6 recherche sur le mot-clé 'Reinforcement learning'

Probabilistic reinforcement learning in adults with autism spectrum disorders / Marjorie SOLOMON in Autism Research, 4-2 (April 2011)  

Public ISBD [article]  inAutism Research > 4-2 (April 2011) . - p.109-120 Titre : Probabilistic reinforcement learning in adults with autism spectrum disorders Type de document : Texte imprimé et/ou numérique Auteurs : Marjorie SOLOMON, Auteur ; Anne C. SMITH, Auteur ; Michael J. FRANK, Auteur ; Stanford LY, Auteur ; Cameron S. CARTER, Auteur Année de publication : 2011 Article en page(s) : p.109-120 Langues : Anglais (eng) Mots-clés : autism spectrum disorders  probabilistic  reinforcement learning  basal ganglia  orbito-frontal cortex  computational model Index. décimale : PER Périodiques Résumé : Background: Autism spectrum disorders (ASDs) can be conceptualized as disorders of learning, however there have been few experimental studies taking this perspective. Methods: We examined the probabilistic reinforcement learning performance of 28 adults with ASDs and 30 typically developing adults on a task requiring learning relationships between three stimulus pairs consisting of Japanese characters with feedback that was valid with different probabilities (80%, 70%, and 60%). Both univariate and Bayesian state–space data analytic methods were employed. Hypotheses were based on the extant literature as well as on neurobiological and computational models of reinforcement learning. Results: Both groups learned the task after training. However, there were group differences in early learning in the first task block where individuals with ASDs acquired the most frequently accurately reinforced stimulus pair (80%) comparably to typically developing individuals; exhibited poorer acquisition of the less frequently reinforced 70% pair as assessed by state–space learning curves; and outperformed typically developing individuals on the near chance (60%) pair. Individuals with ASDs also demonstrated deficits in using positive feedback to exploit rewarded choices. Conclusions: Results support the contention that individuals with ASDs are slower learners. Based on neurobiology and on the results of computational modeling, one interpretation of this pattern of findings is that impairments are related to deficits in flexible updating of reinforcement history as mediated by the orbito-frontal cortex, with spared functioning of the basal ganglia. This hypothesis about the pathophysiology of learning in ASDs can be tested using functional magnetic resonance imaging. En ligne : http://dx.doi.org/10.1002/aur.177 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=121 [article] Probabilistic reinforcement learning in adults with autism spectrum disorders [Texte imprimé et/ou numérique] / Marjorie SOLOMON, Auteur ; Anne C. SMITH, Auteur ; Michael J. FRANK, Auteur ; Stanford LY, Auteur ; Cameron S. CARTER, Auteur . - 2011 . - p.109-120. Langues : Anglais (eng) in Autism Research > 4-2 (April 2011) . - p.109-120 Mots-clés : autism spectrum disorders  probabilistic  reinforcement learning  basal ganglia  orbito-frontal cortex  computational model Index. décimale : PER Périodiques Résumé : Background: Autism spectrum disorders (ASDs) can be conceptualized as disorders of learning, however there have been few experimental studies taking this perspective. Methods: We examined the probabilistic reinforcement learning performance of 28 adults with ASDs and 30 typically developing adults on a task requiring learning relationships between three stimulus pairs consisting of Japanese characters with feedback that was valid with different probabilities (80%, 70%, and 60%). Both univariate and Bayesian state–space data analytic methods were employed. Hypotheses were based on the extant literature as well as on neurobiological and computational models of reinforcement learning. Results: Both groups learned the task after training. However, there were group differences in early learning in the first task block where individuals with ASDs acquired the most frequently accurately reinforced stimulus pair (80%) comparably to typically developing individuals; exhibited poorer acquisition of the less frequently reinforced 70% pair as assessed by state–space learning curves; and outperformed typically developing individuals on the near chance (60%) pair. Individuals with ASDs also demonstrated deficits in using positive feedback to exploit rewarded choices. Conclusions: Results support the contention that individuals with ASDs are slower learners. Based on neurobiology and on the results of computational modeling, one interpretation of this pattern of findings is that impairments are related to deficits in flexible updating of reinforcement history as mediated by the orbito-frontal cortex, with spared functioning of the basal ganglia. This hypothesis about the pathophysiology of learning in ASDs can be tested using functional magnetic resonance imaging. En ligne : http://dx.doi.org/10.1002/aur.177 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=121

