Anticipated Relevance Prepares Visual Processing for Efficient Memory-Guided Selection

Anticipated Relevance Prepares Visual Processing for Efficient Memory-Guided Selection | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Anticipated Relevance Prepares Visual Processing for Efficient Memory-Guided Selection Lasse Dietz , Christoph Strauch , Kabir Arora , Stefan Van der Stigchel , Samson Chota , Surya Gayet doi: https://doi.org/10.1101/2025.08.20.671232 Lasse Dietz 1 Experimental Psychology, Helmholtz Institute, Utrecht University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: l.dietz1{at}uu.nl Christoph Strauch 1 Experimental Psychology, Helmholtz Institute, Utrecht University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kabir Arora 1 Experimental Psychology, Helmholtz Institute, Utrecht University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stefan Van der Stigchel 1 Experimental Psychology, Helmholtz Institute, Utrecht University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Samson Chota 1 Experimental Psychology, Helmholtz Institute, Utrecht University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Surya Gayet 1 Experimental Psychology, Helmholtz Institute, Utrecht University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Finding an object typically involves the use of working memory to prioritize relevant visual information at the right time . For example, successfully detecting a highway exit sign is useless when your announced exit is still ten minutes away, but becomes relevant with only thirty seconds to go. Using EEG, we investigated how predictable changes in stimulus relevance influence topdown (i.e., memory-guided) visual selection. Participants memorized an oriented grating, followed by a cue indicating which of two sequentially presented probes was relevant for a memory-match/-mismatch judgment. Consistent with earlier work, relevant probes evoked stronger univariate responses than irrelevant probes. Furthermore, multivariate responses to (memory-matching and memory-mismatching) probes were more distinct when they were relevant compared to irrelevant. Crucially, using rapid invisible frequency tagging (RIFT), we found that early visual responses to the (empty) probe location were enhanced even before the presentation of relevant (compared to irrelevant) probes. These results demonstrate that predictable changes in stimulus relevance induce both pro-active and re-active changes in visual processing. We conclude that anticipating the (ir)relevance of upcoming visual events enables the visual system to prepare ahead of time, enabling efficient memory-guided visual selection. Introduction Picture yourself driving on the highway, and you see a blue sign announcing your vacation destination, Utrecht, in 5km. Because you roughly know how much time it takes to reach this exit, the immediately upcoming exit signs (e.g., to Nieuwegein) are not relevant to you. Instead, you start to look for the blue highway exit sign to Utrecht after a short wait, find your exit, and enjoy your holiday. In this example, visual working memory (VWM) prioritizes relevant visual input by enhancing the processing of VWM-matching visual events (here: a blue sign) ( Olivers et al., 2011 ; Soto et al., 2008 ). More specifically, VWM content enhances neural responses ( Gayet et al., 2017 ), conscious access ( Gayet et al., 2013 ), attentional capture ( Downing, 2000 ), and gaze biases ( Olivers et al., 2006 ; Olmos-Solis et al., 2017 ; Silvis & Van Der Stigchel, 2014 ) to VWM-matching compared to mismatching visual input. More generally, VWM allows observers to guide visual selection toward currently relevant visual events ( Desimone & Duncan, 1995 ; Duncan & Humphreys, 1989 ; Eimer, 2014 ; Kastner & Ungerleider, 2001 ; Wolfe, 1994 ; Wolfe & Horowitz, 2004 ). But what will be relevant later may not yet be relevant now. In our highway example, the to-be-selected visual event (i.e., the exit sign) only became relevant later. Anticipating when relevant visual events will appear would allow observers to distribute cognitive resources across time efficiently. Indeed, temporal expectations can aid in perceptual discrimination and/or detection tasks; successfully anticipating visual events is associated with altered neural responses and improved behavioral performance ( Bertelson & Boons, 1960 ; Coull & Nobre, 1998 ; Cravo et al., 2013 , 2017 ; Klemmer, 1956 ; Miniussi et al., 1999 ; Nobre & Van Ede, 2018 ; Rohenkohl et al., 2012 ; Vangkilde et al., 2012 ). Similarly, memory-guided (i.e., VWM-based) visual selection may benefit from anticipating the appearance of relevant visual events. While the role of temporal expectations has been extensively studied in the domain of sensory processing ( Nobre & Van Ede, 2018 , 2023 ), its role in memory-guided (i.e., VWM-based) visual selection is heavily understudied. This is surprising because, as our highway example illustrates, memory-guided visual selection (“what are we looking for?”) in daily life is also strongly dependent on temporal expectations (“when are we looking for it?”). Thus, temporal expectations may profoundly influence working memory-guided behavior. Initial research studying anticipation in the context of memory-guided behavior shows that participants are more accurate (and faster) in change-detection tasks when the probe appears after the anticipated interval ( Heuer & Rolfs, 2022 ; Jin et al., 2020 ). These findings show that temporal expectations improve performance in memory recognition tasks. Such improvements may be the result of pro-actively enhancing the representation of the memory content, improved sensory processing of the probe item, or both. A large body of neuroimaging studies has shown that, indeed, observers can alter the representational strength of memory content over time, when anticipating the recall task to occur early or late ( Christophel et al., 2018 ; LaRocque et al., 2017 ; Lewis-Peacock et al., 2012 ; Rose et al., 2016 ; Sahan et al., 2020 ; van Loon et al., 2018 ; Yu et al., 2020 ). However, measuring how visual processing changes in anticipation of a relevant visual event remains notoriously challenging because there is (by definition) no visual event to measure a response to. Here, we leveraged a novel EEG-based method that allows tracking of early visual processing changes in the absence of any perceptible stimulation. In doing so, we directly tested whether visual processing is enhanced in anticipation of visual events that are relevant (versus irrelevant) to memory-guided selection. Participants memorized a single oriented grating on each trial, followed by a cue. The cue indicated which of two subsequent gratings had to be compared to the initial memory item (relevant probe) and which could be ignored (irrelevant probe; see Figure 1 for the paradigm). This design allowed us to investigate (1) how the anticipation of relevant versus irrelevant visual events affects visual processing proactively (before stimulus presentation), and how (2) the memory content influences the response to relevant versus irrelevant visual events re-actively (upon stimulus presentation). Importantly, relevant and irrelevant probes were physically identical across trials and only differed in terms of task-relevance, since stimulus changes (cue, orientation, etc) were counterbalanced within participants. Download figure Open in new tab Figure 1: The left side displays the visual events as perceived by the participants. The luminance changes of a single RIFT cycle (8 frames including the left side) for each of these events is depicted horizontally. Luminance changed periodically at 60Hz, rendering the luminance changes imperceptible. The first fixation period and the response period are not tagged (continuous gray background). This is an example of a cue “2” trial (participants have to compare the memory item to the second probe) requiring a “different” response (the orientation of the second probe differs from that of the memory item). Untagged periods (fixation before the VWM Item and the response period) are grayed out. To measure changes in early visual processing evoked by the mere anticipation of (ir)relevant visual events, EEG-based rapid invisible frequency tagging (RIFT) was employed ( Arora et al., 2024 ; Hustá et al., 2025 ). RIFT continuously tracks changes in early visual processing in the absence of any perceptible stimulus. This is achieved by ‘tagging’ a location of interest on the screen (here: the stimulus area). Tagging refers to periodic luminance modulation of the screen at 60Hz (imperceptible to participants). This luminance modulation induces a corresponding oscillatory response in the EEG signal. The strength of this oscillatory response in the EEG (measured as RIFT coherence) varies with how much cognitive resources (attention) are allocated to visual processing of the tagged location ( Arora et al., 2025 ; Duecker et al., 2024 ; Ferrante et al., 2023 ; Zhigalov et al., 2019 ). Crucial to the present purpose, RIFT produces an experimenter-controlled marker of early visual processing, which enables us to isolate pro-active effects on visual processing that are not triggered (or modulated) by the perception of a stimulus. In contrast, typical approaches using visible probes inevitably confound pro-active effects with re-active effects, which may not have emerged without said probes. Also, approaches using endogenous markers of visual processing (e.g., alpha oscillations) cannot be unequivocally attributed to early visual processing, because they are not experimentally controlled (’tagged’) and may therefore reflect a variety of (early and late, perceptual and post-perceptual) processes. To summarize, RIFT is a non-invasive, location-specific, experimenter-induced oscillatory signal with high temporal resolution that tracks early visual processing at the tagged location. If observers indeed (pro-actively) enhance sensory processing in anticipation of relevant visual events during memory-guided selection, the RIFT response should increase before presentation of a relevant probe compared to an irrelevant probe. Methods Participants Twenty-five participants with normal or corrected-to-normal vision were recruited at Utrecht University. Each participant attended a 2-hour session and was compensated with either participation credits or €24. Informed consent was obtained, and participants provided their age and gender. The study was approved by the Faculty of Social and Behavioral Sciences Ethics Committee of Utrecht University. Out of the initial 25 participants, two exhibited significantly poorer behavioral performance on either cue 1 or cue 2 trials (cue 1: 48.61% versus cue 2: 96.53% and cue 1: 94.44% versus cue 2: 62.50%). As these participants did not seem to adhere to the task instructions (they likely ignored the cue information), they were excluded from further analysis. Additionally, one participant was excluded due to poor EEG data quality. Consequently, a total of 22 participants were included in the eventual analyses. Setup & Apparatus Stimuli were created using Psychtoolbox for MATLAB ( Brainard & Vision, 1997 ; Pelli, 1997 ) and presented on a projection screen using a ProPixx projector (VPixx Technologies Inc., QC Canada; resolution = 960×540px; refresh rate 480Hz). The projected screen size was 48×27.5cm (35.05×20.51 dva) in a rear-projection format. Interfacing with the projector (e.g., turning on/off 480 Hz mode) was achieved using the custom Matlab software provided by the company, which was integrated into the experimental code. Participants were positioned 76 cm from the screen, using a chin and headrest for alignment. Task & Procedure Participants performed a modified delayed match-to-sample task (see Figure 1 , “Participant View”). Each trial began by pressing the space bar, which initiated a 1500ms baseline of untagged fixation, followed by a first oriented grating (hereafter: the memory item), which was presented for 200ms. Participants were instructed to memorize the orientation of this memory item for a subsequent match/mismatch judgment (i.e., they reported whether the cued probe had the same or a different orientation as the memory item). After a second fixation period (1000ms), a cue was presented for 200ms (“1” or “2”) informing participants which of two subsequent oriented gratings (the probes; each presented for 200ms, with a 1500ms inter-stimulus interval) was relevant for the memory match/mismatch judgment. After a 500ms fixation period following the offset of the second probe, a question mark was presented, indicating the forced-choice response window (1000ms). Participants indicated whether the orientation of the cued probe (i.e., first or second) matched the orientation of the memory item by pressing either the left or right control key with the corresponding hand. Each trial ended with a blue circle, signaling a self-paced break. Participants were asked to refrain from using strategies to circumvent the memory component of the task. As an example, we specifically prohibited using one’s fingers as a memory aid for stimulus orientation. Participants were instructed that the irrelevant (i.e., uncued) probe could be ignored. Stimuli The stimuli were oriented square-wave gratings with a diameter of 6°of visual angle (dva). Twelve orientations, ranging from 10 to 190°to avoid fully vertical and horizontal orientation in 15°steps, were randomly selected. The square-wave gratings consisted of alternating shades of gray (127.5 RGB & 95% of 127.5 RGB). The phase value was a random integer between -180 and 180, excluding 0, to prevent fixation on the stripes. The background color was a darker gray (100 RGB) to reduce eyestrain. The fixation dot, response indicator, and stimulus area frame were white (200 RGB). The gratings had soft borders to prevent edge-based orientation memorization or recall strategies. 60Hz RIFT was achieved by multiplying the stimulus area RGB values with eight discrete steps of a sinusoidal curve from 0 to 1. A complete tagging cycle takes ∼16.667ms, which corresponds to a frequency of 60 Hz ( Figure 1 - Projector view). This caused the lighter parts of the grating stimuli and the framed stimulus area to vary fully from 0 to 255 RGB, while the darker parts of the grating stimuli varied from 0 to 95% of 255 RGB. RIFT started at the onset of the memory item and ended at the onset of the response screen. Consequently, each trial had a period of 5300ms of uninterrupted 60Hz tagging. Design Participants completed 12 blocks of 24 trials each, lasting approximately one hour in total, excluding one practice block. The additional practice block consisted of 12 trials, with feedback provided for the first six trials. Per two blocks, trials were balanced for relevant/irrelevant (cue 1/2) probe, the 12 orientation conditions (10° to 190°), and presented in random sequence. Probes were rotated in one-third of the trials (equally 45° to the right, left, or by 90°). Orientations of all gratings were independent of each other. Each memory item orientation appeared four times per two blocks. Trials within each set of two blocks were presented in a different random order for each participant. At the start of a block, participants were informed about the current block number and afterwards about their accuracy in the past block for 3000ms. Eyetracking Eye-tracking data were binocularly collected at 500Hz using an Eyelink 1000 eye tracker (SR Research, Ontario, Canada), with nine-point calibration and validation performed at the start and after each block. Invalid Trials If participants failed to give a response within the forced-choice window, the response was considered invalid. To ensure adherence to central gaze, trials were aborted and considered invalid if the gaze was outside the stimulus area for more than 208ms (25 frames at 120Hz), thus allowing a few short blinks. Blinks were monitored, and participants were reminded on screen to avoid blinking during trials. All invalid trials were repeated up to two times at the end of blocks in a random sequence. EEG Data Collection & Preprocessing EEG data were collected using a 64-channel ActiveTwo BioSemi system at 2048Hz. Preprocessing was done using the Fieldtrip toolbox for MATLAB ( Oostenveld et al., 2011 ). Data was re-referenced by subtracting the electrode average, line noise removed (50, 100, 150Hz) using a discrete Fourier transform, high-pass filtered (0.01Hz), and split into trials of 6800ms (1500ms before VWM onset & 5300ms until response screen start). Independent component analysis was used to remove oculomotor artifacts. Trials containing other artifacts were identified using visual inspection and removed (average trials excluded per participant = 40.41, SD = 26.62). Univariate Analysis To confirm that participants engaged with the temporal structure of the task, we compared univariate EEG responses to relevant versus irrelevant visual events. Previous research suggests that responses to relevant (i.e., prioritized) compared to irrelevant visual events should be stronger in posterior channels (parietal/occipital) ( Correa et al., 2006 ; Hillyard et al., 1998 ; Mangun & Hillyard, 1990 ; Miniussi et al., 1999 ). For the univariate analysis, additional preprocessing steps were taken. First, the data were downsampled to 512 Hz. To remove noise, we applied high (0.1Hz) and lowpass (40Hz) filtering (two-pass Butterworth bandpass filter; 4th order, Hamming taper). The data were baseline corrected by subtracting the average of the baseline window (-200 to 0, before probe onset) from each trial per channel. The EEG data were segmented around probe onset from -500 to 700ms. Trials (only correct ones included) were balanced across cue (1 or 2) conditions, ensuring an equal number of trials where probes (1 and 2) were relevant and irrelevant. As the analysis goal was a general comparison between responses to