“Leader–Follower” Dynamic Perturbation Manipulates Multi-Item Working Memory in Humans

Abstract Manipulating working memory (WM) is a central yet challenging notion. Previous studies suggest that WM items with varied memory strengths reactivate at different latencies, supporting a time-based mechanism. Motivated by this view, here we developed a purely bottom-up “Leader–Follower” behavioral approach to manipulate WM in humans. Specifically, task-irrelevant flickering color disks that are bound to each of the memorized items are presented during the delay period, and the ongoing luminance sequences of the color disks follow a Leader–Follower relationship, that is, a hundreds of milliseconds temporal lag. We show that this dynamic behavioral approach leads to better memory performance for the item associated with the temporally advanced luminance sequence (Leader) than the item with the temporally lagged luminance sequence (Follower), yet with limited effectiveness. Together, our findings constitute evidence for the essential role of temporal dynamics in WM operation and offer a promising, noninvasive WM manipulation approach.


Introduction
Manipulating working memory (WM) is an important yet challenging notion and would also provide crucial causal evidence for the WM neural mechanism.It is suggested that WM information undergoes reactivation or refreshing to overcome memory decay during the delay period (Curtis and D'Esposito, 2003;Vogel and Machizawa, 2004), a process that facilitates memory storage via short-term neural plasticity (STP) principles (Wang et al., 2006;Mongillo et al., 2008;Miller et al., 2018).When multiple items are retained, previous models suggest that the item-specific reactivations compete with each other over time (Oberauer andLewandowsky, 2008, 2011), wherein an individual item fires at varied phases according to its respective memory strength (Lisman and Idiart, 1995;Lisman and Jensen, 2013).The item with stronger memory strength, given its higher neural excitability, fires at an earlier latency, whereas the less excitable item reactivates relatively late (Siegel et al., 2009;Bahramisharif et al., 2018;Huang et al., 2018Huang et al., , 2021)), enabling the transformation of memory strengths into neural activities with varied latencies.Hence, a potential yet unexplored WM manipulation approach is to alter the temporal relationship between item-specific reactivations during retention so their relative memory performance could be modified.
Previous research on noninvasive WM modulation in humans has highlighted several approaches, such as frequency-specific transcranial magnetic stimulation (TMS) and transcranial alternating current stimulation (tACS; Sauseng et al., 2009;Hoy et al., 2015;Beynel et al., 2019).Moreover, presentation of a retro cue could prioritize recalling performance via top-down attentional modulations (Griffin and Nobre, 2003;Landman et al., 2003;Oberauer and Hein, 2012;Myers et al., 2017).Recently we developed a purely bottom-up behavioral dynamic perturbation approach to interfere with the multi-item neural dynamics of sequence WM (Li et al., 2021).Notably, this approach draws on many theoretical models and empirical findings.First, color features, even task irrelevant, tend to be automatically bound to memorized items, that is, object-based WM (Luck and Vogel, 1997;Johnson et al., 2008;Huang et al., 2018;Li et al., 2021).Accordingly, presentation of color disks that are attached to memorized items could possibly reactivate and even modify memories.Second, although it has been suggested that WM information is stored in an active or activity-silent manner (Goldman-Rakic, 1995;Curtis and D'Esposito, 2003;Rose et al., 2016;Wolff et al., 2017;Miller et al., 2018), memory manipulation still relies on active states to drive STP-based modifications of synaptic efficacies (Masse et al., 2019(Masse et al., , 2020;;Barbosa et al., 2020).This idea is akin to the reconsolidation process in long-term emotional memories, whereby the stored information is rendered labile after being retrieved so that new information could be incorporated into and modify old memories (Schiller et al., 2010;Agren et al., 2012;Lane et al., 2015).Finally, flickering color disks have been found to be able to tag item-specific neural reactivations (Huang et al., 2018).Therefore, altering the temporal relationship between luminance sequences of color disks that are linked to each memorized item would presumably perturb the multi-item reactivation profiles to manipulate their memory performances.These points motivate the dynamic perturbation approach developed in our previous study, wherein we demonstrate that temporally synchronized luminance sequences disrupt the recency effect, whereas temporally independent luminance sequences keep the recency intact (Li et al., 2021).Nevertheless, the recency effect is just a behavioral index for the WM sequence, and an efficient bottom-up behavioral approach to modulate multi-item WM performance at a general level is still lacking.
Here, we developed a new "Leader-Follower" approach for WM manipulation during which participants temporarily hold two or three items simultaneously.We introduced a temporal lag at hundreds of milliseconds based on previous findings (Lisman and Idiart, 1995;Mongillo et al., 2008;Mi et al., 2017;Bahramisharif et al., 2018;Huang et al., 2018;Herweg et al., 2020) to the luminance sequences of flickering color disks during retention.Specifically, one luminance sequence (Leader), although a randomly generated white noise that does not contain any regularities, always precedes another sequence (Follower) by a certain temporal lag.We hypothesize that the item bound to the Leader luminance sequence reactivates earlier than the item with the Follower sequence and therefore has better memory performance.Four behavioral experiments on 120 participants provided modest evidence that the item associated with the temporally advanced luminance sequence turns out to have better memory performance than the item modulated by temporally lagged luminance sequence.Together, our results not only offer a new bottom-up behavioral approach to manipulating WM performance but also constitute new evidence supporting the critical role of temporally sequenced reactivations in multi-item WM.

