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Dream research, lucid dreaming, and consciousness studies

Calvin Hall and the Cognitive Theory of Dreaming

By Ryan Hurd

Any survey of modern dream research must include Calvin Hall (1909-1985).  Hall was a behavioral psychologist who explored the cognitive dimensions of dreaming.  His work began before the discovery of REM sleep, so little was known about the biology of sleep and dreams.  Hall drew worldwide attention for his cognitive theory of dreaming , which was among the first scientific theories of dream interpretation based on quantitative analysis… rather than wishful thinking.

Dreams Images are the Embodiment of Thought

Central to Hall’s cognitive theory is that dreams are thoughts displayed in the mind’s private theater as visual concepts. Like Jung, Hall dismissed the Freudian notion that dreams are trying to cover something up.  In his classic work The Meaning of Dreams (1966), Hall writes, “The images of a dream are the concrete embodiments of the dreamer’s thoughts; these images give visual expression to that which is invisible, namely, conceptions.” (p. 95).

So dreams reveal the structure of how we envision our lives, a display that is clearly valuable for anyone who remembers and studies their own dreams.

The Way We See the World

After studying thousands of dreams collected from his students and from around the world, Hall suggested that the main cognitive structures that dreams reveal include:

• conceptions of self (how we appear to ourselves, the roles we play in life)
• conceptions of others (the people in our lives and how we react to their needs 
• conceptions of the world (our environment: is it a barren wasteland or a nurturing place?)
• conceptions of penalties (how we view the Man.  What is allowed? What is forbidden?)
• conceptions of conflict (our inner discord and how we struggle with resolving it).

As a behavioral psychologist, Hall believed these conceptions are antecedents to our behavior in the waking world.  They’re like maps to our actions, and “with these maps we are able to follow the course of man’s behavior, to understand why he selects one road rather than another, to anticipate the difficulties and obstacles he will encounter, and to predict his destinations.” (as qtd in Van De Castle, p. 190)

Content Analysis: the Hall-Van de Castle Scale

Hall’s work is still widely cited today, but his greatest legacy is the system of dream content analysis he developed with psychologist Robert Van De Castle in the 1960s.

Known as the Hall Van De Castle scale, this quantitative system scores a dream report with 16 empirical scales.  Some scales are settings, objects, people, animals, and mythological creatures.  You know, the sort of things you see walking down the street on any given day.  (If you haven’t seen any chimeras or griffins recently, then you’re working too much). Other scales include emotions, sexual content, aggression, etc. .

The value of the project is that there are now hundreds of thousands of dreams measured using the HVdC system, creating a “baseline” for normal dreaming cognition.  So researchers can add dreams from special interest groups (children, Vietnam vets, Armenian students) to measure their profiles against the norm. (see Figure 1 for an example of the possibilities)

This innovation is a huge milestone in the scientific study of dreams.  Now researchers can easily get a snapshot of dreaming cognition that is measurable, quantitative, and statistically significant. Besides psychologists, this scale is still used widely today by sociologists and anthropologists.

And thanks to Hall’s student Bill Domhoff, now a powerful dream research figure in his own right, much of Hall and Van De Castle’s database is available online .

Dream content has coherent meaning—that is the main message behind Hall’s work with dreams.  This view later came under fire by the controversial work of neuroscientist Allan Hobson , who implied that dreams may be nothing more than images stitched together from random brain pulses.   This rift may be the central conflict in dream studies today.

Reader Interactions

December 4, 2009 at 10:16 pm

Thanks for the thorough presentation of Hall’s theory, Ryan. I’ve never encountered his name before. I’m a little bit perplexed though as to why a behavioral psychologist, like Hall, got involved in psychoanalytic research. I guess there’s not much difference between the two fields at that time. Anyway, I think his system of content analysis is indeed useful in providing quantitative data for the investigation of dreams knowing that the latter is filled with abstractions and that subjects who report their dreams may find it difficult to remove subjective interpretations while trying to recall dreams from memory.

December 6, 2009 at 8:53 pm

Ryhen, you’re right about the issues with dream recall and after-the-fact revision… that is actually one of the central criticisms of Hall and Van de Castle’s system.

That’s why it’s important to understand that dream researchers don’t study dreams per say… we study dream reports. a dream is actually a memory, and memory is highly unpredictable. the critique isn’t devastating to the enterprise of dream studies, but certainly a dangerous pothole along the path.

December 8, 2009 at 4:00 pm

The story, as I heard it from Bill Domhoff, was that Hall was originally trained as a behaviorist in the 30’s and 40’s, the heyday of that psychological system. He focused on the study of anxiety in lab rats, which he measured by giving them mild electric shocks and then counting the resulting number of feces (which are apparently an accurate index of a rat’s level of fear). I don’t know why Hall shifted toward psychoanalysis and cognitive psychology, but I find it strangely appropriate that his approach to dream content analysis has its roots in counting rat turds.

December 8, 2009 at 5:48 pm

Kelly, thanks for coming by and also for the behaviorist scoop on Hall!

December 12, 2009 at 6:54 am

Great overview of an important perspective on dreamwork. I’m very opposed to a purely materialistic “random neural firings” hypothesis about dreams.

January 31, 2010 at 11:15 pm

Some nits for you Calvin Hall’s name does not have Jr. in the title Our Dreaming Mind was published in 1994 not 93 Domhoff’s book on Finding Meaning etc was published in 1996, not 97

February 1, 2010 at 1:07 pm

thanks, Bob, for stopping by! I’ll pick those nits.

February 9, 2010 at 10:30 am

Thanks for your overview on the cognitive theory of dreams. I’m writting a research paper on dreams where I’m looking at this theory and Freud’s theory and my argument is that the cognitive theory provides the most clarity in understanding our dreams. I was wondering if you know of any study a psychologist has conducted where he has concluded that a particular dream supports the cognitive dream theory,

I have looked into Domhoff’s dreambank.net website, and found one study that I think I may be able to use in my paper;however, I was just curious to see whether you’ve come across any such study

Thank you so much for your updates on your blog, I’d appreciate a response as soon as you can

Thanks! Tina

April 16, 2010 at 10:41 am

hi Tania am not sure how late this feedback but ..try looking into Deirdre Barrett work she has numerous books and articles on that part she is the chief editor of Dreaming Journal hope this helps. Star

April 16, 2010 at 10:52 am

thanks Star — and sorry Tina — another comment that got by my radar. in general the studies that support cognitive theory are by the researchers who use cognitive theory. same goes for Freudian theory, Jungian theory, etc. There is no final authority on the meaning of dreams when it comes down to individual dreams. in my opinion — and many dream workers — the final authority is YOU. what feels right? what can you learn? studying dreaming like all cognitive artifacts is a study in meaning-making, and nobody can tell you what something means, they can only lead the way…

States of Consciousness

Dreams and Dreaming

Learning objectives.

• Describe and differentiate between theories on why we dream

The meaning of dreams varies across different cultures and periods of time. By the late 19th century, German psychiatrist Sigmund Freud had become convinced that dreams represented an opportunity to gain access to the unconscious. By analyzing dreams, Freud thought people could increase self-awareness and gain valuable insight to help them deal with the problems they faced in their lives. Freud made distinctions between the manifest content and the latent content of dreams.

Manifest content is the actual content, or storyline, of a dream. Latent content , on the other hand, refers to the hidden meaning of a dream. For instance, if a woman dreams about being chased by a snake, Freud might have argued that this represents the woman’s fear of sexual intimacy, with the snake serving as a symbol of a man’s penis.

Freud was not the only theorist to focus on the content of dreams. The 20th century Swiss psychiatrist Carl Jung believed that dreams allowed us to tap into the collective unconscious . The collective unconscious, as described by Jung, is a theoretical repository of information he believed to be shared by everyone. According to Jung, certain symbols in dreams reflected universal archetypes with meanings that are similar for all people regardless of culture or location.

The sleep and dreaming researcher Rosalind Cartwright, however, believes that dreams simply reflect life events that are important to the dreamer. Unlike Freud and Jung, Cartwright’s ideas about dreaming have found empirical support. For example, she and her colleagues published a study in which women going through divorce were asked several times over a five month period to report the degree to which their former spouses were on their minds. These same women were awakened during REM sleep in order to provide a detailed account of their dream content. There was a significant positive correlation between the degree to which women thought about their former spouses during waking hours and the number of times their former spouses appeared as characters in their dreams (Cartwright, Agargun, Kirkby, & Friedman, 2006). Recent research (Horikawa, Tamaki, Miyawaki, & Kamitani, 2013) has uncovered new techniques by which researchers may effectively detect and classify the visual images that occur during dreaming by using fMRI for neural measurement of brain activity patterns, opening the way for additional research in this area.

Recently, neuroscientists have also become interested in understanding why we dream. For example, Hobson (2009) suggests that dreaming may represent a state of protoconsciousness. In other words, dreaming involves constructing a virtual reality in our heads that we might use to help us during wakefulness. Among a variety of neurobiological evidence, John Hobson cites research on lucid dreams as an opportunity to better understand dreaming in general. Lucid dreams are dreams in which certain aspects of wakefulness are maintained during a dream state. In a lucid dream, a person becomes aware of the fact that they are dreaming, and as such, they can control the dream’s content (LaBerge, 1990).

Theories on Dreaming

While the Freudian theory of dreaming may be the most well known, and Cartwright’s suggestions on dreaming the most plausible, there are several other theories about the purpose of dreaming. The threat-simulation theory suggests that dreaming should be seen as an ancient biological defense mechanism. Dreams are thought to provide an evolutionary advantage because of their capacity to repeatedly simulate potential threatening events. This process enhances the neurocognitive mechanisms required for efficient threat perception and avoidance.

The expectation-fulfillment theory  posits that dreaming serves to discharge emotional arousals (however minor) that haven’t been expressed during the day. This practice frees up space in the brain to deal with the emotional arousals of the next day and allows instinctive urges to stay intact. In effect, the expectation is fulfilled (the action is “completed”) in a metaphorical form so that a false memory is not created. This theory explains why dreams are usually forgotten immediately afterwards.

One prominent neurobiological theory of dreaming is the activation-synthesis theory , which states that dreams don’t actually mean anything. They are merely electrical brain impulses that pull random thoughts and imagery from our memories. The theory posits that humans construct dream stories after they wake up, in a natural attempt to make sense of the nonsensical. However, given the vast documentation of the realistic aspects of human dreaming, as well as indirect experimental evidence that other mammals such as cats also dream, evolutionary psychologists have theorized that dreaming does indeed serve a purpose.

