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

Hiroshi Matsui, Kota Yamada, Takayuki Sakagami, Takayuki Tanno

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Streams of operant responses are arranged in bouts separated by pauses and differences in performance in reinforcement schedules with identical inter-reinforcement intervals (IRIs) are primarily due to differences in within–bout response rate, not in bout–initiation rate. The present study used hierarchical Bayesian modeling as a new method to quantify the properties of the response bout. A Bernoulli distribution was utilized to express the probability to stay in bout/pause, while a Poisson distribution was utilized to quantify the within–bout response rates. We compared bout/pause patterns between variable–ratio (VR) and variable–interval (VI) schedules across IRIs. The model estimation revealed no difference in within-bout staying probability between schedules. However, response rates of within–bout responses were higher in VR than VI across IRIs. These results are consistent with previous analyses using a log–survivor plot to describe within–bout responses and bouts–initiation responses. In addition, a simulation study was performed to examine how sensitively the model estimate the parameters according to different bout initiation rates. These result showed that the within–bout staying probability was affected by changes in between–bout while within–bout response rate parameters were not. This suggests model estimation robustness of the model estimation to dissociate within–bout and between–bout parameters during different reinforcement schedules.

Original languageEnglish
Pages (from-to)346-353
Number of pages8
JournalBehavioural Processes
Publication statusPublished - 2018 Dec


  • Bayesian modeling
  • Bout
  • Response rate
  • Variable interval
  • Variable ratio

ASJC Scopus subject areas

  • Animal Science and Zoology
  • Behavioral Neuroscience


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