Kirsten Adam

The contribution of attentional lapses to individual differences in visual working memory capacity

Attentional control and working memory capacity are important cognitive abilities that substantially vary between individuals. Although much is known about how attentional control and working memory capacity relate to each other and to constructs like fluid intelligence, little is known about how trial-by-trial fluctuations in attentional engagement impact trial-by-trial working memory performance. Here, we employ a novel whole-report memory task that allowed us to distinguish between varying levels of attentional engagement in humans performing a working memory task. By characterizing low-performance trials, we can distinguish between models in which working memory performance failures are caused by either (1) complete lapses of attention or (2) variations in attentional control. We found that performance failures increase with set-size and strongly predict working memory capacity. Performance variability was best modeled by an attentional control model of attention, not a lapse model. We examined neural signatures of performance failures by measuring EEG activity while participants performed the whole-report task. The number of items correctly recalled in the memory task was predicted by frontal theta power, with decreased frontal theta power associated with poor performance on the task. In addition, we found that poor performance was not explained by failures of sensory encoding; the P1/N1 response and ocular artifact rates were equivalent for high- and low-performance trials. In all, we propose that attentional lapses alone cannot explain individual differences in working memory performance. Instead, we find that graded fluctuations in attentional control better explain the trial-by-trial differences in working memory that we observe.

The reliability and stability of visual working memory capacity

Because of the central role of working memory capacity in cognition, many studies have used short measures of working memory capacity to examine its relationship to other domains. Here, we measured the reliability and stability of visual working memory capacity, measured using a single-probe change detection task. In Experiment 1, the participants (N = 135) completed a large number of trials of a change detection task (540 in total, 180 each of set sizes 4, 6, and 8). With large numbers of both trials and participants, reliability estimates were high (α > .9). We then used an iterative down-sampling procedure to create a look-up table for expected reliability in experiments with small sample sizes. In Experiment 2, the participants (N = 79) completed 31 sessions of single-probe change detection. The first 30 sessions took place over 30 consecutive days, and the last session took place 30 days later. This unprecedented number of sessions allowed us to examine the effects of practice on stability and internal reliability. Even after much practice, individual differences were stable over time (average between-session r = .76).

Confident failures: Lapses of working memory reveal a metacognitive blind spot

Working memory performance fluctuates dramatically from trial to trial. On many trials, performance is no better than chance. Here, we assessed participants’ awareness of working memory failures. We used a whole-report visual working memory task to quantify both trial-by-trial performance and trial-by-trial subjective ratings of inattention to the task. In Experiment 1 (N = 41), participants were probed for task-unrelated thoughts immediately following 20% of trials. In Experiment 2 (N = 30), participants gave a rating of their attentional state following 25% of trials. Finally, in Experiments 3a (N = 44) and 3b (N = 34), participants reported confidence of every response using a simple mouse-click judgment. Attention-state ratings and off-task thoughts predicted the number of items correctly identified on each trial, replicating previous findings that subjective measures of attention state predict working memory performance. However, participants correctly identified failures on only around 28% of failure trials. Across experiments, participants’ metacognitive judgments reliably predicted variation in working memory performance but consistently and severely underestimated the extent of failures. Further, individual differences in metacognitive accuracy correlated with overall working memory performance, suggesting that metacognitive monitoring may be key to working memory success.

Contralateral Delay Activity Tracks Fluctuations in Working Memory Performance

Neural measures of working memory storage, such as the contralateral delay activity (CDA), are powerful tools in working memory research. CDA amplitude is sensitive to working memory load, reaches an asymptote at known behavioral limits, and predicts individual differences in capacity. An open question, however, is whether neural measures of load also track trial-by-trial fluctuations in performance. Here, we used a whole-report working memory task to test the relationship between CDA amplitude and working memory performance. If working memory failures are due to decision-based errors and retrieval failures, CDA amplitude would not differentiate good and poor performance trials when load is held constant. If failures arise during storage, then CDA amplitude should track both working memory load and trial-by-trial performance. As expected, CDA amplitude tracked load (Experiment 1), reaching an asymptote at three items. In Experiment 2, we tracked fluctuations in trial-by-trial performance. CDA amplitude was larger (more negative) for high-performance trials compared with low-performance trials, suggesting that fluctuations in performance were related to the successful storage of items. During working memory failures, participants oriented their attention to the correct side of the screen (lateralized P1) and maintained covert attention to the correct side during the delay period (lateralized alpha power suppression). Despite the preservation of attentional orienting, we found impairments consistent with an executive attention theory of individual differences in working memory capacity; fluctuations in executive control (indexed by pretrial frontal theta power) may be to blame for storage failures.

Tuning in by tuning out distractions

Working memory is a limited workspace for temporarily holding information in mind, and it is critical for thinking and problem solving. A person’s ability to perform a variety of complicated intelligent behaviors, such as abstract reasoning, mathematics, and acquiring new languages, depends greatly on his or her specific working memory capacity. Those with a high capacity perform better on measures of fluid intelligence and scholastic aptitude than their low-capacity counterparts (1, 2). For the past 15 years, much of the work on individual differences in capacity has indicated that differences between people are largely because of attentional control, which allows one to focus on relevant information and ignore distractions (3, 4). However, it has always been unclear whether the advantages of high-capacity individuals are due to their ability to “tune in” the important stuff or to their ability to “tune out” the unimportant stuff. In PNAS, Gaspar et al. (5) provide exciting new evidence that the main benefit of a high-capacity mind is the ability to quickly and effectively suppress distracting information.

Is Feature-Based Attention Always Spatially Global during Visual Search?

