Abstract
Visual Working Memory (WM) literature has traditionally focused on how the visual system maintains relevant information. On the other hand, visual attention studies demonstrated the crucial role of active suppression. Given the close relationship between visual WM and visual attention, it stands to reason that active suppression plays an important, and yet often ignored, role in WM. To better characterize this, we adapted a classical change detection task (Luck & Vogel, 1997) to include irrelevant information. In this task, participants were simultaneously presented with items that were to-be-memorized (memory targets) and to-be-ignored items (memory distractors). Critically, memory targets and distractors were systematically lateralized, enabling us to use lateralized ERP components to isolate the neural markers of suppression from WM. Specifically, we were interested in an N2pc subcomponent, the distractor positivity (PD). The PD is typically observed in visual search tasks in which salient items need to be actively suppressed (Hickey et al., 2009). We hypothesized that this ERP component would also be implicated in this WM task given that active suppression was required. We found that the PD component increased with the number of distractors to be suppressed from WM, with the WM capacity being identical. This suggests that in order to sufficiently maintain relevant information in WM, more active suppression was required with an increasing number of irrelevant items. Furthermore, individual differences in WM capacity predicted the PD amplitude. This demonstrates that the ability to suppress irrelevant information from WM contributes to better WM performance. In addition we found contralateral delay activity of positive polarity (CDAp) starting at around 450 ms, suggesting lingering active suppression of irrelevant items from WM. In sum our results suggest that active suppression of irrelevant information plays an important role in visual WM and its neural markers are the ERP components PD and CDAp.
Meeting abstract presented at VSS 2017