The role of Working Memory in Theory of Mind:

A behavioral study coupled with EEG

hyperscanning.

Bachelor’s Project Thesis

Jelle Deelstra

Supervisors: dr. M.K. (Marieke) van Vugt & Lionel Newman

Abstract: People use Theory of Mind (ToM) to create models of other’s thoughts and actionsin order to anticipate and coordinate better. For example, when playing volleyball you thinkabout how your teammate is going to pass you the ball in order to be ready for this pass. Inthis study the role of working memory (WM) in ToM related problems is studied using a tacitcoordination task. WM load was manipulated by means of an N-back task to study how it affectsperformance on the task. A significant difference in accuracy was found between low and highWM load conditions, which may suggest that ToM related tasks use WM resources. One neuralmechanism that might explain this coordination without communication is the synchronizationbetween brain activity of two people. During our experiment EEG hyperscanning was used torecord brain activity in participants simultaneously and ultimately to analyse the Inter BrainSynchrony (IBS) between participants. Combining the IBS scores with the behavioral resultshelps us to better understand how coordination between individuals is achieved.

1 Introduction

In daily life we are often faced with problems thatrequire us to reason about others’ actions. Some ofthese problems go a step further and require us toreason about other’s intentions, beliefs and knowl-edge. Examples of such problems are participatingin traffic on a bike, playing a sport in a team orany social interaction. This reasoning about others’mental states is called Theory of Mind (ToM). Rep-resentations of others’ minds do not have to be inline with the world or the subjects own thoughts,but are projections of our own mind. Using ToMwe can anticipate what others will do and chooseactions based on that; coordination. In order touse ToM we need sensory input and some domaingeneral brain functions, one of which might beworking memory (WM). Some studies (Maehara &Saito, 2011; Meyer & Lieberman, 2016) have shownthat by increasing WM load, ToM performance de-creases which suggests that WM is indeed a vitalkeystone of ToM. In addition, the development ofWM and ToM in children is extensively studied andsome studies found that WM and ToM develop-

ment were positively correlated (Lecce & Bianco,2018; He et al., 2019). This is in conflict with earlyresearch on ToM (Baron-Cohen, 1995; Fodor, 1992;Leslie, 1994) which often explained the source ofToM to be modular and not using general resourceslike WM. In recent years social and cognitive psy-chology research consider the resources needed forToM to be a collaboration between modular andgeneral ones (Leslie et al., 2004, 2005; McKinnon& Moscovitch, 2007). This would imply that ToMis not a mechanism on its own but an emergentfeature of the brain instead.

Working memory can be considered as an execu-tive function of the brain. Executive functions mod-ulate the activity of other cognitive functions in agoal directed way. They can occur consciously, me-diated by thoughtful processing, or unconsciously.WM is one executive function that temporarilymaintains and manipulates information that is notcurrently available to senses. Some studies arguethat sensory information is stored in the same brainareas that process these sensory stimuli (Rose,2020) and that WM is used to access and use this

1

information. Most research on executive functionshas implicated the prefrontal cortex to have a largerole in executive functions like WM. This is mostlybecause patients with brain damage in this area al-most always have severe impairments in performingcomplex tasks and understanding concepts that re-quire more than simple input processing. Despitethis evidence suggesting that executive functionsare contingent on the prefrontal cortex, this is notthe only area responsible for these functions. Theleading idea in the field of the cognitive sciencesis that executive functions depend on a larger setof brain regions including the prefrontal cortex, theparietal cortex, the basal ganglia and possibly otherregions (Purves et al., 2018). Because WM dependson a lot of different brain areas it is safe to assumethat it is relevant to many processes that constituteToM.