Participants
One hundred thirty-one participants (50 males, age ranging from 17 to 25 years) took part in five experiments.Two participants in experiment 1, two in experiment 2, three in experiment 3, and four in experiment 4 were removed because of their extreme memory performance (beyond 2.5 * s ) or for not finishing the whole experiment, which resulted in 30 participants for each experiment.An a priori power analysis run in G*Power software (Faul et al., 2009) revealed that to obtain an effect of Cohen's d ¼ 0.55 for a two-sided paired sample t test with a power of 0.8, data from 28 participants needed to be collected.The expected effect size of interest for a difference in normalized target probability between the Leader and the Follower condition was derived based on a pretest on 25 subjects, using a similar paradigm as in experiment 1.All the participants had normal or corrected-to-normal vision with no history of neurologic disorders.They were naive to the purpose of the experiments and provided written informed consent before the start of the experiment.All experiments were conducted in accordance with the Declaration of Helsinki and have been approved by the Research Ethics Committee at Peking University.

Stimuli and tasks
Participants sat in a dark room in front of a Display11 monitor with a 100 Hz refresh rate and a resolution of 1920 * 1080 with their head stabilized on a chin rest.
Participants performed a multi-item working memory task.At the beginning of the trial, multiple bars (0.56°Â 1.67°visual angle; two bars in experiments 1 and 2, three bars in experiments 3 and 4) were simultaneously presented at different locations on the screen in different colors.Participants were instructed to memorize the orientations of the bars and their colors (experiments 1 and 3) or their spatial locations (experiments 2 and 4).During memory maintenance, colors disks flickered for 5 s, and participants performed a central fixation task by monitoring an abrupt luminance change of the central fixation cross.Finally, participants needed to rotate a horizontal test bar by pressing corresponding keys to one instructed memorized orientation as precisely as possible, without a time limit.The luminance of the flickering disk was randomly generated (ranging from 0 cd/m 2 to 15 cd/m 2 ) and was tailored to have equal power at all frequencies by normalizing the amplitudes of its Fourier components before applying an inverse Fourier transform separately for the red and blue colors.The colors and the spatial locations of the bars and disks were carefully balanced across trials to eliminate possible color-specific or spatial-specific effect.Participants completed 192 trials in total in experiments 1 and 2, which took ;1 h, and they completed 162 trials in total in experiments 3 and 4, which also took ;1 h.

Experiment 1
In each trial, after a 0.5 s fixation period, two bars in red and blue were presented at a 3°visual angle above and below the fixation for 2 s.The orientations of the two bars were chosen randomly, with a difference of at least 10°.The colors and spatial locations of the two bars were counterbalanced across trials.Participants were instructed to memorize the orientations and colors of the bars.After a blank interval (0.6 ; 1 s), two disks (3°in radius) with the same colors as the two memorized bars were presented at the left or right side of the fixation (7°i n eccentricity) for 5 s.The colors and spatial locations of the two disks were counterbalanced across trials.Crucially, the luminance of the two color disks was continuously modulated according to two 5 s temporal sequences ranging from dark (0 cd/m 2 ) to bright (15 cd/m 2 ).Specifically, in each trial, a 5 s temporal sequence was first randomly generated (Leader sequence), and then we shifted the Leader sequence 200 ms rightward and moved the final 200 ms segment of the Leader sequence to the beginning to generate a new sequence (Follower sequence).Note that the luminance sequences were generated anew in each trial, and it was quite hard to differentiate between the Leader and Follower sequences.Throughout the 5 s maintenance period, participants performed a central fixation task by continuously monitoring an abrupt luminance change of the central fixation cross while simultaneously holding the two bars.The fixation task is used to eliminate the effect of attentional bias.After finishing the fixation task, a horizontal test bar in red or blue was presented to instruct participants to recall the orientation of the red or blue bar and rotate the test bar to the target orientation as precisely as possible.

