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Howard Yang, Peng Sung, Charles Chubb, George Sperling; Does Feature-Based Attention for Grayscale Vary Across Visual Tasks with Identical Stimuli?. Journal of Vision 2017;17(10):45. doi: 10.1167/17.10.45.
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© ARVO (1962-2015); The Authors (2016-present)
Are feature-based visual attention filters for dark versus light items invariant across different tasks? Method. Stimuli were briefly flashed (300 ms) clouds comprising 16 bars (length, width = .72°, .045°), two each of 8 Weber contrasts ±0.25, ±0.5, ±0.75, ±1 on a mean gray background. Bar orientations had a fixed dispersion of 22.5 deg. around a mean that varied randomly across trials. In the centroid task, the participant strove to mouse-click the centroid of a "target set" of bars, giving equal weight to all bars in this set while ignoring all the other "distractor" bars. In the slant task, participants adjusted the orientation of a central response bar to match the mean-orientation of the bars in the target set, giving equal weight to all target bars while ignoring the distractor bars. In each of the two tasks, in separately blocked conditions, the target set included 1) all bars, 2) bright bars only [bars more luminous than the background] or 3) dark bars only. Results. In each condition in each task, we derived an attention filter that reflected the impact exerted on the participant's responses by bars of different Weber contrasts. In both tasks, participants' attention filters in the all-bars condition gave nearly equal weight to all 8 Weber contrasts. In the bright-bars-only and dark-bars-only selective attention conditions, participants' centroid-task attention filters more accurately approximated equal weight to all target bar luminances than slant-task filters, which despite contrary instructions and feedback, weighted bars more nearly in proportion to absolute Weber contrast. On the other hand, in selective attention to bright-bars-only and dark-bars-only conditions, slant-task filters assigned very little weight to distractors yielding excellent target-to-distractor-weight ratios: >14:1, whereas centroid-task filters were less selective, yielding target-to-distractor-weight ratios: 5:1. Conclusion. Attention filters for gray-scale can differ between different tasks using identical stimuli.
Meeting abstract presented at VSS 2017
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