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Past work has shown that storage in working memory elicits stimulus-specific neural activity that tracks the stored content. Here, we present evidence for a distinct class of load-sensitive neural activity that indexes items without representing their contents per se. We recorded electroencephalogram (EEG) activity while adult human subjects stored varying numbers of items in visual working memory. Multivariate analysis of the scalp topography of EEG voltage enabled precise tracking of the number of individuated items stored and robustly predicted individual differences in working memory capacity. Critically, this signature of working memory load generalized across variations in both the type and number of visual features stored about each item, suggesting that it tracked the number of individuated memory representations and not the content of those memories. We hypothesize that these findings reflect the operation of a capacity-limited pointer system that supports on-line storage and attentive tracking.

The change detection paradigm has been a widely used approach for measuring capacity in visual working memory (WM). In this task, subjects see an array of visual items, followed by a short blank delay and a single test item. Their task is to indicate whether that test item changed relative to the item in the sample array. This task provides reliable measurements of WM capacity that exhibit robust correlations with many outcome variables of interest. Here, we offer a new variant of this task that we call "change localization." This task is closely modeled after the change detection task described above, except that the test array contains the same number of items as the sample array, and one item has always changed in each trial. The subject's task is to select the changed item in the test array. Using both color and shape stimuli, scores in the change localization task were highly correlated with those in the change detection task, suggesting that change localization taps into the same variance in WM ability. Moreover, the change localization task was far more reliable than change detection, such that only half the number of trials were required to achieve robust reliability. To further validate the approach, we replicated known effects from the literature, demonstrating that they could be detected with far fewer trials than with change detection. Thus, change localization provides a highly reliable and sensitive approach for measuring visual working memory capacity.

Representations in working memory need to be flexibly transformed to adapt to our dynamic environment and variable task demands. Recent work has demonstrated that activity in the alpha frequency band enables precise decoding of visual information during both perception and sustained storage in working memory. Extant work, however, has focused exclusively on the representation of static visual images. Here we used EEG recordings to examine whether alpha-band activity supports the dynamic transformation of representations in spatial working memory using an imagery task that required the active shifting of a stored position to a new position. In line with recent findings, a common format of alpha-band activity precisely tracked both the initial position stored in working memory as well as the transformed position, with the latter emerging approximately 800-1200 ms following an auditory cue to “rotate” to a new position. Moreover, the time course of this transformation of alpha activity predicted between-subject differences in manual reaction time to indicate the new position (Experiment 1), as well as within-subject variations in saccade latency in a speeded version of the task (Experiment 2). Finally, cross-training analyses revealed robust generalization of alpha-band reconstruction of working memory contents before and after mental transformation. These findings demonstrate that alpha activity tracks dynamic transformations of representations in spatial working memory, and that the format of this activity is preserved across the initial and transformed memory representations. These findings highlight a new approach for measuring voluntary shifts in online memory representations and show common representational formats during dynamic mental imagery and the maintenance of static representations in working memory.

Visual working memory (WM) must maintain relevant information, despite the constant influx of both relevant and irrelevant information. Attentional control mechanisms help determine which of this new information gets access to our capacity-limited WM system. Previous work has treated attentional control as a monolithic process—either distractors capture attention or they are suppressed. Here, we provide evidence that attentional capture may instead be broken down into at least two distinct subcomponent processes: (1) Spatial capture, which refers to when spatial attention shifts towards the location of irrelevant stimuli and (2) item-based capture, which refers to when item-based WM representations of irrelevant stimuli are formed. To dissociate these two subcomponent processes of attentional capture, we utilized a series of electroencephalography components that track WM maintenance (contralateral delay activity), suppression (distractor positivity), item individuation (N2pc), and spatial attention (lateralized alpha power). We show that new, relevant information (i.e., a task-relevant distractor) triggers both spatial and item-based capture. Irrelevant distractors, however, only trigger spatial capture from which ongoing WM representations can recover more easily. This fractionation of attentional capture into distinct subcomponent processes provides a refined framework for understanding how distracting stimuli affect attention and WM.

