Edward K. Vogel’s research while affiliated with University of Chicago and other places

Working memory tasks often require comparing remembered visual arrays to test displays, yet little is known about how people edit the contents of working memory at test. Across three experiments, we used contralateral delay activity (CDA) as a neural index of working memory load to examine how memory representations are selectively accessed at test. In Experiment 1, when a single test item was probed, CDA amplitudes increased with larger set sizes, indicating that untested items were still actively maintained, suggesting minimal editing based on spatial location. To test whether this was due to spatial grouping, Experiment 2 presented memory items sequentially in different temporal frames but identical spatial locations. The continued maintenance of all items at test suggested that simple spatial grouping could not explain the lack of editing effect seen in Experiment 1. In Experiment 3, however, when items belonged to distinct mnemonic categories, CDA amplitudes at test were reduced, consistent with selective editing based on category relevance. These findings suggest that working memory editing during retrieval is guided by categorical structure rather than spatial position. Supporting this, analysis of the P3 old-new effect revealed that decision speed and strength were influenced by the number of items maintained at test. Together, our results show that while people do not edit their working memory load based on spatial cues, they edit their working memory based on categorical relevance, allowing for more efficient retrieval of task-relevant information.

Decades of research have shown working memory (WM) relies on sustained pre-frontal cortical activity and visual extrastriate activity, particularly in the alpha (8-12 Hz) frequency range. This alpha activity tracks the spatial location of WM items, even when spatial position is task-irrelevant and there is no stimulus currently being presented. Traditional analyses of putative oscillations using bandpass filters, however, conflate oscillations with non-oscillatory aperiodic activity. Here, we reanalyzed seven different human electroencephalography (EEG) visual WM datasets to test the hypothesis that aperiodic activity–which is thought to reflect the relative contributions of excitatory and inhibitory drive–plays a distinct role in visual WM from true alpha oscillations. To do this, we developed a novel, time-resolved spectral parameterization approach to disentangle oscillations from aperiodic activity during WM encoding and maintenance. Across all seven tasks, totaling 112 participants, we captured the representation of spatial location from total alpha power using an inverted encoding model (IEM), replicating traditional analyses. We then trained separate IEMs to estimate the strength of spatial location representation from aperiodic-adjusted alpha (reflecting just the oscillatory component) and aperiodic activity, and find that IEM performance improves for aperiodic-adjusted alpha compared to total alpha power that blends the two signals. We also discover a novel role for aperiodic activity, where IEM performance trained on aperiodic activity is highest during stimulus presentation, but not during the WM maintenance period. Our results emphasize the importance of controlling for aperiodic activity when studying neural oscillations while uncovering a novel functional role for aperiodic activity in the encoding of visual WM information. Significance statement Working memory is a crucial component of cognition, yet its neural mechanisms are not fully understood. Research shows that alpha activity – presumed to reflect neural oscillations – tracks the location of items we hold in memory. However, these analyses assume that all alpha power is oscillatory, even though oscillations are mixed with non-oscillatory, aperiodic activity that may be physiologically and functionally distinct. Here, we use a novel analytical approach for separating alpha oscillations and aperiodic activity dynamically across time. Our results reveal distinct roles for each in human visual working memory: aperiodic activity encodes the spatial location of information whereas alpha oscillations maintain the location of that information.

Sustained attention is the ability to maintain focus on a specific goal over time, but lapses in attention are frequent. Many theories have attributed these lapses to a transient failure of control in maintaining the goal in mind. However, these proposals have been challenged because recent findings have shown more engagement of cognitive control during states more prone to lapses. We hypothesized that lapses occur during periods of high competition between goals, requiring stronger cognitive control. To test this goal-competition hypothesis, we developed a Switch-Continuous Performance Task in which subjects alternated task goals between blocks-either switching or holding the same goal-in an effort to manipulate periods of higher and lower competition between goals. Participants viewed a bilateral display showing a scene (indoor/outdoor) and a face (male/female) on each trial. At the start of each 20-trial block, a cue instructed participants to perform either the scene task (e.g., press for frequent indoor scenes, not infrequent outdoor scenes) or the face task (e.g., press for frequent male faces, not infrequent female faces). Results showed more attention lapses during switch than repeat blocks, suggesting that lapses occur during periods of high competition between goals. In a second study, we monetarily rewarded performance on only one task (scene or face) to create unequal competition between goals. We found that switching to an unrewarded goal-but not a rewarded goal-produced more lapses. Together, these findings support the goal-competition hypothesis as an explanation for the occurrence of sustained attention lapses.