relevant and irrelevant visual events, we combined probes appearing in the first or second segment. Therefore, potential effects are reflective of responses to both probes. Finally, data for each condition were averaged over trials and the 20 posterior channels (P1, P3, P5, P7, P9, PO7, PO3, O1, Iz, Oz, POz, Pz, P2, P4, P6, P8, P10, PO8, PO4, O2) that were also used for the multivariate pattern analysis described below. RIFT - Data Analysis Magnitude Squared Coherence (as described in Pan et al., 2022 ) was used to quantify the strength of the EEG responses at our tagging frequency (60Hz). To this end, the EEG trials were band-pass filtered (±1.9Hz) at 60 Hz using a two-pass Butterworth filter (4th order, Hamming taper). The filtered data was then Hilbert transformed to obtain the analytic signal, which was used to calculate time-varying coherence: With n being the number of trials. For each time sample t in trial tr , the timevarying magnitude of an EEG signal is represented by and by for a synthetic sinusoidal reference signal. The phase difference as a function of time is defined by . Coherence, therefore, reflects the degree to which the 60 Hz phase (weighed by magnitude) is consistently aligned across trials for each channel. The 60Hz coherence traces per condition (cue 1, cue 2) were averaged across each participant’s top five RIFT coherence channels. The top five channels were identified per participant by averaging RIFT coherence across all conditions (cue 1 and 2) and time points throughout a trial. Previous literature indicates that the number of top channels included does not impact the results ( Arora et al., 2025 ). Subsequently, per participant, the difference between the cue 1 RIFT trace and the cue 2 RIFT trace was calculated to reveal changes in visual processing for the two probe intervals. To summarize these findings and address our research question, we sign-reversed the period preceding and including Probe 1 (1700ms) and concatenated it with the period preceding and including Probe 2 (1700ms), as both periods were expected to yield an opposite pattern of results. Multivariate Pattern Analysis Time-resolved MVPA was used to determine whether responses to visual events were modulated by memory content. Specifically, support vector machines (SVM) (Matlab: fitclinear.m) were trained to differentiate between memory-matching and mismatching trials. The analysis was run for a period before to after probes 1 and 2 (-500/+1000ms Probe 1 & -500/+700ms Probe 2 onset) and additionally when combining all trials for probes 1 and 2 (-500ms and +700ms probe onset). Before classification, some additional preprocessing steps were taken. First, data were resampled to 512Hz, to then be high- (0.1Hz) and low-pass (40Hz) filtered using a two-pass Butterworth filter (4th order, Hamming taper). Subsequently, we applied a baseline correction (200ms; -500ms to -300ms before memory item onset) using the mean. Similar to previous research, the 20 posterior channels (P1, P3, P5, P7, P9, PO7, PO3, O1, Iz, Oz, POz, Pz, P2, P4, P6, P8, P10, PO8, PO4, O2) were selected ( Wolff et al., 2017 ). Furthermore, a normalization procedure described by Wolff et al., 2020 was employed. Instead of basing decoding on data of a single timestep only, the data were aggregated across a time window and included in the features provided to the decoder. While this procedure decreased the temporal resolution and specificity (i.e., duration/trajectory/offset) of the decoding, it offers more information (across time) to the decoder, potentially improving decoding accuracy. The procedure was the same for each sample in the EEG timeseries (8ms steps): First, each preceding 100ms window was mean-centered (per trial and channel) to separate neural response dynamics from more stable brain states (e.g., voltage fluctuations). Subsequently, the window was smoothed using a 15ms moving average and then downsampled with a 15ms step size (66.67Hz) to remove high-frequency fluctuations likely uninformative to decoding and reduce feature dimensionality; (Using a 10ms step size, downsampling the data to 100Hz did not change results). Finally, the data were reshaped so that every sample in the time series contained both the channel and the normalized 100ms window in the feature dimension (Channel x Time window). Classification was performed using 8-fold cross-validation for 10 repetitions. Classification accuracy was averaged per participant to then be smoothed using an 80ms moving average. Because memory-match and memory-mismatch probes required a different manual response (e.g., left and right response hand, respectively), the results of the match-mismatch classification analysis described above may (partially) reflect manual response preparation instead of memory-based modulations of the visual response to the probes. To isolate these memory-based modulations of the visual response to the probes, we conducted a cross-participant analysis, capitalizing on the counterbalancing of response hand across participants. The goal of this crossparticipant analysis was to counterbalance response hand across training/testing folds (to prevent decoding solely on manual response preparation), while leaving the general analysis procedure the same. To this end, participants were divided into two groups based on the response button counterbalancing. A ‘Leave One Out’ procedure was used for each group to split the data into train/test folds. Therefore, each test fold contained two participants (one of each response button condition), with the training fold containing the rest of the participants. Additionally, trials both in the train and test folds were randomly subsampled based on the response button condition, ensuring equal trials from each response button condition. This procedure resulted in ten folds, which was repeated for ten repetitions. The combination of folds and repetitions produced an accuracy trace distribution [Folds*Repetitions, Accuracy trace], which