Covert spatial attention has a variety of effects on the responses of individual neurons. However, relatively little is known about the net effect of these changes on sensory population codes, even though perception ultimately depends on population activity. Here, we measured the electroencephalogram (EEG) in human observers (male and female), and isolated stimulus-evoked activity that was phase-locked to the onset of attended and ignored visual stimuli. Using an encoding model, we reconstructed spatially selective population tuning functions from the pattern of stimulus-evoked activity across the scalp. Our EEG-based approach allowed us to measure very early visually evoked responses occurring ~100 ms after stimulus onset. In Experiment 1, we found that covert attention increased the amplitude of spatially tuned population responses at this early stage of sensory processing. In Experiment 2, we parametrically varied stimulus contrast to test how this effect scaled with stimulus contrast. We found that the effect of attention on the amplitude of spatially tuned responses increased with stimulus contrast, and was well-described by an increase in response gain (i.e., a multiplicative scaling of the population response). Together, our results show that attention increases the gain of spatial population codes during the first wave of visual processing.Significance StatementWe know relatively little about how attention improves population codes, even though perception is thought to critically depend on population activity. In this study, we used an encoding-model approach to test how attention modulates the spatial tuning of stimulus-evoked population responses measured with EEG. We found that attention multiplicatively scales the amplitude of spatially tuned population responses. Furthermore, this effect was present within 100 ms of stimulus onset. Thus, our results show that attention improves spatial population codes by increasing their gain at this early stage of processing.

Covert spatial attention has long been thought to speed visual processing. Psychophysics studies have shown that target information accrues faster at attended locations than at unattended locations. However, with behavioral evidence alone, it is difficult to determine whether attention speeds visual processing of the target or subsequent postperceptual stages of processing (e.g., converting sensory responses into decision signals). Moreover, although many studies have shown that attention can boost theamplitudeof visually evoked neural responses, no robust effect has been observed on thelatencyof those neural responses. Here, we offer new evidence that may reconcile the neural and behavioral findings. We examined whether covert attention influenced the latency of the N2pc component, an electrophysiological marker of visual selection that has been linked with object individuation—the formation of an object representation that is distinct from the background and from other objects in the scene. To this end, we manipulated whether or not human observers (male and female) covertly attended the location of an impending search target. We found that the target evoked N2pc onset ∼20 ms earlier when the target location was cued than when it was not cued. In a second experiment, we provided a direct replication of this effect, confirming that the effect of attention on N2pc latency is robust. Thus, although attention may not speed the earliest stages of sensory processing, attention does speed the critical transition between raw sensory encoding and the formation of individuated object representations.SIGNIFICANCE STATEMENTCovert spatial attention improves processing at attended locations. Past behavioral studies have shown that information about visual targets accrues faster at attended than at unattended locations. However, it has remained unclear whether attention speeds perceptual analysis or subsequent postperceptual stages of processing. Here, we present robust evidence that attention speeds the N2pc, an electrophysiological signal that indexes the formation of individuated object representations. Our findings show that attention speeds a relatively early stage of perceptual processing while also elucidating the specific perceptual process that is speeded.

Our attention is critically important for what we remember. Prior measures of the relationship between attention and memory, however, have largely treated “attention” as a monolith. Here, across three experiments, we provide evidence for two dissociable aspects of attention that influence encoding into long-term memory. Using spatial cues together with a sensitive continuous report procedure, we find that long-term memory response error is affected by both trial-by-trial fluctuations of sustained attention and prioritization via covert spatial attention. Furthermore, using multivariate analyses of EEG, we track both sustained attention and spatial attention before stimulus onset. Intriguingly, even during moments of low sustained attention, there is no decline in the representation of the spatially attended location, showing that these two aspects of attention have robust but independent effects on long-term memory encoding. Finally, sustained and spatial attention predicted distinct variance in long-term memory performance across individuals. That is, the relationship between attention and long-term memory suggests a composite model, wherein distinct attentional subcomponents influence encoding into long-term memory. These results point toward a taxonomy of the distinct attentional processes that constrain our memories.

Multiple neural signals have been found to track the number of items stored in working memory (WM). These signals include oscillatory activity in the alpha band and slow-wave components in human EEG, both of which vary with storage loads and predict individual differences in WM capacity. However, recent evidence suggests that these two signals play distinct roles in spatial attention and item-based storage in WM. Here, we examine the hypothesis that sustained negative voltage deflections over parieto-occipital electrodes reflect the number of individuated items in WM, whereas oscillatory activity in the alpha frequency band (8–12 Hz) within the same electrodes tracks the attended positions in the visual display. We measured EEG activity while participants stored the orientation of visual elements that were either grouped by collinearity or not. This grouping manipulation altered the number of individuated items perceived while holding constant the number of locations occupied by visual stimuli. The negative slow wave tracked the number of items stored and was reduced in amplitude in the grouped condition. By contrast, oscillatory activity in the alpha frequency band tracked the number of positions occupied by the memoranda and was unaffected by perceptual grouping. Perceptual grouping, then, reduced the number of individuated representations stored in WM as reflected by the negative slow wave, whereas the location of each element was actively maintained as indicated by alpha power. These findings contribute to the emerging idea that distinct classes of EEG signals work in concert to successfully maintain on-line representations in WM.

