Establishing gaze markers of perceptual load during multi-target visual search

Cognitive Research: Principles and Implications

Original Article

Nilli Lavie¹  Joshua O. Eayrs²  Anthony M. Harris³ 

[1] Institute of Cognitive Neuroscience, University College London, London, UK;Institute of Cognitive Neuroscience, University College London, London, UK;Department of Experimental Psychology, Ghent University, Ghent, Belgium;Institute of Cognitive Neuroscience, University College London, London, UK;Queensland Brain Institute, The University of Queensland, Brisbane, Australia;

关键词: Attention;    Perceptual load;    Visual search;    Eye movements;    Cross-modal processing;   

DOI  :  10.1186/s41235-023-00498-7

 received in 2022-10-10, accepted in 2023-06-22,  发布年份 2023

来源: Springer

【 摘 要 】

Highly-automated technologies are increasingly incorporated into existing systems, for instance in advanced car models. Although highly automated modes permit non-driving activities (e.g. internet browsing), drivers are expected to reassume control upon a ‘take over’ signal from the automation. To assess a person’s readiness for takeover, non-invasive eye tracking can indicate their attentive state based on properties of their gaze. Perceptual load is a well-established determinant of attention and perception, however, the effects of perceptual load on a person’s ability to respond to a takeover signal and the related gaze indicators are not yet known. Here we examined how load-induced attentional state affects detection of a takeover-signal proxy, as well as the gaze properties that change with attentional state, in an ongoing task with no overt behaviour beyond eye movements (responding by lingering the gaze). Participants performed a multi-target visual search of either low perceptual load (shape targets) or high perceptual load (targets were two separate conjunctions of colour and shape), while also detecting occasional auditory tones (the proxy takeover signal). Across two experiments, we found that high perceptual load was associated with poorer search performance, slower detection of cross-modal stimuli, and longer fixation durations, while saccade amplitude did not consistently change with load. Using machine learning, we were able to predict the load condition from fixation duration alone. These results suggest monitoring fixation duration may be useful in the design of systems to track users’ attentional states and predict impaired user responses to stimuli outside of the focus of attention.

【 授权许可】

CC BY   
© The Psychonomic Society 2023

【 图 表 】

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