Brendan Valentine¹ (), Xiaoli Zhang¹, Melisa Menceloglu¹, Taosheng Liu¹; ¹Michigan State University

Previous work on feature-based attention has established two prominent models of the selection profile: feature-similarity gain and surround suppression. The former predicts a monotonic decrease in task performance as the target feature becomes more different from the attended feature, whereas the latter predicts a non-monotonic performance pattern where the lowest performance occurs for targets close to the attended feature with a rebound in performance for more distant features. While support for both models have been found using simple features, it is unclear whether the selection profile for object-based attention aligns with either model. The current study assessed the selection profile for simple shapes, as a first step toward more parametric investigations of object-based attention. The study used a newly developed standardized circular shape space that allowed object difference to be quantitatively measured. In two experiments, participants were directed to attend to two target shapes that systematically varied along the shape circle. Two distractor shapes then appeared, overlapping with the target shapes, and one shape in each pair underwent a brief luminance change. Participants reported the status of each target shape (no change, dimmer, brighter). Experiment 1 used finer sampling of the shape space with a maximum target difference of 90°, and Experiment 2 used a coarser sampling with maximum target difference of 180°. For both experiments, performance accuracy peaked when the two target shapes matched and then decreased in a monotonic manner as the two shapes became more different. These results align more with the feature-similarity gain model and suggest that an analogous shape-similarity gain effect operates at a higher level of complexity. Such a gain effect may support object-based selection to differentiate target objects along higher-order, holistic dimensions like shape.

Acknowledgements: This work was supported by NSF grant 2019995.