A hallmark of episodic memory is the phenomenon of mentally reexperiencing the details of past events, and a well-established concept is that the neuronal activity that mediates encoding is reinstated at retrieval. Evidence for reinstatement has come from multiple modalities, including functional magnetic resonance imaging and electroencephalography (EEG). These EEG studies have shed light on the time course of reinstatement but have been limited to distinguishing between a few categories. The goal of this work was to use recently developed experimental and technical approaches, namely continuous report tasks and inverted encoding models, to determine which frequencies of oscillatory brain activity support the retrieval of precise spatial memories. In experiment 1, we establish that an inverted encoding model applied to multivariate alpha topography tracks the retrieval of precise spatial memories. In experiment 2, we demonstrate that the frequencies and patterns of multivariate activity at study are similar to the frequencies and patterns observed during retrieval. These findings highlight the broad potential for using encoding models to characterize long-term memory retrieval. NEW & NOTEWORTHY Previous EEG work has shown that category-level information observed during encoding is recapitulated during memory retrieval, but studies with this time-resolved method have not demonstrated the reinstatement of feature-specific patterns of neural activity during retrieval. Here we show that EEG alpha-band activity tracks the retrieval of spatial representations from long-term memory. Moreover, we find considerable overlap between the frequencies and patterns of activity that track spatial memories during initial study and at retrieval.

Brady, Konkle, and Alvarez (2009) argued that statistical learning boosts the number of colors that can be held online in visual working memory (WM). They showed that when specific colors are consistently paired together in a WM task, subjects can take optimal advantage of these regularities to recall more colors, an effect they labeled memory compression. They proposed that memory compression is a product of visual statistical learning, an automatic apprehension of statistical regularities that has been shown in prior work to be disconnected from explicit learning. If statistical learning enables an expansion of the number of individuated representations in visual WM, it would require revision of virtually all models of capacity in this online memory system. That said, this provocative claim is inconsistent with multiple studies that have found no improvement in WM performance following numerous repetitions of specific sample displays (e.g., Logie, Brockmole, & Vandenbroucke, 2009; Olson & Jiang, 2004). Here, we replicate the Brady et al. (2009) findings but show that memory compression effects were restricted to subjects who had perfect explicit recall of the color pairs at the end of the study, suggesting that statistical regularities boosted performance by enabling contributions from long-term memory. Thus, while memory compression effects provide an interesting example of the tight collaboration between online and offline memory representations, they do not provide evidence that statistical regularities can augment the number of individuated representations that can be concurrently stored in visual WM. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Attention and working memory are clearly intertwined, as shown by co-variations in individual ability and the recruitment of similar neural substrates. Both processes fluctuate over time(1–5), and these fluctuations may be a key determinant of individual variations in ability(6,7). If these fluctuations are due to the waxing and waning of a common cognitive resource, attention and working memory should co-vary on a moment-to-moment basis. To test this, we developed a hybrid task that interleaved a sustained attention task and a whole-report working memory task. Experiment 1 established that performance fluctuations on these tasks correlated across and within participants: attention lapses led to worse working memory performance. Experiment 2 extended this finding using a real-time triggering procedure that monitored attention fluctuations to probe working memory during optimal (high-attention) or suboptimal (low-attention) moments. In low-attention moments, participants stored fewer items in working memory. Experiment 3 ruled out task-general fluctuations as an explanation for these co-variations by showing that the precision of colour memory was unaffected by variations in attention state. In summary, we demonstrate that attention and working memory lapse together, providing additional evidence for the tight integration of these cognitive processes.