was tested for significant deviation from chance. Significance Testing Significance for all analyses was determined using a non-parametric cluster-based permutation test (see Maris & Oostenveld, 2007 ). The testing procedure consisted of three steps: 1) For each time point of the to-be-tested trace, a t-test was run to identify samples that differed significantly from zero (one-sided, p < 0.1). For clusters of at least 5 consecutively significant samples, the sum of t-values was computed, resulting in a cluster-level-t-mass. 2) To produce a null-distribution of t-masses (reflecting the expected distribution under the null hypothesis), all traces were randomly sign-reversed (preserving auto correlations between time points in the null distribution) 5000 times, resulting in 5000 new datasets containing a randomized trace per participant. The same analysis procedure used for the observed data (Step 1) was applied to each of these 5000 datasets; except, only the largest cluster identified in each of the datasets was included in the null distribution. 3) t-mass values of the observed clusters higher than 95% (i.e., α = 0.05) of the t-mass values in the null distribution were marked as significant. For all significant clusters, we report the exact p-value (2 decimals) and the time range where the 95%-CIs of the observed data differ from zero. Note that we do not report the time range of the cluster itself, because cluster-level significance establishes only that an effect exists, not where it is situated in time ( Sassenhagen & Draschkow, 2019 ). Since, the procedure only tested for positive deviations from zero, time-series with suspected negative deviations were sign-reversed. Results Behavioral Performance All participants completed the task with an average accuracy (percentage of correctly identified relevant probes as mis-/matching the VWM Item) of M = 94.44% across all trials ( SD = 4.90%). On average, participants performed similarly for both cues, with 94.89% ( SD = 5.53%) for cue 1 and 93.96% ( SD = 4.95%) for cue 2. RIFT reveals anticipatory early visual processing enhancements prior to relevant visual events First, we addressed the main question by testing for pro-active neural responses before probe onset. As expected, 60 Hz RIFT coherence was observed strongest in the posterior electrodes (Supplementary Figure 1). To determine whether visual processing was enhanced in anticipation of relevant compared to irrelevant visual events, we investigated the level of RIFT coherence before probe presentation. In the interval before Probe 1 presentation, 60Hz RIFT coherence was higher when Probe 1 was cued to be relevant versus when it was cued to be irrelevant (permutation test, p = .04), supporting our hypothesis ( Figure 2 ); 95%-CIs [2.00, 2.40]s. Similarly, RIFT coherence was higher before Probe 2 presentation when Probe 2 was cued as relevant compared to irrelevant (permutation test, p < .001), again supporting our hypothesis ( Figure 2 ); 95%-CIs [3.57, 4.821]s. The average RIFT difference between relevant and irrelevant probes (i.e., Probe 1 & 2 relevant versus irrelevant) across significant intervals was localized in posterior electrodes. Download figure Open in new tab Figure 2: Anticipating a relevant (compared to irrelevant) probe enhanced visual processing before probe presentation. The top of the figure depicts the time course of events as seen by participants. Average RIFT coherence (quantifying early visual processing; left y-axis) over time, separated for trials in which Probe 1 (dark blue) or Probe 2 (light blue) was relevant. The paired difference between conditions (right y-axis) is depicted in black. Shaded area: 95%-CI. Horizontal black line: significance according to a permutation-based cluster analysis, p <.05. Topography: The average RIFT coherence difference between relevant and irrelevant probes (i.e., Probe 1 & 2 relevant versus irrelevant) across significant intervals. Confirming these findings, we found an overall stronger RIFT coherence preceding relevant visual events compared to irrelevant visual events across the collapsed intervals prior to the probes (permutation test, p < .001; 95%-CIs [-1.03, 0.18]s; Figure 3 ). These findings show that participants enhanced visual processing in anticipation of relevant relative to irrelevant visual events. Download figure Open in new tab Figure 3: Visual processing was enhanced in anticipation of relevant compared to irrelevant visual events. Overlapped difference between relevant and irrelevant probes (Probe 1 & 2 combined) 1500ms before and including 600ms after probe presentation (blue shaded area: 95%-CI). Horizontal lines indicate significance using a permutation-based cluster analysis at * p < .05. Interestingly, these differences in RIFT coherence did not persist beyond the anticipatory period. That is, the difference in RIFT responses between relevant and irrelevant conditions drops to baseline shortly after probe presentation. Stronger univariate responses to relevant than to irrelevant probes Next, we analyzed re-active neural responses to the probes to confirm that participants engaged with the temporal structure of the task. We expected that a probe would evoke a stronger (univariate) response when it was relevant than when it was irrelevant. Combining intervals for Probe 1 and Probe 2, we observed an overall stronger decreased EEG response to relevant visual events compared to irrelevant visual events for posterior electrodes (permutation test, p < .001; Figure 4 ); 95%-CIs [0.17, 0.61]s. In the same context (combined Probe 1 and 2 intervals), memorymatching visual events did not differ from memory-mismatching visual events. This finding shows that participants leveraged the temporal structure of the task, resulting in a stronger (re-active) response to relevant compared to irrelevant visual events. Importantly, relevant and irrelevant stimuli did not systematically differ in their visual features. The results align with