Purchase this article with an account.
Gisella Diaz, Edward Vogel, Edward Awh; Examining distinct neural signals that track the contents of working memory. Journal of Vision 2018;18(10):110. https://doi.org/10.1167/18.10.110.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Oscillatory brain activity in the alpha-band (8-12 Hz) and slow wave EEG activity have both been strongly implicated in the maintenance of information in visual working memory (VWM). For instance, increasing VWM load leads to monotonic declines in alpha power and monotonic increases in the amplitude of a parieto-occipital negative slow wave (Fukuda et al., 2015). While both signals tracked performance, they were uncorrelated and explained distinct variance in VWM capacity. Here, we replicated this empirical pattern using color and spatial memoranda. Additionally, we tested the hypothesis that the negative slow wave is an item-based signal given that its lateralized counterpart tracks the number of individuated items in VWM despite differences in sensory stimulation (Luria et al., 2016). Meanwhile, alpha power might be a spatial index given its role in tracking spatial locations in VWM (Foster et al., 2016). To test this account, we used grouping by collinearity to manipulate the number of individuated items, while holding constant the number of locations. The stimuli were either aligned to create perceptual groups or misaligned to encourage the individuation of each element. The probability of report and response precision were higher for grouped stimuli than for ungrouped stimuli (grouping effect) and for two stimuli than for four stimuli (set size effect). Alpha power suppression was modulated by the number of display elements but not by grouping condition, which suggests that alpha power tracked the number of locations rather than the number of individuated items. The negative slow wave was also modulated by set size, though it is unclear whether the negative slow wave was affected by grouping condition given a trending grouping effect. The current work begins to shed light on a taxonomy of different delay signals, advancing our understanding of cognitive models of online memory processes.
Meeting abstract presented at VSS 2018
This PDF is available to Subscribers Only