Summary Human brains share a broadly similar functional organization with consequential individual variation. This duality in brain function has primarily been observed when using techniques that consider the spatial organization of the brain, such as MRI. Here, we ask whether these common and unique signals of cognition are also present in temporally sensitive but spatially insensitive neural signals. To address this question, we compiled electroencephalogram (EEG) data from individuals of both sexes while they performed multiple working memory tasks at two different data-collection sites (n = 171 and 165). Results revealed that trial-averaged EEG activity exhibited inter-electrode correlations that were stable within individuals and unique across individuals. Furthermore, models based on these inter-electrode correlations generalized across datasets to predict participants’ working memory capacity and general fluid intelligence. Thus, inter-electrode correlation patterns measured with EEG provide a signature of working memory and fluid intelligence in humans and a new framework for characterizing individual differences in cognitive abilities.

Past work has demonstrated that active suppression of salient distractors is a critical part of visual selection. Evidence for goal-driven suppression includes below-baseline visual encoding at the position of salient distractors (Gaspelin and Luck, 2018) and neural signals such as the distractor positivity (Pd) that track how many distractors are presented in a given hemifield (Feldmann-Wüstefeld and Vogel, 2019). One basic question regarding distractor suppression is whether it is inherently spatial or nonspatial in character. Indeed, past work has shown that distractors evoke both spatial (Theeuwes, 1992) and nonspatial forms of interference (Folk and Remington, 1998), motivating a direct examination of whether space is integral to goal-driven distractor suppression. Here, we use behavioral and EEG data from adult humans (male and female) to provide clear evidence for a spatial gradient of suppression surrounding salient singleton distractors. Replicating past work, both reaction time and neural indices of target selection improved monotonically as the distance between target and distractor increased. Importantly, these target selection effects were paralleled by a monotonic decline in the amplitude of the Pd, an electrophysiological index of distractor suppression. Moreover, multivariate analyses revealed spatially selective activity in the θ-band that tracked the position of the target and, critically, revealed suppressed activity at spatial channels centered on distractor positions. Thus, goal-driven selection of relevant over irrelevant information benefits from a spatial gradient of suppression surrounding salient distractors.SIGNIFICANCE STATEMENTPast work has shown that distractor suppression is an important part of goal-driven attentional selection, but has not yet revealed whether suppression is spatially directed. Using behavioral data, event-related potentials (ERPs) of the EEG signal [N2pc and distractor positivity (Pd) component], as well as a multivariate model of EEG data [channel tuning functions (CTF)], we show that suppression-related neural activity increases monotonically as the distance between targets and distractors decreases, and that spatially-selective activity in the θ-band reveals depressed activity in spatial channels that index distractor positions. Thus, we provide robust evidence for spatially-guided distractor suppression, a result that has important implications for models of goal-driven attentional control.

Current theories propose that the short-term retention of information in working memory (WM) and the recall of information from long-term memory (LTM) are supported by overlapping neural mechanisms in occipital and parietal cortex. However, the extent of the shared representations between WM and LTM is unclear. We designed a spatial memory task that allowed us to directly compare the representations of remembered spatial information in WM and LTM with carefully matched behavioral response precision between tasks. Using multivariate pattern analyses on functional magnetic resonance imaging data, we show that visual memories were represented in a sensory-like code in both memory tasks across retinotopic regions in occipital and parietal cortex. Regions in lateral parietal cortex also encoded remembered locations in both tasks, but in a format that differed from sensory-evoked activity. These results suggest a striking correspondence in the format of representations maintained in WM and retrieved from LTM across occipital and parietal cortex. On the other hand, we also show that activity patterns in nearly all parietal regions, but not occipital regions, contained information that could discriminate between WM and LTM trials. Our data provide new evidence for theories of memory systems and the representation of mnemonic content.

Current theories propose that the short-term retention of information in working memory (WM) and the recall of information from long-term memory (LTM) are supported by overlapping neural mechanisms in occipital and parietal cortex. Both are thought to rely on reinstating patterns of sensory activity evoked by the perception of the remembered item. However, the extent of the shared representations between WM and LTM are unclear, and it is unknown how WM and LTM representations may differ across cortical regions. We designed a spatial memory task that allowed us to directly compare the representations of remembered spatial information in WM and LTM. Critically, we carefully matched the precision of behavioral responses in these tasks. We used fMRI and multivariate pattern analyses to examine representations in (1) retinotopic cortex and (2) lateral parietal cortex (LPC) regions previously implicated in LTM. We show that visual memories were represented in a sensory-like code in both tasks across retinotopic regions in occipital and parietal cortex. LPC regions also encoded remembered locations in both WM and LTM, but in a format that differed from the sensory-evoked activity. These results suggest a striking correspondence in the format of WM and LTM representations across occipital and parietal cortex. On the other hand, we show that activity patterns in nearly all parietal regions, but not occipital regions, contained information that could discriminate between WM trials and LTM trials. Our data provide new evidence for theories of memory systems and the representation of mnemonic content.

