A novel free-operant framework enables experimental habit induction in humans

Rani Gera*, Segev Barak, Tom Schonberg*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Habits are a prominent feature of both adaptive and maladaptive behavior. Yet, despite substantial research efforts, there are currently no well-established experimental procedures for habit induction in humans. It is likely that laboratory experimental settings, as well as the session-based structure typically used in controlled experiments (also outside the lab), impose serious constraints on studying habits and other effects that are sensitive to context, motivation, and training duration and frequency. To overcome these challenges, we devised a unique real-world free-operant task structure, implemented through a novel smartphone application, whereby participants could freely enter the app (24 hours a day, 7 days a week) to win rewards. This procedure is free of typical laboratory constraints, yet well controlled. Using the canonical sensitivity to outcome devaluation criterion, we successfully demonstrated habit formation as a function of training duration, a long-standing challenge in the field. Additionally, we show a positive relationship between multiple facets of engagement/motivation and goal-directedness. We suggest that our novel paradigm can be used to study the neurobehavioral and psychological mechanism underlying habits in humans. Moreover, the real-world free-operant framework can potentially be used to examine other instrumental behavior-related questions, with greater face validity in naturalistic conditions.

Original languageEnglish
JournalBehavior Research Methods
DOIs
StateAccepted/In press - 2023

Funding

FundersFunder number
Fields-Rayant Minducate Learning Innovation Research Center
Israel Data Science Initiative
Israel Science Foundation1996/20

    Keywords

    • Free-operant
    • Goal-directed behavior
    • Habits
    • Learning
    • Mobile application
    • Model-based learning
    • Model-free learning
    • Motivation
    • Real-world
    • Reward

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