cognitive development research paper pdf

Stage Theory of Cognitive Development—Jean Piaget

• First Online: 09 September 2020

Cite this chapter

• Brinda Oogarah-Pratap 3 ,
• Ajeevsing Bholoa 3 &
• Yashwantrao Ramma 3  

Part of the book series: Springer Texts in Education ((SPTE))

23k Accesses

1 Citations

1 Altmetric

This chapter outlines Piaget’s Stage Theory of Cognitive Development and its application in a teacher training institute involving graduate physics trainee teachers predicted to operate at the formal operational stage. It is presumed that, at this stage, having assimilated and accommodated logical understanding from the concrete stage, the trainees have a good articulation of theoretical, combinatorial and proportional reasoning. In this study, an attempt has been made to investigate this hypothesis through a case study involving six physics trainee teachers. The trainees were assigned a written task on the motion of a golf ball after which they were required to verbalise their reasoning patterns during individual oral presentations. The trainees demonstrated adequate theoretical reasoning but lacked combinatorial and proportional reasoning patterns. We opine that teacher training institutions should design programmes that make provision for the interconnection of concrete and formal operational stages into a cyclical mode so as to promote reflective practices in trainees.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Barouillet, P. N. (2015). Theories of cognitive development: From Piaget to today. Developmental Review , 1–12.

Google Scholar  

Bastable, S. B., & Dart, M. A. (2008). Developmental stages of the learner. In S. B. Bastable (Ed.), Nurse as educator: Principles of teaching and learning for nursing practice (pp. 165–216). London: Jones and Bartlett Publishers.

Beilin, H., & Pufall, P. (1992). Piaget’s theory . New Jersey: Erbaulm.

Bholoa, A., Walshe, G., & Ramma, Y. (2017). Curriculum implications of the integration of mathematics into science. In K. S. Taber, & B. Akpan. Science education. New directions in mathematics and science education (pp. 211–220). Rotherham: Sense Publishers. Retrieved from https://link.springer.com/chapter/10.1007%2F978-94-6300-749-8_16 .

Bitner, B. L. (1991). Formal operational reasoning modes: Predictors of critical thinking abilities and grades assigned by teachers in science and mathematics for students in grades nine to twelve. Journal of Research in Science Teaching, 28, 275–285.

Article   Google Scholar  

Diamond, B. S., Maeten-Rivera, J., Rohrer, R. E., & Lee, O. (2014). Effectiveness of a curricular and professional development intervention at improving elementary teachers’ science content knowledge and student achievement outcomes: Year 1 results. Journal of Research in Science Teaching, 51 (5), 635–658.

Eggen, P. D., & Kauchal, D. P. (2000). Educational psyhology: Windows on classroom (5th ed.). Upper Saddle River, NJ: Prentice Hall.

Feldman, R. S. (2001). Child development . Upper Saddle River, NJ: Prentice Hall.

Galotti, K. M. (1989). Approaches to studying formal and everyday reasoning. Psychological Bulletin, 105 (3), 331–351.

Hidi, S. (2006). Interest: A unique motivational variable. Educational Research Review, 1, 69–82.

Lawson, A. E., & Snitgen, D. A. (1982). Teaching formal reasoning in a college biology course for preservice teachers. Journal of Research in Science Teaching, 4 (19), 233–248.

Lutz, S., & Huitt, W. (2004). Connecting cognitive development and constructivism: Implications from theory for instruction and assessment. Constructivism in the Human Sciences, 9 (1), 67–90.

Michael, A. (2014). Misconception in primary science . Berkshire: Mc Graw-Hill.

Nixon, R. S., Campbell, B. K., & Luft, J. A. (2016). Effects of subject-area degree and classroom experience on new chemistry teachers’ subject matter knowledge. International Journal of Science Education, 38 (10), 1636–1654. https://doi.org/10.1080/09500693.2016.1204482 .

Satterly, D. (1987). Piaget and education. In L. R. Gregory (Ed.), The Oxford companion to the mind . Oxford: Oxford University Press.

Seltman, M., & Seltman, P. (2006). Piaget’s logic: A critique of genetic epistemology . London: Routledge.

Thomas, R. M. (1992). Comparing theories of child development . Belmont, CA: Wadsworth Publishing Company Inc.

Tom, A. R. (1997). Redesigning teacher education . New York: State University of New York Press.

Valanides, C. N. (2006). Formal reasoning and science teaching. School Science and Mathematics, 96 (2), 99–107.

Download references

Author information

Authors and affiliations.

Mauritius Institute of Education, Moka, Mauritius

Brinda Oogarah-Pratap, Ajeevsing Bholoa & Yashwantrao Ramma

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Brinda Oogarah-Pratap .

Editor information

Editors and affiliations.

Science Teachers Association of Nigeria, Abuja, Nigeria

University of Texas at Tyler, Tyler, TX, USA

Teresa J. Kennedy

Recommended Resources

Lourenci, O., & Machado, A. (1996). In defense of Piaget’s theory: A reply to 10 common criticisms. Psychological Review, 103 (1), 143–264.

Ojose, B. (2008). Applying Piaget’s theory of cognitive development to Mathematics instruction. The Mathematics Educator, 18 (1), 26–30.

Scholnick, E. K. (1999). Conceptual development: Piaget’s legacy . New Jersey: Lawrence Erbaulm Associates.

Young, G. (2011). Development and causality: Neo-Piagetian perspectives . New York: Springer.

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Oogarah-Pratap, B., Bholoa, A., Ramma, Y. (2020). Stage Theory of Cognitive Development—Jean Piaget. In: Akpan, B., Kennedy, T.J. (eds) Science Education in Theory and Practice. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-030-43620-9_10

Download citation

DOI : https://doi.org/10.1007/978-3-030-43620-9_10

Published : 09 September 2020

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-43619-3

Online ISBN : 978-3-030-43620-9

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Search Menu
• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Urban Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Literature
• Classical Reception
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Epigraphy
• Greek and Roman Law
• Greek and Roman Papyrology
• Greek and Roman Archaeology
• Late Antiquity
• Religion in the Ancient World
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Emotions
• History of Agriculture
• History of Education
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Acquisition
• Language Evolution
• Language Reference
• Language Variation
• Language Families
• Lexicography
• Linguistic Anthropology
• Linguistic Theories
• Linguistic Typology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies (Modernism)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Religion
• Music and Media
• Music and Culture
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Science
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Lifestyle, Home, and Garden
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Politics
• Law and Society
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Neuroanaesthesia
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Toxicology
• Medical Oncology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Clinical Neuroscience
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Medical Ethics
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Security
• Computer Games
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Psychology
• Cognitive Neuroscience
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Strategy
• Business Ethics
• Business History
• Business and Government
• Business and Technology
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic Systems
• Economic History
• Economic Methodology
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Natural Disasters (Environment)
• Social Impact of Environmental Issues (Social Science)
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• International Political Economy
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Political Behaviour
• Political Economy
• Political Institutions
• Political Theory
• Politics and Law
• Public Administration
• Public Policy
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Developmental and Physical Disabilities Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

• < Previous chapter
• Next chapter >

16 Cognitive Development: An Overview

David F. Bjorklund, Developmental Evolutionary Psychology Lab, Department of Psychology, Florida Atlantic University.

• Published: 16 December 2013
• Cite Icon Cite
• Permissions Icon Permissions

In this overview, I focus on contemporary research and theory related to five “truths” of cognitive development: (1) cognitive development proceeds as a result of the dynamic and reciprocal transaction of endogenous and exogenous factors; (2) cognitive development involves both stability and plasticity over time; (3) cognitive development involves changes in the way information is represented, although children of every age possess a variety of ways to represent experiences; (4) children develop increasing intentional control over their behavior and cognition; and (5) cognitive development occurs within a social context. Cognitive development happens at a variety of levels, and developmental scientists are becoming increasingly aware of the need to be cognizant of this and the interactions among the various levels to produce a true developmental science.

Cognitive development proceeds as a result of the dynamic and reciprocal transaction of endogenous and exogenous factors.

Cognitive development involves both stability and plasticity over time.

Cognitive development involves changes in the way information is represented, although children of every age possess a variety of ways to represent experiences.

Children develop increasing intentional control over their behavior and cognition.

Cognitive development occurs within a social context.

Human infants and children have strong dispositions/intuitive information-processing biases, but our species’ thinking is highly sensitive to context and highly plastic, and this is particularly true early in life, when developmental trajectories are put in motion.

The ability to represent the intentions and goals of other people allows children to learn through observation and direct teaching, permitting the acquisition of knowledge and skills that were foreign to our ancestors.

The development of executive function involves age-related changes in working memory, inhibition, and cognitive flexibility and plays a central role in the development of higher-level cognition and the regulation of one’s emotions and behaviors.

Background knowledge, or knowledge base, has a significant influence on how children think.

Cultural “explanations” for cognitive development do not provide alternative interpretations to those based on biology (e.g., neurological factors, evolutionary explanations) or specific experience (e.g., how mothers talk to their babies); rather, cognitive development must be seen as the result of interacting factors at multiple levels of organization, with the social environment being a critical ingredient in this mix.

Although there are many characteristics of human beings that make us distinct from our simian cousins, our cognition is high among them. Humans’ abilities to represent relationships, contemplate the past, anticipate the future, and adapt to a broader range of environments than any other mammal make us intellectually distinct in the animal kingdom. We are not the only “thinking” animal, of course, and our impressive suite of cognitive abilities has deep evolutionary roots, some of which can be inferred by studying our close genetic relatives, the great apes. But Homo sapiens ’ intellectual wherewithal has resulted in our species attaining ecological dominance over the globe, for better or worse, making the study of cognition perhaps the most central topic in attaining an understanding of humankind. Most critical for the current handbook, human cognition develops, emerging over infancy and childhood as a result of a continuous interaction of species-typical abilities and environment, broadly defined, and becoming adapted to the specific cultural environment in which children grow up. An understanding of cognitive development is not only of great theoretical importance but also has some obvious practical implications, especially with respect to the education of children and the modification of intellectual deficits attributed to deleterious early environments.

The field of cognitive development is a vast and varied one, and, on the surface, some of the topics classified under the rubric of “cognitive development” seem quite disparate and unrelated. For instance, many psychologists focus on lower-level mechanisms, such as developmental differences in speed of processing or memory span, which can seem light years away from topics such as theory of mind, metacognition, and scientific reasoning. The disparity is due, in part, to the exceptional range over which human cognition extends. Human cognition is affected by basic-level processes that influence how information is encoded, stored, and processed, much as the cognition of other animals with complex brains is. However, these basic-level abilities also develop in conjunction with a representational system that is far different from those of other animals, permitting the development of symbolic thought and forms of thinking and problem solving that require explanations beyond those afforded solely via basic-level analyses. Yet, despite the difference in levels of analysis (and other differences, such as examining developmental function vs. individual differences), the field of cognitive development is unified by some basic beliefs and themes. Some of the themes represent points of controversies as opposed to areas of agreement (e.g., the extent to which cognitive development is influenced by endogenous vs. exogenous factors), and each scientist will have his or her own pet issues that may not be shared with the same level of enthusiasm by others in the field.

I have not attempted in this chapter to provide a complete description of all issues, controversies, or topics of modern cognitive development; any overview chapter by necessity must be incomplete. Rather, I have organized the chapter around what I see as five general “truths” about cognitive development. These truths are actually generalizations, and I make no pretense that they have the authority of scientific law. Other researchers may have a different set of “truths,” and I might (and in fact have; Bjorklund, 1997 , 2005 ) generate a different list depending on the audience or points I wish to address. In the process of discussing these truths, I have slipped in other issues that I believe are important to understanding cognitive development (my set of pet issues), including the importance of taking an evolutionary perspective, the use of comparative animal data, and the distinction between domain-general and domain-specific mechanisms. The five “truths” are as follows:

I believe these “truths” will be familiar to most cognitive developmental psychologists and at least some of the topics will be central to the theoretical and research questions that stimulate all developmental scientists’ quest for knowledge.

Cognitive Development Proceeds as a Result of the Dynamic and Reciprocal Transaction of Endogenous and Exogenous Factors

One issue central to all of psychology is that of nature versus nurture. Traditionally, this has been posed as a dichotomy: Is human thought and behavior genetically/biologically determined or is it shaped by learning/experience/culture? This is dealt with in a more sophisticated way today, in that everyone is an interactionist, with the issue being better expressed as “how do biological/endogenous factors interact with environmental/exogenous factors to produce the adult phenotype?” From this perspective, cognitive development does not simply mature, or bloom, over time, nor is it solely a product of a child’s culture; rather, it emerges over the course of ontogeny as a result of the dynamic and reciprocal transaction between a child’s biological constitution, including genetics, and his or her physical and social environment ( Bjorklund, Ellis, & Rosenberg, 2007 ; Gottlieb, 2007 ). This can be seen in a wide range of research in cognitive development, from the ontogeny of the brain ( Greenough, Black, & Wallace, 1987 ) and the development of perceptual systems ( Lickliter, 1990 ), to the interaction between specific genes associated with intelligence and whether a child is breastfed or bottle-fed ( Caspi et al., 2007 ).

Developmental Systems and Cognitive Development

At the crux of cognitive development (in fact, of development in general) is the idea that development is not simply “produced” by genes, nor constructed by the environment, but emerges from the continuous, bidirectional interaction between all levels of biological and environmental factors ( Gottlieb, 2007 ; Gottlieb, Wahlsten, & Lickliter, 2006 ; Oyama, 2000 ; see chapters by Lickliter and by Moore in this handbook). From this perspective, even phenomena usually identified as innate, such as imprinting in precocial birds, result from the interaction of genetic and environmental factors. For example, research by Gottlieb (1992) demonstrated that ducklings required auditory experience prior to hatching—hearing their mother’s call, the call of brood mates, or even their own vocalization—in order to approach the appropriate (i.e., same-species) maternal call hours after hatching. In other research, birds that received visual experience prior to hatching showed enhanced visual discrimination abilities shortly after hatching, but species-atypical experiences interfered with auditory attachment behaviors ( Lickliter, 1990 ). Bobwhite quail that were exposed to patterned light days before hatching generally failed to approach the species-typical maternal call in a subsequent test, with some approaching the call of a chicken! In other words, even for usually reliably developing phenomena, experience necessarily interacts with genes to affect their expression.

Such interactions are seen in the development of individual differences in intelligence in children (see the chapter by Flynn & Blair in this handbook). For example, it is well established that children growing up in emotionally supportive homes and receiving cognitively rich experiences tend to have higher IQs than do children growing up in high-risk homes who receive less intellectual stimulation ( NICHD Early Child Care Research Network, 2005 ). However, the adverse effects of a nonstimulating environment are often exacerbated for children with medical problems. For example, classic research by Zeskind and Ramey (1978 , 1981 ) revealed that children from impoverished homes who were given educational daycare beginning in their first year of life showed enhanced IQs relative to control children. However, the effects of the intervention were moderated by the biological constitution of the infants at birth. By chance, approximately half of the infants in their rural, poverty sample were fetally malnourished. Fetal malnourishment is associated with slower development, more aversive cries, and less responsiveness in infants. Whereas fetally malnourished babies in the educational daycare group displayed normal IQs comparable to nonfetally malnourished infants in the educational group by 18 months, the fetally malnourished infants in the control group showed the lowest IQ (71 at 36 months of age), 14 points less than the IQs of the biologically normal children in the control group. Some of the differences in the cognitive outcomes of the fetally malnourished children were attributed to ways mothers interacted with their children and how this changed over time. The general lethargy shown by fetally malnourished infants in the control condition did not evoke much in the way of social interaction from their impoverished, highly stressed mothers, which set the stage for future interactions. Mothers tended to initiate little in the way of interaction with their infants, and their infants, in turn, reciprocated. This pattern of less attention and social give-and-take between infant and mother persisted long after children had “recovered” from the poor prenatal diet. In contrast, the social interaction received by the fetally malnourished children in the educational daycare resulted in increased responsiveness, behaviors that they brought home with them. These more outgoing children affected their mothers and set the stage for a more positive interactional style, which, by 18 months of age, was associated with significantly higher IQs.

Gene–Environment Interactions and the Development of Intelligence

Genetic versus environmental effects on the development of intelligence have been the topic of controversy for nearly 100 years (see Gould, 1981 ). Most behavioral genetic accounts put the heritability of intelligence as measured by IQ between 0.50 and 0.60 (i.e., between 50% and 60% of differences in IQ among people can be attributed to differences in genetics), with shared environmental effects (mainly home environment) being significantly less ( Plomin et al., 2008 ). However, estimates of heritability and shared environmental effects vary as a function of the family in which children grow up ( Rowe, Jacobson, & der Oord, 1999 ; Turkheimer et al., 2003 ). For example, in one study of 3,139 adolescent sibling pairs, Rowe and his colleagues reported a heritability of IQ of 0.57 and an effect of shared environment of 0.13. When the sample was divided into adolescents who came from homes where parents had greater than a high-school education versus those with a high-school education or less, the pattern changed substantially. For the high-education group the heritability of IQ was now 0.74 and the effect of shared environment was 0; in contrast, for children from the low-education families, heritability of IQ was reduced to 0.26 and the effect of shared environment was 0.23 (see also Turkheimer et al., 2003 ). Consistent with earlier theorizing ( Bronfenbrenner & Ceci, 1994 ; Scarr, 1993 ), these findings indicate that heritability of IQ varies with environmental conditions. When the environment is “good enough” to support intellectual accomplishments, as presumably the high-education homes were, individual differences in genes presumably contribute more to IQ level than individual differences in environment; when environmental conditions are less than optimal for supporting IQ, however, individual differences in genes are less predictive of IQ, with shared-environment effects increasing in significance.

More straightforward gene × environment interactions are found in contemporary behavioral genetics studies that have identified specific genes associated with intelligence, but only under certain environments. For example, Caspi and his colleagues (2007) identified a variant of a gene associated with higher IQ, but only for children who were breastfed. The gene, located on chromosome 11, is associated with the processing of fatty acids. In two large-scale samples, one from New Zealand and the other from Great Britain, people who had either of two variants of the gene, and were breastfed as infants, had significantly higher IQs (between about 5 and 10 points) than people with the gene who were not breastfed, and people with a third variant of the gene. For this latter group of people, adult IQ did not vary as a function of whether they were breastfed as infants or not. This is a typical type of finding from recent behavioral genetics literature; individual genes have small effects that are usually mediated by the environment, with likely many genes being associated with complex psychological characteristics, such as the development of intelligence ( Plomin, Kennedy, & Craig, 2006 ).

Fleshing Out of Skeletal Competencies

Debates among contemporary researchers often revolve around the extent to which infants enter the world “prepared” by natural selection to encounter a species-typical environment and are constrained to process some information more efficiently than others, with some arguing that infants and young children inherit skeletal competencies ( Geary, 2005 ) or core knowledge ( Baillargeon, 2008 ; Carey, 2009 , 2011 ; Spelke & Kinzler, 2007 ) in specific domains (folk physics, folk biology, and folk psychology), with these competencies being fleshed out over the course of development as children explore, play, and engage in social interactions. Consider the case of processing human faces. In adults, portions of the right frontal cortex appear to be specialized for processing human faces, and adults are especially skilled at processing upright faces, although these special face-processing skills do not apply to upside-down faces or extend to faces of animals from other species—monkeys, for instance. This general pattern is evident by 9 months of age, with infants displaying an upright-face advantage for human faces but not for monkey faces. However, 6-month-old infants process both human and monkey upright faces more efficiently than upside-down faces, displaying a more general “face-processing” bias. This is consistent with the suggestion that infants’ brains are biased to process faces, but that the processing of human faces becomes more specialized with age and experience (e.g., de Haan, Oliver, & Johnson, 1998 ; Johnson & de Haan, 2001 ; Pascalis, de Haan, & Nelson, 2002 ). According to Pascalis and his colleagues (2002 , p. 1321), “the ability to perceive faces narrows with development, due in large measure to the cortical specialization that occurs with experience viewing faces. In this view, the sensitivity of the face recognition system to differences in identity among the faces of one’s own species will increase with age and with experience in processing those faces.”

Even perspectives that have been labeled as neo-nativism (e.g., Spelke, 1991 ; Spelke & Kinzler, 2007 ) do not attribute fully formed “innate ideas” to infants and children, but argue instead that infants inherit a small set of knowledge systems, shaped by natural selection, that serve as the basis for the development of flexible skills and belief systems (e.g., mathematics, knowledge of the properties of objects, reasoning about other people’s thoughts). For example, Geary (1995) proposed that children possess sets of universal biologically primary abilities that have been shaped by natural selection over our species’ phylogeny that children use spontaneously and that will emerge in a species-typical fashion if children experience a species-typical environment. Language and simple quantitative abilities are examples of biologically primary abilities. These are contrasted with culturally determined biologically secondary abilities that do not have an evolutionary history, often require external motivation for their mastery, and are based on biologically primary abilities. Reading and more advanced forms of mathematics are examples of biologically secondary abilities. Although children may be prepared by natural selection to acquire language, for instance, appropriate environmental input is necessary (social interaction in a language-using culture), and when learning to read children require substantial adult support and instruction in applying a series of biologically primary abilities to achieve mastery.

Intuitive mathematics . As an example of biologically primary abilities, consider those Geary proposed for mathematics: numerosity, ordinality, simple arithmetic, and counting. Numerosity refers to the ability to determine quickly the number of items in a set without counting. Using looking-time procedures, 6-month-old infants have been shown to be able to make discriminations between arrays of three versus four items ( Starkey, Spelke, & Gelman, 1990 ; van Loosbroek & Smitsman, 1990 ), as have many mammal and bird species (see Davis & Pérusse, 1988 ), including cats, chimpanzees, and an African grey parrot. Ordinality refers to a basic understanding of more than and less than relationships, and there is evidence for this late in infancy. In one study, Strauss and Curtis (1981) conditioned infants to point to either the larger or smaller array of dots. For instance, infants may have been shown arrays of three and four dots and trained to point to the smaller array. After training, infants were shown two new arrays, in this case two versus three dots. If they had learned merely to point to the array with three dots, they should continue to point to the three-dot array on the new trials. However, if they had learned an ordinal relation (i.e., point to the smaller array), they should point to the two-dot array on the new trial. Infants did the latter, suggesting they had learned an ordinal relationship.

With respect to simple arithmetic, some researchers have interpreted patterns of infants’ attention to unexpected events (using the violation-of-expectation procedure ; see the chapter by Rakison & Lawson in this handbook) as evidence that they can add and subtract small quantities (e.g., 1 + 1 = 2; 2 − 1 = 1). In an experiment by Wynn (1992) , on one set of trials, 5-month-old infants saw a doll placed on a stage, and a screen was raised to hide the object. Infants watched as a hand holding a second doll moved behind the screen and then exited the stage, empty-handed. If infants have some notion of simple arithmetic, they should infer that there are now two dolls behind the screen. When the screen was then lowered, the possible outcome revealed exactly this, two dolls; for the impossible outcome, only one doll was behind the screen. Infants increased their looking time to the impossible condition, consistent with the idea that they expected two dolls to be behind the screen, and they expressed surprise (reflected by increased looking time) when their expectation was violated when only one doll appeared. This phenomenon has been replicated numerous times (e.g., Simon, Hespos, & Rochat, 1995 ; Walden et al., 2007 ), although some question whether this finding reflects not simple addition but rather a more perceptually based phenomenon (e.g., Clearfield & Westfahl, 2006 ).

Counting is a later-emerging ability, with children acquiring the various principles of counting (e.g., each item in an array is associated with one and only one number name; number names must be in a stable, repeatable order; the final number in a series represents the quantity of the set; the order in which things are counted is irrelevant) over the preschool years ( Gelman & Gallistel, 1978 ). Preschool children spontaneously count things, gradually acquiring the principles of counting and the number names used in their culture before they enter school.

Young children’s tool use . Infants and young children also seem prepared to assume that tools are designed for an intended function, referred to as the design stance ( Dennett, 1990 ). That is, once children see a tool being used, or use a tool themselves, for a specific purpose, they assume the tool is “for” that purpose. This is illustrated in a study in which 12- and 18-month-old children watched an experimenter use the straight end of a spoon or a novel spoonlike object to insert into a hole in a box to turn on a light ( Barrett, David, & Needham, 2007 ). When infants were given the opportunity to turn on the light, they used the novel tool appropriately (i.e., grabbed the spoonlike end and inserted the straight end) most of the time, but did so less than 25% of the time when the familiar spoon was used as a tool. By 12 months of age, infants had apparently formed the category “spoon” and knew how this tool should be used. Although such a design stance can lead to less effective problem solving, it also functions to constrain learning in a way that, on average, likely results in infants and children learning the utility of tools from watching other people use them, greatly facilitating their understanding and use of tools, something that is ubiquitous in human cultures. This is something that other tool-using primates seem not to realize. For example, when selecting a tool to solve a problem, tool-using monkeys are not influenced by having used a tool before, as human children are, but will use any equally useful but novel tool ( Cummins-Sebree & Fragaszy, 2005 ; see also Buttelmann et al., 2008 , for similar studies with great apes).

There has always been debate among developmentalists about the extent to which ontogeny is governed by biological versus environmental factors. Contemporary research and theory has changed substantially the nature of this debate, however. The nativists and empiricists of the old days are gone. Advances in genetics and brain research make it clear that biological development always occurs in an environmental context, and this extends to the expression of genes. The cognition of infants and children is constrained by biological factors, yet there is sufficient neuronal plasticity for the considerable influence of experience, broadly defined. Development is a transaction between endogenous and exogenous factors, with hormones and the firing of neighboring neurons being microenvironmental factors for other neurons, and thus for cognition and its development. Debates related to the old nature/nurture issue persist among cognitive developmentalists, but they are framed differently than in the past, and today’s “extremists” share far more ground than their arguments often seem to suggest.

Cognitive Development Involves Both Stability and Plasticity over Time

Cognitive development is about change over time—yet once a level of cognitive competence is established, will it remain stable over time? Will infants with good visual memories grow up to be children and adults with superior memory abilities? Will high-IQ 4-year olds retain their intellectual advantage relative to their peers by high-school graduation? To what extent can patterns or levels of cognition be changed once established? That is, how plastic, or modifiable, is cognition?

There is evidence that some basic-level processes are relatively stable over development, beginning in infancy. For example, in one study, measures of visual reaction times (the time it takes infants to begin an eye movement toward a picture after it appeared) at 3.5 months of age correlated significantly with visual reaction times 4 years later ( r = 0.51; Dougherty & Haith, 1997 ). In other research, measures of visual recognition memory at 7 months of age were significantly correlated with perceptual speed at age 11 years ( Rose & Feldman, 1995 ). Perhaps more compelling, measures of basic information processing in infancy, as assessed by visual recognition memory (usually determined by infants showing a preference for novel pictures) and rate of habituation (how quickly infants tire of attending to a repeated stimulus), have been found to correlate significantly with childhood IQ (e.g., Bornstein et al., 2006 ; Dougherty & Haith, 1997 ; Rose & Feldman, 1995 ; Rose, Feldman, & Wallace, 1992 ; see Bornstein, 1989 ; McCall & Carriger, 1993 ; Fagan & Singer, 1983 , for reviews), which tends to remain highly stable across childhood and into adulthood ( Bayley, 1949 ; Honzik, MacFarlane, & Allen, 1948 ).

The significant relation between mechanisms for basic information processing in infancy and childhood IQ has caused some theorists to propose these infant abilities, as tapped by recognition memory and habituation tasks, are the basis for intelligence, arguing that cognitive development can be best expressed as reflecting continuity of cognitive function with stability ( Fagan, 1992 ). That is, developmental changes in cognitive abilities are quantitative in nature (e.g., increases in speed of processing, working memory), with individual differences being stable over time. The origins of this stability seem to lie both within children themselves and their environments, as measures of both the home environment (e.g., aspects of mother–child interaction) and habituation rate independently predict childhood IQ (e.g., Bornstein et al., 2006 ; Tamis-LeMonda & Bornstein, 1989 ).

But cognition is multifaceted, and other aspects of children’s thinking do not show levels of stability over time. For example, although some aspects of memory, such as memory span and story recall, show moderate to high degrees of stability over childhood (between 4 and 10 years), the cross-age correlations for other aspects of memory, such as free recall and use of memory strategies, are quite low and usually nonsignificant ( Schneider & Weinert, 1995 ). In other research, cross-age correlations of performance on psychometric tests in infancy tended to be high when infants were within a Piagetian-defined stage (e.g., between 8 and 12 months, corresponding to Piaget’s substage of the coordination of secondary circular reactions) but low when measures were taken between stages ( McCall, Eichorn, & Hogarty, 1977 ). This suggests that when there is discontinuity of cognitive change (as reflected by qualitative changes in cognition as in stage theories such as Piaget’s), there is instability of individual differences.

Although some aspects of cognition show high levels of stability over childhood into adulthood, this does not mean that once some level of cognitive accomplishment has been established it is “permanent.” Rather, intellectual functioning once established must be maintained and in some circumstances can be drastically modified, either for the better or worse. The plasticity of cognition is perhaps best exemplified by research examining changes in IQ levels of children originally reared in stultifying institutions and later placed in intellectually stimulating foster or adoptive homes. Research dating back to the 1930s has demonstrated significant and long-lasting enhancements of IQs for such children (e.g., Beckett et al., 2006 ; Nelson et al., 2007 ; O’Connor et al., 2000 ; Skeels, 1966 ; Skeels & Dye, 1939 ; St. Petersburg-USA Orphanage Research Team, 2008 ; Windsor et al., 2011 ). Not surprisingly, the degree of recovery is related to the age at which children are removed from the deleterious environment and placed in supportive homes. For example, recent research examining the IQs of children removed from Romanian orphanages and placed in British adoptive homes revealed no deficits in IQ at ages 6 or 11 years for children adopted within their first 6 months ( Beckett et al., 2006 ). IQs were lower for children adopted at later ages, particularly those adopted after 24 months. However, the 11-year IQs (83) were higher than the 6-year IQs (77) for these late-adopted children, suggesting a catch-up effect for the children who experienced the longest deprivation.