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 (Unsworth et al., 2015) and set sizes 2 and 4 items (Hakim et al., 2019), 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 modelling 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 was 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.

There exists an intricate relationship between attention and working memory. Recent work has further established that attention and working memory fluctuate synchronously, by tightly interleaving sustained attention and working memory tasks. This work has raised many open questions about physiological signatures underlying these behavioral fluctuations. Across two experiments, we explore pupil dynamics using real-time triggering in conjunction with an interleaved sustained attention and working memory task. In Experiment 1, we use behavioral real-time triggering and replicate recent findings from our lab (deBettencourt et al., 2019) that sustained attention fluctuates concurrently with the number of items maintained in working memory. Furthermore, highly attentive moments, detected via behavior, also exhibited larger pupil sizes. In Experiment 2, we develop a novel real-time pupil triggering technique to track pupil size fluctuations in real time and trigger working memory probes. We reveal that this pupil triggering procedure elicits differences in sustained attention, as indexed by response time. These experiments reflect methodological advances in real-time triggering and further characterize an important biomarker of sustained attention.

A long-standing question in the field of vision research is whether scalp-recorded EEG activity contains sufficient information to identify stimulus chromaticity. Recent multivariate work suggests that it is possible to decode which chromaticity an observer is viewing from the multielectrode pattern of EEG activity. There is debate, however, about whether the claimed effects of stimulus chromaticity on visual evoked potentials (VEPs) are instead caused by unequal stimulus luminances, which are achromatic differences. Here, we tested whether stimulus chromaticity could be decoded when potential confounds with luminance were minimized by (1) equating chromatic stimuli in luminance using heterochromatic flicker photometry for each observer and (2) independently varying the chromaticity and luminance of target stimuli, enabling us to test whether the pattern for a given chromaticity generalized across wide variations in luminance. We also tested whether luminance variations can be decoded from the topography of voltage across the scalp. In Experiment 1, we presented two chromaticities (appearing red and green) at three luminance levels during separate trials. In Experiment 2, we presented four chromaticities (appearing red, orange, yellow, and green) at two luminance levels. Using a pattern classifier and the multielectrode pattern of EEG activity, we were able to accurately decode the chromaticity and luminance level of each stimulus. Furthermore, we were able to decode stimulus chromaticity when we trained the classifier on chromaticities presented at one luminance level and tested at a different luminance level. Thus, EEG topography contains robust information regarding stimulus chromaticity, despite large variations in stimulus luminance.

Visual working memory allows us to maintain information in mind for use in ongoing cognition. Research on visual working memory often characterizes it within the context of its interaction with long-term memory (LTM). These embedded-processes models describe memory representations as existing in three potential states: inactivated LTM, including all representations stored in LTM; activated LTM, latent representations that can quickly be brought into an active state due to contextual priming or recency; and the focus of attention, an active but sharply limited state in which only a small number of items can be represented simultaneously. This chapter extends the embedded-processes framework of working memory. It proposes that working memory should be defined operationally based on neural activity. By defining working memory in this way, the important theoretical distinction between working memory and LTM is maintained, while still acknowledging that they operate together. It is additionally proposed that active working memory should be further subdivided into at least two subcomponent processes that index item-based storage and currently prioritized spatial locations. This fractionation of working memory is based on recent research that has found that the maintenance of information distinctly relies on item-based representations as well as prioritization of spatial locations. It is hoped that this updated framework of the definition of working memory within the embedded-processes model provides further traction for understanding how we maintain information in mind.

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 4 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 (WM) must maintain relevant information, despite the constant influx of both relevant and irrelevant information. Attentional control mechanisms help determine which of this new information gets access to our capacity-limited WM system. Previous work has treated attentional control as a monolithic process–either distractors capture attention or they are suppressed. Here, we provide evidence that attentional capture may instead be broken down into at least two distinct sub-component processes: 1) spatial capture, which refers to when spatial attention shifts towards the location of irrelevant stimuli, and 2) item-based capture, which refers to when item-based WM representations of irrelevant stimuli are formed. To dissociate these two sub-component processes of attentional capture, we utilized a series of EEG components that track WM maintenance (contralateral delay activity), suppression (distractor positivity), item individuation (N2pc), and spatial attention (lateralized alpha power). We show that relevant interrupters trigger both spatial and item-based capture, which means that they undermine WM maintenance more. Irrelevant interrupters, however, only trigger spatial capture from which ongoing WM representations can recover more easily. This fractionation of attentional capture into distinct sub-component processes provides a framework by which the fate of ongoing WM processes after interruption can be explained.

