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Normalization is a general neural mechanism for context-dependent decision making.

Kenway Louie1, Mel W Khaw, Paul W Glimcher

  • 1Center for Neural Science, New York University, New York, NY 10003, USA. klouie@cns.nyu.edu

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Neural divisive normalization, a brain computation, influences decision-making. This study reveals how normalization in value signals leads to context-dependent choices in humans and monkeys, impacting rational decision-making.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Decision Science

Background:

  • Divisive normalization is a key neural computation in sensory systems, involved in contrast adaptation and attention.
  • Recent research suggests a similar normalization process operates on value signals guiding decision-making.
  • The impact of this neural normalization on actual choice behavior remains largely unexplored.

Purpose of the Study:

  • To investigate the effects of neural normalization on choice behavior.
  • To determine if normalization models can explain context-dependent choice phenomena.
  • To link neural mechanisms of value coding to observable decision-making patterns.

Main Methods:

  • Developed choice models incorporating divisive normalization.
  • Conducted value-guided choice experiments with both human and monkey participants.
  • Analyzed choice data to identify behaviors consistent with normalization predictions.

Main Results:

  • Choice models with normalization accurately predicted significant, context-dependent choice phenomena.
  • Observed behaviors in both humans and monkeys align with normalization-driven predictions.
  • Normalization effects were evident based on the value and number of alternative options.

Conclusions:

  • Neural normalization of value signals critically influences stochastic choice behavior.
  • Normalization provides a quantitative framework for understanding context effects in decision-making.
  • Findings bridge neural computation and behavioral economics, linking brain mechanisms to choice outcomes.