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Significance statement:
Our days are filled with looking for relevant objects, while ignoring irrelevant visual information. Such visual search activity is thought to be driven by current goals activated in working memory. However, working memory not only serves current goals, but also future goals, with differential impact upon visual selection....
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... setup of trials and timings of displays was the same in all conditions. Our design contained four main conditions (Table 1; Fig. 1b), with as most important manipulations whether the current template or the prospective template was presented lateralized, and whether either one or both templates had to be remembered. In Load 2 conditions, both a current and a prospective template had to be remembered, and there were two versions: In the Current Lateralized condition, the current template was presented left or right from central fixation, whereas the prospective template was presented on the central meridian, above or below fixation. ...
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Citations
... Specifically, the EEG signal was further processed by applying a 110 to 140 Hz band-pass filter to capture muscle activity and transform it into z scores. A subject-specific z-score threshold was determined based on the within-subject variance of z-scores within the windows of interest (de Vries et al., 2017;Duncan et al., 2023). To minimize false alarms, instead of immediately removing epochs that exceeded the z-score . ...
Attentional capture by an irrelevant salient distractor is attenuated when the distractor is presented more frequently in one location compared to other locations, suggesting that people learn to suppress an irrelevant salient location. However, to date it is unclear whether this suppression is proactive, applied before attention has been directed to the distractor location, or reactive, occurring after attention has been directed to that specific location. The aim of the present study is to investigate how suppression is accomplished by using the pinging technique which allows one to probe how attention is distributed across the visual field prior to the presentation of the search display. In an EEG experiment, participants performed a visual search task wherein they were tasked with identifying a shape singleton in the presence of an irrelevant color singleton. Compared to all other locations, this color singleton appeared more frequently at a specific location, which was termed the high-probability location. Prior to the search task, we introduced a continuous recall spatial memory task to reveal the hidden attentional priority map. Participants had to memorize the location of a memory cue continuously and report this location after the visual search task. Critically, after the presentation of the memory cue but before the onset of the search display, a neutral placeholder display was presented to probe how hidden priority map is reconfigured by the learned distractor suppression. Behaviorally, there was clear evidence that the high-probability location was suppressed, as search was more efficient when the distractor appeared at this location. To examine the priority map prior to search, we adopted an inverted encoding approach to reconstruct the tuning profile of the memorized position in the spatial memory task. Inverted modeling resulted in reliable tuning profiles during memory maintenance that gradually decayed and that were revived again by the onset of a neutral placeholder display preceding search. After the onset of the placeholders, the tuning profile observed was characterized by a spatial gradient centered over the high-probability location, with tuning being most pronounced at the-to-be suppressed location. This finding suggests that while learned suppression is initiated prior to search display onset, it is preceded by an initial phase of spatial selection, which is in line with a reactive suppression account. Together these results further our understanding of the mechanism of spatial distractor suppression.
... Research in the field of visual WM has consistently shown that relevant representations of visual memories can be dynamically modulated by means of attentional prioritization (Olivers et al., 2011;Sprague, Ester, & Serences, 2016). For example, one out of several encoded visual items can be retrospectively brought in the focus of attention, and serve as active target template during a visual search task (de Vries, van Driel, Karacaoglu, & Olivers, 2018;de Vries, van Driel, & Olivers, 2017;van Driel, Ort, Fahrenfort, & Olivers, 2019;van Loon, Olmos-Solis, Fahrenfort, & Olivers, 2018). The distinction between active and latent representations can be potentially supported by qualitatively distinct neural states, although the exact mechanisms allowing for such differential maintenance remain elusive (Stokes et al., 2020). ...
Information in working memory (WM) is crucial for guiding behavior. However, not all WM representations are equally relevant simultaneously. Current theoretical frameworks propose a functional dissociation between ‘latent’ and ‘active’ states, in which relevant representations are prioritized into an optimal (active) state to face current demands, while relevant information that is not immediately needed is maintained in a dormant (latent) state. In this context, task demands can induce rapid and flexible prioritization of information from latent to active state. Critically, these functional states have been primarily studied using simple visual memories, with attention selecting and prioritizing relevant representations to serve as templates to guide subsequent behavior. It remains unclear whether more complex WM representations, such as novel stimulus-response associations, can also be prioritized into different functional states depending on their task relevance, and if so how these different formats relate to each other. In the present study, we investigated whether novel WM-guided actions can be brought into different functional states depending on current task demands. Our results reveal that planned actions can be flexibly prioritized when needed and show how their functional state modulates their influence on ongoing behavior. Moreover, they suggest the representations of novel actions of different functional states are maintained in WM via a non-orthogonal coding scheme, thus are prone to interference.
