Invariant inter-subject relational structures in the human visual cortex

TL;DR

This study demonstrates that relational coding, based on similarity distances between activation patterns, is the most invariant neuronal coding scheme across different individuals' visual cortices, highlighting a shared basis for visual perception.

Contribution

The paper identifies relational coding as the invariant neuronal coding scheme across individuals, advancing understanding of shared visual perception at the neural level.

Findings

Relational coding shows high inter-subject consistency.

Alternative coding schemes like population vector coding are less consistent.

Relational coding underpins shared perceptual content in the human brain.

Abstract

It is a fundamental behavior that different individuals see the world in a largely similar manner. This is an essential basis for humans' ability to cooperate and communicate. However, what are the neuronal properties that underlie these inter-subject commonalities of our visual world? Finding out what aspects of neuronal coding remain invariant across individuals' brains will shed light not only on this fundamental question but will also point to the neuronal coding scheme as the basis of visual perception. Here, we address this question by obtaining intracranial recordings from three cohorts of patients taking part in a different visual recognition task (overall 19 patients and 244 high-order visual contacts included in the analyses) and examining the neuronal coding scheme most consistent across individuals' visual cortex. Our results highlight relational coding - expressed by the set of similarity distances between profiles of pattern activations - as the most consistent representation across individuals. Alternative coding schemes, such as population vector coding or linear coding, failed to achieve similar inter-subject consistency. Our results thus support relational coding as the central neuronal code underlying individuals' shared perceptual content in the human brain.