Research has demonstrated that extensive structural and functional brain development continues throughout adolescence. A popular notion emerging from this work states that a relative immaturity in frontal cortical neural systems could explain adolescents' high rates of risk-taking, substance use and other dangerous behaviours. However, developmental neuroimaging studies do not support a simple model of frontal cortical immaturity. Rather, growing evidence points to the importance of changes in social and affective processing, which begin around the onset of puberty, as crucial to understanding these adolescent vulnerabilities. These changes in social-affective processing also may confer some adaptive advantages, such as greater flexibility in adjusting one's intrinsic motivations and goal priorities amidst changing social contexts in adolescence.

Understanding adolescence as a period of social–affective engagement and goal flexibility.

Adolescence is a period of formative biological and social transition. Social cognitive processes involved in navigating increasingly complex and intimate relationships continue to develop throughout adolescence. Here, we describe the functional and structural changes occurring in the brain during this period of life and how they relate to navigating the social environment. Areas of the social brain undergo both structural changes and functional reorganization during the second decade of life, possibly reflecting a sensitive period for adapting to one's social environment. The changes in social environment that occur during adolescence might interact with increasing executive functions and heightened social sensitivity to influence a number of adolescent behaviors. We discuss the importance of considering the social environment and social rewards in research on adolescent cognition and behavior. Finally, we speculate about the potential implications of this research for society.

Is Adolescence a Sensitive Period for Sociocultural Processing

Given assertions of the theoretical, empirical, and practical importance of self-control, this meta-analytic study sought to review evidence concerning the relationship between dispositional self-control and behavior. The authors provide a brief overview over prominent theories of self-control, identifying implicit assumptions surrounding the effects of self-control that warrant empirical testing. They report the results of a meta-analysis of 102 studies (total N = 32,648) investigating the behavioral effects of self-control using the Self-Control Scale, the Barratt Impulsiveness Scale, and the Low Self-Control Scale. A small to medium positive effect of self-control on behavior was found for the three scales. Only the Self-Control Scale allowed for a fine-grained analysis of conceptual moderators of the self-control behavior relation. Specifically, self-control (measured by the Self-Control Scale) related similarly to the performance of desired behaviors and the inhibition of undesired behaviors, but its effects varied dramatically across life domains (e.g., achievement, adjustment). In addition, the associations between self-control and behavior were significantly stronger for automatic (as compared to controlled) behavior and for imagined (as compared to actual) behavior.

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Taking Stock of Self-Control A Meta-Analysis of How Trait Self-Control Relates to a Wide Range of Behaviors

Contents Historical, Contemporary, and Future Perspectives on Self-Regulated Learning and Performance Dale H. Schunk and Jeffrey A. Greene Section I. Basic Domains of Self-Regulation of Learning and Performance Social Cognitive Theoretical Perspective of Self-Regulation Ellen L. Usher and Dale H. Schunk Cognition and Metacognition Within Self-Regulated Learning Philip H. Winne Developmental Trajectories of Skills and Abilities Relevant for Self-Regulation of Learning and Performance Rick H. Hoyle and Amy L. Dent Motivation and Affect in Self-Regulated Learning: Does Metacognition Play a Role? Anastasia Efklides, Bennett L. Schwartz, and Victoria Brown Self-Regulation, Co-Regulation and Shared Regulation in Collaborative Learning Environments Allyson Hadwin, Sanna Jarvela, and Mariel Miller Section II. Self-Regulation of Learning and Performance in Context Metacognitive Pedagogies in Mathematics Classrooms: From Kindergarten to College and Beyond Zemira R. Mevarech, Lieven Verschaffel, and Erik De Corte Self-Regulated Learning in Reading Keith W. Thiede and Anique B. H. de Bruin Self-Regulation and Writing Steve Graham, Karen R. Harris, Charles MacArthur, and Tanya Santangelo The Self-Regulation of Learning and Conceptual Change in Science: Research, Theory, and Educational Applications Gale M. Sinatra and Gita Taasoobshirazi Using Technology-Rich Environments to Foster Self-Regulated Learning in the Social Studies Eric G. Poitras and Susanne P. Lajoie Self-Regulated Learning in Music Practice and Performance Gary E. McPherson, Peter Miksza, and Paul Evans Self-Regulation in Athletes: A Social Cognitive Perspective Anastasia Kitsantas, Maria Kavussanu, Deborah B. Corbatto, and Pepijn K. C. van de Pol Self-Regulation: An Integral Part of Standards-Based Education Marie C. White and Maria K. DiBenedetto Teachers as Agents in Promoting Students' SRL and Performance: Applications for Teachers' Dual-Role Training Program Bracha Kramarski Section III. Technology and Self-Regulation of Learning and Performance Emerging Classroom Technology: Using Self-Regulation Principles as a Guide for Effective Implementation Daniel C. Moos Understanding and Reasoning About Real-Time Cognitive, Affective, and Metacognitive Processes to Foster Self-Regulation With Advanced Learning Technologies Roger Azevedo, Michelle Taub, and Nicholas V. Mudrick The Role of Self-Regulated Learning in Digital Games John L. Nietfeld Self-Regulation of Learning and Performance in Computer-Supported Collaborative Learning Environments Peter Reimann and Maria Bannert Section IV. Methodology and Assessment of Self-Regulation of Learning and Performance Validity and the Use of Self-Report Questionnaires to Assess Self-Regulated Learning Christopher A. Wolters and Sungjun Won Capturing and Modeling Self-Regulated Learning Using Think-Aloud Protocols Jeffrey A. Greene, Victor M. Deekens, Dana Z. Copeland, and Seung Yu Assessing Self-Regulated Learning Using Microanalytic Methods Timothy J. Cleary and Gregory L. Callan Advancing Research and Practice About Self-Regulated Learning: The Promise of In-Depth Case Study Methodologies Deborah L. Butler and Sylvie C. Cartier Examining the Cyclical, Loosely Sequenced, and Contingent Features of Self-Regulated Learning: Trace Data and Their Analysis Matthew L. Bernacki Data Mining Methods for Assessing Self-Regulated Learning Gautam Biswas, Ryan S. Baker, and Luc Paquette Section V. Individual and Group Differences in Self-Regulation of Learning and Performance 26. Calibration of Performance and Academic Delay of Gratification: Individual and Group Differences in Self-Regulation of Learning Peggy P. Chen and Hefer Bembenutty 27. Academic Help Seeking as a Self-Regulated Learning Strategy: Current Issues, Future Directions Stuart A. Karabenick and Eleftheria N. Gonida 28. The Three Faces of Epistemic Thinking in Self-Regulated Learning Krista R. Muis and Cara Singh 29. Advances in Understanding Young Children's Self-Regulation of Learning Nancy E. Perry, Lynda R. Hutchinson, Nikki Yee, and Elina Maatta 30. Self-Regulation: Implications for Individuals With Special Needs Linda H. Mason and Robert Reid 31. Culture and Self-Regulation in Educational Contexts Dennis M. McInerney and Ronnel B. King

