social cognitive theory research paper

ORIGINAL RESEARCH article

Applying social cognitive theory in predicting physical activity among chinese adolescents: a cross-sectional study with multigroup structural equation model.

• 1 Division of Sports Science and Physical Education, Tsinghua University, Beijing, China
• 2 Vanke School of Public Health, Tsinghua University, Beijing, China
• 3 Department of Kinesiology, Hebei Institute of Physical Education, Shijiazhuang, China
• 4 Department of Social Sciences, Hebei Sport University, Shijiazhuang, China

This cross-sectional study aimed to assess the applicability of social cognitive determinants among the Chinese adolescents and examine whether the predictability of the social cognitive theory (SCT) model on physical activity (PA) differs across gender (boys and girls) and urbanization (urban and suburban). A total of 3,000 Chinese adolescents ranging between the ages of 12–15 years were randomly selected to complete a set of questionnaires. Structural equation modeling (SEM) was applied to investigate the relationships between social cognitive variables and PA in the urbanization and gender subgroups. The overall model explained 38.9% of the variance in PA. Fit indices indicated that the structural model of SCT was good: root mean square error of approximation (RMSEA) = 0.047, (root mean square residual) RMR = 0.028, goodness of fit index (GFI) = 0.974, adjusted goodness of fit index (AGFI) = 0.960, Tucker–Lewis coefficient (TLI) = 0.971, and comparative fit index (CFI) = 0.978. Regarding the subgroup analysis, social support (critical ratios [CRs] = 2.118; p < 0.001) had a more substantial impact on the PA of adolescents in suburban areas than that in urban areas, whereas self-regulation (CRs = −2.896, p < 0.001) had a more substantial impact on the PA of adolescents in urban areas than in suburban areas. The results indicate that the SCT model predicts the PA of Chinese adolescents substantially. An SCT model could apply over a range of subgroups to predict the PA behavior and should be considered comprehensively when designing interventions. These findings would benefit PA among the Chinese adolescents, especially across genders and urbanization.

Introduction

Regular physical activity (PA) is essential for the health of adolescents, as it reduces the risk of chronic diseases, obesity, and mental health problems and improves cognitive health indicators ( Kruk, 2007 ; Lipnowski et al., 2012 ; Poitras et al., 2016 ). Despite the considerable evidence showing the benefits of PA, 80.3% of adolescents (13–15 years old) failed to meet the recommendation of 60 min of PA daily ( Hallal et al., 2012 ). Physical inactivity among adolescents is widespread worldwide ( Sisson et al., 2010 ; Patnode et al., 2011 ). In 2016, adolescents aged 11–17 years had a prevalence of physical inactivity of 81.0% (77.6% of boys and 84.7% of girls) globally ( Guthold et al., 2020 ). According to a 2017 survey in China, only 34.1% of children and adolescents met the recommendation of 60 min or more of moderate to vigorous PA (MVPA) per day ( Zhu et al., 2019 ). Evidence indicates that the habits of PA behavior established in adolescence are likely to track into adulthood ( Patnode et al., 2011 ; Hayes et al., 2019 ). Therefore, engaging in PA during this period is particularly important for the whole life span of the individual.

Evidence suggests that interventions using health behavior theories would effectively change the population-level behavior in “real world” contexts ( Hagger and Weed, 2019 ) and may help in explaining the maintenance of health-related behaviors ( Kwasnicka et al., 2016 ). Furthermore, health behavior theories provide an effective framework to understand the mediators and moderators of the behavior. Bandura (1986) proposed that human behavior, personal factors (such as, cognition), and environmental factors are affected by each other within a framework of reciprocal determinism, termed as triadic reciprocal causation. Social cognitive theory (SCT) represents a causal model in which the self-efficacy is set to the influence human behavior directly and indirectly via other mediating processes that include outcome expectations, social support, and self-regulation ( Bandura, 1991 , 1998 , 2004 ; Bandura et al., 1997 ). Self-efficacy reflects the judgment of own ability of an individual to accomplish a specific health behavior. Outcome expectancy reflects the individual’s perception of the likely social, physical, self-evaluation outcome of completing a specific health behavior. Social support is the perceived support for health behaviors from important others, such as family and friends. Self-regulation operates through a set of psychological subfunctions to influence the health behavior (e.g., self-monitoring, judgmental, and self-reactive influences).

Key SCT determinants of PA include social supports, self-efficacy, outcome expectations, and self-regulation of participating in PA ( Bandura et al., 1997 ; Anderson-Bill et al., 2011a ; Ramirez et al., 2012 ). In previous studies, SCT indicates that social and psychological determinants influence the behavior ( Bandura, 1986 , 1998 ) and that self-efficacy is the most relevant determinant to PA in SCT among all key determinants ( Dewar et al., 2013 ; Ah Hong et al., 2017 ; Beauchamp et al., 2019 ). Researchers have used SCT to predict the PA behavior in different countries (e.g., the United States, Australia, and Iran) ( Gao, 2012 ; Dewar et al., 2013 ; Bagherniya et al., 2015 ). The SCT has been widely applied to explain and change the PA behavior across a wide range of age groups and proved successful. Researchers found that the SCT serves as a good framework for researchers studying health promotion and PA in the parents of African American children ( Webber-Ritchey et al., 2018 ). Furthermore, SCT was proved to be effective when used in the intervention in persons with multiple sclerosis and breast cancer ( Auster-Gussman et al., 2021 ; Silveira et al., 2021 ). The overall predictability of SCT ranged from 5 to 52% among adolescents ( Taymoori et al., 2008 ; Lubans et al., 2011 ; Martin et al., 2011 ). However, there are not many pieces of research that use SCT to predict the PA behavior in Chinese adolescents.

The positive effects of social-cognitive determinants on PA prompted researchers to examine the invariance of SCT and the differences between the subgroups of different demographic variables. Identifying the differences in the predictive power of the SCT model and its key determinants among the demographic subgroups is essential for improving our understanding of PA behavior and designing effective interventions. One study reported the predictive power of SCT on PA among urban and underserved middle school students and found that the overall SCT predictability was 19% in urban areas and 12% in underserved areas ( Martin et al., 2011 ). There are numerous gaps in the levels of PA between adolescents living in urban and suburban areas. These differences can be partially explained by socioeconomic status, environment/equipment barriers, social support, and other factors ( Felton et al., 2002 ; Hartley, 2004 ). However, few studies have compared the difference of social-cognitive determinants in urban and suburban areas. Additionally, the predictive power of SCT’s determinants has been analyzed in several research studies. Self-efficacy, parental social support, and friend social support showed no differences between boys and girls ( Martin and McCaughtry, 2008 ). In contrast, boys are more physically active when a parent praises them for being physically active ( Ah Hong et al., 2017 ). Therefore, the predictive power of SCT and its determinants for PA in different gender groups should be further confirmed.

In terms of the estimates of invariance, few studies have confirmed the invariance of SCT across different gender or community subgroups ( Beauchamp et al., 2019 ). Examining the invariance is important to ensure that any differences reported in communities or genders are not merely a function of differences in interpretation of the measures. There is a possibility that the responses to items may be influenced by gender or region of respondents ( Duda and Hayashi, 1998 ). Investigating factorial invariance across the country, age, or gender subgroups allows the examination of the construction of questionnaires, which may result in the measurement of a latent construct similarly across samples. Furthermore, a majority of previous research studies focus on adolescents from western countries. People of Eastern cultural backgrounds have been long overlooked. Therefore, the present study aims to (a) assess the generalizability of the SCT measurements among the Chinese adolescents, (b) test the invariance of the SCT model across gender and urbanization, and (c) examine whether the predictive power of the SCT model on PA differs across the gender (boys and girls) and community subgroups (urban and suburban).

According to evidence provided by previous studies in other countries ( Dewar et al., 2013 ; Ah Hong et al., 2017 ), we hypothesize that (1) SCT could predict the PA behavior of Chinese adolescents, and that (2) there are no differences in the predictive power of SCT on PA behavior in different gender subgroups. Moreover, previous studies reported numerous gaps in PA levels among adolescents living in urban and suburban areas ( Felton et al., 2002 ; Hartley, 2004 ). Support from parents and social community members might differ between these two subgroups ( Ma et al., 2019 ). Thus, we hypothesize that (3) social support and self-regulation would differ in urban and suburban subgroups to predict PA.

Materials and Methods

Design and participants.

A cross-sectional survey was conducted in three cities in China (Beijing in the northeast, Shanghai in the southeast, and Urumchi in the west) from March to May 2018. A total of 3,000 students ranging between 12 and 15 years were randomly selected from local middle schools in each city to complete the questionnaires. The sample size in a structural equation modeling (SEM) study is usually 10 times the number of variables according to the rule of thumb. Thus, the sample size is sufficient for this study. The exclusion criteria are as follows: (a) those who were not between 12 and 15 years old; (b) those who were unable to participate in normal physical activity in the previous week due to injuries or sickness; and (c) those who completed less than 50% of their questionnaire. In total, 2,502 valid questionnaires were obtained, and the validity rate of the questionnaire was 83.4%. Missing values in the dimensions of social-cognitive variables were imputed with mean values of each dimension.

Simple random sampling was used in the study. First, all the schools in these cities were coded. A random number generator was used to randomly select 18 schools, 11 of them from urban areas and 7 from suburban or semirural areas. Second, the teachers in each selected school were contacted, and the purpose of the study was explained to acquire the permission for contacting their students. With approval, the survey was conducted in the classroom setting, and participants were assured that the participation was voluntary and that they were free to withdraw at any time. Before administering the survey, we explained that it was not a test and that we were interested in the actual PA and social cognitive status of participants. All answers were confidential, and all identifying information was kept anonymous to minimize the risk of social desirability bias. Participants were reminded to complete all the questions within 15–25 min. Informed consent was obtained from each student and their parents. Ethics approval to conduct the study was obtained from the ethics committee at the host university.

Instruments

Participants were asked to fill out their demographic information (e.g., age, gender, and living area), SCT-related psychological scales, and the PA questionnaire.

Self-efficacy was measured by using the Self-efficacy for Exercise Scale (SES). SES was used to test the situational confidence in persisting with exercising ( Benisovich et al., 1998 ). The head of the scale reads, “please evaluate how much confidence you have when participating in regular PA during your leisure time.” The 28-items SES uses a five-point Likert-type scale ranging from not confident at all (1) to completely confident (5), and consists of six dimensions ( Norman, 1998 ): (a) negative effects, (b) resistance from others, (c) making excuses, (d) bad weather, (e) exercising alone, and (f) inconveniency. A sample questionnaire item was “I’m tired.” This scale has been proven to have good validity and reliability in assessing psychological problems among Chinese adolescents (Cronbach’s alpha = 0.91) ( Yin, 2007 ; Ren et al., 2020 ). The reliability test of the scales in the present study is α = 0.93.

Self-regulation was measured by using the Exercise Goal-Setting Scale (EGS) and the Exercise Planning and Scheduling Scale (EPS) ( Weinberg, 2002 ). A five-point Likert scale from 1 (“does not describe”) to 5 (“describes completely”) was used for scoring both the EGS and EPS (10-items each) ( Rovniak et al., 2002 ). The EGS and EPS have been proven to be valid and reliable in assessing psychological problems among Chinese adolescents (Cronbach’s alpha = 0.88) ( Zhao, 2008 ; Xu et al., 2017 ). The head of the scale reads, “please evaluate whether the following descriptions are consistent with your when participating in PA.” A sample item was “I always schedule a week’s exercise time in advance.” The reliability test of the scales in the present study is α = 0.91.

Outcome expectation was measured by a scale developed by Lewis for the Physical Activity for Risk Reduction study and was further amended by Lewis et al. (1993) and Rogers et al. (2007) . Participants were asked to rate their agreement on statements that include 12 psychological, social, or physical benefits on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The scale has proven good validity and reliability in assessing psychological problems among Chinese adolescents (Cronbach’s alpha = 0.94) ( Xu et al., 2020 ). The head of the scale reads, “please select the appropriate option and feeling when you participate in regular PA.” A sample item was “Regular exercise will increase my muscle mass.” The reliability test of the scales in the present study is α = 0.90.

Social support was measured by a scale constructed by Sallis et al. (1987) . The items described supportive actions and words that encourage PA. Subjects were asked to rate family and friends’ frequency to perform as the items described. The scale has been proven to have good validity and reliability among the Chinese and American children and adolescents ( Hsu Y. W. et al., 2011 ; Liang et al., 2014 ). The scale used 12 items for family members and 10 items for friends on a five-point scale ranging from 1 (never) to 5 (always). The head of the scale reads, “please rate what your family (e.g., parents, siblings, and grandparents) and friends (e.g., close friends, acquaintances, classmates) have said and done in the past 3 months concerning PA.” An example item is “Offer to exercise with me.” The reliability test of the scales in the present study is α = 0.853.

Physical activity was measured by the PA questionnaire for adolescents (PAQ-A) developed by Kowalski et al. (2004) . The PAQ-A is a self-administered, 7-day recall instrument. It was developed to measure the general levels of PA for adolescents. The questionnaire classifies PA of adolescents into different activity levels and investigates the relationship between PA and health outcomes ( MacKelvie et al., 2001 ). A five-point Likert scale from 1 (“Never”) to 5 (“Above six times a week”) was used to measure that how many times subjects participated in PA in a whole week. PA level is classified as low (≤2), moderate (>2 and ≤3), and high activity (>3), according to the mean score of nine items ( Chen et al., 2008 ). An example question is, “In the past 7 days, how many days have you exercised after school (not including weekends)?” PAQ-A has been proven to have good validity and reliability in assessing psychological problems among adolescents ( Voss et al., 2017 ).

Data Analyses

Descriptive statistics (i.e., means and SD) were computed using Stata 15.0. SEM was performed by AMOS 25.0, and the model was estimated using the maximum likelihood techniques to investigate the relationships between social cognitive variables and PA in urbanization and gender subgroups ( Byrne, 2010 ). First, a confirmatory factor analysis (CFA) was conducted considering the latent variables and observed variables following Yuan et al.’s method ( Yuan et al., 1997 ). A CFA was conducted to test the validity of the scale and the relationship between the latent variable. All standardized factor loading within this single factor should be larger than 0.5 and statistically significant. Model fit was assessed using root mean square error of approximation (RMSEA), root mean square residual (RMR), comparative fit index (CFI), normed fit index (NFI), incremental fit index (IFI), and goodness of fit index (GFI). The accepted cut-offs for the values of CFI, NFI, IFI, and GFI should be greater than 0.90; the thresholds for RMSEA and RMR should be less than 0.05.

Second, the overall fit of the resultant models was computed to assess the predictability of SCT among the Chinese adolescents. A number of the goodness of fit indices representing absolute, comparative, and residual aspects of fit were used, specifically chi-square (χ 2 ), degree of freedom ( df ), RMSEA, RMR, GFI, CFI, adjusted goodness of fit index (AGFI), Tucker–Lewis coefficient (TLI), parsimony goodness of fit index (PGFI), and parsimony normalized fit index (PNFI). The RMSEA and RMR of less than 0.05 indicate excellent model fit ( Loehlin and Beaujean, 2016 ). The GFI, AGFI, TLI, and CFI greater than 0.9 are excellent. PGFI and PNFI greater than 0.5 are excellent ( Bollen and Long, 1993 ).

Third, the invariance analysis across groups is a logical prerequisite for conducting the multigroup comparisons ( Vandenberg and Lance, 2000 ). Invariance models within the pattern of relationships among theoretical constructs (i.e., covariances) and the latent mean difference were estimated to compare if the SCT operates equivalently across the gender and urbanization subgroups. The differences between the groups can be evaluated by examining differences between the models that assume equalities among the parameters with models. A theoretical model is separately applied to each subgroup, and then the invariance analyses are conducted. Before the invariance models can be estimated, it must be established that a model without any invariances (i.e., a model that is different in each group) is reasonable. This model can be used as a basis of assessments for more constrained models. Thus, Model 1 represents unrestricted model (non-invariant, unconstrained model); Model 2 represent measurement equivalent model: (identical factor loading across the subsamples); Model 3 includes Model 2 constraints plus equal factor variances and covariances; Model 4 including Model 3 constraints plus equal paths; Model 5 includes Model 4 constraints plus equal factor residuals (“fully constrained”). Models 4 and 5 refer to the latent construct level, which deals with more substantive information about how subsamples may differ and are similar. The chi-square difference test and the TLI were applied to test the equality constraints to compare the models. If the χ 2 difference is statistically significant, then evidence of cross-group inequality exists. However, the χ 2 difference test would be too strict for a large sample study ( Quintana and Maxwell, 1999 ). TLI estimates the models for the groups separately and sums the chi-squares and the degrees of freedom. A difference of more than 0.05 in TLI or 0.01 in CFI is considered trivial in practical terms ( Cheung and Rensvold, 2002 ; Nigg et al., 2009 ). To complete the final research objective (i.e., determining the explained variance and comparing the strength of regression paths of the SCT constructs in predicting PA across democratic groups), pairwise critical ratios (CRs) for differences between parameters were examined to determine if there were significantly different regression paths for the demographic groups. A CR value larger than 1.96 indicates statistically significant differences in the latent mean.

