Predicting the attention of others

Author:

Ziman Kirsten1ORCID,Kimmel Sarah C.1,Farrell Kathryn T.1,Graziano Michael S. A.12ORCID

Affiliation:

1. Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544

2. Department of Psychology, Princeton University, Princeton, NJ 08544

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.

Funder

HHS | NIH | National Institute of Mental Health

AE Studios

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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