One methodological problem has plagued all of these “natural experiments,” in that children are not randomly assigned to “institution” and “adoptive” conditions. Perhaps the brighter or more maturationally advanced children are more likely to be selected for adoption than less-advanced children, for example. This problem was overcome in a recent study by randomly assigning Romanian infants who had been abandoned at birth to either foster care or to continued institutional care. These infants were followed to 54 months of age and also compared to a group of never-institutionalized infants who were being reared by their biological families in Bucharest, Romania ( Nelson et al., 2007 ). Similar to other studies, Nelson and colleagues reported higher IQs for children in foster care than for those who remained institutionalized, with IQ levels of the foster children being higher the earlier they were removed from the institution (IQs at 0 to 18 months = 85.8; 18 to 24 months = 86.7; 24 to 30 months = 78.1; 30-plus months = 71.5). In fact, children placed in foster care after 30 months of age had IQs similar to those of children in the institutionalized group (72 vs. 73).

It is not surprising that the brains of once-institutionalized children show signs of dysfunction in structure and processing in several areas ( Chugani et al., 2001 ; Eluvathingal et al., 2006 ). Nelson (2007) proposed that the stimulus-poor environments in which these children spend their early lives fail to provide the species-typical experiences human infants have evolved to expect, including sensory stimulation, social stimulation from a caregiver, and language, among others. Nelson suggested that the normal process of selective cell death may go awry in these children, resulting in excess neurons and synapses being lost, most of which can never be replaced.

Institutionalization studies indicate that patterns of cognitive growth can be facilitated when children experience a change from a nonstimulating to a stimulating intellectual environment. Similar changes can also occur in the opposite direction, however, when the supportive environments responsible for the establishment of intellectual accomplishment are changed. For instance, infant and preschool enrichment programs provided intellectually stimulating environments for children at risk for mental retardation, usually through kindergarten. These programs typically resulted in significant gains in IQ and academic performance relative to control children who did not experience educational enrichment (e.g., Bradley, Burchinal, & Casey, 2001 ; Klaus & Gray, 1968 ; Ramey et al., 2000 ). However, with only a handful of exceptions ( Campbell et al., 2002 ; Reynolds et al., 1996 , 2011 ), the gains shown by children in these enrichment preschool programs dwindled with time, with average IQs and school achievement of children attending these programs being comparable to those of control children by fourth grade (see Barnett, 1995 ; Lazar et al., 1982 , for reviews).

The children who attended preschool enrichment programs did, of course, get smarter with age (i.e., showed gains in cognitive development); however, as they returned to their homes and schools, they lost the supportive environment responsible for establishing intellectual accomplishments, and thus lost their intellectual edge relative to control children. Not surprisingly, at-risk children who stay in compensatory education programs once they begin formal school continue to maintain an academic advantage over their peers, but these gains, too, diminish after the completion of the program (e.g., Becker & Gersten, 1982 ).

Human intellectual plasticity is one of our species’ greatest claims to fame (see the chapters by Markant & Thomas in this handbook, and the chapter by Maurer and Lewis on sensitive periods). It permits us to adapt to a broad range of environments and to perform complex cognitive tasks, such as reading and calculus, that our ancestors never faced. Homo sapiens ’ cognitive flexibility is as much a part of our evolved nature as is our upright stance. Human infants and children have strong dispositions/intuitive information-processing biases, but our species’ thinking is highly sensitive to context, and this is particularly true early in life, when developmental trajectories are put in motion. This plasticity early in life is afforded by humans’ slow-developing brain that permits children to adjust to a wide range of circumstances. From this perspective, cognition is always expressed in an environment (usually a social environment, see discussion below), and when the conditions supporting the expression of those intellectual abilities change, one can expect corresponding changes in patterns of cognitive development. This makes humans the most educable of animals—that is, able to learn through experience.

Cognitive Development Involves Changes in the Way Information is Represented, Although Children of Every Age Possess a Variety of Ways to Represent Experiences

Central to all major theories of cognitive development are age-related changes in how objects, people, and experiences are represented (e.g., Brainerd & Reyna, 2002 ; Bruner, 1966 ; Case, 1992 ; Fischer, 1980 ; Karmiloff-Smith, 1991 ; Piaget, 1983 ). Piaget’s stage theory is the classic example in which major changes in how children represent the world reflect qualitative changes in cognition. According to Piaget, infants during their first 18 months or so represent objects and events by means of self-produced action (including sensory “action” such as looking at things), termed sensorimotor intelligence. Beginning around their second birthdays, children are able to represent objects and events symbolically, as reflected by their use of language, mental imagery, deferred imitation, and symbolic play, among other expressions of the symbolic (or semiotic) function. Although symbolic, the thinking of children in this preoperational stage (ranging from about 2 to 7 years) is intuitive and lacks logical operations, such as reversibility (e.g., a cognitive operation can be reversed, as in the case of subtraction, the effects of which can be reversed by addition). The thinking of children in the next state, concrete operations (ranging in age from about 7 to 11 years), although logical, is limited, as the stage name denotes, to concrete entities; abstract reasoning comes on line beginning around 11 or 12 years of age with the advent of formal operations .

Piaget’s stage theory has served as the jumping-off point for other theories proposing developmental differences in representational abilities (e.g., Case, 1992 ; Fischer, 1980 ; Fischer & Bidell, 1998 ; Pascual-Leone, 1970 , 2000 ). It has been critiqued widely (e.g., Brainerd, 1978 ; see papers in Brainerd, 1996 ), and I will not provide a detailed examination of this influential theory here. Rather, I devote most space to what is perhaps the most studied and controversial transition reflected in Piaget’s theory, the change from sensorimotor to symbolic representation. The advent and widespread use of symbolic representation marks a major milestone in cognitive development, and although humans may not be the only species capable of representational thought (see, e.g., Parker & McKinney, 1999 ), the extent to which humans apply such thinking differentiates us from all other species.

As I noted, Piaget believed that the symbolic function was expressed via children’s language, mental imagery, deferred imitation, and symbolic play, among others, each emerging around 18 to 24 months of age. For example, although children typically speak their first words around 10 months of age, they usually don’t put them into sentences until around 18 months, and Piaget (1962) observed, and others confirmed (e.g., Kaye & Marcus, 1981 ), that children display deferred imitation (copying the actions of a model some significant time after observing the behavior) late in the second year of life. However, more recent research indicates that infants show signs of symbolic representation much earlier than Piaget proposed when simplified and age-appropriate tasks are used.

Perhaps the best-documented case of infants displaying symbolic representational abilities much earlier than Piaget proposed is for deferred imitation (see Bauer, 2007 , and the chapters by Bauer and Meltzoff & Williamson in this handbook for reviews). Although infants’ ability to imitate multistep actions increases with age, infants as young as 9 months old will imitate simple actions for up to 5 weeks (e.g., Carver & Bauer, 1999 ); 6-month-olds have been shown to imitate simple behaviors after a 24-hour delay ( Collie & Hayne, 1999 ); and preverbal toddlers have shown evidence of deferred imitation for as long as 1 year (e.g., Bauer, 2002 , 2007 ; Bauer et al., 2000 ). Other research indicates that infants in their first year of life may be able to add and subtract small quantities (e.g., Wynn, 1992 , discussed earlier) and may possess some precocious problem-solving strategies based on analogical reasoning ( Chen, Sanchez, & Campbell, 1997 ; Willatts, 1990 ), and newborns have been shown to copy facial expressions (e.g., Meltzoff & Moore, 1977 ) and integrate information from multiple senses (e.g., Meltzoff & Borton, 1979 ). These and other findings lead Meltzoff (1990 , p. 20) to conclude that “ in a very real sense, there may be no such thing as an exclusively ‘sensorimotor period’ in the normal human infant ” (italics in the original).

Although there are alternate interpretations of some of the findings purported to reflect infant representational abilities (e.g., infant “addition” may actually be the result of perceptual, not conceptual, processes, Clearfield & Westfahl, 2006 ; neonatal imitation may have a communicative and/or affiliative function and is not related to the imitation observed later in infancy, Bjorklund, 1987 ; Byrne, 2005 ), most contemporary theorists concur that representational cognition does not suddenly appear around children’s second birthdays, but rudimentary abilities are seen late in the first and early in the second year of life.

Representing Others as Intentional Agents

In addition to evidence from studies of deferred imitation in infancy (see Bauer, 2007 ), representational competency in infancy is supported by research examining children’s understanding of seeing both themselves and other people as intentional agents —as beings whose behavior is based on what they know and what they want, and who act deliberately to achieve their goals (i.e., they do things “on purpose”; see Bandura, 2006 ; Tomasello & Carpenter, 2007 ).

On the surface, viewing others as intentional agents may not appear to be a major intellectual accomplishment, but it serves as the basis for human social cognition, which includes social learning and teaching, the foundation for culture—the nongenetic transmission of information between generations. Although the first signs of intentional representation appear late in the first year, children’s understanding of others as intentional agents develops over childhood, culminating in the ability to pass false-belief tasks around 4 years of age, the benchmark for attaining theory of mind (see the chapter by Astington & Hughes in this volume 2).

The earliest sign of infants’ understanding of others as intentional agents is seen in shared (or joint ) attention , which involves a triadic interaction between the child, another person, and an object ( Tomasello & Carpenter, 2007 ; Tomasello et al., 2005 ). For example, parents often draw children’s attention to an object by pointing or gazing at the object, a form of referential communication , which indicates that the “pointer” understands that he or she sees something that the observer does not. Despite parents’ actions, infants do not engage in shared attention until about 9 months of age, although they do display some biases toward social stimuli from birth. For example, newborns orient to human faces and learn to seek their mothers’ faces ( Feldman & Eidelman, 2004 ), and by 3 or 5 months infants can recognize self-produced biological motion ( Bertenthal, Proffitt, & Cutting, 1984 ) and turn to look in the same direction of another person ( Tomasello et al., 2005 ).

Beginning around 9 months of age, infants will gaze in the direction adults are looking or pointing, engage in repetitive interaction with an adult and an object, imitate an adult’s action, and point or hold up objects to another person (see Carpenter et al., 1998 ; Tomasello, 1999 ). Shared attention and related abilities increase over the next year. For example, 12-month-olds will point to objects and events that others are unaware of ( Liszkowski, Carpenter, & Tomasello, 2007 ); between 12 and 18 months infants learn to use where others are looking to inform their own attention ( Brooks & Meltzoff, 2002 ) and to point to objects to direct an adult’s attention to something he or she is searching for ( Liszkowski et al., 2006 ).

Although shared attention may seem to reflect a low-level form of representation, it may be unique to humans. For example, although chimpanzees and even monkeys will follow the gaze of another individual in some contexts (e.g., Bering & Povinelli, 2003 ; Bräuer, Call, & Tomasello, 2005 ) and point out things to other individuals (e.g., Leavens, Hopkins, & Bard, 2005 ), there is little evidence that chimpanzees engage in shared attention (e.g., Herrmann et al., 2007 ; Tomasello & Carpenter, 2005 ).

The importance of seeing others as intentional agents can be seen in social learning. The most sophisticated forms of social learning, including teaching, require that the observer not only copy significant aspects of a model’s behavior, but also understand that the model has a specific goal, or intention, in mind. That is, behavior is not copied just for the sake of reproducing the actions of another individual, but to achieve some specific outcome. This is seen early in the second year of life. For example, 14- and 18-month-old infants will copy the behavior an adult intended to perform (e.g., pulling the ends off a dumbbell), even if the adult failed to complete the action (e.g., Meltzoff, 1995 ; see also Carpenter, Akhtar, & Tomasello, 1998 ). In fact, preschool children will generally reproduce most of an adult model’s actions even if all the actions are not necessary to achieve a goal ( Gardiner, Greif, & Bjorklund, 2011 ; Horner & Whiten, 2005 ; Nagell, Olguin, & Tomasello, 1993 ; Nielsen & Tomaselli, 2010 ). For instance, in one study 3- and 4-year-old children were shown a transparent puzzle box and an adult demonstrated a series of three actions, two of which were necessary and one of which was not, to retrieve a gummy bear from inside the box ( Horner & Whiten, 2005 ). Children copied all of the adult’s actions, even those that were obviously irrelevant for attaining the goal. One interpretation of findings such as these is that young children may believe that all of an adult’s actions are goal-directed, making imitation of those actions a reasonable course to take ( Lyons, Young, & Keil, 2007 ).

Although chimpanzees and the other great apes clearly engage in sophisticated forms of social learning, passing information from one generation to the next, the minimal criterion for culture (e.g., van Schaik et al., 2003 ; Whiten, 2007 ; Whiten et al., 1999 ), they tend not to engage in true imitation (understanding the model’s goal and copying most behaviors to achieve that goal) as young children do. Rather, they are more apt to engage in emulation , attaining the same goal as the model but using different, and sometimes more effective, actions in doing so (e.g., Call, Carpenter, & Tomasello, 2004 ; Horner & Whiten, 2005 ; Nagell et al., 1993 ). Thus, despite being apparently able to represent the goals of a model, chimpanzees seem not possess the same degree of recognition of other beings as intentional agents as human preschoolers do, perhaps accounting for the greater effectiveness of social learning in humans than in great apes.

Another major representational change in understanding others as intentional agents seems to occur around 4 years of age when children can pass false-belief tasks. Much before this time, children have great difficulty attributing a false belief to others. For example, if a 3-year-old knows that a cookie, originally hidden in a cupboard, has been moved to a jar, he or she believes that another person, although not privy to the change in location, will also know the correct whereabouts of the cookie (e.g., Baron-Cohen et al., 1985 ; Wimmer & Perner, 1983 ). Although performance on false-belief tasks is affected by task characteristics and by basic-level processes such as executive function (e.g., Flynn, O’Malley, & Wood, 2004 ; Henning, Spinath, & Aschersleben, 2011 ; Hughes & Ensor, 2007 ; see the chapter by Carlson, Zelazo, and Faja in this handbook), 3-year-olds seem to truly lack the conceptual/representational competence to solve such tasks that most 4-year olds possess ( Wellman, Cross, & Watson, 2001 ).

Representational Insight

Most aspects of mental representation and symbolic functioning would seem to require the knowledge that one entity can stand for something other than itself, termed representational insight ( DeLoache, 1987 ; DeLoache & Marzolf, 1992 ). This can be seen in how children interpret pictures or photographs. In one study, children between 9 and 19 months of age in the United States and the Ivory Coast were given photographs of objects to inspect ( DeLoache et al., 1998 ). Most of the youngest children treated the photos as if they were real objects, sometimes even trying to pick them off the page. In contrast, most of the older children pointed at the depicted objects rather than trying to manipulate them, realizing they were representations of things.

In other studies, researchers showed children scale models or photographs of rooms, including the location of a hidden toy. Children were then given the opportunity to find the toy in a “real” room (e.g., DeLoache, 1987 ; DeLoache & Marzolf, 1992 ; Kuhlmeier, 2005 ; Suddendorf, 2003 ). Somewhat surprisingly, children were first able to use the photograph as a cue to where the toy was hidden in the real room (at around 2.5 years of age), but only later were able to find the object when a scale model was used (about 3 years of age). One explanation for this pattern was that the scale model was an interesting object itself, making it difficult for children to treat it as a representation for something else, or what DeLoache (2000) referred to as dual representation . In support of this explanation, when the model was made less interesting (e.g., by having children look at it through a window), 2.5-year-old children were able to use it to find the toy in the real room ( DeLoache, 1991 ).

Implicit/Explicit Representation

One distinction frequently made in cognitive psychology is that between implicit and explicit cognition. Implicit cognition refers to cognition without awareness, whereas explicit cognition refers to cognition with conscious awareness. Generally, human infants and all nonhuman animals may be limited to implicit cognition (but see Bjorklund & Rosenberg, 2005 , for discussion of possible explicit cognition in chimpanzees), and the evolution of conscious awareness, with a well-developed sense of self, has been proposed to be essential for evaluating the causes of one’s behavior and the behavior of others—that is, treating other people as intentional agents ( Bering & Bjorklund, 2007 ). Although implicit cognition may lack the important ingredient of self-awareness, it can be quite sophisticated, as reflected by the knowledge spiders have for building webs, birds have for building nests, or people have for complicated motor tasks, such as skiing down a twisting slope.

Karmiloff-Smith (1991 , 1992 ) developed a theory of representational redescription in which implicit representations are transformed, or redescribed, into various forms of explicit cognition. According to Karmiloff-Smith, redescription permits children to use their representations more flexibly, including taking one piece of information (watching mother as she points in the distance) and making some inferences (perhaps she wants me to look at the object she’s pointing at). With redescription , knowledge that was once implicit becomes explicit, allowing children to generate new insights by reflecting on what they already know.

As with other aspects of cognitive development, there seems not to be a definitive point in time before which self-awareness is not present and after which it is. Perhaps the classic demonstration of self-awareness is mirror self-recognition , in which children realize that it is themselves and not another child that they see in the mirror. Children “pass” this task, usually by pointing to a mark on their face that was surreptitiously placed there rather than pointing at the mirror, around 18 months of age (e.g., Brooks-Gunn & Lewis, 1984 ; Nielsen, Suddendorf, & Slaughter, 2006 ), as do chimpanzees, orangutans, and a few gorillas ( Gallup, 1979 ; Suddendorf & Whiten, 2001 ), dolphins ( Reiss & Marino, 2001 ), elephants ( Plotnik, de Waal, & Reiss, 2006 ), and magpies ( Prior, Schwarz, & Güntürkün, 2008 ). However, when researchers placed stickers on children’s heads, most 2- and 3-year-old children failed to reach for the stickers when shown photographs or videos of themselves (e.g., Povinelli, Landau, & Perilloux, 1996 ; Povinelli & Simon, 1998 ), suggesting that children’s sense of self develops gradually over the preschool years, as their ability to deal with different modes of representation (mirrors, photos, videos) develops (see also Skouteris, Spataro, & Lazaridis, 2006 ; Zelazo, Sommerville, & Nichols, 1999 ).

Other research suggests that some aspects of self-awareness and explicit cognition develop much earlier. For example, as I mentioned previously, infants as young as 9 months old display deferred imitation (see Bauer, 2007 ), which has been proposed to be a nonverbal form of explicit memory. This is seen in studies of adults with hippocampal damage, who are unable to acquire new explicit knowledge but can learn new implicit knowledge. For instance, when given a mirror-drawing task (trace figures while watching one’s hand in a mirror), patients with hippocampal damage don’t remember performing the task from day to day (explicit memory) but nonetheless improve their performance as a result of practice (implicit memory) ( Milner, 1964 ). When these patients are given deferred-imitation tasks similar to those used with infants (observe a novel behavior and then reproduce it a day later), they behave much as they do on verbal explicit memory tasks—they are unable to remember seeing the task performed and fail to reproduce the modeled behavior ( McDonough et al., 1995 ).

Children of all ages beyond infancy (and perhaps during) have both implicit and explicit representations available to them, and operations involving both systems are used in processing information. However, tasks that tap mostly explicit representations show larger development differences than tasks that tap mostly implicit representations (e.g., Billingsley, Smith, & McAndrews, 2002 ; Newcombe et al., 1998 ). For example, in one study, 4-, 5-, and 6-year-old children saw a series of pictures and were asked to identify them or to answer some questions about them (for example, “What would you use an X for?”) ( Hayes & Hennessy, 1996 ). Two days later children were shown a series of fragmented pictures, some of which they had seen earlier and some of which were new. The initial picture in each series was substantially degraded and gradually more detail was provided until children identified the picture. Children were also asked if they remembered each picture from 2 days ago. Recognition memory, a measure of explicit cognition, improved with age; however, children of all ages identified the fragmented “old” pictures (i.e., those they had seen with less detail provided) earlier than the fragmented “new” pictures, a measure of implicit memory. This effect held regardless of whether children remembered seeing the pictures 2 days earlier or not.

Infants and children often display greater cognitive competence on tasks when their knowledge is assessed by implicit rather than explicit measures ( Keen, 2003 ). For example, in a false-belief task, after a piece of cheese is moved from its original container to a new one, children were asked where Sam, who saw where the object was hidden initially but did not see it moved, will look. Most 3-year-olds stated, erroneously, that Sam will look for the cheese in the new location. This is a measure of explicit representation. However, when 3-year-olds were asked this question, they first gazed at the original location, where Sam saw the cheese being hidden ( Clements & Perner, 1994 ; Clements, Ruffman, & McCallum, 2000 ). Looking behavior is a nonverbal and implicit measure, and when it is used as an indication of children’s knowledge, it appears that even 3-year-olds understand (at least implicitly) the possibility that others can hold a false belief.

Other research using infants’ implicit looking behavior (e.g., increasing looking time to an unexpected event, such as a screen that continues to descend when its trajectory should be stopped by an object) indicates that babies possess knowledge of physical objects, such as object permanence ( Baillargeon, 1987 ), months earlier than observed by Piaget using more explicit reaching behaviors as measures (e.g., reaching and retrieving a covered object). Other research using similar looking-time measures has shown that 5- and 6-month-old infants realize that items that are unsupported will fall ( Baillargeon, 1994 ; see Baillargeon, 2008 ; Spelke & Kinzler, 2007 , for reviews). In contrast, 2-year-old children fail to show this knowledge when explicit searching behavior is used as a measure ( Berthier et al., 2000 ; Hood, Carey, & Prasada, 2000 ). For example, after watching a ball dropped onto a stage behind a screen and seeing the resting ball on the floor, 2- and 2.5-year-old children watched as the experimenter placed a cup on the floor of the stage, a shelf over the cup, and then a second cup on that shelf ( Hood et al., 2000 ). The screen was then replaced and the ball dropped again. If the children understood the solidity of objects, as 6-month-old infants presumably do, they should search in the cup on the top shelf. Most 2.5-year-old children did so (93%), but only 40% of the 2-year-old children searched in the top cup, suggesting that, when using explicit measures, their understanding of solidity was tenuous. These and other findings (see Keen, 2003 ) suggest that implicit knowledge develops before explicit knowledge, and we must be cautious when we state that infants or children either possess, as reflected by implicit knowledge, or don’t possess, as reflected by explicit knowledge, a particular concept.

Dual-Process/Representation Theories of Cognitive Development

The implicit/explicit distinction just discussed suggests that children have multiple ways of representing information. Such theories are often referred to as dual-process theories , and most theorists postulate that people have (at least) two basic ways of representing information (e.g., implicit vs. explicit; experiential vs. analytic; exact, verbatim traces vs. inexact, “fuzzy” traces) and that there are developmental differences in how children use these various forms of representation (e.g., Barrouillet, 2011 ; Brainerd & Reyna, 2002 , 2005 ; Klaczynski, 2009 ). One dual-process theory that has been widely applied to children’s cognition is fuzzy-trace theory (e.g., Brainerd & Reyna, 1993 , 2002 , 2005 ). Brainerd and Reyna propose that people represent experiences on a fuzzy-to-verbatim continuum . At one extreme are verbatim traces , which are elaborated, exact representations of recently encoded information. At the other extreme are fuzzy traces , or gist, which are vague, degenerated representations that maintain only the sense or pattern of recent experiences.

Although people of all age process information along the entire continuum, young children are biased to represent experiences in terms of verbatim traces, with this bias shifting in middle childhood. This has implications for children’s performance on a host of tasks, because verbatim and fuzzy traces are processed differently. For example, verbatim traces are more likely to be forgotten and are more susceptible to output interference than fuzzy traces. Although space prevents me from providing a detailed description of research performed following fuzzy-trace theory, it has been applied to a wide range of domains within cognitive development, including memory (e.g., Brainerd & Reyna, 2005 ), arithmetic (e.g., Brainerd & Gordon, 1994 ), and reasoning (e.g., Reyna & Farley, 2006 ), and has generated a number of counterintuitive predictions that have been confirmed by research. For example, under some circumstances, children’s false memories (e.g., remembering an event that didn’t happen) are more resistant to forgetting than true memories (e.g., Brainerd & Mojardin, 1999 ; see Brainerd & Reyna, 2005 ). This was predicted premised on the fact that correct recognition is based, in part, on literal, or verbatim, memory traces. Because there are no verbatim memory traces for falsely remembered events, they are based solely on the more durable fuzzy traces. As a result, true memories are more likely to be forgotten than false memories.

Representation has been one of the most investigated and theorized-about aspects of cognitive development. Counter to Piaget’s original proposal, children, beginning in infancy, have multiple ways of representing information, although their ability to mentally represent people, objects, and events increases in sophistication over infancy and childhood. The ability to represent the intentions of other people, a form of social cognition, may be of special significance, for with it children can represent the goals of other people and are able to learn through observation and direct teaching, permitting the acquisition of knowledge and skills that were foreign to our ancestors. In fact, many theorists propose that humans’ exceptional intelligence, which affords scientific, artistic, and technological accomplishments, is derived from our social intelligence, evolved for cooperating and competing with fellow conspecifics (e.g., Alexander, 1989 ; Dunbar, 1995 , 2010 ; Geary & Ward, 2005 ; Humphrey, 1976 ), and the result of the confluence of a big brain, an extended juvenile period, and living in socially complex groups (e.g., Bjorklund, Cormier, & Rosenberg, 2005 ; Dunbar, 1995 ). Human representational ability is seemingly unique in the animal world. Although hints of representational thought can be seen in other big-brained animals, including the great apes (e.g., Herrmann et al., 2007 ; Whiten, 2007 ), dolphins (e.g., Bender, Herzing, & Bjorklund, 2009 ; Krützen et al., 2005 ), and elephants (e.g., Plotnik et al., 2006 ), no other species makes use of symbolic representation to the extent that humans do. Although I’ve emphasized that there does not seem to be a single point in development when we can say children “have” representational thought versus when they do not, the change of thinking between the mainly sensorimotor infant and the child who possesses language and theory of mind is substantial, giving the appearance, if not the reality, of a stagelike transformation in cognition.

Children Develop Increasing Intentional Control over Their Behavior and Cognition

The purpose of cognition is to solve problems. Although adult human minds can ponder esoteric questions concerning the meaning of existence, cognition evolved to help animals solve the problems they encounter in everyday life. The ability to solve problems increases with age, and one important issue for developmental psychologists concerns the degree to which children of different ages can intentionally guide their problem solving. Much research on this topic has addressed the use of strategies , usually defined as deliberate, goal-directed mental operations that are aimed at solving a problem (e.g., Harnishfeger & Bjorklund, 1990 ; Pressley & Hilden, 2006 ). However, central to using strategies and intentional control of behavior is self-regulation , the ability to guide not only one’s problem solving but also one’s emotions (e.g., Cole, Martin, & Dennis, 2004 ; Posner, Rothbart, & Sheese, 2007 ).

Several basic-level cognitive abilities are involved in self-regulation, which collectively are referred to as executive function ( Jones, Rothbart, & Posner, 2003 ; Wiebe, Espy, & Charak, 2008 ; Zelazo, Carlson, & Kesek, 2008 ). Executive function refers to the processes involved in regulating attention and in determining what to do with information just gathered or retrieved from long-term memory. It plays a central role in planning and behaving flexibly, particularly when dealing with novel information. It involves a related set of basic information-processing abilities, including working memory , the structures and processes used for temporarily storing and manipulating information; selectively attending to relevant information; inhibiting responding and resisting interference; and cognitive flexibility, as reflected by how easily individuals can switch between different sets of rules or different tasks (see Garon, Bryson, & Smith, 2008 ; McAuley & White, 2011 ; Zelazo et al., 2008 ). In this section, I review the development of various aspects of executive function and then look briefly at children’s development of strategies, topics that will both be examined in more detail later in this handbook (see the chapters by Rueda & Posner; and Carlson, Zelazo, & Faja).

The Development of Executive Function

Executive function seems to include at least three factors—working memory, inhibition, and cognitive flexibility—each of which develops. Working memory is measured by performance on working-memory span tasks. Working-memory span can be contrasted with the more familiar measure of memory span , found on the Stanford-Binet and Wechsler IQ tests. Memory span is typically measured by asking children to recall in exact order a list of items that are presented at a rate of about 1 per second. In contrast, in working-memory span tasks, children are asked to perform simple cognitive operations in addition to remembering the items. For example, in a counting-span task children may see arrays of blue circles and yellow triangles and be asked to count the number of circles. Children must then recall the number of circles in that array and in each prior array. Both memory and working-memory span show regular increases with age, with working-memory span usually being about two items less than a child’s memory span (e.g., Alloway, Gathercole, & Pickering, 2006 ; Case, 1985 ; Dempster, 1981 ).

