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Moving Beyond Reinforcement and Response Strength.

Timothy A Shahan1

  • 1Department of Psychology, Utah State University, Logan, UT 84322 USA.

The Behavior Analyst
|January 25, 2020
PubMed
Summary
This summary is machine-generated.

This study critiques response strengthening in behavior analysis, proposing an alternative focused on learning predictive relations. This approach uses important events to measure learning without assuming response strengthening, enhancing behavior science and technology.

Keywords:
InformationMotivationReinforcementResponse strength

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Area of Science:

  • Behavioral Psychology
  • Learning Theory
  • Cognitive Science

Background:

  • Behavior analysis traditionally relies on reinforcement as response strengthening.
  • This implicit process is often hypothetical, potentially inaccurate, and unfalsifiable.
  • A critical review of this traditional view is necessary to explore alternative frameworks.

Purpose of the Study:

  • To critique the concept of reinforcement as response strengthening in behavior analysis.
  • To propose and explore an alternative framework for behavior analysis that does not rely on response strengthening.
  • To investigate how learning predictive relations and organismic state can account for behavioral changes.

Main Methods:

  • Conceptual analysis of existing behavior analysis theories.
  • Theoretical exploration of an alternative model without response strengthening.
  • Examination of the role of "reinforcers" as measures of learning predictive relations.

Main Results:

  • Identified problematic aspects of reinforcement as response strengthening, including its implicit and unfalsifiable nature.
  • Proposed that "reinforcers" function to measure learning of predictive relations by modulating performance based on predictions and organismic state.
  • Suggested formal descriptions of predictive relations in operant behavior are a crucial first step.

Conclusions:

  • An alternative to response strengthening in behavior analysis is feasible and potentially more scientifically rigorous.
  • Focusing on learning predictive relations and organismic state offers a new perspective on behavior.
  • This approach may improve both the basic science and applied technology of behavior analysis.