Gaze-informed modeling of preference learning and prediction. In press.
Abstract: Learning other people’s preferences is a basic skill required to function effectively in society. However, the process underlying this behavior has been left largely unstudied. Here we aimed to characterize this process, using eye-tracking and computational modeling to study people while they estimated another person’s film preferences. In the first half of the study, subjects received immediate feedback after their guess, whereas in the second half, subjects were presented with four random first-half outcomes to aid them with their current estimation. From a variety of learning models, we identified two that best fit subjects’ behavior and eye movements: k-nearest neighbor and beauty contest. These results indicate that although some people attempt to form a high- dimensional representation of other people’s preferences, others simply go with the average opinion. These strategies can be distinguished by looking at a person’s eye movements. The results also demonstrate subjects’ ability to appropriately weight feedback in their estimates.
Presented at the following conference:
Society for Judgment and Decision Making (SJDM), 2016