A neural phenomenon that may be of relevanceto ToM is inter-brain synchronization (IBS). Thisis measured by the degree to which brain signals atcertain intervals or time points are synchronized.When people coordinate with each other, they es-sentially have a representation of their own inten-tions and beliefs, but also a representation aboutthe others’ intentions and beliefs. This second rep-resentation is created using ToM and is maintainedby WM. During a coordination task, both partiesneed to update their representations in order topredict what the other person will do and antici-pate on that. This would mean that when coordi-nation is successful, both parties likely made sim-ilar updates to the representations in their brains.It would make sense that IBS is greater when peo-ple make similar updates in for example WM, com-pared to when they make very different updates (incases where coordination fails) because similar up-dates would implicate similar brain activity. Thisis backed up by studies that show that functionallinks in the brain appear across participants duringcooperation but not during simultaneous individualtasks that are identical (Sinha et al., 2016; Balconi& Vanutelli, 2018). This suggests that the degree ofIBS could predict how well people are able to useToM.

There are several ways to measure someone’sability to use ToM, most of which are coordinationtasks. Some coordination tasks that have been usedfor a long time are game theory tasks such as thewell known prisoners dilemma. One branch of game

theory is repeated game tasks. These tasks usuallyuse a well documented 2 person game like the pris-oners dilemma as a base game. These base gamesare played repeatedly such that subjects form con-ventions about the others’ choices and as a causethey often reach Nash equilibriums if there are any(Mehta et al., 1994). This would imply that sub-jects form a representation about the other subjectsfuture choices using ToM.

Using a coordinated ToM task we wish to investi-gate the role of WM on ToM and see if the degree ofIBS can predict the performance on the task. Thetask is largely based on a task developed by Albertiand colleagues (Alberti et al., 2012) in which twoparticipants simultaneously need to choose imagesfrom a set of images and get feedback based on thesimilarity to what the other participant chose. Setsof images are not repeated, so the idea is that par-ticipants need to create abstract conventions in or-der to coordinate better. In addition we manipulateWM load by an n-back task. For a more theoreticaland motivational background of the task itself I re-fer to the paper by Alberti and colleagues (Albertiet al., 2012). During the whole experiment EEGsignals from the participants were recorded so thatthe degree of IBS at certain time points can be ana-lyzed. These time points represent key moments atwhich WM should be updated in both participants.If coordination performance is better we expect tosee greater IBS because the updating in both indi-viduals would be similar. This leads us to our firstresearch question: ”Does a high working memoryload negatively affect the performance on a coordi-nated ToM task?”. We believe that WM plays a keyrole in the creation of ToM related thoughts and ac-tions so naturally we think that performance willbe worse in the high load condition compared to thelow load condition due to competing resources. Thesecond research question we wish to answer withthis experiment is: “Can the degree of inter brain-synchronization positively predict the performanceon a coordinated ToM task?”. We believe that thisis possible and that greater IBS will correlate withhigher performance on both low and high WM loadconditions.

2

2 Methods

2.1 Subjects

43 pairs of subjects were recruited of which mostwere international students studying in Groningen.Subjects ranged in age from 19 to 36 with a meanage of 23.52 and a SD of 3.99. 8 Pairs were male and35 pairs were female. Subjects were coupled in pairsof the same gender and were all unfamiliar witheach other prior to the experiment. Only same-sex coupling was studied because of gender gen-der specific coordination differences found in previ-ous studies (Baker et al., 2016; Cheng et al., 2015).The following inclusion criteria were selected forthe recruitment of subjects: normal or corrected-to-normal visual acuity (no color blindness) andright manual dominance. Subjects were paid 8 eu-ros per hour with an additional 0-4 euros based ontheir performance, a session usually took 2 hours in-cluding placing and removing the EEG equipment.Before each session the subjects were given a ver-bal overview of the study and an informed consentform. After they signed the form they proceeded tofill in a questionnaire consisting of question relatedto their use of ToM (see Appendix A). 20 subjectswere excluded from the EEG analysis because ofincomplete EEG data or data lost during process-ing.