The continual-activation theory proposes that dreaming is a result of brain activation and synthesis. Dreaming and REM sleep are simultaneously controlled by different brain mechanisms. The hypothesis states that the function of sleep is to process, encode, and transfer data from short-term memory to long-term memory through a process called consolidation. However, there is not much evidence to back this up. NREM sleep processes the conscious-related memory (declarative memory), and REM sleep processes the unconscious related memory (procedural memory).

The underlying assumption of continual-activation theory is that, during REM sleep, the unconscious part of the brain is busy processing procedural memory. Meanwhile, the level of activation in the conscious part of the brain descends to a very low level as the inputs from the senses are basically disconnected. This triggers the “continual-activation” mechanism to generate a data stream from the memory stores to flow through to the conscious part of the brain.

Link to Learning

Review the purpose and stages of sleep as well as the reasons why we dream in the following CrashCourse video:

You can view the transcript for “To Sleep, Perchance to Dream: Crash Course Psychology #9” here (opens in new window) .

CC licensed content, Original

• Modification, adaptation, and original content. Provided by : Lumen Learning. License : CC BY: Attribution

CC licensed content, Shared previously

• Stages of Sleep. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/4-3-stages-of-sleep . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction
• The Nature and Meaning of Dreams. Provided by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/states-of-consciousness-6/sleep-and-dreaming-42/the-nature-and-meaning-of-dreams-184-12719/ . License : CC BY-SA: Attribution-ShareAlike
• Sleeping woman. Authored by : Craig Adderley. Provided by : Pexels. Located at : https://www.pexels.com/photo/woman-sleeping-1497855/ . License : CC0: No Rights Reserved

All rights reserved content

• To Sleep, Perchance to Dream – Crash Course Psychology #9. Provided by : CrashCourse. Located at : https://www.youtube.com/watch?v=rMHus-0wFSo . License : Other . License Terms : Standard YouTube License

storyline of events that occur during a dream, per Sigmund Freud’s view of the function of dreams

hidden meaning of a dream, per Sigmund Freud’s view of the function of dreams

common psychological tendencies that have been passed down from one generation to the next

people become aware that they are dreaming and can control the dream’s content

suggests that dreaming should be seen as an ancient biological defense mechanism that provides an evolutionary advantage because of its capacity to repeatedly simulate potential threatening events, thus enhancing the mechanisms required for efficient threat avoidance.

states that dreams don't actually mean anything. Instead, dreams are merely electrical brain impulses that pull random thoughts and imagery from our memories.

proposes that dreaming is a result of brain activation and synthesis; its assumption is that, during REM sleep, the unconscious part of the brain is busy processing procedural memory

General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Dreams and creative problem-solving

Affiliation.

• 1 Department of Psychiatry, Harvard Medical School, Cambridge, Massachusetts.
• PMID: 28640937
• DOI: 10.1111/nyas.13412

Dreams have produced art, music, novels, films, mathematical proofs, designs for architecture, telescopes, and computers. Dreaming is essentially our brain thinking in another neurophysiologic state-and therefore it is likely to solve some problems on which our waking minds have become stuck. This neurophysiologic state is characterized by high activity in brain areas associated with imagery, so problems requiring vivid visualization are also more likely to get help from dreaming. This article reviews great historical dreams and modern laboratory research to suggest how dreams can aid creativity and problem-solving.

Keywords: REM sleep; creativity; dream incubation; dreams; problem-solving.

© 2017 New York Academy of Sciences.

Publication types

• Brain / physiology*
• Creativity*
• Dreams / physiology*
• Dreams / psychology*
• Problem Solving / physiology*

Deciphering the Functions and Theories Behind Dreams

Brenda Jackson

Table of Contents

• Dreams have fascinated humans for centuries, with various theories and perspectives attempting to explain their meanings.
• Some theories, such as the activation-synthesis hypothesis and Freud’s theory of dream analysis, suggest that dreams are a product of random brain activity or symbolic representations of repressed desires and conflicts.
• Dreams serve multiple functions, including emotional processing, memory consolidation, preparation for potential threats, cognitive activation, and problem-solving.
• Dream interpretation is subjective and can vary based on personal beliefs, cultural factors, and individual experiences. Keeping a dream journal and reflecting on the content of dreams can provide insights into one’s unconscious mind and personal struggles.

The meaning of dreams has fascinated us for centuries, with many different theories existing about their meanings. While the true purpose of dreaming is not fully understood, we will explore different perspectives on dreams, including theories, processes, impacts, interpretations, and cultural and historical views.

Various Theories About Dreams

Dreams have fascinated and perplexed people for centuries. People have long sought to uncover the true meaning behind their dreams, believing that they offer insights into our subconscious minds and provide glimpses into the mysteries of our lives. There are several theories that attempt to explain why we dream and decipher the true significance of our dreams. While each theory offers a unique perspective on dream meanings, no single theory has been proven definitive. Let’s explore some of the most prominent theories below:

1. Activation-Synthesis Hypothesis

The activation-synthesis hypothesis suggests that dreams are simply the result of random brain activity during REM (rapid eye movement) sleep. According to this theory proposed by Harvard psychiatrists John Allan Hobson and Robert McCarley, our brains create stories from these random thoughts and images as a way to make sense of them. The content of our dreams is not significant and does not carry any hidden meanings, but rather it is our brain’s attempt to create a coherent narrative from the chaotic firing of neurons.

2. Freud’s Theory of Dream Analysis

Sigmund Freud, the father of psychoanalysis, believed that dreams were a window into the unconscious mind . According to Freud, dreams are symbolic representations of repressed desires, fears, and unresolved conflicts. He argued that the manifest content of a dream (the actual imagery and storyline ) is a disguise for the latent content (the hidden meaning). Freud believed that by analyzing dreams, one could gain insight into their true desires and conflicts , thus providing a pathway to self-discovery.

3. Threat Simulation Theory

The threat simulation theory suggests that dreams serve as a defense mechanism to prepare us for potential threatening events in waking life. This evolutionary-derived theory proposes that dreams allow us to practice cognitive mechanisms necessary for threat perception and avoidance . Through dream simulations, we can better prepare ourselves for potential dangers in reality.

4. Continual-Activation Theory

The continual-activation theory proposes that dreaming is a process by which our brains remain active during sleep to keep the mind functioning properly. This theory suggests that dreams serve as a way to maintain neural activation and keep the brain stimulated . While the exact purpose of this continual activation is not yet fully understood, some researchers believe it may contribute to memory consolidation and learning processes.

5. Other Theories

In addition to these prominent theories, there are several other theories about dream meanings. Some researchers propose that dreaming helps us process emotions and regulate our emotional experiences. Others suggest that dreams help us make sense of daily experiences and memories or serve as a creative outlet for problem-solving and idea generation.

It is important to note that these theories are not mutually exclusive, and dreams likely serve multiple functions based on individual experiences and circumstances. Additionally, the significance of dreams may vary from person to person, as personal beliefs and cultural factors can influence dream interpretation.

6. Making Sense of Your Dreams

While the true meaning of dreams remains elusive, many people find value in exploring their dream experiences. Keeping a dream journal and reflecting on the content of your dreams can help you uncover patterns, symbols, and emotions that may offer insights into your unconscious mind. By paying attention to your dreams and their connections to your waking life, you can gain a better understanding of yourself, your desires, and your challenges.

The Process and Impact of Dreaming

Dreaming is a fascinating and mysterious experience that occurs when we sleep. It involves vivid mental imagery, emotional shifts, and varying levels of awareness. But what exactly is the purpose and impact of dreaming? Let’s explore the process of dreaming and its effects on our emotional and cognitive wellbeing.

1. The Purpose of Dreams

Why do we dream? While scientists are still trying to fully understand the purpose of dreams, there are several theories that shed light on this phenomenon.

Emotional Processing

Dreams may serve as a way for our brains to process and regulate emotions. During REM sleep, which is when dreams are most vivid, our brains actively engage in emotional memory consolidation. This means that the brain sorts through the emotions we experienced during the day, deciding what to keep and what to let go. Dreams can help us process our feelings , making problems less daunting after a good night’s sleep.

Memory Consolidation

Another important function of dreams is memory consolidation. Our brains go through various stages of sleep throughout the night, and dreams can occur during both REM and non-REM sleep. During these different stages, our brains process and consolidate memories from the day. Dreams may help organize and integrate these memories, turning short-term memories into long-term ones.

Practice and Preparation

Some theories suggest that dreaming serves as a way for us to practice and prepare for real-life scenarios . Our early human ancestors faced daily threats to their survival, and dreams could have been a way for them to rehearse escaping danger or finding shelter. This practice in dreams may have prepared our ancestors to deal with similar situations in real life without any actual risk.

2. The Impact of Dreaming

Dreaming can have a significant impact on our sleep quality and overall wellbeing . Here are some ways in which dreams affect us:

Emotional Regulation

Dreaming provides an opportunity for emotional regulation . When we dream, our brains engage in emotional processing and can help defuse traumatic or distressing emotions. Dreams allow us to confront and work through feelings we might have suppressed during our waking hours, offering a safe space to explore and resolve emotional conflicts.

Cognitive Processing

Dreaming also supports cognitive processing , especially when it comes to memory consolidation. Dreams help organize and integrate information acquired during the day, turning it into long-term memories. This cognitive processing is vital for learning and memory formation .

Problem-Solving

Have you ever woken up with a solution to a problem or a new idea? Dreams have been known to inspire creativity and provide insights into difficult situations . They can offer new perspectives and innovative solutions by allowing us to think outside the box.

While not all dreams are pleasant, even nightmares play a role in our emotional and psychological wellbeing. Nightmares can be intense and distressing, but they can also serve as a way for us to confront fears, process traumatic experiences, and build resilience. Working through nightmares with the help of therapy or self-reflection can lead to emotional growth and healing .

Understanding and Interpreting Dreams

Dreams have fascinated and puzzled humans for centuries. We often wake up after a vivid dream and wonder if there is any deeper meaning behind it. While the true meaning of dreams remains a mystery , there are several theories and techniques that can help us understand and interpret them. In this section, we will explore some of the common theories of dream interpretation and provide tips for analyzing your own dreams.

1. Theories of Dream Interpretation

Freud’s theory of wish fulfillment.

Sigmund Freud, the renowned psychologist, believed that dreams are a manifestation of our unconscious desires . According to Freud, dreams serve as a way for our unconscious mind to fulfill our deepest wishes that may be repressed or unfulfilled in our waking life.

