Purchase this article with an account.
Umesh Rajashekar, Alan C. Bovik, Lawrence K. Cormack; Visual search in noise: Revealing the influence of structural cues by gaze-contingent classification image analysis. Journal of Vision 2006;6(4):7. doi: 10.1167/6.4.7.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Visual search experiments have usually involved the detection of a salient target in the presence of distracters against a blank background. In such high signal-to-noise scenarios, observers have been shown to use visual cues such as color, size, and shape of the target to program their saccades during visual search. The degree to which these features affect search performance is usually measured using reaction times and detection accuracy. We asked whether human observers are able to use target features to succeed in visual search tasks in stimuli with very low signal-to-noise ratios. Using the classification image analysis technique, we investigated whether observers used structural cues to direct their fixations as they searched for simple geometric targets embedded at very low signal-to-noise ratios in noise stimuli that had the spectral characteristics of natural images. By analyzing properties of the noise stimulus at observers' fixations, we were able to reveal idiosyncratic, target-dependent features used by observers in our visual search task. We demonstrate that even in very noisy displays, observers do not search randomly, but in many cases they deploy their fixations to regions in the stimulus that resemble some aspect of the target in their local image features.
This PDF is available to Subscribers Only