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Habits without values.

Kevin J Miller1, Amitai Shenhav2, Elliot A Ludvig3

  • 1Princeton Neuroscience Institute, Princeton University.

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|January 25, 2019
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Summary
This summary is machine-generated.

This study introduces a new computational model for habit formation, proposing habits arise from direct action strengthening, not outcome evaluation. This model explains key habit behaviors like insensitivity to outcome changes and perseveration.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychiatry
  • Behavioral Science

Background:

  • Habits are a key aspect of behavior.
  • Current models link habits to model-free reinforcement learning and outcome evaluation.
  • Traditional views define habits as stimulus-triggered behaviors independent of outcome evaluation.

Purpose of the Study:

  • To develop a computational model of habit formation based on the traditional view of stimulus-response associations.
  • To explain key behavioral phenomena associated with habits.
  • To provide a parsimonious account for existing behavioral and neural data.

Main Methods:

  • Developed a computational model where habits strengthen direct action selection based on recent use.
  • The model does not encode outcome values.
  • Simulated habit formation and tested against established behavioral findings.

Main Results:

  • The model successfully replicates insensitivity to outcome devaluation and contingency degradation.
  • It explains the impact of reinforcement schedules on habit formation rates.
  • The model accounts for perseverative behavior in repeated-choice tasks.

Conclusions:

  • Habitual behaviors can be parsimoniously explained by value-free mechanisms.
  • This framework offers a new foundation for modeling habit interactions with goal-directed behaviors.
  • It guides future research into the neural basis of instrumental behavior control.