2.2 Procedure

When the subjects were finished with the question-naires, the EEG equipment was applied in a stan-dardized manner. The distance between the nasionand the inion was measured and the A32 electrode(see figure 2.3) was positioned exactly in the mid-dle. Four external electrodes were used to filter outeye movement and blinks, they were placed to theouter side of the eyes and above and below the lefteye. Additionally two electrodes were placed on themastoids behind the ears to use for referencing.There is little to no brain signal recorded at themastoids, but they are relatively close to the otherelectrodes. This makes them a good place to usefor referencing since signals recorded in those elec-trodes definitely do not come from the brain. Beforethe experiment began, all channels were inspectedand noisy channels were fixed by either applyingmore gel or switching out the electrode for a new

one.Each session consisted of two same-sex subjects

doing the task in the same room, separated by acloset in between. The matching of the subjects wasdone at random, besides the same-gender criterion.The subjects got the instructions not to commu-nicate via speech or any other methods from theexperimenter and got further written instructionsshown to them on the screen (see Appendix B). Be-fore the start of the session there were two practicesessions. One for practicing the number task andone for practicing the image task. These practicesessions served to familiarize the subjects with theimage task and the time available in the numbertask.

2.3 Design

This experiment uses a 2x2 design where WMload and stimulus type both have two conditions;low/high WM load x shape/color simuli. We ma-nipulated WM load by using two different numbertasks in between the main image tasks, creating alow and high WM load condition. In the high WMload condition subjects were shown a number be-fore a trial which they needed to keep in mind. Twotrials later they were shown a number between 0-9on the screen again and needed to decide whetherthat number was the same or different to the num-ber from 2 trials ago. The numbers shown aftertwo trials were controlled and had a 33% chance ofbeing the same number that they had to keep inmind. This was done to keep subjects from gettinga 50% accuracy by randomly guessing. In the lowWM load condition subjects were shown a num-ber between 0-9 after each trial and need to decidewhether they are odd or even. Both of these num-ber tasks were time restricted by 2495ms, this limitwas chosen based on similar studies using a tacitcoordination task.

There were two types of stimuli used so that theconventions that the subjects formed in the firsthalf of the experiment could not be carried over tothe second part. Both stimuli types consisted of agrid of 64 squares that were either fully colored, orcontained an abstract shape. The stimuli were bothrandomly generated and presented to the subjectsin a random order. Examples of the stimuli usedare shown in figure 2.1 and figure 2.2.

One session of the experiment consisted of two

3

Figure 2.1: Shape stimuli - first guess presenta-tion.

Figure 2.2: Color stimuli - correct trial, bothfirst guesses were the left-most image.

parts that both contained 90 trials. Both parts hada different combination of WM load and stimulitype. An example condition would be shape stimuliwith a high WM load.

In image trials the subjects had two guesses, theirfirst and second-best guess for which there was notime limit. A guess implied a chance to choose thesame image as the other subject, two guesses wereused to give subjects more information so to un-derstand the other’s guesses better. Only the first-best guess was used for the analyses. The result ofthe first guess was only shown after both subjectshad entered their first and second guess. This wasbecause previous research indicated that this pos-itively affected learning performance and invokedgreater IBS (Balconi & Vanutelli, 2018). Their ownchoice was indicated by the text ”YOUR CHOICE”and the other’s choice was indicated by the text”OTHER’S CHOICE”. The entire experiment wascontrolled by the subjects using a keyboard.

Subjects were instructed to base their choices onfeatures of the images instead of their position onthe screen and to pay an equal amount of attentionto both the number and the image tasks. Subjectswere aware that image positions could be differentfor both of them. After the experiment had ended adebriefing form was filled out containing questionsabout the strategies they used.

Figure 2.3: Layout of the electrodes on the head.

2.4 EEG Recording and Data Pre-processing

Two 32-channel EEG systems were used for theEEG signal recording with an additional 6 ’exter-nal’ electrodes used for filtering out noise. The 32electrodes were placed on the subjects scalps us-ing two ElectroCaps with 32 holes. The layout ofthe electrodes is shown in figure 2.3. Two externalelectrodes were placed on the mastoids behind theears, two were placed horizontally next to the eyesand two were placed above and below the left eye.The electrodes around the eyes are mainly for fil-tering out eye blinks and the ones behind the earsare reference electrodes since no brain signal shouldbe coming out of these areas.

