Manipulation of perception using HsMM-MVPA

analysis on EEG data

Bachelor’s Project Thesis

Melle Berentschot, s3508072, m.berentschot@student.rug.nl

Supervisors: H. Berberyan & Dr J.P. Borst

Abstract: Back in 1868, Donders claimed that humans go through multiple cognitive stagesbefore making a decision (Donders, 1868). Although he did find indications that cognitive stagesexist, he has never been able to measure them directly due to the lack of methods to measurebrain activity. However, with the arrival of the cognitive revolution (1950) and the inventionof the electroencephalogram (EEG), people have been able to measure the electrical activityof the brain. Evidence was found supporting that cognitive processing stages can be derivedfrom EEG data by using a hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA) by Anderson, Zhang, Borst, and Whalsh (2016). This claim of using HsMM-MVPA asa method to derive cognitive stages out of an EEG signal was supported with evidence foundby Berberyan, van Maanen, van Rijn, and Borst (2021). Our research uses the same method inorder to investigate whether there is a difference in the perceptual processing stage when usingtransparent stimuli, compared to Berberyan and colleagues using non-transparent stimuli for thesame task. The method was replicated with the difference that we used transparent stimuli inorder to accurately investigate whether the use of transparent stimuli made a difference. Therewas no significant difference found in our reaction times or cognitive stage durations comparedto the results of Berberyan and colleagues. This implies that the transparent stimuli used for thisexperiment did not result in perception manipulation. However, as we did replicate the resultsof Berberyan and colleagues, it can be said that the validity of using HsMM-MVPA as a methodto derive cognitive stages out of EEG data is supported by our findings.

1 Introduction

Over one and a half century ago, the idea of go-ing through different processing stages during athought process was put forward for the first time(Donders, 1868). Donders claimed that people gothrough different cognitive stages before each deci-sion they make. He tried to investigate these stagesprimarily by means of behavioral data such as re-sponse times of his participants. He found an indi-cation of the existence of cognitive stages by analyz-ing the behavioral responses. However, he was neverable to support this indication with exact stage du-rations or temporal onsets of the individual stages,due to the lack of methods to measure brain activ-ity.The cognitive revolution started about a centuryafter that, this brought back the idea Donders had

about cognitive processing stages. This time, bettermethods to measure brain activity were available toresearchers. A well-known method used in order torecord brain activity uses an electroencephalogram(EEG), which is used to record electrical activity ofthe brain. The EEG recordings can be used to di-rectly derive cognitive stages, because EEG enablesthe ability to measure brain activity as it unfoldsin real time, at the level of milliseconds. This is anadvantage of using EEG compared to, for example,using MRI or fMRI. As EEG has the ability to mea-sure brain activity at the level of milliseconds andcognitive stages also appear in the range of millisec-onds, EEG is the method chosen for this research. Arecent method for deriving cognitive stages out ofan EEG signal uses a hidden semi-Markov modelmultivariate pattern analysis (HsMM-MVPA) fordoing this (Anderson et al., 2016). Anderson and

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colleagues gathered evidence that the start of eachcognitive processing stage is indicated by a nega-tive or positive peak in the EEG signal, referred toas ”bumps” in the signal.The validity of using HsMM-MVPA as a methodto derive cognitive stages out of a EEG-signalwas tested by Berberyan et al. (2021). This wasdone by two visual discrimination tasks; one sim-ple and one complex discrimination task. Theyfound that the complexity of the task influencedthe response times, and supported this claim bymeans of an HsMM-MVPA analysis performed onthe EEG data. The HsMM-MVPA results showedthat the duration of certain cognitive processingstages differed between the simple and complextask, whereas the duration of other stages stayedthe same. This supported the idea that some ofthe cognitive stages are related to decision making,while other cognitive stages are not. The cognitivestages that are not related to decision making arestages one goes through during perceiving the stim-uli (at the beginning of a trial) and during motor-related events, pushing the button on the keyboardat the end of a trial in this case.In this research, the aim is to manipulate percep-tion using similar stimuli as Berberyan and col-leagues used in their experiment, with the differ-ence that our stimuli will be presented as trans-parent stimuli. Manipulating perception will beachieved when the perceptual processing stages(i.e. the cognitive stages during perceiving a stimu-lus) prolongs, whereas the duration of all cognitivestages that have to do with the decision-makingitself stays the same compared to the cognitivestages gathered by the research of Berberyan andcolleagues.The use of transparent stimuli as the method tomanipulate perception was chosen because of theresults of a pilot study done under supervision ofBerberyan (Kamsteeg, 2020). This pilot study wasconducted by Kamsteeg using 4 participants andwithout using EEG data. She suggested that trans-parent stimuli could possibly cause a prolongationin the perceptual stage duration based on higherresponse times gathered from transparent stimulicompared to the response times gathered from thebasic stimuli and based on the Shifted-Wald modelsshe used as evidence accumulation models (Heath-cote, 2004; Matzke and Wagenmakers, 2009). Withour research, we aimed to support this finding by