Jung’s Theory of Compensation

Carl Jung, another influential psychologist, proposed that dreams provide a balance or compensation for aspects of our personality that are in conflict with each other. He believed that dreams could reveal unseen parts of ourselves and offer insights into our psyche.

Hall’s Theory of Self-Reflection

Psychologist Calvin S. Hall suggested that dreams are a reflection of our thoughts and ideas. He believed that dreams could provide valuable insight into our sense of self, relationships, conflicts, and external environment.

Modern Theories

More recent theories on dream interpretation focus on the function and purpose of dreaming . Some scientists propose that dreams help with emotional processing, memory consolidation, performance improvement, and creativity enhancement.

2. Common Dream Themes

While the content of dreams can vary greatly from person to person, certain themes tend to recur in many people’s dreams. Here are some common dream topics and possible interpretations:

3. Tips for Analyzing and Interpreting Dreams

Analyzing and interpreting dreams can be a personal and introspective process. Here are some tips to help you understand the deeper meaning of your dreams:

• Keep a Dream Journal Record your dreams in a journal immediately after waking up. Write down as many details as you can remember, including emotions, people, objects, and locations.
• Look for Patterns and Symbols Pay attention to recurring patterns or symbols in your dreams. These may hold personal significance and can offer clues about underlying meanings.
• Consider Context and Personal Associations Reflect on the context of your dream and how it relates to your personal life. What was happening in your waking life before you had the dream? Are there any personal associations you make with the symbols or events in the dream?
• Seek Different Perspectives Share your dreams with trusted friends or family members and ask for their insights. Different perspectives can offer new insights and interpretations that you may not have considered.
• Consult with a Professional If you have recurring or troubling dreams that persistently impact your well-being, consider speaking with a mental health professional or dream analyst. They can help guide you through the interpretation process and provide support.

Remember that dream interpretation is subjective, and the meaning of your dream may be unique to you. Trust your intuition and allow yourself to explore different possibilities and interpretations.

Cultural and Historical Perspectives on Dreams

Dreams have always been a source of fascination and intrigue for humans throughout history. Different cultures and societies have developed their own interpretations and beliefs about the meaning and significance of dreams. Let’s explore some cultural and historical perspectives on dreams that shed light on this fascinating phenomenon .

1. Ancient Civilizations and Dreams

In ancient civilizations, dreams were often seen as messages from the gods or a medium for spiritual communication . For example, in ancient Egypt, dreams were believed to be important omens that could provide guidance and insight into the future. The Egyptians even had professional dream interpreters who would help decipher the hidden meanings behind dreams.

2. Freud and the Unconscious Mind

In the early 20th century, Sigmund Freud, the father of psychoanalysis, revolutionized the field of dream interpretation. Freud believed that dreams were a window to the unconscious mind and could reveal repressed desires, conflicts, and wishes. He introduced the concept of dream symbolism, suggesting that the images and events in dreams were representations of hidden thoughts and emotions.

3. Jung and Collective Unconscious

Another influential figure in dream psychology was Carl Jung, who believed that dreams could provide access to the collective unconscious, a shared repository of symbols, archetypes, and experiences. Jung saw dreams as a way to tap into universal themes and connect with the deeper aspects of human nature.

4. Cross-Cultural Perspectives

Dreams also vary across different cultures, reflecting their unique beliefs and traditions . In indigenous cultures, dreams often hold spiritual significance and are seen as a means of communication with ancestors or spirits. These cultures pay close attention to dreams, considering them valuable sources of wisdom and guidance .

In many African cultures, dreams are regarded as sources of healing or divination. They are believed to offer insights into illness or provide answers to important questions. Dream rituals and ceremonies are often performed to harness the power of dreams for spiritual or practical purposes.

5. Modern Perspectives on Dreams

Today, psychologists and anthropologists continue to study dreams and explore their cultural dimensions . Research has shown that dreams can be influenced by cultural practices, social experiences, and even political events.

Dreams have been found to reflect the collective anxieties and tensions within a society. For example, during times of social change or crisis, such as wars or revolutions, dreams may reflect the fears, hopes, and aspirations of individuals or communities. They can offer a unique perspective on the underlying social and psychological dynamics at play [1] during these transformative times.

6. Dream Interpretation Today

Dream interpretation remains a popular field of study and interest for many individuals. While there are no definitive answers to what dreams mean, exploring their cultural and historical significance can provide valuable insights into the human experience.

It’s important to remember that dream interpretation is subjective and personal. Dreams are deeply rooted in an individual’s unique experiences, emotions, and cultural background . What holds true for one person may not apply to another.

When it comes to interpreting your own dreams, it can be helpful to pay attention to the symbols, emotions, and themes that stand out. Keeping a dream journal and reflecting on the connections between your dreams and your waking life can provide valuable insights into your thoughts, feelings, and aspirations.

Ultimately, the real meaning of dreams is deeply personal and can vary from person to person. Exploring your dreams with an open mind and a curiosity about their cultural and historical dimensions can offer a richer understanding of yourself and the world around you.

Dreams remain a beautiful and mysterious aspect of our lives, providing glimpses into our innermost selves and offering us the chance to explore new possibilities. Whether you interpret them as mere products of random neural firings or as reflections of your deepest longings and fears, there is no denying the power and significance of our dreams. So next time you close your eyes and drift off to sleep, pay attention to the images that flit through your mind – who knows what treasures you might uncover? Remember to approach your dreams with curiosity and compassion, and you may find that they can offer you genuine insights into your life. Sweet dreams!

[1] The Cultural Dimensions of Dreaming

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A comprehensive neurocognitive theory of dreaming based on the theories, methodologies, and findings of cognitive neuroscience and the psychological sciences.

G. William Domhoff's neurocognitive theory of dreaming is the only theory of dreaming that makes full use of the new neuroimaging findings on all forms of spontaneous thought and shows how well they explain the results of rigorous quantitative studies of dream content. Domhoff identifies five separate issues — neural substrates, cognitive processes, the psychological meaning of dream content, evolutionarily adaptive functions, and historically invented cultural uses — and then explores how they are intertwined. He also discusses the degree to which there is symbolism in dreams, the development of dreaming in children, and the relative frequency of emotions in the dreams of children and adults.

During dreaming, the neural substrates that support waking sensory input, task-oriented thinking, and movement are relatively deactivated. Domhoff presents the conditions that have to be fulfilled before dreaming can occur spontaneously. He describes the specific cognitive processes supported by the neural substrate of dreaming and then looks at dream reports of research participants. The "why" of dreaming, he says, may be the most counterintuitive outcome of empirical dream research. Though the question is usually framed in terms of adaptation, there is no positive evidence for an adaptive theory of dreaming. Research by anthropologists, historians, and comparative religion scholars, however, suggests that dreaming has psychological and cultural uses, with the most important of these found in religious ceremonies and healing practices. Finally, he offers suggestions for how future dream studies might take advantage of new technologies like smartphones.

Download the book

MIT Press has made The Neurocognitive Theory of Dreaming available as an "Open Access" publication under a Creative Commons BY-ND license, so you can download a PDF of the entire book .

Buy the book

The neurocognitive theory of dreaming in a nutshell.

For a quick overview of the theory and links to relevant references and videos, you can download a 12-page PDF handout ; it was originally created for a presentation to the 2019 meeting of the Society for the Neuroscience of Creativity, but has been updated as of September 2023.

• Context and Narrative
• Dream Setting
• Dreamers Perspective
• Archetypal and Universal Themes
• Cultural and Symbolic References
• Dream Symbols
• Dreams and Creativity
• Dreams and Health
• Dreams and Memory Consolidation
• Interactions and Relationships
• Dreamers Actions and Choices
• Dreamers Feelings and Emotions
• Dreamers Feelings upon Awakening
• Cross-Cultural Dreaming
• Dreams in Society
• Meeting Famous People
• Social Dreaming Theory
• Lucidity and Control
• Personal Experiences
• Spiritual and Transcendent Dreams
• Applications and Implications
• Critiques and Controversies
• Dream Interpretation Techniques
• Overview and Historical Context
• Activation-Input-Modulation Model
• Activation-Synthesis Theory
• Cognitive Dream Theory
• Information-Processing Theory
• Neurocognitive Dream Theory
• Freudian Dream Theory
• Jungian Dream Theory
• Psychoanalytic Dream Theory
• Continual-Activation Theory
• Evolutionary Dream Theory
• Expectation Fulfillment Theory
• Problem-Solving Dream Theory
• Threat Simulation Theory
• Nightmares and Night Terrors
• Recurring Dreams
• Parallel Universe Dreams
• Past Life Dreams
• Telepathic Dreams
• Time-Travel Dreams
• Healing Dreams
• Prophetic Dreams
• Surreal Dreams
• Visitation Dreams
• Dreams Within Dreams
• Epic or Adventure Dreams
• False Awakenings
• Flying Dreams
• Lucid Dreams
• Privacy Policy
• Terms and Conditions
• Mission Statement

Exploring Key Concepts of Cognitive Dream Theory

Have you ever wondered what drives the  theories  behind our  dreams ?  Cognitive Dream Theory  suggests that our cognitive processes are at the heart of why we dream. By revealing the  key concepts  and  principles  of this fascinating  theory , we can begin to understand the role that  memory ,  information processing , and  imagination  play in constructing our dreams.

Does this theory hold the answers to the mysteries of our nightly visions? Delve deeper to uncover how cognitive functions intertwine with our dream worlds, leading you to a more profound comprehension of the  mind ‘s nocturnal adventures.

Table of Contents

• 🧠 Interplay Between Cognition and Dreaming
• 📚 Overview of Cognitive Dream Theory
• 💤 Cognitive Dream Theory Processes in Dreams
• 🧩 Memory and Information Processing in Cognitive Dream Theory
• 🎨 Dream Construction and Imagination to Cognitive Dream Theory
• 📜 Cognitive Dream Theory Historical Development
• 👥 Notable Contributors to Cognitive Dream Theory

The Interplay Between Cognition and Dreaming

Have you ever pondered over the connection between your waking thoughts and the dreams you experience at night? Cognitive Dream Theory offers a compelling explanation, suggesting that our reasoning and problem-solving abilities don’t simply evaporate when we sleep. Instead, they shape the narratives and content of our dreams. This theory encompasses concepts such as memory consolidation , information processing , and the role of imagination in dream creation. But what exactly does this mean for you and the way you dream?

