Modeling information demand in the framework of probabilistic reasoning

Abstract

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 across three independent participant samples (N = 50, 50, and 150), we show that a model that incorporates non-linear probability and value perception outperforms a model based on a linear mixture of motives in explaining instrumental and non-instrumental information demand. Moreover, individual non-linearities that best explained information demand were correlated with personality traits and with non-linearities explaining risk seeking/aversion in standard choice tasks. The results suggest that a computational framework rooted in the subjective perception of probability furthers our understanding of information demand and its relationship with decision making under risk and uncertainty. The study shows that non-linear perceptions of probabilistic rewards explain non-normative information demand in instrumental and noninstrumental conditions, and correlate with personality traits and nonlinearities in risky-choice tasks

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 1 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.