Decision problems are often characterized by the presence of many choice options described by several attributes, and people need to limit how much information they search for. We propose that humans search and thereby devote attention to relevant information in an efficient and goal-directed, but not necessarily optimal, manner. Thereto, a novel hierarchical Bayesian cognitive model of information search in multiattribute and multialternative decisions is presented and tested. In the model, the search process is governed by the desire to quickly identify the option that meets the choice goal best, which in turn depends on the importance and uncertainty of information and on the accumulated evidence. The theory accounts for many established empirical findings on the interplay of attention and decision making, including the positive correlation of gaze time and choice probability as well as the preference for sampling promising choice candidates. To rigorously test a series of top-down effects of attention, we present results of a new and preregistered eye-tracking experiment on multiattribute decisions, in which the influence of bottom-up attention on visual search is minimized. Our theory accounts for various interactions of attention and choice in this experiment, while variants of it and another extant theory, which assume different search rules, fail to capture these effects. Furthermore, the theory predicts additional choice dynamics such as the dependency of decision speed on the number and overall value of options. The proposed framework provides a general approach to understanding the intricate dynamics of search and valuation mechanisms in multiattribute decisions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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