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Neural autopilot and context-sensitivity of habits

Camerer, Colin F. and Li, Xiaomin (2021) Neural autopilot and context-sensitivity of habits. Current Opinion in Behavioral Sciences, 41 . pp. 185-190. ISSN 2352-1546. doi:10.1016/j.cobeha.2021.07.002.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription
Camerer, Colin F.0000-0003-4049-1871
Li, Xiaomin0000-0002-1286-4012
Additional Information:© 2021 Elsevier Ltd. Available online 10 September 2021. Conflict of interest statement: Nothing declared.
Group:Tianqiao and Chrissy Chen Institute for Neuroscience
Funding AgencyGrant Number
Alfred P. Sloan FoundationUNSPECIFIED
University of PennsylvaniaUNSPECIFIED
Record Number:CaltechAUTHORS:20211123-211149863
Persistent URL:
Official Citation:Colin F Camerer, Xiaomin Li, Neural autopilot and context-sensitivity of habits, Current Opinion in Behavioral Sciences, Volume 41, 2021, Pages 185-190, ISSN 2352-1546,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112020
Deposited By: Tony Diaz
Deposited On:23 Nov 2021 21:41
Last Modified:23 Nov 2021 21:41

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