Work

Disentangling the Effects of Counterfactual Feedback on Maximization and Risk Preference across Gains and Losses

Year: 2025

Type: preprint

Abstract: Decisions under risk can be characterized by two complementary but distinct behavioral variables: the propensity to choose the expected value-maximizing option and the tendency to select the risky option. Human reinforcement learning (RL) research has traditionally focused more on the former (maximization), providing many insights into how fundamental features of the learning environment influence behavior. For instance, previous studies have shown that counterfactual feedback about unchosen options exerts a strong influence on maximization. However, comparatively little is known about scenarios in which maximization and risk propensity are assessed orthogonally and simultaneously. To address this gap, we designed a series of RL experiments in which we manipulated key factors, including the feedback information regime (partial vs. complete) and the relative expected values of risky and safe options. Contrary to the conventional assumption that “more information is better,” our results clearly show that, in such contexts, the effect of foregone outcomes on risk preference outweighs its influence on maximization. We further compared RL choices with description-based versions of the same decision problems. While replicating established findings on the description–experience gap, we found that complete information had little impact on its expression, suggesting that this gap does not arise primarily from instant feedback or insufficient outcome sampling.

Source: Research Square

DOI: 10.21203/rs.3.rs-9149770/v1

Authors: Ali Shiravand, Maëlle Gueguen, Sophie Bavard, Dirk Wulff, Julien Bastin, Stefano Palminteri