Experiment 2
Experiment 2 had the same stimuli and similar paradigm as experiment 1.The only difference was that instead of requiring participants to memorize the orientations of two bars and their colors, we asked participants to memorize the orientations and spatial locations of two bars.Specifically, after finishing the fixation task, a retrospective cue (top or bottom character) was presented for 1 s to instruct participants to recall the orientation at the top or bottom.Then a horizontal bar in white was presented, and participants rotated it to the instructed memorized orientation.Therefore, in experiment 2, color information was totally task irrelevant.

Experiment 3
Experiment 3 was a three-item memory task with one task similar to that in experiment 1.In each trial, three bars colored in red, blue, and green were presented at the same eccentricity as the fixation (3°visual angle) for 3 s.The orientations of the three bars were chosen randomly, with a difference of at least 10°between any two orientations.The colors and spatial locations of the three bars were randomized.Participants were instructed to memorize the orientations and colors of the bars.After a blank interval (0.6 ; 1 s), three disks (3°in radius) with the same colors as the three memorized bars were presented at a 7°eccentricity to the fixation for 5 s.The disk and bar with the same color were presented in the same direction of the fixation but different spatial locations.Similarly, the luminance of the three color disks was continuously modulated according to three 5 s temporal sequences ranging from dark (0 cd/m 2 ) to bright (15 cd/m 2 ).Specifically, in each trial, a 5 s temporal sequence was first randomly generated (Leader sequence), and then we shifted it 150 ms rightward to generate the Follower 1st sequence.Similarly, we shifted the Follower 1st sequence 150 ms rightward to generate the Follower 2nd sequence.Although the three sequences were presented simultaneously, their temporal relationship showed that Leader lead Follower 1st 150 ms, Follower 1st lead Follower 2nd 150 ms, and Leader lead Follower 2nd 300 ms.After finishing the fixation task, a horizontal bar in red, blue, or green was presented, and participants were instructed to recall the orientation of the red, blue, or green bar and rotate it to the target orientation as precisely as possible.

Experiment 4
Experiment 4 had the same stimuli and a similar paradigm as experiment 3, except that instead of requiring participants to memorize the orientations and colors of the three bars, we asked them to memorize the orientations and spatial locations of the three bars.Specifically, after finishing the fixation task, a retrospective cue (left, middle, or right character) was presented for 1 s to instruct participants to recall the orientation at the left, middle, or right location (horizontal direction).Then, a horizontal bar in white was presented, and participants were asked to rotate it to the instructed memory orientation.Therefore, as experiment 2, the color information was also totally task irrelevant in experiment 4.

Data analysis
To quantify the memory performance for each item, a probabilistic mixture model (Bays et al., 2009) was applied to fit behavioral performance.Specifically, the mixture model simultaneously characterizes the contribution of the memory for the target item, nontarget item, and random guess to the final report.Specifically, this model calculates the probability of correctly reporting the feature value of the target item with some variability, the probability of mistakenly reporting the feature value of one of the other nontarget items held in memory with the same variability, and the probability of generating a random response unrelated to either target or nontarget items.In the present study, we focused on target probability because it represents the memory accuracy for the target and has been widely used to quantify memory performance (Gorgoraptis et al., 2011;Van Ede et al., 2018;Li et al., 2021).Moreover, considering that the target probability is not normally distributed, we performed an empirical logit transformation as follows: logit(p , where p is the target probability and n is the number of observation transformations (De Smith, 2018).The normalized target probabilities were used for further statistical tests in all the experiments.In addition, memory precision was estimated by calculating the reciprocal of the circular SD of response error (the circular difference between the reported orientation and the true target orientation).

Statistics
Classical frequentist statistics, for example, repeated ANOVA and paired t tests, were applied to test experimental effect.Considering there are three conditions in experiments 3 and 4, a Holm correction was applied for post hoc analysis.
Apart from classical frequentist statistics, we also implemented Bayesian statistics using JASP software (version 0.16.4.0).Specifically, for the paired t test, we provided Bayes factor BF 10 , which quantifies how many times the observed data are more likely under the alternative hypothesis that postulates the presence of the experimental effect (e.g., the perturbation effect) than under the null hypothesis.For repeated ANOVA, we reported the inclusion Bayes Factor, BF incl , which reflects the evidence for all models with a particular experimental effect compared with all models without that particular effect.A Bayes factor .1 can be interpreted as evidence against the null; one convention is that a Bayes factor .3 can be considered as substantial evidence against the null and vice versa (a Bayes factor smaller than one-third indicates substantial evidence in favor of the null model; Wetzels et al., 2011).Bayesian post hoc tests were applied in experiments 3 and 4. We reported the uncorrected Bayes factor, BF 10,U , and posterior odds, which have been corrected for multiple testing by fixing the prior probability that the null hypothesis holds across all comparisons to 0.5 (Westfall et al., 1997).