The data was split up into two sets, one for eachsubject. After this, the data of each subject was re-referenced to the average of the electrodes on themastoids separately and then padded to preventedge artifacts. Following this, high and low passfilters were applied to filter out any data outsideof the 0.1Hz - 50Hz range. After this step, manualinspection of the channels was done to identify anybad channels that were too noisy.

4

After filtering the data was split into trials andthen detrended. Following this the trials were man-ually inspected for rejection due to obvious noise.Then an independent component analysis was runto identify specific noise components that cause ahigh amount of variance in the data. Componentsare manually inspected and possibly removed basedon amount of variance caused and localization. Allof these pre-processing steps were done in MatLabusing the Fieldtrip package, for more informationsee Fieldtrip package .

2.5 Analysis

2.5.1 Behavioral

To investigate whether WM load has a significanteffect on mean accuracy we created two mixed lin-ear effects models which we compared using a likeli-hood ratio test. The null model contained an inter-cept, the stimulus type as a fixed effect, the subjectnumber as a random effect and the accuracy as thedependant variable. The full model also containedthe WM load condition as fixed effect. An interac-tion variable between WM load and stimulus typedid not produce a better fitting model so this vari-able was not included.

To test the significance of the stimulus type, twosimilar mixed linear effects models were created.The null model contained the same variables as theprevious null model, but with WM load conditionas a fixed effect instead of stimulus type. The fullmodel included stimulus type as a predictor of ac-curacy. Again we compared the null model to thefull model with a likelihood ratio test.

2.5.2 Interbrain synchronization

Our analysis on the EEG data is similar to theanalysis on the behavioral data, we made multiplelinear mixed effects models and compared themusing a likelihood ratio test. The mean IBS in atrial was introduced as a variable which reflectsthe mean IBS between two subjects within theprefrontal brain area. The beginning of each imagetrial functioned as the trigger point where we mea-sure IBS. These are the moments where we wouldexpect the degree of IBS to be higher, becausewhen subjects see an image they immediately needto think about what their partner will choose. We

calculated the IBS using the phase locking value(PLV) measure, which reflects the mean coherenceof the phases. The PLV is calculated by usingformula 2.1.

PLVt =1

T

∣∣∣∣∣T∑

n=1

exp(jφ(t, n))|

∣∣∣∣∣ , j =√−1 (2.1)

Where t is time, T is the number of timesteps ina trial and φ(t, n) is the phase difference at time tand timestep n. The PLV’s are averaged to get onevalue per trial for each pair of electrodes.

Only data from the frontal electrodes Fp1, Fp2,AF3, AF4, F7, F3, Fz, F4 and F8 was used to cal-culate the PLV, as we are particularly interestedin the IBS in the prefrontal cortex. We used fre-quencies from the alpha band (9Hz - 14Hz) be-cause previous studies found that IBS in the al-pha band frequencies is associated with coordina-tion (Hu et al., 2018; Sinha et al., 2016). All the rawdata was looked at and strong artifacts such as headmovement or other unclear noise were manually re-moved. Channels that produced a lot of noise wereinterpolated using the neighbouring channels andtrials that included only noise were removed. Aftermanually removing obvious irregular noise, an in-dependent component analysis was run to identitycommon components such as eye blinks, that causea lot of variance in the data. These componentswere manually inspected and rejected if necessary.

We set up multiple linear mixed effects models inorder to test whether IBS has an influence of accu-racy, or the other way around. In all these modelswe controlled for stimulus type by adding it as afixed effect. We also controlled for the differencebetween subject pairs by adding session numberas a random effect. When the predictor and out-come variables are not related to the WM load weadded this as a fixed effect to control for the vari-ance caused by the number trials.

3 Results

3.1 Behavioral results

First we look at the average accuracy between WMload conditions, and we see that there is a highermean accuracy of the first guess in the low WMload condition compared to the high WM load con-

5

Figure 3.1: Accuracy in all four combinations ofWM load and stimuli.

dition. In the low WM load condition the mean per-centage of correct guesses was 66.905% whereas inthe high WM load condition this was 61.918%.

