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Related Experiment Video

Updated: Apr 23, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Numerical discrimination is mediated by neural coding variation.

Richard W Prather1

  • 1University of Maryland, 3304 Benjamin Bld, College Park, MD 20742, USA.

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|September 20, 2014
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Numerical cognition accuracy depends on both value differences and absolute magnitude, challenging the Weber-Fechner law. This finding impacts understanding the approximate number system (ANS) and its predictive power.

Keywords:
Computational modelingNumerical cognitionPerceptual decision making

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Psychology

Background:

  • Numerical cognition relies on proportional differences, following the Weber-Fechner law.
  • Non-symbolic numerical discrimination measures the approximate number system (ANS) acuity.
  • ANS acuity predicts academic and economic outcomes.

Purpose of the Study:

  • Investigate if numerical discrimination depends solely on proportional differences or also absolute values.
  • Examine the neural basis for potential deviations from the Weber-Fechner law.
  • Develop a computational model to explain observed behavioral patterns.

Main Methods:

  • Behavioral experiments measuring numerical discrimination accuracy.
  • Computational modeling based on neural coding principles.
  • Analysis of the interaction between ratio difference and absolute value.

Main Results:

  • Numerical discrimination accuracy is influenced by both proportional difference and absolute value.
  • A significant interaction between ratio difference and absolute value was observed.
  • Behavioral and computational data corroborated this interaction.

Conclusions:

  • The Weber-Fechner law may not fully explain non-symbolic numerical discrimination.
  • Neural coding noise variation across numerosities could underlie the absolute value effect.
  • Re-evaluation of ANS measurement and outcome prediction is warranted.