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Test-retest reliability of reinforcement learning parameters.

Jessica V Schaaf1,2,3, Laura Weidinger4,5, Lucas Molleman6,5

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Behavior Research Methods
|September 8, 2023
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Summary
This summary is machine-generated.

Computational phenotyping uses model parameters to study individual differences. However, this study found poor test-retest reliability for reinforcement learning model parameters, suggesting participant variability, like mood, influences results.

Keywords:
Computational modelingComputational phenotypingComputational psychiatryReinforcement learningTest–retest reliability

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Area of Science:

  • Computational psychiatry
  • Cognitive neuroscience
  • Psychometric research

Background:

  • Computational phenotyping leverages computational model parameters to understand individual differences in cognitive processes.
  • A key assumption for computational phenotyping is the temporal stability of both behavior and model parameters.
  • The test-retest reliability of computational models, particularly reinforcement learning models, is largely uncharacterized.

Purpose of the Study:

  • To investigate the test-retest reliability of canonical reinforcement learning models.
  • To assess the reliability of model parameters in two common learning paradigms: a two-armed bandit task and a reversal learning task.
  • To determine if computational phenotyping is a reliable method for assessing individual differences.

Main Methods:

  • Two independent cohorts (N=69 and N=47) completed learning tasks online.
  • A five-week interval was used between test and retest sessions.
  • Test-retest reliability was assessed using intraclass correlation coefficients (ICCs) for model parameters and compared with personality and cognitive measures.

Main Results:

  • High test-retest reliability was observed for personality and cognitive measures (ICCs: .67–.93).
  • Reinforcement learning model parameters exhibited generally poor test-retest reliability (bandit task ICCs: .02–.52; reversal learning task ICCs: .01–.71).
  • Simulations confirmed the study's capacity to detect high reliability, indicating the poor results reflect genuine instability.
  • Participant mood (stress and happiness) was found to explain some within-participant variability.

Conclusions:

  • The findings challenge the reliability of current computational phenotyping practices in computational psychiatry due to poor parameter stability.
  • Significant within-participant variability, potentially influenced by factors like mood, complicates the interpretation of model parameters.
  • Future development of computational phenotyping must account for and address individual variability to ensure robust and meaningful insights.