Predicting the attention of others

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.

Keywords: attention; eye movement; predictive models; social attention; social cognition.

Fig. 1.

Example of an attention location. The stimuli consisted of a glowing attention spotlight moving around an underlying scene. Attention sequences were derived from patterns of eye movements, from people who viewed the images for 3 s each. The eye movement data was obtained from the publicly available Dutch Image Description and Eye-tracking Corpus or DIDEC (41). One frame of one example movie is shown here. The attention spotlight is on the sunbather in the lower right quadrant of the scene.

Fig. 2.

Results from experiment 1. (A) Percentage of trials in which subjects correctly chose the valid attention movie over the manipulated attention movie. Each white dot represents the average percent correct for an individual participant. The mean of the distribution (63.93%) is significantly above chance. (B) Percent correct, separated by subjects’ confidence in their judgment. Subjects were most accurate, on average, in trials where they reported the most confidence in their answers (mean = 85.04%). (C) Percent correct, separated by the number of attended locations in the trial’s video stimulus. (D) Percent correct, for each of the 70 video stimulus pairs in the experiment.

Fig. 3.

Percentage of trials in which subjects correctly chose the valid attention movie over the manipulated attention movie, in experiments 1–6. Each white dot represents the average percent correct for an individual participant. See text for details of statistics.

Fig. 4.

Data from experiment 7. (A) Percentage of responses in which subjects correctly chose individual A over B on the basis of their attention traces. Mean of all subjects, across all trials. Error bars show the SE. (B) Percentage correct, split by experiment block number (10 trials per block). Block 1 shows the results of trials 1–10, block 2 of trials 11–20, and so on. Error bars show the SE.

Fig. 5.

Average responses of subjects to valid and scrambled attention movies in experiment 8. Subjects rated each movie on four attributes, on a 1–4 scale. On average, movies that showed valid attention were rated differently from movies that showed temporally scrambled attention. The error bars show the SE.