Neural Activity Reveals Preferences without Choices
研究了能否根据人们不参与实际决策时的神经反应推断其选择，对缺乏选择数据时估计偏好有用。
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.