One popular account of working memory and its development was presented by Baddeley and Hitch ( Baddeley, 1986 ; Baddeley & Hitch, 1974 ), who proposed that working memory consists of a central executive that stores information and two temporary systems, one for coding verbal information called the articulatory , or phonological , loop , and another for coding visual information, referred to as the visuospatial scratch pad , or visuospatial working memory . Developmental differences in verbal memory span are primarily due to age differences in the articulatory loop. Age differences in the rate of decay of verbal representations held in the articulatory loop and/or the rate that that information can be rehearsed contribute to developmental differences in memory and working-memory span (see Cowan & Alloway, 2009 ). Support for this contention comes from research reporting a relationship between the speed with which individual words can be articulated and memory span. Researchers have found reliable age differences in speed of processing , with younger children taking more time to process information and make decisions than older children ( Kail & Ferrer, 2007 ; Miller & Vernon, 1997 ). With age, children are able to read or say words at a faster rate, and memory span increases accordingly (e.g., Chuah & Maybery, 1999 ; Hulme et al., 1984 ). When adults’ speed of processing is slowed down to be comparable to that of 6-year-olds (e.g., by making them remember digits using a foreign language), their memory and working-memory spans are similarly reduced to be comparable to those of 6-year-olds (e.g., Case, Kurland, & Goldberg, 1982 ).

The relationship between speed of enunciating individual items and memory span is nicely illustrated by some cross-cultural research. For example, Chinese-speaking children have longer memory spans than English-speaking children ( Chen & Stevenson, 1988 ; Geary et al., 1993 ), and this is related to the fact that the digits 1 through 9 can be articulated more rapidly in Chinese than in English. A similar effect has been found for bilingual Welsh children, who have longer digit spans in English, their second language, than in Welsh, their first language. This counterintuitive effect is attributed to the fact that number words can be articulated more rapidly in English than in Welsh ( Ellis & Hennelley, 1980 ).

Children’s familiarity with the to-be-remembered items also affects span length (e.g., Dempster, 1981 , 1985 ). For example, in a much-cited study, Chi (1978) reported that a group of 10-year-old chess experts had longer memory spans for game-possible positions on a chessboard than a group of adults who knew how to play chess but were not experts. However, their greater memory span was limited to their area of expertise; the adults had longer memory spans than the children for digits (see also Schneider et al., 1993 ). Memory and working-memory span, then, should not be viewed as absolute limits of children’s information-processing abilities, but rather they are influenced by factors including the speed with which individual items can be processed and children’s knowledge for the to-be-remembered information.

Yet there is evidence that there may be some absolute limits in how much information children of different ages can hold in working memory ( Cowan et al., 1999 , 2011 ). For example, Cowan and his colleagues (1999) evaluated age differences in span of apprehension ( Sperling, 1960 ), which refers to the amount of information that people can attend to at a single time. The span of apprehension of adults is about four items, compared to memory span, which is 7 ± 2 items. In the study by Cowan and colleagues, first- and fourth-grade children and adults heard series of digits over headphones, which they were to ignore, while simultaneously playing a video game. Occasionally and unexpectedly, however, they were asked to recall, in exact order, the most recently presented set of digits they had heard. The average span of apprehension increased with age: about 2.5 digits for first graders, about 3.0 for fourth graders, and about 3.5 digits for adults. Cowan and his colleagues interpreted these significant age differences as reflecting a true developmental difference in the capacity of the short-term store that serves as the foundation for age differences on memory-span tasks.

Individual differences in children’s working memory are related to a host of higher-order cognitive abilities. For example, working memory correlates moderately with IQ (see Fry & Hale, 2000 ) and is significantly associated with the speed and accuracy of arithmetic computation (e.g., Adams & Hitch, 1997 ; Zheng, Swanson, & Marcoulides, 2011 ), reading comprehension (e.g., Daneman & Blennerhassett, 1984 ; Daneman & Green, 1986 ), writing ability (e.g., Swanson & Beringer, 1996 ), and the use of arithmetic (e.g., Berg, 2008 ) and memory strategies (e.g., Lehmann & Hasselhorn, 2007 ; Woody-Dorning & Miller, 2001 ). Children with math (e.g., Geary et al., 1991 ) and reading (e.g., Gathercole et al., 2006 ; Swanson & Jerman, 2007 ) disabilities have smaller working memories than nondisabled children, and children with precocious mathematical skills have higher levels of executive function (e.g., working memory, inhibition) than typically devleoping children ( Johnson, Im-Bolter, & Pascual-Leone, 2003 ; Swanson, 2006 ).

A second basic-level ability included in executive function is inhibition , which refers to the ability to prevent making some cognitive or behavioral response. Researchers have proposed that children’s abilities to inhibit preferred or well-established responses plays an important role in cognitive development (e.g., Bjorklund & Harnishfeger, 1990 ; Dempster, 1992 ; Diamond & Taylor, 1996 ; Harnishfeger, 1995 ). Related to inhibition is resistance to interference ( Dempster, 1993 ), which refers to “susceptibility to performance decrements under conditions of multiple distracting stimuli” ( Harnishfeger, 1995 , pp. 188–189). Resistance to interference is seen in dual tasks, when performing one task (watching television) interferes with performance on a second task (comprehending a story one is reading), or in selective attention, when one must focus on “central” information (reading a story) and ignore “peripheral” information (the plot of a television sitcom).

Inhibition and the ability to resist interference increase with age. For example, in Piaget’s A-not-B object permanence tasks, infants much younger than about 12 months continue to search at location A, where they had retrieved a hidden object several times previously, despite seeing it hidden at a new location (B). One factor hypothesized to be related to performance on this task is infants’ ability to inhibit their previous correct responses, which improves over the latter part of the first year ( Diamond, 1985 ; Holmboe et al., 2008 ).

Inhibition abilities continue to develop over childhood and adolescence (see, e.g., Kochanska et al., 1996 ; Luria, 1961 ) and are assessed by a variety of simple tests. For instance, in the tapping task children must tap once each time the examiner taps twice and tap twice each time the examiner taps once; in the day-night task , children must say “day” each time they see a picture of the moon and “night” each time they see a picture of the sun; and in Simon Says , children must perform an action only when Simon says so (“Simon says, touch your nose”). These tasks require children to inhibit a prepotent response and execute another, and individual and development differences are found on these and other tasks (e.g., Baker, Friedman, & Leslie, 2010 ; Diamond & Taylor, 1996 ; Sabbagh et al., 2006 ). More complicated tasks, appropriate for older children and adolescents, include variants of the Wisconsin Card Sorting Task, in which participants sort cards by one dimension (e.g., shape of object), which is then switched to another dimension (e.g., color). The number of perseverative errors (i.e., continuing to sort by the previously correct category) is used as a measure of inhibition (e.g., Chelune & Baer, 1986 ).

Children’s performance on inhibition and resistance to interference tasks is associated with a host of higher-level cognitive abilities, including false-belief tasks assessing theory of mind (e.g., Sabbagh et al., 2006 ), selective attention (e.g., Ridderinkhof, van der Molen, & Band, 1997 ), selective forgetting (e.g., Harnishfeger & Pope, 1996 ; Lehman et al., 1997 ), incidental learning (e.g., Schiff & Knopf, 1985 ), and intelligence (see Harnishfeger & Bjorklund, 1994 ; McCall & Carriger, 1993 ). Behavioral inhibition has also been identified as the principal cause of attention-deficit/hyperactivity disorder (ADHD; Barkley, 1997 ).

A third basic-level component hypothesized to be involved in the development of executive function is cognitive flexibility , as reflected by the ability to shift between sets of tasks or rules (e.g., Garon et al., 2008 ; Zelazo et al., 2003 ). Many of the tasks used to assess inhibition abilities also require children to shift, or change, between a set of rules. For example, in the Wisconsin Card Sorting Task participants must switch from following one rule (it’s the shape of the object that’s important) to another (it’s the number of objects on a card that’s important). Relatedly, Zelazo and his colleagues have argued that the development of executive function involves the increasing ability to formulate and maintain rules, as illustrated on simplified “shifting tasks,” in which children must change from following one criteria (sort by color) to another (sort by shape) ( Zelazo et al., 2003 , 2008 ).

Developmental differences in executive function have been related to age-related differences in brain development, particularly the frontal lobes (see Zelazo et al., 2008 ). For example, myelination of neurons, which promotes a faster rate of neuronal processing, is not fully developed in the frontal cortex until adolescence or young adulthood (see Lenroot & Giedd, 2007 ; Yakovlev & Lecours, 1967 ). There is substantial research with infants (e.g., Bell, Wolfe, & Adkins, 2007 ) and older children and adults (e.g., Luna et al., 2001 ) pointing to the frontal lobes as the locus of inhibitory control. For example, neuroimaging studies reveal relations between infants’ performance on A-not-B object-permanence tasks and frontal lobe activity (e.g., Baird et al., 2002 ; Segalowitz & Hiscock, 2002 ), and young children’s performance on the Wisconsin Card Sorting Task is similar to that of adults with frontal lesions ( Chelune & Baer, 1986 ). Research by Luna and colleagues (2001) found that inhibition abilities develop gradually between the ages of 8 and 30 years and are associated with activity in the frontal cortex. However, rather than showing a linear relation between age and brain functioning on inhibition tasks, they reported that the prefrontal cortex was more active on these inhibition tasks in adolescents than in either children or adults.

Most accounts of executive function hold that the various processes are domain-general in nature. That is, developmental and individual differences in working memory and inhibition influence children’s performance on a host of tasks in a similar way (e.g., Case, 1992 ). Despite this, one should not expect executive function to be uniform across all tasks, as differences in motivation and knowledge base will influence levels of children’s performance on different tasks, and some aspects of executive function are likely domain-specific in nature. For example, although reading comprehension seems to be associated with a domain-general set of processing resources, the relation between working memory and writing ability appears to be specific to that domain only ( Swanson & Berninger, 1996 ).

Developmental changes in working memory, inhibition, and cognitive flexibility are all related to one another and to changes in neurological development, particularly the frontal cortex. Few higher-level cognitive tasks can be performed without adequate control of one’s attention, and it is difficult to emphasize too much the importance of executive function to the development of higher-level cognition and to the regulation of one’s emotions and behaviors. Some have even speculated that the evolution of executive function may have been an important component in the emergence of the modern human mind (e.g., Geary, 2005 ). The abilities to inhibit inappropriate behavior, resist distraction, and control one’s actions in general are critical to effective function in any social group, as well as for activities such as hunting, preparing meals, or constructing tools, among many others. These abilities are better developed in humans than in other primates and in older children than in younger children, and may be a key to understanding both human cognitive development and evolution.

Becoming Self-Directed Learners: Strategy Development

Once children have sufficient cognitive and behavioral self-control, they can reflect on the problems they face and approach them strategically. Strategies are usually defined as deliberately implemented, nonobligatory (one doesn’t have to use them to perform a task), mentally effortful operations that are aimed at solving a problem and are potentially available to consciousness ( Harnishfeger & Bjorklund, 1990 ; Pressley & Hilden, 2006 ). Children become self-directed learners by using deliberate information-processing operations to achieve specific goals that could not be achieved “without thinking” (i.e., automatically or with implicit cognition).

It should not be surprising that children become better problem solvers with age. What is important is the way in which they become better problem solvers. As I mentioned in the previous section , children’s strategy use is affected by processes such as working memory and inhibition (e.g., Lehmann & Hasselhorn, 2007 ; Woody-Dorning & Miller, 2001 ). However, even young preschool children use simple strategies in some contexts. For example, 18- and 24-month-old children playing a modified game of hide-and-seek looked at the hiding location of a toy or repeated the toy’s name during a delay period between the time the toy was hidden and they were permitted to search for it ( DeLoache & Brown, 1983 ; DeLoache, Cassidy, & Brown, 1985 ). With age, the sophistication of children’s strategies increases. For example, preschool children perform simple addition problems by counting on their fingers; they use counting strategies that involve enunciating all numbers in each addend (e.g., for 5 + 3 = ?, saying “one, two, three, four, five…six, seven, eight,” called the sum strategy ), later using counting strategies in which only the numbers in the smaller addend are enumerated (e.g., for 5 + 3 = ?, saying “five…six, seven, eight,” called the min strategy , because the minimum number of counts is made), to retrieving the answer directly from memory (e.g., for 5 + 3 = ?, saying “eight” immediately after the problem is posed) (see Ashcraft, 1990 ). Use of increasingly sophisticated strategies with age is observed for most other complex cognitive tasks, including memory (see Bjorklund, Dukes, & Brown, 2009 ), problem solving and reasoning (see DeLoache, Miller, & Pierroutsakos, 1998 ), attention (see Miller, 1990 ), and reading (see Garner, 1990 ), among others.

However, it would be misleading to believe that strategies develop in a stagelike fashion, with a less sophisticated strategy being replaced by a more sophisticated one. Rather, children of all ages have multiple strategies available to them at any one time. The number of strategies available to a child increases with age, as does the effectiveness of the modal strategy that is used on any particular task. This is best reflected by Robert Siegler’s (1996 , 2006 ) adaptive strategy choice model . Using Darwin’s metaphor of natural selection as a guide, Siegler proposes that children generate a broad range of strategies to solve a particular class of problem and then select among those strategies. Depending on the child’s goals and the nature of the task, some strategies are selected and used frequently, whereas others that are less effective are used less often and eventually decrease in frequency (and may eventually go “extinct”). Early in development or when a child is first learning a new task, relatively simple strategies “win” most of the time. With practice and maturation, children use other, more effortful (i.e., requiring more mental effort and greater executive control) and effective strategies.

Siegler conceives of development as occurring via as a series of overlapping waves, with the pattern of those waves changing over time. Thus, extending the example of the development of addition strategies, individual preschool and early school-age children actually use multiple strategies that vary with age (older children make greater use of the more sophisticated strategies; see Siegler, 1996 ), specific problems (fact retrieval is more apt to be used on doubles, e.g., 5 + 5 = ?, Siegler & Shrager, 1984 ), and context (e.g., children used less sophisticated strategies in the context of a game using dice than when given problems in a standard format; e.g., Bjorklund & Rosenblum, 2002 ). Multiple and variable strategy use has been found for children for a wide range of tasks, including arithmetic (e.g., Alibali, 1999 ), memory (e.g., Coyle & Bjorklund, 1997 ), spelling (e.g., Kwong & Varnhagen, 2005 ), scientific reasoning ( Schauble, 1990 ), and conservation ( Church & Goldin-Meadow, 1986 ), among others (see Siegler, 1996 , 2006 ).

Children’s strategy use is influenced by a host of factors in addition to executive function, two important ones being knowledge base and metacognition . Knowledge base refers to how much children know about the problems they’re trying to solve. Children over a broad age range for a wide range of tasks use strategies more effectively when they have detailed knowledge for the to-be-processed information. The principal reason for this relationship seems to be that having an extensive knowledge base results in faster processing of information within that domain (e.g., the domain of chess, soccer, or developmental psychology), which in turn results in more efficient processing (see Bjorklund, Muir–Broaddus, & Schneider, 1990 ; Kee, 1994 ). As an example, consider a free-recall task in which children are given lists of words from different categories to remember. Some category members are typical exemplars of their category (e.g., orange, banana, pear for FRUIT), whereas others are atypical category members (e.g., raisin, melon, grapefruit ). Children are more likely to use one or more strategies and to remember more words when recalling the more familiar and more categorically integrated sets of typical items than atypical items (e.g., Best, 1993 ; Schneider, 1986 ; Schwenck, Bjorklund, & Schneider, 2007 ). In general, children’s world knowledge increases with age, and as it does their strategic performance on a host of tasks increases with it.

Metacognition refers to one’s knowledge of one’s cognitive abilities. For each type of cognition there is a corresponding type of metacognition—for example, meta-attention, metamemory, and metalinguistics. Both cognition and metacognition increase with age and are usually (but not always) correlated with one another ( Schneider & Lockl, 2002 ). When problem solving is governed by the use of goal-directed strategies, task performance is considerably enhanced by knowing how well one is doing (i.e., monitoring task performance, procedural metacognition ) and by assessing which strategies will be most effective and when (i.e., declarative metacognition ) ( Schneider & Lockl, 2002 ). This has been found for a host of cognitive domains, including scientific reasoning (e.g., Kuhn et al., 1988 ), arithmetic (e.g., Carr & Jessup, 1995 ), attention (e.g., Miller & Weiss, 1981 ), and memory (e.g., DeMarie et al., 2004 ), among others, although positive relations between cognitive and metacognitive performance are often not found until late childhood (e.g., Hasselhorn, 1990 ; Lange et al., 1990 ), unless simple tasks that involve metamemory questions that are highly related to task performance are used ( Schneider & Sodian, 1988 ). When children are provided metacognitive training, their use of strategies tends to increase, particularly for older children (e.g., Ghatala et al., 1986 ; Ringel & Springer, 1980 ).

The relationship between cognitive performance, strategies, and metacognition is a multidirectional one (e.g., Schneider & Bjorklund, 1998 ). Children’s tendency to use and be aware of the availability and effectiveness of cognitive strategies is related to their level of conceptual development, executive functioning, and familiarity with the materials and tasks. Even very young children use strategies effectively in some situations, but will fail to use them, or use them and fail to enhance task’s performance, in other situations. The latter phenomenon has been referred to as utilization deficiency ( Miller, 1990 ) and has been found for a variety of strategies, including selective attention (e.g., DeMarie-Dreblow & Miller, 1988 ), memory (e.g., Bjorklund et al., 1994 ), reading (e.g., Gaultney, 1995 ), and analogical reasoning (e.g., Muir-Broaddus, 1995 ), among many others. Children who do not use a strategy spontaneously can often be trained to do so, often with increases in task performance (e.g., Flavell, 1970 ; Gelman, 1969 ; see Harnishfeger & Bjorklund, 1990 , for review).

Like most aspects of cognitive development, it is not possible to specify a time in development when children are astrategic and a time when they become strategic. A child who fails to use a strategy on one task may do so given a slightly different context or set of instructions, or a different set of materials. Children’s cognitive functioning is influenced by a host of both endogenous and exogenous factors, and, depending on the amount and type of support children receive for performing a given task, they may display substantial cognitive competence or incompetence. Despite this variability, one can conclude with confidence that children’s problem solving becomes increasingly strategic with age; they have a broad selection of strategies to choose from, and they become more effective with age in their selection and monitoring of problem-solving strategies.

Cognitive Development Is Constructed Within a Social Context

Humans are a social species, and human development can be properly understood only when the influence of social relations and the broader social/cultural environment are considered. Development always occurs within a social context, culturally shaped and historically conditioned, although the specific details of a child’s social environment can vary widely.

This is no less true of cognitive development as it is of social and emotional development. Although the great bulk of cognitive-development research has been conducted in laboratories or quiet rooms in children’s schools, and the topics of study have often been divorced from children’s everyday lives, children’s developing cognitive skills are used to solve everyday problems, and they “learn” to think by interacting with their social environment (see Gauvain, Beebe, & Zhao, 2011 ; Cole, 2006 ; Rogoff, 2003 ; Vygotsky, 1978 ; see the chapter by Gauvain in this volume 2). Acquiring a full understanding of cognitive development requires examining both distal (e.g., evolutionary) and proximal, or immediate (e.g., role of parents, peers, neuronal development) influences. Included in both the distal and the proximal levels of causation is the social environment. First, the opportunities and tools that a culture provides will obviously have an immediate impact on children’s thinking (e.g., learning to read). But many of these tools are products of the sociohistorical context in which a culture developed. The traditions, tools, and languages spoken have deep cultural roots that can influence a child’s intellectual development.

Cultural Contexts for Learning

In all cultures, parents, teachers, siblings, and peers influence children’s cognitive development both by serving as a source of problems (much of humans’ considerable intelligence is used to deal with conspecifics) and by guiding their problem solving. Vygotsky’s (1978) concept of parents and other more cognitively sophisticated people working with children in the zone of proximal development is well known to developmental psychologists and reflects the routine interactions parents and others have with children that fosters cognitive change. Learning is most apt to occur when parents provide children with the appropriate degree of scaffolding ( Wood, Bruner, & Ross, 1976 ), giving neither too little nor too much help with a particular problem. There are cultural and individual differences in the assistance adults give children in solving daily problems, and children learn much about “how to think,” not through explicit teaching by adults, but through what Rogoff (1998 ; Rogoff et al., 1993 ) calls guided participation , “the process and system of involvement of individuals with others, as they communicate and engage in shared activities” ( Rogoff et al., 1993 , p. 6).

Different cultures (and subcultures) construct different experiences for their children, and this has consequences for both what and how children learn. For example, children living in traditional societies are more attentive to what adults do as opposed to what adults say to them, and thus develop a keener ability to learn through observation than children from schooled societies (e.g., Mejia-Arauz, Rogoff, & Paradise, 2005 ; Morelli, Rogoff, & Angelillo, 2003 ). In one study, children of traditional Mexican heritage whose mothers had only basic schooling (on average, a seventh-grade education) and children of Mexican or European background whose mothers had a high-school education or more observed a woman creating origami figures ( Mejia-Arauz et al., 2005 ). When they were later asked to make their own figures, children of traditional Mexican heritage were less likely to request information from the “Origami Lady” than children of the more educated mothers. These findings are consistent with the observations that these “traditional” children pay more attention to the actions of adults and learn more through observation, rather than seeking instructions from adults or learning through verbal instructions (see Cole, 2006 ).

Other research suggests that general cultural perspectives influence some basic aspects of cognitive development. For example, East Asian cultures are proposed to promote a holistic approach to reasoning, whereas Western cultures are hypothesized to promote a more analytic style ( Nisbett et al., 2001 ). Such differences have been hypothesized to affect how children learn to allocate their attention, with East Asians socialized to divide their attention between objects and events in their environments and Westerners socialized to focus their attention on key features of objects ( Duffy & Kitayama, 2007 ). For example, when Japanese and American adults were shown a picture of a box with a line drawn in it and then asked to draw a line in a larger box that is either of the same absolute length or the same relative length as the line in the smaller box, robust cultural differences were found: Japanese adults were more accurate performing the relative task, whereas American adults were more accurate performing the absolute task ( Kitayama et al., 2003 ). This cultural pattern is found as early as age 6 ( Duffy et al., 2009 ; Vasilyeva, Duffy, & Huttenlocher, 2007 ); however, both American and Japanese 4- and 5-year-olds made more errors on the absolute task ( Duffy et al., 2009 ), suggesting that young children from both cultures initially have an easier time dealing with relative information, but, depending on cultural practices, sometime around 6 years of age, some children (in this case, Americans) become socialized to focus their attention, whereas others (in this case, Japanese) become socialized to divide their attention.

Some everyday practices of parents in Western culture serve to prepare their children for life in a schooled and literate society. For example, children attending Western schools are frequently asked questions for which adults already know the answers. They also are asked to learn and discuss things that have no immediate relevance—knowledge for knowledge’s sake. We take such practices for granted, but such context-independent learning is foreign to many cultures, and we are mostly unaware of how evolutionarily novel formal education practices are for our species. Despite the novelty of such practices, children do not enter school totally unprepared for such experiences. For example, Western parents of young children frequently prompt them to name objects or to recall recent events (e.g., “What did we do today? Who did we see? Did you cry? Yes? Who else was there?”) ( Gauvain, 2001 ; Rogoff, 1990 ). In addition to preparing children for the type of discourse they will experience in school, such shared remembering helps children learn how to remember and communicate memories; learn about themselves, which contributes to the development of the self-concept; learn about their own social and cultural history; and learn what is worth remembering. It also promotes social solidarity ( Gauvain, 2001 ).

Sociohistorical Influences

According to Vygotsky (1978) , cultures provide the tools of intellectual adaptation that children learn to use to think and solve problems. These tools include such things as computers, alphabets, abacuses, books, number systems, music, art, and other cultural inventions specifically designed to foster learning and communication, but also more implicit devices that can influence thinking, such as the language spoken and how it represents concepts. Concerning language, something as simple as how a language expresses its numbers can affect important aspects of quantitative development. For instance, we saw earlier that differences in the time it takes to articulate the digit words (one, two, three, etc.) influences digit span (e.g., Geary et al., 1993 ). Differences in how languages name number words have also been shown to be related to aspects of mathematical development. For example, the first 10 digit words have to be memorized in all languages (e.g., one, two, three; eins, zwei, drei ; yee, uhr, shan , in English, German, and Chinese, respectively), but once the teen decade is reached languages differ in terms of how much new vocabulary must be learned and the extent to which one uses the base-10 number system for enunciating number words. For instance, in Chinese numbers from 11 to 19 follow a simple rule: the Chinese word for 10 is shi and the numbers 11 through 19 are made by taking shi and adding the appropriate digit (11 = shi yee , or “ten one”; 12 = shi uhr , or “ten two”; 13 = shi shan , or “ten three,” and so on). In contrast, many of the words denoting the numbers from 11 to 19 in English are arbitrary (e.g., eleven, twelve, thirteen, fifteen), and even for “regular” numbers, the decade name is stated second (nine teen ), unlike numbers beginning with twenty, in which the decade term is stated first (e.g., twenty-one; thirty-two). As a result of these differences, Chinese children learn to count to 20 before English-speaking children, although there are no cultural differences in learning to count to 10 and in counting to 100 once children learn to count to 20 ( Miller et al., 1995 ). Similarly, German children have difficulty when learning how to convert spoken numbers to numerals, because, in German, the decade term follows the unit term (for instance, 42 is said zweiundvierzig , or “two-and-forty”); as a result they frequently invert the order of the numerals (for example, writing “24” instead of “42”) ( Zuber et al., 2009 ).

Other cultures have a limited way of expressing quantities. For instance, the Amazonia languages of the Pirahã and Mundurukú have no number words for quantities larger than five ( Gordon, 2004 ; Pica et al., 2004 ). Adults from these cultures can perform tasks involving small quantities easily, but their performance deteriorates rapidly when attempting tasks with larger quantities. In contrast, Pirahã children who learn Portuguese are able to perform arithmetic calculations with larger quantities, bolstering the interpretation that it is the language’s ability to represent numbers that is responsible for the pattern of numerical thinking in these cultures ( Gordon, 2004 ).

Natural selection has provided humans with a unique nervous system that develops in a species-typical way in all but the most deprived environments. As such, it is easy to think of cognitive development as something that “just happens,” pretty much the same way for children worldwide. Yet intelligence is also rooted in culture, and understanding how cultural practices and technological tools influence cognitive development helps us better comprehend the process of development and our role as adults in fostering that process. Cultural “explanations” for cognitive development do not provide alternative interpretations to those based on biology (e.g., neurological factors) or specific experience (e.g., how mothers talk to their babies); rather, cognitive development must be seen as the result of interacting factors, with the social environment being a critical ingredient in this mix.

Cognitive Development: A Mature and Developing Science

Nearly 20 years ago, a colleague specializing in social development asked me what had happened to cognitive development. It used to “lead the field,” he said, providing a framework for researchers in other areas of development, but now it seemed fragmented. The field was once united behind Piaget’s theory (or united in trying to refute Piaget’s theory), and this provided a framework nearly all psychologists could use to interpret children’s behavior and development. Information-processing approaches replaced Piaget’s account as the dominant metaphor for development, but shortcomings left the field without an overarching metatheory ( Bjorklund, 1997 ).

In the years since I had this conversation, I believe the field of cognitive development has gotten back on track. In place of the theoretical hegemony afforded by Piagetian or information-processing approaches, cognitive developmentalists adopted some principles, many of them based in developmental biology, that served to unify the field. Advances in brain research make it necessary for cognitive developmentalists to provide accounts that are at least not contradictory to what is known about how the brain works and develops (e.g., Lenroot & Giedd, 2007 ; Nelson, Thomas, & de Haan, 2006 ), and in some cases that are usefully informed by neuroscience, as in the case of executive function (e.g., Zelazo et al., 2008 ); new research in genetics points to the complex and bidirectional interactions between genes, environment, and development (e.g., Caspi et al., 2007 ; Rutter, 2007 ); and research and theory in evolutionary developmental biology and psychology (e.g., Bjorklund & Pellegrini, 2002 ; Gardiner & Bjorklund, 2009 ; Ploeger, van der Maas, & Rajimakers, 2008 ; West-Eberhard, 2003 ) make it clear that our ancestors also developed and that an appreciation of human phylogeny can help us acquire a better understanding of human ontogeny without the taint of genetic determinism that was once associated with evolutionary accounts of human behavior. Some researchers are fully aware of these influences on their science, whereas for others they serve as barely noticed background, but influence their thinking nonetheless.

Cognitive developmentalists are not of one mind about development, but they never have been, even when Piaget reigned. Contemporary cognitive-developmental science recognizes the significance of both lower-level and higher-level processes to children’s thinking, that the ontogeny of cognition has both biological and social origins, and that individual differences in cognition and its development are often associated with normative, age-related changes in thought. In brief, cognitive development happens at a variety of levels, and developmental scientists are becoming increasingly aware that we need to be cognizant of this and the interactions among the various levels to produce a true developmental science.

Questions for Future Research

How are patterns of neurological and behavioral/cognitive development coordinated and related?

How does the implicit knowledge/cognition of the infant and young child relate to the subsequent development of explicit knowledge/cognition?

Why do some forms of thinking come easily to children and others do not?

How do children gain intentional control over their thinking and problem solving?

What is the nature of representational change over infancy and childhood?

Adams, J. W. , & Hitch, G. J. ( 1997 ). Working memory and children’s mental addition.   Journal of Experimental Child Psychology , 67, 21–38.

Google Scholar

Alexander, R. D. ( 1989 ). Evolution of the human psyche. In P. Mellers & C. Stringer (Eds.), The human revolution: Behavioural and biological perspectives on the origins of modern humans (pp. 455–513). Princeton, NJ: Princeton University Press.

Google Preview

Alibali, M. W. ( 1999 ). How children change their minds: Strategy change can be gradual or abrupt.   Developmental Psychology , 35, 127–145.

