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ArticleDecrease in signal-related activity by visual training
and repetitive visual stimulation
Andreas Marzoll,
Kazuhisa Shibata,Taro Toyoizumi,
Isha Chavva,
Takeo Watanabe
Takeo_Watanabe@brown.edu

Highlights
Visual stimulation leads todifferent behavioral
changes depending on
the learning stage
Without prior learning,high-frequency
stimulation leads toperformanceenhancement
With prior learning, highfrequency stimulation
leads to performance
impairment
Reversal dynamics mayhelp explain disparate
findings in post-training
processing

Marzoll et al., iScience 25,105492
December 22, 2022 ª 2022
The Author(s).
https://doi.org/10.1016/
j.isci.2022.105492

iSciencellOPEN ACCESS

ArticleDecrease in signal-related activity by visualtraining and repetitive visual stimulation
Andreas Marzoll,1,3 Kazuhisa Shibata,2,3 Taro Toyoizumi,2 Isha Chavva,1 and Takeo Watanabe1,4,*
SUMMARY

While principles governing encoding mechanisms in visual perceptual learning(VPL) are well-known, findings regarding posttraining processing are still unrelated in terms of their underlying mechanisms. Here, we examined the effect ofrepetitive high-frequency visual stimulation (H-RVS) on VPL in an orientation
detection task. Application of H-RVS after a single task session led to enhancedorientation detection performance (n = 12), but not in a sham condition (n =12). If prior training-based VPL had been established by seven sessions in thedetection task, H-RVS instead led to a performance impairment (n = 12). Bothsham (n = 8) and low-frequency stimulation (L-RVS, n = 12) did not lead to asignificant impairment. These findings may suggest reversal dynamics in which
conditions of elevated network excitation lead to a decrease in a signal-related
activity instead of a further increase. These reversal dynamics may represent ameans to link various findings regarding posttraining processing.
INTRODUCTION
Visual perceptual learning (VPL) refers to long-term performance enhancement as a result of perceptual
experience.1 Encoding mechanisms during VPL training have been extensively studied, and important principles have been identified.1–3 However, several research findings indicate that after initial training, several
mechanisms continue to change memory through multiple stages, including stabilization/consolidation
during wakefulness and reactivation followed by reconsolidation. As compared with research on encoding
mechanisms, changes after training related to VPL, termed posttraining processing of VPL, remain enigmatic. Although there have been important findings regarding posttraining processing,4–10 many of thesefindings have been discussed and studied independently without being related to other findings based on
their potentially common principles.
This failure may be due to two factors. First, the findings were not arrived at using the same measure as theirdependent variable. For example, certain studies used behavioral performance,5–10 whereas others used
the ratio of the concentration of excitatory neurotransmitters to the concentration of inhibitory neurotransmitters, termed the E/I ratio.4 Findings based on different measures as dependent variables cannot beeasily linked to one another. Second, the various relevant studies used different and relatively complex
training methods.4–10 This fact also makes it difficult to relate one finding to another.
This study used high-frequency repetitive visual stimulation (H-RVS), in which a visual stimulus intermittently flickers on and off at a relatively high frequency.11–15 The results of using this method could help
relate our findings to those of other studies. H-RVS involves no task, which may prevent H-RVS resultsfrom being contaminated by task processing specific to each study and could be viewed as directly reflecting a common principle with respect to the different findings regarding posttraining processing.16
We conducted two experiments. In a preliminary experiment, H-RVS with an oriented Gabor stimulus wasapplied to a subject group that had not developed VPL through prior training in an orientation detection
task that also employed Gabor stimuli (for details, see STAR methods). Consistent with previous
studies,14,15,17 the subjects in the preliminary experiment exhibited a significant performance improvement
due to H-RVS, while no such improvement occurred in a control group that did not receive H-RVS. In themain experiment, we applied H-RVS or low-frequency RVS (L-RVS) with an oriented Gabor stimulus, or
no RVS, to each of the three subject groups that had already developed VPL as a result of repeatedly
performing training sessions in an orientation detection task (training-based VPL) over 7 days. While

1Department of Cognitive,

Linguistic and Psychological
Sciences, Brown University,
Providence, RI 02912, USA
2Center for Brain Science,

RIKEN, Wako, Saitama 3510106, Japan
3These authors contributed

equally
4Lead contact*Correspondence:
Takeo_Watanabe@brown.
edu
https://doi.org/10.1016/j.isci.
2022.105492

iScience 25, 105492, December 22, 2022 ª 2022 The Author(s).
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Figure 1. Experimental Setup(A) Procedure of the preliminary experiment. Either H-RVS or no RVS was applied after a single pretest session (pre) in theorientation discrimination (OD) task. OD performance was again evaluated in a posttest session (post) after a break of
90 min.
(B) Block structure of an orientation discrimination session (one block = 100 trials). The two orientations (A and B) were
presented in an alternating block-wise fashion.
(C) Procedure of the main experiment. During the first seven days (trn1 – trn7) of the experiment, subjects performed
training sessions in the OD task. Subjects were randomly assigned to one of the H-RVS, L-RVS, and no-RVS groups. On day
8, performance 90 min after (post1) the cessation of RVS and one day after (post2) the cessation of RVS was compared toperformance immediately before RVS (pre).
(D) Stimulus sequence during a two-interval-forced-choice OD task.
(E) Left: Temporal patterns of H-RVS and L-RVS. Right: Stimulus sequence during RVS. Stimulus onset asynchronies (SOA)
was 100 ms for H-RVS (intra-train) and 1000 ms for L-RVS. The stimulated orientations were counter-balanced acrosssubjects and groups. Subjects in the no-RVS group were presented with a fixation point without exposure to flickering
Gabor stimuli (for details, see STAR methods).

