Computational mechanisms underlying latent value updating of unchosen actions
Ben-Artzi,Ido Kessler,Yoav Nicenboim,Bruno Shahar,Nitzan
Ben-Artzi,Ido
Kessler,Yoav
Nicenboim,Bruno
Shahar,Nitzan
Abstract
Current studies suggest that individuals estimate the value of their choices based on observed feedback. Here, we ask whether individuals also update the value of their unchosen actions, even when the associated feedback remains unknown. One hundred seventy-eight individuals completed a multi-armed bandit task, making choices to gain rewards. We found robust evidence suggesting latent value updating of unchosen actions based on the chosen action’s outcome. Computational modeling results suggested that this effect is mainly explained by a value updating mechanism whereby individuals integrate the outcome history for choosing an option with that of rejecting the alternative. Properties of the deliberation (i.e., duration/difficulty) did not moderate the latent value updating of unchosen actions, suggesting that memory traces generated during deliberation might take a smaller role in this specific phenomenon than previously thought. We discuss the mechanisms facilitating credit assignment to unchosen actions and their implications for human decision-making.
Description
Publisher Copyright: Copyright © 2023 The Authors, some rights reserved.
Date
2023-10-20
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Decision-making, Reinforcement, Value-Learning
Citation
Ben-Artzi, I, Kessler, Y, Nicenboim, B & Shahar, N 2023, 'Computational mechanisms underlying latent value updating of unchosen actions', Science Advances, vol. 9, no. 42, eadi2704. https://doi.org/10.1126/sciadv.adi2704, https://doi.org/10.31234/osf.io/sxjez
License
info:eu-repo/semantics/openAccess