Table 1 demonstrates the characteristics of the participants. A total of 2,502 adolescents had a mean age of 13.26 ( SD = 0.87). Most of the participants were 13 or 14 years old; 779 (31.1%) individuals were from Beijing, 388 (15.5%) were from Shanghai, and 1,335 (53.4%) were from Urumqi. Of all the participants, 48% were boys, and 52% were girls. Meanwhile, 58.6% of the participants were from urban areas, and 41.4% were from the suburbs.

Table 1. Descriptive statistics of participants.

Evaluation of the Measurement and Structural Models

Measurement model.

A test of the measurement model indicated a highly satisfactory fit to the data in all five measures: RMSEA = 0.047, CFI = 0.980, NFI = 0.977, IFI = 0.980, and GFI = 0.976. All the factor loading of self-efficacy, self-regulation, outcome expectation, and social support ranged from 0.50 to 0.95 ( p < 0.001), which confirmed the convergent validity of the indicators ( Anderson and Gerbing, 1988 ). Thus, the measurement model was used to test the hypothetical structural model (the figure attached in Appendix A shows the validity of dimensions).

Structural Equation Model

The structural model was tested with all the paths depicted in Figure 1 . Fit indices indicated that the structural model was good: RMSEA = 0.047, RMR = 0.028, GFI = 0.974, AGFI = 0.960, TLI = 0.971, and CFI = 0.978. The overall model explained 38.9% of the variance in PA.

Figure 1. The structural equation model of the overall sample. NE, negative effects; RO, resistance of others; ME, making excuses; BW, bad weather; EA, exercising alone; IC, inconveniency. * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001.

Direct effects, indirect effects, and the portion of total effects mediated by other variables reflect how SCT variables influence PA ( Table 2 ). The total effect of a latent variable on PA is the combination of direct and indirect effects. Social support had the greatest total effect on PA (β total = 0.583, p < 0.001). Higher levels of social support led to the higher levels of self-efficacy, outcome expectations, and self-regulation. Self-regulation exerted a moderate total effect on PA (β total = 0.221, p < 0.001). Higher levels of self-regulatory skills, such as goal setting and planning, directly resulted in the higher PA levels. Self-efficacy had a small total effect on PA (β total = 0.153, p < 0.001). Outcome expectations exhibited a small total effect on PA (β total = 0.093, p < 0.05).

Table 2. The direct, indirect, and total effects of the variables in the social cognitive theory (SCT) model.

Multigroup Analysis

Multigroup SEMs were performed to examine the invariances of the SCT model across the multigroup and whether SCT operates equivalently in regression paths in predicting the PA across gender (boys and girls) and urbanization (urban and suburban).

Urbanization

The SEM was tested in urban and suburban adolescents. In both groups, the hypothesized model is a good representation of the data, as shown in Table 3 . Given that the model results in good fit indices for urban and suburban groups, a multigroup SEM with latent variables was conducted to explore which parameters could be invariant across the urbanization. Table 4 presents the constrained models (Models 2–5) and the different tests of these models with the unconstrained model (Model 1). The RMR, GFI, AGFI, TLI, CFI, and RMSEA indicate excellent model fit. By examining the differences between the constrained and unconstrained models, all models appear to be significantly different in any case at the 1% level. The TLI and IGF indices suggest negligible differences between Models 2, 3, and 4 to the unrestricted model. Thus, Model 1, the unrestricted model, will be used to assess the path coefficients in the two groups ( Figure 2 ). The architecture of the social-cognitive variables on PA was tested to see differences between the urban and suburban groups. After comparing CRs for differences between parameters, social support (CR = 2.118; p < 0.001) had a more substantial impact on the PA of adolescents in suburban areas than that in urban areas, whereas self-regulation (CR = − 2.896, p < 0.001) had a more substantial impact on the PA of adolescents in urban areas than in suburban areas.

Table 3. The goodness of fit index (GFI) of urban and suburban groups.

Table 4. Nested Models of Multigroup SEM across urban and suburban.

Figure 2. The multigroup structural equation modeling (SEM) across urban and suburban. *** indicates p < 0.001, otherwise p > 0.05. Bold indicates significant differences of the path coefficients (critical ratios [CRs] > 1.96).

The SEM was tested in male and female groups. The hypothesized model was a good representation of the data in both groups, as shown in Table 5 . Given that the model offered a good fit for both male and female groups, a multi-structural equation model with latent variables was carried out to study in-depth which parameters could be considered invariant across gender. Table 6 presents the fit indices of the constrained models and the different tests of these models with the unconstrained model. The RMR, GFI, AGFI, TLI, CFI, and RMSEA indicate excellent model fit. The differences between the constrained and unconstrained models indicate that all models appear to be significantly different in all cases at the 1% level. Thus, Model 1, the unrestricted model, was used to assess the path coefficients in the two groups ( Figure 3 ). The architecture of the social-cognitive variables on PA was tested to determine whether there were differences between the urban and suburban groups. After comparing CR for differences between the parameters, the only significant difference was that social support has a more substantial impact on the self-efficacy of male adolescents than on female adolescents. There was no difference in path regression from social cognitive determinants to PA for gender.

Table 5. The goodness of fit index.

Table 6. Nested Models of Multigroup SEM across male and female.

Figure 3. The multigroup comparison of SEM across urban and suburban. Path coefficients are reported male/female subgroup. *** indicates p < 0.001, otherwise p > 0.05. Bold indicates significant differences of the path coefficients (CRs > 1.96).

This study investigated the predictability of SCT among the Chinese adolescents. The overall model explained 38.9% of the variance in PA. Social support had the greatest effect, self-regulation exerted a moderate effect, and self-efficacy and outcome expectations had a small effect on PA. Negligible differences between the constrained and unstrained models proved the invariance of the SCT model across urbanization and gender. The findings indicate that the SCT model applies to various subgroups and should be considered comprehensively when designing interventions. Specifically, social support had a more substantial impact on the PA of adolescents in suburban areas and urban areas, whereas self-regulation had a more substantial impact on the PA in urban areas than in suburban areas. No difference in path regression from social cognitive variables to PA was found across genders.

Two studies conducted in China used the SCT model to predict the exercise frequency of breast cancer survivors ( Hsu H. T. et al., 2011 ) and obesity prevention behaviors (e.g., TV watching, water consumption, fruits and vegetables consumed, and PA) of children ( Murnan et al., 2006 ). To the best of our knowledge, ours is the first article to investigate the predictive power of SCT to PA among Chinese adolescents. Consistent with studies in other countries ( Gao, 2012 ; Dewar et al., 2013 ; Bagherniya et al., 2015 ), we found that the SCT model can predict the PA of Chinese adolescents, which confirmed the first hypothesis. The overall model explained 38.9% of the variance in PA, which can be assessed as substantial ( Cohen, 1988 ; Cohen et al., 2013 ). The results indicate that the intervention based on SCT could effectively change the PA behavior among the Chinese adolescents. In the case of self-efficacy and PA behavior, although previous studies found self-efficacy as a significant predictor of PA ( Gao, 2012 ; Dewar et al., 2013 ; Lee et al., 2018 ), the association between PA and self-efficacy was weak in our study. However, social support was the most critical factor for predicting the PA behavior among Chinese adolescents. The following factors could explain this finding: First, most adolescents in China are engaged in their academic schedule and cannot allocate their time to participate in PA by themselves though they have high self-efficacy. Second, self-efficacy has different categories (e.g., barrier self-efficacy; proxy self-efficacy; and support self-efficacy), which may confine the consistency of the results. In addition, due to the collectivist Chinese culture, their behavior may not be in the control of an individual. They often participate in PA with friends or classmates and have PA scheduled by parents. Further studies that explore the types and sources of social support relevant to adolescents are recommended.

Self-regulation had a moderate effect on PA. Self-regulation is an important set of strategies that could help students gain control over the PA behavior, indicating that individuals need improved goal setting and planning skills to engage in PA. Self-regulation was shown to be a key to success in exercising and adherence to an exercise program, preventing unwanted behavioral tendencies, and focusing on the control of PA behavior ( Bandura et al., 1997 ). Therefore, self-regulation could play a major role in achieving exercise goals, as it serves as an important psychological factor for promoting the positive health behavior, including exercise ( Bandura, 2005 ). This finding has supported the results of studies that found high self-regulation ability predicted the exercise behavior ( Anderson et al., 2006 ; Dishman et al., 2014 ; Gerber et al., 2015 ) and was consistent with the finding that people with high self-regulation abilities participated in the PA for more extended periods ( Stadler et al., 2009 ).

Previous studies did not find any relationship between outcome expectations and PA in children ( Rovniak et al., 2002 ; Gao et al., 2009 ). Our findings found that outcome expectancy had a small effect on the PA behavior, which might be due to the following reasons: first, the adolescents may not sufficiently realize the harmful consequences of physical inactivity, but they recognize the positive social, physical, and psychological expectations of being physically active. Outcome expectancy was reported to be a better predictor of PA among older adults than younger adolescents ( Anderson-Bill et al., 2011b ; Kosteli et al., 2016 ). Second, positive PA outcome expectancy of adolescents may not directly translate to the PA behavior ( Hankonen et al., 2017 ). Thus, exploring the additional constructs impacting the relationship between outcome expectancy and PA may be warranted in the future.

Measurement invariance testing for SCT has been less studied. Most researchers would assume that the instruments operate the same way and contain the same constructs across groups. However, the invariance analysis across groups is a logical prerequisite for conducting multigroup comparisons ( Vandenberg and Lance, 2000 ). If not tested, violations of measurement equivalence assumptions threaten substantive interpretations, as it shows the inability to demonstrate reliability and validity ( Vandenberg and Lance, 2000 ). Our findings indicate that the SCT model has the same construct with items associated equally with the factors for the subgroups of urbanization and gender, which indicated the invariance of SCT across urbanization and gender for PA. Future studies could compare the SCT model across different urbanization and gender subgroups.

Social support and self-regulation were found to differ in urban and suburban subgroups, which confirmed our third hypothesis. Different conclusions were obtained in previous studies concerning the predictability of social support to PA. Laird et al. (2016) found that social support did not predict PA in adolescent girls, even though parents and friends may enhance the PA behavior ( Laird et al., 2016 ). However, Martin’s study found that social support is one of the best predictors of PA among underserved middle school students but did not compare the differences between urban school students with underserved school students ( Martin et al., 2011 ). The findings of our study show that the association between social support and PA was higher in suburban than urban areas. The results indicate that Chinese adolescents in suburban areas might not receive sufficient support from their parents and friends for PA, which may be a restricted factor. The association between self-regulation and PA was higher in urban than in suburban areas, which may be explained by the higher levels of social support, quality of PA environments, and quality of PA equipment that adolescents in urban areas receive and have access to. Therefore, the strategic use of skills to remind, cue, or reinforce PA behavior may be more related to PA in urban areas than suburban areas.

The results largely confirm the second hypothesis that there are no differences in the predictive power of SCT on PA behavior in different gender subgroups. This study found no difference between the predictive power of social-cognitive variables to PA across gender subgroups, consistent with previous research ( Martin and McCaughtry, 2008 ; Chen et al., 2019 ). Results show that the social-cognitive determinants are positively related to PA for boys and girls. One study found that boys were more active and received more support from siblings than girls. The remaining variables did not vary with gender (self-efficacy, parental social support, and friend social support) ( Martin and McCaughtry, 2008 ). Other studies have conflicting results regarding the association between gender and social support. One study found that gender did not moderate the relationship between social support and PA in the Chinese adolescents ( Chen and Dai, 2016 ); however, another reported that boys in primary school were more physically active when a parent praised them for being physically active ( Ah Hong et al., 2017 ), and girls perceived more social support than boys in PA in middle school ( Martin and Smith, 2002 ). Future research should fully explore the relationship between different social support and adolescent PA.

Thus, the SCT model can predict the PA of Chinese adolescents effectively. SCT model could apply to various subgroups to predict the PA behavior and should be considered comprehensively when designing future interventions. However, results from this investigation should be viewed in light of certain limitations. Although the study used Bandura’s classical SCT model, additional relevant PA determinants were not included (e.g., school, peer, and sibling support for PA, barriers, and sociostructurally factors). Second, the self-report questionnaires may have inconsistencies with actual experiences or social desirability bias. Third, given the correlational design of the study, causality cannot be argued. Our findings proved the predictability of SCT on PA among Chinese adolescents, especially the importance of social support and self-regulation. The study investigated the invariances of an SCT model and the difference in the architecture of the social-cognitive variables across urbanization and gender subgroups. The findings indicate that future possibilities for promoting PA interventions among Chinese adolescents should include those variables. However, a questionnaire was used to measure PA due to the large sample size in this study. Future studies should consider measuring PA behavior using accelerometers that can objectively measure PA. Furthermore, future studies should look beyond SCT models and use these in conjunction with broader social-ecological models that incorporate determinants at multiple levels (policy and environment), which could further understand determinants of PA participation among adolescents ( Sallis and Owen, 2015 ).

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the Ethical Committee of the Medical Association of Tsinghua University approved the study (IRB#20190093). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

XM contributed to the idea and supervision. JL wrote the original draft preparation and edited the manuscript. MZ analyzed and interpreted the data. YZ analyzed part of the data. DW and BS revised the manuscript. All authors have read and agreed to the published version of the manuscript.

This study was supported by the National Social Science Fund of China (No. 16BTY065).

Conflict of Interest

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

Publisher’s Note

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

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Appendix Figure 1 The confirmatory factor analysis (CFA) of the model and the validity of scales in different dimensions

Keywords : social cognitive theory, physical activity, Chinese, adolescents, structural equation model

Citation: Liu J, Zeng M, Wang D, Zhang Y, Shang B and Ma X (2022) Applying Social Cognitive Theory in Predicting Physical Activity Among Chinese Adolescents: A Cross-Sectional Study With Multigroup Structural Equation Model. Front. Psychol. 12:695241. doi: 10.3389/fpsyg.2021.695241

Received: 14 April 2021; Accepted: 27 December 2021; Published: 15 March 2022.

Reviewed by:

Copyright © 2022 Liu, Zeng, Wang, Zhang, Shang and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xindong Ma, [email protected]

† These authors have contributed equally to this work and share first authorship

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International Conference on Information Systems, Technology and Management

ICISTM 2010: Information Systems, Technology and Management pp 20–31 Cite as

Social Cognitive Theory in IS Research – Literature Review, Criticism, and Research Agenda

• Kévin D. Carillo 6  
• Conference paper

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 54))

A multitude of research studies have been published investigating individual behavior from the viewpoint of Social Cognitive Theory. We have now reached a point where making sense of such a large number of studies has become a difficult task and where future research efforts must integrate past SCT findings but also express the full potential of SCT in IS research. The aim of the present paper is to organize the literature to provide a clear depiction of the use of SCT in IS research. A review the IS literature which used Social Cognitive Theory of the past 14 years yielded 62 papers that investigated individual behavior using the SCT perspective. This vast literature is mapped into the SCT framework, thus highlighting the main successes but also pitfalls of past research in using the theory. Future research directions are then identified and discussed.

• Social Cognitive Theory
• individual behavior
• literature review

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Carillo, K.D. (2010). Social Cognitive Theory in IS Research – Literature Review, Criticism, and Research Agenda. In: Prasad, S.K., Vin, H.M., Sahni, S., Jaiswal, M.P., Thipakorn, B. (eds) Information Systems, Technology and Management. ICISTM 2010. Communications in Computer and Information Science, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12035-0_4

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A Systematic Review Exploring the Social Cognitive Theory of Self-Regulation as a Framework for Chronic Health Condition Interventions

* E-mail: [email protected]

Affiliations Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada, Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada, Centre for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada

Affiliation Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada

Affiliations Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada, Centre for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada, Canada Research Chair, Dalhousie University, Halifax, Nova Scotia, Canada, Science, Pediatrics, and Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada, Capital District Health Authority, Halifax, Nova Scotia, Canada

Affiliations Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada, Centre for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada

Affiliation Centre for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada

• Michelle E. Tougas, 
• Jill A. Hayden, 
• Patrick J. McGrath, 
• Anna Huguet, 
• Sharlene Rozario

• Published: August 7, 2015
• https://doi.org/10.1371/journal.pone.0134977
• Reader Comments

Theory is often recommended as a framework for guiding hypothesized mechanisms of treatment effect. However, there is limited guidance about how to use theory in intervention development.

We conducted a systematic review to provide an exemplar review evaluating the extent to which use of theory is identified and incorporated within existing interventions. We searched electronic databases PubMed, PsycINFO, CENTRAL, and EMBASE from inception to May 2014. We searched clinicaltrials.gov for registered protocols, reference lists of relevant systematic reviews and included studies, and conducted a citation search in Web of Science. We included peer-reviewed publications of interventions that referenced the social cognitive theory of self-regulation as a framework for interventions to manage chronic health conditions. Two reviewers independently assessed articles for eligibility. We contacted all authors of included studies for information detailing intervention content. We describe how often theory mechanisms were addressed by interventions, and report intervention characteristics used to address theory.

Of 202 articles that reported using the social cognitive theory of self-regulation, 52% failed to incorporate self-monitoring, a main theory component, and were therefore excluded. We included 35 interventions that adequately used the theory framework. Intervention characteristics were often poorly reported in peer-reviewed publications, 21 of 35 interventions incorporated characteristics that addressed each of the main theory components. Each intervention addressed, on average, six of eight self-monitoring mechanisms, two of five self-judgement mechanisms, and one of three self-evaluation mechanisms. The self-monitoring mechanisms ‘Feedback’ and ‘Consistency’ were addressed by all interventions, whereas the self-evaluation mechanisms ‘Self-incentives’ and ‘External rewards’ were addressed by six and four interventions, respectively. The present review establishes that systematic review is a feasible method of identifying use of theory as a conceptual framework for existing interventions. We identified the social cognitive theory of self-regulation as a feasible framework to guide intervention development for chronic health conditions.

