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Training diversity promotes absolute-value-guided choice.

Authors :
Solomyak L
Sharp PB
Eldar E
Source :
PLoS computational biology [PLoS Comput Biol] 2022 Nov 02; Vol. 18 (11), pp. e1010664. Date of Electronic Publication: 2022 Nov 02 (Print Publication: 2022).
Publication Year :
2022

Abstract

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.<br />Competing Interests: The authors have declared that no competing interests exist.<br /> (Copyright: © 2022 Solomyak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Details

Language :
English
ISSN :
1553-7358
Volume :
18
Issue :
11
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
36322560
Full Text :
https://doi.org/10.1371/journal.pcbi.1010664