• Memory Consolidation: Dreams may help strengthen new memories.
• Information Processing: Our brains may sort and interpret daily experiences.
• Problem-Solving: Some theories suggest we work through issues in our dreams.
• Imagination: Dreams utilize our imaginative faculties to create scenarios.
• Emotional Regulation: Dreams could be a way our minds process emotions.
• Neural Activation: Dreaming might be linked to random brain activity during REM sleep.
• Cognitive Development: Dreams might reflect stages of cognitive development.

Interwoven Threads: Exploring the Cognitive Fabric of Dreamscapes

Before delving into these fascinating correlations, let’s consider how they interweave to generate the tapestry of our dreamscapes. These elements are like threads in a complex pattern, each contributing to the overall picture of our dream experiences.

The relationship between these cognitive processes and our dreams isn’t just fascinating—it’s illuminating. It offers a window into the ways our brains continue to work, even as we rest. These cognitive operations play a pivotal role in how we dream, what we dream about, and why certain dreams might be more memorable than others.

Cognitive Dream Theory weaves a narrative that connects our waking cognition to the stories told in sleep. Whether it’s the consolidation of memories or the subconscious processing of daily information, each aspect contributes to the understanding of our dream patterns. Embracing these insights can deepen our appreciation for the remarkable capabilities of the human mind.

An Overview of Cognitive Dream Theory

What is the essence behind the dreams we find ourselves immersed in during the night? Cognitive Dream Theory offers insights into this profound question, tying our dreaming experiences to the cognitive processes of our waking life. This theory sheds light on the intricate relationship between our thoughts , memories , and sensory experiences . It suggests that the way we learn, think, and perceive might directly influence the substance and structure of our dreams. Let’s unpack the foundational aspects of this thought-provoking theory.

• Cognitive Architecture: The mental frameworks that shape waking cognition also structure dreams.
• Consciousness Continuum: Dreams are seen as part of a continuum with waking conscious experience.
• Learning and Memory: Dreams may play a role in the consolidation and rehearsal of memories.
• Perceptual Processing: Sensory information from waking hours can be woven into the dream narrative.
• Reflection and Metacognition: Dreams might reflect one’s thoughts about thinking, or “thinking about thinking.”

Cognitive Sculptors: Understanding the Formation of Dream Experiences

Before we explore the table below, which will illuminate these concepts, it’s important to recognize how they collectively sculpt our dream experiences. Each cognitive component is a piece of the puzzle, contributing to the mental tapestry of our dreams. They hint at a deeper understanding of how and why our internal narratives unfold as they do in the dream world.

Cognitive Dream Theory posits a direct link between our waking cognitive experiences and the dreams we experience while asleep. It provides a framework to understand how dreams might function as extensions of our conscious life, involving similar cognitive processes like memory , learning , and perception . This theory encourages us to view dreams not as random nocturnal musings but as meaningful reflections of our cognitive self.

As we move from outlining the Overview of Cognitive Dream Theory , we’ve laid the groundwork to delve into the Key Concepts and Principles that underpin this fascinating framework. These principles are not standalone ideas but are deeply interwoven with the fabric of our dreaming minds. Moreover, they provide a scaffold for understanding how our cognitive processes manifest as we sleep. Now, let us turn our attention to these foundational elements—the building blocks that offer further clarity on the intricate dance between our cognition and the dreams it produces.

Cognitive Dream Theory Key Concepts and Principles

Diving into Cognitive Dream Theory reveals a realm where our waking life intricately intertwines with our dreams. The key concepts and principles of this theory underscore the continuity between our daily cognitive functions and nocturnal narratives. By understanding the roles of memory , learning , and consciousness , we gain insight into why we dream and what our dreams may signify. These principles serve as the guiding stars to navigate the intriguing landscape of our dreaming minds.

• Cognitive Continuity: Dreams are an extension of waking consciousness.
• Active Simulation: Dreams simulate experiences that could happen while awake.
• Memory Integration: Dreams integrate recent and past experiences.
• Emotional Processing: Dreams often reflect our emotional preoccupations.
• Thought Suppression: Suppressing thoughts by day may cause them to reemerge in dreams.
• Problem-Solving: Dreams may offer creative solutions to problems faced while awake.
• Mental Rehearsal: Practicing skills in dreams may improve waking performance.

Let’s connect these fundamental notions to see how they create the vibrant fabric of our dream experience. These concepts do not function in isolation but rather converge to form a dynamic cognitive dreamscape, enriching our understanding of the dreaming brain.

To encapsulate this section, the key concepts and principles of Cognitive Dream Theory present a comprehensive framework for understanding the intricate dance between our cognition and dream activity. Dreams are not just fantasies; they are the mind’s continuation of thought , emotion , and problem-solving from our waking hours. They offer a safe playground for the brain to explore scenarios, process memories , and even enhance our waking capabilities. This theory holds fascinating implications for the interconnectedness of our cognitive landscapes, whether we are asleep or awake.

Building upon our comprehension of the Key Concepts and Principles of Cognitive Dream Theory, we’ve encountered the intricate patterns that govern our dreamscapes. These principles are the bedrock upon which our understanding of dream cognition is built. Venturing forward, we prepare to explore the Processes in Dreams themselves—how our mental activities during the day continue to resonate through the night. This progression will illuminate the active role our brains play in shaping the dramas we experience in our sleep.

Cognitive Dream Theory Processes in Dreams

Within the enigmatic realm of our dreams, cognitive processes are actively at play. Cognitive Dream Theory posits that the same processes that guide our waking thought— perception , memory , learning , and reasoning —also manifest within our dreams. These processes are not random; rather, they are thought to be instrumental in the formation and progression of our dream narratives. But how do these cognitive processes manifest in our dreams, and what purpose might they serve?

• Perception: Dreams can involve complex sensory experiences.
• Memory: Elements from our waking life are interwoven into dreams.
• Learning: Dreams may help with problem-solving and integrating new knowledge.
• Emotional Regulation: Dreams often process and moderate our emotions.
• Creativity: They provide a rich tapestry for the expression of creative thought.
• Reflection: Dreams allow us to reflect on our sense of self and our beliefs.
• Language: Even in dreams, we often communicate and understand through language.

As we prepare to explore the structure of these cognitive operations , it’s essential to comprehend how they converge to mold our dream experiences. Each process contributes uniquely, offering a potential function or benefit to our waking lives. From memory consolidation to emotional processing, the complexity of dreams reflects the intricacies of our cognitive landscape.

These cognitive processes suggest that dreams are far more than mere echoes of our waking life; they are active rehearsals, creative explorations, and emotional safaris that serve pivotal functions in our cognitive and emotional well-being. They help us integrate new information, solve problems in innovative ways, and process emotions that we might not fully address during the day.

To encapsulate Cognitive Dream Theory’s view on dream processes, we can see dreams as a continuation of our cognitive functions into the night. They serve as a unique space where learning , memory integration , and emotional processing can occur without the constraints of reality. It’s in these nightly narratives that we can encounter the complexity of our cognitive powers, in all their vibrant and transformative glory.

Having explored the Processes in Dreams and how integral cognitive functions persist into our slumber, we’ve glimpsed the seamless continuity between waking and sleeping states. These processes provide a context for understanding the profound complexities of our dream experiences. Next, we’ll transition to a focused discussion on the Role of Memory and Information Processing in dreams, uncovering how these vital cognitive capacities affect the tapestry of our nocturnal narratives and contribute to our understanding of the mind’s inner workings.

Memory and Information Processing in Cognitive Dream Theory

In the intricate dance of dreams, memory and information processing play pivotal roles. According to Cognitive Dream Theory, dreams are not just random sequences; they are a reflection of the mind’s powerful ability to process, store, and recall information . By examining the function of memory within our dreams, we gain insights into how the brain uses the state of sleep to organize and manage the vast quantities of information we encounter daily. But what specific aspects of memory and information processing are at play within our dreams?

• Memory Consolidation: Dreams might aid in transferring information to long-term memory.
• Information Sorting: Our brains could be organizing daily experiences while we dream.
• Emotional Processing: Dreams often deal with emotionally charged memories.
• Problem Resolution: They can provide creative solutions that our waking minds didn’t see.
• Personal Reflection: Dreams may reflect our memories and thoughts about personal life events.
• Learning Integration: Dreams have a potential role in integrating new skills or knowledge.
• Nightly Rehearsals: They may serve as a stage for ‘rehearsing’ possible future scenarios.

In anticipation of a detailed view, let’s recognize the seamless integration of memory and information processing in our dream content. Each night, these cognitive functions collaborate to not just entertain but potentially improve our cognitive capacities during our waking hours.

Dreams reflect the brain’s nocturnal efforts to make sense of the day’s information glut. They appear to be not only a byproduct of our memory processes but also a contributing factor to them, playing a role in everything from emotional regulation to skill development . The night thus becomes an extension of our cognitive laboratory, where the synthesis of new knowledge and the refinement of memory are conducted beneath the veil of sleep.

Cognitive Dream Theory brings a greater appreciation for how dreams and memory intertwine. Dreams are not mere static reflections but dynamic and active rehearsals, highlighting the importance of memory consolidation and information processing in our internal nightly theater. With every dream, we potentially rehearse for life’s stage, wherein our memories and experiences are the script from which we perform.

We have delved into how Memory and Information Processing anchor our dreams to the realities of our daily lives, orchestrating the scenes that play out in the theater of the mind. These cognitive aspects not only store and sort our day’s experiences but also influence the emotional and narrative content of our dreams. Shifting our gaze forward, we will examine the Dream Construction and Imagination in Cognitive Dream Theory, where the boundless creativity of the mind sculpts our dream world, blending memory with the fantastical elements of imagination.

Dream Construction and Imagination to Cognitive Dream Theory

Imagine a world where your deepest thoughts, your most creative ideas, and your unexplored fantasies come to life—this is what happens every time we dream. Cognitive Dream Theory proposes that our imagination is not a passive bystander in this process; rather, it is a dynamic architect. It plays a fundamental role in the construction of dreams, leveraging the brain’s creative capacities to generate complex and vivid dreamscapes. But how exactly does imagination contribute to the way we dream, and what does this reveal about the cognitive processes at work?

