TY - JOUR
T1 - Cortical networks involved in judging the attention of others
AU - Ziman, Kirsten
AU - Kimmel, Sarah C.
AU - Christian, Isaac
AU - Farrell, Kathryn T.
AU - Graziano, Michael S.A.
N1 - Publisher Copyright:
© The Author(s) 2025. Published by Oxford University Press. All rights reserved.
PY - 2025/9/1
Y1 - 2025/9/1
N2 - 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.
AB - 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.
KW - anterior cingulate cortex
KW - anterior insula
KW - attention
KW - dorsomedial prefrontal cortex
KW - precuneus
KW - predictive models
KW - temporoparietal junction
UR - https://www.scopus.com/pages/publications/105017185369
UR - https://www.scopus.com/pages/publications/105017185369#tab=citedBy
U2 - 10.1093/cercor/bhaf266
DO - 10.1093/cercor/bhaf266
M3 - Article
C2 - 41004159
AN - SCOPUS:105017185369
SN - 1047-3211
VL - 35
JO - Cerebral Cortex
JF - Cerebral Cortex
IS - 9
M1 - bhaf266
ER -