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Related Experiment Videos

A model for residence time in concurrent variable interval performance.

Michael A Navakatikyan1

  • 1Department of Psychology, University of Auckland, New Zealand. michael.navakatikyan@brainz.com

Journal of the Experimental Analysis of Behavior
|March 10, 2007
PubMed
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A new component-functions model explains choice behavior on interdependent schedules, outperforming existing models. This behavioral model offers a more accurate and versatile alternative to the quantitative law of effect.

Area of Science:

  • Behavioral psychology
  • Mathematical modeling
  • Reinforcement learning

Background:

  • Interdependent concurrent variable-interval (VI) schedules are complex behavioral paradigms.
  • Existing models like Herrnstein's hyperbola have limitations in explaining choice behavior.
  • Understanding the underlying mechanisms of choice is crucial for behavioral science.

Purpose of the Study:

  • To propose a novel component-functions model for choice behavior.
  • To evaluate the model's performance against existing models using empirical data.
  • To explore the model's potential for extension to various schedule types.

Main Methods:

  • Developed a component-functions model based on the product of behavior-enhancing and behavior-reducing functions.

Related Experiment Videos

  • The model is derived from symmetrical differential equations for excitatory and inhibitory reinforcers.
  • Applied the model to residence time data from interdependent concurrent VI VI schedules.

    • Main Results:

    • The proposed model accounted for 87% of the variance in the data.
    • This significantly outperformed Davison and Hunter's model (64%) and Herrnstein's hyperbola (42%).
    • The model successfully explained matching, undermatching, and overmatching within the same subject.

    Conclusions:

    • The component-functions model provides a superior explanation of choice behavior on interdependent schedules.
    • It offers a viable alternative to Herrnstein's quantitative law of effect.
    • The model's framework has potential for broader applications in behavioral research.