Zhihao Z., Shichun Wang, et al.
PNAS May 2021, 118 (20) e2022685118
DOI: 10.1073/pnas.2022685118
Abstract
Real-world decisions are often open ended, with goals, choice options, or evaluation criteria conceived by decision-makers themselves. Critically, the quality of decisions may heavily rely on the generation of options, as failure to generate promising options limits, or even eliminates, the opportunity for choosing them. This core aspect of problem structuring, however, is largely absent from classical models of decision-making, thereby restricting their predictive scope. Here, we take a step toward addressing this issue by developing a neurally inspired cognitive model of a class of ill-structured decisions in which choice options must be self-generated. Specifically, using a model in which semantic memory retrieval is assumed to constrain the set of options available during valuation, we generate highly accurate out-of-sample predictions of choices across multiple categories of goods. Our model significantly and substantially outperforms models that only account for valuation or retrieval in isolation or those that make alternative mechanistic assumptions regarding their interaction. Furthermore, using neuroimaging, we confirm our core assumption regarding the engagement of, and interaction between, semantic memory retrieval and valuation processes. Together, these results provide a neurally grounded and mechanistic account of decisions with self-generated options, representing a step toward unraveling cognitive mechanisms underlying adaptive decision-making in the real world.
Significance
Life is not a multiple-choice test: Many real-world decisions leave goals, choice options, or evaluation criteria to be determined by decision-makers themselves. However, a mechanistic understanding of how such problem structuring processes influence choice has largely eluded standard models of decision-making. By developing a neurally grounded cognitive model that integrates semantic knowledge retrieval and valuation processes, we offer a computational framework providing strikingly accurate out-of-sample predictions of choices with self-generated options. This framework generates psychological insights into the nature and force of memory retrieval’s substantial influence on choice behavior. Together, these findings represent a step toward predicting complex, ill-structured decisions in the real world, opening up new approaches that may broaden the scope of formal models of decision-making.