previous research observing stronger (re-active) neural responses to targets presented at (cued) relevant compared to irrelevant spatial locations ( Hillyard et al., 1998 ; Mangun & Hillyard, 1990 ), and time points ( Correa et al., 2006 ; Miniussi et al., 1999 ). Download figure Open in new tab Figure 4: Stronger univariate responses to relevant (compared to irrelevant) probes. Univariate responses (i.e., event-related potentials) for the different conditions of interest (Probe 1 & 2 combined; average across 20 posterior channels; left y-axis) and the corresponding difference between the relevant and irrelevant condition (right y-axis) over time. Shaded area: 95%-CI. Horizontal black line: significance according to a permutation-based cluster analysis, p <.05. Decoding reveals increased memory-specific responses to relevant visual events For a final set of analyses, we considered that, if observers make effective use of temporal expectations during memory-guided visual selection, they should better discriminate between memory-matching and memory-mismatching visual events (e.g., matching blue highway exit signs versus mismatching white traffic signs) when they are relevant (i.e., right now) compared to when they are not (i.e., ten minutes from now). To measure these (re-active) mnemonic modulations of the visual events, we used support vector machine classification on the EEG data. Specifically, we compared the multivariate discriminability of memory-matching versus memory-mismatching probes, depending on whether they were relevant or irrelevant. We expected better classification between memory-matching and memorymismatching probes when the probes were relevant to the memory task compared to when they were irrelevant. First, we found that irrespective of probe relevance, multivariate pattern classification allowed for differentiation between matching and mismatching probes. Classification accuracy was increased both when probes were relevant (permutation test, p < .001) and irrelevant (permutation test, p < .001) Figure 5A . 95%-CIs [0.16, 0.64]s (relevant probe) and [0.33, 0.49 & 0.57, 0.70]s (irrelevant probe). To determine whether these memory-modulated visual responses depend on the relevance of the visual input, we tested whether classification accuracy was higher for relevant compared to irrelevant probes. Classification accuracy was, indeed, increased for relevant over irrelevant probes (permutation test, p Difference < .001), supporting our hypothesis ( Figure 5A ); 95%-CIs [0.24, 0.50]s. Similar results were observed when analysing Probe 1 and Probe 2 individually (Supplementary Figure 2). Download figure Open in new tab Figure 5: Multivariate responses to memory-matching versus memory-mismatching visual events (i.e., probes) were more distinct when relevant (compared to irrelevant) for the upcoming task. Multivariate classification accuracy between memorymatching and memorymismatching probes, separated for relevant (purple) and irrelevant (orange) probes (Probe 1 & 2 combined). Horizontal lines: significance using a permutation-based cluster analysis. Dotted line: significantly higher classification accuracy for the relevant over the irrelevant probes, p <.05. (A) The decoding analysis was performed on each participant separately (shaded areas: 95%-CI for the participant distribution). (B) The same decoding analysis was performed across participants, using data from all participants within the different training and testing folds, while ensuring the counterbalancing of hand-response mapping (equal prevalence of left hand for “match” response and left hand for “mismatch” response). Shaded areas: 95%-CI for the test fold distribution. Because memory-match and memory-mismatch probes required a different manual response (e.g., left and right arrow keys, respectively), the match-mismatch classification analysis described above may (partially) reflect manual response preparation instead of memory-based modulations of the visual response to the probes. To isolate these memory-based modulations of the visual response to the probes, we conducted a cross-participant analysis, capitalizing on the counterbalancing of response buttons across participants. When controlling for response hand in the train and test sets, we replicated the better memory-match/mismatch classification for relevant than for irrelevant probes (permutation test, p Difference , < .001; Figure 5B ); 95%-CIs [0.20, 1.10]s. Taken together, memory modulates visual responses; however, the (ir)relevance of the visual events determines the strength of the modulation. Discussion We examined the dynamics of memory-guided visual selection when facing relevant and irrelevant visual events. The results demonstrate that event relevance affects visual processing during memory-guided selection. Specifically, anticipating relevant — rather than irrelevant — visual events (1) increases RIFT coherence prior to the event onset, and subsequently (2a) increases univariate responses to the events themselves, and (2b) improves multivariate differentiation between memorymatching and memory-mismatching events. In sum, the anticipated (ir)relevance of a visual event (1) pro-actively enhances early visual processing, and (2) re-actively modulates the neural response to the visual event itself. Anticipating the (ir)relevance of upcoming events, then, enables the visual system to prepare for efficient memoryguided visual selection. Enhanced visual processing prior to relevant visual events We hypothesized that the temporal structure of the task would allow observers to change the intensity of early visual processing over time, depending on when the relevant visual event was expected to occur. RIFT tracked anticipatory changes in visual processing (before event onset) with high temporal resolution. We found increased RIFT coherence preceding relevant compared to irrelevant probes, despite visual stimulation being constant (i.e., same temporal structure and