Citation: Tougas ME, Hayden JA, McGrath PJ, Huguet A, Rozario S (2015) A Systematic Review Exploring the Social Cognitive Theory of Self-Regulation as a Framework for Chronic Health Condition Interventions. PLoS ONE 10(8): e0134977. https://doi.org/10.1371/journal.pone.0134977

Editor: Delphine Sophie Courvoisier, University of Geneva, SWITZERLAND

Received: January 18, 2015; Accepted: July 15, 2015; Published: August 7, 2015

Copyright: © 2015 Tougas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: MET received funding to complete her Master's thesis from the Nova Scotia Cochrane Resource Centre and the Department of Community Health and Epidemiology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Theory can provide a framework for guiding the development and implementation of health interventions. The use of theory is recommended by the UK Medical Research Council to provide hypotheses of specific mechanisms and interactions [ 1 – 4 ] during the first phase in the development of interventions [ 5 ]. Theory may be particularly useful for interventions that encompass several interacting active management strategies, and are often difficult to evaluate and reproduce, for example interventions directed at chronic health conditions [ 6 ]. Current recommendations to use theory early in the design of interventions, however, do not specifically describe how to incorporate theory in the development process. In health behaviour literature, systematic reviews report that only 22–36% of interventions describe using any theoretical framework or theory components to guide their development [ 7 , 8 ].

The importance of managing chronic health conditions is evident by their increasing prevalence and leading role in worldwide morbidity and mortality [ 9 ]. Many of these conditions can be prevented, treated, and managed through behaviour change interventions, which provide individuals with the skills to have control over and improve their health [ 7 , 9 ]. Using theory to develop chronic health interventions can help to identify what behaviour change mechanisms are influential for improving health outcomes.

The social cognitive theory proposed by Bandura (1986) [ 10 ], is one of the most common behaviour change theories applied in the management of chronic health conditions [ 7 ]. One concept of the theory focuses on the importance of self-regulation as a source of behaviour change, which is broken down into three core components: self-monitoring, self-judgement, and self-evaluation [ 10 , 11 ]. Evidence from randomized controlled trials based on the social cognitive theory of self-regulation supports the clinical benefits of interventions based on this theory for health outcomes in asthma [ 12 ], arthritis [ 13 ], weight loss [ 14 ], and cardiac rehabilitation [ 15 ]. These findings suggest that interventions based on the social cognitive theory of self-regulation can be useful for improving outcomes in some chronic health conditions. Nonetheless, the selection of the specific theory components and associated mechanisms that have been chosen to be addressed with particular intervention characteristics remains unclear.

The objectives of this systematic review of the literature were to evaluate the extent to which theory has been used in the development of existing interventions, and to provide an example of how literature can be systematically reviewed to explore use of theory as a framework for existing interventions. We explored how researchers use the social cognitive theory of self-regulation to inform the management of chronic health conditions. We assessed peer-reviewed publications that reported the evaluation of interventions to identify which theory components and mechanisms were implemented most often, and how the interventions addressed each of the theory mechanisms.

Materials and Methods

Literature search and data sources.

A protocol is available upon request to the first author. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) [ 16 ] was followed for reporting the systematic review ( S1 Table ). We used multiple search strategies to identify relevant studies. First we searched electronic databases PubMed, PsycINFO, and EMBASE from inception to May 2014, using a search strategy of MeSH terms, keywords reflecting the health conditions of interest, and terms associated with the social cognitive theory of self-regulation (see S1 Text for full PubMed search strategy), we searched the Cochrane Central Register of Controlled Trials for completed controlled trials, and the trial registry clinicaltrials.gov for relevant protocols, which were followed up for published studies. Second, we conducted a citation search in Web of Science to identify studies citing Bandura’s first report of the social cognitive theory of self-regulation [ 10 ], or Bandura’s first paper [ 11 ] that comprehensively described the theory components and mechanisms. Third, we examined the reference lists of all included studies, and the reference lists of studies included in systematic reviews of self-monitoring interventions identified through a scoping literature search [ 17 – 20 ]. We searched PubMed for available published studies of identified protocols that met our inclusion criteria by searching for studies published by the protocols’ first author, and searching for publications using the intervention’s name, when available. All of the retrieved citations were imported into an EndNote database, where duplicate citations across data sources were identified and removed.

Inclusion and Exclusion Criteria

We included peer-reviewed publications of studies reporting interventions for chronic health conditions based on the social cognitive theory of self-regulation meeting all of the inclusion criteria described below.

For a homogenous population, we selected studies including participants with chronic health conditions with similar characteristics, which were non-communicable, long-lasting, with a constant non- or slowly-progressive course, and with associated health episodes or behaviour suitable for monitoring. We therefore included the chronic health conditions arthritis, asthma, chronic pain, diabetes, heart disease, and overweight/obesity.

We included studies reporting interventions that stated being designed using the social cognitive theory of self-regulation as the theoretical basis for the intervention, used self-monitoring as an intervention characteristic, and cited one of the main theory publications [ 10 , 11 ]. The social cognitive theory of self-regulation proposes that three main components of the theory, self-monitoring, self-judgement, and self-evaluation, contribute to self-regulation, and influence successful behaviour change. The theory suggests that specific mechanisms related to each of these three main components may be directly associated with successful self-monitoring, self-judgement, and self-evaluation, and influence subsequent behaviour change. The theory identifies self-monitoring as the first and most important step to initiating and informing appropriate self-regulation and behaviour change. We included in this systematic review only interventions that explicitly recommended and expected participants to self-monitor by observing, tracking, and/or recording their own behaviour as a core component of the interventions.

We included peer-reviewed publications of studies that reported the evaluation of relevant interventions, including evaluation of the usability, feasibility, or efficacy/effectiveness of the interventions using observational or experimental designs.

We excluded studies that: 1) cited the social cognitive theory of self-regulation but did not report evaluation of an intervention, 2) were available as conference proceedings, abstracts, case studies, theses, reviews, summaries, commentaries, editorials, letters to the editor, or study protocols without published data, 3) used proxies of the population of interest (e.g., parental administration of an intervention designed to change child behaviour), or used non-human subjects, 4) were not published in English.

Selection Process

We used two screening phases to identify studies reporting potentially relevant interventions from titles and abstracts. First, one reviewer (MT) conducted an initial title and abstract screen to eliminate readily identifiable ineligible types of publications, and studies conducted in clearly irrelevant health conditions. Second, two reviewers (MT, SR) independently screened the remaining titles and abstracts to determine study design eligibility for full text review.

We also used two screening phases at the full text level. In the first phase one reviewer (MT) screened the full-text articles to identify studies citing the social cognitive theory of self-regulation [ 10 , 11 ]. In the second phase, both reviewers independently applied selection criteria to the remaining full text articles. We report the final number of studies (and independent interventions) identified; duplicate publications (i.e., studies reporting the same intervention) were reviewed for any additional information and used to complete data extraction. We calculated interrater reliability for the second phase of title/abstract and full text screening levels using Cohen’s Kappa [ 21 ], and considered Kappa between 0.41–0.60 an indication of moderate level of agreement [ 22 ]. Discrepancies were discussed and resolved, using consultation with a third reviewer (JH) when necessary.

Data Extraction

To supplement information extracted about the content of included interventions, we searched for related publications, protocols, guidelines, and web-based resources. We contacted first authors of included interventions for access to either an intervention manual describing the intervention content, or an intervention guide/outline if a manual was not available. When multiple study publications were identified for one intervention, we contacted the first author of the earliest publication retrieved. When our searches identified information from multiple study publications about the same intervention, we combined this information during extraction. We considered interventions that were published by the same research team across multiple study publications distinct from one another only when at least one main theory component within the intervention was added or removed.

We extracted two types of data from included interventions: 1) study characteristics, and 2) theory-related intervention characteristics.

One reviewer (MT) extracted data on study characteristics, including: study authorship, health condition, inclusion/exclusion criteria, age group, study design, intervention objectives, intervention duration, intervention delivery format, general intervention content, and any additional theories guiding intervention development. A second reviewer (SR) checked the extracted data for accuracy.

We extracted data on the intervention characteristics related to the social cognitive theory of self-regulation. We initially followed a consensus procedure to define the extraction process. We created an outline based on Bandura’s two publications that describe the three main components of the theory that are related to successful self-regulation and behaviour change: self-monitoring, self-judgement, and self-evaluation [ 10 , 11 ]. Self-monitoring involves attention to, noticing, and tracking personal behaviour, which may inform self-judgement. Self-judgement is the process of applying personal standards and values to judge monitored behaviour. Finally, the theory proposes that self-evaluation of monitored behaviour may occur as a result of judgement and directly inform subsequent action, leading all three components to contribute to self-regulation and behaviour change. Within each of the three components, the theory proposes specific mechanisms that may directly influence self-monitoring, self-judgement and self-evaluation, Table 1 .

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https://doi.org/10.1371/journal.pone.0134977.t001

We used the theory definitions to identify intervention characteristics that addressed each of the specific mechanisms proposed to be associated with the three theory components. Three reviewers (MT, JH, AH) independently reviewed four selected interventions that comprehensively described included intervention characteristics. The reviewers used the mechanism definitions to independently code the intervention characteristics that addressed each theory mechanism. We explored agreements and disagreements across the reviewers and reached consensus through discussion about the types of intervention characteristics that were applicable to each of the theory mechanisms. We revised the extraction guide with descriptions to specify the type of intervention characteristics that addressed each theory mechanism ( S2 Table ). We used the extraction guide to develop the final extraction form. Subsequently, two reviewers (MT, SR) extracted the intervention characteristics from remaining studies using the extraction form.

Risk of Bias Assessment

Two reviewers (MT, SR) independently assessed the risk of bias of the extracted studies. We assessed randomized controlled trials for internal validity, using the Cochrane Collaboration’s Risk of Bias tool to assess the selection bias, performance bias, detection bias, attrition bias, and reporting bias ( S3 Table ) [ 23 ]. Following recommendations by Higgins [ 24 ], we judged studies to have an overall ‘high risk of bias’ when at least one of the key domains had a high risk of bias, ‘unclear’ risk of bias when any of the key domains were rated as unclear risk, and ‘low’ risk of bias when all domains were rated as low risk [ 24 ]. We used the Quality in Prognosis Studies (QUIPS) tool [ 25 ] to assess risk of bias for observational studies, while considering the following domains: participant selection, attrition, outcome measurement, confounding, and analysis/reporting (see reference for the full published tool). We rated each domain as high, moderate, or low risk of bias, and judged the overall internal validity across domains by judging studies as low risk only when all of the domains were rated as low, and as high risk of bias when any of the domains were rated as moderate or high. We calculated interrater reliability for risk of bias assessment using Cohen’s Kappa [ 21 ] and considered Kappa between 0.41–0.60 an indication of a moderate level of agreement [ 22 ]. A third reviewer was available for consultation about any unresolved discrepancies, however, consultation was not necessary.

Data Synthesis

We report frequency of use of the social cognitive theory of self-regulation in the development of each intervention, including how often: 1) interventions addressed all three theory components together, two theory components, or only self-monitoring, 2) interventions had characteristics belonging to each specific theory component, and 3) interventions included characteristics belonging to each specific theory mechanism within each of the three theory components. We considered interventions to address a specific mechanism when at least one intervention characteristic included the theory element proposed to be associated with that mechanism, Table 1 . We rated interventions as addressing a specific theory component when at least one mechanism related to that component was judged to be present. Interventions addressed all three theory components when at least one intervention characteristic was judged to be present for at least one mechanism related to each of the self-monitoring, self-judgement, and self-evaluation theory components. Interventions addressed only two theory components when at least one intervention characteristic related to at least one mechanism was present for self-monitoring, along with one other theory component (either self-judgement or self-evaluation). To illustrate the types of intervention characteristics that we judged as addressing the theory mechanisms, we provide some examples of characteristics from the included interventions that clearly represented the descriptions in our extraction guide. We explored whether the study risk of bias impacted how often the main theory components were addressed across the interventions. We present subgroup information about how many theory components were addressed by interventions for each assessed risk of bias.

Description of Included Interventions

We identified and screened 16,188 independent titles and abstracts ( Fig 1 ). We excluded the majority of citations because of ineligible study design. We assessed full text publications for 202 potentially relevant studies that reported interventions for the health conditions of interest, and cited the social cognitive theory of self-regulation [ 10 , 11 ]. Of studies reporting interventions citing the theory, 105 (52%) were excluded because they did not include self-monitoring as a core component of the intervention and therefore did not appropriately address the self-regulation concept of the theory. Our interrater reliability for study selection was moderate for title and abstract, as well as for full-text screening, with a Kappa of 0.60 and 0.55, respectively.

Flow diagram of title/abstract and full-text screening process to identify interventions included in the review.

https://doi.org/10.1371/journal.pone.0134977.g001

We identified 60 relevant studies, which reported 35 unique interventions developed using the social cognitive theory of self-regulation as a conceptual framework, Table 2 . Overweight/obesity was the most common type of health condition addressed by the interventions (14/35) ( Table 3 , S4 Table ). Interventions lasted from four weeks to twelve months, and were delivered through individual-based (17 interventions), group-based (16 interventions), or mixed settings (2 interventions). Most interventions were evaluated using a randomized controlled trial study design (33/35), with an equal distribution of studies assessed as low (13 interventions), high (11 interventions), and unclear (11 interventions) risk of bias. Our interrater reliability for risk of bias assessment was moderate with a Kappa of 0.59. Of the 34 intervention authors contacted (regarding 35 interventions), 11 provided additional information: five provided access to an intervention manual, two provided intervention outlines, and four referred to previous publications.

https://doi.org/10.1371/journal.pone.0134977.t002

https://doi.org/10.1371/journal.pone.0134977.t003

Overview of Theory Component Use

Twenty-one of thirty-five interventions incorporated all three of the main theory components by including at least one intervention characteristic that addressed one or more mechanism for self-monitoring, self-judgement, and self-evaluation. Based on information available in peer-reviewed study publications, only 17 interventions were initially identified that used characteristics addressing mechanisms related to self-evaluation. Additional information from four of five intervention manuals provided by authors, were found to incorporate self-evaluation characteristics that were not previously identified, resulting in 21 interventions identified to address all three of the main theory components.

Self-monitoring.

Each intervention addressed an average of 6.2 of the 8 self-monitoring mechanisms. All mechanisms were frequently used across interventions, with an average of 24 interventions addressing each of the 8 self-monitoring mechanisms. The mechanism ‘Self-diagnosis’ was incorporated least often, by 14 interventions, followed by ‘Focus on success’ and ‘Temporal proximity’, each addressed by 16 interventions. Table 4 describes the intervention characteristics that addressed each self-monitoring theory mechanism. S5 Table provides a summary of how many self-monitoring mechanisms were addressed by each of the individual interventions.

https://doi.org/10.1371/journal.pone.0134977.t004

Self-judgement.

All of the included interventions incorporated characteristics addressing at least one of the mechanisms related to self-judgement. Each intervention addressed an average of 2.4 of the 5 self-judgement mechanisms. The five self-judgement mechanisms were implemented less comprehensively than those of self-monitoring, with an average of 16.6 interventions addressing each of the five mechanisms. The self-judgement mechanisms ‘Social comparison’ and ‘Statistical comparison’ were infrequently addressed, by 11 and seven interventions, respectively. Table 5 describes the intervention characteristics that addressed each of the self-judgement theory mechanisms. S5 Table provides a summary of how many self-judgement mechanisms were addressed by each of the individual interventions.

https://doi.org/10.1371/journal.pone.0134977.t005

Self-evaluation.

We identified a total of 21 interventions that included characteristics related to self-evaluation. Each of these interventions addressed an average of 0.7 of the 3 self-evaluation mechanisms. Self-evaluation was poorly addressed across interventions, with an average of 8.6 interventions addressing each of the three mechanisms. The self-evaluation mechanisms ‘Self-incentives’ and ‘External rewards’ were rarely addressed, by six and four interventions, respectively. Table 6 describes the intervention characteristics that addressed each of the theory mechanisms. S5 Table provides a summary of how many self-evaluation mechanisms were addressed by each of the individual interventions.

https://doi.org/10.1371/journal.pone.0134977.t006

Exploring Differences in Use of the Theory Components

Of the interventions that we evaluated as having a low risk of bias, most (9/13 interventions) used all three of the main theory components. Just over half of the interventions that we evaluated as having high risk of bias (6/11 interventions) and unclear risk of bias (6/11 interventions) used all of the three main theory components.

This review provides an example of how literature can be systematically reviewed to identify the extent to which a selected theory has been used as a framework for existing interventions. To illustrate how researchers can explore theory use for interventions, we provide an overview of the specific theory components and mechanisms that were incorporated into interventions developed using the social cognitive theory of self-regulation as a conceptual framework. From a comprehensive search of multiple sources we identified 202 studies reporting interventions that used the theory, however, only 35 interventions actually incorporated self-monitoring and accurately used the social cognitive theory of self-regulation to develop interventions for the management of arthritis, asthma, diabetes, heart disease, and overweight/obesity. All of the interventions addressed at least two of the main theory components, and 21 of the interventions incorporated characteristics that addressed mechanisms related to all three of the main theory components. We identified that self-monitoring was the theory component used most comprehensively across interventions, with a greater proportion of self-monitoring mechanisms being addressed than those of self-judgement and self-evaluation. Although the self-monitoring mechanisms were often included within interventions, we identified that the self-judgement mechanisms ‘Social comparison’, and ‘Statistical comparison’, and the self-evaluation mechanisms ‘Self-incentives’, and ‘External rewards’ were rarely implemented.

