Attention and Decision Making: A Comprehensive Analysis

How do people acquire and aggregate information when making decisions? This question has been widely studied; however existing theories have not been comprehensively compared on data. We present a general computational framework that allows us to jointly model several different types of attentional and decision processes. We use our framework to test 63 distinct decision models, which apply assumptions from classical decision theories (e.g., the weighted additive rule, the tallying heuristic), contemporary dynamic theories (e.g. decision field theory, the attentional drift diffusion model), as well as several variants of these theories. We combine these decision models with a general attentional model that predicts the dynamics of information acquisition, and fit our models to eye-movement and choice data from three core decision making domains: risk, time, and effort allocation. Our tests identify the set of modeling assumptions necessary to best describe search and choice behavior. In doing so, they reveal the cognitive mechanisms at play in choice, resolve recent theoretical debates on the interplay of attention and choice, and provide new insights on how to generate choice environments aligned with people’s goals.