Intrinsic motivation for choice varies with individual risk attitudes and the controllability of the environment

1Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.
Get full text

Abstract

When deciding between options that do or do not lead to future choices, humans often choose to choose. We studied choice seeking by asking subjects to first decide between a choice opportunity or performing a computer-selected action, after which they either chose freely or performed the forced action. Subjects preferred choice when these options were equally rewarded, even deterministically, and traded extrinsic rewards for opportunities to choose. We explained individual variability in choice seeking using reinforcement learning models incorporating risk sensitivity and overvaluation of rewards obtained through choice. Model fits revealed that 28% of subjects were sensitive to the worst possible outcome associated with free choice, and this pessimism reduced their choice preference with increasing risk. Moreover, outcome overvaluation was necessary to explain patterns of individual choice preference across levels of risk. We also manipulated the degree to which subjects controlled stimulus outcomes. We found that degrading coherence between their actions and stimulus outcomes diminished choice preference following forced actions, although willingness to repeat selection of choice opportunities remained high. When subjects chose freely during these repeats, they were sensitive to rewards when actions were controllable but ignored outcomes -even positive ones- associated with reduced controllability. Our results show that preference for choice can be modulated by extrinsic reward properties including reward probability and risk as well as by controllability of the environment.

Cite

CITATION STYLE

APA

Munuera, J., Agost, M. R., Bendetowicz, D., Kerebel, A., Chambon, V., & Lau, B. (2023). Intrinsic motivation for choice varies with individual risk attitudes and the controllability of the environment. PLoS Computational Biology, 19(8 August). https://doi.org/10.1371/journal.pcbi.1010551

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free