Condition Correct Incorrect Total % CorrectLow 2529 1251 3780 66.905High 2395 1473 3869 61.918

Table 3.1: Results of the first guess.

Secondly we look at how the average accuracydiffers between the two stimulus types. We see thatthe mean accuracy in the color condition is 71.356%compared to 57.063% in the shape condition. Fig-ure 3.1 gives an overview of the accuracy in allfour conditions of the experiment. Here we see thatregardless of stimulus type, the mean accuracy ishigher in the low WM load conditions. Addition-ally the data is slightly skewed to the left for colorstimuli and slightly skewed to the right for shapestimuli.

We compared a model that included WM load asa fixed effect to a model that did not and found thatthe model including WM load produced a lowerAIC score (-88.341 for the full model and -86.273 for

the null model). A likelihood ratio test suggestedthat the model including WM load was a signifi-cantly better fit with X2(1) = 4.0679, p = 0.04371.Next we also compared a model including stim-ulus type as a fixed effect to a model withoutstimulus type. The model with stimulus type in-cluded produced an AIC score of -88.341 comparedto an AIC score of -65.113 for the model with-out stimulus type. Again we ran a likelihood ratiotest which suggested a significantly better fit withX2(1) = 25.228, p = 5.094e−07. The results of thesetests suggest that both WM load and stimulus typeare significant predictors of accuracy.

3.2 Inter brain synchronization re-sults

First of all we took a look at the degree of IBS incorrect and in incorrect trials to see if there willbe higher IBS during correct trials, which couldmean that there was better coordination in thosetrials. Figure 3.2 shows the the mean IBS and theSE for incorrect and correct trials. The mean IBSin incorrect trials (0.233) is slightly higher than incorrect trials (0.231). To test whether this differ-ence is significant we compared a model that in-cluded accuracy as a predictor variable to a nullmodel that did not. The full model produced alower AIC score (-13938) than the null model (-13937) but the fit was not significantly better:X2(2) = 4.9478, p = 0.08426. This suggests thatIBS was not greater in correct trials which couldimply that better coordination does not produce agreater degree of IBS.

To further explore the relation between accuracyand IBS we plotted the mean IBS and mean ac-curacy per session against each other in figure 3.3.Each point in this graph represents a session. They-axis represents the mean IBS over a whole ses-sion and the x-axis represents the mean accuracyof the two subjects combined over a whole session.We see a trend line where the mean IBS slightlyincreases as the mean accuracy goes, up but thisis not a significant relation. A Pearson correlationtest resulted in r(21) = 0.11, p = 0.081 which sug-gests that the IBS scores and the accuracy are notcorrelated. This would imply that pairs that co-ordinate better do not produce a higher degree ofIBS. Because subjects might learn to coordinatethroughout sessions, looking at only the mean IBS

6

Figure 3.2: Mean IBS per correctness.

and accuracy over the sessions as a whole might notbe the most relevant way of looking at this data.Therefore we will also look at how the IBS changeswithin sessions.

To look at how the average IBS changes duringa session the data was binned into five bins. Eachbin represents a different set of trials within thesession. Bin 1 contains trials 1-35, the second bincontains the trials 36-71, the third bin contains tri-als 72-107, the fourth bin contains trials 108-143and the fifth bin contains trials 144-180. For eachsession, the mean IBS was calculated per bin af-ter which the means of all sessions were combinedto compute the mean IBS per bin of sessions. Infigure 3.4 we see that the average IBS score doesnot seem to increase during a session. To furtheranalyse this we compared another linear mixed ef-fects model that included bin as a predictor to amodel that did not. A likelihood ratio test resultedin X2(1) = 0.0117, p = 0.9138 suggesting that theIBS does not change significantly during a session.We compared to similar models to see whether ac-curacy did change during a session. We found that amodel that included bin as a predictor for accuracyproduced a lower AIC score (-130.14) compared tothe model that did not (-131.13). However, the fitof the full model was not significantly better with

Figure 3.3: Mean accuracy and IBS-scores persession.