balanced probe orientations). This was observed in the period preceding both probes, reflected in a mirrored difference wave (cued Probe 1 - cued Probe 2) across the duration of a trial. Specifically, when the first probe was relevant for the memory task, there was a relative increase in the RIFT response before the first probe, and a subsequent decrease before the second probe; conversely, when the second probe was relevant, there was a relative decrease in the RIFT response before the first probe, followed by an increase before the second probe. Our findings show that early visual processing can be flexibly upand down-regulated over time, in anticipation of relevant visual events, relative to irrelevant visual events. These pro-active changes in early visual processing emerged well (approximately up to one second) before the actual stimulus was presented. Based on the present results alone, we cannot determine the absolute direction of these visual processing changes. Observers may up-regulate early visual processing in anticipation of relevant visual events (the cued item) or down-regulate early visual processing in anticipation of irrelevant visual events (the uncued item), or both. Future studies, including a condition without uncued items (i.e., a baseline), may be needed to arbitrate between these possibilities definitively. An increase in visual processing above baseline would indicate up-regulation of visual processing based on temporal expectations, while a decrease would indicate down-regulation. It is worth noting that it is difficult to establish a true baseline of visual processing over time. An alternative approach, would be to reduce the formation (or usefulness) of expectations by making the onset time of the target unpredictable. The present results do align with previous behavioral and neurophysiological research, which has identified a diverse range of processing benefits for inputs that can be anticipated ( Bertelson & Boons, 1960 ; Coull & Nobre, 1998 ; Heuer & Rolfs, 2022 ; Klemmer, 1956 ; Miniussi et al., 1999 ; Nobre & Van Ede, 2018 ; Rohenkohl et al., 2012 ; Vangkilde et al., 2012 ). In most of these studies, there was no “distracting” visual event that required suppression. Typically, participants are cued to expect either an imminent relevant or a distant relevant target. It is plausible that participants up-regulate processing in anticipation of imminent relevant visual events rather than (only) down-regulate processing in anticipation of relevant distant visual events. This suggests that anticipatory effects observed in the present study — at least partly — reflect up-regulation of visual processing in the face of relevant visual events (and cannot be fully explained by the suppression of irrelevant visual events). The present study expands on this previous literature on temporal expectations by measuring how anticipating the (ir)relevance of a visual event affects early visual processing. Studies using RIFT with MEG have localized the source of the RIFT signal to primary and secondary visual cortex ( Duecker et al., 2021 , 2025 ; Schneider et al., 2023 ; Zhigalov & Jensen, 2020 ). Accordingly, RIFT is interpreted to reflect neural excitability of early visual cortex ( Duecker et al., 2021 , 2025 ). Thus, we show here, for the first time, that temporal expectations modulate early visual processing, in the period preceding (ir)relevant stimuli. These anticipatory changes in early visual processing may lie at the basis of behavioral and neural biases observed in extant studies manipulating temporal expectations. Increased mnemonic differentiation of relevant visual events In the context of VWM-guided selection, preparatory visual processing enhancements may facilitate perceptual comparisons between the relevant visual events (what is seen) and VWM content (what is searched for). Moving beyond the anticipatory period, we therefore also asked how relevancy influences the neural responses to the visual events themselves. Specifically, we aimed to determine whether the discriminability between memory-matching and memory-mismatching visual events (i.e., the object you are looking for versus other objects) depends on whether or not the event is relevant at the moment (i.e., do you need to discriminate between objects right now or at a different time?). Firstly, we found dissociable multivariate responses to memory-matching and memory-mismatching visual events, both when visual events were relevant and when they were irrelevant. Importantly, however, responses between memory-matching and memory-mismatching visual events were more distinct when the visual events were relevant for the memory task, compared to when they were not. Thus, temporal expectations are used to regulate the memory-based differentiation between target and distractor objects. This finding makes intuitive sense because — both in our experiment and in daily life — target and distractor objects require different behavioral actions (exit the highway or not) when they are relevant, but not when they are irrelevant. From this perspective, it may be surprising that we also observed memory-based differentiation of the irrelevant visual events (albeit weaker than for relevant events). These findings add to a growing literature investigating how memory-based modulations of visual processing depend on the attentional state of the visual working memory content. Previous literature has shown that currently relevant memory items bias concurrent visual processing more strongly than currently irrelevant memory items ( Olivers et al., 2011 ; Wang et al., 2025 ). This line of research, however, mainly compared how multiple parallel memory items in different attentional (or: relevancy) states influence perception. In this case, however, we explicitly studied how changes in the relevance of a single memory item influenced concurrent visual processing. This may be more similar to real-world situations, in which relevancy changes over