Characterizing Human Habits in the Lab.

computational modeling goal-directed control habit training value-based decision making

Journal

Collabra. Psychology
ISSN: 2474-7394
Titre abrégé: Collabra Psychol
Pays: United States
ID NLM: 101722437

Informations de publication

Date de publication:
28 Feb 2024
Historique:
medline: 11 3 2024
pubmed: 11 3 2024
entrez: 11 3 2024
Statut: ppublish

Résumé

Habits pose a fundamental puzzle for those aiming to understand human behavior. They pervade our everyday lives and dominate some forms of psychopathology but are extremely hard to elicit in the lab. In this Registered Report, we developed novel experimental paradigms grounded in computational models, which suggest that habit strength should be proportional to the frequency of behavior and, in contrast to previous research, independent of value. Specifically, we manipulated how often participants performed responses in two tasks varying action repetition without, or separately from, variations in value. Moreover, we asked how this frequency-based habitization related to value-based operationalizations of habit and self-reported propensities for habitual behavior in real life. We find that choice frequency during training increases habit strength at test and that this form of habit shows little relation to value-based operationalizations of habit. Our findings empirically ground a novel perspective on the constituents of habits and suggest that habits may arise in the absence of external reinforcement. We further find no evidence for an overlap between different experimental approaches to measuring habits and no associations with self-reported real-life habits. Thus, our findings call for a rigorous reassessment of our understanding and measurement of human habitual behavior in the lab.

Identifiants

pubmed: 38463460
doi: 10.1525/collabra.92949
pmc: PMC7615722
doi:

Types de publication

Journal Article

Langues

eng

Pagination

92949

Déclaration de conflit d'intérêts

Competing Interests The authors declare no competing interests.

Auteurs

Stephan Nebe (S)

Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Switzerland.

André Kretzschmar (A)

Individual Differences and Assessment, Department of Psychology, University of Zurich, Switzerland.

Maike C Brandt (MC)

Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Switzerland.

Philippe N Tobler (PN)

Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Switzerland.

Classifications MeSH