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Learning to select computations in recurrent neural circuits.

Sixing Chen1, Frederick Callaway1, Sreejan Kumar1,2

  • 1Department of Psychology, New York University, New York, NY, USA.

Biorxiv : the Preprint Server for Biology
|April 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study presents a neural network model that learns to select computations, explaining how the brain achieves flexible and efficient cognitive control. It unifies meta-reasoning and meta-learning for adaptive thought control.

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Area of Science:

  • Computational neuroscience
  • Cognitive science
  • Artificial intelligence

Background:

  • Biological computation exhibits flexibility and efficiency, often linked to cognitive control.
  • The neural mechanisms underlying adaptive control balancing utility and computational cost remain largely unknown.
  • Prefrontal cortex is implicated in higher-level cognitive functions and decision-making.

Purpose of the Study:

  • To propose a computational framework for adaptive control of thought.
  • To model how the brain implements flexible and efficient cognitive processes.
  • To unify theories of meta-reasoning and meta-learning in neural systems.

Main Methods:

  • Developed a recurrent neural network model integrating rational meta-reasoning theory and a meta-learning algorithm.
  • Utilized the model to simulate performance in simple choice and multi-step planning tasks.
  • Compared model outputs with neural dynamics from macaque orbitofrontal cortex and human planning behaviors.

    • Main Results:

    • The model successfully learned to select computations, approximating optimal symbolic models in choice tasks.
    • It reproduced neural dynamics observed in macaque orbitofrontal cortex.
    • In planning tasks, the model replicated human behavioral strategies and associated neural dynamics.

    Conclusions:

    • The proposed framework offers a mechanistic account of adaptive control of thought.
    • Learning to reason can be framed as learning to learn from internal cognitive operations.
    • This work bridges computational theory and neural implementation of cognitive control.