Alloway, T. P. , Gathercole, S. E. , & Pickering, S. J. ( 2006 ). Verbal and visuospatial short-term and working memory in children: Are the separable?   Child Development , 77 , 1698–1716.

Ashcraft, M. H. ( 1990 ). Strategic processing in children’s mental arithmetic: A review and proposal. In D. F. Bjorklund (Ed.), Children’s strategies: Contemporary views of cognitive development (pp. 185–211). Hillsdale, NJ: Erlbaum.

Baddeley, A. D. ( 1986 ). Working memory . Oxford: Clarendon.

Baddeley, A. D. , & Hitch, G. J. ( 1974 ). Working memory. In G. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic.

Baillargeon, R. ( 1987 ). Object permanence in 3–1/2- and 4–1/2-month-old infants.   Developmental Psychology , 23, 655–664.

Baillargeon, R. ( 1994 ). How do infants learn about the physical world?   Current Directions in Psychological Science , 3 , 133–140.

Baillargeon, R. ( 2008 ). Innate ideas revisited: For a principle of persistence in infants’ physical reasoning.   Perspectives on Psychological Science , 3, 2–13.

Baird, A. A. , Kagan, J. , Gaudette, T. , Walz, K. A. , Hershlag, N. , & Boas, D. A. ( 2002 ). Frontal lobe activation during object permanence: Data from near-infrared spectroscopy.   NeuroImage , 16, 120–126.

Baker, S. T. , Friedman, O. , & Leslie, A. M. ( 2010 ). The opposites task: Using general rules to test cognitive flexibility in preschoolers.   Journal of Cognition and Development , 11, 240–254.

Bandura, A. ( 2006 ). Toward a psychology of human agency.   Perspectives on Psychological Science , 1, 164–180.

Barkley, R. A. ( 1997 ). Behavioral inhibition, sustained attention, and executive functions. Constructing a unifying theory of ADHD.   Psychological Bulletin , 121, 65–94.

Barnett, W. S. ( 1995 ). Long-term effects of early childhood programs on cognitive and school outcomes.   The Future of Children , 5 (No. 3, Winter), 25–50.

Baron-Cohen, S. , Leslie, A. M. , & Frith, U. ( 1985 ). Does the autistic child have a ‘theory of mind’?   Cognition , 21 , 37–46.

Barrett, T. M. , Davis, E. F. , & Needham, A. ( 2007 ). Learning about tools in infancy.   Developmental Psychology , 43, 352–368.

Barrouillet, P. ( 2011 ). Dual-process theories and cognitive development: Advances and challenges.   Developmental Review , 31, 79–85.

Bauer, P. J. ( 2002 ). Long-term recall memory: Behavioral and neuro-developmental changes in the first 2 years of life.   Current Directions in Psychological Science , 11, 137–141.

Bauer, P. J. ( 2007 ). Remembering the times of our lives: Memory in infancy and beyond . Mahwah, NJ: Erlbaum.

Bauer, P. J. , Wenner, J. A. , Dropik, P. L. , & Wewerka, S. S. ( 2000 ). Parameters of remembering and forgetting in the transition from infancy to early childhood.   Monographs of the Society for Research in Child Development , 65 (Issue no. 4, Serial No, 263).

Bayley, N. ( 1949 ). Consistency and variability in the growth of intelligence from birth to eighteen years.   Journal of Genetic Psychology , 75 , 165–196.

Becker, W. C. , & Gersten, R. ( 1982 ). A follow-up of Follow Through: The later effects of the direct instruction model for children in fifth and sixth grades.   American Educational Research Journal , 19, 75–92.

Beckett, C. , Maughan, B. , Rutter, M. , Castle, J. , Colvert, E. , Groothues, C. , Kreppner, J. , Stevens, S. , O-Connor, T. G. , & Sonuga-Barke, E. J. S. ( 2006 ). Do the effects of early sever deprivation on cognition persist into early adolescence? Findings from the English and Romanian Adoptee Study.   Child Development , 77, 696–711.

Bell, M. A. , Wolfe, C. D. , & Adkins, D. R. ( 2007 ). Frontal lobe development during infancy and childhood: Contributions of brain electrical activity temperament, and language to individual differences in working memory and inhibition control. In D. Coch ,K. W. Fischer, & G. Dawson (Eds.), Human behavior, learning, and the developing brain: Typical development (pp. 247–276). New York: Guilford.

Bender, C. E. , Herzing, D. L. , & Bjorklund, D. F. ( 2009 ). Evidence of teaching in Atlantic spotted dolphins ( Stenella frontalis ) by mother dolphins foraging in the presence of their calves. Animal Cognition , 12 , 43–53.

Berg, D. H. ( 2008 ). Working memory and arithmetic calculation in children: The contributory roles of processing speed, short-term memory, and reading.   Journal of Experimental Child Psychology , 99, 288–308.

Bering, J. M. , & Bjorklund, D. F. ( 2007 ). The serpent’s gift: Evolutionary psychology and consciousness. In P. D. Zelazo , M. Moscovitch , & E. Thompson, E. (Eds.). The Cambridge handbook of consciousness (pp. 595–627). New York: Cambridge University Press.

Bering, J. M. , & Povinelli, D. J. ( 2003 ). Comparing cognitive development. In D. Maestripieri (Ed.), Primate psychology (pp. 205–233). Cambridge, MA: Harvard University Press.

Bertenthal, B. I. , Proffitt, D. R. , & Cutting, J. E. ( 1984 ). Infant sensitivity to figural coherence in biomechanical motions.   Journal of Experimental Child Psychology , 37, 213–230.

Berthier, N. E. , DeBois, S. , Poirier, C. R. , Novak, M. A. , & Clifton, R. K. ( 2000 ). Where’s the ball? Two- and three-year-olds reason about unseen events.   Developmental Psychology, 36, 384–401.

Best, D. L. ( 1993 ). Inducing children to generate mnemonic organizational strategies: An examination of long-term retention and materials.   Developmental Psychology , 29, 324–336.

Billingsley, R. L. , Smith, M. L. , & McAndrews, M. P. ( 2002 ). Developmental patterns on priming and familiarity in explicit recollection.   Journal of Experimental Child Psychology , 82 , 251–277.

Bjorklund, D. F. ( 1987 ). A note on neonatal imitation.   Developmental Review , 7, 86–92.

Bjorklund, D. F. ( 1997 ). In search of a metatheory for cognitive development (or, Piaget is dead and I don’t feel so good myself).   Child Development , 68 , 144–148.

Bjorklund, D. F. ( 2005 ). Children’s thinking: Cognitive development and individual differences (4th ed.). Pacific Grove, CA: Brooks/Cole.

Bjorklund, D. F. , Cormier, C. , & Rosenberg, J. S. ( 2005 ). The evolution of theory of mind: Big brains, social complexity, and inhibition. In W. Schneider , R. Schumann-Hengsteler , & B. Sodian (Eds.), Young children’s cognitive development: Interrelationships among executive functioning, working memory, verbal ability and theory of mind (pp. 147–174). Mahwah, NJ: Erlbaum.

Bjorklund, D. F. , Dukes, C. , & Brown, R. D. ( 2009 ). The development of memory strategies. In M. Courage & N. Cowan (Eds.), The development of memory in childhood (pp. 145–175). Hove East Sussex, UK: Psychology Press.

Bjorklund, D. F. , Ellis, B. J. , & Rosenberg, J. S. ( 2007 ). Evolved probabilistic cognitive mechanisms: An evolutionary approach to gene × environment × development. In R. V. Kail (Ed.), Advances in child development and behavior , Vol. 35 (pp. 1–39). Oxford: Elsevier.

Bjorklund, D. F. , & Harnishfeger, K. K. ( 1990 ). Children’s strategies: Their definition and origins. In D. F. Bjorklund (Ed.), Children’s strategies: Contemporary views of cognitive development (pp. 309–323). Hillsdale, NJ: Erlbaum.

Bjorklund, D. F. , Muir-Broaddus, J. E. , & Schneider, W. ( 1990 ). The role of knowledge in the development of strategies. In D. F. Bjorklund (Ed.), Children’s strategies: Contemporary views of cognitive development (pp. 93–128). Hillsdale, NJ: Erlbaum.

Bjorklund, D. F. , & Pellegrini, A. D. ( 2002 ). The origins of human nature: Evolutionary developmental psychology. Washington, DC: American Psychological Association.

Bjorklund, D. F. , & Rosenberg, J. S. ( 2005 ). The role of developmental plasticity in the evolution of human cognition. In B. J. Ellis & D. F. Bjorklund (Eds.). Origins of the social mind: Evolutionary psychology and child development (pp. 45–75). New York: Guilford.

Bjorklund, D. F. , & Rosenblum, K. E. ( 2002 ). Context effects in children’s selection and use of simple arithmetic strategies.   Journal of Cognition and Development , 3, 225–242.

Bjorklund, D. F. , Schneider, W. , Cassel, W. S. , & Ashley, E. ( 1994 ). Training and extension of a memory strategy: Evidence for utilization deficiencies in the acquisition of an organizational strategy in high- and low-IQ children.   Child Development , 65 , 951–965.

Bornstein, M. H. ( 1989 ). Stability in early mental development: From attention and information processing in infancy to language and cognition in childhood. In M. H. Bornstein & N. A. Krasnegor (Eds.), Stability and continuity in mental development: Behavioral and biological perspectives (pp. 147–170). Hillsdale, NJ: Erlbaum.

Bornstein, M. H. , Hahn, C-S. , Bell, C. , Haynes, O. M. , Slater, A. , Golding, J. , Wolke, D. , & the ALSPAC Study Team . ( 2006 ). Stability on cognition across childhood.   Psychological Science , 17, 151–158.

Bradley, R. H. , Burchinal, M. R. , & Casey, P. H. ( 2001 ). Early intervention: The moderating role of the home environment.   Applied Developmental Science , 5, 2–8.

Brainerd, C. J. ( 1978 ). Piaget’s theory of intelligence . Englewood Cliffs, NJ: Prentice-Hall.

Brainerd, C. J. ( 1996 ). Piaget: A centennial celebration.   Psychological Science , 7, 191–195.

Brainerd, C. J. , & Gordon, L. L. ( 1994 ). Development of verbatim and gist memory for numbers.   Developmental Psychology , 30, 163–177.

Brainerd, C. J. , & Mojardin, A. H. ( 1999 ). Children’s and adults’ spontaneous false memories for sentences: Long-term persistence and mere-testing effects.   Child Development , 69 , 1361–1377.

Brainerd, C. J. , & Reyna, V. F. ( 1993 ). Domains of fuzzy trace theory. In M. L. Howe & R. Pasnak (Eds.), Emerging themes in cognitive development: Vol. 1 , Foundations (pp. 50–93). New York: Springer-Verlag.

Brainerd, C. J. , & Reyna, V. F. ( 2002 ). Fuzzy-trace theory and false memory.   Current Directions on Psychological Science , 11, 164–169.

Brainerd, C. J. , & Reyna, V. F. ( 2005 ). The science of false memory . Oxford, UK: Oxford University Press

Bräuer, J. , Call, J. , & Tomasello, M. ( 2005 ). All great ape species follow gaze to distant locations and around barriers.   Journal of Comparative Psychology , 119, 145–154.

Bronfenbrenner, U. , & Ceci, S. J. ( 1994 ). Nature-nurture reconceptualized in developmental perspective: A bioecological model.   Psychological Review , 101 , 568–586.

Brooks, R. , & Meltzoff, A. N. ( 2002 ). The importance of eyes: How infants interpret adult looking behavior.   Developmental Psychology , 38, 958–966.

Brooks-Gunn, J. , & Lewis, M. ( 1984 ). The development of early self-recognition.   Developmental Review , 4, 215–239.

Bruner, J. S. ( 1966 ). On cognitive growth. In J. S. Bruner , R. R. Olver , & P. M. Greenfield (Eds.), Studies in cognitive growth (pp. 1–67). New York: Wiley.

Buttelmann, D. , Carpenter, M. , Call, J. , & Tomasello, M. ( 2008 ). Rational tool use and tool choice in human infants and great apes.   Child Development , 79, 609–626.

Byrne, R. W. ( 2005 ). Social cognition: Imitation, imitation, imitation.   Current Biology , 15 , R489–R500.

Call, J. , Carpenter, M. , & Tomasello, M. ( 2004 ). Copying results and copying actions in the process of social learning: chimpanzees ( Pan troglodytes ) and human children ( Homo sapiens ). Animal Cognition , 8 , 151–163.

Campbell, F. A. , Ramey, C. T. , Pungello, E. , Sparling, J. , & Miller-Johnson, S. ( 2002 ). Early childhood education: Young adult outcomes from the Abecedarian project.   Applied Developmental Science , 6, 42–57.

Carey, S. ( 2009 ). The origins of concepts. Oxford: Oxford University Press.

Carey, S. ( 2011 ). Précis of   The Origins of Concepts. Behavioral and Brain Science , 34 , 133–167.

Carpenter, M. , Akhtar, N. , & Tomasello, M. ( 1998 ). 14- through 18-month-old infants differentially imitate intentional and accidental actions.   Infant Behavior & Development , 21, 315–330.

Carr, M. , & Jessup, D. L. ( 1995 ). Cognitive and metacognitive predictors of mathematics strategy use.   Learning and Individual Differences , 7, 235–247.

Carver, L. J. , & Bauer, P. J. ( 1999 ). When the event is more than the sum of its parts: Individual differences in 9-month-olds long-term ordered recall.   Memory , 2, 147–174.

Case, R. ( 1985 ). Intellectual development: Birth to adulthood . New York: Academic.

Case, R. ( 1992 ). The mind’s staircase: Exploring the conceptual underpinnings of children’s thought and knowledge . Hillsdale, NJ: Erlbaum.

Case, R. , Kurland, M. , & Goldberg, J. ( 1982 ). Operational efficiency and the growth of short-term memory span.   Journal of Experimental Child Psychology , 33, 386–404.

Caspi, A. , Williams, B. , Kim-Cohen, J. , Craig, I. W. , Milne, B. J. , Poulton, R. , Schalkwyk, L. C. , Taylor, A. , Werts, H. , & Moffitt, T. E. ( 2007 ). Moderation of breastfeeding effects on the IQ by genetic variation in fatty acid metabolism.   Proceedings of the National Academy of Science USA , 104 , 18860–18865.

Chelune, G. J. , & Baer, R. A. ( 1986 ). Developmental norms for the Wisconsin Card Sorting Test.   Journal of Clinical and Experimental Neuropsychology , 8, 219–228.

Chen, Z. , Sanchez, R. P. , & Campbell, T. ( 1997 ). From beyond to within their grasp: The rudiments of analogical problem solving in 10- and 13-month olds.   Developmental Psychology , 33, 790–801.

Chen, C. , & Stevenson, H. W. ( 1988 ). Cross-linguistic differences in digit span of preschool children.   Journal of Experimental Child Psychology , 46, 150–158.

Chi, M. T. H. ( 1978 ). Knowledge structure and memory development. In R. Siegler (Ed.), Children’s thinking: What develops ? Hillsdale, NJ: Erlbaum.

Chuah, Y. M. L. , & Maybery, M. T. ( 1999 ). Verbal and spatial short-term memory: common sources of developmental change?   Journal of Experimental Child Psychology , 73 , 7–44.

Chugani, H. T. , Behen, M. E. ; Muzik, O. , Juhász, C. , Nagy, F. , & Chugani, D. C. ( 2001 ). Local brain functional activity following early deprivation: A study of postinstitutionalized Romanian orphans.   NeuroImage , 117, 1290–1301.

Church, R. B. , & Goldin-Meadow, S. ( 1986 ). The mismatch between gesture and speech as an index of transitional knowledge.   Cognition , 23, 43–71.

Clearfield, M. W. , & Westfahl, S. M-C. ( 2006 ). Familiarization in infants’ perception of addition problems.   Journal of Cognition and Development , 7, 27–43.

Clements, W. A. , & Perner, J. ( 1994 ). Implicit understanding of belief.   Cognitive Development , 9, 377–395.

Clements, W. A. , Rustin, C. L. , & McCallum, S. ( 2000 ). Promoting the transition from implicit to explicit understanding: A training study of false belief.   Developmental Science , 3, 81–92.

Cole, M. ( 2006 ). Culture and cognitive development in phylogenetic, historical, and ontogenetic perspective. In W. Damon & R. M. Lerner (Gen. Eds.), Handbook of Child Psychology (6th ed.), D. Kuhn & R. S. Siegler (Vol. Eds.), Vol. 2, Cognition, perception, and language (pp. 636–683). New York: Wiley.

Cole, P. M. , Martin, S. E. , & Dennis, T. A. ( 2004 ). Emotion regulation as scientific construct: Methodological challenges and directions for child development research.   Child Development , 75, 317–333.

Collie, R. , & Hayne, R. ( 1999 ). Deferred imitation by 6–and 9-month-old infants: More evidence for declarative memory.   Developmental Psychobiology , 35, 83–90.

Cowan, N. , & Alloway, T. ( 2009 ). Development of working memory in childhood. In M. Courage & N. Cowan (Eds.), The development of memory in childhood (pp. 303–342). Hove East Sussex, UK: Psychology Press.

Cowan, N. , AuBuchon, A. M. , Gilchrist, A. L. , Ricker, T. J. , & Saults, J. S. ( 2011 ). Age differences in visual working memory capacity: Not based on encoding limitations.   Developmental Science , 14, 1066–1074.

Cowan, N. , Nugent, L. D. , Elliott, E. M. , Ponomarev, I. , & Saults, J. S. ( 1999 ). The role of attention in the development of short-term memory: Age differences in the verbal span of apprehension.   Child Development , 70, 1082–1097.

Coyle, T. R. , & Bjorklund, D. F. ( 1997 ). Age differences in, and consequences of, multiple and variable strategy use on a multitrial sort-recall task.   Developmental Psychology , 33, 372–380.

Cummins-Sebree, S. W. , & Fragaszy, D. M. ( 2005 ). Choosing and using tools: Capuchins ( Cebus apella ) use a different metric than tamarins ( Saguinus oedipus ). Journal of Comparative Psychology , 119, 210–219.

Daneman, M. , & Blennerhassett, A. ( 1984 ). How to assess the listening comprehension skills of prereaders.   Journal of Educational Psychology , 76 , 1372–1381.

Daneman, M. , & Green, I. ( 1986 ). Individual differences in comprehending and producing words in context.   Journal of Memory and Language , 25, 1–18.

Davis, H. , & Pérusse, R. ( 1988 ). Numerical competence in animals: Definitional issues, current evidence, and a new research agenda.   Behavioral and Brain Sciences , 11, 561–615.

DeLoache, J. S. ( 1987 ). Rapid change in the symbolic functioning of very young children.   Science , 238, 1556–1557.

DeLoache, J. S. ( 1991 ). Symbolic functioning in very young children: Understanding of pictures and models.   Child Development , 62, 736–752.

DeLoache, J. S. ( 2000 ). Dual representation and young children’s use of scale models.   Child Development , 71, 329–338.

DeLoache, J. S. , & Brown, A. L. ( 1983 ). Very young children’s memory for the location of objects in a large scale environment.   Child Development , 54, 888–897.

DeLoache, J. S. , Cassidy, D. J. , & Brown, A. L. ( 1985 ). Precursors of mnemonic strategies in very young children’s memory for the location of hidden objects.   Child Development , 56, 125–137.

DeLoache, J. S. , & Marzolf, D. P. ( 1992 ). When a picture is not worth a thousand words: Young children’s understanding of pictures and models.   Cognitive Development , 7, 317–329.

DeLoache, J. S. , Miller, K. F. , & Pierroutsakos, S. L. ( 1998 ). Reasoning and problem solving. In D. Kuhn & R. S. Siegler (Vol. Eds.), Cognitive, language, and perceptual development, Vol. 2, In W. Damon (Gen. Ed.), Handbook of child psychology (pp. 801–850). New York: Wiley.

DeMarie, D. , Miller, P. H. , Ferron, J. , & Cunningham, W. R. ( 2004 ). Path analysis tests for theoretical models of children’s memory performance.   Journal of Cognition and Development , 5, 461–492.

DeMarie-Dreblow, D. , & Miller, P. H. ( 1988 ). The development of children’s strategies for selective attention: Evidence for a transitional period.   Child Development , 59, 1504–1513.

Dempster, F. N ( 1981 ). Memory span: Sources of individual and developmental differences.   Psychological Bulletin , 89 , 63–100.

Dempster, F. N. ( 1985 ). Short-term memory development in childhood and adolescence. In C. J. Brainerd & M. Pressley (Eds.), Basic processes in memory development: Progress in cognitive development research (pp. 209–248). New York: Springer.

Dempster, F. N. ( 1992 ). The rise and fall of the inhibitory mechanism: Toward a unified theory of cognitive development and aging.   Developmental Review , 12, 45–75.

Dempster, F. N. ( 1993 ). Resistance to interference: Developmental changes in a basic processing mechanism. In M. L. Howe & R. Pasnak (Eds.), Emerging themes in cognitive development, Vol. 1 : Foundations (pp. 3–27). New York: Springer-Verlag.

Dennett, D. ( 1990 ). The interpretation of texts, people, and other artifacts.   Philosophy and Phenomenological Quarterly , 1 (supplement), 177–194.

Diamond, A. ( 1985 ). Development of the ability to use recall to guide action as indicated by infants’ performance on AB.   Child Development , 56, 868–883.

Diamond, A. , & Taylor, C. ( 1996 ). Development of an aspect of executive control: Development of the abilities to remember what I said and to “Do as I say, not as I do.”   Developmental Psychobiology , 29 , 315–324.

Dougherty, T. M. , & Haith, M. M. ( 1997 ). Infant expectations and reaction time as predictors of childhood speed of processing and IQ.   Developmental Psychology , 33, 146–155.

Dunbar, R. I. M. ( 1995 ). Neocortical size and language.   Behavioral and Brain Sciences , 18, 388–389.

Dunbar, R. I. M. ( 2010 ). Brain and behaviour in primate evolution. In P. M. Kappler & J. B. Silk (Eds.), Mind the gap: Tracing the origins of human universals (pp. 315–330). New York: Springer.

Duffy, S. , & Kitayama, S. ( 2007 ). Mnemonic context effect in two cultures: Attention to memory representations?   Cognitive Science, 31, 1–12.

Duffy, S. , Toriyama, R. , Itakura, S. , & Kitayama, S. ( 2009 ). Development of cultural strategies of attention in North American and Japanese children.   Journal of Experimental Child Psychology , 102 , 351–359.

Ellis, N. C. , & Hennelley, R. A. ( 1980 ). A bilingual word-length effect: Implications for intelligence testing and the relative ease of mental calculation in Welsh and English.   British Journal of Psychology , 71, 43–52.

Eluvathingal, T. J. , Chugani, H. T. , Behen, M. E. , Juhász, C. , Muzik, O. , Maqbool, M. , Chugani, D. C. , & Makki, M. ( 2006 ). Abnormal brain connectivity in children after early severe socioemotional deprivation: A diffusion tensor imaging study.   Pediatrics , 117, 2093–2100.

Fagan, J. F., III ( 1992 ). Intelligence: A theoretical viewpoint.   Current Directions in Psychological Science , 1 , 82–86.

Fagan, J. F., III , & Singer, J. T. ( 1983 ). Infant recognition memory as a measure of intelligence. In L. P. Lipsitt & C. K. Rovee-Collier (Eds.), Advances in infancy research (Vol. 2, pp. 31–78). Norwood, NJ: Ablex.

Feldman, R. , & Eidelman, A. I. ( 2004 ). Parent–infant synchrony and the social–emotional development of triplets.   Developmental Psychology , 40, 1133–1147.

Fischer, K. W. ( 1980 ). A theory of cognitive development: The control and construction of hierarchies of skills.   Psychological Review , 87 , 477–531.

Fischer, K. W. , & Bidell, T. ( 1998 ). Dynamic development of psychological structures in action and thought. In R. M. Lerner (Vol. Ed.), Theoretical models of human development, Vol. 1, of W. Damon (Gen. Ed.), Handbook of child psychology (5th ed. pp. 467–561). New York: Wiley.

Flavell, J. H. ( 1970 ). Developmental studies of mediated memory. In H. W. Reese & L. P. Lipsitt (Eds.), Advances in child development and child behavior (Vol. 5, pp. 181–211). New York: Academic.

Flinn, M. V. , Geary, D. C. , & Ward, C. V. ( 2005 ). Ecological dominance, social competition, and coalitionary arms races: Why humans evolved extraordinary intelligence.   Evolution and Human Behavior , 26, 10–46.

Flynn, E. , O’Malley, C. , & Wood, D. ( 2004 ). A longitudinal, microgenetic study of the emergence of false belief understanding and inhibition skills.   Developmental Science , 7, 103–115.

Fry, A. , & Hale, S. ( 2000 ). Relationships among processing speed, working memory and fluid intelligence in children.   Biological Psychology , 54, 1–34.

Gallup, G. G., Jr. ( 1979 ). Self-recognition in chimpanzees and man: A developmental and comparative perspective. In M. Lewis & L. A. Rosenblum (Eds.), Genesis of behavior, Vol. 2 . The child and its family (pp. 107–126). New York: Plenum.

Gardiner, A. K. , & Bjorklund, D. F. ( 2009 ). Development, evolution, and the emergence of novel behavior. In B. Myers (Ed.), Encyclopedia of complexity and system science . A. Nowak (Vol. Ed.) Applications of physics and mathematics to social science (pp. 1916–1931). Heidelberg, Germany: Springer.

Gardiner, A. K. , Greif, M. , & Bjorklund, D. F. ( 2011 ). Guided by intention: Preschoolers’ imitation reflects inferences of causation.   Journal of Cognition and Development , 12 , 355–373.

Garner, R. ( 1990 ). Children’s use of strategies in reading. In D. F. Bjorklund (Ed.), Children’s strategies: Contemporary views of cognitive development (pp. 245–268). Hillsdale, NJ: Erlbaum.

Garon, N. , Bryson, S. E. , & Smith, I. M. ( 2008 ). Executive function in preschoolers: A review using an integrative framework.   Psychological Bulletin , 134, 31–60.

Gathercole, S. E. , Alloway, T. P. , Willis, C. , & Adams, A.-M. ( 2006 ). Working memory in children with reading disabilities.   Journal of Experimental Child Psychology , 93, 265–281.

Gaultney, J. F. ( 1995 ). The effect of prior knowledge and metacognition on the acquisition of a reading comprehension strategy.   Journal of Experimental Child Psychology , 59 , 142–163.

Gauvain, M. ( 2001 ). The social context of cognitive development. New York: Guilford.

Gauvain, M. , Beebe, H. , & Zhao, S. ( 2011 ). Applying the cultural approach to cognitive development.   Journal of Cognition and Development , 12 , 121–133

Geary, D. C. ( 1995 ). Reflections of evolution and culture in children’s cognition: Implications for mathematical development and instruction.   American Psychologist , 50 , 24–37.

Geary, D. C. ( 2005 ). The origin of mind: Evolution of brain, cognition, and general intelligence . Washington, DC: American Psychological Association.

Geary, D. C. , Bow-Thomas, C. C. , Fan, L. , & Siegler, R. S. ( 1993 ). Even before formal instructions, Chinese children outperform American children in mental arithmetic.   Cognitive Development , 8, 517–529.

Gelman, R. ( 1969 ). Conservation acquisition: A problem of learning to attend to relevant attributes.   Journal of Experimental Child Psychology , 7 , 167–187

Gelman, R. , & Gallistel, R. ( 1978 ). The child’s understanding of number . Cambridge, MA: Harvard University Press.

Ghatala, E. S. , Levin, J. R. , Pressley, M. , & Goodwin, D. ( 1986 ). A componential analysis of the effects of derived and supplied strategy-utility information on children’s strategy selections.   Journal of Experimental Child Psychology , 41, 76–92.

Gordon, P. ( 2004 ). Numerical cognition without words: Evidence from Amazonia.   Science , 306 (15 October), 496–499.

Gottlieb, G. ( 1992 ). Individual development & evolution: The genesis of novel behavior . New York: Oxford.

Gottlieb, G. ( 2007 ). Probabilistic epigenesis.   Developmental Science , 10, 1–11.

Gottlieb, G. , Wahlsten, D. , & Lickliter, R. ( 2006 ). The significance of biology for human development: A developmental psychobiological systems view. In W. Damon & R. M. Lerner (Gen. Eds.), Handbook of Child Psychology (6th ed.), R. M. Lerner (Vol. Ed.), Vol. 1: Theoretical models of human development (pp. 210–257). New York: Wiley.

Gould, S. J. ( 1981 ). The mismeasure of man. New York: Norton.

Greenough, W. T. , Black, J. E. , & Wallace, C. S. ( 1987 ). Experience and brain development.   Child Development , 58, 539–559.

de Haan, M. , Oliver, A. , & Johnson, M. H. ( 1998 ). Electrophysiological correlates of face processing by adults and 6-month-old infants.   Journal of Cognitive Neural Science (Annual Meeting Supplement), 36.

Harnishfeger, K. K. ( 1995 ). The development of cognitive inhibition: Theories, definitions, and research evidence. In F. Dempster & C. Brainerd (Eds.), New perspectives on interference and inhibition in cognition (pp. 175–294). New York: Academic.

Harnishfeger, K. K. , & Bjorklund, D. F. ( 1990 ). Children’s strategies: A brief history. In D. F. Bjorklund (Ed.), Children’s strategies: Contemporary views of cognitive development (pp. 1–22). Hillsdale, NJ: Erlbaum.