Working memory maintains information in a readily accessible state and has been shown to degrade as the length of the retention interval increases. Previous research has suggested that this decline is attributable to changes in precision as well as sudden loss of item representations. Here, by measuring trial-to-trial variations in performance, we examined an orthogonal distinction between the maximum number of items that an individual can store, and the probability of achieving that maximum. Across two experiments, we replicated the finding that performance declines after long (10 second) retention intervals, as well as past observations that forgetting was due to probabilistic dropping of individual items rather than all-or-none losses of the stored memories. Critically, longer retention intervals did not reduce the maximum amount of information that could be stored in working memory. Instead, lower attentional control accounted for a decreased probability of maintaining the maximum number of items in working memory. Thus, longer retention intervals impact working memory storage via fluctuations in attentional control that lower the probability of achieving a stable maximum storage capacity.

The mangrove forests of Southeast Asia are highly biodiverse and provide multiple ecosystem services upon which millions of people depend. Mangroves enhance fisheries and coastal protection, and store among the highest densities of carbon of any ecosystem globally. Mangrove forests have experienced extensive deforestation owing to global demand for commodities, and previous studies have identified the expansion of aquaculture as largely responsible. The proportional conversion of mangroves to different land use types has not been systematically quantified across Southeast Asia, however, particularly in recent years. In this study we apply a combined geographic information system and remote sensing method to quantify the key proximate drivers (i.e., replacement land uses) of mangrove deforestation in Southeast Asia between 2000 and 2012. Mangrove forests were lost at an average rate of 0.18% per year, which is lower than previously published estimates. In total, more than 100,000 ha of mangroves were removed during the study period, with aquaculture accounting for 30% of this total forest change. The rapid expansion of rice agriculture in Myanmar, and the sustained conversion of mangroves to oil palm plantations in Malaysia and Indonesia, are identified as additional increasing and under-recognized threats to mangrove ecosystems. Our study highlights frontiers of mangrove deforestation in the border states of Myanmar, on Borneo, and in Indonesian Papua. To implement policies that conserve mangrove forests across Southeast Asia, it is essential to consider the national and subnational variation in the land uses that follow deforestation.

Working memory maintains information so that it can be used in complex cognitive tasks. A key challenge for this system is to maintain relevant information in the face of task-irrelevant perturbations. In this series of experiments, we investigated the impact of task-irrelevant interruptions on neural representations of working memory. We recorded electroencephalogram (EEG) activity in humans while they performed a working memory task. On a subset of trials, we interrupted participants with salient, but task-irrelevant objects. To track the impact of these task-irrelevant interruptions on neural representations of working memory, we measured two well-characterized, temporally sensitive EEG markers that reflect active, prioritized working memory representations: the contralateral delay activity (CDA) and lateralized alpha power (8-12hz). Following interruption, we found that CDA momentarily sustained, but was gone by the end of the trial. Lateralized alpha power was immediately influenced by the interrupters, but recovered by the end of the trial. This suggests that dissociable neural processes contribute to the maintenance of working memory information. Additionally, we found that task expectancy modulated the timing and magnitude of how these two neural signals responded to task-irrelevant interruptions, suggesting that the brain’s response to task-irrelevant interruption is shaped by task context. The distinct time courses of and influence of task context on these two neural signatures of working memory have many interesting theoretical implications about how information is actively maintained in working memory.Significance statementWorking memory plays a central role in intelligent behaviors because it actively maintains relevant information that is easily accessible and manipulatable. In everyday life, we are often interrupted while performing such complex cognitive tasks. Therefore, understanding how working memory responds to and overcomes momentary task-irrelevant interruptions is critical for us to understand how complex cognition works. Here, we unveil how two distinct neural signatures of working memory respond to task-irrelevant interruptions by recording electroencephalogram activity in humans. Our findings raise long-standing theoretical questions about how different neural and cognitive processes contribute to the maintenance of information in working memory.