performance levels in the posttest started out at similar levels in all three groups, performance quickly
deteriorated in the H-RVS group and remained at a low level for the remainder of the session. Performancerecovered by the following day. None of the L-RVS and no-RVS groups showed significant impairment.
These findings suggest reversal dynamics in which repetitive orientation detection performance and
H-RVS, possibly after a period of elevated excitation, decrease activity related to stimulus orientations,in contrast to experimental conditions where no prior training-based VPL took place. Such reversal
dynamics may help link important findings that were previously thought to be distinct.4–10

RESULTS
High-frequency visual stimulation without prior training leads to performance enhancement
A number of studies have shown that H-RVS leads to VPL without prior task training.14,15,17 However, theexperimental procedures in these studies are significantly different from those of the main experimentof this study. Thus, we conducted a preliminary experiment (Figure 1A; n = 24) to test whether a performance enhancement would be observed with the same procedures as used in the main experiment, exceptthat no prior training was conducted. Twelve subjects performed an orientation detection task (Figure 1D)
on two orientations (A and B) in alternating blocks (Figure 1B) before (pretest) and after (posttest) H-RVS. To

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Figure 2. H-RVS without preceding training-based VPL leads to performance improvements
Log-threshold differences between pre and post-sessions. Summary statistics are shown as means G SEM.
(A) In the H-RVS group, H-RVS was applied between the pre and post-session. Performance changes for both thestimulated and the nonstimulated orientation are shown.
(B) Performance changes when no RVS was applied between pre and post-sessions. Results for orientations A and B are
shown, which corresponded to the same orientations as in the H-RVS group.

test whether a performance enhancement, if any, is due to a test effect in which the pretest contributes tothe performance enhancement, a different group of 12 subjects was asked to conduct only the pretest andposttest without any H-RVS with otherwise identical procedures.
Following H-RVS (Figure 1E) with one of these orientations (either A or B, counter-balanced acrosssubjects), a significant improvement in orientation detection ability was observed for both orientations(Figure 2A). The improvement cannot be accounted for by a retest effect (Figure 2B).
The results of the subsequent statistical analysis confirmed this observation. A 3-way mixed-design
ANOVA with within-subject factors of session (pretest vs. posttest) and orientation (stimulated vs. nonstimulated) and the between-subject factor group (presence vs. absence of H-RVS) revealed no significant main effects of either orientation (F1,22 = 0.73, p = 0.40), session (F1,22 = 3.67, p = 0.068) or group(F1,22 = 1.82, p = 0.19). There was no significant orientation 3 session 3 group interaction (F1,22 = 0.39,
p = 0.54), which indicates that the effect of H-RVS was not specific to the stimulated orientation. However, there was a significant session 3 group interaction (F1,22 = 5.70, p = 0.026, h2p = 0.21). Post-hoc
paired t tests with Bonferroni-Holm correction reveal a significant difference between sessions in the
H-RVS group (t11 = 3.04, p = 0.036, Cohen’SD = 0.62), but no significant difference in the no-RVS group(t11 = 0.33, p = 1.00). These results indicate that H-RVS in the current paradigm led to performanceenhancement.

High-frequency visual stimulation after prior training leads to performance impairment
We recruited 32 subjects, who were randomly assigned to one of the three RVS groups: H-RVS (n = 12),L-RVS (n = 12), and no RVS (n = 8). Before receiving RVS, all subjects completed seven training sessions(on separate days) in an orientation detection task (Figures 1B–1D). For each subject, two orientations (Aand B) were trained in alternating blocks. On day eight, the subjects were asked to perform the task in apretest session. Then, subjects from the H-RVS and L-RVS groups received 1 h of RVS with either orientation
A or B and whose flicker frequency depended on the group (Figure 1E). Subjects in the no-RVS group wereasked to maintain their gaze at the fixation point presented at the center of the display while no RVS was
being applied to them. In all groups, subjects had to perform a central oddball task to ensure theircontinued fixation (see STAR methods). Ninety minutes after the cessation of RVS in the H-RVS and
L-RVS groups and the cessation of fixation in the no-RVS group, the subjects were asked to perform anothersession of the orientation detection task (‘‘post1’’). We only analyzed the first 16 blocks here (see STARiScience 25, 105492, December 22, 2022