Harnishfeger, K. K. , & Bjorklund, D. F. ( 1994 ). Individual differences in inhibition: Implications for children’s cognitive development.   Learning and Individual Differences , 6, 331–355.

Harnishfeger, K. K. , & Pope, R. S. ( 1996 ). Intending to forget: The development of cognitive inhibition in directed forgetting.   Journal of Experimental Child Psychology , 62, 292–315.

Hasselhorn, M. ( 1990 ). The emergence of strategic knowledge activation in categorical clustering during retrieval.   Journal of Experimental Child Psychology , 50, 59–80.

Hayes, B. K. , & Hennessy, R. ( 1996 ). The nature and development of nonverbal implicit memory.   Journal of Experimental Child Psychology , 63, 22–43.

Henning, A. , Spinath, F. M. , & Aschersleben, G. ( 2011 ). The link between preschoolers’ executive function and theory of mind and the role of epistemic states.   Journal of Experimental Child Psychology , 108, 513–531.

Herrmann, E. , Call, J. , Hernández-Lloreda, M. V. , Hare, B. , & Tomasello, M. ( 2007 ). Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis.   Science , 317, 1360–1366.

Holmboe, K. , Pasco Fearon, R. M. , Csibra, G. , Tucker, L. , & Johnson, M. H. ( 2008 ). “Freeze-Frame”: A new infant inhibition task and its relation to frontal cortex tasks in infancy and early childhood.   Journal of Experimental Child Psychology , 100, 89–114,

Honzik, M. P. , MacFarlane, J. W. , & Allen, L. ( 1948 ). Stability of mental test performance between 2 and 18 years.   Journal of Experimental Education , 17, 309–324.

Hood, B. , Carey, S. , & Prasada, S. ( 2000 ). Predicting the outcomes of physical events: Two-year-olds fail to reveal knowledge of solidity and support.   Child Development , 71, 1540–1554.

Horner, V. , & Whiten, A. ( 2005 ). Causal knowledge and imitation/emulation switching in chimpanzees ( Pan troglodytes ) and children ( Homo sapiens ). Animal Cognition , 8, 164–181.

Hughes, C. , & Ensor, R. ( 2007 ). Executive function and theory of mind: Predictive relations from ages 2 to 4.   Developmental Psychology , 43, 1447–1459.

Hulme, C. , Thomson, N. , Muir, C. , & Lawrence, A. ( 1984 ). Speech rate and the development of spoken words: The role of rehearsal and item identification processes.   Journal of Experimental Child Psychology , 38, 241–253.

Humphrey, N. K. ( 1976 ). The social function of intellect. In P. P. G. Bateson & R. A. Hinde (Eds.), Growing points in ethology (pp. 303–317). Cambridge: Cambridge University Press.

Johnson, M. H. , & de Haan, M. ( 2001 ). Developing cortical specialization for visual-cognitive function: The case of face recognition. In J. L. McClelland , & R. S. Siegler (Eds.), Mechanisms of cognitive development: Behavioral and neural perspectives (pp. 253–270). Mahwah, NJ: Erlbaum.

Johnson, J. , Im-Bolter, N. , & Pascual-Leone, J. ( 2003 ). Development of mental attention in gifted and mainstream children: The role of mental capacity, inhibition, and speed of processing.   Child Development , 74, 1594–1614.

Jones, L. B. , Rothbart, M. K. , & Posner, M. I. ( 2003 ). Development of executive attention in preschool children.   Developmental Science , 6, 498–504.

Kail, R. V. , & Ferrer, E. ( 2007 ). Processing speed in childhood and adolescence: Longitudinal models for examining developmental change.   Child Development , 78, 1760–1770.

Karmiloff-Smith, A. ( 1991 ). Beyond modularity: Innate constraints and developmental change. In S. Carey & R. Gelman (Eds.), The epigenesis of mind: Essays on biology and cognition (pp. 171–197). Hillsdale, NJ: Erlbaum.

Karmiloff-Smith, A. ( 1992 ). Beyond modularity: A developmental perspective on cognitive science . Cambridge, MA: MIT Press.

Kaye, K. , & Marcus, J. ( 1981 ). Infant imitation: The sensory-motor agenda.   Developmental Psychology , 17 , 258–265.

Kee, D. W. ( 1994 ). Developmental differences in associative memory: Strategy use, mental effort, and knowledge-access interactions. In H. W. Reese (Ed.), Advances in child development and behavior (Vol. 25, pp. 7–32). New York: Academic.

Keen, R. ( 2003 ). Representation of objects and events: Why do infants look so smart and toddlers look so dumb?   Current Directions in Psychological Science , 12, 79–83.

Kitayama, S. , Duffy, S. , Kawamura, T. , & Larsen, J. T. ( 2003 ). Perceiving an object and its context in different cultures: A cultural look at new look.   Psychological Science , 14, 201–206.

Klaczynski, P. A. ( 2009 ). Cognitive and social cognitive development: Dual-process research and theory. In J. B. St. T. Evans & K. Frankish (Eds.), In two minds: Psychological and philosophical theories of dual processing (pp. 265–292). Oxford, UK: Oxford University Press.

Klaus, R. A. , & Gray S. ( 1968 ). The early training project for disadvantaged children: A report after five years.   Monographs of the Society for Research in Child Development , 33 (Serial No. 120).

Kochanska, G. , Murray, K. , Jacques, T. Y. , Koenig, A. L. , & Vandegeest, K. A. ( 1996 ). Inhibitory control in young children and its role in emerging internalization.   Child Development , 67, 490–507.

Krützen, M. , Mann, J. , Heithaus, M. R. , Conner, R. C. , Bejder, L. , & Sherwin, W. B. ( 2005 ). Cultural transmission of tool use in Bottlenose dolphins.   Proceedings of the National Academy of Sciences USA , 102, 8939–8943.

Kuhlmeier, V. ( 2005 ). Symbolic insight and inhibitory control: Two problems facing young children an symbolic retrieval tasks.   Journal of Cognition and Development , 6 , 365–380

Kuhn, D. , Amsel, E. , & O’Loughlin, M. ( 1988 ). The development of scientific thinking skills . San Diego: Academic.

Kwong, T. E. , & Varnhagen, C. K. ( 2005 ). Strategy development and learning to spell new words: Generalization of a process.   Developmental Psychology , 41, 148–159.

Lange, G. , Guttentag, R. E. , & Nida, R. E. ( 1990 ). Relationships between study organization, retrieval organization, and general strategy-specific memory knowledge in young children.   Journal of Experimental Child Psychology , 49, 126–146.

Lazar, I. , Darlington, R. , Murray, H. , Royce, J. , & Snipper, A. ( 1982 ). Lasting effects of early education: A report from the Consortium for Longitudinal Studies.   Monographs of the Society for Research in Child Development , 47 (Serial No. 195).

Leavens, D. A. , Hopkins, W. D. , & Bard, K. A. ( 2005 ). Understanding the point of chimpanzee pointing. Epigenesis and ecological validity.   Current Directions in Psychological Science , 14, 185–189.

Lehmann, M. , & Hasselhorn, M. ( 2007 ). Variable memory strategy use in children’s adaptive intratask learning behavior: Developmental changes and working memory influences in free recall.   Child Development , 78, 1068–1082.

Lehman, E. B. , McKinley-Pace, M. J. , Wilson, J. A. , Savsky, M. D. , & Woodson, M. E. ( 1997 ). Direct and indirect measures of intentional forgetting in children and adults: Evidence for retrieval inhibition and reinstatement.   Journal of Experimental Child Psychology , 64, 295–316.

Lenroot, R. K. , & Giedd, J. N. ( 2007 ). The structural development of the human brain as measures longitudinally with magnetic resonance imaging. In D. Coch , K. W. Fischer , & G. Dawson (Eds.), Human behavior, learning, and the developing brain: Typical development (pp. 50–73). New York: Guilford.

Lickliter, R. ( 1990 ). Premature visual stimulation accelerates intersensory functioning in bobwhite quail neonates.   Developmental Psychobiology , 23, 15–27.

Liszkowski, U. , Carpenter, M. , Striano, T. , & Tomasello, M. ( 2006 ). 12- and 18-month-olds point to provide information for others.   Journal of Cognition and Development , 7, 173–187.

Liszkowski, U. , Carpenter, M. , & Tomasello, M. ( 2007 ). Pointing out new news, old news, and absent referents at 12 months of age.   Developmental Science , 10 , F1–F7.

Luna, B. , Thulborn, K. R. , Monoz, D. P. , Merriam, E. P. , Garver, K. E. , Minshew, N. J. , Keshavan, M. S. , Genovese, C. R. , Eddy, W. F. , & Sweeney, J. A. ( 2001 ). Maturation of widely distributed brain function subserves cognitive development.   NeuroImage , 13, 786–793.

Luria, A. R. ( 1961 ). The role of speech in the regulation of normal and abnormal behavior . New York: Liveright.

Lyons, D. E. , Young, A. G. , & Keil, F. C. ( 2007 ). The hidden structure of overimitation.   Proceedings of the National Academy of Sciences USA , 104, 19751–19756.

McAuley, T. , & White, D. A. ( 2011 ). A latent variables examination of processing speed, response inhibition, and working memory during typical development.   Journal of Experimental Child Psychology , 108, 445–468.

McCall, R. B. , & Carriger, M. S. ( 1993 ). A meta-analysis of infant habituation and recognition memory performance as predictors of later IQ.   Child Development , 64 57–79.

McCall, R. B. , Eichorn, D. H. , & Hogarty, P. S. ( 1977 ). Transitions in early mental development.   Monographs of the Society for Research in Child Development , 42 (Serial No. 171).

McDonough, L. , Mandler, J. M. , McKee, R. D. , & Squire, L. R. ( 1995 ). The deferred imitation task as a nonverbal measure of declarative memory.   Proceedings of the National Academy of Sciences USA , 92, 7580–7584.

Mejia-Arauz, R. , Rogoff, B. , & Paradise, R. ( 2005 ). Cultural variation in children’s observation during a demonstration.   International Journal of Behavioral Development , 29, 282–291.

Meltzoff, A. N. ( 1990 ). Towards a developmental cognitive science: The implications of cross-modal matching and imitation for the development of memory in infancy. In A. Diamond (Ed.), Annals of the New York Academy of Sciences : The development and neural bases of higher cognitive functions , (Vol. 608, pp. 1–31). New York: New York Academy of Sciences.

Meltzoff, A. N. ( 1995 ). What infant memory tells us about infantile amnesia: Long-term recall and deferred imitation.   Journal of Experimental Child Psychology , 59, 497–515.

Meltzoff, A. N. , & Borton, R. W. ( 1979 ). Intermodal matching by human neonates.   Nature , 282, 403–404.

Meltzoff, A. N. , & Moore, M. K. ( 1977 ). Imitation of facial and manual gestures by human neonates.   Science , 198, 75–78.

Miller, K. F. , Smith, C. M. , Zhu, J. , & Zhang, H. ( 1995 ). Preschool origins of cross-national differences in mathematical competence.   Psychological Science , 6, 56–60.

Miller, L. T. , & Vernon, P. A. ( 1997 ). Developmental changes in speed of information processing in young children.   Developmental Psychology , 33, 549–554.

Miller, P. H. ( 1990 ). The development of strategies of selective attention. In D. F. Bjorklund (Ed.), Children’s strategies: Contemporary views of cognitive development (pp. 157–184). Hillsdale, NJ: Erlbaum.

Miller, P. H. , & Weiss, M. G. ( 1981 ). Children’s attention allocation, understanding of attention, and performance on the incidental learning task.   Child Development , 52, 1183–1190.

Milner, B. ( 1964 ). Some effects of frontal lobectomy in man. In J. M. Warren & K. Akert (Eds.), The frontal granular cortex and behavior . New York: McGraw-Hill.

Morelli, G. A. , Rogoff, B. , & Angelillo, C. ( 2003 ). Cultural variation in young children’s access to work or involvement in specialized child-focused activities.   International Journal of Behavioral Development , 27, 264–274.

Muir-Broaddus, J. E. ( 1995 ). Gifted underachievers: Insights from the characteristics of strategic functioning associated with giftedness and achievement.   Learning and Individual Differences , 7, 189–206.

Nagell, K. , Olguin, K. , & Tomasello, M. ( 1993 ). Processes of social learning in the tool use of chimpanzees ( Pan troglodytes ) and human children ( Homo sapiens ). Journal of Comparative Psychology , 107, 174–186.

Nelson, C. A. ( 2007 ). A neurobiological perspective on early human deprivation.   Child Development Perspectives , 1, 13–18.

Nelson, C. A. , Thomas, K. M. , & de Haan, M. ( 2006 ). Neural bases of cognitive development. In W. Damon & R. M. Lerner (Gen. Eds.), Handbook of Child Psychology (6th ed.), D. Kuhn & R. S. Siegler (Vol. Eds.), Vol. 2, Cognition, perception, and language (pp. 3–57). New York: Wiley.

Nelson, C. A. III , Zeanah, C. H. , Fox, N. A. , Marshall, P. J. , Smuke, A. T. , & Guthrie, D. ( 2007 ). Cognitive recovery in socially deprived young children: The Bucharest Early Intervention Program.   Science , 318 (21 December), 1937–1940.

Newcombe, N. , Huttenlocher, J. , Drummey, A. B. , & Wiley, J. ( 1998 ). The development of spatial location coding: Use of external frames of reference and dead reckoning.   Cognitive Development , 13, 185–200.

NICHD Early Child Care Research Network ( 2005 ). Duration and developmental timing of poverty and children’s cognitive and social development from birth through third grade.   Child Development , 76 , 795–810.

Nielsen, M. , Suddendorf, T. , & Slaughter, V. ( 2006 ). Mirror self-recognition beyond the face.   Child Development , 77, 176–185.

Nielsen, M. , & Tomaselli, K. ( 2010 ). Overimitation in Kalahari Bushman children and the origins of human cultural cognition.   Psychological Science , 21, 729–736.

Nisbett, R. E. , Peng, K. , Choi, I. , & Norenzayan, A. ( 2001 ). Culture and systems of thought: Holistic vs. Analytic cognition.   Psychological Review , 108, 291–310.

O’Connor, T. G. , Rutter, M. , Beckett, C. , Keaveney, L. , & Kreppner, J. M. , and the English and Romanian Adoptees Study Team ( 2000 ). The effects of global severe privation on cognitive competence: Extension and longitudinal follow-up.   Child Development , 71, 376–390.

Oyama, S. ( 2000 ). The ontogeny of information: Developmental systems and evolution (2nd ed.). Durham, NC: Duke University Press.

Parker, S. T. , & McKinney, M. L. ( 1999 ). Origins of intelligence: The evolution of cognitive development in monkeys, apes, and humans . Baltimore: The Johns Hopkins University Press.

Pascalis, O. , de Haan, M. , & Nelson, C. A. ( 2002 ). Is face processing species-specific during the first year of life?   Science , 296 (17 May), 1321–1323.

Pascual-Leone, J. ( 1970 ). A mathematical model for the transition rule in Piaget’s developmental stages.   Acta Psychologia , 32, 301–345.

Pascual-Leone, J. ( 2000 ). Is the French connection neo-Piagetian? Not nearly enough!   Child Development , 71, 843–845.

Piaget, J. ( 1962 ). Play, dreams, and imitation in childhood . New York: Norton.

Piaget, J. ( 1983 ). Piaget’s theory. In J. H. Flavell & E. M. Markman (Ed.), Cognitive development . Vol. 3 of P. H. Mussen (Gen. Ed.), Handbook of child psychology (4th ed., pp. 630–706). New York: Wiley

Pica, P. , Lemer, C. , Izard, V. , Dehaene, S. ( 2004 ). Exact and approximate arithmetic in an Amazonian indigene group.   Science , 306 (15 October), 499–503.

Plomin, R. , DeFries, J. C. , McClearn, G. E. , & McGuffin, P. ( 2008 ). Behavioral genetics (5th ed.). New York: Worth Publishers.

Plomin, R. , Kennedy, J. K. J. , & Craig, I. W. ( 2006 ). The quest for quantitative trait loci associated with intelligence.   Intelligence , 34, 513–526.

Ploeger, A. , van der Maas, H. L. J. & Rajimakers, M. E. J. ( 2008 ). Is evolutionary psychology a metatheory for psychology? A discussion of four major issues in psychology from an evolutionary developmental perspective.   Psychological Inquiry , 19, 1–18.

Plotnik, J. M. , de Waal, F. B. M. , & Reiss, D. ( 2006 ). Self-recognition in an Asian elephant.   Proceedings of the National Academy of Sciences USA , 103 , 17053–17057.

Posner, M. I. , Rothbart, M. K. , & Sheese, B. E. ( 2007 ). Attention genes.   Developmental Science , 10, 24–29.

Povinelli, D. J. , Landau, K. R. , & Perilloux, H. K. ( 1996 ). Self-recognition in young children using delayed versus live feedback: Evidence of a developmental asynchrony.   Child Development , 67 , 1540–1554.

Povinelli, D. J. , & Simon, B. B. ( 1998 ). Young children’s understanding of briefly versus extremely delayed images of the self: Emergence of the autobiographical stance. Developmental Psychology , 34 , 188–194.

Pressley, M. , & Hilden, K. R. ( 2006 ). Cognitive strategies. In W. Damon & R. M. Lerner (Gen. Eds.), Handbook of Child Psychology (6th ed.), D. Kuhn & R. S. Siegler (Vol. Eds.), Vol. 2: Cognition, perception, and language (pp. 511–556). New York: Wiley.

Prior H. , Schwarz, A. , & Güntürkün, O. ( 2008 ) Mirror-induced behavior in the magpie ( Pica pica ): Evidence of self-recognition. PLoS Biol , 6 (8), e202. doi:10.1371/journal.pbio.0060202. 10.1371/journal.pbio.0060202

Ramey, C. T. , Campbell, F. A. , Burchinal, M. , Skinner, M. L. , Gardner, D. M. , & Ramey, S. L. ( 2000 ). Persistent effects of early childhood education on high-risk children and their mothers.   Applied Developmental Science , 4, 2–14.

Reiss, D. , & Marino, L. ( 2001 ). Mirror self-recognition in the bottlenose dolphin: A case of cognitive convergence.   Proceedings of the National Academy of Sciences USA , 98, 5937–5942.

Reyna, V. F. , & Farley, F. ( 2006 ). Risk and rationality in adolescent decision making: Implications for theory, practice, and public policy.   Psychological Science in the Public Interest , 7, 1–44.

Reynolds, A. J. , Mavrogenes, N. A. , Bezuczko, N. , & Hagemann, M. ( 1996 ). Cognitive and family support mediators of preschool effectiveness: A confirmatory analysis.   Child Development , 67, 1119–1140.

Reynolds, A. J. , Temple, J. A. , White, B. A. B. , Ou, S-R. , & Robertson, D. L. ( 2011 ). Age 26 cost-benefit analysis of the Child-Parent Center Early Education Program.   Child Development , 82, 379–404.

Ridderinkhof, K. R. , van der Molen, M. , & Band, G. P. H. ( 1997 ). Sources of interference from irrelevant information: A developmental study.   Journal of Experimental Child Psychology , 65, 315–341.

Ringel, B. A. , & Springer, C. J. ( 1980 ). On knowing how well one is remembering: The persistence of strategy use during transfer.   Journal of Experimental Child Psychology , 29, 322–333.

Rogoff, B. ( 1990 ). Apprenticeship in thinking: Cognitive development in social context . New York: Oxford University Press.

Rogoff, B. ( 1998 ). Cognition as a collaborative process. In W. Damon (Gen. Ed), Handbook of child psychology (5th ed.), D. Kuhn & R. S. Siegler (Vol. Eds.), Cognition language, and perceptual development, Vol. 2 (pp. 679–744). New York: Wiley

Rogoff, B. ( 2003 ). The cultural nature of human development . New York: Oxford University Press.

Rogoff, B. , Mistry, J. , Göncü, A. , & Mosier, C. ( 1993 ). Guided participation in cultural activity by toddlers and caregivers.   Monographs of the Society for Research in Child Development , 58 (Serial No. 236).

Rogoff, B. , Paradise, R. , Arauz, R. , Correa-Chávez, M. , & Angelillo, C. ( 2003 ). Firsthand learning through intent participation.   Annual Review of Psychology , 54, 175–203.

Rose, S. A. , & Feldman, J. F. ( 1995 ). Prediction of IQ and specific cognitive abilities at 11 years from infancy measures.   Developmental Psychology , 31, 685–696.

Rose, S. A. , Feldman, J. F. , & Wallace, I. F. ( 1992 ). Infant information processing in relation to six-year cognitive outcomes.   Child Development , 63, 1126–1141.

Rowe, D. C. , Jacobson, K. C. , & van der Oord, E. J. C. G. ( 1999 ). Genetic and environmental influences on vocabulary IQ: Parental education level as a moderator.   Child Development , 70, 1151–1162.

Rutter, M. ( 2007 ). Gene–environment interdependence.   Developmental Science , 10, 12–18.

Sabbagh, M. A. , Xu, F. , Carlson, S. M. , Moses, L. J. , & Lee, K. ( 2006 ). The development of executive functioning and theory of mind.   Psychological Science , 17, 74–81.

Scarr, S. ( 1993 ). Biological and cultural diversity: The legacy of Darwin for development.   Child Development , 64 , 1333–1353.

Schauble, L. ( 1990 ). Belief revision in children: The role of prior knowledge and strategies for generating evidence.   Journal of Experimental Child Psychology , 49, 31–57.

Schiff, A. R. , & Knopf, I. J. ( 1985 ). The effect of task demands on attention allocation in children of different ages.   Child Development , 56, 621–630.

Schneider, W. ( 1986 ). The role of conceptual knowledge and metamemory in the development of organizational processes in memory.   Journal of Experimental Child Psychology , 42, 218–236.

Schneider, W. , & Bjorklund, D. F. ( 1998 ). Memory. In W. Damon (Gen. Ed.), Handbook of child psychology. D. Kuhn & R. S. Siegler (Vol. Eds.), Cognitive, language, and perceptual development, Vol. 2 (pp. 467–521). New York: Wiley.

Schneider, W. , Gruber, H. , Gold, A. , & Opwis, K. ( 1993 ). Chess expertise and memory for chess positions in children and adults.   Journal of Experimental Child Psychology , 56, 328–349.

Schneider, W. , & Lockl, K. ( 2002 ). The development of metacognitive knowledge in children and adolescents. In T. Perfect & B. Schwartz (Eds.), Applied metacognition. Cambridge: Cambridge University Press.

Schneider, W. , & Weinert, F. E. ( 1995 ). Memory development during early and middle childhood: Findings from the Munich longitudinal study (LOGIC). In F. E. Weinert & W. Schneider (Eds.), Memory performance and competencies: Issues in growth and development (pp. 263–279). Hillsdale, NJ: Erlbaum.

Schwenck, C. , Bjorklund, D. F. , & Schneider, W. ( 2007 ). Factors influencing the incidence of utilization deficiencies and other patterns of recall/strategy-use relations in a strategic memory task.   Child Development , 78 , 1771–1787.

Segalowitz, S. J. , & Hiscock, M. ( 2002 ). The neuropsychology of normal development: Developmental neuroscience and a new constructivism. In S. J. Segalowitz & I. Rapin (Eds.), Handbook of neuropsychology (2nd ed.), Vol. 8, Part I: Child neuropsychology (pp. 7–28). Amsterdam: Elsevier.

Siegler, R. S. ( 1996 ). Emerging minds: The process of change in children’s thinking. New York: Oxford University Press.

Siegler, R. S. ( 2006 ). Microgenetic analyses of learning. In W. Damon & R. M. Lerner (Gen. Eds.), Handbook of Child Psychology (6th ed.), D. Kuhn & R. S. Siegler (Vol. Eds.), Vol. 2, Cognition, perception, and language (pp. 464–510). New York: Wiley.

Siegler, R. S. , & Shrager, J. ( 1984 ). Strategy choices in addition and subtraction: How do children know what to do? In C. Sophian (Ed.), Origins of cognitive skills (pp. 229–293). Hillsdale, NJ: Erlbaum.

Simon, T. J. , Hespos, S. J. , & Rochat, P. ( 1995 ). Do infants understand simple arithmetic? A replication of Wynn (1992).   Cognitive Development , 10, 253–269.

Skeels, H. M. ( 1966 ). Adult status of children with contrasting early life experiences.   Monographs of the Society for Research in Child Development , 31 (Serial No. 105).

Skeels, H. M. , & Dye, H. B. ( 1939 ). A study of the effects of differential stimulation on mentally retarded children.   Program of the American Association of Mental Deficiency , 44, 114–136.

Skouteris, H. , Spataro, J. , & Lazaridis, M. ( 2006 ). Young children’s use of a delayed video representation to solve a retrieval problem pertaining to self.   Developmental Science , 9, 505–517.

Spelke, E. S. ( 1991 ). Physical knowledge in infancy: Reflections on Piaget’s theory. In S. Carey & R. Gelman (Eds.), Epigenesis of mind: Essays in biology and knowledge (pp. 133–169). Hillsdale, NJ: Erlbaum.

Spelke, E. S. , & Kinzler, K. D. ( 2007 ). Core knowledge.   Developmental Science , 10 , 89–96.

Sperling, G. ( 1960 ). The information available in brief visual presentations.   Psychological Monographs , 74 (No. 11).

St. Petersburg-USA Orphanage Research Team ( 2008 ). The effects of early social-emotional and relationship experience on the development of young orphanage children.   Monographs of the Society for Research in Child Development , 73 (Serial No. 291).

Starkey, P. , Spelke, E. S. , & Gelman, R. ( 1990 ). Numerical abstraction by human infants.   Cognition , 36, 97–127.

Strauss, M. S. , & Curtis, L. E. ( 1981 ). Infant perception of numerosity.   Child Development , 52, 1146–1152.

Suddendorf, T. ( 2003 ). Early representational insight: Twenty-four-month-olds can use a photo to find an object in the world.   Child Development , 74, 896–904.

Suddendorf, T. , & Whiten, A. ( 2001 ). Mental evolution and development: Evidence for secondary representation in children, great apes, and other animals.   Psychological Bulletin 127 , 629–650.

Swanson, H. L. ( 2006 ). Cognitive processes that underlie mathematical precociousness in young children.   Journal of Experimental Child Psychology , 93, 239–264.

Swanson, H. L. , & Berninger, V. W. ( 1996 ). Individual differences in children’s working memory and writing skills.   Journal of Experimental Child Psychology , 63, 358–385.

Swanson, H. L. , & Jerman, O. ( 2007 ). The influence of working-memory on reading growth in subgroups of children with reading disabilities.   Journal of Experimental Child Psychology , 96, 249–283.

Tamis-LeMonda, C. S. , & Bornstein, M. H. ( 1989 ). Habituation and maternal encouragement of attention in infancy as predictors of toddler language, play, and representational competence.   Child Development , 60, 738–751.

Tomasello, M. ( 1999 ). The cultural origins of human cognition . Cambridge, MA: Harvard University Press.

Tomasello, M. , & Carpenter, M. ( 2007 ). Shared intentionality.   Developmental Science , 10, 121–125.

Tomasello, M. , Carpenter, M. , Call, J. , Behne, T. & Moll, H. ( 2005 ). Understanding and sharing intentions: The origins of cultural cognition.   Behavioral and Brain Sciences , 28, 675–692.

Turkheimer, E. , Haley, A. , Waldron, M. , D’Onofrio, B. , & Gottesman, I. I. ( 2003 ). Sociometric status modifies heritability of IQ in young children.   Psychological Science , 14, 623–628.

van Loosbroek, E. , & Smitsman, A. W. ( 1990 ). Visual perception of numerosity in infancy.   Developmental Psychology , 26, 916–922.

Vasilyeva, M. , Duffy, S. , & Huttenlocher, J. ( 2007 ). Developmental changes in the use of absolute and relative information: The case of spatial extent.   Journal of Cognition and Development , 8, 455–471.

Vygotsky, L. S. ( 1978 ). The mind in society: The development of higher psychological processes . Cambridge, MA: Harvard University Press

Walden, T. , Kim, G. , McCoy, C. , & Karrass, J. ( 2007 ). Do you believe in magic? Infants’ social looking during violations of expectations.   Developmental Science , 10, 654–663.

West-Eberhard, M. J. ( 2003 ). Developmental plasticity and evolution. New York: Oxford University Press.

Wiebe, S. A. , Espy, K. A. , & Charak, D. ( 2008 ). Using confirmatory factor analysis to understand executive control in preschool children: I. Latent structure.   Developmental Psychology , 44, 575–587.

Windsor, J. , Benigno, J. P. , Wing, C. A. , Carroll, P. J. , Koga, S. F. , Nelson, III, C. A. , Fox, N. A. , & Zeanah, C. H. ( 2011 ). Effect of foster care on young children’s language learning.   Child Development , 82, 1040–1046.

Wellman, H. M. , Cross, D. , & Watson, J. ( 2001 ). Meta-analysis of theory-of-mind development: The truth about false belief.   Child Development , 72, 655–684.

Whiten, A. ( 2007 ). Pan African culture: Memes and genes in wild chimpanzees.   Proceedings of the National Academy of Sciences USA , 104 (Nov. 6), 17559–17560.

Whiten, A. , Goodall, J. , McGrew, W. C. , Nishida, T. , Reynolds, V. , Sugiyama, Y. , Tutin, C. E. G. , Wrangham, R. W. , & Boesch, C. ( 1999 ). Cultures in chimpanzees.   Nature , 399 (June), 682–685.

