## Abstract

Human alpha-band activity (8–12 Hz) has been proposed to index a variety of mechanisms during visual processing. Here, we distinguished between an account in which alpha suppression indexes selective attention versus an account in which it indexes subsequent working memory storage. We manipulated two aspects of the visual stimuli that perceptual attention is believed to mitigate before working memory storage: the potential interference from distractors and the size of the focus of attention. We found that the magnitude of alpha-band suppression tracked both of these aspects of the visual arrays. Thus, alpha-band activity after stimulus onset is clearly related to how the visual system deploys perceptual attention and appears to be distinct from mechanisms that store target representations in working memory.

## INTRODUCTION

What brain processes are measured by human alpha-band activity has been one of the longest lived mysteries in neuroscience (Walter, 1938; Berger, 1929). One of the earliest hypotheses was that the strength of the 8- to 12-Hz activity evident in the raw EEG might measure the attentional state of the participant (Berger, 1929), with alpha power being strong when not attentive and weak when attending to a stimulus or train of thought. During the subsequent decades, alpha has been proposed to index everything from neural noise, to awareness, to working memory storage (Palva, Monto, Kulashekhar, & Palva, 2010; Woodman, 2010). However, modern versions of Berger's conjecture continue to propose that the alpha-band suppression that follows the onset of a stimulus array may measure the operation of a selective attention mechanism (e.g., Hakim, Adam, Gunseli, Awh, & Vogel, 2019; Wang, Rajsic, & Woodman, 2019; Klimesch, 2012). Our goal here was to distinguish between two competing views: One hypothesizes that alpha-band suppression is generated by a perceptual attention mechanism, and the competing hypothesis proposes that alpha suppression is because of later-stage working memory storage.

Neural oscillations have increasingly become the focus of neuroscientists trying to understand how the brain processes information through specialized neural circuits (Buzsáki & Freeman, 2015; Prescott, Ratté, De Koninck, & Sejnowski, 2008; Makeig et al., 2002). However, the mapping between cognitive mechanisms and the modulations of specific frequency bands is still hotly debated (Cohen, 2017; Hanslmayr & Staudigl, 2014). The 8- to 12-Hz alpha band has received much attention because of it being strongly suppressed during task performance and the signal itself effectively passing through the skulls of human participants (Nunez & Srinivasan, 2006; Cooper, Winter, Crow, & Walter, 1965). However, the relationship between alpha activity and different mechanisms of information processing continues to be debated (Fukuda, Mance, & Vogel, 2015; Jensen & Mazaheri, 2010; Hanslmayr, Spitzer, & Bäuml, 2009; Palva & Palva, 2007; Jensen, 2002). Thus, our goal here was to directly test competing predictions to distinguish between the accounts relating alpha activity to different information processing mechanisms in the brain.

Under the attentional hypothesis, alpha-band suppression indexes a mechanism of perceptual attentional selection that serves to filter out distractors and to focus attention to task-relevant objects (e.g., Jensen & Mazaheri, 2010). Under the working memory hypothesis, alpha-band suppression indexes a mechanism that stores task-relevant information in memory, after front-end perceptual attention has worked to filter distractors and focus the spotlight of attention on task-relevant inputs (Jensen, 2002). To distinguish between these competing hypotheses, we manipulated two characteristics of the visual stimuli that theories propose attentional selection handles before working memory storage. These are the presence of distracting stimuli (in Experiments 1 and 2) and the Spatial extent of the attended area (in Experiment 3).

For many decades, cognitive neuroscientists have studied how mechanisms of perceptual attention operate. This literature has shown that two characteristics of the visual input appear to be mitigated by visual attention, before the later storage of task-relevant information in memory. Specifically, these are factors that would seem to make task-relevant information more difficult to process, but these factors do not impair performance on a memory task because it appears that attention resolves these difficulties before memory storage. First, attention has been shown to effectively resolve competition from distracting stimuli (Wolfe, 1998; Luck, Chelazzi, Hillyard, & Desimone, 1997; Chelazzi, Miller, Duncan, & Desimone, 1993; Broadbent, 1957). Second, the aperture of visual attention has been shown to be adjustable so as to select a set of task-relevant stimuli that may extend across space, such that the aperture of attentional selection has been described as being like a zoom lens (Müller, Bartelt, Donner, Villringer, & Brandt, 2003; Eriksen & St. James, 1986). In both of these cases, it appears that front-end attentional selection mechanisms work to prevent distraction from task-irrelevant objects and to spatially spread attention out across space, such that subsequent mechanisms of information processing are shielded from these aspects of the visual input that could make processing slower or more error prone. We sought to manipulate these characteristics of the visual stimuli to determine whether alpha-band activity tracked our parametric manipulations, as would be expected of an attentional selection mechanism, or was insensitive to these manipulations, as would be expected of a working memory storage mechanism.

To test the hypothesis that alpha-band suppression indexes a mechanism of attentional selection that serves to prevent interference from distractors, we presented single target objects embedded in arrays of distractors and we varied the number of distracting objects (i.e., the set size) as well as the target–distractor similarity, as shown in Figure 1. These experiments were designed to hold target-processing demands constant within each array by presenting a single-colored square that participants needed to remember until the end of the trial, with each trial consisting of three targets that needed to be remembered on each trial. Across trials, we varied the number of distractors. The attention literature has shown that increasing the number of distractors taxes perceptual processing because of the need to filter out these task-irrelevant objects (Vogel, McCollough, & Machizawa, 2005; Wolfe, 1998, 2003; Luck et al., 1997), and this interference is greater when the distractors are more similar to targets in feature space (Woodman, Kang, Thompson, & Schall, 2008; Duncan & Humphreys, 1989; Green & Anderson, 1956). If the amplitude change of alpha-band activity after the onset of a stimulus is because of the brain filtering out task-irrelevant distractors to facilitate target processing (e.g., Jensen & Mazaheri, 2010), then we should see stronger alpha modulation when more distractors are presented (Experiments 1 and 2) and when those distractors are more similar to the targets (Experiment 1 vs. 2). In contrast, if alpha-band activity is because of the need to store targets in memory (e.g., Jensen, 2002), then we should see suppression increase with each additional target (i.e., with each additional array in Experiments 1 and 2), regardless of the nature or number of distractors.

Figure 1.

Illustration of the experimental paradigm and example trials from Experiments 1 and 2. (Top) An example of the sequential working memory task of Experiment 1 from an individual trial with two distractors in the target hemifield. The cue arrows in the example trials point to the right side, instructing participants to attend to the upcoming items appearing in the right hemifield, keeping their eyes fixed on the center fixation. They were instructed to only remember the color of the squares presented in the cued hemifield while ignoring the white circles presented at the same hemifield and all the other items that appeared in the uncued hemifield. After three sequentially presented memory arrays, participants then pressed one key on the keyboard (f or j) to indicate whether one of the colored squares had changed its color or not (key being counterbalanced). In this example trial, the participant would compare their memories of the items presented in the right hemifield to the colored squares presented on the right side of the test array. Below the example trial, we show the possible Distractor set sizes that could occur in Experiment 1. Each memory array could contain 0, 1, 2, 3, or 4 same-color distractors but only one target item. (Bottom) An example of the sequential working memory task of Experiment 2 from an individual trial with two distractors. Similar to Experiment 1, participants were instructed to only remember the color of the squares presented in the cued hemifield while ignoring all the other items. The bottom panel illustrates all the possible Distractor set sizes that could occur during a trial in Experiment 2; each memory array could contain 1, 2, 3, or 4 different-color distractors but only one target item. The colors of the test arrays in both of the example trials were the same (no color changes).

Figure 1.

Illustration of the experimental paradigm and example trials from Experiments 1 and 2. (Top) An example of the sequential working memory task of Experiment 1 from an individual trial with two distractors in the target hemifield. The cue arrows in the example trials point to the right side, instructing participants to attend to the upcoming items appearing in the right hemifield, keeping their eyes fixed on the center fixation. They were instructed to only remember the color of the squares presented in the cued hemifield while ignoring the white circles presented at the same hemifield and all the other items that appeared in the uncued hemifield. After three sequentially presented memory arrays, participants then pressed one key on the keyboard (f or j) to indicate whether one of the colored squares had changed its color or not (key being counterbalanced). In this example trial, the participant would compare their memories of the items presented in the right hemifield to the colored squares presented on the right side of the test array. Below the example trial, we show the possible Distractor set sizes that could occur in Experiment 1. Each memory array could contain 0, 1, 2, 3, or 4 same-color distractors but only one target item. (Bottom) An example of the sequential working memory task of Experiment 2 from an individual trial with two distractors. Similar to Experiment 1, participants were instructed to only remember the color of the squares presented in the cued hemifield while ignoring all the other items. The bottom panel illustrates all the possible Distractor set sizes that could occur during a trial in Experiment 2; each memory array could contain 1, 2, 3, or 4 different-color distractors but only one target item. The colors of the test arrays in both of the example trials were the same (no color changes).