Our review provides a novel example of how to explore the application of theory within existing interventions. Recommendations for the development of theory-driven interventions begin with the suggestion of exploring existing interventions, and conducting a systematic review if relevant synthesized evidence is unavailable for the health condition of interest [ 5 , 57 ]. Reviews exploring theory usually do so by identifying which theories are commonly used [ 7 , 8 ], or by testing theoretical mechanisms associated with change [ 58 , 59 ], rather than identifying intervention characteristics that are used to address theoretical mechanisms. Researchers can use our process as a first step during intervention development to identify which theory mechanisms are commonly or infrequently addressed by interventions, to determine if the selected theory is a feasible framework for development of future interventions for health conditions similar to those included in the review. This type of review is an information source that illustrates examples intervention characteristics used to address theory mechanisms, and can provide direction for use of the characteristics in the development process of future interventions.

Of the 202 full text articles screened, 105 studies evaluating interventions using the social cognitive theory of self-regulation as a conceptual framework were excluded because they did not include self-monitoring as a core component of the intervention and therefore did not appropriately address the self-regulation concept of the theory. Studies evaluating interventions reporting use of the social cognitive theory as a conceptual framework, often either address only specific concepts of the overall theory, or report use of the theory without appropriately including intervention characteristics to approach its theoretical concepts. These findings are similar to those of a review exploring the general use of theory in health behaviour literature, which identified that 70% of all theories were merely mentioned within the research, rather than being appropriately applied [ 7 ]. Researchers and clinicians should cautiously interpret individual studies that report using theory as a conceptual framework, as we found many interventions appear to only cite the theory without actually describing how they addressed each of the main theory components. A systematic review as we have conducted can help to highlight the interventions that appropriately implement theory.

In spite of our comprehensive search across multiple sources that identified over 16 thousand citations, we retrieved only 35 self-monitoring interventions developed using the social cognitive theory of self-regulation, with less than ten interventions identified for four of the five health conditions, and only 21 interventions addressing all three of the main theory components. These numbers are not surprising, considering that only 8% of the published health behaviour literature reports interventions that apply theory as a conceptual framework during development [ 7 ]. Although our review process was successful for identifying interventions developed using a well-known theory, we did so across five health conditions. Researchers interested in exploring theories that are infrequently implemented, or are exploring uncommon health conditions, may find an insufficient availability of relevant reports evaluating interventions of interest.

The Kappa values reporting our interrater reliability for title and abstract screening, full-text, screening, and risk of bias assessment were moderate, yet lower than preferred. We conducted the title and abstract screening in increasing increments, (100, 200, 500, etc.) with discussion between reviewers at each stage. Although our Kappa values improved with each increment from a starting Kappa of 0.37 from screening 100 abstracts to a final Kappa of 0.71 from screening 1,100 abstracts, our overall Kappa (0.60) is a result of the collective interrater reliability. To improve interrater reliability for title and abstract screening, we recommend that reviewers review small increments of abstracts until a stable and acceptable interrater reliability is reached. When we were screening full-text articles, the reviewers were most often discrepant when determining whether or not self-monitoring was a core component of the intervention. This discrepancy is largely a result of poor reporting within the reviewed publications. As a result of unclear or missing information, the reviewers often independently searched for additional information from available protocols, publications, or online websites that reported information about the intervention. The reviewers sometimes explored different sources of supplemental information, resulting in differing opinions as to whether self-monitoring was a core component of the intervention or not. To avoid this type of discrepancy, we recommend that reviewers decide a priori whether or not they will be searching for additional information. If additional information about an intervention will be searched, reviewers should ensure that the same information is examined by all reviewers involved. When assessing risk of bias, the reviewers consistently identified when an intervention was of high risk. The reviewers were most often discrepant when determining if risk of bias was low or unclear, with one reviewer tending to specify ‘low’, while the other tended to specify ‘unclear’. Although discrepancies were regularly discussed throughout this process, the trend in how the reviewers’ rated (low or unclear) was identified retrospectively. To avoid missing the identification this type of trend, we recommend that reviewers search for any patterns in their ratings while discussing discrepancies in their risk of bias assessment. This type of pattern may also arise when screening title and abstracts or full-texts and could be useful for reviewers to identify early during screening and improve overall reliability by guiding decisions about how to address and prevent further discrepancy.

We attempted to comprehensively retrieve information related to interventions through duplicate publications, available resources, and author contact, however, we were only able to judge intervention characteristics based on available information, often provided as a summary or table of contents. Only five of the authors of included interventions provided us with access to full treatment manuals. We were able to use the additional information to identify more theory mechanisms and components that were not addressed in the published materials that we originally extracted. For example, we identified intervention characteristics guiding participants in self-evaluation in four interventions that were not previously identified as using this theory component. Most publications provided overviews of intervention content with broad overarching concepts. Through supplementing our extraction with information from available manuals, it became clear that publications were not comprehensively representing all of the intervention content and application to theory mechanisms and components that we were able to identify from their manuals. Without the option of reviewing entire intervention manuals for the remaining interventions, it is difficult to confirm that we have comprehensively identified all of the intervention characteristics related to relevant theoretical mechanisms. Poor description of intervention content is recognized as a common problem in the reporting of interventions [ 60 ]. To address the problem of underreporting and to improve clarity in the use of theoretically based interventions, intervention investigators should provide access to a comprehensive outline of intervention characteristics and how they apply to each of the related theory mechanisms [ 61 ].

Fourteen of the 35 interventions did not address self-evaluation, one of the three main components of the social cognitive theory of self-regulation. We hypothesize that potential reasons for low frequency of identified self-evaluation mechanisms could be that the mechanisms were either more difficult for intervention researchers to implement, or they may have been incorporated but not reported in the available publications. The self-evaluation mechanism ‘External rewards’, addressed by only four interventions, may have been interpreted by developers as too expensive or time consuming to administer and therefore not addressed by the intervention. It is possible that the other two self-evaluation mechanisms were addressed by intervention characteristics that were not explicitly reported in the identified publications. The mechanism ‘Self-satisfaction’ is associated with positively recognizing achievement of progress or goals, and the ‘Self-incentive’ mechanism is associated with setting and administering personal rewards as sources of motivation and reward. For example, when participants were instructed to actively set goals (to address the ‘Motivation’ mechanism), they may also have been guided to react positively to achievement (‘Self-satisfaction’), or set personal rewards to administer upon achievement of the goals (‘Self-incentives’). Therefore the self-evaluation component may have been underrepresented due to availability of resources, or not been identified due to inaccurate reporting. These issues highlight the importance of comprehensive reporting, to improve replicability of similar interventions, and facilitate empirical and clinical understanding of the mechanisms addressed and intervention characteristics used [ 6 ]. Future researchers can use protocols and publications about intervention development as a source of understanding the process of which intervention characteristics were selected to address specific theory mechanisms. The development of consensus guidelines for guiding the use of theory within interventions is needed to improve both reporting use of theory use as well as implementation of theory throughout intervention development.

In our subgroup analyses that explored the number of theory components that were addressed according to assessed risk of bias, we found that nine of 11 interventions with low risk of bias incorporated intervention characteristics associated with each of the three main theory components in contrast to only six of 11 and 12 interventions with high or unclear risk of bias, respectively. It is possible that these small differences may have been influenced by poor reporting. Since risk of bias assessment relies on reported information [ 24 ], poor reporting may contribute to some interventions being assessed with high or unclear risk of bias and incomplete descriptions of theory mechanisms. These differences highlight the importance of accurate reporting to allow for understanding of mechanisms and intervention characteristics addressed.

Limitations

This review is not without limitations. We may have overlooked relevant interventions that were developed using the social cognitive theory of self-regulation, but that failed to cite either of the two publications we specified for inclusion. However, we expect that our inclusion criteria identified the best examples of interventions developed using the theoretical framework. We surmise that our database searching, citation searching, systematic review reference list searching, and reference list searching of included studies, limited the number of interventions missed in our investigation.

During our consensus process of determining how we would judge whether or not intervention characteristics address theoretical mechanisms, we identified some overlap in concepts of the theory. As a result of this overlap, we may have been overly inclusive when identifying whether each of the theory components and mechanisms were represented. For example, when participants were instructed to select their own rewards contingent on behavioural progress, the characteristic was judged to apply to both ‘Self-incentives’ of the self-evaluation component, and ‘Motivation’ of the self-monitoring component. The available descriptions of the social cognitive theory of self-regulation theory do not provide guidelines as to which mechanisms may overlap, or outline specifically which mechanisms or combination of mechanisms may be most relevant or useful for successful behaviour change. We therefore attempted to explicitly outline in our coding guide potential overlap across mechanisms, and set our criterion of identifying theory components at a minimum to provide a foundation upon which to build future exploration of applying the social cognitive theory of self-regulation in the development of interventions for chronic health conditions. Research is needed to identify and evaluate which specific mechanisms and associated intervention characteristics are most important to address in behaviour change interventions. These evaluations may lead to the development of comprehensive guidelines suggesting how to use the theory mechanisms and components when developing interventions theory.

Future Directions

We conducted this systematic review as a first-step method to inform the process that researchers can take during intervention development. Review authors exploring use of the social cognitive theory of self-regulation are encouraged to use our extraction guide to identify intervention characteristics addressing the theory components ( S2 Table ). For review authors exploring a different theory, following a similar consensus procedure with multiple reviewers to develop an extraction guide that includes comprehensive understanding of the type of intervention characteristics that will be judged as addressing the theory mechanisms may be useful. Our extraction guide may serve as an appropriate starting point and an example of how theory can be identified from exploring intervention characteristics.

If a review identifies that theory is comprehensively addressed across interventions, as our review did for the social cognitive theory of self-regulation for chronic health conditions, sufficient information is likely available for researchers and clinicians to identify which theory mechanisms to consider including during the preliminary phases of developing a theory-driven intervention. Researchers and clinicians can use the review information to choose intervention characteristics that are commonly incorporated to address the theory mechanisms, likely based off of frequency of implementation across interventions. The phases for development of theory-driven interventions suggested by the UK Medical Research Council can then be followed for further testing the intervention components, to identify what intervention version and characteristics can achieve optimum clinical effectiveness [ 5 , 6 ]. If, on the other hand, a review does not identify that theory or specific theoretical mechanisms have been comprehensively used across existing interventions, additional testing and exploration using alternative methods or theories may be required to identify which, if any, of the theory mechanisms are applicable to the population of interest.

We assumed for the purpose of this review that theory-driven interventions provide some benefit over atheoretical interventions. However, the effectiveness of theory-driven interventions compared to those developed without a theoretical framework is unclear. Future research should explore whether there are any benefits when implementing theory-driven in comparison to atheoretical interventions. Even if theory-driven interventions are identified equally as effective as atheoretical interventions, they build on existing knowledge and provide explanations of specific interactions that influence how interventions may work, which is useful for informing improvement and modification of future intervention characteristics and implementation [ 60 ].

Conclusions

The present review establishes that systematic review is a feasible method of identifying use of theory as a conceptual framework for existing interventions. We used the social cognitive theory of self-regulation as an example and identified that it is an adequate and practical theoretical framework to guide the preliminary phases of intervention development for some chronic health conditions. Researchers and clinicians can use this type of systematic review to identify whether a selected theory is a feasible framework to guide intervention development, and which intervention characteristics are used to address the theoretical mechanisms. This work provides a preliminary investigation into exploring use of theory to inform the development of interventions. Further guidelines are needed to assist exploration of theory as a framework in the early phases of intervention development.

Supporting Information

S1 table. prisma checklist..

https://doi.org/10.1371/journal.pone.0134977.s001

S2 Table. Coding Guide of Intervention Characteristics Addressing the Social Cognitive Theory of Self-regulation.

https://doi.org/10.1371/journal.pone.0134977.s002

S3 Table. Cochrane Collaboration’s Risk of Bias Tool.

https://doi.org/10.1371/journal.pone.0134977.s003

S4 Table. Characteristics of Randomized Controlled Trials Evaluating Self-monitoring Interventions for Adults that were Developed Using the Framework of the Social Cognitive Theory of Self-regulation.

https://doi.org/10.1371/journal.pone.0134977.s004

S5 Table. Frequency of Theory Mechanisms Addressed by at Least One Intervention Characteristic for each Included Intervention.

https://doi.org/10.1371/journal.pone.0134977.s005

S1 Text. Systematic Review PubMed Search Strategy.

https://doi.org/10.1371/journal.pone.0134977.s006

Acknowledgments

The authors thank Robin Parker for her help developing the search strategy, and Dalhousie University’s document delivery for assistance in retrieving electronic articles from the search.

Author Contributions

Conceived and designed the experiments: MET JAH PJM AH. Performed the experiments: MET SR. Analyzed the data: MET. Wrote the paper: MET JAH PJM AH SR.

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• 9. WHO. Global status report on noncommunicable diseases 2010. 2011.
• 10. Bandura A. Social foundations of thought and action: A social cognitive theory. NJ; Prentice-Hall: Englewood Cliffs; 1986.
• 24. Higgins JPT, Green S. Cochrane handbook for systematic reviews of interventions. 5.1.0 ed: The Cochrane Collaboration; 2011.

3 Social Cognitive Theory

At the end of this chapter, you will be able to:

• Identify key elements of social cognitive theory
• Explain strategies utilized to implement social cognitive theory
• Summarize the criticisms of social cognitive theory and educational implications
• Explain how equity is impacted by social cognitive theory
• Identify classroom strategies to support the use of social cognitive theory
• Select strategies to support student success utilizing social cognitive theory
• Develop a plan to implement the use of social cognitive theory ​​

SCENARIO: Yesterday, Ms Mitchell felt exhausted at the end of the school day so today she was going to try something new. In her science class, the students could not seem to follow the neatly printed directions on the white board, nor did the color-coordinated handouts seem to make any difference. She had run around the room trying to respond to the different groups attempting the science project but there was a great deal of confusion. It was one of those days where she questioned her career choice- was she really cut out to be a teacher? After a good night’s sleep and coaching from a colleague, Ms. Mitchell was determined to try a different approach and model every step of the process. After she modeled each section, students seemed to get it quickly.  Following some verbal encouragement from Ms. Mitchell, there was soon a happy buzz in the classroom as students engaged with each other in the steps of the science project. Ms. Mitchell was even able to rest her feet, drink her herbal tea and consider what a difference these simple strategies made.

What changes did Ms. Mitchell make? How did modeling the activity change the end result and facilitate their learning? How did her positive remarks reinforce their confidence with the tasks? How did group work also build engagement? As you read through this chapter, consider the power of observation and how learning occurs in a social context with a dynamic and reciprocal interaction of the person, environment, and behavior.

Video 3.1 – Social Cognitive Theory

Introduction.

Albert Bandura (1925-2021) was born in Mundare, Alberta, Canada, the youngest of six children. Both of his parents were immigrants from Eastern Europe. Bandura’s father worked as a track layer for the Trans-Canada railroad while his mother worked in a general store before they were able to buy some land and become farmers. Though times were often hard growing up, Bandura’s parents placed great emphasis on celebrating life and more importantly family. They were also very keen on their children doing well in school. Mundare had only one school at the time so Bandura did all of his schooling in one place.

Bandura attended the University of British Columbia and graduated three years later in 1949 with the Bolocan Award in psychology. Bandura then went to the University of Iowa to complete his graduate work. At the time, the University of Iowa was central to psychological study, especially in the area of social learning theory. By 1952, Bandura completed his Master’s and Ph.D. in clinical psychology. Bandura worked at the Wichita Guidance Center before accepting a position as a faculty member at Stanford University in 1953. Bandura has studied many different topics over the years, including aggression in adolescents (more specifically he was interested in aggression in boys who came from intact middle-class families), children’s abilities to self-regulate and self-reflect, and of course self-efficacy (a person’s perception and beliefs about their ability to produce effects, or influence events that concern their lives).

Bandura is perhaps most famous for his Bobo Doll experiments  in the 1960s. At the time there was a popular belief that learning was a result of reinforcement. In the Bobo Doll experiments, Bandura presented children with social models of novel (new) violent behavior or non-violent behavior towards the inflatable rebounding Bobo Doll.

  As children continue through adolescence toward adulthood, they need to assume responsibility for themselves in all aspects of life. They must master many new skills, and a sense of confidence in working toward the future is dependent on a developing sense of self-efficacy supported by past experiences of mastery. In adulthood, a healthy and realistic sense of self-efficacy provides the motivation necessary to pursue success in one’s life.

  In summary, as we learn more about our world and how it works, we also learn that we can have a significant impact on it. Most importantly, we can have a direct effect on our immediate personal environment, especially with regard to personal relationships, behaviors, and goals. What motivates us to try influencing our environment is specific ways in which we believe, indeed, we can make a difference in a direction we want in life. Thus, research has focused largely on what people think about their efficacy, rather than on their actual ability to achieve their goals (Bandura, 1997).