Willatts, P. ( 1990 ). Development of problem-solving strategies in infancy. In D. F. Bjorklund (Ed.), Children’s strategies: Contemporary views of cognitive development (pp. 23–66). Hillsdale, NJ: Erlbaum.

Wimmer, H. , & Perner, J. ( 1983 ). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception.   Cognition , 13, 103–128.

Wood, D. , Bruner, J. S. , & Ross, G. ( 1976 ). The role of tutoring in problem-solving.   Journal of Child Psychology and Psychiatry , 17, 89–100.

Woody-Dorning, J. , & Miller, P. H. ( 2001 ). Children’s individual differences in capacity: Effects on strategy production and utilization.   British Journal of Developmental Psychology , 19, 543–557.

Wynn, K. ( 1992 ). Addition and subtraction by human infants.   Nature , 358, 749–750.

Yakovlev, P. I. , & Lecours, A. R. ( 1967 ). The myelenogenetic cycles of regional maturation of the brain. In A. Minkowski (Ed.), Regional development of the brain in early life (pp. 3–70). Oxford, England: Blackwell.

Zelazo, P. D. , Carlson, S. M. , & Kesek, A. ( 2008 ). The development of executive function in childhood. In C. A. Nelson & M. Luciana (Eds.), Handbook of cognitive devleopmental neuroscience (2nd ed.) (pp. 553–574), Cambridge, MA: MIT Press.

Zelazo, P. D. , Müller, U. , Frye, D. , & Marcovitch, A. ( 2003 ). The development of executive function in early childhood.   Monographs of the Society for Research in Child Development , 68 (Serial No. 274).

Zelazo, P. D. , Sommerville, J. A. , & Nichols, S. ( 1999 ). Age-related changes in children’s use of external representation.   Developmental Psychology , 35, 1059–1071.

Zeskind, P. S. , & Ramey, C. T. ( 1978 ). Fetal malnutrition: An experimental study of its consequences on infant development in two caregiver environments. Child Development , 49, 1155–1162.

Zeskind, P. S. , & Ramey, C. T. ( 1981 ). Sequelae of fetal malnutrition: A longitudinal, transactional, and synergistic approach.   Child Development , 52, 213–218.

Zheng, X. , Swanson, H. L. , & Marcoulides, G. A. ( 2011 ). Working memory components as predictors of children’s mathematical word problem solving.   Journal of Experimental Child Psychology , 110, 481–498.

Zuber, J. , Pixner, S. , Moeller, K. , & Nuerk, H-C. ( 2009 ). On the language-specificity of basic number processing: Transcoding in a language with inversion and its relation to working memory capacity.   Journal of Experimental Child Psychology , 102, 60–67.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

ORIGINAL RESEARCH article

Determinants of cognitive development in the early life of children in bhaktapur, nepal.

• 1 Child Health Research Project, Department of Pediatrics, Tribhuvan University Teaching Hospital, Kathmandu, Nepal
• 2 Department of Psychosocial Science, Faculty of Psychology, University of Bergen, Bergen, Norway
• 3 Regional Center for Child and Youth Mental Health and Child Welfare, NORCE Norwegian Research Centre AS, Bergen, Norway
• 4 Department of Community Medicine, Kathmandu Medical College, Kathmandu, Nepal
• 5 Department of Research, Innlandet Hospital Trust, Lillehammer, Norway
• 6 Centre for International Health, University of Bergen, Bergen, Norway

Background: Children in low and middle income countries may have many risk factors for poor cognitive development, and are accordingly at a high risk of not reaching their developmental potential. Determinants for cognitive development in early life can be found among biological and socioeconomic factors, as well as in stimulation and learning opportunities.

Objective: The present study aimed to identify determinants of cognitive, language and motor development in 6–11 months old Nepalese infants.

Methods: Six hundred infants with a length-for-age z -score <-1 were assessed with the Bayley Scales of Infant and Toddler development, 3rd edition (Bayley-III). Information on socioeconomic factors, child and maternal demographics, clinical and biological factors, and the home environment were collected. In a manual stepwise variable selection procedure, we examined the association between selected biological, socioeconomic and stimulation and learning opportunity variables and the Bayley-III cognitive, language and motor development subscale scores in multiple linear regression models.

Results: The length-for-age z -scores was positively associated with the cognitive composite score [standardized beta (ß): 0.22, p < 0.001] and the motor composite score [(ß): 0.14, p = 0.001]. Children born with low birth weight (<2500 g) scored significantly lower on all subscale scores. Diarrheal history was associated with poor language composite scores, and females had higher language composite scores than boys [(ß): 0.11, p = 0.015]. Children who had been hospitalized during the first month of life had also lower cognitive and motor composite scores than those who had not been hospitalized. Parental reports of physical punishment and lack of spontaneous vocalization were associated with poor cognitive and language composite scores, respectively. The statistical models with the various subscale scores as dependent variables explained between 8 to 16 percent of the variability in the cognitive developmental outcomes.

Conclusion: Our findings reveal important determinants for developmental scores in infancy, and underline the role of biological risk factors faced by marginalized children in low and middle income countries such as in Nepal.

Introduction

Children in low and middle income countries (LMIC) are at risk of not developing according to their potential, and this represents a major public health problem ( McDonald and Rennie, 2011 ). The South Asian and sub-Saharan African regions have multiple poverty related risks such as malnutrition, poor health and poor quality of stimulation and learning environment for many children ( Grantham-McGregor et al., 2007 ). In these settings, identifying the predictors for early child development will help in initiating early intervention plans to prevent developmental delays ( Persha et al., 2007 ). Known biological risk factors for poor cognitive function that are common in LMICs include short gestational duration ( Gutbrod et al., 2000 ; Espel et al., 2014 ), low birth weight ( Tong et al., 2006 ; Gill et al., 2013 ; Donald et al., 2019 ; Sania et al., 2019 ; Upadhyay et al., 2019 ), anemia ( Sungthong et al., 2002 ) and stunting ( Haile et al., 2016 ; Woldehanna et al., 2017 ). Poor nutrition, one of the causes of stunting ( De Onis and Branca, 2016 ), has crucial impact on the growth and development of the brain and later cognitive functioning ( Georgieff, 2007 ; Georgieff et al., 2018 ). Early childhood illnesses like diarrhea have also shown to predict development in high risk children ( Niehaus et al., 2002 ; Lorntz et al., 2006 ; Kvestad et al., 2015 ). There is also evidence that longer duration of breastfeeding enhances cognitive and language development in infants ( Lee et al., 2016 ).

Indicators of socioeconomic status including economic conditions ( Duc, 2009 ; Ribe et al., 2018 ) and parental education ( Roberts et al., 1999 ; Duc, 2009 ) has consistently been associated with cognitive functioning ( Christensen et al., 2014 ; Ribe et al., 2018 ). Adequate responsive stimulation during the first years of life is also crucial for children to reach their developmental potential ( Yousafzai et al., 2016 ; Nguyen et al., 2018 ).

The first 1,000 days, lasting from conception to the end of the second year of early childhood, is a particularly important period for cognitive development ( Bellieni, 2016 ). During this period, minor impairments of brain because of biological and psychosocial factors can affect the structural and functional development of the brain ( Walker et al., 2011 ).

The current study is conducted in a low and middle income setting with multiple risks factors that might affect child development. We assessed the development of the children and collected information of potential predictors for child development such as biological, socioeconomic, stimulation, and learning opportunities for the children. The main aim of this paper is to identify the determinants of cognitive, motor and language development assessed with the Bayley-III in these Nepalese infants at 6–11 months old.

Materials and Methods

Study design, setting, and population.

The children were participants in a doubled blinded clinical trial entitled “The effect of Vitamin B12 supplementation in Nepali Infants on Growth and Development” ( Strand et al., 2017 ) ( ClinicalTrials.gov : NCT02272842). The study site is the Bhaktapur municipality and surrounding areas of Bhaktapur district in Nepal. We included 600 children aged 6–11 months who were at increased risk of stunting [length for age z -score (LAZ)<-1SD], who plan to reside in the area for the next 12 months and whose parents consented to participate. Children with severe illness requiring hospitalization, severe malnutrition (weight-for-length z -score<-3SD) and with severe anemia (Hb<7 g/dL) were excluded from the study. Those with ongoing acute infections such as fever or infection that required medical treatment were temporarily excluded and enrolled after recovery.

Enrollment and baseline assessments including Bayley test were done from April 2015 to February 2017. The children were identified by field staff from immunization clinics or through door-to-door home visits, and enrolled when their length was confirmed by a supervisor or a physician at the field office. Enrollment procedures included collection of demographic information of the families, length and weight taking, blood sampling and developmental assessments at the same day. After enrollment, the date of the home visit for the home inventory assessment was scheduled with the mother within 1-week. Ethical clearances was obtained from the National Health and Research Council (NHRC; No. 233/2014) in Nepal and from the Regional Committee for Medical and Health Research Ethics (REC; No. 2014/1528) in Norway.

Cognitive, Language and Motor Development

The cognitive, language and motor development at baseline were assessed using the Bayley-III ( Bayley, 2006a ) This is a comprehensive assessment tool of developmental functioning in infants and toddlers aged 1–42 months, takes 40 to 60 min to administer and includes three main subscales; cognitive, language (receptive and expressive communication) and motor (fine and gross motor). The Bayley-III represents the gold standard in developmental assessment of this age group and is widely used for research purposes worldwide. We have used the American norms from a representative American sample ( Bayley, 2006b ). The raw scores of each subscales were converted into scaled scores with a mean of 10 (SD: 3) and a range from 1 to 19 and again converted to the three composite scores with a mean of 100 and standard deviation of 15. The scales for the current study was initially adopted for a study in the same population for children 6 to 24 months ( Murray-Kolb et al., 2014 ), and found to be reliable and feasible in children between 6–11 months in the same study setting ( Ranjitkar et al., 2018 ).

Determinants

Baseline information.

Baseline information was collected within a week from the date of enrollment. The information included family socioeconomic factors, child and maternal demographics, clinical and biological factors and the stimulation and learning opportunities in home environment. At enrollment, length and weight of the child and the mother along with head circumference of the child, was measured by well trained field staffs at the clinic following standard guidelines. Birth weight was recorded according to the parental report. Similarly, blood samples were collected from all the children. Details of the study procedure have been published elsewhere ( Strand et al., 2017 ).

Home Environment

The Home Observation for Measurement of the Environment (HOME Inventory) ( Caldwell and Bradley, 1984 ) is a structured assessment of the home environment that are indicators of stimulation and learning environment of the children. It is performed by a combination of direct observation and an interview with the mother or caregiver of the child at home by trained field staffs. We used selected items from a Bangladeshi adapted version of the Home Inventory that have been found to be a feasible tool in the same population in Nepal ( Jones et al., 2017 ). In the current study, the structured assessment took approximately 20 min to complete with altogether 16 selected items from the original version of HOME Inventory including two items from the “Emotional and verbal responsivity” factor, two items from the ‘Avoidance of restriction and punishment’ factor, four items from the “Caregiver promotes child development” factor, two items from the “Organization of physical and temporal environment”, three items from the “Provision of appropriate play materials”, and three items from the “Opportunities for variety in daily stimulation” ( Table 1 ).

Table 1. Variables assessed in multivariable regression models that measured the association with cognitive, language and motor composite scores of Bayley-III in 600 Nepalese children aged 6–11 months.

Training and Quality Control

Before the start of the study, psychologists responsible for assessing children in the study were trained and standardized in the use of the Bayley-III. A well experienced local psychologist served as “gold standard” during training and throughout the study period, and the study psychologists were required to achieve a high inter-rater agreement (ICC > 0.90) before testing study children. Seven percent of all sessions were scored by two examiners for quality assurances for Bayley with ICC’s ranging from 0.97–1.00 showing excellent inter-rater agreement. All the assessments were video recorded for further check ups when required, and for feedback from the supervising psychologists to the assessors. Any particular issues or challenges with the testing were discussed on weekly Skype-meetings with the supervising team from Norway (IK, MH) during the study period.

For the HOME Inventory, the field staff were trained and validated against a “gold standard” before the study start and they were required to achieve a good inter-rater agreement (ICC = 0.74). Seven percent of all assessments were double scored by the psychologist for quality assurance giving an ICC of 0.88.

Growth measurements and other major activities were also standardized before the study and 5% double scoring were carried out by supervisors during the study period.

Statistical Analyses

Demographic characteristics are presented as numbers (N) and percentages (%), and by means and standard deviations (SD). We used multiple linear regression models to identify determinants of the Bayley-III scores. In these models, the composite scores of the cognitive, motor and language scales of the Bayley-III were used as dependent variables and the variables listed in Table 1 were considered for inclusion in the statistical models.

For the caste variable, we set Newar caste as the reference group, and categorized the remaining in three groups; the Brahmin/Chhetri, Tamang and “Others”. Hospitalizations during the first month of life and history of diarrheal episodes prior to enrolment were dichotomized as “Yes” or “No”. Alcohol consumption by father was dichotomized as “Yes” or “No”. We introduced all the items of the HOME Inventory separately as independent variables.

Variables for the statistical analyses were carefully selected in a manual, stepwise forward procedure as suggested by Hosmer and Lemeshow (Applied logistic regression, Second Edition). In short, the association between each candidate independent variable ( Table 1 ) with the selected outcomes were initially assessed in unadjusted models. Variables that were significant at a P < 0.2 level were kept in multiple models while those that were non-significant in the initial crude assessment were re-introduced one at a time into these multiple models. Only variables that remained significant after this process were kept in the final models that are presented in the paper. This manual stepwise procedure was repeated for each composite score; cognitive, language and motor development. The statistical analyses were performed in STATA version 15 (STATA, College Station, TX, United States).

The mean age of the children was 8 months (SD: 1.7), 309 (51.5%) were male and 62 (10.4%) were born preterm. Approximately 28% of the study children had a history of diarrhea 1 month prior to enrollment and 9% were hospitalized mainly related to their low birth weight or because of jaundice during the first month of life. The mean age of the mothers was 27 (SD: 4) years. Of the mothers, 37% were illiterate or had an educational level up to grade 5. Nearly 70% of the children belonged to the Newar ethnic group. Approximately 52% of the families resided in their own house, and 47% of the families had their own land ( Table 2 ). The mean composite scores of cognitive and motor subscales were close to the American norms while the language scale was 1 SD lower than the norms ( Table 3 ).

Table 2. Baseline information of 600 participant Nepalese infants.

Table 3. Bayley-III composite scores from children 6–11 months of age residing in Bhaktapur, Nepal.

Determinants of the Cognitive Composite Score

The cognitive composite score was positively associated with the length-for-age z -score. Children who were born with low birth weight (<2500 gm) had 5 points lower cognitive composite scores compared to children with birth weight in the normal range. Those who had been hospitalized during the first month of life had an average 4.7 points lower scores compared to those with no such history. A history of alcohol consumption in the father and reports of physical punishment during the past week were also predictors for the cognitive score ( Table 4 ).

Table 4. Linear regression analysis to identify determinants of the Bayley-III cognitive composite score in Nepalese children 6–11 months.

Determinants of the Language Composite Score

Female children had significantly higher language composite scores than male children. Low birth weight and head circumference were significantly associated with lower language scores. Those who had a history of diarrhea 1 month prior to enrollment had 2 points lower language composite scores than those who did not. Tamang and other castes had lower language scores than those belonging to the Newar caste. Children whose mother or caregiver did not show spontaneous vocalization to the child during the home observation had significantly lower scores than those who had mothers or caregivers that showed such stimulation ( Table 5 ).

Table 5. Linear regression analysis to identify determinants of the Bayley-III language composite score in Nepalese children 6–11 months.

Determinants of the Motor Composite Score

The motor composite score was associated with the length-for-age z -score. Other predictors for the motor composite scores were being born with low birth weight, hospitalization during the first month of life and family ownership of house ( Table 6 ).

Table 6. Linear regression analysis to identify determinants of the Bayley-III motor composite score in Nepalese children 6–11 months.

In a high risk sample of young Nepalese children, biological, socioeconomic and home environment factors were associated with cognitive, language and motor development. The assessed risk factors are common in most LMIC. Approximately one third of our participating children were stunted (length-for-age z -score ≤ 2) and approximately 10 percent were born preterm. Length-for-age z -score and low birth weight were the strongest predictors of the cognitive subscale. For the motor subscale, hospitalizations at 1 month of life and length-for-age z -score were the strongest determinants. Newar caste was set as reference group among the castes and caste was the strongest determinant for the language subscale. The cognitive score was associated mainly with the biological factors including growth and hospitalization along with a slightly significant effect of alcohol consumption by the father and reported physical punishment during the past week, while the language domain was associated with both biological and language stimulation and the motor composite was associated with both biological and socioeconomic factors. All models explained 8 to 16 percent variability for the Bayley-III subscales.

Biological Determinants of the Developmental Outcomes

Biological risk factors were consistently associated with all the assessed developmental domains. For instance linear growth was associated with both cognitive and motor development in line with studies that have shown that stunting is one of the main factors of poor child development ( Sudfeld et al., 2015 ; Miller et al., 2016 ). Stunting is one of the most used indicators for denoting malnutrition in early childhood ( Perumal et al., 2018 ) and there are several evidences of cognitive impairment because of malnutrition ( Nyaradi et al., 2013 ). Our findings supports the frequent use of length-for-age as a proxy for neurodevelopment.

Low birth weight was significantly associated with all subscales of the Bayley-III, in line with the known risk of low birth weight for neurodevelopmental outcomes ( Aarnoudse-Moens et al., 2009 ; Oudgenoeg-Paz et al., 2017 ). Our study children showed lower scores on language development with decrease in head circumference. Head growth is consistently associated with cognitive development in previous studies ( Gale et al., 2004 ; Silva et al., 2006 ). For instance, head circumference was a strong predictor of the Bayley scores at all eight sites of Mal-Ed study in which Nepal was one of the site ( Scharf et al., 2018 ), however, some studies contradict to the consistencies of results associated with head circumference and cognitive development ( Martyn et al., 1996 ; Dupont et al., 2018 ). We expected head circumference to show a more overall association across domains in the current study, there was, however, a lack of such associations. The slight differences between studies may be due to differences in study design, sample sizes, age at testing as well as inclusion of other variables.

Twenty-eight percent of the study children has a diarrheal history 1 month prior to enrollment. Both hospitalization and diarrheal history was associated with the cognitive and motor subscales which is in line with previous study results in North India showing decreased neurodevelopmental scores in children with diarrheal history ( Kvestad et al., 2015 ). There may be both direct and indirect pathways from these biological risks and the adverse development. For instance infants who are infected with enteric diseases such as diarrhea during the golden thousand days are affected in the absorptive function of a healthy intestinal tract that is critical for the optimal growth and development of the body and brain ( Petri et al., 2008 ). The consequences of diarrhea and hospitalization may also be mediated by what has been refered to as “Functional isolation” ( Lozoff et al., 1998 ). As a result from the biological conditions, infants may be characterized by irritable and apathetic behavior, and are at risk for receiving less quality responsive care from its caregiver ( Kvestad et al., 2015 ). Thus, biological risks may indirectly limit the stimulation received from the physical and social environment.

Female children have significantly higher language scores than males in the present study. Others have described that female children had better language development than males, especially in communication gestures and vocabulary development ( Eriksson et al., 2012 ). A meta analysis on parent child language interactions showed that mothers talk more to their daughters than their sons ( Leaper et al., 1998 ). Thus, gender differences in communication between parents and children might explain this result.

Socioeconomic Determinants of the Developmental Outcomes

In our results, ownership of house is a predictor for motor development. Houses can be a reflection of economic status of families in a Nepalese context ( Subba et al., 2014 ), and hence, to own a house is one of the most important indicators of socioeconomic status in this setting.

Compared to children in the Newar ethnic group, children from the Tamang group had lower scores on language development. The low score on language development in the Tamang and other castes compared to the Newar can also be verified in relation to differences in the socioeconomic status between these groups. In the study area, the Tamang group are mainly migrated people from neighboring districts. The economic basis for this group is mainly agriculture and other labor work like work in carpet factories ( Ghimire, 2014 ). A study of socioeconomic status of indigenous people in Nepal revealed that Newars have relatively better socioeconomic conditions than other indigenous group including Tamangs ( Subba et al., 2014 ) which may explain the differences we see in language development in our study. The observed differences between the ethnic groups can also be due to variability in communication habits between the groups ( Leonard et al., 2009 ).

The locality of our study setting is rich in cultural activities, and alcohol consumption is very common in this setting ( Maharjan and Magar, 2017 ). In our analysis, father’s alcohol consumption is associated with lower scores on the cognitive subscale. Alcohol consumption in parents have been shown to be related to lower cognitive achievements in children ( Bennett et al., 1988 ; Nordberg et al., 1994 ).

Stimulation and Learning Opportunities as Determinants of the Developmental Outcomes

Of the 16 items from the HOME Inventory that were included in the analyses, only two were significantly related to the Bayley-III scores in these young Nepalese children. Children whose caregiver reported physical punishment during the past week had lower scores on the cognitive subscales. This is in line with studies conducted in the United States, where physical punishment such as spanking predicted lower cognitive scores ( Straus and Paschall, 2009 ; MacKenzie et al., 2013 ). The children whose caregiver did not vocalize spontaneously to the child scored significantly lower on the language subscale. This may be understood in light of the findings in a previous study in the same setting, that showed lack of awareness amongst Nepalese mothers about the importance of interacting with their children ( Shrestha et al., 2019 ). Our results thus confirms the importance of early parent-child communication for early language development especially in vocabulary development ( Topping et al., 2013 ).

A wide range of stimulation and learning opportunities were included, and with two exceptions, physical punishment and caregiver vocalization, none of them were associated with cognitive development. It may be that in this high-risk group at this early age biological risk factors have the largest immediate impact on the childrens development.

The large sample of 600 children is one of the main strengths of this study. The Bayley-III with cultural adaptations have already been tested in the same population and found to be a promising tool in this setting ( Murray-Kolb et al., 2014 ; Pendergast et al., 2018 ; Ranjitkar et al., 2018 ). The study was further strengthened by standardization practices before the assessments ( Ranjitkar et al., 2018 ), and double scorings with the gold standard during the study period to maintain the quality of the data and prevent the examiners drift. The standardized and reliable measurement of predictors including stimulation and learning opportunities and LAZ is also a strength.

Limitations

The sample is a high-risk sample that is part of a clinical trial, and thus, it is not a population-based sample, and care should be taken before generalizing to the population as a whole. A comparison with a typically developing group of Nepalese children would have given additional insight into predictors of development, but was beyond the scope of the present study. One of our inclusion criteria was a LAZ<-1, which reduces the variability and may potentially alter the association between LAZ scores and Bayley scores. We believe, however, that we captured the LAZ-range where there is a linear relation between LAZ and Bayley-scores. This assumption is supported by findings from a study in young children in India where there was a linear association between LAZ and Ages and Stages Questionnaire (ASQ) scores up to -1 LAZ but not beyond that ( Kvestad et al., 2015 ). Birth weight of the children and diahreal episodes were recorded based on the parental reports.

Although our result showed that both biological and social factors were associated with developmental scores of these children, our study underline the role of biological factors faced by marginalized children in low and middle income countries such as in Nepal. Early intervention programs should be encouraged for overall development of children in LMIC setting.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the National Health and Research Council (NHRC; No. 233/2014) in Nepal and Regional Committee for Medical and Health Research Ethics (REC; No. 2014/1528) in Norway. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

TS, MH, IK, and RC designed the study. RC, MU, SR, JS, RS, MS, and LS conducted the research and were responsible for the field implementation and data collection. TS and SR analyzed the data and interpreted the results. SR, MH, IK, and TS had primary responsibility for the final content. All the authors read and approved the final version of the manuscript.

This work was supported by the Thrasher Research Fund (award 11512) and GC Rieber Funds.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We acknowledge the valuable work of all the staff at the Child Health Research Project. We also thank Ravi Prakash Upadhyaya for valuable suggestion in the analysis of the data, Siddhi Memorial Foundation for the collaboration in the conduct of the study, and all the families and their children in our study for their valuable participation.

Aarnoudse-Moens, C. S. H., Weisglas-Kuperus, N., van Goudoever, J. B., and Oosterlaan, J. (2009). Meta-analysis of neurobehavioral outcomes in very preterm and/or very low birth weight children. Pediatrics 124, 717–728. doi: 10.1542/peds.2008-2816

PubMed Abstract | CrossRef Full Text | Google Scholar

Bayley, N. (2006a). Bayley Scales of Infant and Toddler Development. London: Pearson.

Google Scholar

Bayley, N. (2006b). Manual of the Bayley Scales of Infant and Toddler Development. San Antonio, TX: NCS Pearson. Inc.

Bellieni, C. V. (2016). The golden 1,000 days. J. Gen. Pract. 4:250.

Bennett, L. A., Wolin, S. J., and Reiss, D. (1988). Cognitive, behavioral, and emotional problems among school-age children of alcoholic parents. Am. J. Psychiatry 145, 185–190. doi: 10.1176/ajp.145.2.185

Caldwell, B. M., and Bradley, R. H. (1984). Home Observation for Measurement of the Environment. Little Rock: University of Arkansas.

Christensen, D. L., Schieve, L. A., Devine, O., and Drews-Botsch, C. (2014). Socioeconomic status, child enrichment factors, and cognitive performance among preschool-age children: results from the Follow-Up of growth and development experiences study. Res. Dev. Disabil. 35, 1789–1801. doi: 10.1016/j.ridd.2014.02.003

De Onis, M., and Branca, F. (2016). Childhood stunting: a global perspective. Matern. Child Nutr. 12, 12–26. doi: 10.1111/mcn.12231

Donald, K. A., Wedderburn, C. J., Barnett, W., Nhapi, R. T., Rehman, A. M., Stadler, J. A., et al. (2019). Risk and protective factors for child development: an observational South African birth cohort. PLoS Med. 16:e1002920. doi: 10.1371/journal.pmed.1002920

Duc, L. T. (2009). The Effect of Early Age Stunting on Cognitive Achievement Among Children in Vietnam. Oxford: University of Oxford.

Dupont, C., Castellanos-Ryan, N., Séguin, J. R., Muckle, G., Simard, M.-N., Shapiro, G. D., et al. (2018). The predictive value of head circumference growth during the first year of life on early child traits. Sci. Rep. 8:9828. doi: 10.1038/s41598-018-28165-8

Eriksson, M., Marschik, P. B., Tulviste, T., Almgren, M., Pérez Pereira, M., Wehberg, S., et al. (2012). Differences between girls and boys in emerging language skills: evidence from 10 language communities. Br. J. Dev. Psychol. 30, 326–343. doi: 10.1111/j.2044-835X.2011.02042.x

Espel, E. V., Glynn, L. M., Sandman, C. A., and Davis, E. P. (2014). Longer gestation among children born full term influences cognitive and motor development. PLoS One 9:e113758. doi: 10.1371/journal.pone.0113758

Gale, C. R., O’Callaghan, F. J., Godfrey, K. M., Law, C. M., and Martyn, C. N. (2004). Critical periods of brain growth and cognitive function in children. Brain 127, 321–329. doi: 10.1093/brain/awh034

Georgieff, M. K. (2007). Nutrition and the developing brain: nutrient priorities and measurement. Am. J. Clin. Nutr. 85, 614S–620S.

PubMed Abstract | Google Scholar

Georgieff, M. K., Ramel, S. E., and Cusick, S. E. (2018). Nutritional influences on brain development. Acta Paediatr. 107, 1310–1321. doi: 10.1111/apa.14287

Ghimire, M. (2014). Socio-Cultural And Economic Condition Of Tamangs: Case Study of Angsarang, Nepal. Ph.D. Thesis, Springer, Berlin.

Gill, S. V., May-Benson, T. A., Teasdale, A., and Munsell, E. G. (2013). Birth and developmental correlates of birth weight in a sample of children with potential sensory processing disorder. BMC Pediatr. 13:29. doi: 10.1186/1471-2431-13-29

Grantham-McGregor, S., Cheung, Y. B., Cueto, S., Glewwe, P., Richter, L., Strupp, B., et al. (2007). Developmental potential in the first 5 years for children in developing countries. Lancet 369, 60–70. doi: 10.1016/s0140-6736(07)60032-4

Gutbrod, T., Wolke, D., Soehne, B., Ohrt, B., and Riegel, K. (2000). Effects of gestation and birth weight on the growth and development of very low birthweight small for gestational age infants: a matched group comparison. Arch. Dis. Child Fetal Neonatal Ed. 82, F208–F214.

Haile, D., Nigatu, D., Gashaw, K., and Demelash, H. (2016). Height for age z score and cognitive function are associated with academic performance among school children aged 8–11 years old. Arch. Public Health 74:17.