Moreover, to validate the specificity of the alpha-band modulations to the demands of attentional filtering or working memory storage, we compared the pattern of alpha-band effects to a frontal ERP component (i.e., the formerly named “prefrontal bias signal”) that has been proposed to index distractor suppression (Liesefeld, Liesefeld, & Zimmer, 2014) and a posterior parietal component (i.e., the contralateral delay activity [CDA]) that has been shown to track working memory storage. Previous work showed that the prefrontal bias signal appears about 250–300 msec after item onset in the presence of distractors and has been proposed to reflect the filtering of distractors. The CDA is an established neural marker of storing targets in visual working memory (for a review, see Luria, Balaban, Awh, & Vogel, 2016). Its amplitude increases as more representations are held in this limited-capacity visual memory store (Vogel & Machizawa, 2004).

To determine if alpha-band suppression is sensitive to the scope, or Spatial extent, of the deployment of perceptual attention, we manipulated the distance between the target objects while keeping the number of targets constant in Experiment 3. If alpha suppression indexes attentional selection, then this signal should be stronger when larger arrays are shown, because the focus of attention has to zoom out to select all of the objects in the array. In contrast, if alpha suppression indexes working memory storage, alpha suppression would not be expected to be sensitive to the spatial layout of the array, because existing research has shown that visual working memory representations of objects are relatively divorced from spatial location (Xu & Chun, 2006; Phillips, 1974), such that observers can accurately detect changes of simple objects regardless of their spatial layout (Woodman, Vogel, & Luck, 2012). We also compared the pattern of alpha-band effects to those of the CDA component to cross-validate the alpha-band activity as an index of the spatial focus of attention versus working memory storage.

## EXPERIMENTS 1 AND 2

To test the hypothesis that alpha-band suppression indexes a mechanism of attentional selection that serves to filter out distractors, we presented single target objects embedded in arrays of distractors that varied in the number of distracting objects (i.e., the set size) as well as the target–distractor similarity, as shown in Figure 1. Our experiments were designed to hold target-processing demands constant within each array by presenting a single-colored square that participants needed to remember until the end of the trial. Across trials, we varied the number of distractors.

### Materials and Methods

#### Stimuli and Procedures

Stimuli were presented using MATLAB (R2017b 9.3.0; MathWorks) and the Psychophysics Toolbox (Version 3.0.12; Brainard, 1997) on a CRT monitor contained in a Faraday cage. Stimuli were presented on a gray background (x = 283, y = 314, L = 55.1 cd/m2, x and y define chromaticity, L represents luminance in the CIE xyY color space derived from the CIE 1931 color space). Participants were seated approximately 75 cm from the screen.

Figure 9 shows an example trial from Experiment 3. Each trial began with a display containing a black fixation cross (x = 349, y = 313, L = 0.11 cd/m2, 0.75° of visual angle) in the center of the screen for 500 msec, followed by a black arrow cue (1.3° of visual angle in width and 0.4° tall) presented 1.5° above the center fixation for 100 msec indicating the relevant side of the screen (right or left) to be attended. Participants were instructed to attend to the upcoming items appearing in the cued hemifield, keeping their eyes fixated at the center of the screen. After a postcue delay of 900 msec, we presented the memory arrays with either two or four colored squares. To prevent a sensory confound because of unilateral stimulation of the visual system (Woodman, 2010), we presented an equal number of items in both hemifields (i.e., both the task-relevant hemifield and task-irrelevant hemifield). Participants were instructed to only memorize the colored squares that appeared in the cued hemifield while ignoring the squares that presented in the uncued hemifield. The memory array appeared on the screen for 100 msec, which was followed by a 900-msec delay period before the appearance of the test array. The test array consisted of all colored squares presented in the memory array. On 50% of trials, the color of one square in the cued hemifield of the test array was replaced with another color. On these change trials, one of the colored squares would change to a color not previously seen in that hemifield on that trial. The locations of all the colored squares in the test array stayed the same as they appeared in the memory arrays, including the changed one. Participants then pressed one key on the keyboard to indicate that one of the colors of the squares had changed and pressed another key to indicate that all colors of the squares had stayed the same. The change versus no-change keys were counterbalanced across participants, with the two keys used being f and j.

Figure 9.

Illustration of the experimental paradigm and conditions from Experiment 3. (Top) An example of the change detection task of Experiment 3 from an individual trial with four colored squares in the target hemifield. The cue arrow in the example trial points to the right side, instructing participants to attend to the upcoming items appearing in the right hemifield, keeping their eyes fixed on the center fixation. After a short delay, participants then pressed one key on the keyboard (f or j) to indicate whether one of the colored squares that appeared in the right hemifield of the test array had changed its color or not (key being counterbalanced) compared to the colored squares presented on the right side of the memory array. This example trial is a change one. (Bottom) The possible set sizes and the distances between the adjacent squares that could occur in Experiment 3. Each memory array could contain two or four colored squares, and the distance between each adjacent colored square could be 1°, 3°, or 5° of visual angle, with small, medium, or large Spatial extent, respectively.

Figure 9.

Illustration of the experimental paradigm and conditions from Experiment 3. (Top) An example of the change detection task of Experiment 3 from an individual trial with four colored squares in the target hemifield. The cue arrow in the example trial points to the right side, instructing participants to attend to the upcoming items appearing in the right hemifield, keeping their eyes fixed on the center fixation. After a short delay, participants then pressed one key on the keyboard (f or j) to indicate whether one of the colored squares that appeared in the right hemifield of the test array had changed its color or not (key being counterbalanced) compared to the colored squares presented on the right side of the memory array. This example trial is a change one. (Bottom) The possible set sizes and the distances between the adjacent squares that could occur in Experiment 3. Each memory array could contain two or four colored squares, and the distance between each adjacent colored square could be 1°, 3°, or 5° of visual angle, with small, medium, or large Spatial extent, respectively.

In Experiment 3, the colors of the squares were chosen from a pool of seven highly discriminable colors: red (x = 631, y = 328, L = 11.8 cd/m2), green (x = 276, y = 593, L = 48.1 cd/m2), blue (x = 145, y = 66, L = 5.86 cd/m2), magenta (x = 284, y = 140, L = 16.5 cd/m2), yellow (x = 390, y = 507, L = 72.8 cd/m2), white (x = 281, y = 314, L = 76.2 cd/m2), and black (x = 349, y = 313, L = 0.11 cd/m2). Sets of colors were randomly chosen from the color pool, without replacement for the squares in a given hemifield. To manipulate the spatial distribution of the to-be-remembered objects, the colored squares (0.65° of visual angle in width and 0.65° tall) were always presented in a line (horizontal or vertical) at Set Size 2 or in a square at Set Size 4 with a distance of 1°, 3°, or 5° of visual angle between the center of each two adjacent colored squares (as illustrated in Figure 9). In a given trial, the distance between the colored squares was always the same in both the left and right hemifields. To control for other perceptual factors, such as saccades that could influence the function of attention, the matrices of the colored squares (a line at Set Size 2 or a square at Set Size 4) were placed inside the same space area, with the furthest colored square being placed along the circumference of an imaginary circle with the eccentricity of 7° of visual angle to the center of the fixation. To prevent participants from knowing in advance the number of to-be-remembered items and the distance between them in an upcoming trial, all trials with different set sizes (two or four colored squares) and different distances between each adjacent colored square (1°, 3°, or 5° of visual angle) were randomly interleaved within a given block. All participants completed 192 trials with each condition for 1152 trials in total for Experiment 3.

#### Change Detection Task Performance

We calculated the accuracy of remembering the colors and the working memory capacity in Experiment 3 using the same methods as in Experiments 1 and 2.