• Imaginative Flexibility: Dreams reveal an uninhibited form of creativity.
• Scenario Simulation: They create intricate scenarios that simulate real or fantastical experiences.
• Emotional Exploration: Dreams allow for the safe expression of deep emotions.
• Cognitive Experimentation: They can be seen as a mental playground for new ideas.
• Problem-Solving: Dreams sometimes offer unconventional solutions to waking problems.
• Symbolism and Metaphor: They often employ symbols to represent complex thoughts or feelings.
• Sense of Wonder: Dreams can evoke awe and profound curiosity.

As we weave these threads together, we begin to see the vast tapestry of our dreams influenced by our imagination. They reflect the boundless potential of the mind to recreate and explore realities that extend beyond our waking life.

In dreaming, we discover the full scope of our creativity, untethered by the limits of reality. Dreams are a canvas where imagination paints scenes of extraordinary complexity, inviting us to question, explore, and even solve the riddles of our waking lives. They serve as a testament to the richness of our cognitive realm—where every night, we construct worlds that are as real in our minds as the waking world is to our senses.

The construct of dreams within the framework of Cognitive Dream Theory reveals the essence of imagination in our inner life. Dreams are not random; they are shaped by the very same forces that drive our creative endeavors when we are awake. They are proof that the human spirit, with its innate ability to dream, is fueled by an inexhaustible wellspring of inventiveness and insight . It’s through our dreams that we can tap into the profound depths of our imaginative potential.

As we’ve unraveled the role of Imagination in Dream Construction , we’ve seen the creative power of our minds at work, painting our sleep with vivid narratives and emotional landscapes. This intricate process of dream building emerges from a rich history of cognitive exploration. Now, we transition to examining the Historical Development of Cognitive Dream Theory , tracing the evolution of thought that has led to our current understanding of the dreaming mind. Through this historical lens, we gain perspective on the collective journey that has shaped the theories we study today.

Cognitive Dream Theory Historical Development

The evolution of Cognitive Dream Theory is as fascinating as the dreams it seeks to explain. This theory has not emerged in a vacuum; it stands on the shoulders of centuries of philosophical inquiry and decades of neuroscientific research . It represents a confluence of ideas that have been shaped by historical developments in understanding the human mind and its functions. How has Cognitive Dream Theory evolved over time, and what key milestones have influenced its current form?

• Ancient Philosophies: Early musings on the nature of dreams.
• Freudian Influence: The psychoanalytic perspective and its focus on dream symbolism.
• Behaviorism’s Impact: A shift towards observable behavior and away from mental processes.
• Cognitive Revolution: The resurgence of interest in internal mental states.
• Neuroscientific Advances: Modern technology unveiling the workings of the dreaming brain.
• Interdisciplinary Studies: Combining psychology, neuroscience, and other fields to understand dreams.
• Contemporary Theories: Ongoing research and theories that continue to shape our understanding.

As we prepare to chart the course of Cognitive Dream Theory’s historical progression, it’s crucial to appreciate the synergy of past and present thoughts that form the foundation of our current knowledge. Each phase in its evolution has contributed layers of understanding, constructing a comprehensive view of what dreams are and why they matter.

The historical development of Cognitive Dream Theory is a testament to the quest for understanding the hidden workings of the mind. Dreams, once the domain of myth and mysticism, have slowly unraveled to reveal their cognitive core through the contributions of numerous disciplines and epochs.

To encapsulate, Cognitive Dream Theory’s historical lineage showcases a journey from abstract musings to concrete scientific investigations. This evolution mirrors humanity’s own cognitive development, as we grow to understand the most intimate of experiences—our dreams. It’s a testament to the intertwined narrative of human thought and scientific discovery, how one begets the other, and how together, they illuminate the dark recesses of our night-time musings.

Reflecting on the Historical Development of Cognitive Dream Theory , we’ve witnessed a chronological tapestry unfold, revealing how our comprehension of dreams has been sculpted over time. This exploration into the past sets the stage for recognizing the individuals who have significantly shaped this field. Therefore, we segue into acknowledging the Notable Contributors to Cognitive Dream Theory , whose pioneering work and insightful discoveries continue to influence and inspire our quest to understand the enigmatic world of dreams.

Notable Contributors to Cognitive Dream Theory

The rich tapestry of Cognitive Dream Theory is embroidered with the insights of many brilliant minds. From early researchers to contemporary scholars, the contributions of these individuals have shaped our understanding of the cognitive mechanisms behind dreaming. These notable contributors have brought unique perspectives, innovative methodologies, and profound theories that underpin the study of dreams in the context of cognitive science. But who are these intellectual pioneers, and what have they bestowed upon the field?

• Sigmund Freud: The father of dream analysis and interpretation.
• Carl Jung: Emphasized the archetypal and collective unconscious in dreams.
• Calvin Hall: Focused on the cognitive aspects of dream content.
• J. Allan Hobson : Proposed the Activation-Synthesis Hypothesis of dreaming.
• Mark Solms: Integrated neuroscientific findings with psychoanalytic theories.
• G. William Domhoff: Advocated for a neurocognitive model of dreaming.

These contributors serve as beacons in the exploration of our nocturnal cognitive adventures, each illuminating a different facet of dreaming.

Through their groundbreaking work, these scholars have expanded the boundaries of how we comprehend the intricate connection between cognition and dreaming. They’ve provided frameworks, challenged existing paradigms, and opened doors to new realms of research.

Summarizing the contributions of these luminary figures, it becomes clear that Cognitive Dream Theory is a product of both divergent thinking and scholarly rigor. These theorists have given us a lens through which we can examine our dreams—not as mere figments of the imagination, but as complex cognitive phenomena. Their legacies continue to enlighten and inspire ongoing research, ensuring that the study of dreams remains a dynamic and ever-evolving field.

As we pull back the curtain on the mysteries of dreaming through the lens of Cognitive Dream Theory , it becomes apparent that our dreams are not mere byproducts of sleep but rather intricate expressions of our cognitive processes. From the fundamental principles that govern dream construction to the historical milestones that have shaped our understanding, we’ve navigated a realm where psychology, neurobiology, and philosophy converge. Notable contributors have paved the way for this exploration, each adding a unique piece to the dream puzzle.

Reflecting on this journey, one cannot help but marvel at the complex interplay between our waking cognition and the dreams that animate our nights. We’ve seen how memory, information processing, and imagination interweave to create the vivid tableau of our dreamscapes, and how the insights of Freud, Jung, and many others have advanced our understanding.

What do these insights mean for the future of dream research? As we stand at the crossroads of cognitive science and dream analysis, the path ahead promises even deeper insights into the architecture of the mind . We have only begun to chart the vast and unknown territories of our dreaming brains, and each night offers a new opportunity to explore the boundless potential of our consciousness.

With every dream, we are invited to question, to learn, and to wonder at the remarkable capabilities of our cognitive architecture —capabilities that do not sleep, even when we do. The ongoing conversation between our waking lives and our dreams is a testament to the unexplored depths of the human mind, a constant reminder that within each of us lies a world as vast and as mysterious as the universe itself.

• cognitive processes
• dream content
• dream recall
• emotional regulation
• memory consolidation
• neural networks
• problem-solving in dreams
• sleep stages
• Subconscious Mind

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48 Problem Solving

Department of Psychological and Brain Sciences, University of California, Santa Barbara

• Published: 03 June 2013
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Problem solving refers to cognitive processing directed at achieving a goal when the problem solver does not initially know a solution method. A problem exists when someone has a goal but does not know how to achieve it. Problems can be classified as routine or nonroutine, and as well defined or ill defined. The major cognitive processes in problem solving are representing, planning, executing, and monitoring. The major kinds of knowledge required for problem solving are facts, concepts, procedures, strategies, and beliefs. Classic theoretical approaches to the study of problem solving are associationism, Gestalt, and information processing. Current issues and suggested future issues include decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific thinking, everyday thinking, and the cognitive neuroscience of problem solving. Common themes concern the domain specificity of problem solving and a focus on problem solving in authentic contexts.

The study of problem solving begins with defining problem solving, problem, and problem types. This introduction to problem solving is rounded out with an examination of cognitive processes in problem solving, the role of knowledge in problem solving, and historical approaches to the study of problem solving.

Definition of Problem Solving

Problem solving refers to cognitive processing directed at achieving a goal for which the problem solver does not initially know a solution method. This definition consists of four major elements (Mayer, 1992 ; Mayer & Wittrock, 2006 ):

Cognitive —Problem solving occurs within the problem solver’s cognitive system and can only be inferred indirectly from the problem solver’s behavior (including biological changes, introspections, and actions during problem solving). Process —Problem solving involves mental computations in which some operation is applied to a mental representation, sometimes resulting in the creation of a new mental representation. Directed —Problem solving is aimed at achieving a goal. Personal —Problem solving depends on the existing knowledge of the problem solver so that what is a problem for one problem solver may not be a problem for someone who already knows a solution method.

The definition is broad enough to include a wide array of cognitive activities such as deciding which apartment to rent, figuring out how to use a cell phone interface, playing a game of chess, making a medical diagnosis, finding the answer to an arithmetic word problem, or writing a chapter for a handbook. Problem solving is pervasive in human life and is crucial for human survival. Although this chapter focuses on problem solving in humans, problem solving also occurs in nonhuman animals and in intelligent machines.

How is problem solving related to other forms of high-level cognition processing, such as thinking and reasoning? Thinking refers to cognitive processing in individuals but includes both directed thinking (which corresponds to the definition of problem solving) and undirected thinking such as daydreaming (which does not correspond to the definition of problem solving). Thus, problem solving is a type of thinking (i.e., directed thinking).

Reasoning refers to problem solving within specific classes of problems, such as deductive reasoning or inductive reasoning. In deductive reasoning, the reasoner is given premises and must derive a conclusion by applying the rules of logic. For example, given that “A is greater than B” and “B is greater than C,” a reasoner can conclude that “A is greater than C.” In inductive reasoning, the reasoner is given (or has experienced) a collection of examples or instances and must infer a rule. For example, given that X, C, and V are in the “yes” group and x, c, and v are in the “no” group, the reasoning may conclude that B is in “yes” group because it is in uppercase format. Thus, reasoning is a type of problem solving.

Definition of Problem

A problem occurs when someone has a goal but does not know to achieve it. This definition is consistent with how the Gestalt psychologist Karl Duncker ( 1945 , p. 1) defined a problem in his classic monograph, On Problem Solving : “A problem arises when a living creature has a goal but does not know how this goal is to be reached.” However, today researchers recognize that the definition should be extended to include problem solving by intelligent machines. This definition can be clarified using an information processing approach by noting that a problem occurs when a situation is in the given state, the problem solver wants the situation to be in the goal state, and there is no obvious way to move from the given state to the goal state (Newell & Simon, 1972 ). Accordingly, the three main elements in describing a problem are the given state (i.e., the current state of the situation), the goal state (i.e., the desired state of the situation), and the set of allowable operators (i.e., the actions the problem solver is allowed to take). The definition of “problem” is broad enough to include the situation confronting a physician who wishes to make a diagnosis on the basis of preliminary tests and a patient examination, as well as a beginning physics student trying to solve a complex physics problem.

