Attention distribution while detecting conflicts between converging objects

An eye-tracking study

Journal Article (2020)
Author(s)

Yke Bauke B. Eisma (TU Delft - Human-Robot Interaction)

Anouk Looijestijn (Student TU Delft)

Joost C.F. Winter (TU Delft - Human-Robot Interaction)

Research Group
Human-Robot Interaction
Copyright
© 2020 Y.B. Eisma, Anouk E. Looijestijn, J.C.F. de Winter
DOI related publication
https://doi.org/10.3390/vision4030034
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Y.B. Eisma, Anouk E. Looijestijn, J.C.F. de Winter
Research Group
Human-Robot Interaction
Issue number
3
Volume number
4
Reuse Rights

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Abstract

In many domains, including air traffic control, observers have to detect conflicts between moving objects. However, it is unclear what the effect of conflict angle is on observers’ conflict detection performance. In addition, it has been speculated that observers use specific viewing techniques while performing a conflict detection task, but evidence for this is lacking. In this study, participants (N = 35) observed two converging objects while their eyes were recorded. They were tasked to continuously indicate whether a conflict between the two objects was present. Independent variables were conflict angle (30, 100, 150 deg), update rate (discrete, continuous), and conflict occurrence. Results showed that 30 deg conflict angles yielded the best performance, and 100 deg conflict angles the worst. For 30 deg conflict angles, participants applied smooth pursuit while attending to the objects. In comparison, for 100 and especially 150 deg conflict angles, participants showed a high fixation rate and glances towards the conflict point. Finally, the continuous update rate was found to yield shorter fixation durations and better performance than the discrete update rate. In conclusion, shallow conflict angles yield the best performance, an effect that can be explained using basic perceptual heuristics, such as the ‘closer is first’ strategy. Displays should provide continuous rather than discrete update rates.