The visual system has a limited capacity, so visual inputs must compete for representation in visual cortex. Attentional mechanisms resolve this competition by selecting a subset of behaviorally relevant information for processing on the basis of location or nonspatial features, such as color (for

Inverted Encoding Models Assay Population-Level Stimulus Representations, Not Single-Unit Neural Tuning

Inverted encoding models (IEMs) are a powerful tool for reconstructing population-level stimulus representations from aggregate measurements of neural activity (e.g., fMRI or EEG). In a recent report, Liu et al. (2018) tested whether IEMs can provide information about the underlying tuning of single units. Here, we argue that using stimulus reconstructions to infer properties of single neurons, such as neural tuning bandwidth, is an ill-posed problem with no unambiguous solution. Instead of interpreting results from these methods as evidence about single-unit tuning, we emphasize the utility of these methods for assaying population-level stimulus representations. These can be compared across task conditions to better constrain theories of large-scale neural information processing across experimental manipulations, such as changing sensory input or attention. Neuroscience methods range astronomically in scale. In some experiments, we record subthreshold membrane potentials in individual neurons, while in others we measure aggregate responses of thousands of neurons at the millimeter scale. A central goal in neuroscience is to bridge insights across all scales to understand the core computations underlying cognition (Churchland and Sejnowski, 1988). However, inferential problems arise when moving across scales: single-unit response properties cannot be inferred from fMRI activation in single voxels, subthreshold membrane potential cannot be inferred from extracellular spike rate, and the state of single ion channels cannot be inferred from intracellular recordings. These are all examples of an inverse problem in which an observation at a larger scale is consistent with an enormous number of possible observations at a smaller scale. Recent analytical advances have circumvented challenges inherent in inverse problems by instead transforming aggregate signals from their native “measurement space” (e.g., activation pattern across fMRI voxels) into a …

Dissecting the neural focus of attention reveals distinct processes for spatial attention and object-based storage in visual working memory

Working memory (WM) maintains relevant information in an accessible state, and is composed of an active focus of attention and passive offline storage. Here, we dissect the focus of attention by showing that distinct neural signals index the online storage of objects and sustained spatial attention. We recorded EEG activity during two tasks that employed identical stimulus displays while the relative demands for object storage and spatial attention varied. Across four experiments, we found dissociable delay-period signatures for an attention task (which only required spatial attention) and 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 the number of items in the array. Thus, we argue that the focus of attention is maintained via the combined operation of distinct processes for covert spatial orienting and object-based storage.

Item-specific delay activity demonstrates concurrent storage of multiple items in working memory

Abstract A longstanding view holds that information is maintained in working memory (WM) via persistent neural activity that encodes the content of WM. Recent work, however, has challenged the view that all items stored in WM are actively maintained. Instead, “activity-silent” models propose that items can be maintained in WM without the need for persistent neural activity, raising the possibility that only a subset of items – perhaps just a single item – may be actively represented at a given time. While past studies have successfully decoded multiple items stored in WM, these studies cannot rule out an active switching account in which only a single item is actively represented at a time. Here, we directly tested whether multiple representations can be held concurrently in an active state. We tracked spatial representations in WM using alpha-band (8–12 Hz) activity, which encodes spatial positions held in WM. Human observers (male and female) remembered one or two positions over a short delay while we recorded EEG. Using a spatial encoding model, we reconstructed stimulus-specific working memory representations (channel tuning functions, CTFs) from the scalp distribution of alphaband power. Consistent with past work, we found the selectivity of spatial CTFs was lower when two items were stored than when one item was stored. Critically, data-driven simulations revealed that the selectivity of spatial representations in the two-item condition could not be explained by models restricting storage to a single item at a time. Thus, our findings provide robust evidence for the concurrent storage of multiple items in visual working memory. Author Summary Working memory (WM) is a mental workspace where we temporarily hold information “online” in pursuit of our current goals. However, recent activity-silent models of WM have challenged the view that all items are held in an “online” state, instead proposing that only a subset of representations in WM – perhaps just one item – are represented by persistent activity at a time. To directly test a single-item model of persistent activity, we used a spatial encoding model to read out the strength of two representations from alpha-band (8–12 Hz) power in the human EEG signal. We provide direct evidence that both locations were maintained concurrently, ruling out the possibility that declines in stimulus-specific activity are due to storing one of two items in an activity-silent state.

Improvements to visual working memory performance with practice and feedback

Visual working memory capacity is estimated to be around 3–4 items, but on some trials participants fail to correctly report even a single item from the memory array. Such failures of working memory performance are surprisingly common, and participants have poor self-awareness of them. Previous work has shown that behavioral feedback can reduce the frequency of working memory failures, but the benefits of feedback disappeared immediately after it was taken away. Here, we tested whether extended practice with or without trial-by-trial feedback would lead to persistent improvements in working memory performance. Participants were assigned to one of four groups: (1) Working memory practice with feedback (2) Working memory practice without feedback (3) Crossword puzzle active control (4) No-contact control. Consistent with previous work, simple practice with a visual working memory task robustly improved working memory performance across practice sessions. However, we found only partial support for the efficacy of feedback in improving working memory performance. Practicing with feedback improved working memory performance relative to a no-feedback group for some practice sessions. However, the feedback benefits did not persist across all training sessions and did not transfer to a final test session without the feedback. Thus, the benefits of performance feedback did not persist over time. Further, we found only stimulus-specific transfer of visual working memory practice benefits. We also found that participants’ metaknowledge improved with practice, but that receiving feedback about task accuracy actually slightly harmed the accuracy of concurrent metaknowledge ratings. Finally, we discuss important design considerations for future work in this area (e.g. power, expectations, and “spacing effects”). For example, we found that achieved statistical power to detect a between-groups effect declined with practice. This finding has potentially critical implications for any study using a 1-session study to calculate power for a planned multi-session study.