Types of Problems

It is customary in the problem-solving literature to make a distinction between routine and nonroutine problems. Routine problems are problems that are so familiar to the problem solver that the problem solver knows a solution method. For example, for most adults, “What is 365 divided by 12?” is a routine problem because they already know the procedure for long division. Nonroutine problems are so unfamiliar to the problem solver that the problem solver does not know a solution method. For example, figuring out the best way to set up a funding campaign for a nonprofit charity is a nonroutine problem for most volunteers. Technically, routine problems do not meet the definition of problem because the problem solver has a goal but knows how to achieve it. Much research on problem solving has focused on routine problems, although most interesting problems in life are nonroutine.

Another customary distinction is between well-defined and ill-defined problems. Well-defined problems have a clearly specified given state, goal state, and legal operators. Examples include arithmetic computation problems or games such as checkers or tic-tac-toe. Ill-defined problems have a poorly specified given state, goal state, or legal operators, or a combination of poorly defined features. Examples include solving the problem of global warming or finding a life partner. Although, ill-defined problems are more challenging, much research in problem solving has focused on well-defined problems.

Cognitive Processes in Problem Solving

The process of problem solving can be broken down into two main phases: problem representation , in which the problem solver builds a mental representation of the problem situation, and problem solution , in which the problem solver works to produce a solution. The major subprocess in problem representation is representing , which involves building a situation model —that is, a mental representation of the situation described in the problem. The major subprocesses in problem solution are planning , which involves devising a plan for how to solve the problem; executing , which involves carrying out the plan; and monitoring , which involves evaluating and adjusting one’s problem solving.

For example, given an arithmetic word problem such as “Alice has three marbles. Sarah has two more marbles than Alice. How many marbles does Sarah have?” the process of representing involves building a situation model in which Alice has a set of marbles, there is set of marbles for the difference between the two girls, and Sarah has a set of marbles that consists of Alice’s marbles and the difference set. In the planning process, the problem solver sets a goal of adding 3 and 2. In the executing process, the problem solver carries out the computation, yielding an answer of 5. In the monitoring process, the problem solver looks over what was done and concludes that 5 is a reasonable answer. In most complex problem-solving episodes, the four cognitive processes may not occur in linear order, but rather may interact with one another. Although some research focuses mainly on the execution process, problem solvers may tend to have more difficulty with the processes of representing, planning, and monitoring.

Knowledge for Problem Solving

An important theme in problem-solving research is that problem-solving proficiency on any task depends on the learner’s knowledge (Anderson et al., 2001 ; Mayer, 1992 ). Five kinds of knowledge are as follows:

Facts —factual knowledge about the characteristics of elements in the world, such as “Sacramento is the capital of California” Concepts —conceptual knowledge, including categories, schemas, or models, such as knowing the difference between plants and animals or knowing how a battery works Procedures —procedural knowledge of step-by-step processes, such as how to carry out long-division computations Strategies —strategic knowledge of general methods such as breaking a problem into parts or thinking of a related problem Beliefs —attitudinal knowledge about how one’s cognitive processing works such as thinking, “I’m good at this”

Although some research focuses mainly on the role of facts and procedures in problem solving, complex problem solving also depends on the problem solver’s concepts, strategies, and beliefs (Mayer, 1992 ).

Historical Approaches to Problem Solving

Psychological research on problem solving began in the early 1900s, as an outgrowth of mental philosophy (Humphrey, 1963 ; Mandler & Mandler, 1964 ). Throughout the 20th century four theoretical approaches developed: early conceptions, associationism, Gestalt psychology, and information processing.

Early Conceptions

The start of psychology as a science can be set at 1879—the year Wilhelm Wundt opened the first world’s psychology laboratory in Leipzig, Germany, and sought to train the world’s first cohort of experimental psychologists. Instead of relying solely on philosophical speculations about how the human mind works, Wundt sought to apply the methods of experimental science to issues addressed in mental philosophy. His theoretical approach became structuralism —the analysis of consciousness into its basic elements.

Wundt’s main contribution to the study of problem solving, however, was to call for its banishment. According to Wundt, complex cognitive processing was too complicated to be studied by experimental methods, so “nothing can be discovered in such experiments” (Wundt, 1911/1973 ). Despite his admonishments, however, a group of his former students began studying thinking mainly in Wurzburg, Germany. Using the method of introspection, subjects were asked to describe their thought process as they solved word association problems, such as finding the superordinate of “newspaper” (e.g., an answer is “publication”). Although the Wurzburg group—as they came to be called—did not produce a new theoretical approach, they found empirical evidence that challenged some of the key assumptions of mental philosophy. For example, Aristotle had proclaimed that all thinking involves mental imagery, but the Wurzburg group was able to find empirical evidence for imageless thought .

Associationism

The first major theoretical approach to take hold in the scientific study of problem solving was associationism —the idea that the cognitive representations in the mind consist of ideas and links between them and that cognitive processing in the mind involves following a chain of associations from one idea to the next (Mandler & Mandler, 1964 ; Mayer, 1992 ). For example, in a classic study, E. L. Thorndike ( 1911 ) placed a hungry cat in what he called a puzzle box—a wooden crate in which pulling a loop of string that hung from overhead would open a trap door to allow the cat to escape to a bowl of food outside the crate. Thorndike placed the cat in the puzzle box once a day for several weeks. On the first day, the cat engaged in many extraneous behaviors such as pouncing against the wall, pushing its paws through the slats, and meowing, but on successive days the number of extraneous behaviors tended to decrease. Overall, the time required to get out of the puzzle box decreased over the course of the experiment, indicating the cat was learning how to escape.

Thorndike’s explanation for how the cat learned to solve the puzzle box problem is based on an associationist view: The cat begins with a habit family hierarchy —a set of potential responses (e.g., pouncing, thrusting, meowing, etc.) all associated with the same stimulus (i.e., being hungry and confined) and ordered in terms of strength of association. When placed in the puzzle box, the cat executes its strongest response (e.g., perhaps pouncing against the wall), but when it fails, the strength of the association is weakened, and so on for each unsuccessful action. Eventually, the cat gets down to what was initially a weak response—waving its paw in the air—but when that response leads to accidentally pulling the string and getting out, it is strengthened. Over the course of many trials, the ineffective responses become weak and the successful response becomes strong. Thorndike refers to this process as the law of effect : Responses that lead to dissatisfaction become less associated with the situation and responses that lead to satisfaction become more associated with the situation. According to Thorndike’s associationist view, solving a problem is simply a matter of trial and error and accidental success. A major challenge to assocationist theory concerns the nature of transfer—that is, where does a problem solver find a creative solution that has never been performed before? Associationist conceptions of cognition can be seen in current research, including neural networks, connectionist models, and parallel distributed processing models (Rogers & McClelland, 2004 ).

Gestalt Psychology

The Gestalt approach to problem solving developed in the 1930s and 1940s as a counterbalance to the associationist approach. According to the Gestalt approach, cognitive representations consist of coherent structures (rather than individual associations) and the cognitive process of problem solving involves building a coherent structure (rather than strengthening and weakening of associations). For example, in a classic study, Kohler ( 1925 ) placed a hungry ape in a play yard that contained several empty shipping crates and a banana attached overhead but out of reach. Based on observing the ape in this situation, Kohler noted that the ape did not randomly try responses until one worked—as suggested by Thorndike’s associationist view. Instead, the ape stood under the banana, looked up at it, looked at the crates, and then in a flash of insight stacked the crates under the bananas as a ladder, and walked up the steps in order to reach the banana.

According to Kohler, the ape experienced a sudden visual reorganization in which the elements in the situation fit together in a way to solve the problem; that is, the crates could become a ladder that reduces the distance to the banana. Kohler referred to the underlying mechanism as insight —literally seeing into the structure of the situation. A major challenge of Gestalt theory is its lack of precision; for example, naming a process (i.e., insight) is not the same as explaining how it works. Gestalt conceptions can be seen in modern research on mental models and schemas (Gentner & Stevens, 1983 ).

Information Processing

The information processing approach to problem solving developed in the 1960s and 1970s and was based on the influence of the computer metaphor—the idea that humans are processors of information (Mayer, 2009 ). According to the information processing approach, problem solving involves a series of mental computations—each of which consists of applying a process to a mental representation (such as comparing two elements to determine whether they differ).

In their classic book, Human Problem Solving , Newell and Simon ( 1972 ) proposed that problem solving involved a problem space and search heuristics . A problem space is a mental representation of the initial state of the problem, the goal state of the problem, and all possible intervening states (based on applying allowable operators). Search heuristics are strategies for moving through the problem space from the given to the goal state. Newell and Simon focused on means-ends analysis , in which the problem solver continually sets goals and finds moves to accomplish goals.

Newell and Simon used computer simulation as a research method to test their conception of human problem solving. First, they asked human problem solvers to think aloud as they solved various problems such as logic problems, chess, and cryptarithmetic problems. Then, based on an information processing analysis, Newell and Simon created computer programs that solved these problems. In comparing the solution behavior of humans and computers, they found high similarity, suggesting that the computer programs were solving problems using the same thought processes as humans.

An important advantage of the information processing approach is that problem solving can be described with great clarity—as a computer program. An important limitation of the information processing approach is that it is most useful for describing problem solving for well-defined problems rather than ill-defined problems. The information processing conception of cognition lives on as a keystone of today’s cognitive science (Mayer, 2009 ).

Classic Issues in Problem Solving

Three classic issues in research on problem solving concern the nature of transfer (suggested by the associationist approach), the nature of insight (suggested by the Gestalt approach), and the role of problem-solving heuristics (suggested by the information processing approach).

Transfer refers to the effects of prior learning on new learning (or new problem solving). Positive transfer occurs when learning A helps someone learn B. Negative transfer occurs when learning A hinders someone from learning B. Neutral transfer occurs when learning A has no effect on learning B. Positive transfer is a central goal of education, but research shows that people often do not transfer what they learned to solving problems in new contexts (Mayer, 1992 ; Singley & Anderson, 1989 ).

