Neuroelectrical Correlates of Trustworthiness and Dominance Judgments Related to the Observation of...

Research ArticleNeuroelectrical Correlates of Trustworthiness and DominanceJudgments Related to the Observation of Political Candidates

Giovanni Vecchiato1 Jlenia Toppi23 Anton Giulio Maglione4 Elzbieta Olejarczyk5

Laura Astolfi23 Donatella Mattia3 Alfredo Colosimo4 and Fabio Babiloni13

1 Department of Physiology and Pharmacology ldquoSapienzardquo University Piazzale Aldo Moro 5 00185 Rome Italy2 Department of Computer Control and Management Engineering ldquoSapienzardquo University Viale Ariosto 5 00185 Rome Italy3 IRCCS Fondazione Santa Lucia Via Ardeatina 306 00179 Rome Italy4Department of Anatomy Histology Forensic Medicine and Orthopedics ldquoSapienzardquo University Via Borelli 50 00185 Rome Italy5 Nałęcz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences 4 Trojdena Street02-109 Warsaw Poland

Correspondence should be addressed to Giovanni Vecchiato giovannivecchiatouniroma1it

Received 29 May 2014 Accepted 21 July 2014 Published 24 August 2014

Academic Editor Carlo Cattani

Copyright copy 2014 Giovanni Vecchiato et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The present research investigates the neurophysiological activity elicited by fast observations of faces of real candidates duringsimulated political elections We used simultaneous recording of electroencephalographic (EEG) signals as well as galvanic skinresponse (GSR) and heart rate (HR) as measurements of central and autonomic nervous systems Twenty healthy subjects wereasked to give judgments on dominance trustworthiness and a preference of vote related to the politiciansrsquo faces We used high-resolution EEG techniques to map statistical differences of power spectral density (PSD) cortical activity onto a realistic headmodel as well as partial directed coherence (PDC) and graph theory metrics to estimate the functional connectivity networksand investigate the role of cortical regions of interest (ROIs) Behavioral results revealed that judgment of dominance trait is themost predictive of the outcome of the simulated elections Statistical comparisons related to PSD and PDC values highlighted anasymmetry in the activation of frontal cortical areas associated with the valence of the judged trait as well as to the probability tocast the vote Overall our results highlight the existence of cortical EEG features which are correlated with the prediction of voteand with the judgment of trustworthy and dominant faces

1 Introduction

A growing number of research laboratories are involved ininvestigating cerebral areas activated during the observationof pictures showing politicians as well as videos supportingthem during electoral campaigns In the pioneering study inthe field faces of coupled candidates for political electionsin the USA Senate were presented for less than a second [1]Judgments based on such a ldquosuperficialrdquo observation couldpredict the election results being linearly correlated withthe candidatersquos margin of victory In other words emotionalinferences formed on the basis of the observation of aface for less than a second could even overcome rationalconsiderations about the hypothetical future work of the

candidate The consequence of this result is that the appealof a politicianrsquos face might be the principal factor for thecitizenrsquos choice even more than rational considerationsIn fact such a phenomenon has also been confirmed bysubsequent studies [2] suggesting that the scenic presenceby itself mostly influence the decision of vote These resultsconcerning the behavioural psychology are surprising ifwe think about the USA midterm elections of 2006 whencandidates and their supporting groups spent about 1 billiondollars in advertisements in order to inform electors abouttheir political affiliation qualities and ideas to promote[3] Afterwards researchers moved to understand whetherthis first impression is more due to positive effects (iepleasantness adaptation to particular a priori requirements)

Hindawi Publishing CorporationComputational and Mathematical Methods in MedicineVolume 2014 Article ID 434296 19 pageshttpdxdoiorg1011552014434296

