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Pupil size variations reveal Bayesian inference in cognitive arithmetic.

Alexandre Zénon1, Samuel Salvaggio2, Michael Andres2

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Summary
This summary is machine-generated.

The brain uses Bayesian inference for arithmetic, updating beliefs about answers as numbers are processed. Pupil dilation tracks this information gain, showing faster problem-solving with more informative inputs.

Keywords:
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Area of Science:

  • Cognitive Neuroscience
  • Mathematical Cognition
  • Computational Psychiatry

Background:

  • Bayesian inference models brain function, explaining behavior and neurophysiology.
  • Its role in arithmetic, particularly how uncertainty and prior beliefs are managed, is not well understood.

Purpose of the Study:

  • To investigate if Bayesian inference mechanisms underpin arithmetic problem-solving.
  • To examine how information from operands refines response probabilities and how uncertainty is represented.

Main Methods:

  • Three experiments involving participants calculating sums of sequentially presented numbers.
  • Pupillometry to measure pupil-related arousal as an indicator of information gain.
  • Manipulation of operand informativeness to influence response range narrowing.

Main Results:

  • Pupil dilation correlated with information gain from the first operand, indicating updated probability distributions.
  • More informative operands and greater pupil dilation led to faster response times.
  • This suggests enhanced initial information processing facilitates quicker arithmetic problem-solving.

Conclusions:

  • Bayesian inference plays a crucial role in human arithmetic problem-solving.
  • Information gain during operand integration can be monitored using pupillometry.
  • This provides a neurophysiological marker for cognitive processes in arithmetic.