Abstract
The detection of visual blur during a dual task has been shown to be unaffected by cognitive load (Loschky et al., 2014). Blur may be a feature that can be detected preattentively, such as color, orientation, etc. (Treisman & Gelade, 1980). Interestingly, eyetracking studies have shown when blur/clarity contrast is present in a scene, eye movements during free viewing tend to go to regions of clarity rather than blur (Enns & MacDonald, 2012; Kahn, Dinet, & Konik, 2011; Loschky & McConkie, 2002; Smith & Tadmor, 2012). However, these issues have not been studied in the context of visual search, the "gold standard" of attentional selection research. Thus, an eyetracking visual search study investigated if blur/clarity contrast is non-predictive of a target, will unique blur capture or repel visual selective attention or be ignored? A legibility control study showed that identification accuracy and reaction times did not significantly differ between blurred or clear single rotated T-like Ls or Ts (Jiang & Chun, 2001); thus legibility would not explain search results. Then, a rotated L versus T visual search task was performed with an imaginary circle of eight degrees radius, and set sizes 4 & 8, using the control study stimuli. Letters were presented either all-blurred, all-clear, or with a blurred or clear singleton letter among distractors. With set size 4, reaction times weakly supported unique blur being ignored (blurred singleton target = all-blurred). With set size 8, reaction times strongly supported unique blur being ignored (blurred singleton target = all-blurred = all-clear) and clarity captured visual selective attention (clear singleton target all-clear & blurred singleton target). Eyetracking results further supported these conclusions. In set size 8, the first eye movement to a letter most frequently landed on a uniquely clear letter, but not uniquely blurred letters. Overall, the results suggest that unique clarity captures attention while unique blur is ignored.
Meeting abstract presented at VSS 2016