... Consequently, our current results suggest that FMT cannot be interpreted as an index that is specific to working-memory load, per se. Instead, at a most general level, should be interpreted in the context of models of frontal midline systems (e.g., anterior cingulate cortex) involved in assessing the need for and regulation of the level of cognitive control (e.g., de Vries, Savran, van Driel, & Olivers, 2019;Fiebelkorn & Kastner, 2019;Helfrich et al., 2018;de Vries, van Driel, & Olivers, 2017;Shenhav et al., 2013;Sauseng, Hoppe, Klimesch, Gerloff, & Hummel, 2007;de Araújo et al., 2002;Gevins & Smith, 2000;Kahana, Sekuler, Caplan, Kirschen, & Madsen, 1999;Gevins et al., 1997;Pennekamp, Bösel, Mecklinger, & Ott, 1994). ...
Adaptive behavior relies on the selection and prioritization of relevant sensory inputs from the external environment as well as from among internal sensory representations held in working memory. Recent behavioral evidence suggests that the classic distinction between voluntary (goal-driven) and involuntary (stimulus-driven) influences over attentional allocation also applies to the selection of internal representations held in working memory. In the current EEG study, we set out to investigate the neural dynamics associated with the competition between voluntary and involuntary control over the focus of attention in visual working memory. We show that when voluntary and involuntary factors compete for the internal focus of attention, prioritization of the appropriate item is delayed—as reflected both in delayed gaze biases that track internal selection and in delayed neural beta (15–25 Hz) dynamics that track the planning for the upcoming memory-guided manual action. We further show how this competition is paralleled—possibly resolved—by an increase in frontal midline theta (4–8 Hz) activity that, moreover, predicts the speed of ensuing memory-guided behavior. Finally, because theta increased following retrocues that effectively reduced working-memory load, our data unveil how frontal theta activity during internal attentional focusing tracks demands on cognitive control over and above working-memory load. Together, these data yield new insight into the neural dynamics that govern the focus of attention in visual working memory, and disentangle the contributions of frontal midline theta activity to the processes of control versus retention in working memory.
... Second, an automatic artifact-rejection procedure was used to detect electromyography (EMG) noise. Specifically, the epochs were band-pass filtered at 110−140 Hz and transformed into a z-score threshold per participant based on the within-subject variance of z-scores (de Vries et al., 2017). Epochs that exceeded the threshold within the time window of interest were flagged (Duncan et al., 2023). ...
Through statistical learning, humans are able to extract temporal regularities, using the past to predict the future. Evidence suggests that learning relational structures makes it possible to anticipate the imminent future; yet, the neural dynamics of predicting the future and its time-course remain elusive. To examine whether future representations are denoted in a temporally discounted fashion, we used the high-temporal-resolution of electroencephalography (EEG). Observers were exposed to a fixed sequence of events at four unique spatial positions within the display. Using multivariate pattern analyses trained on independent pattern estimators, we were able to decode the spatial position of dots within full sequences, and within randomly intermixed partial sequences wherein only a single dot was presented. Crucially, within these partial sequences, subsequent spatial positions could be reliably decoded at their expected moment in time. These findings highlight the dynamic weight changes within the assumed spatial priority map and mark the first implementation of EEG to decode predicted, yet critically omitted events.
Impact statement
Utilizing high-temporal-resolution EEG, the dynamic weight changes of assumed spatial priority map were visualized by decoding the spatial position of expected, yet omitted, events at their expected moment in time.
... Using a paradigm free from potentially confounding stimulus-driven alpha power fluctuations and evoked neural responses, we revealed subtle attention-driven alpha power dynamics, which seemed to be more informative of behavior than the much stronger and more commonly investigated static alpha power lateralization effects. Such dynamic alpha power modulations might constitute a universal mechanism for the flexible prioritization of information at its most relevant moments in time, independent of the sensory modality (van Ede et al., 2011;Wöstmann et al., 2021) and whether the attentional spotlight shines at information in the external or internal space (de Vries et al., 2017;van Ede et al., 2017). Since our paradigm was also designed to minimize decision-and motor-related processing, our finding of slow negative potentials and their non-trivial correlation with subsequent memory performance further adds to the growing evidence that temporal attention can affect cognitive processes beyond the level of response preparation and execution and suggests that such processes might be reflected by slow negative waves. ...