2 Computational and Mathematical Methods in Medicine

or negative ones (ie negative judgement about pleas-antness implicitly extended to the competence field) [4]The surprising result of this study was that an emotionallyldquonegativerdquo judgement towards a candidate (as being ldquolessappealingrdquo than the opponent) is a prevailing reason for hisdefeat even in a contest of simulated elections In additionthe cerebral activity generated from an emotional state ofldquorejectionrdquo of the candidate is completely different from theone generated from an emotional state of acceptation orsatisfaction [5] Neuropolitical studies have found that theactivation of emotion-related areas in the brain is linked topolitical preferences identifying the neural correlates in theprefrontal cortices of changes in political attitudes towardothers that are linked to social cognition [6] In particular ina study performed by functionalmagnetic resonance imaging(fMRI) Spezio and colleagues [5] revealed that activationin the insulaparainsula and anterior cingulate cortex (ACC)correlates with election loss in a simulated voting paradigm

Another study [7] investigated how political party affil-iation and political attitudes modulate neural activity whileviewing faces of presidential candidates They found thatviewing the candidate from the opposing political partyproduced signal changes in cognitive control circuitry inthe dorsolateral prefrontal cortex and anterior cingulate aswell as in emotional regions such as the insula and anteriortemporal poles

From the traditional political research that is performedwithout any neurophysiologicalmeasurements it was alreadyknown that the negative vote plays an important role in thefinal vote decision [8ndash10] In this context it is important tounderstand the role of peoplersquos emotional behaviour afterobserving a politician compared with the one of a wholeelectoral campaign

It is very well known that the hemodynamic measure-ments of the brain activity have the spatial resolution of theorder of cubic mm being capable of detecting activationsalso in deep brain structures such as amygdala and nucleusaccumbens However the lack of time resolution due to thedelay of the cerebral bloodflow increment after the expositionto the stimuli makes the fMRI unsuitable to follow thebrain dynamics For this reason other authors also adoptdifferent tools such asmagnetoencephalography (MEG)Thistechnique is sensitive to changes of magnetic fields that areinduced by the electrical brain activity and it is able to detectrapid changes of the neural activity on a temporal scale ofmilliseconds and on a spatial scale of centimetres [11]

It is worth noting that in the past several studies elec-troencephalography (EEG) was also used as brain imagingtool for the analysis of brain activity during the observationof faces and emotional stimuli (for a review see [12]) High-resolution EEG technology has been developed to enhancethe poor spatial information content of the EEG activity inorder to detect the brain activity with a spatial resolution ofa squared centimetre and the unsurpassed time resolution ofmilliseconds [13ndash17]

Recently it was realized that the functional connectivitynetworks [18 19] estimated frombrain-imaging data obtainedby EEG MEG (magnetoencephalography) and fMRI can beinvestigated using graph theory [20ndash29] Since a graph is

a mathematical representation of a network that has beenessentially reduced to nodes and connections between themthe use of a graph-theory approach is potentially relevantand useful to quantify and describe the degree and modalityof communication among different cerebral areas as firstdemonstrated on a set of anatomical brain networks [30 31]

The use of EEG allows following the brain activity on amillisecond base but it has the problem that the recordedEEG signals are mainly due to the activity generated inthe cortical structures of the brain In fact the electromag-netic activity elicited by deep structures advocated for thegeneration of emotional processing in humans is almostimpossible to gather from usual superficial EEG electrodes[13 32] It has been underlined that a positive or negativeemotional processing of the commercial advertisements is animportant factor for the formation of stable memory traces[6] Hence it became relevant to infer the emotional engageof the subject by using indirect signs for it In fact indirectsigns of emotional processing could be gathered by pickingvariations of the activity of the anatomical structures linkedto the emotional processing activity in humans such as theactivity of sweat glands on the hands andor the variation ofthe heart rate [33] In particular by monitoring autonomicactivity using devices able to record the variation of the skinconductivity (galvanic skin responses (GSR)) and the heartrate (HR) it is possible to assess the ldquointernalrdquo emotional stateof the subject [34] In fact galvanic skin response (GSR) activ-ity is actually viewed as a sensitive and convenientmeasure ofsympathetic arousal associated with emotion cognition andattention [35] Studies using functional imaging techniques[35 36] have related the generation and level of electrodermalactivity to specific brain areas These specific regions arethe ventromedial prefrontal cortex orbitofrontal cortex leftprimary motor cortex and the anterior and posterior cingu-late which have been shown to be associated with emotionaland motivational behaviours [35 36] Such findings indicatethe close association of peripheral and central measures ofarousal emphasising the close connections between electro-dermal activity arousal attention cognition and emotion Inaddition the link between the heart rate (HR) or the heart ratevariability (HRV) and the sympathovagal balance has alsobeen suggested [37ndash39]

In this study we were interested in analyzing the neuro-electrical and autonomic activity elicited during the obser-vation of real politiciansrsquo faces who actually participated inmunicipal and regional elections held in Italy in the 2004and 2008 The aim of the present research was to investigatethe EEG activity of a group of twenty healthy subjects whilethey were asked to both vote in simulated elections and givea judgment about personal traits of real politicians shownBy means of high-resolution EEG technique we mapped sta-tistical differences of cortical spectral activity elicited duringthe observation of candidates while subjects were asked togive a judgment on dominance trustworthiness traits anda preference of vote on the faces seen To measure boththe electrical brain activity and the autonomic responseswe used simultaneous EEG GSR and HR measurementsduring the whole experiment Moreover analysis of theelectrodermal activity and the heart rate variability related

Computational and Mathematical Methods in Medicine 3

to such as stimuli has also been performed along with thestudy of the participantrsquos choice Thus these measurementshave been able to analyze the proposed social paradigmfrom several perspectives by collecting the explicit judgmentof simulated voters and by analyzing their cognitive andemotional aptitude through the estimation of cortical spectralactivity and the related functional connectivity as well asparameters of the autonomic nervous system

In particular the objectives of the present study canbe summarized through the formulation of the followingexperimental questions

(1) Is it possible to predict the elections outcome throughthe subjectsrsquo rapid and explicit judgment of domi-nance and trustworthiness traits

(2) Are there any EEG features able to predict subjectsrsquopreference of voteAre there any correlations betweenthe cortical functional activity and the judgment oftrustworthiness and dominance

(3) Is there any autonomic signature correlating withdominance and trustworthiness traits of politiciansrsquofaces

2 Material and Methods

21 Experimental Design and Stimuli Twenty healthy volun-teers (mean age 267plusmn37 years 9women) have been recruitedfor this experiment Participants were asked to vote for realpolitical candidates who run against each other during themunicipal and provincial elections of the years 2004 and2008 Pictures of thewinner and the runner-upwere collectedfrom various Internet sources (eg website of the ItalianMinistry of the Interior and local media sources) Each photohas been processed by a professional photographer in orderto convert it into black and white balance contrast andluminance Some races were unusable because of the lowresolution of candidatersquos photos For the remaining 70 racesthe image of each politician was cropped to a common size(250 times 361 pixels) and placed on a grey background All faceshave been adapted to fit the same size and to occupy the visualfield of around 59times91 degrees on a screenwith dimension of337times270mmand resolution of 1024times768 pixels Participantswere sitting at 80 cm from the screen

Our subjects were asked to give a preference of vote(vote condition) according to the images of political candi-dates Moreover subjects were also asked to judge the samepoliticians for their traits of dominance and trustworthiness(dominance and trustworthiness conditions resp) At thebeginning of each trial a neutral face was shown [40] Sub-sequently the question that subjects were asked was shownfollowed by the pictures of the two candidates presentedone by one for 1 s intermingled by blank screens of length2-3 s (random variable uniformly distributed) Finally thepictures of both politicians side by side were presented for amaximumof 2 s During that time subjects were asked to givetheir vote and judgment according to the former question bypressing the leftright arrow button on the keyboard If theydid not express any preferencewithin 2 s interval the trial will

QQQ

Question

Candidate 1

Candidate 2

Decision

Data analysis

Data analysis

∙ Who is more trustworthy (T condition)

∙ Who is more dominant (D condition)

∙ Who will have your vote (V condition)

= 1s

= 2 s

sim[2 3] s

Q

(1s)

(1s)