Data availability
Data and the associated code are available from the Open Science Framework at https://osf.io/cpvdk/.

Results
Leader-Follower dynamic perturbation modulates two-item memory performance (experiment 1) Thirty participants performed a two-item memory task in experiment 1 (Fig. 1A).In each trial, two bars were simultaneously presented at the top and bottom locations, and participants needed to memorize both orientations and colors of the two bars over a 5 s delay period while performing a central fixation task.During the recalling phase, participants adjusted the orientation of a probe bar to match that of the memorized bar of the same color as the probe.Crucially, during the 5 s delay period, two task-irrelevant disks with the same colors as one of the memorized bars, one red and one blue, were bilaterally presented, and their luminance was continuously changing according to two 5 s temporal sequences (Fig. 1B).The two luminance sequences were designed to have a specific temporal relationship, with their cross-correlation coefficient peaking at a 200 ms lag (Fig. 1C).Specifically, one sequence randomly generated per trial (Leader sequence) would be used to generate the other by introducing a 200 ms lag (Follower sequence).In other words, to generate two random sequences with a fixed time lag, we temporally shifted one sequence (Leader) rightward by 200 ms to generate the Follower sequence.Moreover, to ensure their simultaneous occurrence, we cut the last 200 ms segment of the Follower sequence and shift it to its beginning so that the Leader and Follower sequences still had a fixed circular temporal lag.Finally, the color, spatial location, and Leader-Follower conditions were counterbalanced across trials.
All trials were then categorized based on whether the luminance sequence of the corresponding disk during the delay period (i.e., one with the same color as the probe) was a Leader or Follower sequence, regardless of its color or location.For instance, when recalling the orientation of a red bar held in memory, this trial would be labeled according to whether the luminance sequence of the red disk was a Leader or Follower sequence.Similarly, when retrieving the orientation of the blue bar, the trial condition would be determined by the blue disk, that is, Leader or Follower.
We first estimated memory precision for each item by calculating the reciprocal of the circular SD of response error (the circular difference between the reported orientation and the true orientation across trials, 1=s ; Bays et al., 2009).As shown in Figure 1D, the Leader condition showed better memory performance than the Follower condition (Leader, mean ¼ 1.636, SE ¼ 0.100; Follower, mean ¼ 1.483, SE ¼ 0.111; paired t test, t (29) ¼ 2.565, p ¼ 0.016, Cohen's d ¼ 0.468).We then implemented the Bayesian hypothesis test and confirmed the significant memory modulation effect (BF 10 ¼ 3.074).To further assess the contribution of the memory for target item to the final report, we used a probabilistic mixture model (Bays et al., 2009) and focused on the calculated target probability, that is, the proportion of responses attributed to the report of the correct target, to quantify memory performance.Moreover, to ensure normal distribution, we performed an empirical logit transformation (de Smith, 2018) on the target response probability.As shown in Fig. 1E, the Leader condition also showed better memory performance than the Follower condition (Leader, mean ¼ 3.638, SE ¼ 0.223; Follower, mean ¼ 3.011, SE ¼ 0.220; paired t test, t (29) ¼ 2.798, p ¼ 0.009, Cohen's d ¼ 0.511; Bayes factor, BF 10 ¼ 4.901; Extended Data Fig. 1-1, target probability without normalization; Extended Data Fig. 1-2, additional parameters, nontarget and random guess probability, results).
Together, consistent with our hypothesis, the Leader-Follower dynamic perturbation during WM retention effectively modulates memory performance when participants held two items in memory, wherein the item experiencing temporal advances during retention shows better memory performance compared with the item with the relative 200 ms temporal delays.

