Experiential value neglect in hybrid choice is robust to learning and representational manipulations

In decision-making, option values can arise from past experiences with rewards and punishments (experiential) or from explicit descriptions of outcomes and probabilities (descriptive). While most studies have examined experiential and descriptive options separately, real-world decisions, such as deciding between a restaurant we visited before and one known through online reviews, often entail choosing between the two. Recently, Garcia et al. (2023) revealed that experiential options are systematically neglected in such hybrid choices. Yet, the cause of this experiential neglect remains unknown. To test whether this phenomenon results from representational differences between experiential and descriptive options, we manipulated the learning architecture, test-phase feedback, stimulus format, and feedback format over five experiments (n = 375). Additionally, participants showed reduced sensitivity to experiential losses relative to gains. Experiential neglect persisted across all conditions, eliminating these hypotheses as explanations of this phenomenon. Rather, we propose that neglect may stem from retrieval-based uncertainty in experiential valuation. Together, these findings provide evidence for a cognitive constraint on the integration of experiential and described values in decision-making.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-03
DOI
https://doi.org/10.5281/zenodo.23924509
Primary Topic
Decision-Making and Behavioral Economics
Type
preprint
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preprint

Experiential value neglect in hybrid choice is robust to learning and representational manipulations

Basile Garcia, Stefano Palminteri, Maël P. Lebreton, Caroline Pioger
Zenodo (CERN European Organization for Nuclear Research)
Decision-Making and Behavioral Economics
preprint

Experiential value neglect in hybrid choice is robust to learning and representational manipulations

Basile Garcia, Stefano Palminteri, Maël P. Lebreton, Caroline Pioger
preprint en

Abstract

In decision-making, option values can arise from past experiences with rewards and punishments (experiential) or from explicit descriptions of outcomes and probabilities (descriptive). While most studies have examined experiential and descriptive options separately, real-world decisions, such as deciding between a restaurant we visited before and one known through online reviews, often entail choosing between the two. Recently, Garcia et al. (2023) revealed that experiential options are systematically neglected in such hybrid choices. Yet, the cause of this experiential neglect remains unknown. To test whether this phenomenon results from representational differences between experiential and descriptive options, we manipulated the learning architecture, test-phase feedback, stimulus format, and feedback format over five experiments (n = 375). Additionally, participants showed reduced sensitivity to experiential losses relative to gains. Experiential neglect persisted across all conditions, eliminating these hypotheses as explanations of this phenomenon. Rather, we propose that neglect may stem from retrieval-based uncertainty in experiential valuation. Together, these findings provide evidence for a cognitive constraint on the integration of experiential and described values in decision-making.

Zenodo (CERN European Organization for Nuclear Research)
University of Geneva (CH), Centre National de la Recherche Scientifique (FR), Inserm (FR), Université Paris Sciences et Lettres (FR), École Normale Supérieure - PSL (FR), Laboratoire de Neurosciences Cognitives (FR), Laboratoire de Neurosciences Cognitives et Computationnelles (FR), Paris Jourdan Sciences Economiques (FR), Paris School of Economics (FR)
Decision-Making and Behavioral Economics
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Experiential value neglect in hybrid choice is robust to learning and representational manipulations — Basile Garcia, Stefano Palminteri, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS