Kelly McEvoy¹, Sarah Shomstein¹; ¹Department of Psychological & Brain Sciences, The George Washington University

Attentional selection operates on an object-based representation using both simple geometric shapes (e.g., rectangles, trapezoids, letters, semicircles) as well as semantically meaningful objects in real-world scenes (e.g., a cup on a table). While previous work suggests that both low-level (boundaries) and high-level properties (object meaning) contribute to object-based attention, the relative contribution of these features remains unclear. Here, we characterize the relative contribution of a consistent object border (i.e., object outline) and consistent object semantic information (i.e., object identity) to object-based attentional selection. We used an adapted two-rectangle task in which following a brief exogenous cue (150 ms) and a delay (70 ms), the attended real-world object abruptly changed according to one of three conditions: an object with the same border but different semantic category, an object with a different border but same semantic category, or an ‘outlier’ object with neither the border nor category in common. We predicted that both objects with the same border or same category contribute to the perception of objecthood but to varying extents, while ‘outlier’ objects contribute to an overall smaller object-based effect compared to border and category. Robust object-based attentional effects were observed in conditions where border and semantic information were preserved. Additionally, object-based effects were also observed in the outlier condition, yielding a smaller magnitude compared to when the object’s border or category was maintained. Overall, object-based attentional guidance was observed in all three conditions, but was modulated by the degree of low- and high-level changes in object properties. Our findings suggest that object-based attentional guidance persists despite altering the object properties in real-world objects, which can generalize to more complex, naturalistic environments.

Acknowledgements: NSF BCS 2022572 to SS