Impact of Social Cognitive Theory

Bandura is still influencing the world with expansions of Social Cognitive Theory (SCT). SCT has been applied to many areas of human functioning such as career choice and organizational behavior as well as in understanding classroom motivation, learning, and achievement (Lent, Brown, & Hackett, 1994). Bandura (2001) brought SCT to mass communication in his journal article that stated the theory could be used to analyze how “symbolic communication influences human thought, affect and action” (p. 3). The theory shows how new behavior diffuses through society by psychosocial factors governing acquisition and adoption of the behavior. Bandura’s (2011) book chapter “The Social and Policy Impact of Social Cognitive Theory” to extend SCT’s application in health promotion and urgent global issues, which provides insight into addressing global problems through a macro social lens, aiming at improving equality of individuals’ lives under the umbrellas of SCT. This work focuses on how SCT impacts areas of both health and population effects in relation to climate change. He proposes that these problems could be solved through television serial dramas that show models similar to viewers performing the desired behavior.

Bandura (2011) states population growth is a global crisis because of its correlation with depletion and degradation of our planet’s resources. Bandura argues that SCT should be used to get people to use birth control, reduce gender inequality through education, and to model environmental conservation to improve the state of the planet. Green and Peil (2009) reported he has tried to use cognitive theory to solve a number of global problems such as environmental conservation, poverty, soaring population growth, etc.

Criticism of Social Cognitive Theory

• The social cognitive theory is that it is not a unified theory. This means that the different aspects of the theory may not be connected. For example, researchers currently cannot find a connection between observational learning and self-efficacy within the social-cognitive perspective.
• The theory is so broad that not all of its component parts are fully understood and integrated into a single explanation of learning.  The findings associated with this theory are still, for the most part, preliminary.
• The theory is limited in that not all social learning can be directly observed. Because of this, it can be difficult to quantify the effect that social cognition has on development.
• Finally, this theory tends to ignore maturation throughout the lifespan. Because of this, the understanding of how a child learns through observation and how an adult learns through observation are not differentiated, and factors of development are not included.

  Image 3.7

Educational implications of social cognitive theory.

An important assumption of Social Cognitive Theory is that personal determinants, such as self-reflection and self-regulation, do not have to reside unconsciously within individuals . People can consciously change and develop their cognitive functioning. This is important to the proposition that self-efficacy too can be changed, or enhanced. From this perspective, people are capable of influencing their own motivation and performance according to the model of triadic reciprocality in which personal determinants (such as self-efficacy), environmental conditions (such as treatment conditions), and action (such as practice) are mutually interactive influences. Improving performance, therefore, depends on changing some of these influences.

Relevancy to the classroom:

  In teaching and learning, the challenge upfront is to:

• Get the learner to believe in his or her personal capabilities to successfully perform a designated task.
• Provide environmental conditions, such as instructional strategies and appropriate technology, that improve the strategies and self-efficacy of the learner.
• Provide opportunities for the learner to experience successful learning as a result of appropriate action (Self-efficacy Theory, n.d.).

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Social Cognition through the Lens of Cognitive and Clinical Neuroscience

Maria arioli.

1 NEtS Center, Scuola Universitaria Superiore IUSS, Pavia, 27100, Italy

2 Cognitive Neuroscience Laboratory, ICS Maugeri, Pavia, 27100, Italy

Chiara Crespi

Nicola canessa.

Social cognition refers to a set of processes, ranging from perception to decision-making, underlying the ability to decode others' intentions and behaviors to plan actions fitting with social and moral, besides individual and economic considerations. Its centrality in everyday life reflects the neural complexity of social processing and the ubiquity of social cognitive deficits in different pathological conditions. Social cognitive processes can be clustered in three domains associated with (a) perceptual processing of social information such as faces and emotional expressions (social perception), (b) grasping others' cognitive or affective states (social understanding), and (c) planning behaviors taking into consideration others', in addition to one's own, goals (social decision-making). We review these domains from the lens of cognitive neuroscience, i.e., in terms of the brain areas mediating the role of such processes in the ability to make sense of others' behavior and plan socially appropriate actions. The increasing evidence on the “social brain” obtained from healthy young individuals nowadays constitutes the baseline for detecting changes in social cognitive skills associated with physiological aging or pathological conditions. In the latter case, impairments in one or more of the abovementioned domains represent a prominent concern, or even a core facet, of neurological (e.g., acquired brain injury or neurodegenerative diseases), psychiatric (e.g., schizophrenia), and developmental (e.g., autism) disorders. To pave the way for the other papers of this issue, addressing the social cognitive deficits associated with severe acquired brain injury, we will briefly discuss the available evidence on the status of social cognition in normal aging and its breakdown in neurodegenerative disorders. Although the assessment and treatment of such impairments is a relatively novel sector in neurorehabilitation, the evidence summarized here strongly suggests that the development of remediation procedures for social cognitive skills will represent a future field of translational research in clinical neuroscience.

1. Making Sense of Others' Behavior with Social Cognition

Social cognition refers to a set of neurocognitive processes underlying the individuals' ability to “ make sense of others' behavior ” as a crucial prerequisite of social interaction [ 1 ]. Such a complex ability entails a variety of skills, ranging from decoding social information (e.g., faces and emotional expressions) and drawing inferences on others' mental or affective states to making decisions consistent with social norms and others' welfare.

Social abilities emerge as early as 14 months [ 2 ], also in nonhuman species [ 3 ], and remain crucial for the lifespan [ 4 ]. Their centrality in everyday life is clearly shown by those conditions in which a social cognitive impairment results in a variety of adverse outcomes, e.g., mental [ 5 ] and physical [ 6 ] deficits, functional disability [ 7 ], unemployment [ 5 ], and more generally poor quality of life [ 8 ]. The last edition of the American Psychiatric Association's Diagnostic and Statistical Manual for Mental Disorders (DSM-5) has indeed introduced social cognition as one of the six main factors of neurocognitive functioning, impaired in different pathological conditions.

Social cognitive impairments are a prominent concern, or even a core facet, of several neurodegenerative (e.g., behavioral variant of frontotemporal dementia), neuropsychiatric (e.g., schizophrenia, major depressive disorder, and bipolar disorder), and neurodevelopmental (e.g., autism spectrum disorder and attention deficit hyperactivity disorder) conditions, and often occur after acute brain damage (e.g., traumatic brain injury and stroke) [ 9 ]. Moreover, such deficits are critical predictors of functional outcomes because they affect the ability to create and maintain interpersonal relationships, thereby removing their benefits in everyday life [ 7 ]. In this respect, the rewarding and healthy value of social interaction [ 10 ] is shown by growing evidence on the negative consequences of isolation in terms of morbidity and mortality [ 11 – 13 ]. Interestingly, perceived social isolation (i.e., loneliness) is a major risk factor for several diseases, including dementia, independent of objective social isolation [ 14 ].

In order to pave the way for other articles of this special issue on the social cognitive deficits associated with acquired brain injury, this review aims at providing an overview of the social brain and its main functions. We will pursue this goal by summarizing the main findings obtained within the research field popularly known as “social cognitive neuroscience” [ 15 ]. For explanatory purposes, the complexity of social cognition will be addressed in terms of its three main domains, i.e., social perception, social understanding, and decision-making in the social context. Each of these subjects, representing distinct—although strictly intertwined—sectors of social neuroscience, will be first addressed in terms of cognitive processes and their modulating variables and then with regard to the available f MRI evidence on their neural correlates. Since the consequences of brain damage on social cognitive performance might be confounded by aging effects, in the last section we will briefly summarize the main findings of a fast-growing literature concerned with age-related changes in different facets of social cognition. To complement the evidence on the effects of acquired brain injury presented in other articles of this issue, this section will also review few selected findings from a lively interdisciplinary research sector exploring social cognitive deficits in neurodegenerative disorders. To introduce the potential translational implications of research in social cognitive neuroscience, we conclude by discussing selected examples of social cognitive treatment protocols assessed in previous studies and the available meta-analytic evidence about their effectiveness.

2. Three Main Domains of Social Cognition

The ability to establish appropriate social interactions entails several distinct processes. First, the social agent must recognize the others as “living persons,” via the analysis of complex perceptual information including facial expressions, gestures, postures and body language, and voice, [ 16 ]. Once integrated, this information will represent the input for higher-level processes underlying a direct resonance to others' affective states (i.e., “empathy”) and/or the interpretation of their observable behaviors in terms of mental states and dispositions (i.e., “mentalizing” or “theory of mind” [ 17 ]). By modulating decision-making, the outcome of these processes will likely lead the observer to adapt her/his own social behavior [ 18 ]. This framework highlights the three key domains of social cognition which will be discussed in the next sections, i.e., social perception, social understanding, and social decision-making.

2.1.1. Social Perception

A basic prerequisite of social cognition is the ability to distinguish between objects (whose behavior is completely explained by physical forces) and persons (characterized by inner experiences, such as motivations, reasons, and intentions, which make their behavior not completely predictable) (Fiske and Taylor, 2013) [ 19 ].

A related question in social cognitive neuroscience is whether social stimuli represent a qualitatively different perceptual category or rather the specificity of their neural processing can be reduced to “low-level” perceptual dimensions such as vividness, salience or familiarity (Fiske & Taylor, 2013). The former hypothesis fits with the centrality of social stimuli in human life, with their different functions being expressed at various levels of complexity: survival for the single individual, communication in dyads, social coordination in groups, and, finally, culture in institutions [ 20 ]. The prototypical example, in this respect, is represented by the neural processing of human faces [ 21 ], providing multifaceted information on both others' changeable characteristics such as emotions and intentions, and invariant features such as identity. The unique salience of human faces [ 22 ] is indeed considered to reflect their predictive power with respect to others' intentions and thus their potential consequence in social terms [ 23 ]. In line with this view, different experimental paradigms suggest that faces and objects undergo different styles of cognitive processing, i.e., holistic vs. part-based coding, respectively, with parts being integrated into a whole in upright but not inverted faces [ 24 ]. This evidence for the unique status of faces fits with the existence of a dedicated neural circuitry for this category of social stimuli, additionally showing stronger responses to upright than inverted faces [ 25 ].

In particular, the eyes represent the most dynamic and informative social stimulus, capturing our attention more than head/body movements and postures [ 26 ]. Gaze direction reveals overt attention shifts, and the informative value of another's eye-movement patterns with respect to her/his mental states explains why gaze perception is considered a crucial prerequisite of mentalizing [ 27 ]. Alongside gaze, also the emotional expressions produced by the contractions of facial muscles provide crucial social information [ 28 , 29 ]. In addition to the obvious communicative valence of emotions (“ A radar and rapid response system, constructing and carrying meaning across the flow of experience ” [ 30 ]), it is important to stress their adaptive value for appraising experience and preparing to act in response to external stimuli. The popular Ekman and Friesen's (2003) facial action coding scheme (FACS) describes facial expressions as combinations of the action units characterizing different emotions. This model is based on the notion of a set of six basic universal emotions (happiness, anger, sadness, fear, disgust, and surprise) which all humans can express and recognize regardless of sociocultural effects [ 31 ]. It is worth mentioning that more recently, a similar proposal has been made for specific social emotions such as shame and embarrassment (Cordaro et al., 2017). On the other hand, available evidence on the role played by cultural rules on the processing of facial expression and interpretation of emotions strengthens an “interactionist perspective” taking into consideration both biological and social/cultural factors [ 32 ].

While facial expressions represent the most effective means for emotional communication, the latter can involve also the body [ 33 ] and the voice [ 34 ]. In the first case, bodily changes are related to the role of emotions in preparing to act in response to external stimuli. If different emotions involve specific patterns of body movement and posture, this information could support emotional decoding based on visuomotor analyses of body language. Evidence based on point-light displays indeed shows high accuracy in relating such a minimal information to the emotion expressed by a moving body [ 35 ]. In addition, voices reveal our feelings as well, through nonverbal vocalizations (e.g., laugh) and prosody. However, available evidence suggests that the voice conveys mostly unspecific facets of affective states, such as physiological arousal [ 36 ], but no clear cue to specific emotions. On the other hand, the combination of different features could contribute to distinguish emotions in spoken sentences [ 37 ], and there is evidence for intersubject reliability in emotional judgments based on vocalizations [ 38 ]. Moreover, although most studies have addressed the information provided by face, body, and voice in isolation, the typical co-occurrence of multiple input channels improves emotional decoding (Martinez et al., 2015).

According to the “Feedback hypothesis”, faces, voices, and bodies not only express but also influence emotional experiences, because the production of facial expressions, sounds, and postures results in related sensory feedback which in turn modulates the intensity of feelings [ 39 ]. The latter would be thus enhanced by the expression of a congruent emotion and decreased either by the inhibition of a congruent emotion or by the expression of an incongruent emotion [ 40 ]. This hypothesis suggests a tight relationship between the perceptual and “private” facets of emotional processing, which fits with recent evidence on emotion perception. Several theoretical speculations and empirical investigations on this subject revolve around the notion of “embodied simulation.” That is, a mirror-like mechanism [ 41 ] is considered to provide a direct link between the first- and third-person experiences and thus access to the meaning of others' actions and emotions [ 42 ]. In this perspective, mirroring the others' facial emotional expressions, via the engagement of the corresponding motor circuits and muscular contractions (i.e., mimicry; [ 43 , 44 ]), underpins a direct and experiential grasp of their meaning [ 45 ].

The notion of embodiment, however, has been also proposed to underlie even cognitive phenomena exceeding perception and action. According to the “embodied cognition” framework [ 46 ], all cognitive representations and operations would be fundamentally grounded in their physical sensory-motor context [ 47 ]. Even our semantic knowledge would be ultimately represented, at the neural level, in the sensory-motor systems underlying our direct experience with the world (Niedenthal, 2007), so that semantic representations of objects or events involve (some of) the brain sensory-motor states associated with their direct experience (Barsalou, 2008). This approach strongly departs from associative network models, considering memory as a web of semantic concepts that describe objects and events [ 48 ] in terms of basic units represented by propositions [ 49 ]. In the latter framework, any object would be represented in memory by a set of descriptive propositions, interconnected by associative links made through experience. The engagement of an emotion unit would spread activity in this interconnected web [ 50 ], thus increasing the accessibility to words and memories associated with the target emotion [ 51 ]. In the embodied cognition framework, instead, even the somehow “abstract” facets of emotional processing, such as those representing the affective value of an object brought to memory, involves reactivating the motor programs and feelings associated with its direct sensorimotor experience [ 52 ]. The latter would then provide an experiential access to the meaning of concepts, including their affective features.

2.1.2. Neural Correlates of Social Perception

The fast growth of social cognitive neuroscience is providing increasing evidence on the brain networks subserving the different domains previously described, and the available data nowadays allow to fractionate the social brain in distinct sets of areas associated with relatively specific functions. We will focus on the neural processing of visual stimuli, representing the richest source of information in everyday social life as well as in the available literature.

The first nodes of the neural pathways underlying the processing of visual social stimuli involve the occipitotemporal cortex, where distinct brain regions have been associated with a preliminary decomposition of the visual scene into different categories and particularly faces (Occipital Face Area (OFA) in the inferior occipital gyrus and Fusiform Face Area (FFA) in the fusiform gyrus; [ 53 ]) and bodies or body-parts (Extrastriate Body Area (EBA) in the lateral occipito-temporal cortex and Fusiform Body Area (FBA) in the fusiform gyrus [ 54 ]). The activation of these areas has been interpreted as reflecting a dedicated neural circuitry for faces (“ face-selective hypothesis ” [ 21 ]), or a greater expertise in discriminating faces compared with other kinds of stimuli (“ expertise hypothesis ” [ 55 ]). The latter hypothesis found support in the FFA activation in participants trained to identify novel artificial objects sharing some typical constraints of faces (i.e., greebles; [ 56 ]), but subsequent studies reinterpreted this evidence in terms of subjects coding these stimuli as face-related [ 57 ].

While the OFA and EBA appear to underpin the neural representation of parts of faces and bodies, respectively, the FFA and FBA seem to reflect more holistic representations of these stimuli, i.e., processing the configurations of face- and body-parts into wholes [ 58 ]. Alongside the proximity of FFA and FBA in the posterior fusiform gyrus, the latter evidence raises the possibility that their functional integration underpins the ability to identify other individuals based on cues from both faces and bodies, particularly when a single cue-type is not sufficient for recognition [ 54 ]. This proposal fits with the notion that, among distinct neural pathways originating from these areas, a “ventral” pathway, running along the temporal cortex, underpins the semantic representation of specific concepts, i.e., the identity of familiar or unique stimuli. In particular, the polar sectors of the temporal and medial temporal cortex seem to be associated with the processing of unique houses or persons (e.g., the White House or President Obama) [ 59 ]. Along this pathway, single-cell recordings during awake-surgery have highlighted, in the human temporal and hippocampal cortex, neurons showing invariant responses to single persons, landmarks, and object [ 60 ]. The fact that these neurons are activated by different pictures of a same stimulus and some of them even by letter strings reporting its name strongly suggests their role in coding an abstract representation of specific concepts.

