Cortical networks involved in judging the attention of others

Understanding someone else's attention lies at the heart of human interaction. When we perceive something to be in someone else's attention, we understand it to be in that person's mind in the moment, ready to affect that person's choices. It has been proposed that people construct predictive models of the attentional state of others. In that proposal, a working understanding of someone else's attention goes far beyond merely tracking the direction of someone's eyes. For example, in a recent study, participants watched a spotlight of attention moving around a picture and successfully judged whether the trace represented real, human attention or an artificially manipulated version, demonstrating implicit information about how patterns of attention behave over space and time. However, the neuronal underpinnings of attention modeling are almost entirely unstudied. Here we tested people in an fMRI scanner while they performed the attention judgment paradigm. When contrasting attention traces that participants judged to be artificial versus those judged to be real, activity was found bilaterally in the precuneus, dorsomedial prefrontal cortex, anterior cingulate, and anterior insula, as well as in a part of the right temporoparietal junction. These areas partially overlap the theory-of-mind network and the salience network. The activations differ from those known to be involved in processing low-level features of attention such as the sight of other people's eyes. The results provide an initial picture of the cortical networks involved in monitoring the attention patterns of others and recognizing when those patterns deviate from expectation.

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Publication Details

Journal
Cerebral Cortex
Published
2025-08-28
DOI
https://doi.org/10.1093/cercor/bhaf266
Primary Topic
Cognitive Science and Education Research
Type
article
Field-Weighted Citation Impact
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article

Cortical networks involved in judging the attention of others

Michael S. A. Graziano, Kirsten Ziman, Isaac Ray Christian, Sarah C. Kimmel et al.
Cerebral Cortex
Cognitive Science and Education Research
article

Cortical networks involved in judging the attention of others

Michael S. A. Graziano, Kirsten Ziman, Isaac Ray Christian, Sarah C. Kimmel, Kathryn T. Farrell
article en

Abstract

Understanding someone else's attention lies at the heart of human interaction. When we perceive something to be in someone else's attention, we understand it to be in that person's mind in the moment, ready to affect that person's choices. It has been proposed that people construct predictive models of the attentional state of others. In that proposal, a working understanding of someone else's attention goes far beyond merely tracking the direction of someone's eyes. For example, in a recent study, participants watched a spotlight of attention moving around a picture and successfully judged whether the trace represented real, human attention or an artificially manipulated version, demonstrating implicit information about how patterns of attention behave over space and time. However, the neuronal underpinnings of attention modeling are almost entirely unstudied. Here we tested people in an fMRI scanner while they performed the attention judgment paradigm. When contrasting attention traces that participants judged to be artificial versus those judged to be real, activity was found bilaterally in the precuneus, dorsomedial prefrontal cortex, anterior cingulate, and anterior insula, as well as in a part of the right temporoparietal junction. These areas partially overlap the theory-of-mind network and the salience network. The activations differ from those known to be involved in processing low-level features of attention such as the sight of other people's eyes. The results provide an initial picture of the cortical networks involved in monitoring the attention patterns of others and recognizing when those patterns deviate from expectation.

Cerebral CortexVol. 35(9)
Princeton University (US)
Openalex Percentile: Top 74%
Cognitive Science and Education Research
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