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Two pathways to resolve relational inconsistencies.

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Summary
This summary is machine-generated.

When faced with unexpected information, people and artificial neural networks (ANNs) may resist changing their core beliefs. Extreme violations trigger a different learning mechanism, preserving prior expectations.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Machine Learning

Background:

  • Individuals often maintain prior expectations despite contradictory observations.
  • Extreme violations of expectations are paradoxically met with greater resistance to change.

Purpose of the Study:

  • To investigate the mechanisms underlying expectation adaptation in the face of observational violations.
  • To explore whether artificial neural networks (ANNs) exhibit similar phenomena in relational learning.

Main Methods:

  • Testing the adaptation capabilities of ANNs trained on relational learning tasks.
  • Analyzing how ANNs adjust expectations based on the magnitude of violated observations.

Main Results:

  • ANNs demonstrate a similar pattern to humans: small violations lead to relational expectation adjustments.
  • Large violations in ANNs trigger a shift in object representation, bypassing relational adaptation.
  • This suggests a unified learning dynamic across biological and artificial systems.

Conclusions:

  • The stability of prior expectations during large violations is a natural outcome of learning dynamics.
  • No additional cognitive mechanisms are required to explain this observed stability.
  • Intermediate adaptation steps may influence the robustness of prior expectations.