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James Cesaro, Wanghaoming Fang, Taosheng Liu; Surround Suppression in Feature-based Attention to Orientation. Journal of Vision 2018;18(10):308. doi: 10.1167/18.10.308.
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Goal. Feature-based attention (FBA) enhances attended orientation at the cost of unattended ones. However, the precise attentional modulation profile still remains unclear. While the feature-similarity gain model predicts a monotonic modulation profile, the surround suppression model suggests a non-monotonic (Mexican hat) profile. Here, we systematically investigated how attending to an orientation modulates the perception of unattended ones. Methods. Stimuli consisted of 180 oriented bars, which were randomly located within an annulus centered on the fixation. In a two-interval forced choice task, participants reported the target-present interval. Targets were defined as the stimulus in which a proportion of bars shared the same orientation, while noise stimuli contained bars with all different orientations. The proportion of consistently oriented bars in the target (coherence) was first determined individually via an adaptive staircase. Then, in a feature cueing procedure an orientation precue was presented before the stimuli, indicating the coherent target orientation in 60% of trials. In the remaining trials, the target could be ±15°, ±30°, ±45°, ±60°, ±75°, or 90° offset from the cued orientation. We also included a neutral condition (no precue) as a baseline to assess attentional modulation. Results. Compared to the neutral baseline, we found a significant enhancement for valid trials (0°) and a significant suppression effect when cue and target are orthogonal (90°). Importantly, there were also suppressions at ±45° followed by rebounds at ±60°. Model comparison strongly favored a non-monotonic (Mexican hat) profile over a monotonic (Gaussian) profile. Conclusion. Attention to orientation elicits a non-monotonic modulation, suggesting a combination of surround suppression and feature similarity gain at different scales.
Meeting abstract presented at VSS 2018
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