Kamsteeg with gathering EEG data in order to per-form HsMM-MVPA analysis when using transpar-ent stimuli. We assume that if the perception ismanipulated by the use of transparent stimuli, theresults of the HsMM-MVPA analysis will show thisdifference in the duration of the perceptual process-ing state.

2 Methodology

2.1 Participants

In total 30 participants performed our simple visualdiscrimination task. Out of these 30 participants, 6participants had large artifacts in the EEG data.This means that something happened during therecording which caused too much noise to the data.This can be caused by a computer which crashedor because the participant moved too much duringthe recording, which both happened during multi-ple experiments.The participants were all recruited using an ad-vertisement on Facebook. All the participants wereright handed and they had normal or corrected-to-normal vision. They all took the EnChroma ColorBlindness Test in order to test whether they hadnormal color vision. The age of the participantsranged from 19 to 29 years old. The mean age ofthe participants was 22.95 years old.The participants signed an informed consent formand were paid 8 euros compensation for their par-ticipation in this experiment.

2.2 Task Design

The task which the participants had to perform wasa simple visual discrimination task. In this task theparticipants were asked to discriminate between ei-ther shapes, colors or characters. The task was di-vided into 3 blocks. These blocks were presented ina random order.In the first block the participants had to make a dis-crimination between different geometrical shapes,which consisted of circles, squares, triangles andrhombuses, while paying no attention to their color.The geometrical shapes were presented in an equaldistribution to the participants. This means thatthe participants for example encountered just asmany circles as triangles in a single block.

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In the second block the participants had to dis-criminate between colors of objects. Analogous tothe previous block, the participants were asked toignore their geometric shape. The colors were pre-sented in an equal distribution to the participants.In the final block the participants were shown astring of 4 letters or numbers and were asked todifferentiate between the two. The letters and num-bers were also presented in an equal distribution tothe participants, meaning participants would en-counter as many number combinations as lettercombinations.

2.3 Stimuli

Whereas in previous research basic stimuli (see Fig-ure 2.1) were used, the stimuli that were used dur-ing this study were all transparent versions of thesebasic stimuli (Kamsteeg, 2020). The transparentstimuli have a transparency of 75% compared tothe basic stimuli and the black borders are miss-ing.The colors of the shape stimuli varied. The optionswere either red, green, yellow or blue. The charac-ters were set to grey. The geometrical shapes in-cluded circles, squares, triangles and rhombuses.The geometrical shapes and colors were used inboth block 1 and 2. In each block two shapes andtwo colors were used. For block 1, two of four shapesand colors were randomly chosen. For the nextblock, the remaining two shapes and colors wereused. The character and number combinations usedin block 3 were completely randomized.

Figure 2.1: Stimuli comparison, figures fromKamsteeg (2020)

2.4 Experimental procedure

The participants were tested in a quiet room infront of a computer. They were asked to press akey (either ’n’ or ’m’ depending on the stimuli) onthe keyboard using their right hand. Each blockstarted with an instruction slide, which specifiedthe task for that particular block. After each blockthe participants had the chance to take a break.The experiment consisted of, including practice,420 trials. Per block each participant had 20 prac-tice trials in order to get familiar with the task.This means that each participant performed a totalof 360 trials. Each of these trials started with a fix-ation dot. This dot was visible for a random time,between 1500 and 2250 milliseconds, after whichthe trial started.The participants had a timeout of 3000 ms to per-form each trial. If this time has passed and the par-ticipants did not manage to press ’n’ or ’m’ thena screen appeared that stated ’Too late!’. If theypressed one of these keys before the time is up, ascreen would appear which would either state ’Cor-rect’ or ’Incorrect’, depending on the answer.In total, the experiment took approximately onehour, including EEG setup.

