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Shir Hofstetter1,2, Marcus Daghlian3,2,4, Serge O Dumoulin3,2,4,5

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Humans and animals process item ranks using neuronal tuning, not symbols. This brain mechanism supports non-symbolic ordinality perception, crucial for decision-making and social behaviors.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Comparative Psychology

Background:

  • Ordinality, the perception of item rank in a sequence, is a fundamental cognitive skill shared across species.
  • This ability is vital for behaviors like decision-making, foraging, and social organization, independent of symbolic systems.
  • Previous research suggests a neural basis for quantity processing, but the mechanisms for ordinality perception remain less understood.

Purpose of the Study:

  • To investigate the neural mechanisms supporting non-symbolic ordinality perception in the human brain.
  • To test the hypothesis that neuronal tuning, with neurons selectively responding to specific ranks, underlies ordinality processing.
  • To explore whether similar ordinality processing mechanisms exist in artificial neural networks.

Main Methods:

  • Utilized ultra-high field 7 Tesla functional magnetic resonance imaging (fMRI) in human participants.
  • Applied population receptive field (pRF) modeling to identify neural populations tuned to ordinal positions.
  • Trained hierarchical convolutional neural networks on visual tasks to observe spontaneous emergence of ordinality tuning.

Main Results:

  • Identified neural populations in parietal and premotor cortices tuned to non-symbolic ordinal positions.
  • Observed increased tuning width and decreased cortical territory for higher ordinal ranks, indicating reduced precision.
  • Found that these neural responses did not generalize to symbolic ordinality, and similar tuning emerged in convolutional neural networks.

Conclusions:

  • Neuronal tuning properties in parietal and premotor cortices support non-symbolic ordinality perception.
  • The findings suggest that ordinality processing relies on inherent neural processing features, similar to other quantity representations.
  • The emergence of similar tuning in artificial neural networks suggests a fundamental computational principle for rank processing.