Figure 1 Epochs of a typical trial Each trial began with theimage of a neutral face (1 s) to allow a washout of the neurophys-iological variables Afterwards subjects were asked to express ajudgment about the subsequent political candidates related to traitsof trustworthiness dominance and preference of vote which wererandomized among trials (3 s)Then each single face of the politicalcandidates racing for the same election appeared separately and inrandom order (1 s) among subjects and judgment Finally both faceswere presented together for the decision stage (2 s) All stimuli wereintermingled by a black screen (sim[2 3] s)

be classified as an abstentionThe order of presentation of thecandidates as well as their position of appearance in the trialwas fully randomized to show each pair of faces in all threeconditions Epochs of a representative trial are illustrated inFigure 1

22 Behavioural Data For each couple of politicians andjudgment (vote dominance and trustworthiness) we col-lected the subjectrsquos choice For each experimental conditionwe calculated the percentage of correct prediction eachjudgement has been compared with the outcome of theelection through the following formula

PR =

sum119873

119894=1120575 (119881119894 )

119873times 100 (1)

where 120575 is the Kronecker delta 119881119894is the given vote or judg-

ment explicitly expressed by subjects and is the outcomeof the real elections If the subject chose the politician whoactually won the race we considered that the related outcomeof the simulated race has been correctly predicted Subjectswere not aware of the outcome of the real elections At theend of the experiment participants were asked to report if


they recognized some of the seen faces and if they knewthe real outcome of the elections In such a case the relatedtrials were discarded from the analysis Repeated-measuresanalysis of variance (ANOVA) was performed to assessdifferences between percentage of correct prediction amongjudgements For each couple of judgment (votedominancevotetrustworthiness and dominancetrustworthiness) thePearsonrsquos correlation has been calculated between the rates ofcorrect prediction of election races

23 EEG Recordings and Preprocessing The cerebral activityhas been recorded with a 61-channel system (Brain AmpBrainproducts GmbH Germany) with a sampling rate of200Hz The EEG signals have been band pass filtered at 1ndash45Hz and depurated of ocular artefacts by employing theindependent component analysis (ICA) the components dueto eye blinks and ocular movements have been detected byeye inspection and then removed from the original signalThe collected data have been segmented in order to analyzethe neurophysiological activity elicited during the 1 s observa-tion of the politiciansThus each segment was 1 s long for theEEG signal All of them have been classified into six subsetsThey were associated for instance with the observation offaces which have casted the vote (119881+) and those which didnot (119881minus) In the sameway we grouped the segments related tofaces which have been judged moreless dominant (119863+119863minus)and moreless trustworthy (119879+119879minus) respectively For eachdataset a semiautomatic procedure has been also adoptedto reject trials presenting muscular and other kinds of arte-facts Only artefacts-free trials have been considered for thefollowing analysis The extra-cerebrally referred EEG signalshave been transformed by means of the common averagereference (CAR) The individual alpha frequency (IAF) hasbeen calculated for each subject in order to define six bandsof interest according to themethod suggested in the literature[41] Such bands were in the following reported as IAF+119909where IAF is the individual alpha frequency in Hertz and119909 is an integer displacement in the frequency domain whichis employed to define the band In particular we definedthe following six frequency bands theta (IAFminus6 IAFminus2) forexample theta ranges between IAFminus6 and IAFminus2Hz alpha(IAFminus2 IAF+2) beta (IAF+2 IAF+15) and gamma (IAF+15IAF+30)

24 Estimation of Cortical Power Spectral Density The high-resolution EEG technologies [13 15 17 42ndash44] have beenadopted in order to obtain an estimation of the corticalpower spectral density (PSD) The scalp skull and duramater compartments were built by using 1200 triangles foreach structure and the boundary element model was thenemployed to solve the forward electromagnetic problemFor each subject the electrodes disposition in terms ofcoordinates on the scalp surface was calculated through anonlinear minimization procedure [45] The cortical modelconsisted of 7953 dipoles uniformly distributed on the corti-cal surface and the estimation of the current density strengthfor each dipole was obtained by solving the electromagneticlinear inverse problem according to the minimum norm

solution as described in the previous papers [17 45ndash49]Each dipole was modeled to be perpendicular to the corticalsurfaceThe cortical power spectral density (PSD) calculatedwith the Welch method [50] has been computed for eachequivalent cortical current dipole of the realistic average headmodel (Colin template 7953 cortical dipoles) by solving theelectromagnetic linear inverse problem This procedure hasallowed us to obtain a measure of PSDs values for eachcortical dipole and for each trial for all datasets In orderto take into account subjectsrsquo personal baseline activity weused the neurophysiological signals (mean and standarddeviation) related to the observation of the neutral face totransform into 119911-score variables the values of spectral powerof vote dominance and trustworthiness datasets accordingto the following formula

