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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Relational cognition, the ability to infer generalized relationships, is crucial for intelligence.
  • Understanding the neural basis of relational cognition, particularly transitive inference (TI), remains a challenge due to limited predictive models.
  • Working memory (WM) is hypothesized to be essential for relational inference in the brain.

Purpose of the Study:

  • To discover and analyze neural networks (NNs) capable of performing transitive inference (TI).
  • To investigate how NNs generalize and exhibit behaviors observed in biological systems during relational tasks.
  • To compare NN performance and solutions with human behavior in WM-dependent TI tasks.

Main Methods:

  • Developed and analyzed neural networks (NNs) trained on transitive inference (TI) tasks.
  • Tested NN generalization capabilities, including performance under working memory (WM) load.
  • Conducted large-scale experiments with human subjects performing WM-based TI.
  • Compared human behavioral data with predictions from different NN models.

Main Results:

  • NNs demonstrated perfect generalization in TI tasks without explicit transitive structure.
  • NNs exhibited emergent behaviors, including order-dependent patterns, mirroring those in living subjects.
  • Human subjects' behavior in WM-based TI tasks was inconsistent with certain intuitive NN solutions.
  • Identified alternative NN solutions with distinct behavioral and neural predictions.

Conclusions:

  • Neural networks can learn and generalize relational cognition, providing testable hypotheses for brain function.
  • The study reveals discrepancies between NN models and human behavior in TI, suggesting complex neural implementations.
  • Findings offer novel insights into the neural mechanisms underlying relational cognition and its potential computational underpinnings.