Punishment insensitivity and impaired reinforcement learning in preschoolers / Margaret J. BRIGGS-GOWAN in Journal of Child Psychology and Psychiatry, 55-2 (February 2014)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 55-2 (February 2014) . - p.154-161 Titre : Punishment insensitivity and impaired reinforcement learning in preschoolers Type de document : Texte imprimé et/ou numérique Auteurs : Margaret J. BRIGGS-GOWAN, Auteur ; Sara R. NICHOLS, Auteur ; Joel VOSS, Auteur ; Elvira ZOBEL, Auteur ; Alice S. CARTER, Auteur ; Kimberly J. MCCARTHY, Auteur ; Daniel S. PINE, Auteur ; James R. BLAIR, Auteur ; Lauren S. WAKSCHLAG, Auteur Article en page(s) : p.154-161 Langues : Anglais (eng) Mots-clés : Psychopathic tendencies  reinforcement learning  punishment insensitivity  low concern  early childhood  disruptive behavior  development Index. décimale : PER Périodiques Résumé : Background Youth and adults with psychopathic traits display disrupted reinforcement learning. Advances in measurement now enable examination of this association in preschoolers. The current study examines relations between reinforcement learning in preschoolers and parent ratings of reduced responsiveness to socialization, conceptualized as a developmental vulnerability to psychopathic traits. Methods One hundred and fifty-seven preschoolers (mean age 4.7 ± 0.8 years) participated in a substudy that was embedded within a larger project. Children completed the ‘Stars-in-Jars’ task, which involved learning to select rewarded jars and avoid punished jars. Maternal report of responsiveness to socialization was assessed with the Punishment Insensitivity and Low Concern for Others scales of the Multidimensional Assessment of Preschool Disruptive Behavior (MAP-DB). Results Punishment Insensitivity, but not Low Concern for Others, was significantly associated with reinforcement learning in multivariate models that accounted for age and sex. Specifically, higher Punishment Insensitivity was associated with significantly lower overall performance and more errors on punished trials (‘passive avoidance’). Conclusions Impairments in reinforcement learning manifest in preschoolers who are high in maternal ratings of Punishment Insensitivity. If replicated, these findings may help to pinpoint the neurodevelopmental antecedents of psychopathic tendencies and suggest novel intervention targets beginning in early childhood. En ligne : http://dx.doi.org/10.1111/jcpp.12132 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=221 [article] Punishment insensitivity and impaired reinforcement learning in preschoolers [Texte imprimé et/ou numérique] / Margaret J. BRIGGS-GOWAN, Auteur ; Sara R. NICHOLS, Auteur ; Joel VOSS, Auteur ; Elvira ZOBEL, Auteur ; Alice S. CARTER, Auteur ; Kimberly J. MCCARTHY, Auteur ; Daniel S. PINE, Auteur ; James R. BLAIR, Auteur ; Lauren S. WAKSCHLAG, Auteur . - p.154-161. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 55-2 (February 2014) . - p.154-161 Mots-clés : Psychopathic tendencies  reinforcement learning  punishment insensitivity  low concern  early childhood  disruptive behavior  development Index. décimale : PER Périodiques Résumé : Background Youth and adults with psychopathic traits display disrupted reinforcement learning. Advances in measurement now enable examination of this association in preschoolers. The current study examines relations between reinforcement learning in preschoolers and parent ratings of reduced responsiveness to socialization, conceptualized as a developmental vulnerability to psychopathic traits. Methods One hundred and fifty-seven preschoolers (mean age 4.7 ± 0.8 years) participated in a substudy that was embedded within a larger project. Children completed the ‘Stars-in-Jars’ task, which involved learning to select rewarded jars and avoid punished jars. Maternal report of responsiveness to socialization was assessed with the Punishment Insensitivity and Low Concern for Others scales of the Multidimensional Assessment of Preschool Disruptive Behavior (MAP-DB). Results Punishment Insensitivity, but not Low Concern for Others, was