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Figure 3. H-RVS after training-based VPL leads to a transient, orientation-nonspecific impairment in orientation
detection performance
Summary statistics are shown as means G SEM.
(A and B) Sessionwise signal-to-noise thresholds for the stimulated (A) and nonstimulated orientations (B). After VPL(trn1 – trn7), a strong and orientation-nonspecific impairment between the pre-RVS and post1 sessions occurred after
H-RVS (arrow), which returns to pre-RVS levels on the following day (post2).
(C and D) Mean changes in signal-to-noise thresholds from pre to post1 (C) and from pre to post2 (D) across the twoorientations for each group. (**) indicates p < .01, after Bonferroni-Holm correction.

methods). To test the long-term effect of RVS, a final session of the orientation detection task was
performed on the following day (‘‘post2’’).
VPL as a result of repetitive performance on the task for seven training days occurred with both target orientations in all three groups (Figures 3A and 3B; trn1-trn7). This observation was confirmed by the results of
a 3-way mixed-design ANOVA on the within-subject factors of session (trn1-7) and orientation (stimulated
vs. nonstimulated) and on the between-subject factor of group (H-RVS vs. L-RVS vs. no-RVS). There was asignificant main effect of session (F2.7,78.4 = 12.0, p < .001, h2p = .29, Greenhouse-Geisser corrected with ε =.45) but not of group (F2,29 = 1.26, p = .30, h2p = .08) or orientation (F1,29 = 0.06, p = .81, h2p = .002). There was asignificant orientation 3 group interaction (F2,29 = 4.19, p = .025, h2p = .22), although subsequent BonferroniHolm-corrected post-hoc tests could not pinpoint specific significant comparisons (all ps > .21).
A large impairment in orientation detection ability was observed following H-RVS but not following L-RVS orno-RVS (Figures 3A–3C; post1). On the following day, the impaired performance after H-RVS recovered topretest levels (Figure 3D; post2). The following statistical analysis confirmed the preceding observations.
We performed a 3-way mixed-design ANOVA on the within-subject factors of session (pre, post1, vs.
post2) and orientation (stimulated vs. nonstimulated) and on the between-subject factor of group (H-RVS

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vs. L-RVS vs. no-RVS). There was a significant main effect of session (F1.41,40.89 = 6.87, p = .006, h2p = 0.19,
Greenhouse-Geisser corrected with ε = .71). Importantly, there was a significant effect of session 3 groupinteraction (F2.82,40.89 = 3.03, p = .043, h2p = 0.17, Greenhouse-Geisser corrected with ε = .71). This result indicates that changes in performance were dependent on the type of RVS. There was no significant effect ofsession 3 group 3 orientation interaction, indicating that the effect was not specific to orientation (F4,58 =
0.35, p = .76). Because of this lack of evidence for orientation-specific effects, subsequent analyses excluded
this factor of orientation. Levene’s test indicated that the assumption of variance homogeneity was violated
in the post1 session of the H-RVS group for one orientation (stimulated orientation: F2,29 = 2.56, p = .01;
nonstimulated orientation: F2,29 = 5.39; p = .095). This outcome was due to high interindividual variationin susceptibility to performance impairment. However, because the sample sizes of the groups are reasonably balanced, ANOVA can be considered robust to this violation.18 Post-hoc paired t tests with BonferroniHolm correction for the difference between pretest and post1 indicated that this impairment (Figure 3C)
was significant in the H-RVS group (t11 = 4.37, p = 0.002, Cohen’s d = 0.77) but not in the L-RVS (t11 =1.42, p = 1.00) or no-RVS group (t7 = 0.09, p = 1.0). The impairment in the H-RVS group did not persist
to the next day (Figure 3D), as corroborated by a lack of significant differences from pretest to post2 in
the H-RVS (t11 = 0.30, p = 1.0), the L-RVS (t11 = 0.44, p = 1.0), and no-RVS (t7 = 0.29, p = 1.0) groups.
Small performance reductions were also observed at post1 in the L-RVS and no-RVS groups (Figure 3C).
Since no orientation stimulus was presented to the no-RVS group, the reduction may be accounted
for by a general state that occurred across the three groups. However, t tests indicated a significant
performance difference between the H-RVS and no-RVS groups at post1 (t18 = 2.23, p = .039, Cohen’sd = 1.02), whereas no significant difference was found between the L-RVS and no-RVS groups (t18 = 0.62,
p = .53). These results indicate that performance impairment due to H-RVS in fact occurred, whereas no
significant change in performance due to L-RVS occurred. Also, see the supplemental information forfurther analysis results which excluded the possibility that the impairments in the H-RVS group were related
to individual differences prior to RVS.
Although group-level differences did not reach significance in the ANOVA, the possibility remains that the
impairments observed in the H-RVS group were related to individual differences prior to H-RVS. We investigated the following four possibilities through a mediation analysis. As shown later in discussion, none ofthese possibilities turned out to have a significant influence on the performance changes after RVS/no-RVS.
1. Effect of pretraining performance: It is possible that impairment merely reflected a return to thepretraining performance levels observed on Day 1 (Figures 3A and 3B; trn1). Because the H-RVS
group exhibited slightly worse performance than the other groups on Day 1, this may explain—atleast partly—the larger effect size of the impairment after RVS.
2. Effect of training-based VPL: The amount of training-based VPL could explain the observed effect.
We defined the amount of training-based VPL as the percent improvement between training
sessions 1 and 7 [i.e., trn7trn1
trn1 ].
3. Effect of the performance in the pretest session: The H-RVS group displayed somewhat worse performance in the session immediately preceding the intervention (pre) than the other two groups,
which could be related to the lower performance after RVS.
4. Posttraining learning curve slope: Lower performance in the pretest and H-RVS groups could indicate that these subjects had not yet reached asymptotic performance, while the subjects from theother two groups had reached an asymptote in performance and thus differed in the learning stage.
To test this possibility, we fitted learning curves (for details, see STAR methods) to the training performance from Day 1 to Day 7 (Figures 3A and 3B; trn1-trn7). The slope measured on Day 7 of thesefunctions can be taken as an indicator of how settled at their performance level the subjects were. A
slope close to zero indicates asymptotic performance, which was the case for most subjects.
In the mediation analysis (Figure 4), we attempted to predict the percent changes between the pretest sesand post2pre] from the presence or absence of H-RVS stimulasion and the two post-sessions [i.e., post1prepre
pretion. The four variables mentioned above were added as mediators to the model to check if they canpartially explain the observed impairment in the H-RVS condition. A significant mediation might indicate
a confounding effect of these variables on the behavioral results. Consistent with our previous analysis,
H-RVS significantly predicts performance changes in the post1 session (b = 0.79, z = 2.37, p = .018),