Three conceptions of the mechanisms underlying transfer are specific transfer , general transfer , and specific transfer of general principles . Specific transfer refers to the idea that learning A will help someone learn B only if A and B have specific elements in common. For example, learning Spanish may help someone learn Latin because some of the vocabulary words are similar and the verb conjugation rules are similar. General transfer refers to the idea that learning A can help someone learn B even they have nothing specifically in common but A helps improve the learner’s mind in general. For example, learning Latin may help people learn “proper habits of mind” so they are better able to learn completely unrelated subjects as well. Specific transfer of general principles is the idea that learning A will help someone learn B if the same general principle or solution method is required for both even if the specific elements are different.

In a classic study, Thorndike and Woodworth ( 1901 ) found that students who learned Latin did not subsequently learn bookkeeping any better than students who had not learned Latin. They interpreted this finding as evidence for specific transfer—learning A did not transfer to learning B because A and B did not have specific elements in common. Modern research on problem-solving transfer continues to show that people often do not demonstrate general transfer (Mayer, 1992 ). However, it is possible to teach people a general strategy for solving a problem, so that when they see a new problem in a different context they are able to apply the strategy to the new problem (Judd, 1908 ; Mayer, 2008 )—so there is also research support for the idea of specific transfer of general principles.

Insight refers to a change in a problem solver’s mind from not knowing how to solve a problem to knowing how to solve it (Mayer, 1995 ; Metcalfe & Wiebe, 1987 ). In short, where does the idea for a creative solution come from? A central goal of problem-solving research is to determine the mechanisms underlying insight.

The search for insight has led to five major (but not mutually exclusive) explanatory mechanisms—insight as completing a schema, insight as suddenly reorganizing visual information, insight as reformulation of a problem, insight as removing mental blocks, and insight as finding a problem analog (Mayer, 1995 ). Completing a schema is exemplified in a study by Selz (Fridja & de Groot, 1982 ), in which people were asked to think aloud as they solved word association problems such as “What is the superordinate for newspaper?” To solve the problem, people sometimes thought of a coordinate, such as “magazine,” and then searched for a superordinate category that subsumed both terms, such as “publication.” According to Selz, finding a solution involved building a schema that consisted of a superordinate and two subordinate categories.

Reorganizing visual information is reflected in Kohler’s ( 1925 ) study described in a previous section in which a hungry ape figured out how to stack boxes as a ladder to reach a banana hanging above. According to Kohler, the ape looked around the yard and found the solution in a flash of insight by mentally seeing how the parts could be rearranged to accomplish the goal.

Reformulating a problem is reflected in a classic study by Duncker ( 1945 ) in which people are asked to think aloud as they solve the tumor problem—how can you destroy a tumor in a patient without destroying surrounding healthy tissue by using rays that at sufficient intensity will destroy any tissue in their path? In analyzing the thinking-aloud protocols—that is, transcripts of what the problem solvers said—Duncker concluded that people reformulated the goal in various ways (e.g., avoid contact with healthy tissue, immunize healthy tissue, have ray be weak in healthy tissue) until they hit upon a productive formulation that led to the solution (i.e., concentrating many weak rays on the tumor).

Removing mental blocks is reflected in classic studies by Duncker ( 1945 ) in which solving a problem involved thinking of a novel use for an object, and by Luchins ( 1942 ) in which solving a problem involved not using a procedure that had worked well on previous problems. Finding a problem analog is reflected in classic research by Wertheimer ( 1959 ) in which learning to find the area of a parallelogram is supported by the insight that one could cut off the triangle on one side and place it on the other side to form a rectangle—so a parallelogram is really a rectangle in disguise. The search for insight along each of these five lines continues in current problem-solving research.

Heuristics are problem-solving strategies, that is, general approaches to how to solve problems. Newell and Simon ( 1972 ) suggested three general problem-solving heuristics for moving from a given state to a goal state: random trial and error , hill climbing , and means-ends analysis . Random trial and error involves randomly selecting a legal move and applying it to create a new problem state, and repeating that process until the goal state is reached. Random trial and error may work for simple problems but is not efficient for complex ones. Hill climbing involves selecting the legal move that moves the problem solver closer to the goal state. Hill climbing will not work for problems in which the problem solver must take a move that temporarily moves away from the goal as is required in many problems.

Means-ends analysis involves creating goals and seeking moves that can accomplish the goal. If a goal cannot be directly accomplished, a subgoal is created to remove one or more obstacles. Newell and Simon ( 1972 ) successfully used means-ends analysis as the search heuristic in a computer program aimed at general problem solving, that is, solving a diverse collection of problems. However, people may also use specific heuristics that are designed to work for specific problem-solving situations (Gigerenzer, Todd, & ABC Research Group, 1999 ; Kahneman & Tversky, 1984 ).

Current and Future Issues in Problem Solving

Eight current issues in problem solving involve decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific problem solving, everyday thinking, and the cognitive neuroscience of problem solving.

Decision Making

Decision making refers to the cognitive processing involved in choosing between two or more alternatives (Baron, 2000 ; Markman & Medin, 2002 ). For example, a decision-making task may involve choosing between getting $240 for sure or having a 25% change of getting $1000. According to economic theories such as expected value theory, people should chose the second option, which is worth $250 (i.e., .25 x $1000) rather than the first option, which is worth $240 (1.00 x $240), but psychological research shows that most people prefer the first option (Kahneman & Tversky, 1984 ).

Research on decision making has generated three classes of theories (Markman & Medin, 2002 ): descriptive theories, such as prospect theory (Kahneman & Tversky), which are based on the ideas that people prefer to overweight the cost of a loss and tend to overestimate small probabilities; heuristic theories, which are based on the idea that people use a collection of short-cut strategies such as the availability heuristic (Gigerenzer et al., 1999 ; Kahneman & Tversky, 2000 ); and constructive theories, such as mental accounting (Kahneman & Tversky, 2000 ), in which people build a narrative to justify their choices to themselves. Future research is needed to examine decision making in more realistic settings.

Intelligence and Creativity

Although researchers do not have complete consensus on the definition of intelligence (Sternberg, 1990 ), it is reasonable to view intelligence as the ability to learn or adapt to new situations. Fluid intelligence refers to the potential to solve problems without any relevant knowledge, whereas crystallized intelligence refers to the potential to solve problems based on relevant prior knowledge (Sternberg & Gregorenko, 2003 ). As people gain more experience in a field, their problem-solving performance depends more on crystallized intelligence (i.e., domain knowledge) than on fluid intelligence (i.e., general ability) (Sternberg & Gregorenko, 2003 ). The ability to monitor and manage one’s cognitive processing during problem solving—which can be called metacognition —is an important aspect of intelligence (Sternberg, 1990 ). Research is needed to pinpoint the knowledge that is needed to support intelligent performance on problem-solving tasks.

Creativity refers to the ability to generate ideas that are original (i.e., other people do not think of the same idea) and functional (i.e., the idea works; Sternberg, 1999 ). Creativity is often measured using tests of divergent thinking —that is, generating as many solutions as possible for a problem (Guilford, 1967 ). For example, the uses test asks people to list as many uses as they can think of for a brick. Creativity is different from intelligence, and it is at the heart of creative problem solving—generating a novel solution to a problem that the problem solver has never seen before. An important research question concerns whether creative problem solving depends on specific knowledge or creativity ability in general.

Teaching of Thinking Skills

How can people learn to be better problem solvers? Mayer ( 2008 ) proposes four questions concerning teaching of thinking skills:

What to teach —Successful programs attempt to teach small component skills (such as how to generate and evaluate hypotheses) rather than improve the mind as a single monolithic skill (Covington, Crutchfield, Davies, & Olton, 1974 ). How to teach —Successful programs focus on modeling the process of problem solving rather than solely reinforcing the product of problem solving (Bloom & Broder, 1950 ). Where to teach —Successful programs teach problem-solving skills within the specific context they will be used rather than within a general course on how to solve problems (Nickerson, 1999 ). When to teach —Successful programs teaching higher order skills early rather than waiting until lower order skills are completely mastered (Tharp & Gallimore, 1988 ).

Overall, research on teaching of thinking skills points to the domain specificity of problem solving; that is, successful problem solving depends on the problem solver having domain knowledge that is relevant to the problem-solving task.

Expert Problem Solving

Research on expertise is concerned with differences between how experts and novices solve problems (Ericsson, Feltovich, & Hoffman, 2006 ). Expertise can be defined in terms of time (e.g., 10 years of concentrated experience in a field), performance (e.g., earning a perfect score on an assessment), or recognition (e.g., receiving a Nobel Prize or becoming Grand Master in chess). For example, in classic research conducted in the 1940s, de Groot ( 1965 ) found that chess experts did not have better general memory than chess novices, but they did have better domain-specific memory for the arrangement of chess pieces on the board. Chase and Simon ( 1973 ) replicated this result in a better controlled experiment. An explanation is that experts have developed schemas that allow them to chunk collections of pieces into a single configuration.

In another landmark study, Larkin et al. ( 1980 ) compared how experts (e.g., physics professors) and novices (e.g., first-year physics students) solved textbook physics problems about motion. Experts tended to work forward from the given information to the goal, whereas novices tended to work backward from the goal to the givens using a means-ends analysis strategy. Experts tended to store their knowledge in an integrated way, whereas novices tended to store their knowledge in isolated fragments. In another study, Chi, Feltovich, and Glaser ( 1981 ) found that experts tended to focus on the underlying physics concepts (such as conservation of energy), whereas novices tended to focus on the surface features of the problem (such as inclined planes or springs). Overall, research on expertise is useful in pinpointing what experts know that is different from what novices know. An important theme is that experts rely on domain-specific knowledge rather than solely general cognitive ability.

Analogical Reasoning

Analogical reasoning occurs when people solve one problem by using their knowledge about another problem (Holyoak, 2005 ). For example, suppose a problem solver learns how to solve a problem in one context using one solution method and then is given a problem in another context that requires the same solution method. In this case, the problem solver must recognize that the new problem has structural similarity to the old problem (i.e., it may be solved by the same method), even though they do not have surface similarity (i.e., the cover stories are different). Three steps in analogical reasoning are recognizing —seeing that a new problem is similar to a previously solved problem; abstracting —finding the general method used to solve the old problem; and mapping —using that general method to solve the new problem.