Another neural pathway of social perception involves the posterior portion of the lateral temporal cortex, where a hierarchical organization includes brain areas responding to pure motion (area MT/V5 in the inferior/middle temporal cortex), the typical motion of objects (middle temporal cortex), and biological motion (posterior portion of superior temporal sulcus; pSTS) [ 61 ] ( Figure 2(a) ). The pSTS represents a crucial hub of the brain network of social perception, processing the changeable features of biological stimuli and particularly their action-related motion patterns [ 62 ]. Neurophysiological studies have highlighted, in this region, single neurons responding to the observation of movements performed by different biological effectors, including eye-gaze [ 63 ]. Some of these neurons respond to complex visual patterns, such as the interaction between effector and objects, or a reaching action but only if the agent's gaze is directed to the target object [ 64 ]. Overall, the available evidence suggests that the pSTS plays a key role in the sensory binding of different features of biological motion, likely generating a superordinate representation of perceived actions [ 65 ]. Since pSTS neurons do not discharge during active movements, this region of the monkey brain does not display a “mirror-like” response. However, both neurophysiological data from the monkey [ 62 ] and neuroimaging evidence in human subjects [ 66 , 67 ] suggest that the pSTS sends higher-level perceptual inputs to the frontoparietal mirror system associated with the analysis of the meaning of others' actions (see Section 2.2.1 and Figure 2(b) ).

Brain networks of social cognition. Meta-analytic evidence for the neural networks underlying social perception (a), action observation (mirror system) (b), and mentalizing (Theory of Mind system) (c). As shown in the bottom sector of the figure, these three networks overlap in the STS, a crucial hub of the social brain providing inputs to both the mirror and mentalizing systems [ 66 ]. Reproduced with permission from Yang, Rosenblau, Keifer, and Pelphrey, An Integrative Neural Model of Social Perception, Action Observation, and Theory of Mind , Neuroscience and Biobehavioral Reviews, 51 (2015) 263–275, doi:10.1016/j.neubiorev.2015.01.020.

The pSTS is also part of another network, including the amygdala and orbitofrontal cortex (Amaral et al., 1992), associated with the processing of the affective value of observed stimuli. The amygdala is a key node of the social brain (Brothers, 1990), in which neuroimaging studies are associated with the emotional facets of social perception, such as the processing of facial expressions (Todorov et al., 2012) and judgments of trustworthiness [ 68 ]. This correlational evidence found support in lesional data showing the consequences of its damage, or abnormal functioning, on social cognitive processing [ 69 ] and real social interactions [ 70 ].

In line with the recent emphasis on the notion of “connectome” [ 71 ], diffusion imaging studies have started to address the structural connections underpinning the different facets of social cognition [ 72 ]. In the case of face processing, converging evidence shows the involvement of the inferior longitudinal fasciculus (IFL) and inferior frontooccipital fasciculus (IFOF), projecting from the occipital cortex to the anterior temporal and frontal cortex, respectively [ 73 ]. Their crucial role in connecting the nodes of the network subserving face processing is shown by studies relating distinct metrics of structural connectivity to face perception skills in normal conditions [ 74 ], physiological aging (disruption of the right IFL [ 75 ]), and in association with face blindness in developmental prosopoagnosia (disruption of both the right IFL and IFOF [ 76 ]). Preliminary evidence additionally shows the involvement of the superior longitudinal fasciculus (SLF), connecting temporal, parietal, and frontal regions [ 77 ] and particularly face-responsive portions of the STS with orbitofrontal and inferior frontal cortex ([ 78 , 79 ].

2.2.1. Social Understanding: Representing Others' Behavior

Since others' behavior is not completely predictable, the success of social interactions depends on the ability to decode their mental and, particularly, intentional states [ 80 ]. Interpreting others' behavior in terms of mental states, such as beliefs, desires, intentions, goals, experiences, sensations, and emotions, is thus a critical step for predicting their future actions [ 81 ]. This natural disposition to mentalizing entails the development of a “Theory of Mind” (ToM) based on the awareness that people have mental states, information, and motivations that may differ from one's own (Frith and Frith, 2006) [ 82 ]. On this assumption, mentalizing performance is typically measured with tasks assessing whether an individual is able to represent mental states, attributes them to oneself vs. other persons, and then, based on such attribution, correctly understands and/or predicts others' behavior [ 83 – 85 ].

Far from being a unique process, mentalizing involves several components and the integration of different facets of social understanding [ 86 , 87 ]. Neuroimaging studies are providing increasing knowledge on the neural correlates of such components. A first crucial distinction regards the ability to attribute mental states vs. affective states, i.e., cold or cognitive ToM vs. hot or affective ToM , respectively [ 88 ]. Moreover, representing others' thoughts, desires, feelings, and traits, i.e., mentalizing, differs from grasping and automatically sharing affective states, i.e., empathy [ 89 ]. On the other hand, these constructs are partially overlapping [ 90 ], and an influential model considers cognitive ToM a prerequisite for affective ToM, which additionally requires empathic skills ([ 91 ] see Figure 1 ).

Empathy and mentalizing. The figure depicts Shamay-Tsoori et al.'s [ 91 ] model of the relationship between the key processes of social understanding, i.e., empathy and mentalizing. According to the model, cognitive mentalizing is a prerequisite for affective mentalizing, which however interacts with emotional empathy. Reproduced with permission from Shamay-Tsoori, Harari, Aharon-Peretz, and Levckovitz, [ 91 ].

In addition, a dissociation has been proposed between implicit and explicit mentalizing [ 80 ]: while the former would be present even in infants, who can ascribe false beliefs to agents from nonverbal behavior [ 2 ], explicit mentalizing represents a cognitively demanding sociocultural skill acquired by verbal instructions. Considerable evidence nowadays shows that explicit mentalizing develops slowly in the childhood [ 87 ]. Finally, based on computational complexity it is common to distinguish between first and higher-order Theory of Mind processing. First-order ToM, involving the representation of another individual's mental states (inclusive of both its affective and cognitive components) [ 92 ], develops between the age of 4 and 5 [ 93 ]. Second-order ToM, i.e., mentalizing what someone else is thinking or feeling about a third person's mental states [ 94 ], typically develops at the age of 6.

Social perception and in particular emotion decoding are considered to precede mentalizing [ 85 ]. The former stage would indeed reflect low-level perceptual processes providing inputs to the higher-level integrative and inferential processes associated with mentalizing [ 95 ]. On the other hand, mentalizing can influence social perception via top-down mechanisms mediated by long-term knowledge. This bidirectional relationship represents a core element of the influential Mindreading model [ 96 ], in which social perception and mentalizing represent different components of a larger system subserving the ability to perceive and respond appropriately to others' emotions and intentions [ 97 ]. This model entails three key perceptual detectors for mental states, gaze, and affective states, alongside a shared attention mechanism supporting the ability to selectively focus on specific stimuli and integrating the outcome of detector-specific basic perceptual processes. On top of this hierarchy, an advanced mentalizing ability allows us to perceive and respond appropriately to others' emotions, beliefs, and behaviors.

The kind of processes underpinning the mentalizing ability is, however, strongly debated (Goldman and Sripada, 2005). According to so-called “Theory-theory”, people act as naïve social scientists, developing psychological theories to infer others' mental states [ 98 ]. Based on the aforementioned mirroring process, “Simulation theory” rather states that we attribute mental states to others by simulating them in our own mind [ 45 , 99 ]. A considerable literature, mostly based on neuroimaging data, suggests that different processes, revolving around simulative mechanisms vs. inferential routines, are recruited depending on the type of stimuli (visual vs. verbal) and instructions (implicit vs. explicit) ([ 66 , 100 ] see [ 101 ]).

An alternative to both these approaches is represented by so-called “interaction theory” [ 102 ], stressing the role played by embodiment and direct perception when experiencing real social interactions (Froese and Gallagher, 2012). Based on the uniqueness of social interaction, in terms of the richness of incoming information and complexity of the responses, the advocates of this perspective aim to address social cognition from an interactor's point of view [ 103 ], also with innovative experimental designs grounded in virtual reality [ 104 , 105 ], to investigate the mechanisms whereby individuals modulate their actions online [ 106 ]. This change of perspective involves shifting from “open-loop” to “closed-loop” scenarios where interactors influence one another dynamically, reciprocally, and continuously [ 107 ]. Neuroimaging studies based on this approach have shown that compared with the mere observation of social stimuli, being actively engaged in a social interaction activates a more extensive network of areas associated with perception-action coupling and affective evaluations, promoting motor responses coherent with the social stimulus [ 107 ]. These results highlight the potential implications of such an ecological approach not only for studying the neural bases of social cognition in normal individuals, but also for characterizing related disorders in pathological populations and for rehabilitation after brain damage. For example, recent evidence based on human-avatar online interactions shows that apraxics' motor impairments in a social reach-to-grasp task are abolished when patients are asked to interact with a virtual partner rather than performing actions on their own [ 108 ].

2.2.2. Neural Correlates of Social Understanding

Distinct research lines, within social cognitive neuroscience, have addressed the neural bases of the ability to understand others' behaviors and decode their intentions and feelings. Most of the related evidence revolves around the mirror and mentalizing brain networks which, based on inputs from the pSTS (see Section 2.1.2 ), appear to underpin distinct levels of the hierarchy of social understanding [ 66 , 109 ].

The mirror system includes inferior frontal, premotor, and parietal regions which are activated both when performing an action and when observing the same action performed by someone else [ 41 ] ( Figure 2(b) ). This network is considered to underpin a variety of action-related social functions, from action recognition [ 110 ] and imitation learning (Vogt et al., 2007) to the context-based decoding of so-called “private goals,” e.g., grasping a cup to drink vs. to clean the table (Iacoboni et al., 2005). The mirror system is anatomically and functionally distinct from the mentalizing system, which includes the medial prefrontal cortex (mPFC), temporoparietal junction (TPJ), medial precuneus/posterior cingulate cortex, and temporal poles [ 86 , 111 , 112 ] ( Figure 2(c) ). This network of areas is typically engaged when others' intentions cannot be automatically derived from visual cues and must thus be inferred in terms of thoughts and beliefs [ 101 , 109 ].

Therefore, a superordinate dimension eliciting the specific recruitment of the mirror vs. mentalizing systems is represented by the aim to identify, respectively, how (executed movements associated with a behavioral state) vs. why (beliefs and intentions associated with a mental state) an action is performed [ 113 – 115 ]. The mirror and mentalizing systems seem thus to play complementary roles in processing others' intentions, driven by the presence of, respectively, biological actions vs. abstract information (e.g., observing real scenes vs. reading stories) or implicit vs. explicit instructions (e.g., to passive observe vs. to infer characters' intentions) [ 101 ], and by identifying how vs. why the character is expressing a feeling (i.e., explicit identification vs. attribution [ 114 ]).

While the evidence reviewed above involves the attribution of intentions and cognitive states, other research lines have addressed the neural bases of empathy, i.e., grasping others' feelings through their direct resonance in the observer's brain. This process seems to recruit a mirror-like mechanism specific for different kinds of empathic responses, involving the same brain regions associated with their first-person experience rather than the frontoparietal mirror network. This is the main finding of a series of studies which have reported the involvement of (a subset of) the same brain regions when directly experiencing, and when attending in someone else, specific affective or sensory stimulations. Such a mechanism has been described for the direct and vicarious experience of pain (anterior insula and anterior cingulate cortex, i.e., the affective sector of the so-called pain matrix [ 116 , 117 ]), disgust (anterior insula [ 118 ]), tactile sensations (secondary somatosensory cortex SII [ 119 ]), and even regret for the outcomes of choices (orbitofrontal cortex and anterior cingulate cortex [ 120 , 121 ]). In keeping with the notion of “mirroring,” these results suggest that the observation, or even the mere awareness [ 116 , 117 ], of another person in a particular emotional state may automatically activate the neural representation of the same state in the observer. Such representation includes its associated autonomic and somatic responses, neurally associated with the activation of the anterior insula and dorsal anterior cingulate cortex [ 122 , 123 ], which provides support to the concept of a mirroring, sensorimotor, and nature of empathy [ 124 ]. This notion is strengthened by recent evidence on the neurophysiological correlates of facial mimicry, i.e., the unconscious and unintentional automatic response to the facial expressions of others [ 125 ]. The simultaneous recording of facial muscular reactivity (via electromyography, EMG) and brain activity (via f MRI) highlighted a correlation between spontaneous facial muscle reactions to facial expressions and brain activity in the frontoinsular and inferior parietal “mirror” sectors associated with their motor simulation. Overall, considerable evidence indicates that such a limbic, visceromotor, mirroring system for shared sensory and emotional experience provides the neural framework for emotional insights into other minds.

Both mirroring and mentalizing have been associated with structural connections between temporal, parietal, and frontal lobes underpinned by the superior longitudinal fasciculus [ 72 ]. The latter has been indeed associated with individual differences in abilities such as emotion recognition [ 126 , 127 ], empathy [ 128 ], and imitation [ 129 ]. Other facets of embodied cognition have been ascribed to further limbic tracts, i.e., the uncinate fasciculus linking medial temporal and orbitofrontal cortex [ 130 ], involved in socioemotional processing [ 131 ], and the anterior thalamic radiation connecting the hypothalamus and limbic structures to prefrontal and anterior cingulate cortex [ 132 ], associated with affective processing and emotional regulation [ 133 ]. In keeping with their role in face processing, the inferior longitudinal fasciculus (ILF) and inferior frontooccipital fasciculus (IFOF) have been also associated with emotion recognition and empathy skills both in healthy [ 128 , 134 ] and brain-lesioned [ 135 , 136 ] individuals. In addition to the SLF, mentalizing seems to be supported also by the cingulum (linking medial prefrontal, posterior cingulate and medial temporal cortex [ 130 ]) and arcuate fasciculus (connecting the temporoparietal junction with prefrontal cortex [ 137 ]). Mentalizing abilities have been related to the degree of axonal injury in the left cingulum in brain-lesioned children [ 138 ] and in the arcuate fasciculus, near the temporoparietal junction, in high-functioning autistic individuals [ 139 ]. Strong evidence for this association comes also from direct electrical stimulation during neurosurgery, showing that the virtual disconnection of these tracts results in a marked decrease of mentalizing performance [ 140 , 141 ].

2.3.1. Social Decision-Making

Understanding others' behaviors in terms of dispositions and intentions is often critical for making appropriate decisions in a variety of social contexts. Most choices are made within direct or indirect social interactions within complex and dynamic environments. They will thus depend either on the choices already made by others (if they are known) or on our prediction of the choices they will make (if concurrent with our own ones) and more generally on the awareness of their consequences for both ourselves and others [ 142 ]. From the economic standpoint, studying decisions made in different types of social context, or even within social interactions, is aimed at identifying the optimal choice among the available ones. On the other hand, psychological studies have shown several examples of preferences which seem to reflect prosocial and/or affective considerations even more than economic utilities. Researchers have thus begun to investigate the social and cognitive variables modulating social decision-making using tasks originally developed in distinct research fields within the economic sciences.

One typical example is represented by studies modeling agents' choices with the tools of Game Theory. The latter is based on rigorous models aiming to identify the optimal choice for interacting agents, in different possible situations in which they know the respective outcomes of each possible choice and they can, or cannot, make agreements before choosing. As anticipated, however, real human choices often deviate from the predictions of such models. For instance, classical Game Theory predicts that a group of rational players will make decisions to reach outcomes, known as Nash equilibria [ 143 ], from which no player can increase his/her own playoff unilaterally. Still, considerable evidence shows that people introduce psychological and prosocial considerations in their strategies, which appear to be less selfish and more fairness-oriented than predicted by economic models [ 144 ]. Typical examples of such prosocial attitude are represented by the usual response patterns observed in three tasks entailing two interacting players, popularly known as Ultimatum, Dictator, and Trust games (Fehr and Fischbacher, 2006).

In the Ultimatum Game [ 145 ], the proposer is asked how much of a financial endowment she/he is willing to send to an unknown responder. The latter can accept or reject the offer: in the first case, the sum is divided as proposed; in case of rejection, instead, no one receives anything. Against the economic prescription, i.e., to accept any offer as a responder and thus to offer as less as possible as a proposer [ 146 ], people usually propose “fair” offers [ 147 ] and reject unfair offers [ 148 ], although with some cultural differences [ 149 ], rejection-rates increase substantially as offers decrease in magnitude. A similar trend is found in the Dictator game , although the responder can only accept the proposer's offer.

In the Trust Game , two players receive the same initial endowment. Then, the “trustor” player decides how much of this sum to send to a trustee. Both players know that the transferred amount will be multiplied by a factor >1. The trustee must then decide whether to return some of her/his payoff to the trustor. If she/he honors trust, both players end up with a net monetary increase. If instead the trustee keeps the entire amount, the trustor ends up with a loss. In the case of a single interaction (i.e., “one-shot”), a rational and selfish trustee would not be expected to honor the first player's trust. Therefore, the latter should never trust the other player. Against this prediction, instead, in most studies the first player sends some money to the second one, with trust being typically reciprocated [ 150 ].

Both in their “one-shot” and iterated versions, these tasks typically highlight the willingness to punish, at own expenses, defectors who will never be met again [ 144 , 151 ]. Considerable evidence seems indeed to show the role played, in real human interactions, by an expectation of reciprocity . The latter is the basis of the “tit-for-tat” strategy, i.e., trusting the partner at the first move and then replicating her/his next moves, in which both informatic simulations and psychological studies highlight as the natural strategy in social interactions (Axelrod and Hamilton, 1981). Importantly, this strategy requires the identification and punishment of defectors, even when this is not directly beneficial to the punisher. Since the simple presence vs. absence of the possibility to punish has been shown to increase vs. reduce cooperation in social interaction [ 151 ], this behavior has been called “altruistic punishment” because its costs will benefit individuals other than the punisher. While representing another puzzling behavior for economic theories, real interaction-games have shown that altruistic punishment is a key prerequisite for cooperative behavior to spread in a society [ 151 ]. There must exist, then, some incentive to behaviors which are socially advantageous, but individually expensive. A possible incentive for altruistic punishment by single individuals has been found in the strong negative emotions associated with unfairness, defection, and abuse of one's own trust, eliciting a “desire of revenge” [ 151 ]. In simpler words, anticipating the pleasure inherent in satisfying such desire would represent the incentive to punishment behaviors which, although irrational in purely economic terms for the single individual, exert prosocial consequences at the society level.