significantly associated with reinforcement learning in multivariate models that accounted for age and sex. Specifically, higher Punishment Insensitivity was associated with significantly lower overall performance and more errors on punished trials (‘passive avoidance’). Conclusions Impairments in reinforcement learning manifest in preschoolers who are high in maternal ratings of Punishment Insensitivity. If replicated, these findings may help to pinpoint the neurodevelopmental antecedents of psychopathic tendencies and suggest novel intervention targets beginning in early childhood. En ligne : http://dx.doi.org/10.1111/jcpp.12132 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=221

The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk preference between autistic and neurotypical youth / Kentaro KATAHIRA ; Hironori AKECHI ; Atsushi SENJU in Molecular Autism, 16 (2025)  

Public ISBD [article]  inMolecular Autism > 16 (2025) . - 3 Titre : The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk preference between autistic and neurotypical youth Type de document : Texte imprimé et/ou numérique Auteurs : Kentaro KATAHIRA, Auteur ; Hironori AKECHI, Auteur ; Atsushi SENJU, Auteur Article en page(s) : 3 Langues : Anglais (eng) Mots-clés : Humans  Adolescent  Child  Male  Female  Reinforcement, Psychology  Young Adult  Autistic Disorder/psychology  Adult  Choice Behavior  Risk-Taking  Learning  Autism  Developmental changes  Reinforcement learning  Risk preference  Surprise  the Declaration of Helsinki and was approved by the Committee on Ethics of  Experimental Research on Human Subjects, Graduate School of Arts and Sciences,  University of Tokyo (156-17). Consent for publication: Not applicable. Competing  interests: The authors declare no competing interests. Index. décimale : PER Périodiques Résumé : BACKGROUND: Risk preference changes nonlinearly across development. Although extensive developmental research on the neurotypical (NTP) population has shown that risk preference is highest during adolescence, developmental changes in risk preference in autistic (AUT) people, who tend to prefer predictable behaviors, have not been investigated. Here, we aimed to investigate these changes and underlying computational mechanisms. METHOD: We ran a game-like risk-sensitive reinforcement learning task on 75 participants aged 6-30 years (AUT group, n = 31; NTP group, n = 44). Focusing on choices between alternatives with the same objective value but different risks, we calculated the risk preference and stay probability of a risky choice after a rewarding or non-rewarding outcome. Analyses using t-tests and multiple regression analyses were conducted. Using the choice-related data of each participant, we fit four reinforcement learning models and compared the fit of each model to the data. Furthermore, we validated the results of model fitting with multiple methods, model recovery, parameter recovery, and posterior predictive check. RESULTS: We found a significant difference in nonlinear developmental changes in risk preference between the AUT and NTP groups. The computational modeling approach with reinforcement learning models revealed that individual preferences for surprise modulated such preferences. CONCLUSIONS: These findings indicate that for NTP people, adolescence is a developmental period involving risk preference, possibly due to lower surprise aversion. Conversely, for AUT people, who show opposite developmental change of risk preference, adolescence could be a developmental period involving risk avoidance because of low surprise preference. En ligne : https://dx.doi.org/10.1186/s13229-025-00637-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555 [article] The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk preference between autistic and neurotypical youth [Texte imprimé et/ou numérique] / Kentaro KATAHIRA, Auteur ; Hironori AKECHI, Auteur ; Atsushi SENJU, Auteur . - 3. Langues : Anglais (eng) in Molecular Autism > 16 (2025) . - 3 Mots-clés : Humans  Adolescent  Child  Male  Female  Reinforcement, Psychology  Young Adult  Autistic Disorder/psychology  Adult  Choice Behavior  Risk-Taking  Learning  Autism  Developmental changes  Reinforcement learning  Risk preference  Surprise  the Declaration of Helsinki and was approved by the Committee on Ethics of  Experimental Research on Human Subjects, Graduate School of Arts and Sciences,  University of Tokyo (156-17). Consent for publication: Not applicable. Competing  interests: The authors declare no competing interests. Index. décimale : PER Périodiques