Evidence map›Paper›PMID 37812722›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Predicting the attention of others.

Kirsten Ziman, Sarah C Kimmel, Kathryn T Farrell, Michael S A Graziano

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Monitoring Attention in Self and Others.Journal of cognitive neuroscience · 2025
    Article
  5. Cortical networks involved in judging the attention of others.Cerebral cortex (New York, N.Y. : 1991) · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kirsten ZimanPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08544.ORCID 0000-0002-8942-3362
Sarah C KimmelPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08544.
Kathryn T FarrellPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08544.
Michael S A GrazianoPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08544.ORCID 0000-0002-7727-3475

Funding

NRSA Training Grant in Quantitative NeuroscienceT32MH065214 · NIMH · PRINCETON UNIVERSITY · PI Nathaniel Douglass Daw, KENNETH A NORMAN · 2002 to 2026
$7.9M
NIMH NIH HHS T32 MH065214
6 · The paper itself

Abstract

As social animals, people are highly sensitive to the attention of others. Seeing someone else gaze at an object automatically draws one's own attention to that object. Monitoring the attention of others aids in reconstructing their emotions, beliefs, and intentions and may play a crucial role in social alignment. Recently, however, it has been suggested that the human brain constructs a predictive model of other people's attention that is far more involved than a moment-by-moment monitoring of gaze direction. The hypothesized model learns the statistical patterns in other people's attention and extrapolates how attention is likely to move. Here, we tested the hypothesis of a predictive model of attention. Subjects saw movies of attention displayed as a bright spot shifting around a scene. Subjects were able to correctly distinguish natural attention sequences (based on eye tracking of prior participants) from altered sequences (e.g., played backward or in a scrambled order). Even when the attention spot moved around a blank background, subjects could distinguish natural from scrambled sequences, suggesting a sensitivity to the spatial-temporal statistics of attention. Subjects also showed an ability to recognize the attention patterns of different individuals. These results suggest that people possess a sophisticated model of the normal statistics of attention and can identify deviations from the model. Monitoring attention is therefore more than simply registering where someone else's eyes are pointing. It involves predictive modeling, which may contribute to our remarkable social ability to predict the mind states and behavior of others.

Indexed as

BrainCognitionEmotionsEyeHumansVision, Ocularattentioneye movementpredictive modelssocial attentionsocial cognition

Identifiers

PMID37812722
PMCPMC10589679

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.