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Figure 4. Path plot of the mediation analysis
Numbers on the arrows indicate standardized coefficients estimated by the model. Black arrows indicate the direct effects
and post2pre].
of H-RVS on percent changes in orientation detection performance in the post1 and post2 session [post1preprepretrn7trn1
Red arrows indicate the indirect effects via mediating variables (trn1: pretraining performance, trn1 : training-based
performance improvement [percent], pre: performance in the pre-stimulation session, posttraining slope: modeled
learning curve slope at the end of the training phase). Blue arrows indicate residual covariances. Numbers next torecurrent arrows indicate residuals.

but not in the post2 session (b = 0.16, z = 0.46, p = .65). An analysis of the indirect effects shows that none of
the four variables significantly mediates these changes. This was the case for the changes in the post1 session (pretraining performance: b = 0.26, z = 0.94, p = .35; training-based VPL: b = 0.26, z = 0.76, p = .45;
pretest performance: b = 0.15, z = 1.08, p = .28; posttraining learning curve slope: b = 0.08, z = 0.25,
p = .80) and in the post2 session (pretraining performance: b = 0.10, z = 0.52, p = .61; training-based
VPL: b = 0.16, z = 0.70, p = .48; pretest performance: b = 0.16, z = 1.13, p = .26; posttraining learning curve
slope: b = 0.09, z = 0.25, p = .80). The direct effects of H-RVS on performance changes were significant in
the case of post1 (b = 0.86, z = 3.16, p = .002) but not post2 (b = 0.04, z = 0.14, p = .89). Consequently, thedirect effect of H-RVS on performance changes in post1 accounts for 91.8% of the explained variance in thismodel. This suggests that the four potentially confounding variables had a negligible effect on theobserved pattern of results.

The effects of high-frequency high-frequency visual stimulation require a period of build-up
to occur
In order to track the time course of the same-day effects after H-RVS (i.e. during post1), we looked at theblock-wise performance of all 32 blocks in the orientation detection task (Figure 5A). This session spans thetime from 90 min until about 180 min after the offset of RVS. Rather than a strong initial effect in the H-RVS
group, the opposite was the case. This group started at a similar performance level to the others (thiscounts against the possibility that a relatively lower performance with this group before H-RVS caused alower performance after H-RVS). However, during the first few blocks, performance decreases rapidly.
Furthermore, there is no sign of attenuation in performance decreases until the end of the session. We
quantified these observations by fitting power functions to individual block-wise performances andcomparing estimated performance changes for the first and last 10 blocks of the session (Figure 5B). A
mixed-design ANOVA with within-subject factor phase (early vs. late) and between-subject factor group(H-RVS vs. L-RVS vs. no-RVS) revealed significant main effects of phase (F1,29 = 9.07, p = .005, h2p = .24)

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A

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Figure 5. The effects of high-frequency RVS require a period of build-up to occur
Summary statistics are shown as Mean G SEM.
(A) Block-wise signal-to-noise thresholds in post1, from 90 min until about 180 min after the stimulation offset for bothorientations combined.
(B) Comparison of performance changes from individually fitted power functions for blocks 1–10 (early) and 23–32 (late).
Early impairment in the H-RVS condition was significantly larger than in all other conditions and phases. (*) indicatesp < .05, (**) indicates p < .01, (***) indicates p < .001.

and group (F2,29 = 4.90, p = .015, h2p = .25). Importantly, there is a significant phase 3 group interaction(F2,29 = 3.76, p = .035, h2p = .21). Subsequent Bonferroni-Holm-corrected post-hoc t tests show that the earlychange in the H-RVS group is significantly larger than in all other combinations of phase and group(vs H-RVS-late: t11 = 4.11, p = .004, Cohen’s d = 0.73; vs L-RVS-early: t22 = 3.89, p = .003, Cohen’s d =0.69; vs L-RVS-late: t22 = 4.30, p < .001, Cohen’s d = 0.76; vs sham-early: t18 = 3.00, p = .044, Cohen’sd = 0.53; vs sham-late: t18 = 4.03, p = .002, Cohen’s d = 0.71). There were no significant differences between
other combinations of phase and group (all absolute t values <0.92, ps = 1.0). These results are robust tomoderate changes to the number of included blocks.

DISCUSSION
In this study, we applied H-RVS to a group of subjects after training-based VPL was established throughseven days of training. At the beginning of the posttest, in which the same orientation detection task as
in the VPL training stage was performed 90 min after RVS, no effect of H-RVS was observed. However, as
progressively more test trials were conducted, performance decreased rather than increased. The performance impairment disappeared on the following day. In contrast, H-RVS with an untrained subject groupthat was otherwise subjected to the same procedures induced a significant performance improvement.
Performance changes (i.e., impairment or enhancement) after H-RVS depended on whether training-based
VPL had been established before H-RVS. This outcome indicates that the performance impairment cannot
be entirely accounted for by general fatigue or other unspecific effects.
Among the three groups of subjects who had established training-based VPL, the mean performances on
the first day of training and in the pretest of the H-RVS group were lower to some degree, but not significantly, lower than those of the L-RVS and no-RVS groups. However, our statistical analysis indicates thatthese differences cannot account for the impairment after H-RVS. Moreover, at the beginning of the posttest, there was no significant difference in the performance among the three groups of subjects. Therefore,
it is unlikely that the lower performance in the H-RVS group on the first day of training or in the pretest of the
H-RVS group can account for the performance impairment in the H-RVS group.
Our findings may be regarded as a manifestation of interesting network activity changes related to visualsignal processing. Both training-based VPL and performance enhancement by high-frequency sensory

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stimulation are each associated with increased levels of cortical excitation in involved sensory networks.19–
21
In the case of H-RVS without prior training-based VPL, this increased cortical excitation may lead to stronger signal-related activity in the related visual areas, leading to increased performance by facilitating offline
gains (cf.19,22). From this viewpoint, our findings with H-RVS after prior training-based VPL can be understood as evidence of reversal dynamics. Within these dynamics, conditions of elevated network excitation,
possibly beyond the level of either training-based VPL or high-frequency stimulation individually, lead to a
net decrease in a signal-related activity instead of a further increase, as is the case after either intervention
alone. Such ‘‘paradoxical’’ effects on excitation, measured by phosphene thresholds, have been observed
after high-frequency repetitive transcranial magnetic stimulation (rTMS) in conditions of abnormallyelevated baseline excitation.20,23–26 Given that the results from stimulation using different paradigms(H-RVS vs. high-frequency rTMS) on significantly different measures (behavioral performance vs. phosphene thresholds) are in accordance with the reversal dynamics, the reversal dynamics of signal-related
activity could be a broadly applicable principle.
Related alternative explanations, that could also lead to the observed decrease in signal-related
activity, are an increase of internal noise in the affected sensory systems, which would cause diminishedperformance through a hampered readout of sensory signals by high-level decision-making areas.27,28
Furthermore, combined training-based VPL and H-RVS could cause sensory neurons to reach the limitsof their dynamic firing range in a form of response saturation due to overexcitation, which would cause
them to lose sensitivity. On the flip side, H-RVS without prior training-based VPL could have led toincreased signal-related activity by increasing the signal strength itself or reducing internal noise.
Whatever the exact neural mechanism behind these findings may be, it is apparent that the underlying
mechanism of VPL consolidation before and after long periods of training are different, which has earlier
been suggested by a VPL study involving sleep deprivation.29
An interesting possibility is that posttraining phenomena in VPL previously considered to be unrelated to
one another4–10 could be linked by these reversal dynamics.
In perceptual deterioration, performance increases with an increasing amount of practice on a visual task,followed by a performance decrease after the amount of practice exceeds a certain level within asession.5–10 Perceptual deterioration may be at least partially regarded as a manifestation of the reversal
dynamics suggested above. Indirect evidence that shows an association between perceptual deterioration, decreased blood-oxygen-related dependent (BOLD) signals30 and, in turn, decreased cortical excitation31 gives additional credence to this possibility.
Shibata et al.4 found that training on a visual task beyond asymptotic performance increases (overlearning)
rapidly changed E/I ratios from higher to lower levels compared to pretraining baselines. A higher E/I ratio
has been found to be correlated with a larger amplitude of VPL,32,33 which is also associated with higher corticalexcitation.21,34–38 Therefore, the rapid decrease in the E/I ratio after overlearning may be related to the reversal
dynamics. In the Shibata et al. study,4 no significant difference was found between the performance before overlearning training on the same day and that on the following day. In our results, performance impairments disappeared by the following day. Thus, results of both the overlearning study and this study are consistent in thatintervention effects were not observed on the following day. However, in the overlearning study, performance
was not measured after overlearning training on the same day. Thus, in a future study, it is necessary to test
whether a temporary performance decrease did in fact occur on the same day after the overlearning training.
In summary, we found that H-RVS increased performance without prior training, whereas H-RVS severely
impaired performance due to preceding visual training. These results may be a manifestation of reversal
dynamics in which conditions of increased overall excitation in networks lead to a decrease instead of afurther increase of signal-related activity in visual networks. This principle would be of substantial importance to rehabilitative39 and augmented cognition applications40,41 that make use of sensory stimulation
protocols such as H-RVS.

