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Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes.

Asieh Abolpour Mofrad1, Anis Yazidi2, Hugo L Hammer3

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This study introduces the equivalence projective simulation (EPS) model, linking stimulus equivalence (SE) in humans to artificial agents. The EPS model successfully simulates complex behavioral patterns, offering an interpretable alternative to black box models.

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Area of Science:

  • Behavioral psychology
  • Artificial intelligence
  • Machine learning

Background:

  • Stimulus equivalence (SE) and projective simulation (PS) are frameworks for studying complex behavior in humans and artificial agents, respectively.
  • Existing models often lack interpretability and flexibility.
  • Bridging behavior analysis and machine learning is a significant challenge.

Discussion:

  • The equivalence projective simulation (EPS) model is proposed, adapting the PS framework to model SE formation.
  • EPS simulates the emergence of equivalence relations using a matching-to-sample (MTS) procedure.
  • The model operates within a varying action set and derives new relations without environmental feedback.

Key Insights:

  • This research marks the first link between equivalence theory in behavior analysis and artificial agents within machine learning.
  • The EPS model demonstrates advantages over traditional neural network models due to its interpretability and flexibility.
  • The model successfully replicates experimental results, including typical and atypical participant behavior and the nodal effect.

Outlook:

  • The EPS model provides a valuable tool for understanding and simulating complex cognitive processes.
  • Further research can explore the application of EPS in diverse areas of equivalence theory.
  • The model's interpretability facilitates modifications for advanced research questions in artificial intelligence and cognitive science.