Working memory (WM) is an online memory system that is critical for holding information in a rapidly accessible state during ongoing cognitive processing. Thus, there is strong value in methods that provide a temporally resolved index of WM load. While univariate EEG signals have been identified that vary with WM load, recent advances in multivariate analytic approaches suggest that there may be rich sources of information that do not generate reliable univariate signatures. Here, using data from four published studies (n = 286 and >250,000 trials), we demonstrate that multivariate analysis of EEG voltage topography provides a sensitive index of the number of items stored in WM that generalizes to novel human observers. Moreover, multivariate load detection ("mvLoad") can provide robust information at the single-trial level, exceeding the sensitivity of extant univariate approaches. We show that this method tracks WM load in a manner that is (1) independent of the spatial position of the memoranda, (2) precise enough to differentiate item-by-item increments in the number of stored items, (3) generalizable across distinct tasks and stimulus displays, and (4) correlated with individual differences in WM behavior. Thus, this approach provides a powerful complement to univariate analytic approaches, enabling temporally resolved tracking of online memory storage in humans.

Visual working memory is the ability to hold visual information temporarily in mind. A key feature of working memory is its starkly limited capacity, such that only a few simple items can be remembered at once. Prior work has shown that this capacity limit cannot be circumvented by providing additional encoding time, whether providing just 200 ms or up to 1300 ms, capacity is still limited to only three to four items. In contrast, Brady et al. (2016) hypothesized that real-world objects, but not simple items used in prior research, benefit from additional encoding time and are not subject to traditional capacity limits. They supported this hypothesis with results from both behavior and the contralateral delay activity (CDA), an EEG marker of working memory storage, and concluded that familiar, complex stimuli are necessary to observe encoding time effects. Here, we conducted three replications of Brady et al.'s key manipulation with a larger number of human participants and more trials per condition. We failed to replicate their primary behavioral result (objects benefit more than colors from additional encoding time) and failed to observe an object-specific increase in the CDA. Instead, we found that encoding time benefitted both simple color items and real-world objects, in contrast to both the findings by Brady et al., and some prior work on this topic. Overall, we observed no support for the hypothesis that real-world objects have a different capacity than colored squares. We discuss the implications of our findings for theories of visual working memory (VWM).

With the increasing prevalence of legal cannabis use and availability, there is an urgent need to identify cognitive impairments related to its use. It is widely believed that cannabis, or its main psychoactive component Δ⁹-tetrahydrocannabinol (THC), impairs working memory, i.e., the ability to temporarily hold information in mind. However, our review of the literature yielded surprisingly little empirical support for an effect of THC or cannabis on working memory. We thus conducted a study with three main goals: (1) quantify the effect of THC on visual working memory in a well-powered sample, (2) test the potential role of cognitive effects (mind wandering and metacognition) in disrupting working memory, and (3) demonstrate how insufficient sample size and task duration reduce the likelihood of detecting a drug effect. We conducted two double-blind, randomized crossover experiments in which healthy adults (N = 23, 23) performed a reliable and validated visual working memory task (the "Discrete Whole Report task", 90 trials) after administration of THC (7.5 and/or 15 mg oral) or placebo. We also assessed self-reported "mind wandering" (Exp 1) and metacognitive accuracy about ongoing task performance (Exp 2). THC impaired working memory performance (d = 0.65), increased mind wandering (Exp 1), and decreased metacognitive accuracy about task performance (Exp 2). Thus, our findings indicate that THC does impair visual working memory, and that this impairment may be related to both increased mind wandering and decreased monitoring of task performance. Finally, we used a down-sampling procedure to illustrate the effects of task length and sample size on power to detect the acute effect of THC on working memory.

Covert spatial attention is essential for humans' ability to direct limited processing resources to the relevant aspects of visual scenes. A growing body of evidence suggests that rhythmic neural activity in the alpha frequency band (8-12 Hz) tracks the spatial locus of covert attention, which suggests that alpha activity is integral to spatial attention. However, extant work has not provided a compelling test of another key prediction: that alpha activity tracks the temporal dynamics of covert spatial orienting. In the current study, we examined the time course of spatially specific alpha activity after central cues and during visual search. Critically, the time course of this activity tracked trial-by-trial variations in orienting latency during visual search. These findings provide important new evidence for the link between rhythmic brain activity and covert spatial attention, and they highlight a powerful approach for tracking the spatial and temporal dynamics of this core cognitive process.