119885119881119863119879

=119883119881119863119879

minus 120583119873

120590119873

(2)

where 119885119881119863119879

is the 119911-score value related to the vote dom-inance and trustworthiness dataset whereas 120583

119873and 120590

119873

are mean and standard deviation related to the neutral facedataset

To improve the normality properties of such distri-butions all variables have been transformed into normaldistributions [51] Finally for each of the three conditions weused the 119905-test (119875 lt 005) to compare the transformed PSDdistribution for each equivalent cortical current dipole andthe aforementioned frequency bands of interest and adoptedthe false discovery rate correction for multiple comparisons[49]

25 Partial Directed Coherence The partial directed coher-ence PDC [52] is a full multivariate spectral measure usedto determine the directed influences between any given pairof signals in a multivariate data set PDC is a frequencydomain representation of the existing multivariate relation-ships between simultaneously analyzed time series that allowsthe inference of functional relationships between them Thisestimator was demonstrated to be a frequency version ofthe concept of Granger causality [53] according to whicha time series 119909[119899] can be said to have an influence onanother time series 119910[119899] if the knowledge of past samples of119909 significantly reduces the prediction error for the presentsample of 119910 In this study the PDC technique was appliedto the set of several regions of interest (ROIs) in which thecortical surface has been segmented according to Brodmannareas (BAs) Namely we defined the following bilateral areasof interest 10 8 5 7 37 19 946 2122 and 4142 Thischoice of the selected ROIs can be justified at the light ofthe results provided by the published literature [5ndash7] showinghow these cerebral areas are involved during the observationof political candidates (photos and videos) as well as intheir judgment Due to computational limitations of the PDCmethod we could not cover the whole cortical surface butonly select the most representative regions However thiscortical segmentation has not been taken into account forthe PSD analysis because we preferred to exploit the enhanceof the spatial resolution and to highlight changes of activity


of single equivalent cortical current dipoles estimated Insuch a way a total of eighteen ROIs has been taken intoaccount for the subsequent functional connectivity analysisperformed via partial directed coherence (PDC) and graph-theory metrics In the following we denote the ROIs signals119878

119878 = [1199041 (119905) 1199042 (119905) 119904119873 (119905)]

119879 (3)

Let us to suppose that the following MVAR process is anadequate description of the data set 119878

119901

sum

119896=0

Λ119896119878 (119905 minus 119896) = 119864 (119905) with Λ

0= 119868 (4)

In this expression 119864(119905) = [1198901(119905) 1198902(119905) 119890

119873(119905)]119879 is a vector

of multivariate zero-mean uncorrelated white noise processΛ1 Λ2 Λ

119901are the119873times119873matrices of model coefficients

and 119901 is the model order chosen in this case by means ofthe Akaike information criteria (AIC) for MVAR processes[54] Once an MVAR model is adequately estimated itbecomes the basis for subsequent spectral analysis In orderto investigate the spectral properties of the examined process(2) is transformed to the frequency domain

Λ (119891) 119878 (119891) = 119864 (119891) (5)

where

Λ (119891) =

119901

sum

119896=0

Λ119896119890minus1198952120587119891Δ119905119896

(6)

and Δ119905 is the temporal interval between two samplesIt is then possible to define PDC as

120587119894119895(119891) =

Λ119894119895(119891)

radicsum119873

119896=1Λ119896119895(119891)Λ

lowast

119896119895(119891)

(7)

Such formulation was derived by the well-known conceptof partial coherence [52] The PDC from 119895 to 119894 120587

