Neural autopilot and context-sensitivity of habits

This paper is about the background of two new ideas from neuroeconomics for understanding habits. The main idea is a two-process 'neural autopilot' model. This model hypothesizes that contextually cued habits occur when the reward from the habitual behavior is numerically reliable (as in related models with an 'arbitrator'). This computational model is lightly parameterized, has the essential ingredients established in animal learning and cognitive neuroscience, and is simple enough to make nonobvious predictions. An interesting set of predictions is about how consumers react to different kinds of changes in prices and qualities of goods ('elasticities'). Elasticity analysis expands the habit marker of insensitivity to reward devaluation, and other types of sensitivities. The second idea is to use machine learning to discover which contextual variables seem to cue habits, in field data.