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Jongsoo Baek, Yukai Zhao, Zhong-Lin Lu, Barbara Dosher; Visual attention in spatial cuing and visual search. Journal of Vision 2011;11(11):162. doi: https://doi.org/10.1167/11.11.162.
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Signal detection theory (Green & Swets, 1966) based uncertainty models (Palmer, 1994; Eckstein, 1998) with an unlimited capacity attention system have provided an excellent account of the set size effects in visual search accuracy. However, spatial cuing task experiments found strong effects of attention — precuing improves accuracy, especially when the target is embedded in high level of external noise (Dosher & Lu, 2000, Lu & Dosher, 2000). In this research, we attempted to resolve the apparent contradictory conclusions from these two major lines of inquiry in spatial attention. We hypothesize that the conditions under which an effect of spatially cued attention is substantial should correspond to the circumstances in which attention effects over and above uncertainty should occur in visual search. Many of the classical visual search experiments have been carried out using stimulus conditions where attention effects are least likely to be found. We studied visual search in a range of external noise, signal contrast, and target-distractor similarity conditions. In each trial, 8 Gabor patches were shown in each of two brief intervals, with one target at a different orientation from the distractors in one of the presentations. Subjects were pre-cued to a subset of the stimuli (1, 2, 4 or 8) and asked to report (1) which interval contained the target, and (2) where the target was. In both zero noise and low signal contrast, and all high external noise conditions, the set size effects were greater than that predicted from decision uncertainty model. All these results are well accounted by a model that combines the elaborated perceptual template model (ePTM, Jeon, Lu & Dosher, 2009), the attention mechanisms developed in the PTM framework (Lu & Dosher, 1998), and the SDT based uncertainty calculations. Our empirical results and theoretical model generate a common taxonomy of visual attention in spatial cuing and visual search.
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