#### EEG Acquisition

The EEG was recorded in an electrically shielded, soundproof booth from a 20-channel cap (Electro-Cap International), embedded with tin electrodes that make contact with the skin through electrode gel. The 20 electrodes were positioned according to the International 10–20 system (F3, F4, C3, C4, P3, P4, PO3, PO4, O1, O2, PO7, PO8, T3, T4, T5, T6, Fz, Cz, and Pz). Impedance values were kept below 4 kΩ. The reference electrodes were affixed with stickers to the left and right mastoids, and a ground electrode was placed in the elastic cap at Fpz. Data were referenced online to the right mastoid and rereferenced offline to the algebraic average of the left and right mastoids. EOG activity was recorded with two HEOG electrodes placed ∼1 cm lateral to the outer canthi of the two eyes, and one vertical EOG electrode was placed below the right eye to detect eye movements and blinks. All channels were bandpass filtered from 0.01 to 100 Hz and recorded with a 250-Hz sampling rate.

#### EEG Analyses

##### Artifact rejection.

Trials were rejected using the same three-step procedure used in Experiments 1 and 2 with two exceptions: First, the rejection interval of the segmentations is from the onset of the memory array to 1000 msec (instead of 2600 msec in Experiments 1 and 2) after it because there was only single memory array in Experiment 3. Second, we used a threshold of ±3 μV (Woodman & Luck, 2003) to reject trials contaminated by HEOG because of a relatively short display time of the memory array in Experiment 3, unlike the three sequentially presented memory arrays in Experiments 1 and 2. After the rejection, participants were replaced when their data did not contain enough trials in each cell for analyses. In Experiment 3, 12 participants were replaced because of excessive eye movements and artifacts. For the remaining participants, an average of 2.04% (SD = 2.64%) of trials were excluded. It appears our sample included a run of participants who found fixation particularly difficult. We note that the same patterns of results were observed when we focused our analyses on the usable data from the replaced participants.

##### Alpha power analysis.

To measure the alpha power change on each trial, the raw EEG data were first bandpass filtered by the eegfilt.m function and then Hilbert transformed by the hilbert.m function in MATLAB to obtain the instantaneous power values for the alpha band (8–12 Hz). Percent change in alpha power was then calculated relative to a baseline period before the onset of the cue (−400 to 0 msec relative to the cue onset, −1400 to −1000 msec relative to the memory array onset) similar to previous studies' analyses of this frequency band (e.g., Wang et al., 2019; Adam et al., 2018). Next, the ipsilateral alpha power and the contralateral alpha power were calculated by averaging the percent change in alpha power for ipsilateral electrodes relative to the cued hemifield and contralateral electrodes relative to the cued hemifield, respectively. Global alpha power was calculated by averaging the percent change in alpha power for both ipsilateral electrodes and contralateral electrodes across the same lateral-occipital and posterior-parietal electrodes we used in Experiments 1 and 2 (i.e., P3, P4, T7, T8, PO3, PO4, PO7, PO8, O1, and O2; Adam et al., 2018; Fukuda et al., 2015).

##### CDA analysis.

To relate the alpha-band activity that we measured to a canonical electrophysiological measure of visual working memory storage, we also calculated the CDA in Experiment 3. Before calculating the CDA, the signal was first low-pass filtered with a two-way least squares finite impulse response filter function from the EEGLAB Toolbox (eegfilt.m; Delorme & Makeig, 2004) with the low pass at 30 Hz to remove the high-frequency noise of the EEG signal. After filtering, trials were baseline corrected to the mean amplitude of the ERPs from −200 to 0 msec relative to the onset of the memory array. The CDA amplitude was then computed by subtracting the activity of the electrodes ipsilateral to the cued hemifield from the activity of the electrodes contralateral to the cued hemifield across the lateral-occipital and posterior-parietal electrodes: P3, P4, T7, T8, PO3, PO4, PO7, PO8, O1, and O2.

#### Experimental Design and Statistical Analyses

To examine the influence of set size and Spatial extent on behavioral performance in Experiment 3, separate two-way repeated-measures ANOVAs with the within-participant factor of Set size (2 or 4) and Spatial extent (small, medium, or large) were performed on the behavioral estimates (accuracy and K), the alpha power percent change, and the CDA amplitude. Preplanned pairwise comparisons with Bonferroni corrections for multiple comparisons were used across each neighboring Spatial extent to verify the nature of the Spatial extent effects. All these statistical analyses were performed in SPSS 19.0 (IBM Inc.).

### Results

#### Behavior

Figure 10 shows our behavioral findings in Experiment 3. We found that our manipulation of the distance between the adjacent colored squares had no impact on participants' ability to remember either two or four targets presented on each trial.

Figure 10.

Behavioral performance of Experiment 3. The mean visual working memory capacity (left) and the mean accuracy rate (right) estimates for different set sizes and Spatial extents. The x axis represents Spatial extent (small, medium, or large), and the y axis represents visual working memory capacity (K; left) or accuracy rate (right). Red and blue bars represent Set Size 2 and Set Size 4, respectively. Error bars indicate the SEMs.

Figure 10.

Behavioral performance of Experiment 3. The mean visual working memory capacity (left) and the mean accuracy rate (right) estimates for different set sizes and Spatial extents. The x axis represents Spatial extent (small, medium, or large), and the y axis represents visual working memory capacity (K; left) or accuracy rate (right). Red and blue bars represent Set Size 2 and Set Size 4, respectively. Error bars indicate the SEMs.

Participants' capacity estimates (K) and change-detection accuracy were entered into separate two-way repeated-measures ANOVAs with within-participant factors of Set size (2 or 4) and Spatial extent (small, medium, or large). The analysis of capacity estimates (K) revealed a significant Set size effect, with a significant increase of WM capacity from Set Size 2 to Set Size 4, F(1, 17) = 65.910, p < .001, ηp2 = .795. However, there was no significant Spatial extent effect, F(2, 34) = 0.779, p = .467, ηp2 = .044, nor any interaction between Set size and Spatial extent, F(2, 34) = 0.590, p = .560, ηp2 = .034. Similarly, the analysis on raw accuracy yielded a significant Set size effect, with a significant decrease of the response accuracy from Set Size 2 to Set Size 4, F(1, 17) = 34.882, p < .001, ηp2 = .672. However, there was no significant Spatial extent effect, F(2, 34) = 0.045, p = .956, ηp2 = .003, nor any interaction between Set size and Spatial extent, F(2, 34) = 0.137, p = .873, ηp2 = .008. Thus, our behavioral findings from Experiment 3 suggest that presenting the same amount of targets across different Spatial extents had no influence on the storage of these colors in visual working memory.

To provide stronger inferential power regarding the null results of the Spatial extent effect on behavior performance, we also computed Bayes factors to determine how much more likely the null hypothesis than the possibility that different Spatial extent did show an effect (Rouder et al., 2017). We found the null hypothesis was 11.1 and 11.5 times more likely than the hypothesis that a Spatial extent effect on working memory capacity (K) and response accuracy existed, demonstrating that we did not find a reliable modulation of different Spatial extents on behavior performance.

#### EEG and ERPs

##### Alpha power suppression.

In contrast to the participants' behavior that was invariant across Spatial extent, the alpha power suppression was modulated by both the number of targets and the Spatial extent. Figure 11A shows the pattern of the alpha power suppression changes with set size and the Spatial extent. We measured stronger global alpha power suppression as more targets were simultaneously presented in a memory array, consistent with recent findings that varied memory set size using multiobject arrays (Fukuda et al., 2015; Sauseng et al., 2009). In addition, we found that this alpha power suppression increased as the Spatial extent increased, supporting our hypothesis that alpha-band suppression indexes a mechanism of attentional selection that serves to control the Spatial extent of attention. These observations about the pattern of the global alpha power in Experiment 3 were statistically verified with the following steps.

Figure 11.

The alpha power suppression across set sizes and Spatial extents in Experiment 3. (A) Left: the global alpha power suppression in Experiment 3 averaged over electrode pairs, namely, P3/P4, T7/T8, PO3/PO4, PO7/PO8, and O1/O2, separated by set size and Spatial extent. The solid lines and dashed lines represent Set Size 2 and Set Size 4, respectively. Blue, red, and green lines represent small, medium, and large Spatial extent, respectively. The cyan bar and the black bar on the time axis represent the onset and duration of the cue and the memory array. The gray interval represents the time window of the global alpha power. Right: the mean global alpha power suppression after the onset of the memory array, separated by set size and Spatial extent. The x axis represents Spatial extent (small, medium, or large), and the y axis represents the mean global alpha power suppression. Red bars and blue bars represent Set Size 2 and Set Size 4, respectively. Error bars indicate the SEMs. (B) Left: the ipsilateral alpha power suppression in Experiment 3 averaged over electrode pairs, namely, P3/P4, T7/T8, PO3/PO4, PO7/PO8, and O1/O2, separated by set size and Spatial extent. Right: the mean ipsilateral alpha power suppression after the onset of the memory array, separated by set size and Spatial extent. (C) Left: the contralateral alpha power suppression in Experiment 3 averaged over electrode pairs, namely, P3/P4, T7/T8, PO3/PO4, PO7/PO8, and O1/O2, separated by set size and Spatial extent. Right: the mean contralateral alpha power suppression after the onset of the memory array, separated by set size and Spatial extent.