Jones, P. C., Pendergast, L. L., Schaefer, B. A., Rasheed, M., Svensen, E., Scharf, R., et al. (2017). Measuring home environments across cultures: invariance of the HOME scale across eight international sites from the MAL-ED study. J. Sch. Psychol. 64, 109–127. doi: 10.1016/j.jsp.2017.06.001

Kvestad, I., Taneja, S., Hysing, M., Kumar, T., Bhandari, N., and Strand, T. A. (2015). Diarrhea, stimulation and growth predict neurodevelopment in young north Indian children. PLoS One 10:e0121743. doi: 10.1371/journal.pone.0121743

Leaper, C., Anderson, K. J., and Sanders, P. (1998). Moderators of gender effects on parents’ talk to their children: a meta-analysis. Dev. Psychol. 34, 3–27. doi: 10.1037/0012-1649.34.1.3

Lee, H., Park, H., Ha, E., Hong, Y.-C., Ha, M., Park, H., et al. (2016). Effect of breastfeeding duration on cognitive development in infants: 3-year follow-up study. J. Korean Med. Sci. 31, 579–584. doi: 10.3346/jkms.2016.31.4.579

Leonard, K. M., Van Scotter, J. R., and Pakdil, F. (2009). Culture and communication: cultural variations and media effectiveness. Adm. Soc. 41, 850–877. doi: 10.1177/0095399709344054

CrossRef Full Text | Google Scholar

Lorntz, B., Soares, A. M., Moore, S. R., Pinkerton, R., Gansneder, B., Bovbjerg, V. E., et al. (2006). Early childhood diarrhea predicts impaired school performance. Pediatr. Infect. Dis. J. 25, 513–520. doi: 10.1097/01.inf.0000219524.64448.90

Lozoff, B., Klein, N. K., Nelson, E. C., McClish, D. K., Manuel, M., and Chacon, M. E. (1998). Behavior of infants with iron-deficiency anemia. Child Dev. 69, 24–36. doi: 10.1111/j.1467-8624.1998.tb06130.x

MacKenzie, M. J., Nicklas, E., Waldfogel, J., and Brooks-Gunn, J. (2013). Spanking and child development across the first decade of life. Pediatrics 132:e1118-25. doi: 10.1542/peds.2013-1227

Maharjan, P., and Magar, K. (2017). Prevalence of alcohol consumption and factors associated with the alcohol use among the youth of suryabinayak Municipality, Bhaktapur. J. Pharm. Care Health Syst. 4:168.

Martyn, C. N., Gale, C. R., Sayer, A. A., and Fall, C. (1996). Growth in utero and cognitive function in adult life: follow up study of people born between 1920 and 1943. BMJ 312, 1393–1396. doi: 10.1136/bmj.312.7043.1393a

McDonald, L. A., and Rennie, A. C. (2011). Investigating developmental delay/impairment. Paediatr. Child Health 21, 443–447. doi: 10.1016/j.paed.2011.02.008

Miller, A. C., Murray, M. B., Thomson, D. R., and Arbour, M. C. (2016). How consistent are associations between stunting and child development? Evidence from a meta-analysis of associations between stunting and multidimensional child development in fifteen low-and middle-income countries. Public Health Nutr. 19, 1339–1347. doi: 10.1017/S136898001500227X

Murray-Kolb, L. E., Rasmussen, Z. A., Scharf, R. J., Rasheed, M. A., Svensen, E., Seidman, J. C., et al. (2014). The MAL-ED cohort study: methods and lessons learned when assessing early child development and caregiving mediators in infants and young children in 8 low-and middle-income countries. Clin. Infect. Dis. 59(Suppl._4), S261–S272. doi: 10.1093/cid/ciu437

Nguyen, P. H., DiGirolamo, A. M., Gonzalez-Casanova, I., Young, M., Kim, N., Nguyen, S., et al. (2018). Influences of early child nutritional status and home learning environment on child development in Vietnam. Matern. Child Nutr. 14:e12468. doi: 10.1111/mcn.12468

Niehaus, M. D., Moore, S. R., Patrick, P. D., Derr, L. L., Lorntz, B., Lima, A. A., et al. (2002). Early childhood diarrhea is associated with diminished cognitive function 4 to 7 years later in children in a northeast Brazilian shantytown. Am. J. Trop. Med. Hyg. 66, 590–593. doi: 10.4269/ajtmh.2002.66.590

Nordberg, L., Rydelius, P. A., and Zetterström, R. (1994). Parental alcoholism and early child development. Acta Paediatr. 83, 14–18. doi: 10.1111/j.1651-2227.1994.tb13378.x

Nyaradi, A., Li, J., Hickling, S., Foster, J., and Oddy, W. H. (2013). The role of nutrition in children’s neurocognitive development, from pregnancy through childhood. Front. Hum. Neurosci. 7:97. doi: 10.3389/fnhum.2013.00097

Oudgenoeg-Paz, O., Mulder, H., Jongmans, M. J., van der Ham, I. J., and Van der Stigchel, S. (2017). The link between motor and cognitive development in children born preterm and/or with low birth weight: a review of current evidence. Neurosci. Biobehav. Rev. 80, 382–393. doi: 10.1016/j.neubiorev.2017.06.009

Pendergast, L. L., Schaefer, B. A., Murray-Kolb, L. E., Svensen, E., Shrestha, R., Rasheed, M. A., et al. (2018). Assessing development across cultures: invariance of the Bayley-III scales across seven international MAL-ED sites. Sch. Psychol. Q. 33:604. doi: 10.1037/spq0000264

Persha, A., Arya, S., Nagar, R., Behera, P., Verma, R., and Kishore, M. (2007). Biological and psychosocial predictors of developmental delay in persons with intellectual disability: retrospective case-file study. Asia Pac. Disabil. Rehabil. J. 18, 93–100.

Perumal, N., Bassani, D. G., and Roth, D. E. (2018). Use and misuse of stunting as a measure of child health. J. Nutr. 148, 311–315. doi: 10.1093/jn/nxx064

Petri, W. A., Miller, M., Binder, H. J., Levine, M. M., Dillingham, R., and Guerrant, R. L. (2008). Enteric infections, diarrhea, and their impact on function and development. J. Clin. Investig. 118, 1277–1290. doi: 10.1172/jci34005

Ranjitkar, S., Kvestad, I., Strand, T. A., Ulak, M., Shrestha, M., Chandyo, R. K., et al. (2018). Acceptability and reliability of the bayley scales of infant and toddler development-III among children in Bhaktapur, Nepal. Front. Psychol. 9:1265. doi: 10.3389/fpsyg.2018.01265

Ribe, I. G., Svensen, E., Lyngmo, B. A., Mduma, E., and Hinderaker, S. G. (2018). Determinants of early child development in rural Tanzania. Child Adolesc. Psychiatry Ment. Health 12:18. doi: 10.1186/s13034-018-0224-5

Roberts, E., Bornstein, M. H., Slater, A. M., and Barrett, J. (1999). Early cognitive development and parental education. Infant Child Dev.Int. J. Res. Pract. 8, 49–62. doi: 10.1002/(sici)1522-7219(199903)8:1<49::aid-icd188>3.3.co;2-t

Sania, A., Sudfeld, C. R., Danaei, G., Fink, G., McCoy, D. C., Zhu, Z., et al. (2019). Early life risk factors of motor, cognitive and language development: a pooled analysis of studies from low/middle-income countries. BMJ Open 9:e026449. doi: 10.1136/bmjopen-2018-026449

Scharf, R. J., Rogawski, E. T., Murray-Kolb, L. E., Maphula, A., Svensen, E., Tofail, F., et al. (2018). Early childhood growth and cognitive outcomes: findings from the MAL-ED study. Matern. Child Nutr. 14:e12584. doi: 10.1111/mcn.12584

Shrestha, M., Ulak, M., Strand, T. A., Kvestad, I., and Hysing, M. (2019). How much do Nepalese mothers know about child development? Early Child Dev. Care 189, 135–142. doi: 10.1080/03004430.2017.1304391

Silva, A., Metha, Z., and O’Callaghan, F. J. (2006). The relative effect of size at birth, postnatal growth and social factors on cognitive function in late childhood. Ann. Epidemiol. 16, 469–476. doi: 10.1016/j.annepidem.2005.06.056

Strand, T. A., Ulak, M., Chandyo, R. K., Kvestad, I., Hysing, M., Shrestha, M., et al. (2017). The effect of vitamin B 12 supplementation in Nepalese infants on growth and development: study protocol for a randomized controlled trial. Trials 18:187. doi: 10.1186/s13063-017-1937-0

Straus, M. A., and Paschall, M. J. (2009). Corporal punishment by mothers and development of children’s cognitive ability: a longitudinal study of two nationally representative age cohorts. J. Aggress. Maltreat. Trauma 18, 459–483. doi: 10.1080/10926770903035168

Subba, C., Pyakuryal, B., Bastola, T. S., Subba, M. K., Raut, N. K., and Karki, B. (2014). A Study on the Socio-Economic Status of Indigenous Peoples in Nepal. Kathmandu: Lawyer’s Association for Human Rights of Nepalese Indigenous Peoples (LAHURNIP).

Sudfeld, C. R., McCoy, D. C., Fink, G., Muhihi, A., Bellinger, D. C., Masanja, H., et al. (2015). Malnutrition and its determinants are associated with suboptimal cognitive, communication, and motor development in Tanzanian children. J. Nutr. 145, 2705–2714. doi: 10.3945/jn.115.215996

Sungthong, R., Mo-suwan, L., and Chongsuvivatwong, V. (2002). Effects of haemoglobin and serum ferritin on cognitive function in school children. Asia Pac. J. Clin. Nutr. 11, 117–122. doi: 10.1046/j.1440-6047.2002.00272.x

Tong, S., Baghurst, P., and McMichael, A. (2006). Birthweight and cognitive development during childhood. J. Paediatr. Child Health 42, 98–103. doi: 10.1111/j.1440-1754.2006.00805.x

Topping, K., Dekhinet, R., and Zeedyk, S. (2013). Parent–infant interaction and children’s language development. Educ. Psychol. 33, 391–426. doi: 10.1080/01443410.2012.744159

Upadhyay, R. P., Naik, G., Choudhary, T. S., Chowdhury, R., Taneja, S., Bhandari, N., et al. (2019). Cognitive and motor outcomes in children born low birth weight: a systematic review and meta-analysis of studies from South Asia. BMC Pediatrics 19:35. doi: 10.1186/s12887-019-1408-8

Walker, S. P., Wachs, T. D., Grantham-McGregor, S., Black, M. M., Nelson, C. A., Huffman, S. L., et al. (2011). Inequality in early childhood: risk and protective factors for early child development. Lancet 378, 1325–1338. doi: 10.1016/s0140-6736(11)60555-2

Woldehanna, T., Behrman, J. R., and Araya, M. W. (2017). The effect of early childhood stunting on children’s cognitive achievements: evidence from young lives Ethiopia. Ethiop. J. Health Dev. 31, 75–84.

Yousafzai, A. K., Obradović, J., Rasheed, M. A., Rizvi, A., Portilla, X. A., Tirado-Strayer, N., et al. (2016). Effects of responsive stimulation and nutrition interventions on children’s development and growth at age 4 years in a disadvantaged population in Pakistan: a longitudinal follow-up of a cluster-randomised factorial effectiveness trial. Lancet Glob. Health 4, e548–e558. doi: 10.1016/S2214-109X(16)30100-0

Keywords : cognitive development, Bayley scales of infant and toddler development, biological factors, socioeconomic factors, environmental stimulation, manual stepwise procedure

Citation: Ranjitkar S, Hysing M, Kvestad I, Shrestha M, Ulak M, Shilpakar JS, Sintakala R, Chandyo RK, Shrestha L and Strand TA (2019) Determinants of Cognitive Development in the Early Life of Children in Bhaktapur, Nepal. Front. Psychol. 10:2739. doi: 10.3389/fpsyg.2019.02739

Received: 11 September 2019; Accepted: 20 November 2019; Published: 06 December 2019.

Reviewed by:

Copyright © 2019 Ranjitkar, Hysing, Kvestad, Shrestha, Ulak, Shilpakar, Sintakala, Chandyo, Shrestha and Strand. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tor A. Strand, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Psychol

The Place of Development in the History of Psychology and Cognitive Science

In this article, I analyze how the relationship of developmental psychology with general psychology and cognitive science has unfolded. This historical analysis will provide a background for a critical examination of the present state of the art. I shall argue that the study of human mind is inherently connected with the study of its development. From the beginning of psychology as a discipline, general psychology and developmental psychology have followed parallel and relatively separated paths. This separation between adult and child studies has also persisted with the emergence of cognitive science. The reason is due essentially to methodological problems that have involved not only research methods but also the very object of inquiry. At present, things have evolved in many ways. Psychology and cognitive science have enlarged their scope to include change process and the interaction between mind and environment. On the other hand, the possibility of using experimental methods to study infancy has allowed us to realize the complexity of young humans. These facts have paved the way for new possibilities of convergence, which are eliciting interesting results, despite a number of ongoing problems related to methods.

Introduction

In this paper, I intend to analyze how the relationship of developmental psychology to general psychology and cognitive science has unfolded. This historical analysis will provide a background for a critical examination of the present state of the art.

Psychology emerged as a scientific discipline with the founding of Wundt’s Laboratory in Leipzig at the end of the nineteenth century (1879) 1 . Wundt’s method, both experimental and introspective, was directed to the study of an adult’s mind and behavior. It is less well-known that only 10 years later, James Baldwin, who had attended Wundt’s seminars in Germany, founded a laboratory of experimental psychology in Toronto in which experiments devoted to the study of mental development were performed. If the occasion that aroused Baldwin’s interest was the birth of his first daughter, actually, “that interest in the problems of genesis–origin, development, evolution–became prominent; the interest which was to show itself in all the subsequent years” ( Baldwin, 1930 ). Baldwin’s work was a source of inspiration for Piaget, certainly one of the most prominent figures in developmental psychology ( Morgan and Harris, 2015 ).

From the origins of psychology as a discipline, general psychology and developmental psychology have followed parallel and relatively separate paths. Two questions are particularly relevant to explain this fact.

From a theoretical point of view, developmental psychology has all along been greatly influenced by biology and evolutionary theory. The founders of developmental psychology have widely analyzed the relation between ontogenesis and phylogenesis ( Baldwin, 1895 ; Piaget, 1928 ). This analysis resulted in accepting the challenge of explaining development in a broad sense. In his autobiography, Baldwin affirms that already in the 10 years that he spent in Princeton between 1893 and 1903, where he founded another laboratory of experimental psychology, “the new interest in genetic psychology and general biology had become absorbing, and the meagerness of the results of the psychological laboratories (apart from direct work on sensation and movement) was becoming evident everywhere.” Thus, developmental psychology has followed an approach that in general psychology appeared much later 2 .

A second question regards method. Developmental researchers, while manifesting their attachment to experimental procedures, have been confronted with their insufficiency in the study of development. Both for deontological and practical reasons, many aspects of development, in particular in infants and young children, can hardly be investigated experimentally. Thus, a great number of studies in developmental psychology make use of observational methods based on different techniques such as ethnographic methods or parent reports, and the reliability of these methods has been questioned.

This relative separation between studies of adults and children has also persisted with the emergence of cognitive science. Actually, the primary aim of cognitive science, at least at the outset, was to model what we could call an adult static mind. Given a certain output, for instance an action, the task of the psychologist was to reconstruct the inference processes that were at the origin of this same action.

At the beginning of the twenty-first century, psychology and cognitive science have enlarged their scope to include change processes and the interaction between mind and environment, including other minds. Developmental psychology, for its part, has developed nonverbal methods such as looking measures and choice measures that also make it possible to carry out experiments with infants. These facts have paved the way for new possibilities of convergence, which are eliciting interesting results, despite a number of ongoing problems related to methods.

Psychology, Cognitive Science and Artificial Intelligence

The beginning of cognitive science.

According to the American psychologist George Miller, cognitive science was born on September 11, 1956, the second day of the Second Symposium on Information Theory held at MIT. That day began with a paper read by Allen Newell and Herbert Simon on the state of art of the Logic Theory Machine: a proof on computer of theorem 2.01 of Whitehead and Russell’s Principia Mathematica . That very same day ended with the first version of Chomsky’s The Structures of Syntax . Miller left the symposium convinced that experimental psychology, theoretical linguistics, and computer simulation of cognitive processes could become parts of a wider whole and that the future of research would be found in the elaboration of this composite whole (reported in Bruner, 1983a ). It is Miller who in 1960, together with Eugene Galanter and Karl Pribram, authored a text that may be considered the manifesto of cognitive science and that proclaimed the encompassing of cognitive psychology within the more general framework of information processing ( Miller et al., 1960 ). The assumption was that newly born information science could provide a unifying framework for the study of cognitive systems ( Schank and Abelson, 1977 ).

From a theoretical point of view, the core of this project is the concept of representation. Intentional mental states, such as beliefs and perceptions, are defined as relations to mental representations. The semantic properties of mental representations explain intentionality ( Pitt, 2017 ). Representations can be computed and thus constitute the basis for some forms of logic systems. According to the Cognitive Science Committee (1978) , which drew up a research project for the Sloan Foundation, all those disciplines, which belong to cognitive science, share the common goal of investigating the representational and computational capacities of the mind and the structural and functional realization of these capacities in the brain.

This point of view constitutes the foundation for what has been called functionalism in the philosophy of mind, i.e., the hypothesis that what defines the mind are those features that are independent of its natural realization. The classic functionalist stance is expressed by Pylyshyn in his book on computation and cognition ( Pylyshyn, 1984 ). He maintains that a clear distinction must be made between the functional architecture of the cognitive system and the rules and representations that the system employs.

Functionalism has been greatly discussed and criticized from the beginning ( Block, 1978 ; Dreyfus, 1979 ). Harnad (1990) identified what has been defined as the symbol grounding problem : “How can the semantic interpretation of a formal symbol system be made intrinsic to the system?”

The most exhaustive and most deeply argued critique of functionalism was advanced by Searle, who developed his arguments over time, publishing a number of essays which have given rise to heated debate ( Searle, 1980 , 1990 , 1992 ). The position taken up by functionalism is that the relationship between the brain and its products, that is to say conscious processes, is mediated by an intermediate level of unconscious rules. This intermediate level is, for functionalists, the level of the program. It is postulated that the rules are computational and that, consequently, the aim of research in cognitive science is to reconstruct these rules. Searle’s objection is that there are only two types of natural phenomena, the brain and the mental states that the brain brings into being and that humans experience. The brain produces mental states due to its specific biological characteristics. When we postulate the existence of unconscious rules, according to Searle, we invent a construct whose aim is to highlight a function, which we believe is especially significant. Such a function is not intrinsic and has no causal power. This argument is particularly interesting because it is founded on the impassable biological nature of the mind. Neither logic nor mathematical or statistical procedures may replace brain as a biological organ.

From another perspective, some scholars have emphasized that functionalism leads to a new form of behaviorism. Putnam (1988) claimed that reducing mental processes exclusively to their functional descriptions is tantamount to describe such processes in behavioristic terms 3 . In psychology, one of the most polemical critics of functionalism as a dangerous vehicle toward a new form of anti-mentalism, which would render vain all the battles waged by cognitivists against classic behaviorism, was a developmental psychologist, Bruner (1990) . The centrality of computability as the criterion for the construction of models in cognitive science leads naturally, in Bruner’s opinion, to abandoning “meaning making,” which was the central concern of the “Cognitive Revolution.”

Thus, at least at the outset, cognitive science was devoted to constructing computational models of human inference processes and of the knowledge that is used in performing these inferences. This definition of the object of cognitive science has led at first to designing and implementing problem-solving systems, where the complexity was located in the inference mechanisms, supposed to be the same for all problems ( Newell and Simon, 1972 ). Later, systems were implemented where reasoning was associated with specific and articulated knowledge representation ( Levesque and Brachman, 1985 ).

Notably, the aspect that was absent from this view of cognitive science was learning. This lack, according to Gentner (2010) , could be partly explained as a reaction to behaviorism, which was completely centered on learning. In fact, there were also philosophical reasons. Chomsky and Fodor, who were among the most influential members of the cognitive science community, were highly critical of the concept of learning. In their view, learning as a general mechanism does not exist, and Fodor even went so far as to state explicitly that no theory of development exists either ( Fodor, 1985 ).

Thus, cognitive science was born essentially as a reaction to behaviorism and took its legitimacy from the use of methodologies developed within artificial intelligence. These methodologies were supposed to make explicit how mental representations produced human activity in specific domains. However, this approach had a price: it separated the mind from its biological basis and from the context in which human activity takes place. There was no place for development, interaction, and variation due to biological or social causes 4 . This theoretical choice explains Bruner’s disillusion. For Bruner, cognitive science had fallen back into the behaviorism against which it originated, and no interesting relation could be established with developmental psychology. Developmental psychology is founded on the premise that a human being develops in interaction with the physical world and the society of other humans.

Cognitive Science in the Twenty-First Century

Cognitive science has changed considerably from its beginning. An obvious novelty concerns the increased importance assumed by learning with the emergence of connectionism ( Hinton, 1989 ).

When connectionist models were introduced, there was much debate regarding the relation of neural networks with the functioning of the human brain and their ability to address higher forms of thought ( Fodor and Pylyshyn, 1988 ; Quinlan, 1991 ; Chalmers, 1993 ). Later, philosophical discussion was replaced by empirical considerations. Networks are an efficient computational tool in some domains and are often used jointly with symbolic computations ( Wermter and Sun, 2000 ). Moreover, in recent advancements of artificial Intelligence, neural networks have been largely replaced by a variety of techniques of statistical learning ( Forbus, 2010 ).

More interesting for my purpose is the changes that the general philosophy of cognitive science has undergone due to the problems that have emerged with classic symbolic models. At its origin, the core of cognitive science was the relation between psychology and artificial intelligence. In the original project, this marriage was to be fruitful for both disciplines. Artificial intelligence expected from psychology the analysis of high-level mental mechanisms that, once simulated on a computer, could improve the efficiency of artificial systems. With computer simulation, psychology was to acquire a method to validate its models. However, this marriage, which for a while has been very productive and has generated many interesting ideas, ultimately failed. Artificial intelligence has evolved computing techniques that produce efficient systems without asking anymore if these techniques replicate human mental processes more or less faithfully. In psychology, the constraint to produce computational models has again restricted its scope ( Airenti and Colombetti, 1991 ).

Thus, the results of cognitive science of the twentieth century have led to a shift in cognitive science that has emerged with this century. Some researchers have proclaimed that the theoretical hypothesis that minds functionalities can be modeled disregarding the fact that they operate on the external world through the body could no longer be accepted. This new approach implies accounting for the biology of the mind/body unity and the interaction with the external world, both physical and social. One source of inspiration for this new turn came from Varela et al. (1991) , who proposed the concept of the embodied mind . Actually, the concept of embodiment includes many rather disparate inspirations, from Merleau-Ponty and phenomenology to Buddhism. I do not analyze these questions here. What interests me is the mere assumption that cognition is grounded in the world.

This new turn corresponds to the major importance assumed by robotics. It might be exaggerated to say that the role played by artificial intelligence in the past is now assumed by robotics. However, it is clear that the aim of constructing artificial actors that interact with the world and/or with humans has again established a link between the study of humans and the production of artificial systems. With respect to the past, the focus is no longer on the symbolic function of the mind, but on the mind embedded into a physical device that interacts with the external world. This evolution is linked to the enlarged scope of present robotics that goes well beyond traditional tasks such as farm automation. The ambition is to construct robots that may cooperate with humans in a multiplicity of tasks, including, for instance, assisting aged or disabled people or interacting with autistic children. Social robotics has then evolved toward biologically inspired systems, based on the notions of self-organization and embodiment ( Pfeifer et al., 2007 ). This new development has led to question once again psychologists about those characteristics that make humans what they are. If robots must be able to interact with humans, they should show those same characteristics ( Kahn et al., 2007 ). Can robots be endowed with intentionality, emotions, and possibly empathy?

Here, again a functionalist position appears. For some authors, the fact that the robot’s internal mechanisms are grounded in physical interactions with the external environment means that they truly have the potentiality of intrinsic intentionality ( Zlatev, 2001 ). This means, for them, that a mind is embodied in a robot. To the question of whether robots can have emotions, Arbib and Fellous (2004) answer that a better knowledge of biological systems will allow us in the future to single out “brain operating principles” independent of the physical medium in which they are implemented. This new form of functionalism is currently contrasted with an approach that considers that mental states and emotions are not intrinsic but can only be attributed to robots by humans ( Ziemke et al., 2015 ). Robots’ embodiment does not overcome the objection that was addressed to traditional artificial intelligence, namely that mental states and emotions can only be produced by a biological brain ( Ziemke, 2008 ). This latter position maintains that the relevant question for human-robot interaction is not that robots must be intentional beings, but that they must be perceived as such by humans ( Airenti, 2015 ; Wiese et al., 2017 ).

In conclusion, we can say that cognitive science was born as a way to renew psychology through a privileged connection with artificial intelligence. In the present state of research, it is social robotics that is attempting to establish a connection with biological sciences, psychology, and neuroscience, in order to build into robots those functionalities that should allow them to successfully interact with the external physical and social world. However, the main fundamental philosophical problems remain unchanged. One could still argue, as Searle did, that human mentality is an emergent feature of biological brains and no logical, mathematical or statistical procedure can produce it.

Present Questions for Cognitive Science

The question that we may raise today is this: what is cognitive science for? The relation that psychology has established with the sciences of the artificial has hidden the fact that a number of phenomena, which are essential for explaining the functioning of the human mind, have been largely ignored. This failure in explanation, which has concerned, for instance, the managing of mental states and emotions, and many complex communicative phenomena, is fundamentally linked to the fact that the mind is constantly in interaction with the physical and social world in a process of development. The primitive idea of cognitive science was to go beyond traditional psychology to enrich the study of mind with the contributions of other disciplines that also investigated human mind, such as linguistics, philosophy, and anthropology. This approach, which concerns the definition of the field of cognitive science, has been quite early reinterpreted as a problem of formalism. The question posed has been: how could psychology produce scientific models of human thought? Hence, the importance assumed by computer modeling as a means of replacing more traditional logical, mathematical, and statistical models. However, this theoretical choice has generated a major ambiguity, because computer models that are founded on logical, mathematical, or statistical formalisms have been seen as possibly equivalent to the mind. Once the fallacy of this equivalence appears—because no artificial model may replace the causal power of the human brain—we are left with some formal models with very limited psychological significance. What has been lost is the richness that cognitive science was supposed to acquire by connecting different disciplines. In particular, for many years, this approach has prevented general psychology from connecting with developmental psychology, a field of studies that, since Baldwin, had already posed the problem of the construction of the human mind as the result of biological development and social interaction.

The Study of Development

Biology and development in the debate between piaget and chomsky.

Studying development necessarily implies considering the fact that humans are biological systems that are certainly particularly complex but also share many characteristics with other living beings. Thus, in the field of developmental psychology, many questions have emerged concerning the link between development and evolution, the relation between genetic endowment and the influence on acquisition of environment (a concept that includes physical environment, parenting, social rules, etc.), and the nature of learning.

For Piaget, who came to developmental psychology from natural sciences, development had to be seen in the light of the theories of evolution. Intelligence, for him, is a particular case of biological adaptation, and knowledge is not a state but a process. Through action, children explore space and objects in the external world, and in this way, for instance, they learn the properties of the objects and their relations. These ideas, which sound rather contemporary to us, were considered as problematic in the past and prevented the establishment of a relationship between the study of development and the study of cognition in general. It is only in this century that development has been integrated into evolution studies via the so-called evo-devo approach and that these ideas have given rise to an interest in psychology ( Burman, 2013 ).

Actually, some aspects of Piaget’s perspective were problematic. Piaget supported his theory using what was considered a Lamarckian vision of evolution that assumed the inheritance of acquired characteristics. He had a well-known debate at the end of his life (1975) with Noam Chomsky on language acquisition, and outstanding biologists who also participated to the debate contested the validity of his use of the concept of phenocopy ( Piattelli-Palmarini, 1979/1980 ). In fact, on this point, Piaget had been influenced by Baldwin, who proposed what is known as Baldwin’s effect ( Simpson, 1953 ). This effect manifests in three stages: (1) Individual organisms interact with the environment in such a way as to produce nonhereditary adaptations; (2) genetic factors producing similar traits occur in the population; and (3) these factors increase in frequency under natural selection (taken from Waddington, 1953 ). Later, Piaget revised his own theory and updated Baldwin’s effect under the influence of Waddington ( Burman, 2013 ). Recently, epigenetic theories have emerged in biology, and the importance of development is generally accepted. On the developmental side, it has been proposed that Piaget’s theory might be replaced as a metatheory for cognitive development by evolutionary psychology ( Bjorklund, 2018 ).

The debate between Chomsky and Piaget is interesting because it is a clear example of the impossibility of dialogue between one of the fathers of cognitive science and the scholar who, at that moment, personified developmental psychology. Piaget was unable to justify his position that grammar rules could also be accounted for by sensorimotor schemata, and Chomsky appeared to have won the debate. At the same time, Chomsky presented the emergence of syntactic rules in the child’s mind, excluding in principle any possible form of learning. However, in hindsight, we know how the task of establishing abstract principles of universal grammar proved to be arduous, underwent many substantial changes and is not yet realized.

Another controversial aspect of Piaget’s position was his adherence to the recapitulation theory, i.e., the idea originally proposed by Haeckel, that ontogeny recapitulates phylogeny. It is this principle that motivated Piaget’s study of development as a way of contributing to the study of the evolution of human thought ( Koops, 2015 ). However, this position has as its consequence the idea that primitive populations would exist wherein we might find adult thought processes that in modern civilizations are typical of young children.

What is striking in this debate is that the specific biological model that Piaget adopted was not the only point of disagreement. What was questioned was in general the relevance of development for the study of a basic human ability such as language. Certainly, in the work of the first figures of developmental psychology, we find a baffling mix of very interesting ideas regarding the place of humans as biological entities in evolution and a difficulty in taking into account the complexities of actual biological theories and of social aspects such as cultural variation. At the same time, these scholars were confronted with objections from cognitive scientists who did not admit the relevance of investigating development for the study of the human mind.

