Computational modeling reveals strategic and developmental differences in the behavioral impact of reward across adolescence

Developmental Differences in Probabilistic Reversal Learning: A Computational Modeling Approach

Frontiers in Neuroscience ◽

10.3389/fnins.2020.536596 ◽

2021 ◽

Vol 14 ◽

Author(s):

Eileen Oberwelland Weiss ◽

Jana A. Kruppa ◽

Gereon R. Fink ◽

Beate Herpertz-Dahlmann ◽

Kerstin Konrad ◽

...

Keyword(s):

Computational Modeling ◽

Cognitive Flexibility ◽

Reversal Learning ◽

Learning Task ◽

Developmental Differences ◽

Model Parameters ◽

Time Data ◽

Conflicting Information ◽

Perseverative Errors ◽

Probabilistic Reversal Learning

Cognitive flexibility helps us to navigate through our ever-changing environment and has often been examined by reversal learning paradigms. Performance in reversal learning can be modeled using computational modeling which allows for the specification of biologically plausible models to infer psychological mechanisms. Although such models are increasingly used in cognitive neuroscience, developmental approaches are still scarce. Additionally, though most reversal learning paradigms have a comparable design regarding timing and feedback contingencies, the type of feedback differs substantially between studies. The present study used hierarchical Gaussian filter modeling to investigate cognitive flexibility in reversal learning in children and adolescents and the effect of various feedback types. The results demonstrate that children make more overall errors and regressive errors (when a previously learned response rule is chosen instead of the new correct response after the initial shift to the new correct target), but less perseverative errors (when a previously learned response set continues to be used despite a reversal) adolescents. Analyses of the extracted model parameters of the winning model revealed that children seem to use new and conflicting information less readily than adolescents to update their stimulus-reward associations. Furthermore, more subclinical rigidity in everyday life (parent-ratings) is related to less explorative choice behavior during the probabilistic reversal learning task. Taken together, this study provides first-time data on the development of the underlying processes of cognitive flexibility using computational modeling.

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Computational Modeling of Cognition and Behavior

10.1017/cbo9781316272503 ◽

2018 ◽

Cited By ~ 32

Author(s):

Simon Farrell ◽

Stephan Lewandowsky

Keyword(s):

Computational Modeling ◽

And Behavior

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Recent Developments in the Assessment and Diagnosis of Child Psychopathology

European Journal of Psychological Assessment ◽

10.1027/1015-5759.11.3.203 ◽

1995 ◽

Vol 11 (3) ◽

pp. 203-212 ◽

Cited By ~ 3

Author(s):

Frank C. Verhulst

Keyword(s):

Problem Behaviors ◽

Child Psychopathology ◽

Developmental Differences ◽

Multiple Informants ◽

Cultural Research ◽

Assessment Approach ◽

Multicultural Knowledge ◽

Recent Developments ◽

Basic Characteristics ◽

Assessment And Diagnosis

In this article, recent developments in the assessment and diagnosis of child psychopathology are discussed with an emphasis on standardized methodologies that provide data that can be scored on empirically derived groupings of problems that tend to co-occur. Assessment methodologies are highlighted that especially take account of the following three basic characteristics of child psychopathology: (1) the quantitative nature of child psychopathology; (2) the role of developmental differences in the occurrence of problem behaviors, and (3) the need for multiple informants. Cross-cultural research is needed to test the applicability of assessment procedures across different settings as well as the generalizability of taxonomic constructs. Assessments of children in different cultures can be compared or pooled to arrive at a multicultural knowledge base which may be much stronger than knowledge based on only one culture. It is essential to avoid assuming that data from any single source reveal the significance of particular problems. Instead, comprehensive assessment of psychopathology requires coordination of multisource data using a multiaxial assessment approach.

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