Handbook of Self-Regulation of Learning and Performance

It has been hypothesized that reward-seeking and impulsivity develop along different timetables and have different neural underpinnings, and that the difference in their timetables helps account for heightened risk-taking during adolescence. In order to test these propositions, age differences in reward-seeking and impulsivity were examined in a socioeconomically and ethnically diverse sample of 935 individuals between the ages of 10 and 30, using self-report and behavioral measures of each construct. Consistent with predictions, age differences in reward-seeking follow a curvilinear pattern, increasing between preadolescence and mid-adolescence, and declining thereafter. In contrast, age differences in impulsivity follow a linear pattern, with impulsivity declining steadily from age 10 on. Heightened vulnerability to risk-taking in middle adolescence may be due to the combination of relatively higher inclinations to seek rewards and still maturing capacities for self-control.

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A Dual Systems Model of Adolescent Risk-Taking

Age differences in future orientation are examined in a sample of 935 individuals between 10 and 30 years using a delay discounting task as well as a new self-report measure. Younger adolescents consistently demonstrate a weaker orientation to the future than do individuals aged 16 and older, as reflected in their greater willingness to accept a smaller reward delivered sooner than a larger one that is delayed, and in their characterizations of themselves as less concerned about the future and less likely to anticipate the consequences of their decisions. Planning ahead, in contrast, continues to develop into young adulthood. Future studies should distinguish between future orientation and impulse control, which may have different neural underpinnings and follow different developmental timetables.

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Age Differences in Future Orientation and Delay Discounting

The presence of peers increases risk taking among adolescents but not adults. We posited that the presence of peers may promote adolescent risk taking by sensitizing brain regions associated with the anticipation of potential rewards. Using fMRI, we measured brain activity in adolescents, young adults, and adults as they made decisions in a simulated driving task. Participants completed one task block while alone, and one block while their performance was observed by peers in an adjacent room. During peer observation blocks, adolescents selectively demonstrated greater activation in reward-related brain regions, including the ventral striatum and orbitofrontal cortex, and activity in these regions predicted subsequent risk taking. Brain areas associated with cognitive control were less strongly recruited by adolescents than adults, but activity in the cognitive control system did not vary with social context. Results suggest that the presence of peers increases adolescent risk taking by heightening sensitivity to the potential reward value of risky decisions.

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Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry

Adolescents take more risks in the presence of their peers, but the mechanism through which peer presence affects risky decision-making is unknown. We propose that the presence of peers increases the salience of the immediate rewards of a risky choice. The current study examined the effect of peer presence on reward sensitivity in a sample of 100 late adolescents ages 18 through 20 (M=18.5) using a delay discounting task, which assesses an individual's preference for immediate versus delayed rewards. Participants were randomly assigned to complete the task alone or with 2 same-age, same-sex peers observing. Consistent with our prediction, adolescents demonstrated a greater preference for immediate rewards when with their peers than when alone. Heightened risk taking by adolescents in the company of their friends may be due in part to the effect that being with one's peers has on reward sensitivity.

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Lia O’Brien

Papers

Age Differences in Future Orientation and Delay Discounting

Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry

Adolescents Prefer More Immediate Rewards When in the Presence of their Peers