Résumé : BACKGROUND: Risk preference changes nonlinearly across development. Although extensive developmental research on the neurotypical (NTP) population has shown that risk preference is highest during adolescence, developmental changes in risk preference in autistic (AUT) people, who tend to prefer predictable behaviors, have not been investigated. Here, we aimed to investigate these changes and underlying computational mechanisms. METHOD: We ran a game-like risk-sensitive reinforcement learning task on 75 participants aged 6-30 years (AUT group, n = 31; NTP group, n = 44). Focusing on choices between alternatives with the same objective value but different risks, we calculated the risk preference and stay probability of a risky choice after a rewarding or non-rewarding outcome. Analyses using t-tests and multiple regression analyses were conducted. Using the choice-related data of each participant, we fit four reinforcement learning models and compared the fit of each model to the data. Furthermore, we validated the results of model fitting with multiple methods, model recovery, parameter recovery, and posterior predictive check. RESULTS: We found a significant difference in nonlinear developmental changes in risk preference between the AUT and NTP groups. The computational modeling approach with reinforcement learning models revealed that individual preferences for surprise modulated such preferences. CONCLUSIONS: These findings indicate that for NTP people, adolescence is a developmental period involving risk preference, possibly due to lower surprise aversion. Conversely, for AUT people, who show opposite developmental change of risk preference, adolescence could be a developmental period involving risk avoidance because of low surprise preference. En ligne : https://dx.doi.org/10.1186/s13229-025-00637-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555

Do autistic individuals show atypical performance in probabilistic learning? A comparison of cue-number, predictive strength, and prediction error / Lei ZHANG ; Fang LIU in Molecular Autism, 16 (2025)  

Public ISBD [article]  inMolecular Autism > 16 (2025) . - 15 Titre : Do autistic individuals show atypical performance in probabilistic learning? A comparison of cue-number, predictive strength, and prediction error Type de document : Texte imprimé et/ou numérique Auteurs : Lei ZHANG, Auteur ; Fang LIU, Auteur Article en page(s) : 15 Langues : Anglais (eng) Mots-clés : Humans  Autistic Disorder/psychology/physiopathology/diagnosis  Cues  Male  Adult  Female  Probability Learning  Young Adult  Reinforcement, Psychology  Learning  Associative learning  Bayesian  Prediction errors  Predictive coding  Probabilistic learning  Reinforcement learning  reviewed and approved by the University Research Ethics Committee (UREC) at the  University of Reading (reference number: UREC 20/28). All participants provided  their written informed consent prior to their participation. Consent for  publication: Not applicable. Competing interests: The authors declare no  competing interests. Index. décimale : PER Périodiques Résumé : BACKGROUND: According to recent models of autism, autistic individuals may find learning probabilistic cue-outcome associations more challenging than deterministic learning, though empirical evidence for this is mixed. Here we examined the mechanism of probabilistic learning more closely by comparing autistic and non-autistic adults on inferring a target cue from multiple cues or integrating multiple target cues and learning from associations with various predictive strengths. METHODS: 52 autistic and 52 non-autistic participants completed three tasks: (i) single-cue probabilistic learning, in which they had to infer a single target cue from multiple cues to learn cue-outcome associations; (ii) multi-cue probabilistic learning, in which they had to learn associations of various predictive strengths via integration of multiple cues; and (iii) reinforcement learning, which required learning the contingencies of two stimuli with a probabilistic reinforcement schedule. Accuracy on the two probabilistic learning tasks was modelled separately using a binomial mixed effects model whereas computational modelling was performed on the reinforcement learning data to obtain a model parameter on prediction error integration (i.e., learning rate). RESULTS: No group differences were found in the single-cue probabilistic learning task. Group differences were evident for the multi-cue probabilistic learning task for associations that are weakly predictive (between 40 and 