Figure 11.

The alpha power suppression across set sizes and Spatial extents in Experiment 3. (A) Left: the global alpha power suppression in Experiment 3 averaged over electrode pairs, namely, P3/P4, T7/T8, PO3/PO4, PO7/PO8, and O1/O2, separated by set size and Spatial extent. The solid lines and dashed lines represent Set Size 2 and Set Size 4, respectively. Blue, red, and green lines represent small, medium, and large Spatial extent, respectively. The cyan bar and the black bar on the time axis represent the onset and duration of the cue and the memory array. The gray interval represents the time window of the global alpha power. Right: the mean global alpha power suppression after the onset of the memory array, separated by set size and Spatial extent. The x axis represents Spatial extent (small, medium, or large), and the y axis represents the mean global alpha power suppression. Red bars and blue bars represent Set Size 2 and Set Size 4, respectively. Error bars indicate the SEMs. (B) Left: the ipsilateral alpha power suppression in Experiment 3 averaged over electrode pairs, namely, P3/P4, T7/T8, PO3/PO4, PO7/PO8, and O1/O2, separated by set size and Spatial extent. Right: the mean ipsilateral alpha power suppression after the onset of the memory array, separated by set size and Spatial extent. (C) Left: the contralateral alpha power suppression in Experiment 3 averaged over electrode pairs, namely, P3/P4, T7/T8, PO3/PO4, PO7/PO8, and O1/O2, separated by set size and Spatial extent. Right: the mean contralateral alpha power suppression after the onset of the memory array, separated by set size and Spatial extent.

To quantify the global alpha effects in Experiment 3, we first calculated the mean global alpha suppression after the memory array (averaged over 300–700 msec after the presentation of the memory array) for each set size and Spatial extent. The two-way repeated-measures ANOVA of Set size (2 vs. 4) and Spatial extent (small vs. medium vs. large) on the mean global alpha suppression revealed a significant Set size effect, F(1, 17) = 8.146, p = .019, ηp2 = .209, with a significantly stronger alpha power suppression at Set Size 4 than Set Size 2. Most importantly, for the hypothesis, the analysis also revealed a significant Spatial extent effect, F(2, 34) = 20.714, p < .001, ηp2 = .549. The preplanned pairwise comparisons across each neighboring Spatial extent show that the global alpha power suppression significantly increased from the small Spatial extent to the medium Spatial extent, F(1, 17) = 5.347, p = .034, ηp2 = .239, and kept increasing from the medium Spatial extent to the large Spatial extent, F(1, 17) = 17.943, p = .001, ηp2 = .513. Finally, there was no interaction of Set Size × Spatial extent, F(2, 34) = 0.805, p = .455, ηp2 = .045.

The findings that the global alpha suppression changes with set size and Spatial extent support our hypothesis that this signal indexes an attention mechanism that serves to control the size of the focus of attention. In parallel with our analyses from Experiments 1 and 2, we next compared the alpha activity contralateral to the target objects with the alpha activity ipsilateral to the target objects to test the conjecture that these lateralized signals measure different cognitive mechanisms. To measure these lateralized signals, we calculated the average alpha suppression of the lateral-posterior electrodes ipsilateral to the task-relevant hemifield and the electrodes contralateral to the task-relevant hemifield, respectively. The results of this lateralized averaging are shown in Figure 11B and C. As evidenced by these means, the patterns of alpha suppression were similar in both hemispheres, with both ipsilateral and contralateral hemispheres exhibiting the parametric modulation across set sizes and Spatial extents.

A three-way repeated-measures ANOVA with within-participant factors of Hemisphere (ipsilateral vs. contralateral), Set size (2 vs. 4), and Spatial extent (small vs. medium vs. large) on the mean ipsilateral and contralateral alpha suppression revealed a significant main effect of Hemisphere, F(1, 17) = 10.146, p = .005, ηp2 = .374, because of the contralateral alpha suppression being stronger than the ipsilateral alpha suppression. Moreover, there was a significant Set size effect, F(1, 17) = 8.146, p = .011, ηp2 = .324, with a significantly stronger alpha power suppression at Set Size 4 than Set Size 2. The analysis also revealed a significant Spatial extent effect, F(2, 34) = 20.714, p < .001, ηp2 = .549. The preplanned pairwise comparisons across each neighboring Spatial extent show that both the contralateral and ipsilateral alpha power suppression significantly increased from the small Spatial extent to the medium Spatial extent, F(1, 17) = 5.347, p = .034, ηp2 = .239, and kept increasing from the medium Spatial extent to the large Spatial extent, F(1, 17) = 17.943, p = .001, ηp2 = .513. However, we found no interaction of Hemisphere × Set Size, F(1, 17) = 2.215, p = .155, ηp2 = .115, no interaction of Hemisphere × Spatial extent, F(2, 34) = 0.717, p = .496, ηp2 = .040, and no interaction of Hemisphere × Set Size × Spatial extent, F (2, 34) = 0.572, p = .570, ηp2 = .033. These results suggest that contralateral and ipsilateral alpha activity behave similarly as a function of memory set size and the Spatial extent of attention.

In summary, in Experiment 3, we found that, when we spread the to-be-remembered items across a larger Spatial extent, alpha power suppression increased, confirming the predictions of the attentional hypothesis that alpha power suppression measures the operation of a perceptual attention mechanism that operates early in the flow of information processes, before working memory storage. In Experiment 3, we also replicated previous studies showing that the alpha power suppression is stronger when larger arrays of task-relevant objects are presented.

##### CDA.

We next wanted to compare the pattern of alpha power to an established measure of working memory storage, the CDA (for a review, see Luria et al., 2016). We had based the logic of our manipulation on the fact that Spatial extent does not seem to influence how colored squares are stored in visual working memory (e.g., Woodman et al., 2012). However, by measuring the CDA, we can validate this assumption and strengthen our conclusions that alpha is tracking the operation of attention and not working memory storage per se. As shown in Figure 12, we found that the CDA amplitude increased as the working memory set size increased. Unlike alpha suppression, the Spatial extent had no influence on the CDA amplitude. These observations about the pattern of the CDA effects were supported by the following statistical analyses.

Figure 12.

The CDA activity changes with set size and Spatial extent in Experiment 3. (A) The CDA waveforms in Experiment 3 averaged over electrode pairs, namely, P3/P4, T7/T8, PO3/PO4, PO7/PO8, and O1/O2, separated by set size and Spatial extent. The solid lines and dashed lines represent Set Size 2 and Set Size 4, respectively. Blue, red, and green lines represent small, medium, and large Spatial extent, respectively. The black bar on the time axis represents the onset and duration of the memory array. The gray interval represents the time window of the CDA. (B) The mean CDA amplitude after the onset of the memory array, separated by set size and Spatial extent. The x axis represents Spatial extent (small, medium, or large), and the y axis represents the mean CDA amplitude (averaged across the gray area in A). Red bars and blue bars represent Set Size 2 and Set Size 4, respectively. Error bars indicate the SEMs.

Figure 12.

The CDA activity changes with set size and Spatial extent in Experiment 3. (A) The CDA waveforms in Experiment 3 averaged over electrode pairs, namely, P3/P4, T7/T8, PO3/PO4, PO7/PO8, and O1/O2, separated by set size and Spatial extent. The solid lines and dashed lines represent Set Size 2 and Set Size 4, respectively. Blue, red, and green lines represent small, medium, and large Spatial extent, respectively. The black bar on the time axis represents the onset and duration of the memory array. The gray interval represents the time window of the CDA. (B) The mean CDA amplitude after the onset of the memory array, separated by set size and Spatial extent. The x axis represents Spatial extent (small, medium, or large), and the y axis represents the mean CDA amplitude (averaged across the gray area in A). Red bars and blue bars represent Set Size 2 and Set Size 4, respectively. Error bars indicate the SEMs.

