TL;DR
This study investigates how non-human primates combine reinforcement learning and working memory during a trial-and-error problem-solving task, revealing individual differences and potential species-specific coordination dynamics.
Contribution
It introduces a computational model combining RL and WM to explain primate behavior, highlighting inter-individual variability and implications for cross-species comparisons.
Findings
Monkeys' behavior is better explained by combined RL and WM models.
Different monkeys exhibit distinct coordination dynamics between RL and WM.
Pretraining may influence the dominance of certain RL-WM coordination patterns.
Abstract
Accumulating evidence suggest that human behavior in trial-and-error learning tasks based on decisions between discrete actions may involve a combination of reinforcement learning (RL) and working-memory (WM). While the understanding of brain activity at stake in this type of tasks often involve the comparison with non-human primate neurophysiological results, it is not clear whether monkeys use similar combined RL and WM processes to solve these tasks. Here we analyzed the behavior of five monkeys with computational models combining RL and WM. Our model-based analysis approach enables to not only fit trial-by-trial choices but also transient slowdowns in reaction times, indicative of WM use. We found that the behavior of the five monkeys was better explained in terms of a combination of RL and WM despite inter-individual differences. The same coordination dynamics we used in a previous…
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Taxonomy
MethodsSoftmax · Q-Learning
