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Adaptive neural coding: from biological to behavioral decision-making.

Kenway Louie1, Paul W Glimcher1, Ryan Webb2

  • 1Center for Neural Science, New York University, New York, NY 10003 ; Institute for the Interdisciplinary Study of Decision Making, New York University, New York, NY 10003.

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Deviations from optimal decision-making in animals may stem from biological mechanisms evolved for performance under neural constraints. These mechanisms use relative value coding, explaining context-dependent choices and linking neural circuits to behavior.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Behavioral Economics

Background:

  • Empirical decision-making often deviates from normative choice theory predictions.
  • The underlying reasons for these suboptimal behaviors in biological systems remain largely unknown.

Purpose of the Study:

  • To propose that suboptimal decision-making arises from evolved biological mechanisms operating under biophysical constraints.
  • To link the computational principles of neural circuits to observed choice behaviors.

Main Methods:

  • Examining decision-making at the computational level.
  • Analyzing sensory processing computations like divisive normalization.
  • Investigating analogous computations in decision-related brain areas.

Main Results:

  • Biological decision mechanisms may have evolved to maximize performance within inherent biophysical limits.
  • Adaptive computations, such as relative value coding, can explain context-dependent decision-making.
  • These computations reconcile behavioral violations of normative theory with neural constraints.

Conclusions:

  • Deviations from optimality are not necessarily errors but adaptations to biological constraints.
  • Computational approaches provide a critical bridge between neural circuit architecture and empirical choice behavior.
  • Understanding these mechanisms offers insights into the evolution of decision-making across species.