Re-evaluating the relationships among filtering activity, unnecessary storage, and visual working memory capacity

Abstract

The amount of task-irrelevant information encoded in visual working memory (VWM), referred to as unnecessary storage, has been proposed as a potential mechanism underlying individual differences in VWM capacity. In addition, a number of studies have provided evidence for additional activity that initiates the filtering process originating in the frontal cortex and basal ganglia, and is therefore a crucial step in the link between unnecessary storage and VWM capacity. Here, we re-examine data from two prominent studies that identified unnecessary storage activity as a predictor of VWM capacity by directly testing the implied path model linking filtering-related activity, unnecessary storage, and VWM capacity. Across both studies, we found that unnecessary storage was not a significant predictor of individual differences in VWM capacity once activity associated with filtering was accounted for; instead, activity associated with filtering better explained variation in VWM capacity. These findings suggest that unnecessary storage is not a limiting factor in VWM performance, whereas neural activity associated with filtering may play a more central role in determining VWM performance that goes beyond preventing unnecessary storage.Keywords Working memory . Attention . ERP . Short-term memory . fMRIOver the past few decades, there has been enormous interest in understanding the nature of visual working memory (VWM), which enables the on-line maintenance of a limited amount of visual information over short periods of time. In particular, a number of studies have focused on understanding the origins of individual differences in VWM capacity (Luck & Vogel, 2013). That is, VWM has a limited bandwidth, and as such, the number of items (capacity) and/or the fidelity of information stored in VWM appear to be severely limited, with significant individual differences in this ability.Although the precise origins of individual differences in VWM capacity remains unclear, one mechanism that has been proposed to account for these differences is the ability to efficiently allocate limited capacity resources through the filtering of unnecessary (distractor) information, thereby minimizing the unnecessary storage of task-irrelevant information (Awh & Vogel, 2008;Luck & Vogel, 2013). Specifically, if VWM is characterized as having a limited number of storage Bslots,t hen any item that is encoded into VWM will occupy one of these storage units. According to this model, the more control you have over which information gains access to VWM, the less likely you are to encode task-irrelevant information that will occupy these limited storage slots, thereby freeing up these resources for the task-relevant items. This efficient allocation of capacity-limited resources has been linked to VWM capacity, as shown by a positive correlation between measures of filtering efficiency and VWM capacity (Vogel, Mccollough, & Machizawa, 2005). From this perspective, filtering efficiency can be indexed by the amount of unnecessary activity -that is,...

Addressing properties and limitations of flexible resource allocation in visual short-term memory

“…It is unknown how flexible allocation would be reflected in this activity, but we suggest it is accomplished through those networks that mediate top-down attention and executive control. This proposal is consistent with the findings of Edin et al (2009) finding functional connectivity between frontal activity in the dorsolateral prefrontal cortex and the IPS, as well as the direct path found by Emrich and Busseri (2015) suggesting attention related activity plays a central role in VSTM performance.…”

“…However looking at structures alone can only explains part of the mechanism. Emrich and Busseri (2015), tested the implied path from frontal regions controlling access (filtering) to storage activity in the parietal cortex that would predict working memory capacity using the data from McNab & Klingberg (2008). When they tested a path model, the direct path of from filtering activity to working memory capacity was a stronger predictor than the indirect path of frontal activity controlling access to memory storage (unnecessary storage) predicting VSTM performance.…”

Executive and perceptual distraction in visual working memory.

“…For example, Emrich and Busseri (2015) reanalyzed outcomes from McNab and Klingberg (2008) and Liesefeld et al (2014) , and found that individual differences in working memory capacity were predicted by filtering-related activity, rather than unnecessary storage of distractor items. Emrich and Busseri (2015) suggested that this filtering activity might reflect general top-down attentional control, and would be critical for the selection and processing of target items, even when to-be-ignored distractors are absent. Taking a slightly different approach, Shipstead et al (2014) distinguished between memory for visual arrays with and without additional distraction.…”

A Daytime Nap Enhances Visual Working Memory Performance and Alters Event-Related Delay Activity

“…Consistent engagement of attentional control mechanisms is required in tasks of visual WM to prevent errors from complete attentional lapses and more subtle causes such as insufficient individuation of items or swapping item positions (Adam, Mance, Fukuda, & Vogel, 2015). Moreover, neural measures of attentional control also predict WMC directly (Emrich & Busseri, 2015). Both sleep deprivation and sleep restriction are known to result in deficits to sustained attention and executive control of attention (Durmer & Dinges, 2005), and such deficits may account for impaired WM under conditions of sleep loss (Frenda & Fenn, 2016).…”

“…Specifically, they found in distractor-present conditions a negative-going peak about 245ms post-stimulus, and (although not analyzed in their report), a pattern of more positive amplitude on average over the second half of the delay period. Importantly, the activity associated with filtering processes has been shown to predict WMC independent of the presence of distractors, and it has been proposed that such activity may index the efficient processing of target information (Emrich & Busseri, 2015).…”