2.5 Behavioral data analysis

The relevant data from the behavioral data are er-ror rates and reaction times. When analyzing re-action times, we removed the trials that deviatedmore than two standard deviations from the meanreaction time. Trials where incorrect answers weregiven were removed too.The amount of errors indicate whether the difficultyof the task is manipulated. The reaction time is re-quired to compare with previous studies. A com-parison can be the first clue whether the cognitiveprocesses are manipulated in terms of duration. At-test was performed on both reaction time and theerror rates in order to compare them across condi-tions.

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2.6 Recording and preprocessing ofEEG data

The participants were seated in front of the screen.Six electrodes were placed on the face. Two ver-tically aligned above and under the left eye. Twohorizontally aligned, one electrode on each temple.And one electrode placed behind each ear. Four outof six electrodes function to measure any musclemovements and eye blinks. The two electrodes be-hind the ears, mastoid electrodes, function as ref-erence. Afterwards a cap with 32 electrode slotswas placed on top of the head. The electrodes usedare active Ag-AgCI electrodes (Biosemi Active Twosystem) digitized with a sampling rate of 512 Hz.The international 10-20 system layout was usedto place the electrodes. Next to 32 electrodes aCommon Mode Sense (CMS) and Drive Right Leg(DRL) were attached to the cap.The collected EEG data were passed through ahigh-pass filter of 1 Hz and a low-pass filter of40 Hz and finally down-sampled to 256 Hz. After-wards manual rejection of artifacts was applied tothe data. This process required the researchers tomanually go through the data and to delete anynoise. On average 1.2 % of the original EEG datais deleted during manual artifact rejection.For three participants, channels were removed.After manual rejection of data, the data was fil-tered from eye blinks and muscle contractions de-tectable in the measured EEG signal. This was doneby using a technique called independent componentanalysis (ICA). On average one to two componentswere removed per data set.

2.7 HsMM-MVPA

2.7.1 Preprocessing for HsMM-MVPA

To perform HsMM-MVPA analysis further prepro-cessing was necessary. First, we down-sampled ourdata to 100 Hz. Then, the data was epoched trial-by-trial from the moment the stimuli was presenteduntil the consecutive response. After that, the datawas separated into two conditions, derived from theinitial three conditions. In this way, the first condi-tion included all trials of the shape-discriminationtask and the color-discrimination task and the sec-ond condition included all trials of the character-discrimination task. The merging of the colors and

shapes conditions was done because these two tasksare too similar to treat separately. Moreover, bothconditions differ with the characters-discriminationcondition, we were interested in this difference andthis was also a reason to merge the colors andshapes conditions to one condition.After this, the outliers were excluded based on theresponse times. Then the data was baselined from400ms prior to the stimuli until the moment of ap-pearance of the stimuli. Based on the baselineddata, only complete trials were kept for analysisand incomplete trials (due to artifact rejection)were removed. This is done to prevent includingincomplete trials where the first cognitive stage(s)of the decision process might be missing. Then, bymeans of a covariance matrix computed for eachtrial and subject separately, principal componentanalysis (PCA) was performed on the data. Thefirst 10 PC components were retained. Finally, z-scores were calculated in order to normalize thedata.

2.7.2 HsMM-MVPA analysis

The purpose of applying the HsMM-MVPA anal-ysis is to discover the cognitive stages which canbe generalized across all trials of all participants.The HsMM-MVPA analysis aims to find cognitivestages that are hidden in the EEG-signal. Thesecognitive stages can be identified by bumps andflats. Each bump is followed by a flat, and a com-bination of a bump and a flat is called a cognitivestage. During the HsMM-MVPA analysis, cognitivestages are tried to be found within each epoch ex-tracted from the EEG data. This is done by look-ing at the principal components extracted fromthe EEG signal. This repeated signal consists ofn bumps, which results in n+1 flats, because thefirst stage always starts with a flat.Cognitive stages are obtained from the PC com-ponents by searching for the best model to fit thedata. The goal of model fitting is to find the modelwith the optimal number of bumps and thus the op-timal number of stages. This will be done by meansof the following few steps. First, the best magni-tude parameters are obtained for each of the twoconditions separately. Then, these parameters areused for performing a leave-one-out cross valida-tion (LOOCV) procedure for both conditions. Thisprocedure is performed to prevent overfitting. The