Memory-irrelevant dynamic perturbation (experiment 2)
In experiment 1, the color feature was memory relevant as participants retained both the orientation and color of the two items.In experiment 2, we examined whether the dynamic perturbation would still be effective when color is memory irrelevant.Thirty new participants participated in experiment 2 (Fig. 2A), wherein two bars were simultaneously presented at the top and bottom locations.Instead of memorizing colors as in experiment 1, participants held the locations and orientations of the two bars over a 5 s delay period in memory while performing a central fixation task.During the memory test, participants were first presented with a location cue (top or bottom), and based on this they adjusted a probe bar to match the memorized orientation, regardless of its color.In other words, the color feature was completely memory irrelevant in experiment 2. Similar to experiment 1, the Leader-Follower dynamic perturbation was applied to the two colored disks during retention (Fig. 2B,C).
Unfortunately, as shown in Figure 2D, there is no significant difference between the Leader and Follower Figure 2. Task-irrelevant Leader-Follower dynamic perturbation (experiment 2, N ¼ 30).A, Task-irrelevant dynamic perturbation paradigm.Experiment 2 was the same as experiment 1, except that participants needed to memorize the orientations and locations (top or bottom) of the two bar stimuli regardless of their colors.During the memory test period, a location cue (top or bottom) was first presented, and based on this participants rotated the horizontal white bar to the corresponding memorized orientation.Critically, a Leader-Follower dynamic perturbation as in experiment 1 was applied during the delay period; that is, two disks of the same color as each of the memorized bars (blue and red) were presented bilaterally, with their luminance continuously modulated by a Leader or Follower sequence, respectively.B, The Leader temporal sequence was a 5 s white noise randomly generated per trial, and the Follower sequence was created by circular shifting the Leader sequence 200 ms rightward.The two luminance sequences were presented simultaneously rather than asynchronously.C, The Leader-Follower cross-correlation over time as a function of temporal lag, peaking at 200 ms.D, Memory performance.Grand averaged (mean 1 SEM) memory precision during recalling test for Leader (purple) and Follower (turquoise) conditions, with dots denoting individual participants.E, Same as D, but for normalized target probability.Extended Data Figure 1-1 shows target probability without normalization.Extended Data Figure 1-2 shows additional parameter results (nontarget and random guess probability).
Overall, the Leader-Follower dynamic perturbation still seems to modulate memory in terms of target probability when the color feature that the dynamic perturbation operates on is memory irrelevant but with a less stronger modulation effect than the memory-relevant perturbation (experiment 1).
Leader-Follower dynamic perturbation modulates three-item memory performance (experiment 3) After demonstrating the limited effectiveness of the Leader-Follower dynamic perturbation approach in the two-item memory task, we next tested the effectiveness of the approach on a three-item memory display.Thirty new participants participated in experiment 3 (Fig. 3A), in which they memorized both the orientations and colors (red, blue, green) of three bars over a 5 s delay period.Similar to experiment 1, during the memory test phase participants adjusted the orientation of a probe bar to match that of the memorized bar with the same color.Critically, the Leader-Follower dynamic perturbation was now applied to three task-irrelevant disks with the same colors as one of the memorized bars (red, blue, green) during the 5 s delay period; their luminance was continuously modulated by three temporally related sequences (Fig. 3B).Specifically, one sequence randomly generated in each trial (Leader sequence) was used to generate the other two sequences by introducing a 150 or 300 ms lag, corresponding to the Follower 1st and Follower 2nd sequences, respectively (Fig. 3B,C).Using 150 and 300 ms instead of 200 ms derives from previous neural findings revealing that three-item sequence memory entails a more temporally compressed reactivation than the two-item sequence memory (Huang et al., 2018).Finally, the color, spatial location, and Leader-Follower conditions were counterbalanced across trials.
Trials were categorized as Leader, Follower 1st , or Follower 2nd conditions, based on the corresponding luminance sequence (i.e., having the same color as the probe).As shown in Fig. 3D, the dynamic perturbation showed weak modulation on memory precision (Leader, mean ¼ 1.153, SE ¼ 0.054; Follower 1st , mean ¼ 1.117, SE ¼ 0.069; Follower 2nd , mean ¼ 1.024, SE ¼ 0.058; one-way repeated measures ANOVA; main effect of perturbation, F (2,58) ¼ 2.506, p ¼ 0. Together, on a descriptive level, the Leader-Follower dynamic perturbation approach is also effective in a three-item paradigm; that is, the item associated with earlier temporal reactivations shows better memory performance compared with those endowed with relatively delayed reactions during the delay period.However, on a statistical level, the results provide a trend in the suggested direction at best.