time, and observers typically never load more than one item in memory ( Draschkow et al., 2021 ; Qing et al., 2025 ; Sahakian et al., 2023 ; Van der Stigchel et al., 2019 ). Here we show that as the relevance of a single memory item for behavior waxes and wanes, its influence on visual selection follows. Anticipatory changes in memory representations An outstanding question pertains to whether and how memory representations change in anticipation of an upcoming visual event. Indeed, in parallel to the observed enhancement of visual processing prior to relevant visual events, one may also expect an anticipatory sharpening of the visual working memory content. In support of this latter possibility, recent studies investigating the impact of temporal expectations on memory recall revealed that VWM content is more resistant to predictable than to unpredictable visual interference ( Gresch et al., 2021 ; Van Ede et al., 2018 ). However, these studies did not directly measure the representational format or strength of the memory content in anticipation of the (relevant and irrelevant) visual events. More direct evidence comes from fMRI decoding studies, in which a cue indicated which of two items held in VWM would be relevant for the next of two consecutive recall tasks. These studies have shown that observers can indeed flexibly enhance relevant VWM representations in anticipation of a memory task ( Christophel et al., 2018 ; LaRocque et al., 2017 ; Lewis-Peacock et al., 2012 ; Rose et al., 2016 ; Sahan et al., 2020 ; van Loon et al., 2018 ; Yu et al., 2020 ). In the context of visual search, recent fMRI decoding studies have shown that working memory representations of a search target — generated during search preparation — depend on contextual expectations (e.g., about the location of a target object, or the appearance of distractor objects) ( Gayet & Peelen, 2022 ; Lerebourg et al., 2024 ). These findings demonstrate that VWM contents can be strategically altered to match situational demands in a search task context. Relatedly, VWM representations can be dynamically adjusted (from a sensory to an action-oriented format) to meet current task demands ( Henderson et al., 2022 ). Together, these findings highlight the adaptability of working memory contents to the changing demands of various dynamic environments. By extension, it seems likely that VWM representations also vary in response to the type of temporal expectations investigated in the present study. Unfortunately, EEG decoding methods thus far have been relatively unsuccessful in decoding the content of visual working memory over a prolonged period. Therefore, future work is needed (e.g., using fMRI-EEG fusion) to directly test whether the changes in early visual processing established here, are accompanied by a corresponding sharpening of the VWM content. Conclusion In sum, when driving on a highway, you do not continuously and indiscriminately compare the visual input of the right side of the road with the highway exit sign that you are looking for. Instead, you prioritize visual input during the period in time when you expect the sign to appear. Specifically, when the anticipated highway exit is imminent, you preemptively enhance early visual processing, which may benefit the comparison between the visual input to the exit sign that you hold in memory. Taken together, the anticipated (ir)relevance of visual events plays a crucial role during memory-guided visual selection. Anticipating upcoming relevant visual events allows for the efficient distribution of resources relevant to visual processing over time. Declarations Funding This project was funded by Utrecht University. Conflicts of interest/Competing interests The authors declare no conflicting interests. Ethics approval The study was carried out in accordance with the protocol approved by the Faculty of Social and Behavioral Sciences Ethics Committee of Utrecht University. Author Contributions Conceptualization, L.D., C.S., S.V.d.S., S.C., and S.G.; data curation, L.D.; formal analysis, L.D., C.S., K.A., S.C., and S.G.; funding acquisition, S.C. and S.G.; methodology, L.D., C.S., K.A., S.C., and S.G.; project administration, L.D., C.S., S.C., and S.G.; software, L.D., K.A., and S.C.; supervision, C.S., S.V.d.S., S.C., and S.G.; visualization, L.D.; writing — original draft preparation, L.D. and S.G.; writing — review and editing, C.S., S.V.d.S., S.C., and S.G. Footnotes ↵ * Shared authorship References ↵ Arora , K. , Gayet , S. , Kenemans , J. L. , Van Der Stigchel , S. , & Chota , S. ( 2025 ). Dissociating external and internal attentional selection . iScience , 28 ( 4 ), 112282 . doi: 10.1016/j.isci.2025.112282 OpenUrl CrossRef PubMed ↵ Arora , K. , Gayet , S. , Kenemans , J. 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Share Anticipated Relevance Prepares Visual Processing for Efficient Memory-Guided Selection Lasse Dietz , Christoph Strauch , Kabir Arora , Stefan Van der Stigchel , Samson Chota , Surya Gayet bioRxiv 2025.08.20.671232; doi: https://doi.org/10.1101/2025.08.20.671232 Share This Article: Copy Citation Tools Anticipated Relevance Prepares Visual Processing for Efficient Memory-Guided Selection Lasse Dietz , Christoph Strauch , Kabir Arora , Stefan Van der Stigchel , Samson Chota , Surya Gayet bioRxiv 2025.08.20.671232; doi: https://doi.org/10.1101/2025.08.20.671232 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7636) Biochemistry (17704) Bioengineering (13898) Bioinformatics (41967) Biophysics (21460) Cancer Biology (18599) Cell Biology (25525) Clinical Trials (138) Developmental Biology (13384) Ecology (19909) Epidemiology (2067) Evolutionary Biology (24326) Genetics (15613) Genomics (22512) Immunology (17740) Microbiology (40423) Molecular Biology (17191) Neuroscience (88645) Paleontology (667) Pathology (2835) Pharmacology and Toxicology (4825) Physiology (7646) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4302) Systems Biology (9825) Zoology (2271)