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HsMM-MVPA model on all subjects but one is esti-mated, and after that the fit of this model is testedon the left-out subject. In this way, training andtesting of each model is separated. Multiple mod-els are fitted, ranging from a model with one bumpto a model with the maximum number of bumpspossible.Because of the difference in duration across tri-als, the onset of bumps can occur at different timepoints at each trial. To account for this, the datais analyzed at the single-trial level while all partici-pants and all trials are taken into account simulta-neously. The topology of the bumps is constant foreach trial because the method assumes that eachtrial consists of the same cognitive processes. How-ever, the variability in duration of the cognitive pro-cesses is accounted for by making the duration ofthe flats variable. This makes it so that the widthand amplitude of each bump is the same for eachtrial, yet the stage durations are kept variable byimplementing the variability of the duration of theflats between the bumps across the trials. In thisway, the maximum number of bumps depends onthe duration of the trials. In this case, the maxi-mum was five bumps. Therefore, the fitted modelsranged from a model with one bump to a modelwith five bumps.

For each of these models, the mean log-likelihoodwas determined among all participants by theLOOCV procedure. For each model with an ad-ditional bump, we calculated the number of partic-ipants for whom this model fitted significantly bet-ter as determined by performing a sign test. Finally,the model with the highest mean log-likelihood waschosen as the model with the optimal number ofbumps and stages. This model was used as a finalHsMM-MVPA model.

3 Results

In this section, the results gathered from our datawill be shown. We gathered two types of data; be-havioral data and EEG data. Notice that for thepurpose of clarity, from now on, ”condition 1” willrefer to the condition for which the participants hadto distinguish between different shapes or colors.Subsequently, ”condition 2” will refer to the condi-tion for which the participants had to distinguishbetween different characters.

3.1 Behavioral data results

For the results of the behavioral data, the firstpoints of interest are whether there is a differencein reaction time and error rate between the twoconditions. Looking at the reaction times in Figure3.1, it can be seen that the mean reaction time ofcondition 2 is higher compared to the mean reac-tion time of condition 1. Welch Two Sample t-testwas applied to test the difference in mean reactiontimes between the two conditions. A p < 0.05 wasobtained (See Table 3.1), thus it can be said thatthere is a significant difference in reaction times be-tween the two conditions.

Figure 3.1: Reaction times of 2 conditions

Looking at the mean error rate in Figure 3.2, itcan be seen that the mean error rate of condition2 is higher than the mean error rate of condition1. Similar to testing the difference in mean reac-tion times, Welch Two Sample t-test was appliedto test the difference in mean error rates betweenthe two conditions. A p > 0.05 was obtained (SeeTable 3.1), thus it can be said that there is no sig-nificant difference in mean error rates between thetwo conditions.

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Table 3.1: Results of Welch Two Sample t-test

Figure 3.2: Mean error rate of 2 conditions

Now that the difference in reaction times anderror rates between the two conditions are deter-mined, the next step is to compare our behavioralresults with the behavioral results of Berberyan etal. (2021). Looking at Figure 3.3, the mean reac-tion time of the results of Berberyan and colleaguescan be seen on the left plot and our reaction timescan be seen on the right plot. It can be seen thatthere are a number of similarities between the twoplots. For both datasets, the mean reaction timesfor condition 2 is higher compared to condition 1.It can also be seen that the mean reaction timeof condition 1 is around 500 milliseconds for bothdatasets. Also, the mean reaction time for condi-tion 2 is around 600 milliseconds. It can be seenin the figure that the reaction times of Berberyanand colleagues is even a bit higher compared to ourreaction times. This suggests that there is no sig-nificant difference between the two datasets. Thisclaim is supported by the results of the Welch Two-Sample t-test performed on the difference in meanreaction times, for p > 0, 05 (see Table 3.2).

Figure 3.3: Comparison of reaction times, databy Berberyan et al. (2021) on the left versus ourdata on the right.

