Proceedings of the Annual Meeting of the Cognitive Science Society

Nonlinear probability weighting allows cumulative prospect theory (CPT) to account for seminal phenomena in riskychoice (e.g., the certainty effect). The attentional drift diffusion model (aDDM) formalizes that attentional biases canshape preferences as a sequential sampling process. We simulated choices between safe and risky options using the aDDMwith varying attentional biases to safe or risky options and modeled these choices with CPT. Changes in preferences dueto attentional biases were systematically reflected in the parameters of CPT’s weighting function (curvature, elevation).We demonstrate that this also holds empirically, in the sampling paradigm in decision from experience. Hence, nonlinearprobability weighting can arise from option-specific attentional biases in information search. This challenges commoninterpretations of probability-weighting parameters, suggests novel attentional explanations for empirical phenomena as-sociated with characteristic shapes of CPT’s probability-weighting function, and adds to the integration of two prominentcomputational frameworks for decision making.