60%) but not when they are strongly predictive (10-20% or 80-90%). Computational modelling on the reinforcement learning task revealed that, as a group, autistic individuals had a higher learning rate than non-autistic individuals. LIMITATIONS: Due to the online nature of the study, we could not confirm the diagnosis of our autistic sample. The autistic participants were likely to have typical intelligence, and so our findings may not be generalisable to the entire autistic population. The learning tasks are constrained by a relatively small number of trials, and so it is unclear whether group differences will still be seen when given more trials. CONCLUSIONS: Autistic adults showed similar performance as non-autistic adults in learning associations by inferring a single cue or integrating multiple cues when the predictive strength was strong. However, non-autistic adults outperformed autistic adults when the predictive strength was weak, but only in the later phase. Autistic individuals were also more likely to incorporate prediction errors during decision making, which may explain their atypical performance on the weakly predictive associations. Our findings have implications for understanding differences in social cognition, which is often noisy and weakly predictive, among autistic individuals. En ligne : https://dx.doi.org/10.1186/s13229-025-00651-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555 [article] Do autistic individuals show atypical performance in probabilistic learning? A comparison of cue-number, predictive strength, and prediction error [Texte imprimé et/ou numérique] / Lei ZHANG, Auteur ; Fang LIU, Auteur . - 15. Langues : Anglais (eng) in Molecular Autism > 16 (2025) . - 15 Mots-clés : Humans  Autistic Disorder/psychology/physiopathology/diagnosis  Cues  Male  Adult  Female  Probability Learning  Young Adult  Reinforcement, Psychology  Learning  Associative learning  Bayesian  Prediction errors  Predictive coding  Probabilistic learning  Reinforcement learning  reviewed and approved by the University Research Ethics Committee (UREC) at the  University of Reading (reference number: UREC 20/28). All participants provided  their written informed consent prior to their participation. Consent for  publication: Not applicable. Competing interests: The authors declare no  competing interests. Index. décimale : PER Périodiques Résumé : BACKGROUND: According to recent models of autism, autistic individuals may find learning probabilistic cue-outcome associations more challenging than deterministic learning, though empirical evidence for this is mixed. Here we examined the mechanism of probabilistic learning more closely by comparing autistic and non-autistic adults on inferring a target cue from multiple cues or integrating multiple target cues and learning from associations with various predictive strengths. METHODS: 52 autistic and 52 non-autistic participants completed three tasks: (i) single-cue probabilistic learning, in which they had to infer a single target cue from multiple cues to learn cue-outcome associations; (ii) multi-cue probabilistic learning, in which they had to learn associations of various predictive strengths via integration of multiple cues; and (iii) reinforcement learning, which required learning the contingencies of two stimuli with a probabilistic reinforcement schedule. Accuracy on the two probabilistic learning tasks was modelled separately using a binomial mixed effects model whereas computational modelling was performed on the reinforcement learning data to obtain a model parameter on prediction error integration (i.e., learning rate). RESULTS: No group differences were found in the single-cue probabilistic learning task. Group differences were evident for the multi-cue probabilistic learning task for associations that are weakly predictive (between 40 and 60%) but not when they are strongly predictive (10-20% or 80-90%). Computational modelling on the reinforcement learning task revealed that, as a group, autistic individuals had a higher learning rate than non-autistic individuals. LIMITATIONS: Due to the online nature of the study, we could not confirm the diagnosis of our autistic sample. The autistic participants were likely to have typical intelligence, and so our findings may not be generalisable to the entire autistic population. The learning tasks are constrained by a relatively small number of trials, and so it is unclear whether group differences will still be seen when given more trials. CONCLUSIONS: Autistic adults showed similar performance as non-autistic