Sustained attention is the ability to maintain focus on a specific goal over time,but lapses in attention are frequent. Many theories have attributed these lapses to atransient failure of control in maintaining the goal in mind. However, these proposalshave been challenged because recent findings have shown more engagement ofcognitive control during states more prone to lapses. We hypothesized that lapses occurduring periods of high competition between goals, requiring stronger cognitive control.To test this goal-competition hypothesis, we developed a Switch-ContinuousPerformance Task in which subjects alternated task goals between blocks—eitherswitching or holding the same goal—in an effort to manipulate periods of higher andlower competition between goals. Participants viewed a bilateral display showing ascene (indoor/outdoor) and a face (male/female) on each trial. At the start of each 20-trial block, a cue instructed participants to perform either the scene task (e.g., press forfrequent indoor scenes, not infrequent outdoor scenes) or the face task (e.g., press forfrequent male faces, not infrequent female faces). Results showed more attention lapsesduring switch than repeat blocks, suggesting that lapses occur during periods of highcompetition between goals. In a second study, we monetarily rewarded performance ononly one task (scene or face) to create unequal competition between goals. We foundthat switching to an unrewarded goal—but not a rewarded goal—produced more lapses.Together, these findings support the goal-competition hypothesis as an explanation forthe occurrence of sustained attention lapses.

Individuals differ in their ability to sustain attention. However, whether differences in sustained attention reflect differences in processes related to attentional control and working memory or long-term memory (LTM) remains underexplored. In Experiment 1, we conducted an online study (n = 136) measuring participants' sustained attention, attention control and working memory, and LTM. We measured sustained attention with an audio-visual continuous performance task (avCPT) in which participants responded to images while inhibiting responses to infrequent targets; attention control and working memory with Flanker, change localization, and Simon tasks; and LTM with recognition and source memory tests. Factor analyses revealed that sustained attention formed a distinct factor from attention control and working memory and LTM. Individual differences in the sustained attention factor robustly predicted individual differences in LTM and, to a lesser extent, attention control and working memory. In Experiment 2, to test how neural signatures of sustained attention related to attention control and working memory and LTM, we analyzed fMRI functional connectivity patterns collected as 20 participants performed the avCPT. A pre-trained connectome-based model of sustained attention predicted participants' performance on out-of-scanner LTM, but not attention control and working memory, tasks. Together these results suggest that individual differences in sustained attention, although correlated with attention control and working memory, are more closely related to LTM.

Sustained attention fluctuates between periods of good and poor attentional performance. Two major methodologies exist to study these fluctuations: an objective approach that identifies “in-the-zone” states of consistent response times (RTs) and “out-of-the-zone” states of erratic RTs and a subjective approach that asks participants whether they are on-task or mind wandering. Although both approaches effectively predict attentional lapses, it remains unclear whether they capture the same or distinct attentional fluctuations. We combined both approaches within a single sustained attention task requiring frequent responses and response inhibition to rare targets to explore their consistency (N = 40). Behaviorally, both objective out-of-the-zone and subjective mind-wandering states were associated with more attentional lapses. However, the percentage of time spent out-of-the-zone did not differ between on-task and mind-wandering periods and both objective and subjective states independently predicted error-proneness, suggesting that the two methods do not capture the same type of attention fluctuations. Whereas attentional preparation before correct inhibitions was greater during out-of-the-zone compared with in-the-zone periods, preparation did not differ by subjective state. In contrast, posterror slowing differed by both objective and subjective states, but in opposite directions: slowing was observed when participants were objectively out-of-the-zone or subjectively on-task. Overall, our results provide evidence that objective and subjective approaches capture distinct attention fluctuations during sustained attention tasks. Integrating both objective and subjective measures is crucial for fully understanding the mechanisms underlying our ability to remain focused.

Individual differences in working memory predict a wide range of cognitive abilities. However, little research has been done on whether working memory continues to predict task performance after repetitive learning. Here, we tested whether working memory ability continued to predict long-term memory (LTM) performance for picture sequences even after participants showed massive learning. In Experiments 1–3, subjects performed a source memory task in which they were presented a sequence of 30 objects shown in one of four quadrants and then were tested on each item’s position. We repeated this procedure for five times in Experiment 1 and 12 times in Experiments 2 and 3. Interestingly, we discovered that individual differences in working memory continually predicted LTM accuracy across all repetitions. In Experiment 4, we replicated the stable working memory demands with word pairs. In Experiment 5, we generalized the stable working memory demands model to attentional control abilities. Together, these results suggest that people, instead of relying less on working memory, optimized their working memory and attentional control throughout learning.