When comparing our error rates with the errorrates gathered by Berberyan and colleagues (Figure3.4), it can be seen that the error rate of condition 1are similar. It can also be seen that the error rate ofcondition 2 is higher for our data compared to thedata of Berberyan and colleagues. However, there isno significant difference in error rates, as indicatedby p > 0.05 in Table 3.2.

Figure 3.4: Comparison of error rates, data byBerberyan et al. (2021) on the left versus ourdata on the right.

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Table 3.2: Results of Welch Two Sample t-testwhen comparing our behavioral data to the be-havioral data of Berberyan et al. (2021).

3.2 HsMM-MVPA results

To gain more insight in the different processingstages during each trial, the HsMM-MVPA analysiswas performed separately for the two conditions tofind the optimal number of stages in order to fit themodel. Looking at Figure 3.5 and Figure 3.6, it canbe seen that for both conditions, the mean log likeli-hood (acquired as explained in section 2.7.2) is thehighest for a model with four bumps. Also, lookingat the number of participants for whom adding thefourth bump is significantly better when perform-ing a sign test, it can be seen that this is the casefor 21 out of 24 participants for condition 1 and for18 out of 24 participants for condition 2. Therefore,a model with four bumps fits best to the data forboth conditions. This implies that there will be fivestages in the optimal model.

Figure 3.5: Log likelihoods for all number ofbumps for condition 1, significance of numberof participants is indicated with the red star in-side of the points

Figure 3.6: Log likelihoods for all number ofbumps for condition 2, significance of numberof participants is indicated with the red star in-side of the points

Now that the optimal amount of bumps andstages is found, we want to find out whether there isa difference in the bump topologies and stage dura-tions between the two conditions. To test this, threedifferent HsMM-MVPA models were constructedwhich investigated whether there are shared bumpsand stage durations between the two conditions.The first model fit is a combined model in whichit is hypothesized that the bumps and stage dura-tions are the same for the two conditions (Combinedmodel in Table 3.3). For the second model it washypothesized that only the second bump and theduration of the third stage would vary between thetwo conditions (Bump2Stage3Vary in Table 3.3).The third model hypothesized that all bumps andstage durations would vary between the two condi-tions, all bumps and stage durations were separatedfor this model fit (Sum of separate models in Table3.3). The model fitting was done with a forwardstepsize fiting routine. This was done to preventrandom effects potentially leading to statistical er-rors. The model fits were compared for each par-ticipant, in Table 3.3 the number of participantsfor whom the row-model fits better than the col-umn model is indicated in the cells. A model waspreferred if the fit improved compared to a simplermodel for a significant number of participants, thecells for which this is the case are indicated with agrey background in Table 3.3. The best estimatedmodel is indicated with an asteriks (*).

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Table 3.3: Results of model comparison for theestimated HsMM-MVPA models

It can be seen in the table thatBump2Stage3Vary and Sum of separate mod-els are improved fits compared to the combinedmodel for a significant number of participants.Therefore, all three appointed models fit betterthan the combined model. As stated before, amodel was preferred if the fit improved comparedto a simpler model. However, looking at thecomplexity of the two models, it can be said thatthe Bump2Stage3Vary model is a simpler modelcompared to Sum of separate models, for Sum ofseparate models requires more parameters becauseit assumes all bumps and stage durations vary be-tween the two conditions instead of the one bumpand one stage duration which Bump2Stage3Varyassumes. Looking at Table 3.3, Sum of separatemodels does not improve the fit compared to theBump2Stage3Vary model for a significant numberof participants. Therefore, it can be said thatBump2Stage3Vary is the preferred model to fit thedata.Looking at the scalp topologies of the four bumpsfor the two conditions (Figure 3.7), it can be seenthat the topologies of bump one, two and four areshared between the two conditions. However, look-ing at the scalp topology of the second bump, itcan be seen that there is a difference here betweenthe two conditions. The activation of the secondbump seems higher for the characters conditioncompared to the shapes and colors condition.

Figure 3.7: Scalp topologies for the two condi-tions from the Bump2Stage3Vary model, wherethe first, third and fourth bump are shared andthe second bump differs.

Looking at our stage durations (top of Figure

3.8), it can be seen that the stage durations of stageone, four and five are the same for the two condi-tions. It can also be seen that the duration of stagetwo slightly differs between the two conditions andthat the duration of stage three differs over 100 msin duration between the two conditions.

