The Computational Development of Reinforcement Learning during Adolescence.
Adolescence is a period of life characterised by changes in learning and decision-making. Learning and decision-making do not rely on a unitary system, but instead require the coordination of different cognitive processes that can be mathematically formalised as dissociable computational modules. He...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2016-06-01
|
Series: | PLoS Computational Biology |
Online Access: | http://europepmc.org/articles/PMC4920542?pdf=render |
id |
doaj-a7a6b15311b540fe8a243afb572f0ee0 |
---|---|
record_format |
Article |
spelling |
doaj-a7a6b15311b540fe8a243afb572f0ee02020-11-24T21:12:25ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-06-01126e100495310.1371/journal.pcbi.1004953The Computational Development of Reinforcement Learning during Adolescence.Stefano PalminteriEmma J KilfordGiorgio CoricelliSarah-Jayne BlakemoreAdolescence is a period of life characterised by changes in learning and decision-making. Learning and decision-making do not rely on a unitary system, but instead require the coordination of different cognitive processes that can be mathematically formalised as dissociable computational modules. Here, we aimed to trace the developmental time-course of the computational modules responsible for learning from reward or punishment, and learning from counterfactual feedback. Adolescents and adults carried out a novel reinforcement learning paradigm in which participants learned the association between cues and probabilistic outcomes, where the outcomes differed in valence (reward versus punishment) and feedback was either partial or complete (either the outcome of the chosen option only, or the outcomes of both the chosen and unchosen option, were displayed). Computational strategies changed during development: whereas adolescents' behaviour was better explained by a basic reinforcement learning algorithm, adults' behaviour integrated increasingly complex computational features, namely a counterfactual learning module (enabling enhanced performance in the presence of complete feedback) and a value contextualisation module (enabling symmetrical reward and punishment learning). Unlike adults, adolescent performance did not benefit from counterfactual (complete) feedback. In addition, while adults learned symmetrically from both reward and punishment, adolescents learned from reward but were less likely to learn from punishment. This tendency to rely on rewards and not to consider alternative consequences of actions might contribute to our understanding of decision-making in adolescence.http://europepmc.org/articles/PMC4920542?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Stefano Palminteri Emma J Kilford Giorgio Coricelli Sarah-Jayne Blakemore |
spellingShingle |
Stefano Palminteri Emma J Kilford Giorgio Coricelli Sarah-Jayne Blakemore The Computational Development of Reinforcement Learning during Adolescence. PLoS Computational Biology |
author_facet |
Stefano Palminteri Emma J Kilford Giorgio Coricelli Sarah-Jayne Blakemore |
author_sort |
Stefano Palminteri |
title |
The Computational Development of Reinforcement Learning during Adolescence. |
title_short |
The Computational Development of Reinforcement Learning during Adolescence. |
title_full |
The Computational Development of Reinforcement Learning during Adolescence. |
title_fullStr |
The Computational Development of Reinforcement Learning during Adolescence. |
title_full_unstemmed |
The Computational Development of Reinforcement Learning during Adolescence. |
title_sort |
computational development of reinforcement learning during adolescence. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2016-06-01 |
description |
Adolescence is a period of life characterised by changes in learning and decision-making. Learning and decision-making do not rely on a unitary system, but instead require the coordination of different cognitive processes that can be mathematically formalised as dissociable computational modules. Here, we aimed to trace the developmental time-course of the computational modules responsible for learning from reward or punishment, and learning from counterfactual feedback. Adolescents and adults carried out a novel reinforcement learning paradigm in which participants learned the association between cues and probabilistic outcomes, where the outcomes differed in valence (reward versus punishment) and feedback was either partial or complete (either the outcome of the chosen option only, or the outcomes of both the chosen and unchosen option, were displayed). Computational strategies changed during development: whereas adolescents' behaviour was better explained by a basic reinforcement learning algorithm, adults' behaviour integrated increasingly complex computational features, namely a counterfactual learning module (enabling enhanced performance in the presence of complete feedback) and a value contextualisation module (enabling symmetrical reward and punishment learning). Unlike adults, adolescent performance did not benefit from counterfactual (complete) feedback. In addition, while adults learned symmetrically from both reward and punishment, adolescents learned from reward but were less likely to learn from punishment. This tendency to rely on rewards and not to consider alternative consequences of actions might contribute to our understanding of decision-making in adolescence. |
url |
http://europepmc.org/articles/PMC4920542?pdf=render |
work_keys_str_mv |
AT stefanopalminteri thecomputationaldevelopmentofreinforcementlearningduringadolescence AT emmajkilford thecomputationaldevelopmentofreinforcementlearningduringadolescence AT giorgiocoricelli thecomputationaldevelopmentofreinforcementlearningduringadolescence AT sarahjayneblakemore thecomputationaldevelopmentofreinforcementlearningduringadolescence AT stefanopalminteri computationaldevelopmentofreinforcementlearningduringadolescence AT emmajkilford computationaldevelopmentofreinforcementlearningduringadolescence AT giorgiocoricelli computationaldevelopmentofreinforcementlearningduringadolescence AT sarahjayneblakemore computationaldevelopmentofreinforcementlearningduringadolescence |
_version_ |
1716750993723490304 |