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Test-retest reliability of reinforcement learning parameters. | LitMetric

Test-retest reliability of reinforcement learning parameters.

Behav Res Methods

Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands.

Published: August 2024

AI Article Synopsis

  • The study explores the concept of computational phenotyping in the context of computational psychiatry, which uses parameter estimates from models to understand individual differences in behavior.
  • It examines the test-retest reliability of reinforcement learning models across two experimental tasks, finding that while personality and cognitive measures showed high reliability, the model parameter estimates were generally low.
  • The results indicate that a significant portion of variability in the model parameters may be due to individual participant differences, with mood factors like stress and happiness contributing to this variability.

Article Abstract

It has recently been suggested that parameter estimates of computational models can be used to understand individual differences at the process level. One area of research in which this approach, called computational phenotyping, has taken hold is computational psychiatry. One requirement for successful computational phenotyping is that behavior and parameters are stable over time. Surprisingly, the test-retest reliability of behavior and model parameters remains unknown for most experimental tasks and models. The present study seeks to close this gap by investigating the test-retest reliability of canonical reinforcement learning models in the context of two often-used learning paradigms: a two-armed bandit and a reversal learning task. We tested independent cohorts for the two tasks (N = 69 and N = 47) via an online testing platform with a between-test interval of five weeks. Whereas reliability was high for personality and cognitive measures (with ICCs ranging from .67 to .93), it was generally poor for the parameter estimates of the reinforcement learning models (with ICCs ranging from .02 to .52 for the bandit task and from .01 to .71 for the reversal learning task). Given that simulations indicated that our procedures could detect high test-retest reliability, this suggests that a significant proportion of the variability must be ascribed to the participants themselves. In support of that hypothesis, we show that mood (stress and happiness) can partly explain within-participant variability. Taken together, these results are critical for current practices in computational phenotyping and suggest that individual variability should be taken into account in the future development of the field.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289054PMC
http://dx.doi.org/10.3758/s13428-023-02203-4DOI Listing

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