A neural autopilot theory of habit: Evidence from consumer purchases and social media use.

Abstract

This article applies a two-process "neural autopilot" model to field data. The autopilot model hypothesizes that habitual choice occurs when the reward from a behavior has low numerical "doubt" (i.e., reward prediction errors are small). The model toggles between repeating a previous choice (habit) when doubt is low and making a goal-directed choice when doubt is high. The model has ingredients established in animal learning and cognitive neuroscience and is simple enough to make nonobvious predictions. In two empirical applications, we fit the model to field data on purchases of canned tuna and posting on the Chinese social media site Weibo. This style of modeling is called "structural" because there is a theoretical model of how different variables influence choices by agents (the "structure"), which tightly restricts how hidden variables lead to observed choices. There is empirical support for the model, more strongly for tuna purchases than for Weibo posting, relative to a baseline "reduced-form" model in which current choices are correlated with past choices without a mechanistic (structural) explanation. An interesting set of predictions can also be derived about how consumers react to different kinds of changes in prices and qualities of goods (this is called "counterfactual analysis").