Alpha power modulations and slow negative potentials have previously been associated with anticipatory processes in spatial and temporal top‐down attention. In typical experimental designs, however, neural responses triggered by transient stimulus onsets can interfere with attention‐driven activity patterns and our interpretation of such. Here, we investigated these signatures of spatio‐temporal attention in a dynamic paradigm free from potentially confounding stimulus‐driven activity using electroencephalography. Participants attended the cued side of a bilateral stimulus rotation and mentally counted how often one of two remembered sample orientations (i.e., the target) was displayed while ignoring the uncued side and non‐target orientation. Afterwards, participants performed a delayed match‐to‐sample task, in which they indicated if the orientation of a probe stimulus matched the corresponding sample orientation (previously target or non‐target). We observed dynamic alpha power reductions and slow negative waves around task‐relevant points in space and time (i.e., onset of the target orientation in the cued hemifield) over posterior electrodes contralateral to the locus of attention. In contrast to static alpha power lateralization, these dynamic signatures correlated with subsequent memory performance (primarily detriments for matching probes of the non‐target orientation), suggesting a preferential allocation of attention to task‐relevant locations and time points at the expense of reduced resources and impaired performance for information outside the current focus of attention. Our findings suggest that humans can naturally and dynamically focus their attention at relevant points in space and time and that such spatio‐temporal attention shifts can be reflected by dynamic alpha power modulations and slow negative potentials.
... Disks had a radius of 0.6º and were equally distanced from each other. The color pool comprised 12 colors and it was obtained from de Vries et al. (2017). Colors were firstly determined in DKL color space and later converted to RGB. ...
... They were chosen to have the same contrast and luminance, differing only in hue. The 12 colors were discrete colors from an imaginary wheel in which consecutive colors were more similar than colors at the other extreme of the wheel (see methods in de Vries et al. (2017), for a detailed description of color extraction). Four non-consecutive colors were then randomly chosen for the memory set of each trial. ...
Flexible updating of information in Visual Working Memory (VWM) is crucial to deal with its limited capacity. Previous research has shown that the removal of no longer relevant information takes some time to complete. Here, we sought to study the time course of such removal by tracking the accompanying drop in load through behavioral and neurophysiological measures. In a first experimental session, participants completed a visuospatial retro-cue task in which the Cue-Target Interval (CTI) was manipulated. Performance revealed that it takes about half a second to make full use of the retro-cue. In a second session, we sought to study the dynamics of load-related electroencephalographic (EEG) signals to track the removal of information. We applied Multivariate Pattern Analysis (MVPA) to EEG data from the same task. However, contrary to previous research indicating that MVPA can be used to uniquely decode VWM load, the results suggested that the classifiers were mainly sensitive to selection, visual cue variations, or eye movements that accompany load manipulations, and not so much to load per se. These findings advise caution when using MVPA to decode VWM load, as classifiers may be sensitive to confounding operations.
... Consequently, our current results suggest that FMT cannot be interpreted as an index that is specific to working-memory load, per se. Instead, at a most general level, should be interpreted in the context of models of frontal midline systems (e.g., anterior cingulate cortex) involved in assessing the need for and regulation of the level of cognitive control (e.g., Shenhav et al., 2013;Araújo et al., 2002;Kahana et al., 1999;de Vries et al., 2019;Fiebelkorn & Kastner, 2019;Gevins, 1997;Gevins & Smith, 2000;Helfrich et al., 2018;Pennekamp et al., 1994;Sauseng et al., 2007;Vries et al., 2017). ...
Adaptive behaviour relies on the selection and prioritisation of relevant sensory inputs from the external environment as well as from among internal sensory representations held in working memory. Recent behavioural evidence suggests that the classic distinction between voluntary (goal-driven) and involuntary (stimulus-driven) influences over attentional allocation also applies to the selection of internal representations held in working memory. In the current EEG study, we set out to investigate the neural dynamics associated with the competition between voluntary and involuntary control over the focus of attention in visual working memory. We show that when voluntary and involuntary factors compete for the internal focus of attention, prioritisation of the appropriate item is delayed - as reflected both in delayed gaze biases that track internal selection and in delayed neural beta (15-25 Hz) dynamics that track the planning for the upcoming memory- guided manual action. We further show how this competition is paralleled - possibly resolved - by an increase in frontal midline theta (4-8 Hz) activity that, moreover, predicts the speed of ensuing memory-guided behaviour. Finally, because theta increased following retrocues that effectively reduced working-memory load, our data unveil how frontal theta activity during internal attentional focusing tracks demands on cognitive control over and above working-memory load. Together, these data yield new insight into the neural dynamics that govern the focus of attention in visual working memory, and disentangles the contributions of frontal midline theta activity to the processes of control versus retention in working memory.