The Interactionist Perspective

Piaget’s perspective was, in a sense, paradoxical. This perspective considered children’s development as the product of their action on the environment, but at the same time postulated a rather rigid succession of stages that led to adult thought and excluded the importance of the social aspects of this environment in the first years. In fact, infants and young children were considered closed in their egocentrism and unable to take advantage of their interactions with adults and peers.

These aspects have been criticized within developmental psychology, where a cultural turn, fathered by Vygotsky (1962/1986) and mainly interpreted in the United States by Bruner (1990) , has arisen. For both these authors, biological factors are considered an endowment of potentialities that develop in a society of co-specifics and are submitted to variability and to cultural variation.

Bruner was, at the outset, an enthusiastic supporter of cognitive science and in particular of the mentalist theory of language proposed by Chomsky ( Bruner, 1983b ). Later, however, the primacy that Chomsky assigns to syntax turned out to be unsatisfactory to Bruner, according to whom language is fundamentally a communicative device. The problem of language acquisition is thus redefined as the development of a communicative capacity that appears in the prelinguistic stage. This position was the result of Bruner’s work on preverbal communication carried out at the Center for Cognitive Studies at Harvard University starting in 1966.

For Bruner, language requires the maturation of cognitive structures, which underlie intentional action in general. His debt to Piaget with regard to the importance of action is evident. Language is “a specialized and conventionalized extension of cooperative action” ( Bruner, 1975 ). In this, he rejoins the communication theories proposed within the philosophy of language by Austin (1962) and Grice (1989) .

Bruner’s studies are part of a revolution in developmental studies in which more careful scrutiny and more sophisticated experimentation led to the discovery that children begin to engage in rather complex cognitive activity very early on. Prior to these studies, many of the aspects relating to infant cognition were not taken into consideration. The prejudice that saw human development as the slow acquisition of rationality prevented researchers from seeking elements of complexity in the cognition of a new-born.

In brief, since its origin, developmental psychology has undergone an important change. At the outset, the idea was that what characterized human cognition was adult rational thought, and studying development meant understanding the stages that led to this achievement. Later, the goal became understanding the development of the different faculties that characterize cognition starting from birth. This goal has also opened the door to comparative studies.

The Problems of Method

Developmental psychologists have always struggled with problems of method.

Piaget frequently discussed his observations of his three children. Studies on language acquisition have often benefited from researchers’ observations of their own children (see, for instance, Stern and Stern, 1928 ). These procedures, which have been considered as barely scientific by other psychologists, have provided useful inspiration for further research. Note that Darwin’s observations of his children were a fundamental source for his work on emotions ( Darwin, 1872/1965 ).

Ethical reasons forbid experiments, which may perturb children. Moreover, conceiving experiments that have ecological validity is even more difficult to do with young children than with adults. Hence, the necessity of using different methods in order to produce data that cannot be collected using classic experimental procedures. Without using observational methods, for instance, it is not possible to assess the spontaneous appearance of a given phenomenon ( Airenti, 2016 ). Furthermore, some behaviors may appear only in specific situations and would go unnoticed if they were not observed by caregivers who may see children at different moments of the day and in different situations. Thus, developmental psychologists have used different methodologies, classic experiments but also fieldwork, ethological observation, and parent reports.

A fundamental advancement was the development of techniques permitting to assess infants’ and young children’s abilities in experiments. A key element was the elaboration of the habituation paradigm ( Fantz, 1964 ; Bornstein, 1985 ). After repeated exposure to a stimulus, infants’ looking time decreases due to habituation and increases when a novel stimulus is presented. Habituation allows us to understand if infants discriminate among different stimuli.

In particular for language studies, nonnutritive sucking ( Siqueland and De Lucia, 1969 ) has been used. This is an experimental method based on operant conditioning allowing one to test infants’ discrimination of and preference for different stimuli. This technique has been used to show, for instance, that infants already acquire in the mother’s womb the ability to recognize and prefer the prosody of a language and of familiar voices ( DeCasper and Fifer, 1980 ).

Currently, the most utilized technique with infants is preferential looking or reaching. In this technique, two stimuli are presented together and what is measured is the infant’s preference. Specific types of this technique are used to claim surprise, anticipation, and preferences for novel or familiar stimuli and to evaluate preference over and above novelty or familiarity ( Hamlin, 2014 ) 5 .

Another technique presently used to investigate infant cognitive development is EEG recordings, even if creating infant-friendly laboratory environments, age-appropriate stimuli, and infant- friendly paradigms requires special care ( Hoehl and Wahl, 2012 ).

The development of these experimental techniques has vastly enlarged the scope of infant studies. In particular, a new research trend has emerged aimed at discovering what has been called the core knowledge ( Spelke, 2000 ; Spelke and Kinzler, 2007 ). The idea is that at the basis of human cognition, there is a set of competencies, such as representing objects, action, number and space, which are already present in infants and which underlie and constrain later acquisitions. Researchers have also been working on other possible basic competencies such as social cognition ( Baillargeon et al., 2016 ) and morality ( Wynn and Bloom, 2014 ).

In the literature, debate continues surrounding the replicability and robustness of findings obtained within these experimental paradigms, in particular with respect to infants’ and toddlers’ implicit false belief and morality ( Hamlin, 2014 ; Tafreshi et al., 2014 ; Baillargeon et al., 2018 ; Sabbagh and Paulus, 2018 ).

This debate also involves the relation between development and evolution. For Tafreshi and colleagues, for instance, the idea of core knowledge would involve a consideration of high-level cognitive capacities as biologically predetermined instead of constructed in interaction with the environment. This is not the perspective of those who consider that development does exist in the social environment but is constrained by a number of basic competencies ( Hamlin, 2014 ). An important element of this perspective is comparing human and animal capacities. In fact, research has shown that such basic competencies also exist in some form in animals. For instance, numerous studies have shown that adult nonhuman primates have the core systems of object, number, agent representations, etc. ( Spelke and Kinzler, 2007 ).

These preoccupations have also informed work by Tomasello and the Leipzig group. “All we can claim to have done so far–writes Tomasello–is to establish some comparative facts–organized by some theoretical speculations–that hopefully get us started in the right direction toward an evolutionary informed account of the ontogeny of uniquely human psychology” ( Tomasello, 2018 ). Comparing experimental work on great apes and young children has led him to formulate the hypothesis that the factors marking the difference between these two groups are different aspects of social cognition. Nonhuman primates have some basic capacities in these areas. In humans, the evolved capacity for shared intentionality transforms them in the species-unique human cognition and sociality ( Tomasello and Herrmann, 2010 ).

Tomasello’s work has also aroused criticism. In this case, the criticism is because his research, both with young children and primates, uses experimental methods and is carried out in a laboratory. Fieldwork primatologists have claimed that primates in captivity, tested by someone of another species, cannot display the abilities that their conspecifics display in their natural environment ( Boesch, 2007 ; De Waal et al., 2008 ). Tomasello answered this criticism by maintaining that the fact of being raised in a human environment enhances primates’ capacities ( Tomasello et al., 1993 ; Tomasello and Call, 2008 ).

In conclusion, in developmental psychology, a multiplicity of methods has been applied, and the debate over their respective validity and correct application continues. However, what is not in question is that development is a complex and multifaceted phenomenon that must be analyzed as such and from different points of view.

A paradigmatic case in the present research is the study of the theory of mind. Discovering how subjects represent their own mind and other minds was proposed in 1978 by Premack and Woodruff as a problem of research on primates, and in a short time, it has become one of the main topics in developmental research ( Premack and Woodruff, 1978 ). It is currently being studied in groups of different ages, from infants to the elderly, both in typical and clinical subjects and using different methodologies, from classic experiments to clinical observation. Moreover, a number of studies investigate individual and cross-cultural variation and its role in human-robots interactions. Philosophers have contributed to the definition of this phenomenon, and neuroscientists are working to discover its neural basis.

Computational Models of Development

Some researchers have pursued the goal of constructing computational models of cognitive development using different computational approaches (for a review, see Mareschal, 2010 ). However, as the author of this review remarks, all the models have explored cognition “as an isolated phenomenon”, i.e., they did not consider the physical and social context in which development unfolds.

Karmiloff-Smith, a developmental psychologist who proposed the most interesting theory about developmental change as an alternative to Piaget’s, considered that a number of features of her RR ( representational redescription ) model happened to map onto features of connectionist models ( Karmiloff-Smith, 1992 ; for a review of these models, see Plunkett et al., 1997 ). However, she also remarks that connectionist models have modeled tasks, while development is not simply task-specific learning, as it involves deriving and using previously acquired knowledge 6 .

One result of the dissatisfaction with the results deriving from the relation between cognitive psychology and artificial intelligence and the concomitant increase in interest in embodied cognition has been the growth of developmental robotics ( Lungarella et al., 2003 ). The aim of this field is to produce baby robots endowed with sensorimotor and cognitive abilities inspired by child psychology and to model developmental changes ( Cangelosi and Schlesinger, 2018 ). This approach has led to the comparison of results in experiments with robots and children. This is a promising field, even if it does not overcome the problems described above regarding the specificity of tasks that does not allow to account for infants’ ability to utilize previously differently acquired knowledge in the performance of a given task.

In conclusion, some approaches within cognitive science have acknowledged the usefulness of studying children in order to understand the mechanisms of development. Especially in the case of developmental robotics, this has allowed for studying the interaction of different capacities such as sensorimotor abilities, perception, and language. At the same time, the computational constraints do not allow for overcoming task specificity.

Concluding Remarks

I have argued that since their beginning, general psychology and developmental psychology have followed parallel paths that have only occasionally converged. The reason is due essentially to methodological problems that have involved not only research methods but also the very object of inquiry.

Psychology was founded with the ambition of becoming a science performed in laboratories and based on experimental work. However, as early as in 1934, Vygotsky had already deplored the attempt to achieve scientific standards by limiting the importance of general issues. “As long as we lack a generally accepted system incorporating all available psychological knowledge, any important factual discovery inevitably leads to the creation of a new theory to fit the newly observed facts” ( Vygotsky, 1962/1986 , p. 13).

The birth of cognitive science has taken important steps toward constructing links with other disciplines and also other ways to study cognition. However, this opening was soon transformed in the search for a unifying methodology, namely computer modeling, as a guarantee of scientific results. Many interesting ideas have been generated. However, after four decades of work in this direction, it has become impossible to ignore that too many important aspects of the human mind and activity have been eluded.

The relative isolation of developmental psychology came from the prejudice, also shared by eminent developmental psychologists like Piaget, that what characterizes human cognition are adult cognitive abilities.

However, from the start, developmental psychology was not limited to investiganting the specificity of children’s cognition. It devoted attention to what makes development possible, including biological endowment and cultural transmission; whether an infant should be considered a blank slate or if one can define some pre-existent basic abilities; what makes humans different from animals and nonhuman primates; and how specific human abilities such as language have evolved.

At present, a rapprochement between adult and child studies is made possible by different factors. The possibility of using experimental methods to study infancy has allowed us to realize the complexity of young humans. Moreover, development is increasingly being considered as a phenomenon not only characterizing childhood but also present over the life span, including both the acquisition and the decay of mental abilities ( Bialystok and Craik, 2006 ). Studying the human mind means studying how the human mind changes in interaction with the external environment all life long. In this sense, the study of human mind is inherently connected with the study of its development.

An important question of method emerges here. We have observed that over the years, developmental psychologists have sought to construct methods that can be reliable and at the same time can adequately address the topics under discussion here. The achievement of finding ways to carry out experiments with infants and nonhuman primates has been an important advancement in this perspective. This advancement has garnered both praise and criticism. To be reliable, experiments with infants require very rigorous procedures. Frequently, a detailed analysis of procedures is necessary to explain divergent results. However, it can be noted that reproducibility is an open problem for psychological science in general ( Open Science Collaboration, 2015 ). For nonhuman primates, the ecological validity of laboratory experiments has been questioned. More generally, it has been shown that in the field of developmental psychology, experimental studies do not completely replace other methodologies, but rather should coexist with them.

The human mind is complex, and all the methods that have been proposed in different disciplines may be useful in advancing our knowledge of it. The explanation of this complexity was the main goal underlying the proposal of cognitive science and is the perspective we must pursue in the future.

On this ground, the paths of psychology and developmental psychology may reconverge.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer MT declared a shared affiliation, with no collaboration, with the author to the handling editor at time of review.

1 The very earliest date was 1875 and that same year William James’ laboratory at Harvard in the United States was established ( Harper, 1950 ).

2 William James was influenced by Darwin and this appears in particular in his conceiving the mind as a function and not as a thing ( Bredo, 1998 ). However, his book The Principles of Psychology , first published in 1890 and later revised several times, ignored child development. In the chapter devoted to methods and snares in psychology, he adds to introspective observation and experimental method the comparative method. “So it has come to pass that instincts of animals are ransacked to throw light on our own; and that the reasoning faculties of bees and ants, the minds of savages, infants, madmen, idiots, the deaf and blind, criminals, and eccentrics, are invoked in support of this or that special theory about some part of our own mental life” ( James, 1983 , p. 193). If he admits that “information grows and results emerge”, he also cautions that “there are great sources of error in the comparative method” and that “comparative observation, to be definite, must usually be made to test some pre-existing hypothesis” ( James, 1983 ).

3 Putnam was actually the first to employ the term functionalism , and his aim in doing so was anti-reductionist. In his 1975 work he used the comparison with a computer program to show that psychological properties do not have a physical and chemical nature, even though they are realized by physical and chemical properties ( Putnam, 1975 ).

4 , Hewitt (1991) highlights the difficulties inherent in constructing artificial systems, which, like social systems, are founded on concepts such as commitment, cooperation, conflict, negotiation, and so forth.

5 Gaze and eye-tracking techniques are normally used in psychological research with adults ( Mele and Federici, 2012 ) but it is in developmental studies that they have had a dramatic impact on the possibilities of inquiry.

6 A different approach that has given origin to formal models and simulations is the paradigm that views the developmental process as a change within a complex dynamic system. Cognition in this perspective is embodied in the processes of perception and action ( Smith and Thelen, 2003 ).

• Airenti G. (2015). The cognitive bases of anthropomorphism: from relatedness to empathy . Int. J. Soc. Robot. 7 , 117–127. 10.1007/s12369-014-0263-x [ CrossRef ] [ Google Scholar ]
• Airenti G. (2016). Playing with expectations: a contextual view of humor development . Front. Psychol. 7 :1392. 10.3389/fpsyg.2016.01392 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Airenti G., Colombetti M. (1991). “ Artificial intelligence and the representation problem ” in Proceedings of the first world conference on the fundamentals of artificial intelligence. (Paris: Angkor; ), 17–28. [ Google Scholar ]
• Arbib M. A., Fellous J.-M. (2004). Emotions: from brain to robot . Trends Cogn. Sci. 8 , 554–561. 10.1016/j.tics.2004.10.004, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Austin J. A. (1962). How to do things with words. (Oxford: Oxford University Press; ). [ Google Scholar ]
• Baillargeon R., Buttelmann D., Southgate V. (2018). Invited commentary: interpreting failed replications of early-false belief findings: methodological and theoretical considerations . Cogn. Dev. 46 , 112–124. 10.1016/j.cogdev.2018.06.001 [ CrossRef ] [ Google Scholar ]
• Baillargeon R., Scott R. M., Bian L. (2016). Psychological reasoning in infancy . Annu. Rev. Psychol. 67 , 159–186. 10.1146/annurev-psych-010213-115033, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baldwin J. M. (1895). Mental development in the child and the race. (New York, NY: Macmillan; ). [ Google Scholar ]
• Baldwin J. M. (1930). “ Autobiography of James Mark Baldwin ” in History of psychology in autobiography. ed. Murchison C., Vol. 1 (Worchester, MA: Clark University Press; ), 1–30. [ Google Scholar ]
• Bialystok E., Craik F. I. M. (2006). Lifespan cognition: Mechanisms of change. (New York: Oxford University Press; ). [ Google Scholar ]
• Bjorklund D. F. (2018). A metatheory for cognitive development (or “Piaget is dead” revisited) . Child Dev. 89 , 2288–2302. 10.1111/cdev.13019, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Block N. (1978). Troubles with functionalism. Minnesota studies in the philosophy of science. Vol. 9 (Minneapolis: University of Minnesota Press; ), 261–325. [ Google Scholar ]
• Boesch C. (2007). What makes us human (Homo sapiens)? The challenge of cognitive cross-species comparison . J. Comp. Psychol. 121 , 227–240. 10.1037/0735-7036.121.3.227, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bornstein M. H. (1985). “ Habituation of attention as a measure of visual information processing in human infants: summary, systematization, and synthesis ” in Measurement of audition and vision during the first year of postnatal life: A methodological overview. eds. Gottlieb G., Krasnegor N. A. (Norwood, NJ: Ablex; ), 253–300. [ Google Scholar ]
• Bredo E. (1998). The Darwinian center to the vision of William James. Paper presented at the Annual Meeting of the American Educational Research Association . (SanDiego, CA, April 13–17, 1998).
• Bruner J. S. (1975). The ontogenesis of speech acts . J. Child Lang. 2 , 1–19. [ Google Scholar ]
• Bruner J. S. (1983a). In search of mind: Essays in autobiography. (New York, NY: Harper and Row; ). [ Google Scholar ]
• Bruner J. S. (1983b). Child's talk: Learning to use language. (New York: Norton; ). [ Google Scholar ]
• Bruner J. S. (1990). Acts of meaning. (Cambridge, MA: Harvard University Press; ). [ Google Scholar ]
• Burman J. T. (2013). Updating the Baldwin effect. The biological levels behind Piaget’s new theory . New Ideas Psychol. 31 , 363–373. 10.1016/j.newideapsych.2012.07.003 [ CrossRef ] [ Google Scholar ]
• Cangelosi A., Schlesinger M. (2018). From babies to robots: the contribution of developmental robotics to developmental psychology . Child Dev. Perspect. 12 , 183–188. 10.1111/cdep.12282 [ CrossRef ] [ Google Scholar ]
• Chalmers D. (1993). Why Fodor and Pylyshyn were wrong: the simplest refutation . Philos. Psychol. 6 , 305–319. 10.1080/09515089308573094 [ CrossRef ] [ Google Scholar ]
• Cognitive Science Committee (1978). Report of the State of the Art, Ms.
• Darwin C. (1872/1965). The expression of emotions in man and animals. (Chicago: University of Chicago Press; ). [ Google Scholar ]
• De Waal F. B., Boesch C., Horner V., Whiten A. (2008). Comparing social skills of children and apes . Science 319 :569. 10.1126/science.319.5863.569c, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• DeCasper A. J., Fifer W. P. (1980). Of human bonding: newborns prefer their mothers’ voices . Science 208 , 1174–1176. 10.1126/science.7375928, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dreyfus H. (1979). What computers can’t do: A critique of artificial reason. (New York, NY: Harper & Row; ). [ Google Scholar ]
• Fantz R. L. (1964). Visual experience in infants: decreased attention to familiar patterns relative to novel ones . Science 146 , 668–670. 10.1126/science.146.3644.668, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fodor J. A. (1985). Fodor’s guide to mental representation: the intelligent auntie’s vademecum . Mind 94 , 76–100. [ Google Scholar ]
• Fodor J., Pylyshyn Z. (1988). Connectionism and cognitive architecture: a critical analysis . Cognition 28 , 3–71. 10.1016/0010-0277(88)90031-5, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Forbus K. D. (2010). AI and cognitive science: the past and next 30 years . Top. Cogn. Sci. 2 , 345–356. 10.1111/j.1756-8765.2010.01083.x, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gentner D. (2010). Psychology in cognitive science: 1978–2038 . Top. Cogn. Sci. 2 , 328–344. 10.1111/j.1756-8765.2010.01103.x, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grice H. P. (1989). Studies in the way of words. (Cambridge, MA: Harvard University Press; ). [ Google Scholar ]
• Hamlin J. K. (2014). The conceptual and empirical case for social evaluation in infancy. Commentary on Tafreshi, Thompson, and Racine . Hum. Dev. 57 , 250–258. 10.1159/000365120 [ CrossRef ] [ Google Scholar ]
• Harnad S. (1990). The symbol grounding problem . Physica D 42 , 335–346. 10.1016/0167-2789(90)90087-6 [ CrossRef ] [ Google Scholar ]
• Harper R. S. (1950). The first psychological laboratory . Isis 41 , 158–161. 10.1086/349141 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hewitt C. (1991). Open information systems semantics for distributed artificial intelligence . Artif. Intell. 47 , 79–106. 10.1016/0004-3702(91)90051-K [ CrossRef ] [ Google Scholar ]
• Hinton G. E. (1989). Connectionist learning procedures . Artif. Intell. 40 , 185–234. 10.1016/0004-3702(89)90049-0 [ CrossRef ] [ Google Scholar ]
• Hoehl S., Wahl S. (2012). Recording infant ERP data for cognitive research . Dev. Neuropsychol. 37 , 187–209. 10.1080/87565641.2011.627958, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• James W. (1983). The principles of psychology. (Cambridge, Mass: Harvard University Press; ). [ Google Scholar ]
• Kahn P. H., Jr., Ishiguro H., Friedman B., Kanda T., Freier N. G., Severson R. L., et al. (2007). What is a human? Toward psychological benchmarks in the field of human-robot interaction . Inter. Stud. 8 , 363–390. 10.1075/is.8.3.04kah [ CrossRef ] [ Google Scholar ]
• Karmiloff-Smith A. (1992). Beyond modularity. (Cambridge, Mass: The MIT Press; ). [ Google Scholar ]
• Koops W. (2015). No developmental psychology without recapitulation theory . Eur. J. Dev. Psychol. 12 , 630–639. 10.1080/17405629.2015.1078234 [ CrossRef ] [ Google Scholar ]
• Levesque H., Brachman R. (1985). “ A fundamental tradeoff in knowledge representation and reasoning (revised version) ” in Readings in knowledge representation. eds. Brachman R., Levesque H. J. (Burlington, MA: Morgan Kaufmann; ), 41–70. [ Google Scholar ]
• Lungarella M., Metta G., Pfeifer R., Sandini G. (2003). Developmental robotics: a survey . Con. Sci. 15 , 151–190. 10.1080/09540090310001655110 [ CrossRef ] [ Google Scholar ]
• Mareschal D. (2010). Computational perspectives on cognitive development . WIREs Cognit. Sci. 1 , 696–708. 10.1002/wcs.67, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mele M. L., Federici S. (2012). Gaze and eye-tracking solutions for psychological research . Cogn. Proc. 13 , S261–S265. 10.1007/s10339-012-0499-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Miller G. A., Galanter E., Pribram K. (1960). Plans and structure of behavior. (New York, NY: Holt, Rinehart and Winston; ). [ Google Scholar ]
• Morgan T. J. H., Harris P. L. (2015). James Mark Baldwin and contemporary theories of culture and evolution . Eur. J. Dev. Psychol. 12 , 666–677. 10.1080/17405629.2015.1074068 [ CrossRef ] [ Google Scholar ]
• Newell A., Simon H. A. (1972). Human problem solving. (Englewood Cliffs, N.J: Prentice-Hall; ). [ Google Scholar ]
• Open Science Collaboration (2015). Estimating the reproducibility of psychological science . Science 349 :aac4716. 10.1126/science.1253751, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pfeifer R., Lungarella M., Iida F. (2007). Self-organization, embodiment, and biologically inspired robotics . Science 318 , 1088–1093. 10.1126/science.1145803, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Piaget J. (1928). Logique génétique et sociologie . Revue Philosophique de la France et de l’Étranger 53 , 167–205. [ Google Scholar ]
• Piattelli-Palmarini M. (1979/1980). Language and learning: The debate between Jean Piaget and Noam Chomsky. (London: Routledge and Kegan Paul; ). [ Google Scholar ]
• Pitt D. (2017). “ Mental representation ” in The Stanford Encyclopedia of philosophy. ed. Zalta E. N. https://plato.stanford.edu/archives/spr2017/entries/mental-representation
• Plunkett K., Karmiloff-Smith A., Bates E., Elman J. L., Johnson M. H. (1997). Connectionism and developmental psychology . J. Child Psychol. Psychiatry 38 , 53–80. 10.1111/j.1469-7610.1997.tb01505.x, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Premack D., Woodruff G. (1978). Does the chimpanzee have a theory of mind? Behav. Brain Sci. 4 , 515–526. [ Google Scholar ]
• Putnam H. (1975). Mind, language and reality. (Cambridge: Cambridge University Press; ). [ Google Scholar ]
• Putnam H. (1988). Representation and reality. (Cambridge, MA: MIT Press; ). [ Google Scholar ]
• Pylyshyn Z. W. (1984). Computation and cognition. (Cambridge, MA: The MIT Press/Bradford; ). [ Google Scholar ]
• Quinlan P. T. (1991). Connectionism and psychology: A psychological perspective on new connectionist research. (Hemel Hempstead: Harvester Wheatsheaf; ). [ Google Scholar ]
• Sabbagh M. A., Paulus M. (2018). Editorial. Replication studies of implicit false belief with infants and toddlers . Cogn. Dev. 46 , 1–3. 10.1016/j.cogdev.2018.07.003 [ CrossRef ] [ Google Scholar ]
• Schank R. C., Abelson R. P. (1977). Scripts, plans, goals and understanding. (Hillsdale, N.J: Lawrence Erlbaum; ). [ Google Scholar ]
• Searle J. R. (1980). Minds, brains and programs . Behav. Brain Sci. 3 , 417–424. 10.1017/S0140525X00005756 [ CrossRef ] [ Google Scholar ]
• Searle J. R. (1990). Consciousness, explanatory inversion, and cognitive science . Behav. Brain Sci. 13 , 585–596. 10.1017/S0140525X00080304 [ CrossRef ] [ Google Scholar ]
• Searle J. R. (1992). The rediscovery of the mind. (Cambridge, MA: The MIT Press; ). [ Google Scholar ]
• Simpson G. G. (1953). The Baldwin effect . Evolution 7 , 110–117. 10.1111/j.1558-5646.1953.tb00069.x [ CrossRef ] [ Google Scholar ]
• Siqueland E. R., De Lucia C. A. (1969). Visual reinforcement of non-nutritive sucking in human infants . Science 165 , 1144–1146. 10.1126/science.165.3898.1144, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith L. B., Thelen E. (2003). Development as a dynamic system . Trends Cogn. Sci. 7 , 343–348. 10.1016/S1364-6613(03)00156-6, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spelke E. S. (2000). Core knowledge . Am. Psychol. 55 , 1233–1243. 10.1037/0003-066X.55.11.1233, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spelke E. S., Kinzler K. D. (2007). Core knowledge . Dev. Sci. 10 , 89–96. 10.1111/j.1467-7687.2007.00569.x, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stern C., Stern W. (1928). Die Kindersprache [The speech of children. (4th Ed.)]. (Oxford, England: Barth; ). [ Google Scholar ]
• Tafreshi D., Thompson J. J., Racine T. P. (2014). An analysis of the conceptual foundations of the infant preferential looking paradigm . Hum. Dev. 57 , 222–240. 10.1159/000363487 [ CrossRef ] [ Google Scholar ]
• Tomasello M. (2018). Great apes and human development: a personal history . Child Dev. Perspect. 12 , 189–193. 10.1111/cdep.12281 [ CrossRef ] [ Google Scholar ]
• Tomasello M., Call J. (2008). Assessing the validity of ape-human comparisons: a reply to Boesch (2007) . J. Comp. Psychol. 122 , 449–452. 10.1037/0735-7036.122.4.449, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tomasello M., Herrmann E. (2010). Ape and human cognition: what’s the difference? Curr. Dir. Psychol. Sci. 19 , 3–8. 10.1177/0963721409359300 [ CrossRef ] [ Google Scholar ]
• Tomasello M., Savage-Rumbaugh S., Kruger A. (1993). Imitative learning of actions on objects by children, chimpanzees and enculturated chimpanzees . Child Dev. 64 , 1688–1705. 10.2307/1131463, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Varela F. D., Rosch E., Thompson E. (1991). The embodied mind: Cognitive science and human experience. (Cambridge, MA: MIT Press; ). [ Google Scholar ]
• Vygotsky L. (1962/1986). Thought and language. (Cambridge, MA: The MIT Press; ). [ Google Scholar ]
• Waddington C. H. (1953). The ‘Baldwin effect,’ ‘genetic assimilation’ and ‘homeostasis’ . Evolution 7 , 386–387. [ Google Scholar ]
• Wermter S., Sun R. (eds.) (2000). Hybrid neural symbolic integration. (Berlin: Springer Verlag; ). [ Google Scholar ]
• Wiese E., Metta G., Wykowska A. (2017). Robots as intentional agents: using neuroscientific methods to make robots appear more social . Front. Psychol. 8 :1663. 10.3389/fpsyg.2017.01663 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wynn K., Bloom P. (2014). “ The moral baby ” in The handbook of moral development. 2nd Edn. eds. Killen M., Smetana J. G. (New York: Psychology Press; ), 435–453. [ Google Scholar ]
• Ziemke T. (2008). On the role of emotion in biological and robotic autonomy . BioSystems 91 , 401–408. 10.1016/j.biosystems.2007.05.015, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ziemke T., Thill S., Vernon D. (2015). “ Embodiment is a double-edged sword in human-robot interaction: ascribed vs. intrinsic intentionality ” in Proceedings of the workshop on cognition: A bridge between robotics and interaction , 9–10.
• Zlatev J. (2001). The epigenesis of meaning in human beings, and possibly robots . Minds Mach. 11 , 155–195. 10.1023/A:1011218919464 [ CrossRef ] [ Google Scholar ]