We calculated the mean amplitude of the CDA after the memory array (400–700 msec after the memory array onset) across different set sizes and Spatial extents. The two-way repeated-measures ANOVA of Set size (2 vs. 4) and Spatial extent (small vs. medium vs. large) on the mean CDA amplitude revealed a significant Set size effect, F(1, 17) = 34.959, p < .001, ηp2 = .673, with a more negative CDA amplitude at Set Size 4 than Set Size 2. However, there was no significant Spatial extent effect, F(2, 34) = 2.179, p = .129, ηp2 = .114, nor a significant interaction of Set Size × Spatial extent, F(2, 34) = 0.355, p = .704, ηp2 = .020. We found the null hypothesis was 2.6 times more likely than the hypothesis that a Spatial extent effect on CDA amplitude existed, supporting our observation that we did not find a reliable modulation of different Spatial extents on CDA amplitude. These findings are consistent with our observation of the behavioral patterns of the capacity (K) estimates, supporting a selective role of the CDA in tracing the number of the target objects instead of the Spatial extent.

### Discussion

In Experiment 3, we found that the strength of alpha suppression tracked the spatial distribution of task-relevant items in the arrays we presented to participants. Classic research in cognitive psychology indicates that the focus of attention is adjustable, such that attention works like a zoom lens to select arrays of task-relevant stimuli that vary in their spatial distribution (Eriksen & St. James, 1986). Here, we showed that alpha tracked this spread of attention across space, with stronger alpha suppression when attention needed to cover more areas of the visual field. Whereas much of the research measuring alpha-band activity has manipulated the characteristics of task-relevant stimuli to see how alpha is influenced, our findings in all three experiments show that alpha is sensitive to these factors of physical stimulus array characteristics that are handled by front-end perceptual attention mechanisms.

## GENERAL DISCUSSION

The goal of this study was to test the competing hypotheses that the alpha-band activity elicited by visual stimulus arrays measures a mechanism of attention versus a mechanism of working memory storage. We focused on two aspects of the visual stimuli that perceptual attention is believed to mitigate before working memory storage. Specifically, two of the classic functions that perceptual attention has been proposed to mitigate is the potential interference from distractors and the size of the focus of attention. To this end, we measured the alpha-band suppression in question while we simultaneously manipulated the target load, the distractor load, and the spacing of the items in the arrays. Consistent with previous behavioral work, we found that neither the distractors nor spacing between items influenced performance in the color change-detection task. However, we found that the magnitude of alpha-band suppression tracked both of these aspects of the visual arrays.

The careful reader might be worried about the fact that the participants in all experiments exhibited behavioral performance that was essentially at ceiling and indifferent to the presence of the distractors or their spatial arrangement. However, these high levels of performance fit well with our goal of examining the role of alpha-band activity in attention mechanisms that resolve perceptual difficulties before working memory storage and are consistent with previous behavior findings that attention could effectively resolve competition from distracting stimuli (Wolfe, 1998; Luck et al., 1997; Chelazzi et al., 1993; Broadbent, 1957). More specifically, the distractors were likely easy to filter in the current study because all of the distractors were the same shape and color (all white circles in Experiment 1 and all circles in Experiment 2). This grouping may have encouraged participants to group them and reject the entire group in one processing step (Bundesen, 1990; Duncan & Humphreys, 1989). Clearly, the job performed by alpha occurs before these distractors are rejected. Thus, our study shows a clear dissociation between the sensitivity of alpha-band power to distractors and spacing, compared to its relative insensitivity at the behavioral level.

We contrasted the parametric sensitivity of the alpha index to the demands of distractor suppression with the modulations of the simultaneously measured ERP components that previous work had associated with the storage of targets (i.e., the CDA) and the need to suppress or filter distracting information (the prefrontal bias signal; Liesefeld et al., 2014). We found that the CDA increased with each new target that needed to be encoded into working memory but was relatively unaffected by the distractors that strongly modulated the alpha-band activity, until working memory was full of targets, at which point it appears that filter may break down, as previously predicted (Vogel et al., 2005). Moreover, whereas the prefrontal bias signal was modulated by the presence of any distractors at all, the alpha signal was parametrically manipulated by the number of distractors in each array.

One seemingly possible explanation for the Distractor set size effects on alpha power suppression that we found in Experiments 1 and 2 is that these effects were only a result of more visual stimuli being presented. In other words, the alpha power suppression may index the total sensory input instead of some kind of attention mechanism. Consistent with this view, we found that the amplitude of the N1 component showed the same pattern of Distractor set size effects that the subsequent alpha suppression showed (see Figure 13 for an example from Experiment 1), and the visual N1 component is believed to be generated by the sensory processing of visual inputs (Woodman, 2010). However, it is unlikely that alpha suppression is because of a purely sensory mechanism for the following reasons. If alpha power suppression simply reflects the total amount of visual input, then we should see its amplitude only increase as the number of distractors increases. However, we found larger alpha suppression in Experiment 2 than Experiment 1, because of Experiment 2 having higher target–distractor similarity, although the number of stimuli was the same across Distractor set sizes in both experiments. In addition, experiments that have manipulated set sizes across a large range have found that alpha suppression stops increasing even as the total amount of sensory stimulation continues to increase (e.g., Fukuda & Woodman, 2017). Moreover, the results of Experiment 3 showed that the strength of alpha power suppression was modulated by the Spatial extent of the array of task-relevant items, even when the number of the task-relevant items was kept constant. Obviously, this rules out the simple idea that alpha power suppression simply reflects the total energy of visual input. Thus, our findings of alpha power suppression modulated by Distractor set size, similarity, and spacing suggest that it reflects an attentional mechanism instead of simply being a lingering sensory response, although alpha suppression does mirror the early ERP responses in much of the present data set.

Figure 13.

The N1 component changes with Distractor set sizes in Experiment 1. The ERP waveforms in Experiment 1 averaged over electrode pairs, namely, P3/P4, P7/P8, PO3/PO4, PO7/PO8, and O1/O2, separated by Distractor set sizes (black, yellow, magenta, red, and blue lines represent zero distractor [D0], one distractor [D1], two distractors [D2], three distractors [D3], and four distractors [D4], respectively). The black bars on the time axis represent the onset and duration of each memory array. The gray intervals represent the time window of the N1 component, whose amplitude shows a trend of linear increase with the increase of Distractor set sizes.

Figure 13.

The N1 component changes with Distractor set sizes in Experiment 1. The ERP waveforms in Experiment 1 averaged over electrode pairs, namely, P3/P4, P7/P8, PO3/PO4, PO7/PO8, and O1/O2, separated by Distractor set sizes (black, yellow, magenta, red, and blue lines represent zero distractor [D0], one distractor [D1], two distractors [D2], three distractors [D3], and four distractors [D4], respectively). The black bars on the time axis represent the onset and duration of each memory array. The gray intervals represent the time window of the N1 component, whose amplitude shows a trend of linear increase with the increase of Distractor set sizes.

In this study, we tested competing predictions of an early attentional account of alpha suppression with an account that proposes these signals arise because of later working-memory-related processes. However, within the theoretical space of an attentional account of alpha, two competing views have been proposed with regard to the function of alpha-band activity: distractor suppression or target enhancement. Empirical studies using spatial cueing tasks and lateralized working memory tasks have shown that alpha-band activity increases ipsilateral to the cued locations and decreases contralateral to the cued locations, with distractor suppression accounts proposing that higher alpha-band power reflects increased suppression of irrelevant stimuli (e.g., Payne & Sekuler, 2014; Händel, Haarmeier, & Jensen, 2011; Jensen & Mazaheri, 2010). The target enhancement account emphasizes that the ipsilateral alpha power increase accompanies the contralateral alpha power decrease to the cued hemifield. Under this perspective, the lower alpha power reflects an increase in target signal enhancement (see a recent review by Foster & Awh, 2019). However, dissociating these two accounts is difficult because enhancing targets in the cued hemifield and suppressing the task-irrelevant distractors in the uncued hemifield may happen at the same time. The present findings are potentially relevant to this debate because we increased the number of distractors while keeping the number of targets constant in each memory array. Because alpha suppression increased as the number of distractors increased, as well as their similarity to the memory targets, it would be intuitive to conclude that alpha-band activity tracks distractor suppression, as we may have implied in the present article in our description of distractor interference. However, it could be that increasing the number of distractors causes a target enhancement mechanism to work harder. For example, if you were talking to a friend in a room that was growing increasingly loud because of the arrival of more loud-talking people, then a natural countermeasure would be to talk more loudly so you can be heard. Although this is fundamentally about increasing the intensity of the target signal, it is done in reaction to the distractors. In the same way, it is possible that the present findings reflect signal enhancement, not distractor suppression, by an early attention mechanism (Noonan et al., 2016; also see a recent review by Foster & Awh, 2019). Thus, we believe that we can firmly conclude that alpha tracks an early attentional selection mechanism, with the flavor of that selection mechanism still to be definitively determined.