While classical economic models had largely ignored the influence of emotions on decision-making, growing evidence at the crossroad between cognitive neuroscience and economics is showing the effect of affective processing on actual choices [ 152 ]. It is now widely acknowledged that decision-making is driven by anticipated outcome-related feelings and particularly by the attempt to experience positive feelings associated with gains and prosociality and to avoid negative feelings such as disappointment for a loss, regret for a foregone outcome, or guilt for the adverse consequences of one's choices for another [ 153 ]. The neural bases of these processes constitute the subject of neuroeconomics, a lively research field at the crossroad among neural, psychological, and social sciences.

2.3.2. Neural Correlates of Social Decision-Making

Understanding others' affective and cognitive states and particularly intentions is often a crucial step for different facets of social decision-making. These might include anticipating others' choices in a strategic context, or planning the reaction to another's defection, e.g., an unfair proposal in the Ultimatum Game, or unreciprocated trust in the Trust Game. While the aforementioned psychological studies have highlighted actual behaviors inconsistent with “rational” economic predictions, neuroscientific data suggest that the typical human prosocial attitude might largely reflect motivational drives associated with brain regions underlying affective and hedonic evaluations. This research field is indeed grounded in the notion that the weight of affective drives, largely acknowledged in individual decision-making (e.g., [ 120 , 121 , 154 , 155 ]), is even enhanced when making choices in a social context [ 156 , 157 ].

A fast-growing literature is unveiling a mosaic of brain regions underlying the different facets of this process. First, the activation of the anterior insula in association with the receipt and rejection of unequal offers by another human subject [ 158 ] might reflect the negative affective reactions elicited by unfairness. Interestingly, accepting unfair offers reflects in increased activity of the dorsolateral prefrontal cortex, a key node of the executive network associated with cognitive control and response inhibition. The latter evidence has been initially interpreted in terms of the role played by this region in inhibiting the negative affects prompting the rejection of unfair offers [ 158 ]. However, against this hypothesis further studies have shown an increase of acceptance rate after its deactivation with transcranial magnetic stimulation (TMS) [ 159 , 160 ]. The dorsolateral prefrontal cortex might thus underpin the selfish drive to accept every offer, rather than the prosocial aptitude toward altruistic punishment. On the other hand, the fact that punishing defectors reflects in the activation of the ventral striatum [ 161 ], the key node of the brain reward network (Schultz et al., 2006), suggests that altruistic punishment might be also stimulated by the rewarding experience implicit in satisfying the desire for revenge against nonreciprocators. Due to its costs, such behavior requires to weigh economic and hedonic outcomes, a tradeoff involving the ventromedial prefrontal cortex [ 161 ]. Overall, the activation of the striatum in association with “tit-for-tat” behaviors and particularly with reciprocated cooperation [ 162 ] highlights a neurobiological interpretation of the, economically irrational, tendency to prefer prosocial behaviors over individual gratifications [ 163 ]: the subjective utility associated with mutual cooperation would represent a short-term social reward outweighing that resulting from unilateral defection (which, in contrast, might additionally reflect in negative feelings such as shame and guilt).

While these data seem to highlight a natural human disposition to prosocial behavior and sharing of resources, less optimistic evidence comes from studies investigating the neural bases of altruism and charity, i.e., costly behaviors providing benefits only to other people. On the one hand, the activation of the ventral striatal hub of the reward network [ 164 , 165 ] might suggest that altruistic behavior is rewarding in itself, which could be interpreted as an evidence against the existence of “pure” altruism. Moreover, other studies have shown, in the same subjects, overlapping ventral striatal activations when deciding to donate money while knowing to be observed and when deciding to keep the money while knowing that no one was observing them [ 166 ]. These results suggest the opportunity to reframe the theoretical speculations and empirical analyses of the putative human prosocial, or even altruistic, disposition in a broader perspective merging economic, psychological and neuroscientific evidence.

3. Age-Related Changes in Social Cognition

A growing literature on age-related changes in cognitive proficiency reveals that physiological aging entails both losses and gains of functions (Kensinger et al., 2017) [ 167 ]. Despite a global decrease of cognitive efficiency, some facets of social cognitive and affective processing remain stable or even improve with age [ 168 ], bringing potential benefits to everyday functioning [ 169 ].

Such changes involve the interaction of multiple processes, i.e., disruption of functions, resource limitations, and reallocation, as well as compensative mechanisms (Kensinger et al., 2017). In turn, these processes are modulated by a wide range of factors including, among others, individual differences in education [ 170 ], level of fluid cognition [ 171 ], and resource availability [ 172 ]. Within this complex scenario, two variables are considered to provide the strongest contribution to age-related changes in social cognition [ 173 ].

The first variable concerns the cooperation vs. competition between automatic and controlled processes. Since aging mainly impacts executive control (von Hippel and Henry 2012), a significant reduction of the ability to inhibit automatic responses can result in socially disinhibited and inappropriate behaviors [ 174 ]. The same mechanism appears to facilitate stereotypical thoughts, which are automatically activated in the presence of stereotyped group members, making older adults more inclined to show prejudices than younger adults (von Hippel and Henry 2012).

Secondly, changes in social cognition seem to depend on whether and to what extent tasks rely on novel information processing vs. accumulated experience [ 175 ]. Despite a global decrease of cognitive efficiency (in terms of speed processing, memory, complex reasoning, attention, and inhibitory control), as well as physical [ 176 ] and perceptual [ 177 ] functioning, several studies reported smaller age-related effects in domains related to past experience, including vocabulary and general knowledge [ 175 ]. This is a crucial notion, since these skills might contribute to specific facets of social cognitive and affective processing and thus partially compensate the overall cognitive decline.

For instance, although older adults perform worse than young adults on memory recall tasks, even when presented with social and affective stimuli [ 178 ], they show equally, or even more, effective emotion regulation skills [ 171 ]. While the latter evidence may appear at odds with an executive decline, emotion regulation may require less resources in older than young adults due to the improved procedural knowledge accumulated throughout life [ 168 ]. In addition, older adults may allocate a greater proportion of resources to emotion regulation compared to younger adults [ 179 ], due both to the possible prioritization of arousing and to self-relevant information (Kensinger et al., 2017), and to age-related motivational changes toward the maximization of the emotional satisfaction in the “here and now” [ 168 ].

Overall, these findings highlight the complexity of age-related changes in social cognition, which are deeply intertwined with both the physiological decrease of cognitive functioning and the shifts in life goals. We will briefly review the available evidence on the changes reported in the three domains of social cognition previously described.

3.1. Age-Related Changes in Social Perception

As discussed in Section 2.1.1 , faces represent a crucial source of social signals, and emotion recognition from facial expressions is a critical prerequisite for appropriate interpersonal communication and social functioning [ 180 ] (von Hippel and Henry, 2012).

While aging is associated with significantly decreased performance in recognizing negative emotions such as fear, sadness, and anger [ 180 ], older adults perform better than younger ones in the case of positive emotions (i.e., happiness and surprise) and disgust. This evidence has been ascribed to the top-bottom bias , indicating age-related changes in face-processing strategies: older, compared with younger, adults are more likely to focus on the bottom half of the face (mouth or nose), which provides information concerning the muscular contractions specifically associated with happiness and disgust [ 181 ], rather than on the eyes [ 182 ]. This pattern might reflect functional and/or structural age-related changes within the face-processing brain network—including the STS, medial PFC and amygdala—associated with eye-gaze perception and decoding [ 167 ].

On the other hand, the decline in recognizing negative emotions from faces might be also attributed to the “ age-related positivity effect” [ 183 ], indicating the older adults' tendency to focus more on positive than negative stimuli compared with younger adults. This effect, consistently described also in attention ad memory domains [ 184 ], has been linked to age-related changes in emotion regulation mechanisms, helping elders to preserve a better mood [ 185 ]. These changes might reflect the fact that, in the elderly, only negative stimuli are associated with the activation of the medial prefrontal cortex (PFC), possibly supporting top-down emotion regulation processes aimed to inhibit negative emotions [ 186 ].

3.2. Age-Related Changes in Social Understanding

The preservation of functions underlying social understanding, such as emotional sharing and the attribution of cognitive or affective states to others, predicts successful outcomes in aging [ 187 ]. In the attempt to disentangle specific changes in the different facets of social understanding, several studies have shown a prominent age-related decline in its cognitive components (i.e., mentalizing and social metacognition), with a relative conservation, or even an enhancement, of the affective ones (i.e., empathy and compassion) [ 167 , 173 , 188 ]. Also in this case, the former evidence may reflect an overall decline in executive control and fluid intelligence [ 189 ], associated with reduced activity of the dorsolateral PFC [ 167 ]. Additionally, older adults seem to shift their motivations: according to the socioemotional selectivity theory they disengage their focus from future-oriented goals and prioritize social and emotional meaningful activities by selectively allocating more resources on emotional processing and emotion regulation strategies [ 190 ]. This view is supported by a study reporting age-related neurostructural changes in 883 healthy individuals. While cortical thickness decreased with age in brain regions related to executive functioning, such as the dorsal ACC alongside the superior and lateral sectors of the PFC, no age affect was found in regions typically engaged in emotion regulation, such as the ventromedial PFC and ventral ACC [ 191 ].

3.3. Age-Related Changes in Social Decision-Making

Alongside an enhancement of affective processing skills, different facets of social behavior and decision-making, like generativity and prosociality, undergo substantial changes with age.

Generativity, i.e., the tendency to expand the individual focus of concern beyond oneself [ 192 ], becomes a prominent challenge in late life, prompting the desire of cooperation between generations and the need for older adults to offer emotional support and mediate conflicts, which are perceived as essential goals for survival (Gurven and Kaplan, 2009). Compared with young people, older adults endorse more generative goals and other-focused problem solving [ 193 ]. Moreover, both the feeling of pity and the willingness to help others seem to progressively increase with age [ 194 ].

Closely related to social affective skills, also the tendency to prosociality seems to increase in late life [ 195 ]. In line with the socioemotional selectivity theory , contexts relevant to social and affective goals might motivate older adults, even more than younger ones, to help others, since empathy and/or compassion represent powerful skills capable of promoting prosocial behaviors [ 195 ]. This is the core of “empathic concern” [ 196 ], whereby acting to benefit needy others can mitigate the negative emotional arousal induced by experiencing their needs. The enhancement of emotion regulation skills might thus mediate the higher prosociality displayed by older adults, ultimately increasing their well-being, satisfaction and emotional fulfillment [ 193 ].

4. Altered Social Cognition in Neurodegenerative Diseases

Increasing evidence highlights a variety of social cognitive impairments in different neurological (e.g., neurodegenerative diseases, traumatic brain injuries, and brain tumors) and psychiatric (e.g., mood disorders, autism, and schizophrenia) conditions [ 197 – 200 ]. These alterations are mainly associated with the functional consequences of neuropathological processes or brain lesions affecting regions and networks underlying social cognition skills.

Within the realm of neurodegenerative diseases, pathological changes in social cognition and behavior are a major hallmark of the frontotemporal dementia (FTD) disease spectrum, including the primary progressive aphasias (i.e., semantic, nonfluent, and logopenic variants) [ 201 ] and the behavioral variant (bvFTD) [ 202 ]. Due to the progressive degeneration of frontobasal and limbic networks associated with the processing of emotional and social cues [ 203 – 207 ], bvFTD represents a prototypical example of the breakdown of social cognition. A marked neurocognitive impairment has been reported, in this disease, in all the domains previously discussed, from emotion recognition and social understanding to judgment involving social dilemmas and violations (Elamin et al., 2013). Despite similar deficits in emotion recognition and social understanding [ 208 , 209 ], bvFTD and both the semantic and the nonfluent FTD variants have been associated with different patterns of structural damage within a frontoinsular-temporal network which is also known as “social context network” [ 210 ]. This model is based on the notion that different social cognitive processes are encapsulated into specific context circumstances, having an intrinsic social meaning. Specific patterns of social cognitive impairment, typical of neurological and psychiatric diseases, might thus arise from selective dysfunctions within a distributed network causing a global impairment in the processing of social context information. This network is considered to include three main hubs with specific functions, i.e., (1) frontal areas, supporting the updating of context cues to make predictions; (2) temporal cortex, underlying the consolidation of value-based learning of contextual associations; (3) insular cortex, managing the convergence between emotional and cognitive states related to the coordination between external and internal milieus and thus facilitating frontotemporal interactions in processing social contexts.

Further cues into the abnormal social brain come from the literature revolving around the FTD-Amyotrophic Lateral Sclerosis (ALS) continuum hypothesis [ 211 ]. The growing evidence on the neuropathological, genetic, neuroimaging, and clinical commonalities between the two conditions [ 212 – 217 ] now includes social cognitive deficits, which have been revealed also in ALS patient without dementia [ 218 ].

Social cognitive impairments have been described also in other neurodegenerative disorders. Although these symptoms are not considered central or typical expressions of these diseases, the impairment can involve one or more of the domains reviewed before. As discussed in the next paragraphs, social perception and social understanding are, to date, the most frequently investigated domains in neurodegenerative disorders.

4.1. Altered Social Perception in Neurodegenerative Diseases

With respect to social perception, evidence exists for abnormal visual and/or auditory (i.e., based on prosodic cues) recognition of basic emotions, especially involving negative emotions, in bvFTD [ 219 – 222 ]. Interestingly, emotion recognition from faces discriminates bvFTD from other neurodegenerative, as well as psychiatric, diseases [ 207 ]. However, abnormal affective processing and emotion recognition (particularly for negative emotions) have been found also in other disorders (Elamin et al., 2013) [ 223 ], such as ALS [ 126 , 224 ], Parkinson's disease [ 225 ], corticobasal syndrome and progressive supranuclear palsy [ 226 ], and Huntington's disease [ 223 , 227 ], as well as Alzheimer's disease (AD) and mild cognitive impairment, particularly when subtle or static emotional stimuli are presented [ 8 , 228 ].

4.2. Altered Social Understanding in Neurodegenerative Diseases

The studies addressing social understanding in neurodegenerative diseases are contributing to unveil a complex scenario, with different disorders reflecting in distinct patterns of functional impairment. Defective mentalizing skills have been reported in bvFTD and AD [ 229 ]. However, while in AD this deficit likely reflects a global cognitive breakdown, bvFTD patients display a relatively selective impairment in affective mentalizing [ 229 ], likely reflecting their marked difficulties with empathic abilities [ 230 ]. In line with the continuum hypothesis, this pattern has been also described in a subset of ASL patients displaying a prominent impairment in the processing of emotional cues (Cerami et al., 2013) [ 127 ]. In Parkinson's disease, early mentalizing deficits are accompanied by decreased empathic skills in the later disease stages, reflecting the progression of the pathology from the dorsolateral prefrontal to orbitofrontal circuits (Elamin et al., 2013). In Huntington's disease, the impairment of both cognitive and affective components of social understanding is often associated with the severity of executive decline and motor symptoms [ 223 ].

4.3. Altered Social Decision-Making in Neurodegenerative Diseases

Abnormal performance in tasks assessing individual decision-making has been described in different neurodegenerative diseases, such as FTD, AD, Parkinson's disease, and Huntington disease (see [ 231 ] for a review). Instead, the evidence on social decision-making in neurodegenerative diseases is still limited and mainly related to bvFTD and AD [ 223 ]. In particular, bvFTD patients display a significant reduction in the tendency to prosociality [ 232 ] and cooperative behavior [ 233 ] (O'Callagan et al., 2015). In line with the “social context network” model described above [ 210 ], such changes might reflect the damage in frontostriatal areas supporting the generation and update of predictions based on social contextual information.

5. Conclusions

The data reviewed here summarize the main results of social cognitive neuroscience in the attempt to unveil the brain networks underlying the humans' automatic disposition to make sense of others' behavior. While most of the initial efforts within this lively research field dealt with the “social brain” in healthy individuals, its most recent developments are concerned with identifying the changes associated with physiological aging or different pathological conditions. A growing literature shows that the multilevel approach of social cognitive neuroscience, connecting seemingly distinct drivers of human behavior such as hormones or prosocial motivations [ 234 ], constitutes a platform providing experimental paradigms for targeting specific social cognitive processes, as well as objective metrics for assessing their impairment, or the effectiveness of remediation procedures, in different neuropsychiatric diseases [ 7 ].

