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The dimensionality of neural representations for control.

David Badre1, Apoorva Bhandari1, Haley Keglovits1

  • 1Department of Cognitive, Linguistic, and Psychological Sciences, Carney Institute for Brain Science, Brown University.

Current Opinion in Behavioral Sciences
|September 1, 2020
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Summary

This review explores how representational dimensionality shapes cognitive control. Understanding this neural code property is key to flexible behavior and future neuroscience research.

Keywords:
cognitive controlexecutive functionfrontal lobesneural computationneural representation

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Cognitive control enables flexible goal-directed behavior.
  • Control representations are central to cognitive control theories.
  • Neural codes must balance separability and generalizability.

Purpose of the Study:

  • To review the role of representational dimensionality in cognitive control.
  • To discuss the implications of dimensionality for neural computation.
  • To summarize current neuroscience findings on control representation dimensionality.

Main Methods:

  • Literature review of cognitive control theories.
  • Analysis of representational dimensionality in neural codes.
  • Synthesis of neuroscience findings, focusing on the prefrontal cortex.

Main Results:

  • Representational dimensionality is a critical property of control representations.
  • Dimensionality influences the separability/generalizability trade-off in neural computation.
  • Evidence suggests specific dimensionality patterns in prefrontal cortex control representations.

Conclusions:

  • Dimensionality is crucial for understanding how the brain implements cognitive control.
  • Future research should further investigate the dimensionality of neural codes in cognitive control.
  • Open questions remain regarding the precise neural mechanisms and optimal dimensionality for cognitive control.