Modeling Information Demand in the Framework of Probabilistic Reasoning

Deciding whether, when, and which information to sample is critical for making effective decisions, yet the cognitive mechanisms of this process are not well understood. Here, we propose that key aspects of human information demand are explained by non-linear subjective perceptions of probabilistic losses or gains. Using behavioral testing and quantitative model comparisons, we show that a new model that incorporates non-linearities in perceived probability and value outperforms a mixed-motive approach in explaining deviations from normative information demand. Across participants, individual subjective non-linearities that best accounted for information demand were correlated with personality traits and with non-linearities explaining risk seeking/aversion in standard choice tasks. The results support a novel computational framework that is rooted in the subjective probabilistic perception and furthers our understanding of information demand and its relationship with decision making under risk and uncertainty.