We respond to the comments of Logie and Vandierendonck to our article proposing benchmark findings for evaluating theories and models of short-term and working memory. The response focuses on the two main points of criticism: (a) Logie and Vandierendonck argue that the scope of the set of benchmarks is too narrow. We explain why findings on how working memory is used in complex cognition, findings on executive functions, and findings from neuropsychological case studies are currently not included in the benchmarks, and why findings with visual and spatial materials are less prevalent among them. (b) The critics question the usefulness of the benchmarks and their ratings for advancing theory development. We explain why selecting and rating benchmarks is important and justifiable, and acknowledge that the present selection and rating decisions are in need of continuous updating. The usefulness of the benchmarks of all ratings is also enhanced by our concomitant online posting of data for many of these benchmarks. (PsycINFO Database Record

A recent study found that visual working memory performance was enhanced when pairs of colors were predictably paired, and it was interpreted as a form of “memory compression” which implies that more colors could be stored online in a more efficient format. Here we propose an alternative hypothesis that does not entail any increase in the number of individuated representations stored online. Instead, familiar ensembles of items may be attached to a content-free label (e.g., remembering red-white-blue as “American flag”) that can be used to retrieve the constituents of a chunk when they are needed to guide a response. If accessing “compressed” memories requires an additional retrieval process, then access to compressed items should be slower than for uncompressed items. Indeed, Experiments 1 (visual) and 2 (verbal) showed that response times were substantially longer in patterned (i.e., compressed) than in control conditions. In Experiments 3 and 4, regularity-based advantages were eliminated with brief (1000 or 875 ms) response deadlines, in line with our hypothesis that accessing compressed memories requires a slow retrieval process. In sum, while statistical regularities can enable access to larger amounts of information, this information may not be available “online” in the same way as singleton items.

There is a consensus that visual working memory (WM) resources are sharply limited, but debate persists regarding the simple question of whether there is a limit to the total number of items that can be stored concurrently. Zhang and Luck (2008) advanced this debate with an analytic procedure that provided strong evidence for random guessing responses, but their findings can also be described by models that deny guessing while asserting a high prevalence of low precision memories. Here, we used a whole report memory procedure in which subjects reported all items in each trial and indicated whether they were guessing with each response. Critically, this procedure allowed us to measure memory performance for all items in each trial. When subjects were asked to remember 6 items, the response error distributions for about 3 out of the 6 items were best fit by a parameter-free guessing model (i.e. a uniform distribution). In addition, subjects' self-reports of guessing precisely tracked the guessing rate estimated with a mixture model. Control experiments determined that guessing behavior was not due to output interference, and that there was still a high prevalence of guessing when subjects were instructed not to guess. Our novel approach yielded evidence that guesses, not low-precision representations, best explain limitations in working memory. These guesses also corroborate a capacity-limited working memory system - we found evidence that subjects are able to report non-zero information for only 3-4 items. Thus, WM capacity is constrained by an item limit that precludes the storage of more than 3-4 individuated feature values.

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.

It is well known that salient yet irrelevant singleton can capture attention, even when this is inconsistent with the current goals of the observer (Theeuwes, 1992; 2010). Others however have claimed that capture is critically contingent on the goals of the observer: Capture is strongly modulated (or even eliminated) when the irrelevant singleton does not match the target-defining properties (Folk, Remington, & Johnston, 1992). There has been a long-standing debate on whether attentional capture can be explained by goal-driven and/or stimulus-driven accounts. Here, we shed further light on this phenomenon by using EEG activity (raw EEG and alpha power) to provide a time-resolved index of attentional orienting. Participants searched for a target defined by a pre-specified color. The search display was preceded by a singleton cue that either matched the color of the upcoming target (contingent cues), or that appeared in an irrelevant color (non-contingent cues). Multivariate analysis of raw EEG and alpha power revealed preferential tuning to the location of both contingent and non-contingent cues, with a stronger bias towards contingent than non-contingent cues. The time course of these effects, however, depended on the neural signal. Raw EEG data revealed attentional orienting towards the cue early on in the trial (>156 ms), while alpha power revealed sustained spatial selection in the cued locations at a later moment in the trial (>250 ms). Moreover, while raw EEG showed stronger capture by contingent cues during this early time window, the advantage for contingent cues arose during a later time window in alpha band activity. Thus, our findings suggest that raw EEG activity and alpha-band power tap into distinct neural processes that index movements of covert spatial attention. Both signals provide clear neural evidence that both contingent and non-contingent cues can capture attention, and that this process is robustly shaped by the target-defining properties in the current block of trials.