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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Characterizing Human Habits in the Lab |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Collabra: Psychology |
| Publisher | University of California Press |
| Geographical Reach | international |
| ISSN | 2474-7394 |
| Volume | 10 |
| Number | 1 |
| Page Range | 92949 |
| Date | 2024 |
| Abstract Text | 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. |
| Free access at | DOI |
| Digital Object Identifier | 10.1525/collabra.92949 |
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| Keywords | habit, value-based decision making, goal-directed control, computational modeling, training |