Memory-irrelevant dynamic perturbation in three-item memory task (experiment 4)
Finally, we tested the memory-irrelevant dynamic perturbation approach in a three-item memory task (experiment 4).Thirty new participants participated in the experiment (Fig. 4A) in which they held the locations and orientations of the three bars over a 5 s delay period in memory.During the memory test phase, participants were first presented with a location cue (left, middle, or right); based on this they adjusted a probe bar to match the memorized orientation, regardless of its color.Thus, similar to experiment 2, the color feature was completely memory irrelevant here.Moreover, the same Leader-Follower dynamic perturbation as used in experiment 3 was applied to the three colored disks during retention (Fig. 4B,C).
As shown in Figure 4D, the memory precision for Leader, Follower 1st , and Follower 2nd conditions exhibited a gradual decrease (Leader, mean ¼ 1.510, SE ¼ 0.091; Follower 1st , mean ¼ 1.329, SE ¼ 0.094; Follower 2nd , mean ¼ 1.170, SE ¼ 0.084; one-way repeated measures ANOVA; main effect of perturbation, F (2,58) ¼7.303, p ¼ 0.001, h p 2 ¼ 0.201; Bayes factor, BF incl ¼ 22.737; post hoc analysis, Leader vs Follower 1st , t (29) ¼ 2.033, p cor ¼ 0.093, Cohen's d ¼ 0.368; Bayesian post hoc tests, BF 10,U ¼ 0.883, posterior odds ¼ 0.519; Leader vs Follower 2nd , t (29) ¼ 3.819, p cor , 0.001, Cohen's d ¼ 0.692; Bayesian post hoc tests, BF 10,U ¼ 40.869, posterior odds ¼ 24.007; Follower 1st vs Follower 2nd , t (29) ¼ 1.786, p cor ¼ 0.093, Cohen's d ¼ 0.323; Bayesian post hoc tests, BF 10,U ¼ 1.197, posterior odds ¼ 0.703).The normalized target probability also showed a significant modulation effect (Leader, mean ¼ 3.656, SE ¼ 0.217; Follower 1st , mean ¼ 3.064, SE ¼ 0.236; Follower 2nd , mean ¼ 2.630, SE ¼ 0.208; one-way repeated measures ANOVA; main effect of Figure 3. Leader-Follower dynamic perturbation modulates three-item memory performance (experiment 3, N ¼ 30).A, Experiment 3 paradigm.In each trial, participants were presented with three bars, and they memorized their orientations and colors.During the memory test, participants adjusted the orientation of a probe bar to match that of the memorized bar that had the same color as the probe.During the 5 s delay period, participants performed a central fixation task while three task-irrelevant flickering disks of the same color as each of the memorized bars (blue, red, green) were presented simultaneously, with their luminance continuously modulated by three 5 s temporal sequences (Leader, Follower 1st , Follower 2nd ), respectively.The color, spatial location, and Leader-Follower conditions were counterbalanced across trials.B, The Leader temporal sequence was a 5 s white noise randomly generated per trial, and the Follower 1st and Follower 2nd sequences were created by circular shifting the Leader sequence 150 and 300 ms rightward, respectively.C, The Leader-Follower 1st and Leader-Follower 2nd cross-correlation over time as a function of temporal lag, peaking at 150 and 300 ms, respectively.D, Memory performance.Grand averaged (mean 1 SEM) memory precision for Leader (purple), Follower 1st (turquoise), and Follower 2nd (yellow) conditions.Dots denote individual participants.E, Same as D, but for normalized target probability.Extended Data Figure 1-1 shows target probability without normalization.Extended Data Figure 1-2 shows additional parameter results (nontarget and random guess probability).To examine the manipulation consistency between experiments 3 and 4, both of which used a three-item WM task, we conducted a mixed-design ANOVA analysis (Experiment * Perturbation) again.The results reveal a A, Task-irrelevant dynamic perturbation paradigm.Experiment 4 was the same as experiment 3, except that participants needed to memorize the orientations and locations (left/middle/right) of the three bar stimuli regardless of their color features.During the memory test period, a location cue was first presented; based on this participants rotated the horizontal white bar to the corresponding memorized orientation.Critically, a Leader-Follower dynamic perturbation as in experiment 3 was applied during the delay period; that is, three disks of the same color as each of the memorized bars (blue, red, green) were presented simultaneously, with their luminance continuously modulated by the Leader, Follower 1st , or Follower 2nd sequence, respectively.B, The Leader temporal sequence was a 5 s white noise randomly generated per trial, and the Follower 1st and Follower 2nd sequences were created by circular shifting the Leader sequence 150 and 300 ms rightward, respectively.C, The Leader-Follower 1st and Leader-Follower 2nd cross-correlation over time as a function of temporal lag, peaking at 150 and 300 ms, respectively.D, Memory performance.Grand averaged (mean 1 SEM) memory precision for Leader (purple), Follower 1st (turquoise), and Follower 2nd (yellow) conditions.Dots denote individual participants.E, Same as D, but for normalized target probability.Extended Data Figure 1-1 shows target probability without normalization.Extended Data Figure 1-2 shows additional parameter results (nontarget and randomly guess probability).
To provide a possible explanation for the nonrobust memory modulation effect in experiments 2 and 3, we compared the memory precision between experiments, which we thought should largely reflect the task difficulty.Experiment 2 (two-item location memory) showed significant higher memory precision compared with experiment 1(two-item color memory; Experiment effect, F (1,58) ¼ 5.264, p ¼ 0.025, h p 2 ¼ 0.083; BF incl ¼ 2.191), whereas experiment 4 (three-item location memory) also showed significant higher memory precision than experiment 3 (three-item color memory; Experiment effect, F (1,58) ¼ 7.242, p ¼ 0.009, h p 2 ¼ 0.111; BF incl ¼ 5.030).These results indicated that this purely bottom-up perturbation may only have significant effectiveness when the task is of moderate difficulty instead of being too easy (twoitem location memory) or too difficult (three-item color memory) to accomplish.