Figure 3.8: Stage durations of the five stages.Top figure shows the results of our data. Bot-tom figure shows the results of data gatheredby Berberyan et al. (2021).

Finally, these stage durations have to be com-pared with the stage durations of Berberyan andcolleagues, because we are interested in whetherthere is a difference in stage durations which mighthave been caused by using transparent stimuli.Comparing the stage durations of Berberyan andcolleagues with the stage durations of our data(Figure 3.8), it can be seen that the difference inthe third stage duration is also present for the re-sults of Berberyan et al. (2021). The duration ofthe third stage for the characters condition is theonly stage with a duration above 200 ms for bothdata sets. The durations of stage 1, 2, 4 and 5 seemto be similar between the two conditions for bothdata sets.

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4 Discussion

The goal of the current paper was to investigate thepossibility of using transparent stimuli as a methodto manipulate perception. That is, we wanted toinvestigate whether presenting transparent stimuliinstead of basic stimuli would prolong the percep-tual processing state. This is done by gatheringboth behavioral data and EEG data, and by com-paring the results with the results gathered fromthe simple stimuli used by Berberyan et al. (2021).First, we compared the behavioral data, it can besaid that the difference in response times and er-ror rates was not significant, this does not supportthe idea that perception is manipulated when us-ing transparent stimuli. Second, we used HsMM-MVPA to process and analyze the EEG data. TheHsMM-MVPA results were compared with those ofBerberyan and colleagues. There was no significantdifference found in bump topologies or stage du-rations between the HsMM-MVPA results. A sug-gestion for explaining the lack of a difference in re-sults might be that the stimuli were not transpar-ent enough, which would explain why the stimuliwere perceived in a similar way as non-transparentstimuli were perceived. Future research could in-vestigate whether using more transparent stimuliwould result in a difference in the duration of theperceptual processing stage.The idea of using transparent stimuli was derivedfrom a pilot study done by Kamsteeg (2020), shefound a difference in her behavioral data resultswhen using transparent stimuli compared to usingbasic stimuli. The fact that she did find a differ-ence in behavioral data and we did not could beexplained by the fact that she only gathered 4 par-ticipants, which could have caused the results to beunreliable.Both Kamsteeg and Berberyan and colleagues useddrift diffusion models in order to accumulate evi-dence for their findings about stage durations. Thekind of drift diffusion model they used was called aShifted-Wald model (Heathcote, 2004; Matzke andWagenmakers, 2009). The goal of such a modelis to measure both the non-decision and the de-cision time. Kamsteeg used a Shifted-Wald modelto support her finding about the prolongation ofthe perceptual processing stage. She found thatthe non-decision time prolonged and the decisiontime stayed similar when comparing them with the

Shifted-Wald models found by Berberyan and col-leagues. We did not use any drift diffusion modelto support our findings. Since we did not find anyprolongation in the perceptual processing stage, itwould be interesting to see whether there wouldalso be no difference in non-decision time when fit-ting a Shifted-Wald model. Future research coulduse a drift diffusion model in order to accumulateany evidence found by performing HsMM-MVPAon EEG data.

5 Conclusion

The goal of this experiment was investigate whetherit is possible to manipulate perception using trans-parent stimuli. This could have been supported bythe fact that our data resulted in higher reactiontimes compared to the data of Berberyan et al.(2021). After that, we had to check which stageduration prolonged when using transparent stim-uli compared to basic stimuli by means of HsMM-MVPA. If the perceptual processing stage wouldhave prolonged when using transparent stimuli,then we would conclude that it is possible to manip-ulate the perceptual processing stage using trans-parent stimuli. This would then also confirm thepossible evidence of manipulating perception usingtransparent stimuli by Kamsteeg (2020). However,this was not the case, as our results and the resultsof Berberyan et al. (2021) were too similar. Thissimilarity can be found for both the behavioral re-sults and the HsMM-MVPA results. Therefore, itcan be said that there is no evidence found for ma-nipulating the duration of the perceptual process-ing stage when using transparent stimuli. However,it can be said that because of the similarities of ourresults and the results gathered by Berberyan andcolleagues, the validity of using HsMM-MVPA asa method of deriving cognitive stages out of EEGdata is supported by our findings.

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