Valdes-Sosa, Cobo, and Pinilla (1998) introduced a transparent-motion design that provided evidence of object-based attention whereby attention embraces all features of an attentionally cued perceptual object including new unpredictable features such as a brief translation. Subsequent studies using variants of that design appeared to provide further behavioral, electrophysiological, and brain imaging evidence of object-based attention. Stoner and Blanc (2010) observed, however, that these previous results could potentially be explained by feature-based competition/normalization models of attention. To distinguish between the object-based and feature-based accounts, they introduced “feature swaps” into a delayed-onset variant of the transparent-motion design (Reynolds, Alborzian, & Stoner, 2003). Whereas the object-based attention account predicted that the effect of cueing would survive these feature swaps, the motion-competition account predicted that the effect of cueing would be reversed by these feature swaps. The behavioral results of Stoner and Blanc (2010) supported the object-based account, and in doing so, provided evidence that the attentional advantage in this design is spatially selective at the scale of the intermixed texture elements (i.e., dots) of the overlapping and moving dot fields. In the present study, we used the design of Stoner and Blanc (2010) to investigate both psychophysical performance and evoked activities under different cueing and feature swapping conditions. We confirmed that the behavioral effects of attentional cueing survived feature swaps and found event-related potential (ERP) correlates of those effects in the N1 component range over occipital and parieto-occipital scalp sites. These modulations of the neural activity were, moreover, significantly associated with variation in behavioral performance values across the different conditions. Our findings thus provide the first evidence of the role of the N1 component in object-based attention in this transparent-motion design under conditions that rule out feature-based mechanisms and that reveal selective processing at a fine spatial scale.
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