Discussion
In the present study, we sought to capitalize on the Leader-Follower dynamic perturbation as a new behavioral manipulation mechanism to interfere with the multi-item neural dynamics and alter WM performance in humans.Four experiments with 120 participants demonstrate the effectiveness of the approach.Specifically, temporally advanced manipulation (Leader) during retention leads to better recalling performance than temporally delayed perturbation (Follower), regardless of its relevance to the memory task.These findings, together with previous works (Miller et al., 2018;Barbosa et al., 2020;Li et al., 2021), support the substantial role of STP-based neural dynamics in mediating WM operation.Our work also offers a new bottom-up behavioral approach to manipulating human WM.However, it is notable that the memory modulation effect is not very robust across experiments and measures, which indicates that this purely bottom-up perturbation approach has limited effectiveness and needs further exploration.
There are many noninvasive approaches to altering WM performance in humans.For instance, applying TMS to relevant brain regions could modulate memory behavior (Lee and D'Esposito, 2012) and even reactivate information retained in WM (Rose et al., 2016).Oscillatory interference methods, such as rhythmic physical stimulus (Clouter et al., 2017), repetitive TMS (Sauseng et al., 2009;Beynel et al., 2019), or tACS with rhythmic (Hoy et al., 2015) or theta-gamma coupling (Alekseichuk et al., 2016), have also been found to efficiently affect memory performance.Here, we developed a purely bottom-up, behavioral approach by presenting task-irrelevant flickering color probes during WM retention.Notably, because participants could not discriminate the temporal relationship of the luminance sequences at the perceptual level, that is, which sequence leads and which sequence lags, the manipulation is indeed operated in an unconscious way.Moreover, the luminance sequences are randomly generated per trial, and therefore it is only their temporal relationship instead of a specific sequence that influences WM performance.Furthermore, the Leader-Follower dynamic perturbation aims to alter multi-item WM performance, which is different from our previous work focusing on sequence working memory (Li et al., 2021), thus offering a memory manipulation approach at a general level.Finally, distinct from the retrocue behavioral paradigm, whereby the cued item would enter the focus of attention and get prioritized in WM (Oberauer and Hein, 2012;Oztekin et al., 2010), our method is a purely bottom-up manipulation and does not rely on top-down attentional modulations.
Crucially, our Leader-Follower dynamic perturbation approach draws on accumulating findings and models advocating the central function of temporal dynamics in WM.First, multiple WM items are postulated to undergo item-by-item sequential reactivations with items of greater strength firing earlier (Lisman and Idiart, 1995;Oberauer andLewandowsky, 2008, 2011;Lisman and Jensen, 2013), a framework that has received empirical evidence support (Axmacher et al., 2010;Friese et al., 2013;Burke et al., 2016;Heusser et al., 2016).Recently, we also demonstrate that a sequence of items is serially reactivated during the delay period, and the late item in the sequence is accompanied by better memory performance (i.e., recency effect) and earlier reactivation (Huang et al., 2018(Huang et al., , 2021)), also in line with the latency-based view.Interestingly, this latency-or time-based coding of input strength extends beyond memory findings and also occurs in perception and attention (Landau and Fries, 2012;Fiebelkorn et al., 2013Fiebelkorn et al., , 2018;;Jensen et al., 2014;Song et al., 2014;Jia et al., 2017;Mo et al., 2019;Huang and Luo, 2020).Here, we speculate that altering the early-late time relationship of neural responses indeed modifies the subsequent WM performance.Second, the time lag between luminance sequences is set also according to previous experimental findings and STP neural model, that is, temporally compressed reactivation within 200 and 150 ms for two-and three-item sequences, respectively (Herweg et al., 2020;Huang et al., 2018;Li et al., 2021;Mi et al., 2017;Mongillo et al., 2008).Overall, the dynamic perturbation approach is motivated by previous findings, allowing us to exploit the time perspective of the brain to manipulate multi-item neural dynamics and in turn alter WM performance.
We developed a Leader-Follower dynamic perturbation aiming to introduce a specific temporal lag in the reactivation profiles of memorized items to manipulate their memory strengths.We hypothesize that items with relatively earlier reactivation during retention would have better memory performance than items with relatively later reactivation.The manipulation is implemented by generating temporally shifted luminance sequences (i.e., Leader sequence, Follower sequence) for color disks that are bound to each memorized item during retention.Although the temporal manipulation is possibly at an unconscious level, that is, participants could not tell which sequence advances over time, our brain is known to be indeed endowed with tremendous capabilities to calculate the temporal lag between events, from tens of milliseconds to hundreds of milliseconds.Moreover, the continuous attractor neural network model established in our previous work, by incorporating plausible biological principles, also supports that temporal lag is encoded in the system and influences memory representations (Li et al., 2021).
Retaining information in WM has traditionally been hypothesized to rely on persistent firing, but computational models and recent findings propose a hidden-state WM view; that is, items could be silently retained in STP-based synaptic weights (Huang et al., 2021;Miller et al., 2018;Mongillo et al., 2008;Rose et al., 2016;Trübutschek et al., 2019;Wolff et al., 2017), even lasting for tens of seconds long with a periodical refresh (Fiebig and Lansner, 2017).
Then how could we access information in this activity-silent network?Previous studies demonstrate that presenting a nonspecific impulse during retention could transiently perturb the WM network and reactivate memories (Fan et al., 2021;Huang et al., 2021;Wolff et al., 2017).This methodological advance has allowed researchers to directly access WM information and predict subsequent behavior.Here, we use task-irrelevant luminance sequences to first reactivate memory information and then apply continuous perturbation to impose temporal relationships between items to interfere with their neural dynamics and manipulate WM.This approach resembles the reconsolidation process in long-term memory such that the stored fear memory would be rendered labile when retrieved, and new information could be inserted and modify old memories within this period (Agren et al., 2012;Lane et al., 2015;Schiller et al., 2010).Meanwhile, different from long-term memory relying on long timescales, our approach is operated at a shorter temporal scale, that is, 100-200 ms, a critical time scale in STP-based WM operation.
Together, based on accumulating neural findings and theoretical models, we develop a new Leader-Follower dynamic perturbation behavioral approach to alter multiitem WM in humans by presenting temporally related luminance sequences during the delay period.We demonstrate that the item associated with the Leader luminance sequence shows better memory performance than the item bound to the Follower luminance sequence.Our results suggest the essential role of neural temporal dynamics in WM operation and offer a promising, noninvasive WM manipulation approach.

