Proceedings of the Annual Meeting of the Cognitive Science Society

Probability weighting is a central component of cumulative prospect theory, but its cognitive basis remains contested. We propose a process-level account in which weighting emerges from \emph{intrapersonal aggregation}: decision makers integrate an externally provided probability with internally generated estimates from memory or simulation. We formalize this as minimization of a weighted Kullback--Leibler divergence and show that the solution is logarithmic pooling in log-odds space, yielding the standard two-parameter weighting function. Curvature reflects the relative influence of external information, while elevation captures directional bias in internal sampling. The model predicts that sampling depth and external reliability primarily affect curvature, whereas framing or affective biases primarily shift elevation. An analytic Beta-sampling benchmark makes these dependencies explicit.