Neural Activity Reveals Preferences without Choices

• 成果类型: Article
• 署名作者: Smith, Alec; Bernheim, B. Douglas; Camerer, Colin F.; Rangel, Antonio
• 署名单位: California Institute of Technology; Stanford University; National Bureau of Economic Research
• 刊物名称: AMERICAN ECONOMIC JOURNAL-MICROECONOMICS
• ISSN/ISSBN: 1945-7669
• DOI: 10.1257/mic.6.2.1
• 发表日期: 2014
• 页码: 1-36
• 关键词: orbitofrontal cortex; logistic-regression; subjective value; goal values; valuation; fmri; prediction; models; CLASSIFICATION; regularization

摘要

We investigate the feasibility of inferring the choices people would make (if given the opportunity) based on their neural responses to the pertinent prospects when they are not engaged in actual decision making. The ability to make such inferences is of potential value when choice data are unavailable, or limited in ways that render standard methods of estimating choice mappings problematic. We formulate prediction models relating choices to nonchoice neural responses, and use them to predict out-of-sample choices for new items and for new groups of individuals. The predictions are sufficiently accurate to establish the feasibility of our approach.