Mirroring the sensitivity of alpha to the presence and number of distractors, we found that the strength of alpha-band activity was also sensitive to the Spatial extent of an array of task-relevant items. Again, we found that manipulating the Spatial extent of the memory array had no influence on participants' behavioral performance, showing another example in which attention mitigates a factor of the stimulus input that shields downstream processes like memory storage. This effect would seem to provide a potential alternative explanation for previous findings in which the set size of task-relevant information was varied (Fukuda & Woodman, 2017). That is, previous reports that interpreted set-size-dependent alpha suppression as being an index of the processing of the targets themselves might instead be largely because of inherent confound in which presenting arrays with larger memory set sizes takes up more space in the visual field and requires the participant to select a larger or smaller area of the visual field with attention. Thus, the present findings may require us to reconsider the theoretical meaning of previous empirical reports. In addition to demonstrating the utility of alpha-band suppression to study the ability of the brain to deploy attention, our findings impact our theoretical understanding of attention.

Theories of visual attention have long debated whether the selection of targets and the suppression of distractors are performed by a unitary mechanism or are separable attentional subroutines (Hickey, Di Lollo, & McDonald, 2009; Ruff & Driver, 2006; Carrasco, Ling, & Read, 2004; Awh, Matsukura, & Serences, 2003; Lu, Lesmes, & Dosher, 2002; Carrasco, Penpeci-Talgar, & Eckstein, 2000; Dosher & Lu, 2000; Cheal & Gregory, 1997; Hawkins et al., 1990). Unitary models have been favored by some because of parsimony and the fact that the same neurons in the visual system show increased firing rates when a target is in its receptive field and show decreased firing rates when a distractor appears in its receptive field, particularly when the distractor flanks the target in the visual field (e.g., Thompson, Hanes, Bichot, & Schall, 1996). However, recent work in human electrophysiology has supported the proposal that separate mechanisms perform target selection and distractor suppression (Sawaki & Luck, 2014; Sawaki, Geng, & Luck, 2012; Hickey et al., 2009). The present findings appear problematic for models that propose a unitary attention mechanism that is used to keep multiple target representations active in mind and to select new perceptual inputs from potentially distracting information from the visual field (Cowan, 2001, 2012; Jonides et al., 2008; Oberauer, 2002). Our observation that alpha-band suppression is sensitive to the distractor load, but not the target load that accumulates across the trial, is contrary to predictions of a unitary attention mechanism model, as we should have observed both factors increase the magnitude of suppression if both operations are performed by the same mechanism.

Of further potential theoretical impact, we observed independence between the alpha-band suppression signal and other components that have been proposed to specialize in target processing. The CDA is believed to provide a sensitive index of the number of objects simultaneously maintained in visual working memory (Vogel et al., 2005; Vogel & Machizawa, 2004). Consistent with this idea, we found that this ERP component did track the number of colored targets as the trial unfolded. It was relatively insensitive to the presence of distractors or the spatial layout of the targets. The exception to this was when the number of targets approached capacity (i.e., three objects; Luck & Vogel, 2013; Vogel et al., 2001). After the presentation of the third target, we did observe that the CDA was larger in amplitude when distractors were present than when they were not. Previous work has suggested that, when we are tasked with an informational load that taxes working memory capacity, attentional filtering may breakdown (Vogel et al., 2005). Our simultaneous measurements of alpha and the CDA suggest that we can observe the dynamics of this breakdown as working memory is sequentially loaded with items in the face of distraction.

Although our manipulations in this study were done to isolate the potential contribution of variation in the number of distractors and spatial deployment of attention on alpha, these experiments leave open an important question. It is possible that alpha-band suppression might index a mechanism that serves to not only filter distracting objects and control the focus of attention but also filter distracting features within target objects themselves. We do not yet know whether this alpha suppression mechanism operates on fine-grained representations. Does it operate to filter irrelevant features from our representations of task-relevant information, as researchers have previously proposed is possible (e.g., Woodman & Vogel, 2008)? One useful avenue for future research would be to manipulate the nature of the targets that need to be selected. It is possible that, when targets are defined by a single feature (e.g., color), the presence of additional irrelevant object features could modulate alpha suppression, with each additional distracting feature increasing alpha suppression (e.g., shape, orientation, the presence of a bar). In contrast, it is possible that the alpha suppression mechanism only operates on grouped object representations (Vecera, Behrmann, & McGoldrick, 2000; Vecera & Farah, 1994), so alpha would not vary in this way.

Our findings stand in contrast to recent work that attempted to test a similar hypothesis about alpha indexing attentional selection versus distractor suppression (Bacigalupo & Luck, 2019). That study manipulated the distance between a target and a distractor and reported a nonlinear effect in which lateralized alpha suppression was strongest when the distance between a target and a distractor was in a sweet spot. Here, we show that the relationship between alpha suppression and distractors may be more straightforward in that it tracks the spatial deployment of attention necessary to filter out distracting information. However, in our experiments, the minimum distance between objects was outside the range studied in this previous article, and the increases in alpha suppression are difficult to account for with such a crowding explanation alone. We believe that experiments are needed that contrast spatial proximity with the number of distractors presented, with such experiments addressing the spatial version of the granularity question raised in our previous paragraph.

It is also worth noting that alpha power suppression, sometimes referred to as alpha power desynchronization, functions differently from alpha power synchronization. Previous studies have shown stronger alpha power synchronization (i.e., an increase in alpha power) across electrode sites ipsilateral to task-irrelevant items presented in an uncued hemifield (Sauseng et al., 2009) and increased alpha power synchronization elicited by the presence of a task-irrelevant distractor compared to the presence of task-relevant items in a sequentially presented memory task (Payne, Guillory, & Sekuler, 2013). Thus, the present findings observed across our three experiments appear to be surprising in not clearly showing this same pattern of alpha power increases as was observed in these two other experiments, although the present arrays used much denser arrays of distractors and heavier memory demands. Future studies are needed to verify the functional difference of alpha power synchronization relative to the desynchronization measures of attentional filtering and distractor suppression that we observed here.

In conclusion, the present findings challenge previous proposals about the nature of the cognitive mechanism that is indexed by alpha suppression after the onset of a visual array. Recent work had proposed that alpha might index attending to task-relevant information (Bacigalupo & Luck, 2019; Fukuda et al., 2016), storing that information in working memory (Fukuda & Woodman, 2017; Fukuda et al., 2015), or even be related to action selection (Deiber et al., 2012). Our findings suggest that alpha-band activity after stimulus onset is related to how the visual system deploys attention to handle potentially distracting objects and appears to be distinct from mechanisms that select target representations or store them in working memory.

## Author Contributions

Sisi Wang: Conceptualization; Data curation; Formal analysis; Investigation; Project administration. Emma E. Megla: Data curation; Formal analysis; Investigation; Project administration. Geoffrey F. Woodman: Conceptualization; Formal analysis; Funding acquisition; Supervision; Writing-review & editing.

## Funding Information

National Eye Institute (http://dx.doi.org/10.13039/100000053), Grant number: P30-EY08126, R01-EY019882, T32-EY007135. National Institute of Mental Health (http://dx.doi.org/10.13039/100000025), Grant number: R01-MH110378.

## Acknowledgments

The present work was supported by grants from the National Institutes of Health (R01-EY019882, R01-MH110378, P30-EY08126, and T32-EY007135) to G. F. W. and a China Scholarship Council scholarship (201706140083) to S. W.

Reprint requests should be sent to Geoffrey F. Woodman, Department of Psychology, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN 37240, or via e-mail: geoffrey.f.woodman@vanderbilt.edu.