X2(1) = 1.0131, p = 0.3142. The results of thesetests imply that participants did not improve theircoordination during a session.

In order to further explore the relation betweenWM load and IBS, we plotted the average IBS inthe four conditions in figure 3.5. We see that thestandard error varies somewhat between conditionsbut the there is no clear difference in means be-tween the conditions. We compared a model thatincluded WM load as a predictor variable for IBSto a model that did not. We found that the modelincluding WM load produced a higher AIC score(-13937 for the full model and -13938 for the nullmodel). The fit of the null model did not producea significantly better fit with X2(1) = 1.0318, p =0.3097. This would imply that WM load has no sig-nificant effect on IBS and that the resources usedby WM are not needed to create higher degrees ofIBS.

Lastly we will look at how IBS and accuracy di-rectly affect each other to see if the degree of IBScan predict the accuracy of the first guess. We com-pared another two models, one with IBS as a pre-dictor for accuracy and one with a random inter-cept as a predictor. The null model without IBS asa predictor produced a lower AIC score (-331.01)than the full model (-331.00), but did not producea significantly better fit:X2(1) = 1.976, p = 0.1598.

7

Figure 3.4: Mean IBS-scores of all sessions, di-vided into trial bins.

This suggests that IBS cannot be used to predictthe accuracy of the first guess.

4 Discussion

4.1 Conclusions

The primary goal of this study was to build on thefindings of Maehara & Saito (2011) by showing thatWM load can affect coordination on a coordinatedToM task. We found a higher mean accuracy onthe first guess in the low WM load condition com-pared to the high WM load condition, irrespectiveof the stimulus type used. By comparing a modelthat included WM load as a predictor for accuracyto a model that did not, we found that WM loadhas a significant effect on the accuracy. Specifically,accuracy in the high WM load condition was sig-nificantly lower than accuracy in the low WM loadcondition. This suggests that WM and ToM do in-deed use shared resources from the brain.

The secondary goal of our experiment was to seeif we could predict the accuracy of the first guessin a trial based on the degree of IBS in that trial.We predicted that a higher degree of IBS wouldcorrelate with a higher accuracy because a higherdegree of IBS could mean more or better use ofToM. The Pearson correlation test suggested that

Figure 3.5: IBS in all four combinations of WMload and stimuli.

the accuracy is not affected by the degree of IBSand we also saw that the mean IBS does not signif-icantly differ between correct and incorrect trials.Furthermore, we saw no significant effect of IBS onaccuracy by comparing a model that included IBSas predictor for accuracy to a model without IBSas a predictor. Bases on the combination of thesefindings we assume that we can not predict the per-formance on a coordinated ToM task based on onlythe degree of IBS. This would also suggest that thedegree of IBS is not directly related to coordinationthat requires the use of ToM.

4.2 Limitations

There were a few limitations to the design of thisstudy, of which the most prominent was the differ-ence in difficulty between the two stimulus types.We observed that the accuracy was a lot higherin conditions where color stimuli were used, com-pared to conditions that used shape stimuli. We didcontrol for this in our statistical models by addingstimulus type as a fixed effect, but this is not aperfect way to handle this. It would be preferredto have a different way of preventing the conven-tions learned in the first block to carry over intothe second block, because then the effect of WM

8

load could be isolated better.Another limitation were sessions where there was

one or more EEG channels that contained purenoise. We excluded these channels and used inter-polation to recreate them, however there was stilldata lost in this process.

4.3 Future research

If WM and ToM indeed use shared brain resources,this would imply that by improving WM you alsoimprove your ability to use ToM. This would bereally useful since we already know that we canimprove WM through training. Consequently thiswould mean that we can also train our ability to useToM which is useful in a lot of situations like co-ordination in the work place, teaching, and generalsocial skills.

This would be an interesting topic to further ex-plore with an experiment. A study on this couldfocus on training subjects’ WM and seeing if thisalso improves their ability to use ToM. The WMperformance and ability to use ToM of the subjectsshould be indexed at the start to have a baselineafter which the subjects would be split up into twogroups. One group would train their WM while theother group would not. After a certain period oftime both WM performance and ability to use ToMis measured again and the two groups can be com-pared to each other. If ToM improved with a linearrelationship to WM this would support our findingsthat WM and ToM use shared brain resources.

