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Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking
The dual systems model posits that adolescent risk-taking results from an imbalance between a cognitive control system and an incentive processing system. Researchers interested in understanding the development of adolescent risk-taking use a diverse array of behavioral and self-report measures to i...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886471/ https://www.ncbi.nlm.nih.gov/pubmed/28082127 http://dx.doi.org/10.1016/j.dcn.2016.12.007 |
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author | Harden, K. Paige Kretsch, Natalie Mann, Frank D. Herzhoff, Kathrin Tackett, Jennifer L. Steinberg, Laurence Tucker-Drob, Elliot M. |
author_facet | Harden, K. Paige Kretsch, Natalie Mann, Frank D. Herzhoff, Kathrin Tackett, Jennifer L. Steinberg, Laurence Tucker-Drob, Elliot M. |
author_sort | Harden, K. Paige |
collection | PubMed |
description | The dual systems model posits that adolescent risk-taking results from an imbalance between a cognitive control system and an incentive processing system. Researchers interested in understanding the development of adolescent risk-taking use a diverse array of behavioral and self-report measures to index cognitive control and incentive processing. It is currently unclear whether different measures commonly interpreted as indicators of the same psychological construct do, in fact, tap the same underlying dimension of individual differences. In a diverse sample of 810 adolescent twins and triplets (M age = 15.9 years, SD = 1.4 years) from the Texas Twin Project, we investigated the factor structure of fifteen self-report and task-based measures relevant to adolescent risk-taking. These measures can be organized into four factors, which we labeled premeditation, fearlessness, cognitive dyscontrol, and reward seeking. Most behavioral measures contained large amounts of task-specific variance; however, most genetic variance in each measure was shared with other measures of the corresponding factor. Behavior genetic analyses further indicated that genetic influences on cognitive dyscontrol overlapped nearly perfectly with genetic influences on IQ (r(A) = −0.91). These findings underscore the limitations of using single laboratory tasks in isolation, and indicate that the study of adolescent risk taking will benefit from applying multimethod approaches. |
format | Online Article Text |
id | pubmed-6886471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-68864712019-12-03 Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking Harden, K. Paige Kretsch, Natalie Mann, Frank D. Herzhoff, Kathrin Tackett, Jennifer L. Steinberg, Laurence Tucker-Drob, Elliot M. Dev Cogn Neurosci Article The dual systems model posits that adolescent risk-taking results from an imbalance between a cognitive control system and an incentive processing system. Researchers interested in understanding the development of adolescent risk-taking use a diverse array of behavioral and self-report measures to index cognitive control and incentive processing. It is currently unclear whether different measures commonly interpreted as indicators of the same psychological construct do, in fact, tap the same underlying dimension of individual differences. In a diverse sample of 810 adolescent twins and triplets (M age = 15.9 years, SD = 1.4 years) from the Texas Twin Project, we investigated the factor structure of fifteen self-report and task-based measures relevant to adolescent risk-taking. These measures can be organized into four factors, which we labeled premeditation, fearlessness, cognitive dyscontrol, and reward seeking. Most behavioral measures contained large amounts of task-specific variance; however, most genetic variance in each measure was shared with other measures of the corresponding factor. Behavior genetic analyses further indicated that genetic influences on cognitive dyscontrol overlapped nearly perfectly with genetic influences on IQ (r(A) = −0.91). These findings underscore the limitations of using single laboratory tasks in isolation, and indicate that the study of adolescent risk taking will benefit from applying multimethod approaches. Elsevier 2016-12-26 /pmc/articles/PMC6886471/ /pubmed/28082127 http://dx.doi.org/10.1016/j.dcn.2016.12.007 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Harden, K. Paige Kretsch, Natalie Mann, Frank D. Herzhoff, Kathrin Tackett, Jennifer L. Steinberg, Laurence Tucker-Drob, Elliot M. Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking |
title | Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking |
title_full | Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking |
title_fullStr | Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking |
title_full_unstemmed | Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking |
title_short | Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking |
title_sort | beyond dual systems: a genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886471/ https://www.ncbi.nlm.nih.gov/pubmed/28082127 http://dx.doi.org/10.1016/j.dcn.2016.12.007 |
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