Computational mechanisms underlying latent value updating of unchosen actions

Ido Ben-Artzi, Yoav Kessler, Bruno Nicenboim, Nitzan Shahar

    Research output: Contribution to journalArticleScientificpeer-review

    4 Citations (Scopus)

    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.

    Original languageEnglish
    Article numbereadi2704
    Number of pages15
    JournalScience Advances
    Volume9
    Issue number42
    DOIs
    Publication statusPublished - 20 Oct 2023

    Keywords

    • Decision-making
    • Reinforcement
    • Value-Learning

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