adults in learning associations by inferring a single cue or integrating multiple cues when the predictive strength was strong. However, non-autistic adults outperformed autistic adults when the predictive strength was weak, but only in the later phase. Autistic individuals were also more likely to incorporate prediction errors during decision making, which may explain their atypical performance on the weakly predictive associations. Our findings have implications for understanding differences in social cognition, which is often noisy and weakly predictive, among autistic individuals. En ligne : https://dx.doi.org/10.1186/s13229-025-00651-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555

Annual Research Review: Developmental computational psychiatry / T. U. HAUSER in Journal of Child Psychology and Psychiatry, 60-4 (April 2019)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 60-4 (April 2019) . - p.412-426 Titre : Annual Research Review: Developmental computational psychiatry Type de document : Texte imprimé et/ou numérique Auteurs : T. U. HAUSER, Auteur ; G. J. WILL, Auteur ; M. DUBOIS, Auteur ; R. J. DOLAN, Auteur Article en page(s) : p.412-426 Langues : Anglais (eng) Mots-clés : Developmental computational psychiatry  apathy  dopamine  motivation  prediction error  reinforcement learning  self-esteem Index. décimale : PER Périodiques Résumé : Most psychiatric disorders emerge during childhood and adolescence. This is also a period that coincides with the brain undergoing substantial growth and reorganisation. However, it remains unclear how a heightened vulnerability to psychiatric disorder relates to this brain maturation. Here, we propose 'developmental computational psychiatry' as a framework for linking brain maturation to cognitive development. We argue that through modelling some of the brain's fundamental cognitive computations, and relating them to brain development, we can bridge the gap between brain and cognitive development. This in turn can lead to a richer understanding of the ontogeny of psychiatric disorders. We illustrate this perspective with examples from reinforcement learning and dopamine function. Specifically, we show how computational modelling deepens an understanding of how cognitive processes, such as reward learning, effort learning, and social learning might go awry in psychiatric disorders. Finally, we sketch the promises and limitations of a developmental computational psychiatry. En ligne : https://dx.doi.org/10.1111/jcpp.12964 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=388 [article] Annual Research Review: Developmental computational psychiatry [Texte imprimé et/ou numérique] / T. U. HAUSER, Auteur ; G. J. WILL, Auteur ; M. DUBOIS, Auteur ; R. J. DOLAN, Auteur . - p.412-426. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 60-4 (April 2019) . - p.412-426 Mots-clés : Developmental computational psychiatry  apathy  dopamine  motivation  prediction error  reinforcement learning  self-esteem Index. décimale : PER Périodiques Résumé : Most psychiatric disorders emerge during childhood and adolescence. This is also a period that coincides with the brain undergoing substantial growth and reorganisation. However, it remains unclear how a heightened vulnerability to psychiatric disorder relates to this brain maturation. Here, we propose 'developmental computational psychiatry' as a framework for linking brain maturation to cognitive development. We argue that through modelling some of the brain's fundamental cognitive computations, and relating them to brain development, we can bridge the gap between brain and cognitive development. This in turn can lead to a richer understanding of the ontogeny of psychiatric disorders. We illustrate this perspective with examples from reinforcement learning and dopamine function. Specifically, we show how computational modelling deepens an understanding of how cognitive processes, such as reward learning, effort learning, and social learning might go awry in psychiatric disorders. Finally, we sketch the promises and limitations of a developmental computational psychiatry. En ligne : https://dx.doi.org/10.1111/jcpp.12964 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=388

Annual Research Review: Transdiagnostic neuroscience of child and adolescent mental disorders – differentiating decision making in attention-deficit/hyperactivity disorder, conduct disorder, depression, and anxiety / Edmund J. S. SONUGA-BARKE in Journal of Child Psychology and Psychiatry, 57-3 (March 2016)  

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