Matthew Tong¹, Ziyao Zhang², Nancy B. Carlisle¹; ¹Lehigh University, ²University of Texas at Austin

An important aspect of performing efficient visual search is the ability to modulate attention to ignore distractions. This attentional suppression can occur as a result of selection history. Through repeated experience with a salient distractor over time, individuals can implicitly learn to ignore it and find their target more efficiently. Research examining the underlying mechanisms of this learned suppression effect leaves some open questions. The signal suppression hypothesis suggests that learned suppression can occur proactively and specific to feature values. Alternatively, stimulus-driven accounts suggest that learned suppression occurs reactively, after an initial attentional capture and followed by a rapid disengagement of attention. In addition, second-order suppression models suggest that suppression may not be specific to individual features. To clarify these open questions, we measured the power of steady-state visually evoked potentials (SSVEPs) throughout a learned suppression task. In this task, participants had to find a target shape in a search array with a distractor color singleton appearing on half of the trials. We used SSVEPs as a neural correlate of feature-specific attention by manipulating the color of the SSVEP-eliciting stimuli. We also analyzed time periods both before and after the onset of the search array to compare proactive and reactive mechanisms. We observed a significant decrease in SSVEP power corresponding to the color of a learned distractor singleton compared to both target and neutral baseline colors in the proactive time period. In addition, this reduction in SSVEP power for the distractor color was maintained into the reactive time period. Thus, we provide support for the signal suppression hypothesis by finding neural evidence for proactive, feature-specific learned suppression. These findings highlight important neural mechanisms underlying learned distractor suppression, address open questions in the literature, and more broadly, advance our current understanding of attention during visual search.