... To identify EMG contaminated epochs, an adapted version of an automated trial-rejection procedure was used, as implemented in Fieldtrip (Oostenveld et al., 2011). Muscle activity was specifically captured using a 110-140 Hz band-pass filter, and variable z-score thresholds per subject were allowed based on withinsubject variance of z-scores (Vries et al., 2017). Rather than immediately removing epochs exceeding the zscore threshold, the five electrodes that contributed most to accumulated z-score within the time period containing the marked EMG artefact were first identified. ...
It is well established that attention can be sharpened through the process of statistical learning (e.g., visual search becomes faster when targets appear at high-relative-to-low probability locations). Although this process of statistically learned attentional enhancement differs behaviorally from the well-studied top–down and bottom–up forms of attention, relatively little work has been done to characterize the electrophysiological correlates of statistically learned attentional enhancement. It thus remains unclear whether statistically learned enhancement recruits any of the same cognitive mechanisms as top–down or bottom–up attention. In the current study, EEG data were collected from while participants searched for an ambiguous unique shape in a visual array (the additional singleton task). Unbeknown to the participants, targets appeared more frequently in one location in space (probability cuing). Encephalographic data were then analyzed in two phases: an anticipatory phase and a reactive phase. In the anticipatory phase preceding search stimuli onset, alpha lateralization as well as the Anterior Directing Attention Negativity and Late Directing Attention Positivity components—signs of preparatory attention known to characterize top–down enhancement—were tested. In the reactive phase, the N2pc component—a well-studied marker of target processing—was examined following stimuli onset. Our results showed that statistically learned attentional enhancement is not characterized by any of the well-known anticipatory markers of top–down attention; yet targets at high probability locations did reliably evoke larger N2pc amplitudes, a finding that is associated with bottom–up attention and saliency. Overall, our findings are consistent with the notion that statistically learned attentional enhancement increases the perceptual salience of items appearing at high-probability locations relative to low-probability locations.
... In line with previous studies (Bazanova, 2012;Brickwedde et al., 2019;Mahjoory et al., 2019), our results confirmed the functional significance of resting alpha activity in cognitive task performance. Specifically, alpha power at rest can be indicative of an inhibitory attentional filter which helps to keep relevant target information activated, allowing for more efficient anticipatory attention before completing a cognitive task (de Vries et al., 2017;Klimesch, 2012;Jensen and Mazaheri, 2010;Sadaghiani et al., 2010). Thus, theoretically, the increase in resting alpha oscillations may help individuals to gate neural resources, build up an 'attentional buffer', modulate levels of preparedness for upcoming processing, and thereby lead to higher levels of word leaning efficiency. ...
Individuals exhibit considerable variability in their capacity to learn and retain new information, including novel vocabulary. Prior research has established the importance of vigilance and electroencephalogram (EEG) alpha rhythm in the learning process. However, the interplay between vigilant attention, EEG alpha oscillations, and an individual's word learning ability (WLA) remains elusive. To address this knowledge gap, here we conducted two experiments with a total of 140 young and middle-aged adults who underwent resting EEG recordings prior to completing a paired-associate word learning task and a psychomotor vigilance test (PVT). The results of both experiments consistently revealed significant positive correlations between WLA and resting EEG alpha oscillations in the occipital and frontal regions. Furthermore, the association between resting EEG alpha oscillations and WLA was mediated by vigilant attention, as measured by the PVT. These findings provide compelling evidence supporting the crucial role of vigilant attention in linking EEG alpha oscillations to an individual's learning ability.
... First, EMG contaminated epochs were identified with an adapted version of an automated trialrejection procedure as implemented in Fieldtrip 100 . To specifically capture muscle activity we used a 110-140 Hz band-lass filter and allowed for variable z-score thresholds per subject based on withinsubject variance of z-scores 101 . Moreover, to reduce the number of false alarms, rather than immediate removal of epochs exceeding the z-score threshold, the algorithm first identified the five electrodes that contributed most to the accumulated z-score within the time period containing the marked EMG artefact (We chose to deviate from our preregistered method here as the new interpolated method allowed us to preserve more data. ...
Attention has been usefully thought of as organized in priority maps – putative maps of space where attentional priority is weighted across spatial regions in a winner-take-all competition for attentional deployment. Recent work has highlighted the influence of past experiences on the weighting of spatial priority – called selection history. Aside from being distinct from more well-studied, top-down forms of attentional enhancement, little is known about the neural substrates of history-mediated attentional priority. Using a task known to induce statistical learning of target distributions, in an EEG study we demonstrate that this otherwise invisible, latent attentional priority map can be visualized during the intertrial period using a ‘pinging’ technique in conjunction with multivariate pattern analyses. Our findings not only offer a method of visualizing the history-mediated attentional priority map, but also shed light on the underlying mechanisms allowing our past experiences to influence future behavior.
