The interplay between gaze and consistency in scene viewing: Evidence from visual search by young and older adults

The Influence of Competing Social and Symbolic Cues on Observers' Gaze Behaviour

The effects of social (eye gaze, pointing gestures) and symbolic (arrows) cues on observers' attention are often studied by presenting such cues in isolation and at fixation. Here, we extend this work by embedding cues in natural scenes. Participants were presented with a single cue (Experiment 1) or a combination of cues (Experiment 2) embedded in natural scenes and were asked to 'simply look at the images' while their eye movements were recorded to assess the effects of the cues on (overt) attention. Single-gaze and pointing cues were fixated for longer than arrows but at the cost of shorter dwell times on the cued object. When presented together, gaze and pointing cues were fixated faster and for longer than simultaneously presented arrows. Attention to the cued object depended on the combination of cues and whether both cues were directed towards or away from the target object. Together, the findings confirm earlier observations that people attract attention more strongly than arrows but that arrows more strongly direct attention.

Age Differences in Gaze Following: Older Adults Follow Gaze More than Younger Adults When free-viewing Scenes

Previous research investigated age differences in gaze following with an attentional cueing paradigm where participants view a face with averted gaze, and then respond to a target appearing in a location congruent or incongruent with the gaze cue. However, this paradigm is far removed from the way we use gaze cues in everyday settings. Here we recorded the eye movements of younger and older adults while they freely viewed naturalistic scenes where a person looked at an object or location. Older adults were more likely to fixate and made more fixations to the gazed-at location, compared to younger adults. Our findings suggest that, contrary to what was observed in the traditional gaze-cueing paradigm, in a non-constrained task that uses contextualized stimuli older adults follow gaze as much as or even more than younger adults.

Benchmarking Human Performance for Visual Search of Aerial Images

Aerial images are frequently used in geospatial analysis to inform responses to crises and disasters but can pose unique challenges for visual search when they contain low resolution, degraded information about color, and small object sizes. Aerial image analysis is often performed by humans, but machine learning approaches are being developed to complement manual analysis. To date, however, relatively little work has explored how humans perform visual search on these tasks, and understanding this could ultimately help enable human-machine teaming. We designed a set of studies to understand what features of an aerial image make visual search difficult for humans and what strategies humans use when performing these tasks. Across two experiments, we tested human performance on a counting task with a series of aerial images and examined the influence of features such as target size, location, color, clarity, and number of targets on accuracy and search strategies. Both experiments presented trials consisting of an aerial satellite image; participants were asked to find all instances of a search template in the image. Target size was consistently a significant predictor of performance, influencing not only accuracy of selections but the order in which participants selected target instances in the trial. Experiment 2 demonstrated that the clarity of the target instance and the match between the color of the search template and the color of the target instance also predicted accuracy. Furthermore, color also predicted the order of selecting instances in the trial. These experiments establish not only a benchmark of typical human performance on visual search of aerial images but also identify several features that can influence the task difficulty level for humans. These results have implications for understanding human visual search on real-world tasks and when humans may benefit from automated approaches.