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A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

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Published on: November 9, 2018

A further application of the active time model to multiple concurrent variable-interval schedules.

Andrew T McKenzie1, J Mark Cleaveland

  • 1Department of Psychology, Vassar College, Box 298, 124 Raymond Avenue, Poughkeepsie, NY 12603, United States.

Behavioural Processes
|October 10, 2009
PubMed
Summary

The active time model (ATM) accurately predicts behavior under concurrent variable interval schedules. This model outperformed scalar expectancy theory in fitting experimental data from birds trained on different reinforcement schedules.

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Area of Science:

  • Behavioral Psychology
  • Animal Cognition
  • Reinforcement Learning

Background:

  • Concurrent variable interval (VI) schedules are fundamental in understanding choice behavior.
  • Predictive models are crucial for explaining and validating experimental findings in operant conditioning.

Purpose of the Study:

  • To evaluate the predictive accuracy of the active time model (ATM) for probe data under concurrent VI VI schedules.
  • To compare the performance of ATM against a variant of scalar expectancy theory.

Main Methods:

  • Subjects (birds) were trained on concurrent VI 30-s VI 60-s and VI 60-s VI 120-s schedules.
  • Unreinforced probes were conducted, pairing stimuli with equivalent absolute but different relative reinforcement rates, and vice versa.
  • Behavioral data from probes were analyzed using ATM and scalar expectancy theory.

Main Results:

  • Birds showed preferences in probe trials that aligned with reinforcement schedules.
  • The active time model (ATM) accurately fit individual subject data for both probe types.
  • A variant of scalar expectancy theory failed to fit the data at individual or group levels.

Conclusions:

  • The active time model (ATM) provides an accurate account of behavior under concurrent variable interval schedules.
  • ATM demonstrates superior predictive power compared to scalar expectancy theory in this context.
  • Findings support the utility of ATM in modeling complex reinforcement schedules.