References

Alberti, F., Heap, S. & Sugden, R. (2012), ‘Salienceas an emergent property,”’, Journal of EconomicBehavior & Organization 82, issues 2-3, 379–394.

Baker, J., Liu, N., Cui, X., Vrticka, P., Saggar, M.,Hosseini, S. & Reiss, A. (2016), ‘Sex differencesin neural and behavioral signatures of coopera-tion revealed by fnirs hyperscanning.’, ScientificReports 6, issue 1, 26492.

Balconi, M. & Vanutelli, M. (2018), ‘Eeg hyper-scanning and behavioral synchronization duringa joint action’, Neuropsychological Trends pp. 23–47.

Baron-Cohen, S. (1995), Mindblindness: An essayon autism and theory of mind, The MIT Press,Cambridge.

Cheng, X., Li, X. & Hu, Y. (2015), ‘Synchronousbrain activity during cooperative exchange de-pends on gender of partner: A fnirs-based hy-perscanning study.’, Human Brain Mapping 36,issue 6, 2039–2048.

Fodor, J. (1992), ‘A theory of the child’s theory ofmind’, Cognition 44, 283–296.

He, J., Guo, D., Zhai, S., Shen, M. & Gao, Z.(2019), ‘Development of social working mem-ory in preschoolers and its relation to theory ofmind’, Child Development 90, issue 4, 1319–1332.

Hu, Y., Pan, Y., Shi, X., Cai, Q., Li, X. & Cheng,X. (2018), ‘Inter-brain synchrony and coopera-tion context in interactive decision making.’, Bi-ological Psychology 133, 54–62.

Lecce, S. & Bianco, F. (2018), ‘Working memorypredicts changes in children’s theory of mind dur-ing middle childhood: A training study”’, Cogni-tive Development 47, 71–81.

Leslie, A. (1994), Mapping the Mind: DomainSpecificity in Cognition and Culture, CambridgeUniversity Press, Cambridge.

Leslie, A., Friedman, O. & German, T. (2004),‘Core mechanisms in “theory of mind”’, Trendsin Cognitive Sciences 8, 528–533.

Leslie, A., German, T. & Pollizi, P. (2005), ‘Belief-desire reasoning as a process of selection.’, Cog-nitive Psychology 50, 45–85.

Maehara, Y. & Saito, S. (2011), ‘I see into yourmind too well: Working memory adjusts theprobability judgment of others’ mental states’,Acta Psychol. (Amst) 138, no. 3, 367–376.

McKinnon, M. & Moscovitch, M. (2007), ‘Domain-general contributions to social reasoning: Theoryof mind and deontic reasoning re-explored’, Cog-nition 102, issue 2, 179–218.

Mehta, J., Starmer, C. & Sugden, R. (1994), ‘Thenature of salience: An experimental investigationof pure coordination games’, American EconomicReview 83, issue 3, 658–673.

9

Meyer, M. & Lieberman, M. (2016), ‘Social work-ing memory training improves perspective-takingaccuracy’, Soc. Psychol. Personal. Sci. 7, no.4, 381–389.

Purves, D., Huettel, S., Cabeza, R., LaBar, K. &Platt, M. (2018), Principles of Cognitive Neuro-science, Second Edition, Oxford University PressInc, Oxford.

Rose, N. (2020), ‘The dynamic-processing model ofworking memory’, Current Directions in Psycho-logical Science 29(4), 378–387.

Sinha, N., Maszczyk, T., Wanxuan, Z., Tan, J. &Dauwels, J. (2016), ‘Eeg hyperscanning studyof inter-brain synchrony during cooperative andcompetitive interaction’, 2016 IEEE Interna-tional Conference on Systems, Man, and Cyber-netics (SMC) pp. 4813–4818.

10

A ToM Related Question-naire

B Instructions of the experi-ment