## REFERENCES

Adam
,
K. C. S.
,
Robison
,
M. K.
, &
Vogel
,
E. K.
(
2018
).
Contralateral delay activity tracks fluctuations in working memory performance
.
Journal of Cognitive Neuroscience
,
30
,
1229
1240
.
Awh
,
E.
,
Matsukura
,
M.
, &
Serences
,
J. T.
(
2003
).
Top–down control over biased competition during covert spatial orienting
.
Journal of Experimental Psychology: Human Perception and Performance
,
29
,
52
63
.
Bacigalupo
,
F.
, &
Luck
,
S. J.
(
2019
).
Lateralized suppression of alpha-band EEG activity as a mechanism of target processing
.
Journal of Neuroscience
,
39
,
900
917
.
Berger
,
H.
(
1929
).
Über das Elektrenkephalogramm des Menschen
.
Archiv für Psychiatrie und Nervenkrankheiten
,
87
,
527
570
.
Brainard
,
D. H.
(
1997
).
The Psychophysics Toolbox
.
Spatial Vision
,
10
,
433
436
.
Broadbent
,
D. E.
(
1957
).
A mechanical model for human attention and immediate memory
.
Psychological Review
,
64
,
205
215
.
Bundesen
,
C.
(
1990
).
A theory of visual attention
.
Psychological Review
,
97
,
523
547
.
Buzsáki
,
G.
, &
Freeman
,
W.
(
2015
).
Editorial overview: Brain rhythms and dynamic coordination
.
Current Opinion in Neurobiology
,
31
,
v
ix
.
Carrasco
,
M.
,
Ling
,
S.
, &
Read
,
S.
(
2004
).
Attention alters appearance
.
Nature Neuroscience
,
7
,
308
313
.
Carrasco
,
M.
,
Penpeci-Talgar
,
C.
, &
Eckstein
,
M.
(
2000
).
Spatial covert attention increases contrast sensitivity across the CSF: Support for signal enhancement
.
Vision Research
,
40
,
1203
1215
.
Cheal
,
M. L.
, &
Gregory
,
M.
(
1997
).
Evidence of limited capacity and noise reduction with single-element displays in the location-cuing paradigm
.
Journal of Experimental Psychology: Human Perception and Performance
,
23
,
51
71
.
Chelazzi
,
L.
,
Miller
,
E. K.
,
Duncan
,
J.
, &
Desimone
,
R.
(
1993
).
A neural basis for visual search in inferior temporal cortex
.
Nature
,
363
,
345
347
.
Cohen
,
M. X.
(
2017
).
Where does EEG come from and what does it mean?
Trends in Neurosciences
,
40
,
208
218
.
Cooper
,
R.
,
Winter
,
A. L.
,
Crow
,
H. J.
, &
Walter
,
W. G.
(
1965
).
Comparison of subcortical, cortical and scalp activity using chronically indwelling electrodes in man
.
Electroencephalography and Clinical Neurophysiology
,
18
,
217
228
.
Cowan
,
N.
(
2001
).
The magical number 4 in short-term memory: A reconsideration of mental storage capacity
.
Behavioral and Brain Sciences
,
24
,
87
114
.
Cowan
,
N.
(
2012
).
An embedded-processes model of working memory
. In
A.
Miyake
&
P.
Shah
(Eds.),
Models of working memory: Mechanisms of active maintenance and executive control
(pp.
62
101
).
Cambridge, UK
:
Cambridge University Press
.
Deiber
,
M.-P.
,
Sallard
,
E.
,
Ludwig
,
C.
,
Ghezzi
,
C.
,
Barral
,
J.
, &
Ibañez
,
V.
(
2012
).
EEG alpha activity reflects motor preparation rather than the mode of action selection
.
Frontiers in Integrative Neuroscience
,
6
,
59
.
Delorme
,
A.
, &
Makeig
,
S.
(
2004
).
EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
.
Journal of Neuroscience Methods
,
134
,
9
21
.
Dosher
,
B. A.
, &
Lu
,
Z.-L.
(
2000
).
Noise exclusion in spatial attention
.
Psychological Science
,
11
,
139
146
.
Duncan
,
J.
, &
Humphreys
,
G. W.
(
1989
).
Visual search and stimulus similarity
.
Psychological Review
,
96
,
433
458
.
Eriksen
,
C. W.
, &
St. James
,
J. D.
(
1986
).
Visual attention within and around the field of focal attention: A zoom lens model
.
Perception & Psychophysics
,
40
,
225
240
.
Foster
,
J. J.
, &
Awh
,
E.
(
2019
).
The role of alpha oscillations in spatial attention: Limited evidence for a suppression account
.
Current Opinion in Psychology
,
29
,
34
40
.
Fukuda
,
K.
,
Kang
,
M.-S.
, &
Woodman
,
G. F.
(
2016
).
Distinct neural mechanisms for spatially lateralized and spatially global visual working memory representations
.
Journal of Neurophysiology
,
116
,
1715
1727
.
Fukuda
,
K.
,
Mance
,
I.
, &
Vogel
,
E. K.
(
2015
).
α power modulation and event-related slow wave provide dissociable correlates of visual working memory
.
Journal of Neuroscience
,
35
,
14009
14016
.
Fukuda
,
K.
, &
Woodman
,
G. F.
(
2017
).
Visual working memory buffers information retrieved from visual long-term memory
.
Proceedings of the National Academy of Sciences, U.S.A.
,
114
,
5306
5311
.
Green
,
B. F.
, &
Anderson
,
L. K.
(
1956
).
Color coding in a visual search task
.
Journal of Experimental Psychology
,
51
,
19
24
.
Hakim
,
N.
,
Adam
,
K. C. S.
,
Gunseli
,
E.
,
Awh
,
E.
, &
Vogel
,
E. K.
(
2019
).
Dissecting the neural focus of attention reveals distinct processes for spatial attention and object-based storage in visual working memory
.
Psychological Science
,
30
,
526
540
.
Händel
,
B. F.
,
Haarmeier
,
T.
, &
Jensen
,
O.
(
2011
).
Alpha oscillations correlate with the successful inhibition of unattended stimuli
.
Journal of Cognitive Neuroscience
,
23
,
2494
2502
.
Hanslmayr
,
S.
,
Spitzer
,
B.
, &
Bäuml
,
K.-H.
(
2009
).
Brain oscillations dissociate between semantic and nonsemantic encoding of episodic memories
.
Cerebral Cortex
,
19
,
1631
1640
.
Hanslmayr
,
S.
, &
Staudigl
,
T.
(
2014
).
How brain oscillations form memories—A processing based perspective on oscillatory subsequent memory effects
.
Neuroimage
,
85
,
648
655
.
Hawkins
,
H. L.
,
Hillyard
,
S. A.
,
Luck
,
S. J.
,
Mouloua
,
M.
,
Downing
,
C. J.
, &
Woodward
,
D. P.
(
1990
).
Visual attention modulates signal detectability
.
Journal of Experimental Psychology: Human Perception and Performance
,
16
,
802
811
.
Hickey
,
C.
,
Di Lollo
,
V.
, &
McDonald
,
J. J.
(
2009
).
Electrophysiological indices of target and distractor processing in visual search
.
Journal of Cognitive Neuroscience
,
21
,
760
775
.
Jensen
,
O.
(
2002
).
Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short-term memory task
.
Cerebral Cortex
,
12
,
877
882
.
Jensen
,
O.
, &
Mazaheri
,
A.
(
2010
).
Shaping functional architecture by oscillatory alpha activity: Gating by inhibition
.
Frontiers in Human Neuroscience
,
4
,
186
.
Jonides
,
J.
,
Lewis
,
R. L.
,
Nee
,
D. E.
,
Lustig
,
C. A.
,
Berman
,
M. G.
, &
Moore
,
K. S.
(
2008
).
The mind and brain of short-term memory
.
Annual Review of Psychology
,
59
,
193
224
.
Klimesch
,
W.
(
2012
).
α-band oscillations, attention, and controlled access to stored information
.
Trends in Cognitive Sciences
,
16
,
606
617
.
Liesefeld
,
A. M.
,
Liesefeld
,
H. R.
, &
Zimmer
,
H. D.
(
2014
).
Intercommunication between prefrontal and posterior brain regions for protecting visual working memory from distractor interference
.
Psychological Science
,
25
,
325
333
.
Lu
,
Z.-L.
,
Lesmes
,
L.-A.
, &
Dosher
,
B. A.
(
2002
).
Spatial attention excludes external noise at the target location
.
Journal of Vision
,
2
,
312
323
.
Luck
,
S. J.
,
Chelazzi
,
L.
,
Hillyard
,
S. A.
, &
Desimone
,
R.
(
