Abstract
Bayesian belief updating provides a normative framework for how a rational agent should revise probabilities in light of new evidence. However, empirical research in psychology and behavioral economics has consistently documented systematic biases in human belief updating, such as conservatism (under-reaction to new data), base-rate neglect (under-weighting prior information), and confirmation bias (over-weighting confirmatory evidence) [2, 12]. Existing theories either derive Bayesian updating axiomatically or interpret it via information-theoretic principles, but they do not account for these biases in a unified way. In this paper, we propose a generalized Bayesian updating model that incorporates explicit cognitive constraints and information-processing costs. By introducing a parametric distortion of the log Bayes factor and an explicit information cost term, we derive an update rule that recovers classical Bayes’ rule as a special case. The resulting formulation explains well-known cognitive biases as rational outcomes under bounded rationality. Our model bridges normative Bayesian theory and descriptive cognitive phenomena, and has implications for cognitive science, artificial intelligence, and decision support systems.