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  3. Modeling Relational Responding With Artificial Neural Networks.
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  3. Modeling Relational Responding With Artificial Neural Networks.

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Modeling relational responding with artificial neural networks.

Janelle Mendoza1, Stefano Ghirlanda2

  • 1Department of Psychology, CUNY Graduate Center, USA.

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View abstract on PubMed

Summary
This summary is machine-generated.

A neural network model successfully replicated pigeon behavior in a relational responding task, demonstrating generalization from training to novel stimuli. This model reconciles differing theories on how animals learn stimulus relationships.

Keywords:
Artificial neural networksComputational modelingRelational cognitionStimulus generalization

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Area of Science:

  • Comparative psychology
  • Artificial intelligence
  • Cognitive science

Background:

  • Relational responding involves behavior aligned with rules for comparing stimuli.
  • Previous studies trained pigeons on stimulus comparison tasks with mixed results.
  • Contrasting theories exist regarding the mechanisms of relational responding in animals.

Purpose of the Study:

  • To develop and validate an artificial neural network model of relational responding.
  • To analyze the model's generalization capabilities from training to test stimuli.
  • To reconcile theoretical perspectives on relational responding.

Main Methods:

  • Trained pigeons on a task involving choosing smaller or larger circles.
  • Developed a simple artificial neural network model to simulate pigeon behavior.
  • Analyzed the model's performance on novel stimulus pairs.

    • Main Results:

    • The neural network model accurately reproduced pigeon behavior, including systematic failures.
    • The model demonstrated generalization from trained to untrained stimulus pairs.
    • The model's performance aligned with findings from Lazareva et al. (2014).

    Conclusions:

    • A simple artificial neural network can effectively model relational responding in pigeons.
    • The model provides a computational account that integrates stimulus-feature and stimulus-relationship learning.
    • The findings help reconcile Köhler's relational rules theory with Spence's stimulus generalization theory.