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Related Experiment Videos

A selectionist approach to reinforcement

J W Donahoe1, J E Burgos, D C Palmer

  • 1Department of Psychology, University of Massachusetts, Amherst 01003.

Journal of the Experimental Analysis of Behavior
|July 1, 1993
PubMed
Summary
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A new reinforcement principle, modeled in neural networks, explains complex behaviors like learning and stimulus control. This biologically plausible model simulates respondent and operant conditioning phenomena.

Area of Science:

  • Neuroscience
  • Behavioral Science
  • Computational Neuroscience

Background:

  • Traditional models of reinforcement often treat behavior and neural processes separately.
  • Understanding the neural basis of reinforcement is crucial for explaining complex adaptive behaviors.

Purpose of the Study:

  • To introduce a unified principle of reinforcement integrating behavioral and neuroscientific data.
  • To explore the implications of this principle using computer simulations of adaptive neural networks.

Main Methods:

  • Development of a biologically plausible neural network model.
  • Implementation of a single reinforcement principle within the network.
  • Computer simulations to observe emergent network behaviors.

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Main Results:

  • The model successfully simulated key phenomena of respondent and operant conditioning, including acquisition, extinction, and stimulus control (e.g., blocking, discrimination).
  • Simulated networks demonstrated context-dependent behavior guidance, consistent with behavior-analytic principles.
  • Reinforcement acting on network connectivity produced complex behaviors from simple processes.

Conclusions:

  • A single, biologically plausible reinforcement principle can account for a wide range of behavioral phenomena.
  • Adaptive neural networks offer a powerful framework for simulating complex behavior and understanding its neural underpinnings.
  • This selectionist approach provides a promising avenue for simulating the complex behavior of living organisms.