Limitations of the study
The impairment effect after training-based VPL reported here, though strong, largely relies on a subset of
subjects in the H-RVS group. Arguably, only five out of twelve subjects were susceptible to this impairment

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ArticleiSciencell

ArticleOPEN ACCESS(corroborated by a k-means cluster analysis). Further research would need to address the reasons why suchlarge interindividual differences exist.42–45 Though there was some degree of performance differences
prior to H-RVS, these did not have a discernible effect on post-stimulation performance according toour mediation analysis.

STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:d KEY RESOURCES TABLE
d RESOURCE AVAILABILITY

B Lead contact
B Materials availability
B Data and code availabilityd EXPERIMENTAL MODEL AND SUBJECT DETAILSd METHOD DETAILSB Environment & apparatus
B Orientation detection task
B Repetitive visual stimulation protocol
B Experimental procedure
d QUANTIFICATION AND STATISTICAL ANALYSIS

B Statistical analysis
B Orientation detection data
B Repetitive visual stimulation
B Exclusion criteria
B Mediation analysis

ACKNOWLEDGMENTS
The current study was supported by a Gateway Fellowship of the Ruhr University Bochum Research School
PLUS, funded by Germany’s Excellence Initiative [DFG GSC 98/3] to A.M. and NIH R01EY019466, and NIH
R01EY027841 to T.W. K.S. was supported by JSPS Kakenhi 19H01041, JSPS Kakenhi 20H05715, and JST
Moonshot R&D JPMJMS2013.

AUTHOR CONTRIBUTIONS
A.M. and T.W. designed the experiment. A.M., K.S. and I.C. acquired the data. A.M. and K.S. performed
data analysis. A.M., K.S. and T.W. interpreted the data. A.M., K.S. and T.W. drafted and revised the article.

DECLARATION OF INTERESTS
The authors declare no competing interests.

Received: August 1, 2022
Revised: September 7, 2022
Accepted: November 1, 2022
Published: December 22, 2022
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STAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCESOURCEIDENTIFIERThe MathWorks, Inc.

https://www.mathworks.com

Brainard,46 Kleiner et al.47

https://psychtoolbox.org

Software and algorithms
MATLAB Version R2019b (data analysis),
Version 2012b (psychophysics)
Psychophysics Toolbox, Version 3
JASP, Version 0.11.1

JASP Team48

RESOURCE AVAILABILITY
Lead contact
Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Takeo Watanabe (takeo_watanabe@brown.edu).

Materials availability
This study did not generate new unique reagents.

Data and code availabilityd All data reported in this paper will be shared by the lead contact upon request.
d This paper does not report original code.
d Any additional information required to reanalyze the data reported in this paper is available from thelead contact upon request.

EXPERIMENTAL MODEL AND SUBJECT DETAILSWe recruited 61 naive subjects, of which 5 had to be excluded from subsequent analysis (see below). The
remaining 56 subjects (34 female) had a mean age of 25.1 G 1.2 years (range: 18–60 years). The subjects did
not suffer from neurological/psychological conditions and had normal or corrected-to-normal vision. All
subjects gave written, informed consent in accordance with institutional regulations and the Declarationof Helsinki. The subjects were randomly assigned to different experimental groups. There was no discernible influence of sex on the results of this study.

METHOD DETAILS
Environment & apparatus
All experiments were performed in a dimly lit room (<0.5 cd/m2). Subjects placed their heads on an adjustable-height chin rest in front of a computer screen. In the preliminary experiment, stimuli were presented
on an I-O DATA LCD-GC251USB LCD running at a resolution of 1920 3 1080 pixels and a refresh rate of
240 Hz, with 50 cm of viewing distance. In the main experiment, stimuli were presented on a Dell Trinitron
Ultra-Scan P991 CRT monitor, running at a resolution of 1024 3 768 pixels and a refresh rate of 120 Hz, with70 cm of viewing distance. Gamma corrections were performed to linearize the luminance curve. All stimuli
were generated and presented using custom software written for MATLAB R2012b (The MathWorks Inc.,
Natick, MA) and the Psychophysics Toolbox 3.46,47 The stimuli were presented on a uniform graybackground (62 cd/m2). Before and during stimulus presentation, a fixation dot of 0.1 diameter and37.2 cd/m2 luminance was presented in the center of the screen. The subjects were instructed to keep theirgaze on the fixation dot.