Research on analogical reasoning shows that people often do not recognize that a new problem can be solved by the same method as a previously solved problem (Holyoak, 2005 ). However, research also shows that successful analogical transfer to a new problem is more likely when the problem solver has experience with two old problems that have the same underlying structural features (i.e., they are solved by the same principle) but different surface features (i.e., they have different cover stories) (Holyoak, 2005 ). This finding is consistent with the idea of specific transfer of general principles as described in the section on “Transfer.”

Mathematical and Scientific Problem Solving

Research on mathematical problem solving suggests that five kinds of knowledge are needed to solve arithmetic word problems (Mayer, 2008 ):

Factual knowledge —knowledge about the characteristics of problem elements, such as knowing that there are 100 cents in a dollar Schematic knowledge —knowledge of problem types, such as being able to recognize time-rate-distance problems Strategic knowledge —knowledge of general methods, such as how to break a problem into parts Procedural knowledge —knowledge of processes, such as how to carry our arithmetic operations Attitudinal knowledge —beliefs about one’s mathematical problem-solving ability, such as thinking, “I am good at this”

People generally possess adequate procedural knowledge but may have difficulty in solving mathematics problems because they lack factual, schematic, strategic, or attitudinal knowledge (Mayer, 2008 ). Research is needed to pinpoint the role of domain knowledge in mathematical problem solving.

Research on scientific problem solving shows that people harbor misconceptions, such as believing that a force is needed to keep an object in motion (McCloskey, 1983 ). Learning to solve science problems involves conceptual change, in which the problem solver comes to recognize that previous conceptions are wrong (Mayer, 2008 ). Students can be taught to engage in scientific reasoning such as hypothesis testing through direct instruction in how to control for variables (Chen & Klahr, 1999 ). A central theme of research on scientific problem solving concerns the role of domain knowledge.

Everyday Thinking

Everyday thinking refers to problem solving in the context of one’s life outside of school. For example, children who are street vendors tend to use different procedures for solving arithmetic problems when they are working on the streets than when they are in school (Nunes, Schlieman, & Carraher, 1993 ). This line of research highlights the role of situated cognition —the idea that thinking always is shaped by the physical and social context in which it occurs (Robbins & Aydede, 2009 ). Research is needed to determine how people solve problems in authentic contexts.

Cognitive Neuroscience of Problem Solving

The cognitive neuroscience of problem solving is concerned with the brain activity that occurs during problem solving. For example, using fMRI brain imaging methodology, Goel ( 2005 ) found that people used the language areas of the brain to solve logical reasoning problems presented in sentences (e.g., “All dogs are pets…”) and used the spatial areas of the brain to solve logical reasoning problems presented in abstract letters (e.g., “All D are P…”). Cognitive neuroscience holds the potential to make unique contributions to the study of problem solving.

Problem solving has always been a topic at the fringe of cognitive psychology—too complicated to study intensively but too important to completely ignore. Problem solving—especially in realistic environments—is messy in comparison to studying elementary processes in cognition. The field remains fragmented in the sense that topics such as decision making, reasoning, intelligence, expertise, mathematical problem solving, everyday thinking, and the like are considered to be separate topics, each with its own separate literature. Yet some recurring themes are the role of domain-specific knowledge in problem solving and the advantages of studying problem solving in authentic contexts.

Future Directions

Some important issues for future research include the three classic issues examined in this chapter—the nature of problem-solving transfer (i.e., How are people able to use what they know about previous problem solving to help them in new problem solving?), the nature of insight (e.g., What is the mechanism by which a creative solution is constructed?), and heuristics (e.g., What are some teachable strategies for problem solving?). In addition, future research in problem solving should continue to pinpoint the role of domain-specific knowledge in problem solving, the nature of cognitive ability in problem solving, how to help people develop proficiency in solving problems, and how to provide aids for problem solving.

Anderson L. W. , Krathwohl D. R. , Airasian P. W. , Cruikshank K. A. , Mayer R. E. , Pintrich P. R. , Raths, J., & Wittrock M. C. ( 2001 ). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York : Longman.

Baron J. ( 2000 ). Thinking and deciding (3rd ed.). New York : Cambridge University Press.

Google Scholar

Google Preview

Bloom B. S. , & Broder B. J. ( 1950 ). Problem-solving processes of college students: An exploratory investigation. Chicago : University of Chicago Press.

Chase W. G. , & Simon H. A. ( 1973 ). Perception in chess.   Cognitive Psychology, 4, 55–81.

Chen Z. , & Klahr D. ( 1999 ). All other things being equal: Acquisition and transfer of the control of variable strategy . Child Development, 70, 1098–1120.

Chi M. T. H. , Feltovich P. J. , & Glaser R. ( 1981 ). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

Covington M. V. , Crutchfield R. S. , Davies L. B. , & Olton R. M. ( 1974 ). The productive thinking program. Columbus, OH : Merrill.

de Groot A. D. ( 1965 ). Thought and choice in chess. The Hague, The Netherlands : Mouton.

Duncker K. ( 1945 ). On problem solving.   Psychological Monographs, 58 (3) (Whole No. 270).

Ericsson K. A. , Feltovich P. J. , & Hoffman R. R. (Eds.). ( 2006 ). The Cambridge handbook of expertise and expert performance. New York : Cambridge University Press.

Fridja N. H. , & de Groot A. D. ( 1982 ). Otto Selz: His contribution to psychology. The Hague, The Netherlands : Mouton.

Gentner D. , & Stevens A. L. (Eds.). ( 1983 ). Mental models. Hillsdale, NJ : Erlbaum.

Gigerenzer G. , Todd P. M. , & ABC Research Group (Eds.). ( 1999 ). Simple heuristics that make us smart. Oxford, England : Oxford University Press.

Goel V. ( 2005 ). Cognitive neuroscience of deductive reasoning. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 475–492). New York : Cambridge University Press.

Guilford J. P. ( 1967 ). The nature of human intelligence. New York : McGraw-Hill.

Holyoak K. J. ( 2005 ). Analogy. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 117–142). New York : Cambridge University Press.

Humphrey G. ( 1963 ). Thinking: An introduction to experimental psychology. New York : Wiley.

Judd C. H. ( 1908 ). The relation of special training and general intelligence. Educational Review, 36, 28–42.

Kahneman D. , & Tversky A. ( 1984 ). Choices, values, and frames. American Psychologist, 39, 341–350.

Kahneman D. , & Tversky A. (Eds.). ( 2000 ). Choices, values, and frames. New York : Cambridge University Press.

Kohler W. ( 1925 ). The mentality of apes. New York : Liveright.

Larkin J. H. , McDermott J. , Simon D. P. , & Simon H. A. ( 1980 ). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.

Luchins A. ( 1942 ). Mechanization in problem solving.   Psychological Monographs, 54 (6) (Whole No. 248).

Mandler J. M. , & Mandler G. ( 1964 ). Thinking from associationism to Gestalt. New York : Wiley.

Markman A. B. , & Medin D. L. ( 2002 ). Decision making. In D. Medin (Ed.), Stevens’ handbook of experimental psychology, Vol. 2. Memory and cognitive processes (2nd ed., pp. 413–466). New York : Wiley.

Mayer R. E. ( 1992 ). Thinking, problem solving, cognition (2nd ed). New York : Freeman.

Mayer R. E. ( 1995 ). The search for insight: Grappling with Gestalt psychology’s unanswered questions. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight (pp. 3–32). Cambridge, MA : MIT Press.

Mayer R. E. ( 2008 ). Learning and instruction. Upper Saddle River, NJ : Merrill Prentice Hall.

Mayer R. E. ( 2009 ). Information processing. In T. L. Good (Ed.), 21st century education: A reference handbook (pp. 168–174). Thousand Oaks, CA : Sage.

Mayer R. E. , & Wittrock M. C. ( 2006 ). Problem solving. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 287–304). Mahwah, NJ : Erlbaum.

McCloskey M. ( 1983 ). Intuitive physics.   Scientific American, 248 (4), 122–130.

Metcalfe J. , & Wiebe D. ( 1987 ). Intuition in insight and non-insight problem solving. Memory and Cognition, 15, 238–246.

Newell A. , & Simon H. A. ( 1972 ). Human problem solving. Englewood Cliffs, NJ : Prentice-Hall.

Nickerson R. S. ( 1999 ). Enhancing creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 392–430). New York : Cambridge University Press.

Nunes T. , Schliemann A. D. , & Carraher D. W , ( 1993 ). Street mathematics and school mathematics. Cambridge, England : Cambridge University Press.

Robbins P. , & Aydede M. (Eds.). ( 2009 ). The Cambridge handbook of situated cognition. New York : Cambridge University Press.

Rogers T. T. , & McClelland J. L. ( 2004 ). Semantic cognition: A parallel distributed processing approach. Cambridge, MA : MIT Press.

Singley M. K. , & Anderson J. R. ( 1989 ). The transfer of cognitive skill. Cambridge, MA : Harvard University Press.

Sternberg R. J. ( 1990 ). Metaphors of mind: Conceptions of the nature of intelligence. New York : Cambridge University Press.

Sternberg R. J. ( 1999 ). Handbook of creativity. New York : Cambridge University Press.

Sternberg R. J. , & Gregorenko E. L. (Eds.). ( 2003 ). The psychology of abilities, competencies, and expertise. New York : Cambridge University Press.

Tharp R. G. , & Gallimore R. ( 1988 ). Rousing minds to life: Teaching, learning, and schooling in social context. New York : Cambridge University Press.

Thorndike E. L. ( 1911 ). Animal intelligence. New York: Hafner.

Thorndike E. L. , & Woodworth R. S. ( 1901 ). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8, 247–261.

Wertheimer M. ( 1959 ). Productive thinking. New York : Harper and Collins.

Wundt W. ( 1973 ). An introduction to experimental psychology. New York : Arno Press. (Original work published in 1911).

Further Reading

Baron, J. ( 2008 ). Thinking and deciding (4th ed). New York: Cambridge University Press.

Duncker, K. ( 1945 ). On problem solving. Psychological Monographs , 58(3) (Whole No. 270).

Holyoak, K. J. , & Morrison, R. G. ( 2005 ). The Cambridge handbook of thinking and reasoning . New York: Cambridge University Press.

Mayer, R. E. , & Wittrock, M. C. ( 2006 ). Problem solving. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 287–304). Mahwah, NJ: Erlbaum.

Sternberg, R. J. , & Ben-Zeev, T. ( 2001 ). Complex cognition: The psychology of human thought . New York: Oxford University Press.

Weisberg, R. W. ( 2006 ). Creativity . New York: Wiley.

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