SAwSu: An Integrated Model of Associative and Reinforcement Learning

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the in...

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Bibliographic Details
Published inCognitive science Vol. 38; no. 3; pp. 580 - 598
Main Authors Veksler, Vladislav D., Myers, Christopher W., Gluck, Kevin A.
Format Journal Article
LanguageEnglish
Published United States Wiley Subscription Services, Inc 01.04.2014
Subjects
Association Learning
Associative learning
Cognitive ability
Cognitive modeling
Cognitive system
Decision making
Humans
Integrated models
Learning
Models, Psychological
Reinforcement (Psychology)
Reinforcement learning
Reward
Learning
Cognitive modeling
Integrated models
Decision making
Reinforcement learning
Associative learning
Cognitive system
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Summary:Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.
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ISSN:0364-0213
1551-6709
DOI:10.1111/cogs.12103