Training diversity promotes absolute-value-guided choice.

Autor: Levi Solomyak, Paul B Sharp, Eran Eldar
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: PLoS Computational Biology, Vol 18, Iss 11, p e1010664 (2022)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1010664
Popis: Many decision-making studies have demonstrated that humans learn either expected values or relative preferences among choice options, yet little is known about what environmental conditions promote one strategy over the other. Here, we test the novel hypothesis that humans adapt the degree to which they form absolute values to the diversity of the learning environment. Since absolute values generalize better to new sets of options, we predicted that the more options a person learns about the more likely they would be to form absolute values. To test this, we designed a multi-day learning experiment comprising twenty learning sessions in which subjects chose among pairs of images each associated with a different probability of reward. We assessed the degree to which subjects formed absolute values and relative preferences by asking them to choose between images they learned about in separate sessions. We found that concurrently learning about more images within a session enhanced absolute-value, and suppressed relative-preference, learning. Conversely, cumulatively pitting each image against a larger number of other images across multiple sessions did not impact the form of learning. These results show that the way humans encode preferences is adapted to the diversity of experiences offered by the immediate learning context.
Databáze: Directory of Open Access Journals
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