What is it about?

Humans are remarkably flexible in adapting to changing environments. For this purpose, they should learn about contingencies between stimuli, actions, and rewards. By doing this, they learn what actions (behavior) to undertake to achieve their goal (reward) in a given situation (stimuli). Importantly, humans can use different learning strategies. Furthermore, which learning strategy is optimal can differ across environments. The current study investigated across multiple learning tasks which learning strategy was used by human participants. We observed that humans indeed use different learning strategies in different environments. Importantly, the learning strategy that was used by the human participants was also the learning strategy that was most optimal in that environment. Hence, humans can adaptively change their learning strategy based on requirements of the learning task and environment.

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Why is it important?

We investigate changes in learning strategies across multiple datasets from different research labs. Here, the used learning strategies that we observed were consistent for datasets within one learning environment but not when comparing datasets across learning environments. This indicates a highly reliable and robust shift in learning strategies depending on the learning environment. Additionally, our results indicated that the learning strategies that were used by the human participants aligned with the learning strategy that was optimal in that environment. This suggests that, although performance was not always optimal, humans are able to adapt their learning strategy in an optimal manner.

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This page is a summary of: Humans adaptively select different computational strategies in different learning environments., Psychological Review, April 2024, American Psychological Association (APA),
DOI: 10.1037/rev0000474.
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