The contralateral delay activity (CDA) is an event-related potential component commonly used to examine the online processes of visual working memory. Here, we provide a robust analysis of the statistical power that is needed to achieve reliable and reproducible results with the CDA. Using two very large EEG datasets that examined the contrast between CDA amplitude with set sizes 2 and 6 items and set sizes 2 and 4 items, we present a subsampling analysis that estimates the statistical power achieved with varying numbers of subjects and trials based on the proportion of significant tests in 10,000 iterations. We also generated simulated data using Bayesian multilevel modeling to estimate power beyond the bounds of the original datasets. The number of trials and subjects required depends critically on the effect size. Detecting the presence of the CDA-a reliable difference between contralateral and ipsilateral electrodes during the memory period-required only 30-50 clean trials with a sample of 25 subjects to achieve approximately 80% statistical power. However, for detecting a difference in CDA amplitude between two set sizes, a substantially larger number of trials and subjects were required; approximately 400 clean trials with 25 subjects to achieve 80% power. Thus, to achieve robust tests of how CDA activity differs across conditions, it is essential to be mindful of the estimated effect size. We recommend researchers designing experiments to detect set-size differences in the CDA collect substantially more trials per subject.

Despite being intuitive, cognitive effort has proven difficult to define quantitatively. Here, we proposed to study cognitive effort by investigating the degree to which the brain deviates from its default state, where brain activity is scale-invariant. Specifically, we measured such deviations by examining changes in scale-invariance of brain activity as a function of task difficulty and posited suppression of scale-invariance as a proxy for exertion of cognitive effort. While there is some fMRI evidence supporting this proposition, EEG investigations on the matter are scant, despite the EEG signal being more suitable for analysis of scale invariance (i.e., having a much broader frequency range). In the current study we validated the correspondence between scale-invariance (H) of cortical activity recorded by EEG and task load during two working memory (WM) experiments with varying set sizes. Then, we used this neural signature to disentangle cognitive effort from the number of items stored in WM within participants. Our results showed monotonic decreases in H with increased set size, even after set size exceeded WM capacity. This behavior of H contrasted with behavioral performance and an oscillatory indicator of WM load (i.e., alpha-band desynchronization), both of which showed a plateau at difficulty levels surpassing WM capacity. This is the first reported evidence for the suppression of scale-invariance in EEG due to task difficulty, and our work suggests that H suppression may be used to quantify changes in cognitive effort even when working memory load is at maximum capacity.

Complex cognition relies on both on-line representations in working memory (WM), said to reside in the focus of attention, and passive off-line representations of related information. Here, we dissected the focus of attention by showing that distinct neural signals index the on-line storage of objects and sustained spatial attention. We recorded electroencephalogram (EEG) activity during two tasks that employed identical stimulus displays but varied the relative demands for object storage and spatial attention. We found distinct delay-period signatures for an attention task (which required only spatial attention) and a WM task (which invoked both spatial attention and object storage). Although both tasks required active maintenance of spatial information, only the WM task elicited robust contralateral delay activity that was sensitive to mnemonic load. Thus, we argue that the focus of attention is maintained via a collaboration between distinct processes for covert spatial orienting and object-based storage.

Is numerosity an abstraction that arises downstream of basic perceptual processing, or does the brain process numerosity like a primary perceptual feature such as color or motion? Here, we tested whether visual cortex computes number automatically, even when number is task-irrelevant and is being processed unconsciously. We recorded electroencephalography while subjects watched dotcloud stimuli that alternated in numerical content at 15 Hz, under one of three conditions: judging numerosity, judging convex hull, or making no judgments. Under all three conditions, we observed oscillatory activity at 15 Hz in early visual cortex. The strength of this signal depended on the numerical content of the stimuli in all three conditions and did not differ significantly across the conditions. Results show that number can be computed spontaneously by visual cortex, even when participants are attending to non-numerical information, consistent with the proposal that number is a primary perceptual attribute of visual stimuli.

How do humans compute approximate number? According to one influential theory, approximate number representationsarise in the intraparietal sulcus and are amodal (independent of any sensory modality). Alternatively, approximate numbermay be computed initially within sensory systems. We tested for approximate number representations in the visual systemusing steady state visual evoked potentials (SSVEPs). We recorded EEG from human subjects while they viewed dotcloudspresented at 30 Hz. Alternating the dotcloud numerosity at 15 Hz evoked a 15 Hz SSVEP detectable over the occipital lobe(Oz). The SSVEP amplitude increased as the numerical difference between dotclouds increased, indicating that subjectsvisual systems were differentiating dotclouds on the basis of their numerical ratios. Critically, subjects were unable toconsciously discriminate dotcloud numerosity, indicating the rapid presentation disrupted reentrant feedback to visualcortex. Approximate number appears to be computed within the visual system, independently of higher-order areas suchas the intraparietal sulcus.