1997
).
Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex
.
Journal of Neurophysiology
,
77
,
24
42
.
Luck
,
S. J.
, &
Vogel
,
E. K.
(
2013
).
Visual working memory capacity: From psychophysics and neurobiology to individual differences
.
Trends in Cognitive Sciences
,
17
,
391
400
.
Luria
,
R.
,
Balaban
,
H.
,
Awh
,
E.
, &
Vogel
,
E. K.
(
2016
).
The contralateral delay activity as a neural measure of visual working memory
.
Neuroscience & Biobehavioral Reviews
,
62
,
100
108
.
Makeig
,
S.
,
Westerfield
,
M.
,
Jung
,
T. P.
,
Enghoff
,
S.
,
Townsend
,
J.
,
Courchesne
,
E.
, et al
(
2002
).
Dynamic brain sources of visual evoked responses
.
Science
,
295
,
690
694
.
Müller
,
N. G.
,
Bartelt
,
O. A.
,
Donner
,
T. H.
,
Villringer
,
A.
, &
Brandt
,
S. A.
(
2003
).
A physiological correlate of the “zoom lens” of visual attention
.
Journal of Neuroscience
,
23
,
3561
3565
.
Noonan
,
M. A. P.
,
Adamian
,
N.
,
Pike
,
A.
,
Printzlau
,
F.
,
Crittenden
,
B. M.
, &
Stokes
,
M. G.
(
2016
).
Distinct mechanisms for distractor suppression and target facilitation
.
Journal of Neuroscience
,
36
,
1797
1807
.
Nunez
,
P. L.
, &
Srinivasan
,
R.
(
2006
).
Electric fields of the brain: The neurophysics of EEG
(2nd ed.).
New York
:
Oxford University Press
.
Oberauer
,
K.
(
2002
).
Access to information in working memory: Exploring the focus of attention
.
Journal of Experimental Psychology: Learning, Memory, and Cognition
,
28
,
411
421
.
Palva
,
J. M.
,
Monto
,
S.
,
Kulashekhar
,
S.
, &
Palva
,
S.
(
2010
).
Neuronal synchrony reveals working memory networks and predicts individual memory capacity
.
Proceedings of the National Academy of Sciences, U.S.A.
,
107
,
7580
7585
.
Palva
,
S.
, &
Palva
,
J. M.
(
2007
).
New vistas for α-frequency band oscillations
.
Trends in Neurosciences
,
30
,
150
158
.
Payne
,
L.
,
Guillory
,
S.
, &
Sekuler
,
R.
(
2013
).
Attention-modulated alpha-band oscillations protect against intrusion of irrelevant information
.
Journal of Cognitive Neuroscience
,
25
,
1463
1476
.
Payne
,
L.
, &
Sekuler
,
R.
(
2014
).
The importance of ignoring: Alpha oscillations protect selectivity
.
Current Directions in Psychological Science
,
23
,
171
177
.
Pelli
,
D. G.
(
1997
).
The VideoToolbox software for visual psychophysics: Transforming numbers into movies
.
Spatial Vision
,
10
,
437
442
.
Phillips
,
W. A.
(
1974
).
On the distinction between sensory storage and short-term visual memory
.
Perception & Psychophysics
,
16
,
283
290
.
Prescott
,
S. A.
,
Ratté
,
S.
,
De Koninck
,
Y.
, &
Sejnowski
,
T. J.
(
2008
).
Pyramidal neurons switch from integrators in vitro to resonators under in vivo-like conditions
.
Journal of Neurophysiology
,
100
,
3030
3042
.
Rouder
,
J. N.
,
Morey
,
R. D.
,
Morey
,
C. C.
, &
Cowan
,
N.
(
2011
).
How to measure working memory capacity in the change detection paradigm
.
Psychonomic Bulletin & Review
,
18
,
324
330
.
Rouder
,
J. N.
,
Morey
,
R. D.
,
Verhagen
,
J.
,
Swagman
,
A. R.
, &
Wagenmakers
,
E.-J.
(
2017
).
Bayesian analysis of factorial designs
.
Psychological Methods
,
22
,
304
321
.
Ruff
,
C. C.
, &
Driver
,
J.
(
2006
).
Attentional preparation for a lateralized visual distractor: Behavioral and fMRI evidence
.
Journal of Cognitive Neuroscience
,
18
,
522
538
.
Sauseng
,
P.
,
Klimesch
,
W.
,
Heise
,
K. F.
,
Gruber
,
W. R.
,
Holz
,
E.
,
Karim
,
A. A.
, et al
(
2009
).
Brain oscillatory substrates of visual short-term memory capacity
.
Current Biology
,
19
,
1846
1852
.
Sawaki
,
R.
,
Geng
,
J. J.
, &
Luck
,
S. J.
(
2012
).
A common neural mechanism for preventing and terminating the allocation of attention
.
Journal of Neuroscience
,
32
,
10725
10736
.
Sawaki
,
R.
, &
Luck
,
S. J.
(
2014
).
How the brain prevents and terminates shifts of attention
. In
G. R.
Mangun
(Ed.),
Cognitive electrophysiology of attention: Signals of the mind
(pp.
16
29
).
New York
:
Elsevier
.
Thompson
,
K. G.
,
Hanes
,
D. P.
,
Bichot
,
N. P.
, &
Schall
,
J. D.
(
1996
).
Perceptual and motor processing stages identified in the activity of macaque frontal eye field neurons during visual search
.
Journal of Neurophysiology
,
76
,
4040
4055
.
Vecera
,
S. P.
,
Behrmann
,
M.
, &
McGoldrick
,
J.
(
2000
).
Selective attention to the parts of an object
.
Psychonomic Bulletin & Review
,
7
,
301
308
.
Vecera
,
S. P.
, &
Farah
,
M. J.
(
1994
).
Does visual attention select objects or locations?
Journal of Experimental Psychology: General
,
123
,
146
160
.
Vogel
,
E. K.
, &
Machizawa
,
M. G.
(
2004
).
Neural activity predicts individual differences in visual working memory capacity
.
Nature
,
428
,
748
751
.
Vogel
,
E. K.
,
McCollough
,
A. W.
, &
Machizawa
,
M. G.
(
2005
).
Neural measures reveal individual differences in controlling access to working memory
.
Nature
,
438
,
500
503
.
Vogel
,
E. K.
,
Woodman
,
G. F.
, &
Luck
,
S. J.
(
2001
).
Storage of features, conjunctions, and objects in visual working memory
.
Journal of Experimental Psychology: Human Perception and Performance
,
27
,
92
114
.
Walter
,
W. G.
(
1938
).
Critical review: The technique and application of electro-encephalography
.
Journal of Neurology, Neurosurgery and Psychiatry
,
1
,
359
385
.
Wang
,
S.
,
Rajsic
,
J.
, &
Woodman
,
G. F.
(
2019
).
The contralateral delay activity tracks the sequential loading of objects into visual working memory, unlike lateralized alpha oscillations
.
Journal of Cognitive Neuroscience
,
31
,
1689
1698
.
Wolfe
,
J. M.
(
1998
).
Visual search
. In
H.
Pashler
(Ed.),
Attention
(pp.
13
71
).
London, UK
:
Psychology Press
.
Wolfe
,
J. M.
(
2003
).
Moving towards solutions to some enduring controversies in visual search
.
Trends in Cognitive Sciences
,
7
,
70
76
.
Woodman
,
G. F.
(
2010
).
A brief introduction to the use of event-related potentials in studies of perception and attention
.
Attention, Perception, & Psychophysics
,
72
,
2031
2046
.
Woodman
,
G. F.
,
Kang
,
M.-S.
,
Thompson
,
K.
, &
Schall
,
J. D.
(
2008
).
The effect of visual search efficiency on response preparation: Neurophysiological evidence for discrete flow
.
Psychological Science
,
19
,
128
136
.
Woodman
,
G. F.
, &
Luck
,
S. J.
(
2003
).
Serial deployment of attention during visual search
.
Journal of Experimental Psychology: Human Perception and Performance
,
29
,
121
138
.
Woodman
,
G. F.
, &
Vogel
,
E. K.
(
2008
).
Selective storage and maintenance of an object's features in visual working memory
.
Psychonomic Bulletin & Review
,
15
,
223
229
.
Woodman
,
G. F.
,
Vogel
,
E. K.
, &
Luck
,
S. J.
(
2012
).
Flexibility in visual working memory: Accurate change detection in the face of irrelevant variations in position
.
Visual Cognition
,
20
,
1
28
.
Xu
,
Y.
, &
Chun
,
M. M.
(
2006
).
Dissociable neural mechanisms supporting visual short-term memory for objects
.
Nature
,
440
,
91
95
.