Figure 1 .
Figure1.Leader-Follower dynamic perturbation during retention modulates two-item memory performances (experiment 1, N ¼ 30).A, Leader-Follower dynamic perturbation paradigm.In each trial, participants were presented with two bars, and they memorized their orientations and colors.During the memory test, participants adjusted the orientation of a probe bar to match that of the memorized bar that was the same color as the probe.During the 5 s delay period, participants performed a central fixation task while two task-irrelevant flickering disks of the same color as each of the memorized bars (blue and red) were presented bilaterally, with their luminance continuously modulated by two 5 s temporal sequences, Leader or Follower sequences, respectively.The color, spatial location, and Leader-Follower conditions were counterbalanced across trials.B, The Leader temporal sequence was a 5 s white noise randomly generated per trial, and the Follower sequence was created by circular shifting the Leader sequence 200 ms rightward.Note that the two sequences were presented simultaneously rather than asynchronously.C, The Leader-Follower cross-correlation over time as a function of temporal lag, peaking at 200 ms.D, Memory performance.Grand averaged (mean 1 SEM) memory precision during recalling test for Leader (purple) and Follower (turquoise) conditions, with dots denoting individual participants.E, Same as D, but for normalized target probability; *p , 0.05.Extended Data Figure1-1 shows target probability without normalization.Extended Data Figure1-2 shows additional parameter results (nontarget and random guess probability).

Figure 4 .
Figure4.Memory-irrelevant Leader-Follower dynamic perturbation (experiment 4, N ¼ 30).A, Task-irrelevant dynamic perturbation paradigm.Experiment 4 was the same as experiment 3, except that participants needed to memorize the orientations and locations (left/middle/right) of the three bar stimuli regardless of their color features.During the memory test period, a location cue was first presented; based on this participants rotated the horizontal white bar to the corresponding memorized orientation.Critically, a Leader-Follower dynamic perturbation as in experiment 3 was applied during the delay period; that is, three disks of the same color as each of the memorized bars (blue, red, green) were presented simultaneously, with their luminance continuously modulated by the Leader, Follower 1st , or Follower 2nd sequence, respectively.B, The Leader temporal sequence was a 5 s white noise randomly generated per trial, and the Follower 1st and Follower 2nd sequences were created by circular shifting the Leader sequence 150 and 300 ms rightward, respectively.C, The Leader-Follower 1st and Leader-Follower 2nd cross-correlation over time as a function of temporal lag, peaking at 150 and 300 ms, respectively.D, Memory performance.Grand averaged (mean 1 SEM) memory precision for Leader (purple), Follower 1st (turquoise), and Follower 2nd (yellow) conditions.Dots denote individual participants.E, Same as D, but for normalized target probability.Extended Data Figure1-1 shows target probability without normalization.Extended Data Figure1-2 shows additional parameter results (nontarget and randomly guess probability).