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Modeling bout-pause response patterns in variable-ratio and variable-interval schedules using hierarchical Bayesian

Hiroshi Matsui1, Kota Yamada2, Takayuki Sakagami2

  • 1Department of Psychology, Keio University, Tokyo, Japan; Japan Society for the Promotion of Science, Tokyo, Japan.

Behavioural Processes
|July 31, 2018
PubMed
Summary
This summary is machine-generated.

This study reveals that operant response rates within bouts are higher in variable-ratio (VR) schedules than variable-interval (VI) schedules, using hierarchical Bayesian modeling. The analysis confirmed model robustness in distinguishing between-bout and within-bout response parameters.

Keywords:
Bayesian modelingBoutResponse rateVariable intervalVariable ratio

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Area of Science:

  • Behavioral psychology
  • Quantitative psychology
  • Reinforcement learning

Background:

  • Operant responses occur in bouts separated by pauses.
  • Performance differences in reinforcement schedules with identical inter-reinforcement intervals (IRIs) are mainly due to within-bout response rates, not bout initiation rates.

Purpose of the Study:

  • To introduce hierarchical Bayesian modeling for quantifying operant response bout properties.
  • To compare bout/pause patterns between variable-ratio (VR) and variable-interval (VI) schedules.
  • To assess the robustness of the Bayesian model in distinguishing between-bout and within-bout parameters.

Main Methods:

  • Hierarchical Bayesian modeling was employed.
  • Bernoulli distribution modeled the probability of staying in a bout or pause.
  • Poisson distribution quantified within-bout response rates.
  • Comparison of bout/pause patterns across VR and VI schedules with varying IRIs.

Main Results:

  • No significant difference in within-bout staying probability was found between VR and VI schedules.
  • Within-bout response rates were significantly higher in VR schedules compared to VI schedules across all IRIs.
  • Model simulations indicated that within-bout staying probability is sensitive to between-bout changes, while within-bout response rate parameters remain robust.

Conclusions:

  • Hierarchical Bayesian modeling effectively quantifies operant response bout properties.
  • Variable-ratio schedules yield higher within-bout response rates than variable-interval schedules.
  • The developed model demonstrates robustness in dissociating within-bout and between-bout response parameters under different reinforcement schedules.