Reward-specific learning parameters change across normative adolescent development and are blunted in youth with high risk for depression / Holly SULLIVAN-TOOLE in Journal of Child Psychology and Psychiatry, 67-6 (June 2026)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 67-6 (June 2026) . - p.963-975 Titre : Reward-specific learning parameters change across normative adolescent development and are blunted in youth with high risk for depression Type de document : texte imprimé Auteurs : Holly SULLIVAN-TOOLE, Auteur ; Jeremy M. HAYNES, Auteur ; Helen SCHMIDT, Auteur ; Bart LARSEN, Auteur ; Nathaniel HAINES, Auteur ; Thomas M. OLINO, Auteur Article en page(s) : p.963-975 Langues : Anglais (eng) Mots-clés : reward learning  reinforcement learning  adolescence  learning rate  maternal depression  Iowa Gambling Task Index. décimale : PER Périodiques Résumé : Background Reward learning is thought to undergo refinement in adolescence, but little is known about how computational components of reinforcement learning develop. Given that adolescence is a sensitive period for reward system plasticity with associated vulnerability for depression, it is important to understand developmental trajectories of different reinforcement learning parameters in normative development and in youth at risk for depression. Methods Youth aged 9?17?years completed the Play-or-Pass Iowa Gambling Task (PoP-IGT) across five timepoints. We calculated task metrics using a traditional scoring approach ? yielding summary scores for good deck play, bad deck play, and net play ? and a computational modeling approach ? yielding parameters for reward learning rate, punishment learning rate, go bias, and sensitivity to win/loss frequency ignoring outcome magnitude. We examined normative developmental trajectories for each traditional and computational performance metric using multilevel models. Further, we examined whether maternal history of depression was associated with individual differences in these trajectories. Results As hypothesized, youth showed a significant age-related increase in net play (p?=?0.003), a measure of overall good performance. Exploratory analyses found that youth showed significant developmental change in reward-specific learning parameters including age-related increases in win/loss frequency sensitivity (FDR ?=?0.016) and age-related decreases in reward learning rate (FDR ? En ligne : https://doi.org/10.1111/jcpp.70086 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=587 [article] Reward-specific learning parameters change across normative adolescent development and are blunted in youth with high risk for depression [texte imprimé] / Holly SULLIVAN-TOOLE, Auteur ; Jeremy M. HAYNES, Auteur ; Helen SCHMIDT, Auteur ; Bart LARSEN, Auteur ; Nathaniel HAINES, Auteur ; Thomas M. OLINO, Auteur . - p.963-975. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 67-6 (June 2026) . - p.963-975 Mots-clés : reward learning  reinforcement learning  adolescence  learning rate  maternal depression  Iowa Gambling Task Index. décimale : PER Périodiques Résumé : Background Reward learning is thought to undergo refinement in adolescence, but little is known about how computational components of reinforcement learning develop. Given that adolescence is a sensitive period for reward system plasticity with associated vulnerability for depression, it is important to understand developmental trajectories of different reinforcement learning parameters in normative development and in youth at risk for depression. Methods Youth aged 9?17?years completed the Play-or-Pass Iowa Gambling Task (PoP-IGT) across five timepoints. We calculated task metrics using a traditional scoring approach ? yielding summary scores for good deck play, bad deck play, and net play ? and a computational modeling approach ? yielding parameters for reward learning rate, punishment learning rate, go bias, and sensitivity to win/loss frequency ignoring outcome magnitude. We examined normative developmental trajectories for each traditional and computational performance metric using multilevel models. Further, we examined whether maternal history of depression was associated with individual differences in these trajectories. Results As hypothesized, youth showed a significant age-related increase in net play (p?=?0.003), a measure of overall good performance. Exploratory analyses found that youth showed significant developmental change in reward-specific learning parameters including age-related increases in win/loss frequency sensitivity (FDR ?=?0.016) and age-related decreases in reward learning rate (FDR ? En ligne : https://doi.org/10.1111/jcpp.70086 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=587

Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research / Nathaniel HAINES in Development and Psychopathology, 31-3 (August 2019)  

Public ISBD [article]  inDevelopment and Psychopathology > 31-3 (August 2019) . - p.871-886 Titre : Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research Type de document : texte imprimé Auteurs : Nathaniel HAINES, Auteur ; Ziv BELL, Auteur ; Sheila E. CROWELL, Auteur ; Hunter HAHN, Auteur ; Dana KAMARA, Auteur ; Heather MCDONOUGH-CAPLAN, Auteur ; Tiffany SHADER, Auteur ; Theodore P. BEAUCHAINE, Auteur Article en page(s) : p.871-886 Langues : Anglais (eng) Mots-clés : arousal  emotion dysregulation  facial expression  negative valence system  positive valence system Index. décimale : PER Périodiques Résumé : As early as infancy, caregivers’ facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal—core Research Domain Criteria constructs—as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother–daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation. En ligne : http://dx.doi.org/10.1017/S0954579419000312 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=403 [article] Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research [texte imprimé] / Nathaniel HAINES, Auteur ; Ziv BELL, Auteur ; Sheila E. CROWELL, Auteur ; Hunter HAHN, Auteur ; Dana KAMARA, Auteur ; Heather MCDONOUGH-CAPLAN, Auteur ; Tiffany SHADER, Auteur ; Theodore P. BEAUCHAINE, Auteur . - p.871-886. Langues : Anglais (eng) in Development and Psychopathology > 31-3 (August 2019) . - p.871-886 Mots-clés : arousal  emotion dysregulation  facial expression  negative valence system  positive valence system Index. décimale : PER Périodiques Résumé : As early as infancy, caregivers’ facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal—core Research Domain Criteria constructs—as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother–daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation. En ligne : http://dx.doi.org/10.1017/S0954579419000312 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=403

---

1 (1 - 2 / 2) Par page : 25 50 100 200