Cited by 280Neural and Behavioral Psychology StudiesBehavioral Health and InterventionsNeurotransmitter Receptor Influence on Behavior

The DOI matches its Crossref record; 135 of the 136 references it cites were found in a registry or an index; none of the 99 sources its claims cite is checked for support yet.

Habits without values.

Open access
Miller, Kevin J., Shenhav, Amitai, Ludvig, Elliot A.·2019·Psychological Review

Published version of a preprint

Abstract

Habits form a crucial component of behavior. In recent years, key computational models have conceptualized habits as arising from model-free reinforcement learning mechanisms, which typically select between available actions based on the future value expected to result from each. Traditionally, however, habits have been understood as behaviors that can be triggered directly by a stimulus, without requiring the animal to evaluate expected outcomes. Here, we develop a computational model instantiating this traditional view, in which habits develop through the direct strengthening of recently taken actions rather than through the encoding of outcomes. We demonstrate that this model accounts for key behavioral manifestations of habits, including insensitivity to outcome devaluation and contingency degradation, as well as the effects of reinforcement schedule on the rate of habit formation. The model also explains the prevalent observation of perseveration in repeated-choice tasks as an additional behavioral manifestation of the habit system. We suggest that mapping habitual behaviors onto value-free mechanisms provides a parsimonious account of existing behavioral and neural data. This mapping may provide a new foundation for building robust and comprehensive models of the interaction of habits with other, more goal-directed types of behaviors and help to better guide research into the neural mechanisms underlying control of instrumental behavior more generally. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper develops a computational model in which habits are value-free stimulus-response associations strengthened by repetition, rather than model-free reinforcement learning. It shows this model reproduces key habit phenomena, including insensitivity to outcome devaluation and contingency degradation, reinforcement-schedule effects on habit formation, lesion-like dissociations, and perseveration.

Key findings

  • A value-free habitual controller, combined with a model-based goal-directed controller and an arbiter, reproduces overtraining-induced inflexibility: after longer training, simulated agents continued lever-pressing under omission and devaluation, whereas shorter training allowed reversal. Training duration was varied from 100 to 2000 trials in intervals of 100 for omission and from 10 to 2000 trials in intervals of 100 for devaluation.
  • Simulated agents trained on variable-interval (VI6: pellets baited every 6 s, on average 10/min) lever-pressed at a moderate rate and habitized early, whereas agents trained on variable-ratio (VR10: 10% chance per press) lever-pressed at a high rate and habitized much later.
  • Simulated lesions reproduced rodent striatal patterns: decreasing goal-directed parameters (DMS-like) produced a low press rate unaffected by omission or devaluation; decreasing habitual parameters (DLS-like) produced a high press rate that adapted to both manipulations. Control agents adapted after limited training (5,000 trials) but not after extensive training (15,000 trials).
  • In a dynamic two-armed bandit task (10,000 trials; 50 model copies; reinforcer probabilities changed by independent Gaussian random walks, SD = 0.15), the model reproduced both reinforcement-seeking and value-free perseveration; replacing the habitual component with model-free RL failed to reproduce the perseverative pattern.

Methods

  • Computational model with three modules: a habitual controller tracking recency-weighted action history \(H\), a goal-directed controller using model-based RL to learn separate reinforcer expectations \(R\) and utilities \(U\), and an arbiter weighting control by action-outcome contingency \(g\) and overall habitization \(h\).
  • Simulated task environments included reversal learning, omission contingency, outcome devaluation, free-operant VR/VI schedules, simulated lesions, and a two-armed bandit. Most simulations used a single state with two actions; free-operant simulations used continuous press rates from 0 to 150 presses/min with function approximation and an effort cost quadratic in press rate.
  • Analyses averaged simulated performance across agents and, for the two-armed bandit, used logistic regression quantifying influences of past actions, reinforcers, and their interaction on future choice.

Limitations

  • The paper states that the model is described at an abstract computational level, limiting engagement with neurobiological detail, and that a neurobiologically detailed implementation is a critical next step.
  • The paper states that the arbiter’s conditions require future research, that the model is agnostic about whether bypassing model-free RL entirely is appropriate, and that the form of value/motivational modulation of habit learning remains unresolved.
  • Evident from the text: the findings come from simulations rather than new empirical experiments, and many simulations use simplified single-state, two-action environments with specific parameter and utility choices, so generality across richer environments is not established.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1037/rev0000120

Cites

135 of 135 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 99 cited sources checked · 0 supported · 0 flagged

Show all 81 claims

  • It may state two separate claims as one. 21
  • Probably not a substantial claim. 18
38 of 38
  • The paper relies on this without support.

    The simulated task environments capture the essential features of the corresponding animal learning experiments, so that the model's behavior in them can be compared to animal behavior.

    “To illustrate the behavior of the model and the dynamics of its various internal variables, we simulated behavior in a probabilistic reversal learning task (Fig 3).”
  • The paper relies on this without support.

    The model's goal-directed controller corresponds to the dorsomedial striatum (DMS) and its habitual controller corresponds to the dorsolateral striatum (DLS).

    “We lesioned the goal-directed controller (DMS) or habitual controller (DLS) in our model”
    “Lesions to our model’s goal-directed and habitual controllers thus reproduce behavioral patterns typical of DMS and DLS lesioned rats”
  • The authors state this as an assumption.

    In the model, control of behavior is allocated on each trial to either the habitual or the goal-directed system by an arbiter.

    “In our model, control of behavior is allocated on each trial to either the habitual or the goal-directed system by an arbiter.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Training reinforcement probability differs between Methods and Results

    Major

    The Methods specify a 25% pellet probability during omission training, and say devaluation training was identical. The Results describe a 50% pellet probability for both training phases.

  • Habit-weight update uses a different basis function

    Minor

    The habit-strength function uses basis functions γi, so its derivative with respect to weight ci is γi. The stated weight-update equation instead substitutes χi, which is not the defined habit basis function.

2 of 2

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Metadata

Authors
Miller, K. J., Shenhav, A., & Ludvig, E. A.
Affiliations
Princeton University (US)
Venue
Psychological Review
Published
2019-03
Volume
126
Issue
2
Pages
292-311
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
OpenAlex
W2949941589
Cited by
280
References
136
Citations by year
2026: 32 · 2025: 38 · 2024: 52 · 2023: 29 · 2022: 41 · 2021: 41 · 2020: 24 · 2019: 14
FWCI
17.32
Type
journal article
Language
en
Open access
Locations
  • Psychological Review (publishedVersion, other-oa)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • Warwick Research Archive Portal (University of Warwick) (submittedVersion)
Funders
  • National Institutes of Health (T-32 MH065214)
  • Princeton University
  • Sponsor name not included
  • National Institute of General Medical Sciences (P20GM103645)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
Keywords
habit, stimulus-response learning, model-free reinforcement learning, outcome devaluation, contingency degradation, reinforcement schedules, perseveration, instrumental behavior, habit formation
MeSH terms
Animals, Behavior, Animal, Decision Making, Goals, Habits, Humans, Models, Psychological, Reinforcement, Psychology, Prefrontal Cortex

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