Orientation detection task
We employed a two-interval-forced-choice (2IFC) orientation detection task to assess the behavioral effects of RVS (Figure 1D). Orientation detection was chosen over discrimination to be able to align stimulus
orientation during both task and RVS (cf.14). In each trial, the subjects had to indicate which of two successively presented noise fields contained an embedded Gabor stimulus as a signal image. Difficulty of this

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task was varied by changing the signal-to-noise level of the Gabor stimulus (i.e., percentage of signal pixels
through substitution) by a 3-down/1-up staircase procedure. To advance quickly to an adequate difficultylevel, the procedure was changed to a 1-down/1-up at the beginning of a block until the first mistake wasmade. With each step up or down, the signal-to-noise percentage was changed by 0.0612 log-levels. The
initial signal-to-noise level in each block was 32.4%. Signal and noise images were shown in a circular aperture of 5 diameter in the center of the screen. The target Gabor stimulus orientation could be either of two
oblique orientations (45 or +45 ) within one block. Spatial frequency was kept constant at 2 cycles per
degree, while phase was randomized from trial to trial. The maximum Michelson contrast between troughs(49.6 cd/m2) and peaks (74.4 cd/m2) of the sine gratings was 0.2. The Gaussian envelope of the Gabor had as of 1 . Noise pixels were drawn from a Gaussian luminance distribution (m = 62 cd/m2, s = 12.4 cd/m2). To
avoid local contrast cues unrelated to orientation detection, we ensured that local contrast in the noise images was kept similar to that in the signal images. We did this by generating noise images by randomly
shuffling pixels from the signal image within concentric rings. In each trial, the prestimulus interval lasted
for 300 ms. The stimulus and noise images were presented for 50 ms each (with their order randomly varied), with a 300-ms interstimulus interval. The subjects had to respond within 1.5 s by pressing ‘1’ or ‘2’ on a
keyboard to indicate the interval containing the signal image. Before the start of the experiment, thesubjects were instructed to respond as accurately and fast as possible. In the preliminary experiment, no
feedback was presented in order to reduce the amount of training-based VPL. In the main experiment,
feedback was provided by visual cues after each trial: a ‘☺’ glyph after a correct response and a ‘3’ glyph
after an incorrect response. Additionally, a beeping sound was played if the subject did not respond intime. The subjects performed 16 blocks per session of 100 trials each. In the session 90 min after RVS,
the subjects completed 32 blocks to better track the time-course of the effect of RVS on performance.
Of these 32 blocks, only the first 16 were analyzed for comparability. The target Gabor orientation was alternated between blocks (Figure 1B). The order (i.e., 45 or +45 first) was kept consistent for each subject
but was randomly varied between subjects. A cue stimulus informed subjects of the target orientation
before each block.

Repetitive visual stimulation protocol
We tested the effects of three different repetitive visual stimulation paradigms: H-RVS, L-RVS and no-RVS.
In the H-RVS and L-RVS groups, a Gabor stimulus was flickered on and off in protocol-specific temporal
patterns (Figure 1E; see below). The orientation of the Gabor was either 45 or +45 and was randomly
chosen for each subject. The Gabor stimuli had the same parameters as in the orientation detection task
except for contrast and a lack of embedded noise. The maximum Michelson contrast at the center of
the Gabor was 0.6, the luminance varied between 99.2 cd/m2 at the peaks and 24.8 cd/m2 at the troughsof the sine grating. During H-RVS, these Gabor stimuli rapidly flickered on and off in an intermittent fashion.
In each cycle, the stimuli flickered on at a rate of 10 Hz and remained visible for 8.3 ms over a duration of 1 s(Figure 1E). Thereafter, no stimuli were presented for 5 s until the next cycle started. Cycles were repeated
600 times without interruption, which took 60 min. The frequency of 10 Hz was chosen over the 20 Hz used in
our previous study14 because of the small effect sizes found in that study and the greater preponderance of
H-RVS frequencies close to 10 Hz in earlier studies,11–13 suggesting that this frequency may lead to a morerobust effect. In the case of L-RVS, the stimuli flickered at a low, continuous frequency of 1 Hz (Figure 1E). A
cycle in this case consisted of just one Gabor stimulus being displayed for 8.3 ms until the next cycle started
after 1 s. This was repeated for 3600 cycles to match the total duration of H-RVS.
To ensure compliance with central fixation, we employed an oddball task where subjects had to detect
infrequent contrast changes in the Gabor stimuli. Unlike in some earlier experiments,14,15 this did notrender the stimulus task-irrelevant, which may subject it to attentional inhibition and bias results.1,49 In
the case of H-RVS, oddball stimulus trains had a maximum contrast of 0.9 (sine peaks: 117.8 cd/m2; troughs:
6.2 cd/m2) and 0.98 during L-RVS for single oddball stimuli (sine peaks: 122.8 cd/m2; troughs: 1.2 cd/m2).
The number of contrast changes was 50 for both H-RVS and L-RVS.
Only a fixation dot (0.3 diameter) was presented in the no-RVS group, which also lasted for 60 min. During
the fixation task in the no-RVS group, color of the fixation dot occasionally changed from white to red (70%red, 10% green and 10% blue color channels) and returned to white after 8.3 ms. The number of color
changes was 50, the same as in the RVS groups.

