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Beyond Letters: Optimal Transport as a Model for Sub-Letter Orthographic Processing.

Jack E Taylor1,2, Rasmus Sinn1, Cosimo Iaia1

  • 1Department of Psychology, Goethe University Frankfurt, Frankfurt, Germany.

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|October 20, 2025
PubMed
Summary
This summary is machine-generated.

Optimal transport theory models early letter shape processing in visual word recognition. This computational approach, using Wasserstein distance, accurately predicts neural activity and geometric invariances in letter perception.

Keywords:
EEGRSAcharactersgrapheticslettersoptimal transportorthographyvisual word recognition

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Psychology

Background:

  • Letter processing is crucial for visual word recognition.
  • Current models often simplify or ignore early perceptual letter recognition processes.

Purpose of the Study:

  • To propose optimal transport theory as a computational framework for letter shape processing.
  • To investigate the alignment of optimal transport cost with neural activity in early visual processing stages.

Main Methods:

  • Representational similarity analysis was employed.
  • Optimal transport cost (Wasserstein distance) was calculated between letter pairs.
  • Neural activity elicited by visual letter stimuli was recorded and analyzed.

Main Results:

  • Optimal transport cost aligns with neural activity within 225 ms of stimulus onset.
  • This approach outperforms models based on simple shape overlap.
  • Optimal transport successfully captures emergent geometric invariances in letter perception.
  • Wasserstein distance predicts neural activity comparably to features from trained artificial neural networks.

Conclusions:

  • Optimal transport theory provides a computationally explicit framework for modeling early orthographic processes.
  • This theory offers a more constrained and interpretable model of letter perception compared to unconstrained artificial network representations.