The advancements in parcellating social cognitive processes and their neural bases nowadays allow design interventions based on robust evidence at the level of the construct of interest (e.g., face processing), or of deeper neurobiological mechanisms such as the modulation of amygdala activity by oxytocin (Ebert and Brune, 2017). The complexity of social cognition and its multifaceted nature indeed reflect in the variety of different remediation procedures which have been already proposed to improve social skills and to assess their impact beyond the trained process. Different approaches aim to improve either basic cognitive skills, to increase relational competence via training strategies underlying the analysis of social context and emotional information (i.e., “wide interventions; Peyroux and Frank, 2014), or specific components of social cognition such as emotion recognition [ 235 ], mentalizing [ 236 ], or empathy (Klimecki et al., 2013) (i.e., “targeted interventions”), particularly in schizophrenia [ 237 ] and autism [ 238 , 239 ]. Meta-analytic results highlight moderate training effects on emotion recognition and mentalizing, with such improvements being transferred to daily social life [ 240 ], but also limited success in remediating more complex, higher-order social cognitive functions [ 241 ]. Possible explanations for this negative evidence might include the lack of consideration of basic cognitive impairments and of real-world social situations characterized by a basic property of social cognition such as the mutual interdependence between agents. As previously discussed ( Section 2.2.1 ), the potential implications of novels paradigms entailing real or virtual social interactions represent one of the most promising challenges for social neuroscience [ 106 ], already supported by positive outcomes in neurological patients [ 108 ].

More generally, the available evidence suggests that the effectiveness of social cognitive remediation depends on “baseline” skills and that successful programs require adapting management strategies based on individual profiles. A detailed description of social cognitive processes and their neural correlates is thus critical to tailor remediation protocols to target specific brain networks and their associated cognitive functions. By summarizing the extensive available evidence on the neural bases of social cognition, the present review highlights specific domains which should be evaluated in pathological populations, taken into consideration when designing novel tests [ 242 , 243 ] or rehabilitation procedures [ 244 ], and addressed in original studies. As in all areas of empirical research, the quality of the answers depends on the quality of the questions. This is one of the main reasons why the increasing interaction among social and clinical as well as basic and translational research areas represents one of the most exciting developments within cognitive neuroscience.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Social Cognitive Theory Research Paper

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1. Social Cognitive Theory

Human behavior has often been explained in terms of one-sided causation. In these unidirectional models, behavior is depicted as being predominantly shaped and controlled by environmental influences, or impelled by inner drives and dispositions. Social cognitive theory explains human functioning in terms of triadic reciprocal causation (Bandura 1986). In this model of reciprocal determinism personal determinants in the form of cognitive, biological, and emotional factors, behavior patterns, and environmental events all operate as interacting determinants that influence each other bidirectionally.

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Get 10% off with 24start discount code, 2. agentic perspective.

Social cognitive theory is rooted in an agentic perspective. People are self-organizing, proactive, self-reflecting, and self-regulating, not just reactive organisms shaped and shepherded by external events. Human adaptation and change are rooted in social systems. Therefore, personal agency operates within a broad network of sociostructural influences. In these agentic transactions, people are producers as well as products of social systems. Sociostructural and personal determinants are treated as co-factors within a unified causal structure rather than as rival conceptions of human behavior.

3. Fundamental Human Capabilities

In social cognitive theory, people are characterized in terms of a number of fundamental capabilities. These are enlisted in effecting personal and social change.

3.1 Symbolizing Capability

The extraordinary capacity to represent events and their conditional relations in symbolic form provides humans with a powerful tool for comprehending their environment and for creating and managing environmental conditions that touch virtually every aspect of their lives. Most environmental events exert their effects indirectly, through cognitive processing, rather than directly. Cognitive factors partly determine which environmental events are observed, what meaning is conferred on them, what emotional impact and motivating power they have, and how the information they convey is organized and preserved for future use. Through the medium of symbols, people transform transient experiences into cognitive models that serve as guides for reasoning and action. By symbolizing their experiences, people give structure, meaning, and continuity to their lives. The sociocognitive methods for promoting personal and social change draw heavily on this symbolizing capability.

If put to faulty use, the capacity for symbolization can cause distress. Indeed, many human dysfunctions and torments stem from problems of thought. People live in a psychic environment largely of their own making. In their thoughts, they often dwell on painful pasts and on perturbing futures of their own invention. They burden themselves with stressful arousal through apprehensive rumination, debilitate their efforts by self-impeding ideation, drive themselves to despondency by harsh self-evaluation and dejecting modes of thinking, and often act on misconceptions that get them into trouble. Thought can thus be a source of human failings and distress as well as a source of human accomplishments. Faulty styles of thinking can be changed to some extent by verbal analysis of faulty inferences from personal experiences. However, corrective and enabling mastery experiences are more persuasive than talk alone in altering faulty beliefs and dysfunctional styles of thinking (Bandura 1986, 1997, Rehm 1988, Williams 1990).

3.2 Vicarious Capability

There are two basic modes of learning. People learn by experiencing the effects of their actions, and through the power of social modeling. Trial and error learning is a tedious and hazardous process. Fortunately, this process can be short cut by social modeling. Humans have evolved an advanced capacity for observational learning that enables them to expand their knowledge and competencies rapidly through the information conveyed by the rich variety of models (Bandura 1986, Rosenthal and Zimmerman 1978).

Modeling is not simply a process of response mimicry as commonly believed. Modeled activities convey rules for generative behavior. Once observers extract the rules underlying the modeled activities they can generate new patterns of behavior that go beyond what they have seen or heard. Self-regulatory and other cognitive skills can also be developed by using models to verbalize plans, action strategies, and self-guidance to counteract self-debilitating thought patterns, as solutions to problems are worked through (Meichenbaum 1984). Deficient and faulty habits of thought are corrected as participants adopt and enact the verbal self-guidance.

In addition to cultivating new competencies, modeling influences can alter incentive motivation, emotional proclivities, and value systems (Bandura 1986). Seeing others achieve desired outcomes by their efforts can instill motivating outcome expectations in observers that they can secure similar benefits for comparable performances; seeing others punished for engaging in certain activities can instill negative outcome expectations that serve as disincentives. Observers can also acquire lasting attitudes and emotional and behavioral proclivities toward persons, places, or things that have been associated with modeled emotional experiences. Observers learn to fear the things that frightened models, to dislike what repulsed them, and to like what gratified them.

During the course of their daily lives, people have direct contact with only a small sector of the physical and social environment. As a result, their conceptions of social reality are greatly influenced by modeled representations of society, mainly by the mass media (Gerbner 1972). Video and computer systems feeding off telecommunications satellites are now rapidly diffusing new ideas, values, and styles of conduct worldwide. At the societal level, symbolic modeling is transforming how social systems operate and serving as a major vehicle for sociopolitical change (Bandura 1997). As Braithwaite (1994) has shown, the speed with which Eastern European rulers and regimes were toppled was greatly accelerated by televised modeling of successful mass action.

The vast body of knowledge on modeling processes is being widely applied for personal development, therapeutic purposes, and social change (Bandura 1997, Bandura and Rosenthal 1978, Rosenthal and Steffek 1991). For human problems that stem from sociocognitive deficits, the guided mastery approach that is highly effective in cultivating personal efficacy and psychosocial competencies combines three components. First, the appropriate competencies are modeled in a stepwise fashion to convey the basic rules and strategies. Second, the learners receive guided practice under simulated conditions to develop proficiency in the skills. Third, they are provided with a graduated transfer program that helps them to apply their newly learned skills in their everyday lives in ways that will bring them success. In ameliorating anxiety and phobic dysfunction, the modeling provides coping strategies for managing threats. One can eventually overcome fears without the aid of modeling through repeated unscathed contact with threats, although it takes longer and is more stressful. However, modeling of cognitive and behavioral skills is a key ingredient in the development of complex competencies.

Sociocognitive approaches rely on mastery experiences as the principal vehicle of change. The enabling power of social modeling is enhanced by guided mastery enactments. When people avoid what they dread, they lose touch with the reality they shun. Guided mastery treatment quickly restores reality testing in two ways. It provides disconfirming tests of phobic beliefs by persuasive demonstrations that what phobics dread is safe. Even more importantly, it provides confirmatory tests that phobics can exercise control over what they fear. Intractable phobics, of course, are not about to do what they dread. Therapists must, therefore, create environmental conditions that enable phobics to succeed despite themselves. This is achieved by enlisting a variety of performance-mastery aids. Threatening activities are repeatedly modeled to demonstrate coping strategies and to disconfirm people’s worst fears. Intimidating tasks are reduced to graduated subtasks of easily mastered steps. Joint performance with the therapist enables frightened people to do things they would refuse to do on their own. Another method for overcoming resistance is for phobics to perform the feared activity for only a short time. As they become bolder the length of involvement is extended. Protective conditions can be introduced to weaken resistance that retards change. After effective functioning is fully restored, increasingly challenging self-directed mastery experiences are then arranged to strengthen and generalize the sense of coping efficacy. Varied mastery experiences build resiliency by reducing vulnerability to the negative effects of adverse experiences. This is a powerful treatment that eliminates phobias, anxiety arousal, biochemical stress reactions, and wipes out recurrent nightmares and intrusive rumination (Bandura 1997, Williams 1990).

Symbolic modeling lends itself readily for society-wide applications through creative use of the electronic media. For example, the soaring population growth and the environmental devastation it produces is the most urgent global problem. Worldwide use of enabling and motivating dramatic serials is raising people’s efficacy to exercise control over their family lives, enhancing the status of women to have some say in how they live their lives, and lowering the rates of childbearing (Singhal and Rogers 1999, Vaughan et al. 1995).

3.3 Self-Regulatory Capability

People are self-reactors with a capacity to motivate, guide, and regulate their activities through the anticipative mechanism of forethought. People set goals for themselves, anticipate the likely consequences of prospective actions, and plan courses of action that are likely to produce desired outcomes and avoid detrimental ones. The projected future is brought into the present through forethought. A forethoughtful perspective provides direction, coherence, and meaning to one’s life.

Much human motivation and behavior is regulated anticipatorily by the material and social outcomes expected for given courses of action. Social cognitive theory broadens this functionalism to include self-evaluative outcomes. People do things that give them satisfaction and a sense of self-worth, and refrain from actions that evoke self-devaluative reactions. Incentive systems are enlisted, if needed, to promote and help sustain personal and social change (Bandura 1986, 1998, O’Leary and Wilson 1987).

In keeping with the agentic perspective of social cognitive theory, the greatest benefits that psycho- logical treatments can bestow are not specific remedies for particular problems, but the self-regulatory capabilities needed to deal effectively with whatever situations might arise. To the extent that treatment equips people to exercise influence over events in their lives, it initiates an ongoing process of self- regulative change.

Personal standards for judging and guiding one’s actions play a major role in self-motivation and in the exercise of self-directedness (Bandura 1991, Locke and Latham 1990). Self-regulatory control is achieved by creating incentives for one’s own actions and by anticipative reactions to one’s own behavior depending on how it measures up to personal standards. Sociocognitive principles of self-regulation provide explicit guidelines for self-motivation, personal development, and modification of detrimental styles of behavior.

A favorable self-regulatory system provides a continuing source of motivation, self-directedness, and personal satisfaction, but a dysfunctional self-system can breed much human misery. For people who adopt stringent personal standards, most of their accomplishments bring them a sense of failure and self-disparagement (Bandura 1997, Rehm 1988). In its more extreme forms, harsh standards of self-evaluation give rise to despondency, chronic discouragement, and feelings of worthlessness and lack of purposefulness. For example, Hemingway, who took his own life, imposed upon himself demands that were unattainable throughout his life, pushed himself to extraordinary feats, and constantly demeaned his accomplishments.

Effective treatments for despondency arising from stringent self-imposed standards remedy each of the dysfunctional aspects of the self-system (Rehm 1981). They correct self-belittling interpretative biases that minimize one’s successes and accentuate one’s failures. To increase self-satisfaction and a sense of personal accomplishment, participants are taught how to set themselves attainable subgoals in activities of personal significance and to focus their efforts and self-evaluation on progress toward their aspirations. Through proximal structuring, goals become motivating rather than demoralizing. Depressed individuals tend to be less self-rewarding for successes and more denying and self-punishing for failures than the nondepressed for similar performances. They are taught how to be more self-rewarding for their progressive personal attainments.

Deficient or deviant standards also create problems, although the distress is inflicted on others rather than on oneself. Unprincipled individuals who pursue an ethic of expediency, and those who pride themselves in antisocial activities readily engage in conduct that is socially injurious (Bandura 1991). Antisocial and transgressive styles of conduct have diverse sources requiring multifaceted approaches to personal change. Successful treatments promote the development of competencies and prosocial self-regulatory standards and styles of behavior sufficiently attractive to supplant antisocial ones (Bandura 1973, Reid and Patterson 1991, Silbert 1984). Education provides the best escape from crime and poverty. Promoting educational development is, therefore, a key factor in reducing crime and delinquency.

3.4 Self-Reflective Capability

Among the mechanisms of personal agency none is more central or pervasive than people’s beliefs in their capability to exercise control over their own functioning and over environmental events (Bandura 1997). Efficacy beliefs are the foundation of human agency. Unless people believe that they can produce desired results by their actions, they have little incentive to act or to persevere in the face of difficulties. Meta-analyses, which combine the findings of numerous studies, attest to the influential role played by efficacy beliefs in human adaptation (Holden 1991, Holden et al. 1990, Multon et al. 1991, Stajkovic and Luthans 1998).

Self-efficacy beliefs regulate human functioning through their impact on cognitive, motivational, emotional, and choice processes. They determine whether people think pessimistically or optimistically, and in self-enhancing or self-debilitating ways. Efficacy beliefs play a central role in the self-regulation of motivation through goal challenges and outcome- expectations. Whether people act on the outcomes they expect prospective performances to produce depends on their beliefs about whether or not they can produce those performances. It is partly on the basis of efficacy beliefs that people choose what challenges to undertake, how much effort to expend in the endeavor, how long to persevere in the face of obstacles and failures, and whether failures are motivating or demoralizing. Efficacy beliefs play a key role in shaping the courses lives take by influencing the types of activities and environments people choose to enter. By choosing their environments, people can have a hand in what they become.

People’s beliefs in their coping capabilities also play a pivotal role in the self-regulation of affective states (Bandura 1997). Efficacy beliefs influence how potential threats are perceived and cognitively processed. If people believe they can manage threats they are not distressed by them. But if they believe they cannot control potential threats they experience high anxiety. Through inefficacious thinking they distress them- selves and constrain and impair their functioning.

Many human distresses result from failures of thought control. It is not the sheer frequency of disturbing cognitions, but the perceived inability to turn them off that is the major source of distress. Similarly, in obsessional disorders, it is not the ruminations, per se, but the perceived inefficacy to stop them that is perturbing.

Phobics display high anxiety and physiological stress to threats they believe they cannot control. After their perceived efficacy is raised to the maximal level by guided mastery experiences, they manage the same threats with equanimity.

It was widely assumed that phobic behavior was controlled by anxiety. Many therapeutic procedures were, therefore, keyed to extinguishing anxiety arousal. The anxiety control theory has not withstood experimental scrutiny, however. A low sense of coping efficacy produces both anxiety and phobic behavior. People often perform activities even though highly anxious, as long as they believe they can master them. Intense stage fright, for example, does not stop actors from going on stage. Conversely, people avoid situations they believe exceed their coping capabilities without waiting for visceral arousal to tell them to do so. Williams (1992) has shown that people base their actions on efficacy beliefs in situations they regard as risky not on anxiety arousal.

A low sense of efficacy to exercise control over things one values can give rise to feelings of futility and despondency through several pathways. In one pathway, a low sense of efficacy to fulfill personal standards of worth gives rise to self-devaluation and depression. A second pathway occurs through a low sense of social efficacy to develop social relationships that bring satisfaction to people’s lives and enable them to manage chronic stressors. Another is through the exercise of control over depressing thoughts themselves. Low efficacy to regulate ruminative thought contributes to the occurrence of depressive episodes, how long they last, and how often they recur.

People’s beliefs about their efficacy are constructed from four principal sources of information. The most effective way of instilling a strong sense of efficacy is through mastery experiences. Successes build a robust belief in one’s personal efficacy. Failures undermine it. Development of resilient self-efficacy requires experiences in overcoming obstacles through perseverant effort. The second method is by social modeling. Models serve as sources of competencies and motivation. Seeing people similar to oneself succeed by perseverant effort raises observers’ beliefs in their own capabilities. Social persuasion is the third mode of influence. The fourth way of altering self-efficacy beliefs is to enhance physical strength and stamina and alter mood states on which people partly judge their capabilities.

These different modes of influence create and strengthen beliefs of personal efficacy across diverse spheres of functioning. Such beliefs predict the level, scope, and durability of behavioral changes (Bandura 1997). Microanalyses of how given treatments work, reveal that the efficacy belief system is a common pathway through which different types of treatment produce their effects. Knowledge of the determinants and processes governing the formation of efficacy beliefs provides explicit guidelines on how best to structure programs to achieve desired change.

4. Sociostructural Change

Many human problems are sociostructural, not simply individual. Common problems require social solutions through collective action. Social cognitive theory extends the conception of human agency to collective agency (Bandura 1997). People’s shared belief in their collective power to produce desired results is a key ingredient of collective agency. The stronger the people’s perceived collective efficacy the higher their aspirations and motivational investment in their undertakings, the stronger their staying power in the face of impediments and setbacks, the higher their morale and resilience to adversity, and the greater their accomplishments.

Efforts to improve the human condition must, therefore, be directed not only at treating the casualties of adverse social practices, but also at altering the social practices producing the causalities. The models of social change derived from social cognitive theory (Bandura 1986, 1997), draw heavily on knowledge of modeling, motivational, regulatory, and efficacy mechanisms operating at the collective level.

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