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ll

OPEN ACCESSIn all three RVS/no-RVS conditions, the subjects had to press the space bar on a keyboard within 800 ms
after a change in the target stimuli was detected. They received audiovisual feedback, as in the orientation
detection task, on hits, misses and false alarms.

Experimental procedure
Preliminary experiment (Figure 1A): In the pretest (‘‘pre’’), subjects performed the orientation detection
task for 16 blocks that lasted for approximately 45 min. The subjects could take short breaks between
blocks. Immediately thereafter, subjects received 1 h of H-RVS or no intervention at all. After a break of
90 min, subjects performed the orientation detection task for 32 blocks as the posttest (‘‘post’’).
Main experiment (Figure 1C): During the first seven days, the subjects performed 16 blocks of the orientation detection task per day, which lasted for approximately 45–50 min. The subjects could take short breaks
between blocks. On day eight, a pretest of the orientation detection task for 16 blocks (‘‘pre’’) preceded the
1-h session of either H-RVS, L-RVS or no-RVS for a duration of 1 h. After a break of 90 min to ensure time forneuroplastic processes to take hold and for early post-stimulation adaptation effects to subside, a posttestof the orientation detection task was performed for 32 blocks (‘‘post1’’). On the next day, one final session
was performed for 16 blocks to track the persistence of RVS effects to the next day (‘‘post2’’).

QUANTIFICATION AND STATISTICAL ANALYSIS
Statistical analysis
For statistical analysis of the behavioral data, we used JASP 0.11.148 (JASP Team) and custom scripts written
in MATLAB R2019b (The MathWorks Inc., Natick, MA). Unless noted otherwise, statistical tests were twotailed where applicable.

Orientation detection data
During the orientation detection task, we evaluated signal-to-noise thresholds with a 3-down/1-up staircase procedure, which nominally converges on a 79.4% correct response rate.50,51 We calculated the geometric mean of the last 6 reversals of each block as the threshold. Likewise, we used the geometric mean forsession and group means. For purposes of statistical inference, thresholds were then log-transformed. In
the correlation analyses (Figures 4A–4D), we calculated Kendall’s tB as correlation coefficient to reduce theoutsized influence of outliers in some analyses. For block-wise analysis in the first post session of the mainb
experiment, we fitted power functions of the form y = abx b + cb, with x designating the experimental session
and designating the mean signal-to-noise threshold. Parameters could vary without constraint. Initial
binit = 0 and cbinit = Mgeom ðyÞ (i.e., a constant function
binit = 1, b
values for parameter estimation were set to aat the geometric mean of pre-stimulation thresholds). PL training data to learning curves was performedbusing the nonlinear least squares method. Parameters for a power function of the form y = abx b + cb (with conbb; cbR0 and b%0)
straints: awere estimated for each individual subject (collapsed over orientations), with x
designating the experimental session and y designating the mean signal-to-noise threshold. Parameter
constraints were used as VPL was expected to occur. Initial values for parameter estimation were set tobinit = 0 and cbinit = Mgeom ðyÞ. Posttraining slopes of the learning curves were obtained bybinit = 1, badifferentiating each fitted function at x = 7.

Repetitive visual stimulation
As a means of fixation compliance during all types of RVS, we calculated d’ to estimate how well subjects
paid attention to infrequent changes in the target stimuli. Successful responses within 800 ms after thechanges were counted as hits, while responses outside of this time window were counted as false alarms.
The subjects in the no-RVS group, who were not presented with oriented Gabor stimuli, were assigned anarbitrary orientation (45 ) as the ‘stimulated’ orientation for later analysis. Both hit and false alarm rates
were bounded at .02 and .98 for obtaining finite d’ values in all cases. The fixation compliance during RVS
did not significantly correlate with subsequent impairment (not shown).

Exclusion criteria
Some subjects did not approach stable performance in the orientation detection task but instead
showed severe impairment at some point in the training procedure (but before RVS) of the main

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experiment. Therefore, we decided to exclude subjects who demonstrated this pattern, which was likely
related to motivational instead of perceptual issues. We thus excluded 5 subjects from the analysis whose(log-transformed) pre-RVS threshold on day 8 was between 75 and 300% worse than their best daily mean
threshold during the training phase (at most 16% worse for all others).

Mediation analysis
In the mediation analysis, standard errors were calculated using the delta method. We used a maximumlikelihood estimator and the bias-corrected percentile bootstrap method (B = 1000) for confidence interval
estimation. Estimated model coefficients were standardized. As the categorical predictor variable of themodel had to be binary, we recoded subjects according to whether they received H-RVS or not (L-RVS
or no-RVS), instead of using group as the predictor variable.

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