119894119895(119891)

describes the directional flow of information from the signal119904119895(119899) to 119904

119894(119899) whereupon common effects produced by other

electrodes 119904119896(119899) on the latter are subtracted leaving only a

description that is exclusive from 119904119895(119899) to 119904

119894(119899)

PDC values are in the interval [0 1] and the normaliza-tion condition

119873

sum

119899=1

10038161003816100381610038161003816120587119899119895(119891)

10038161003816100381610038161003816

2

(8)

is verified According to this condition 120587119894119895(119891) represents

the fraction of the time evolution of electrode 119895 directed toelectrode 119894 as compared to all of 119895rsquos interactions to otherelectrodes

Even if this formulation derived directly from informa-tion theory the original definition was modified in order togive a better physiological interpretation to the estimationresults achieved on electrophysiological data In particulartwo modifications have been proposed First a new type of

normalization already used for another connectivity estima-tor such as directed transfer function [55] was introduced bydividing each estimated value of PDC for the root squaredsums of all elements of the relative row and then a squaredversion of the PDC was introduced [56]

sPDC119894119895(119891) =

10038161003816100381610038161003816Λ119894119895(119891)

10038161003816100381610038161003816

2

sum119873

119898=1

1003816100381610038161003816Λ 119894119898 (119891)1003816100381610038161003816

2 (9)

The better performances of sPDC have been demonstratedin simulation studies which revealed reduced error levelsboth in the estimation of connectivity patterns on datacharacterized by different lengths and SNR and in distinctionbetween direct and indirect paths [56] Such formulationwas used in this study for the estimation of functionalconnectivity

26 Statistical Validation of Connectivity Patterns Randomfluctuations of signals induced by environmental noisecould lead to the presence of spurious links in the con-nectivity estimation process In order to avoid such falseconnections it is necessary to apply a method for thestatistical validation of estimated connectivity patterns Inorder to assess the significance of the estimated connectivitypatterns the value of effective connectivity for a given pairof electrodes obtained by computing PDC [57 58] mustbe statistically compared with a threshold level which isrelated to the lack of transmission between considered ROIs(null hypothesis) Threshold values were estimated usingasymptotic statistic [59] a recently introduced method basedon the assumption that PDC in the null case follows a1205942 distribution [60] The statistical threshold for the null

case is achieved by applying a percentile related to a givensignificance level on a 120594

2 distribution derived by meansof Monte Carlo method directly from the data The highaccuracy of the asymptotic statistic method in the assessmentprocess has been demonstrated in a simulation study inwhichthis new method was compared with the shuffling procedure[61 62] a time consuming methodology currently availablein the functional connectivity field [63]

The statistical validation process had to be applied on eachcouple of signals for each frequency sample This necessityled to the execution of a high number of simultaneouslyunivariate statistical tests with evident consequences in theoccurrence of type I errors The statistic theory providesseveral techniques that could be usefully applied in thecontext of the assessment of connectivity patterns in orderto avoid the occurrence of false positives [64] In particularwe chose the false discovery rate (FDR) method [65 66]because it has been demonstrated to be a good compromisein preventing both type I and type II errors occurred duringconnectivity estimation [67 68]

After the validation process the PDC estimation isaveraged within four frequency bands defined according toindividual alpha frequency (IAF) [41] in order to take intoaccount the variability among subjects of the alpha peak in thespectrumThe range for each frequency band is theta [IAFminus6IAFminus2] alpha [IAFminus2 IAF+2] beta [IAF+2 IAF+15] and


gamma [IAF+15 IAF+30] PDC connections of the threejudgments (119881 119879 and 119863) have been separately comparedbetween valence (+ minus) for each band of interest by perform-ing multiple Studentrsquos 119905-test on ROIs FDR corrected

27 Graph Theory A graph consists of a set of vertices (ornodes) and a set of edges (or connections) indicating thepresence of some sort of interaction between the verticesThe adjacency matrix 119860 contains the information about theconnectivity structure of the graph When a directed edgeexists from the node 119894 to 119895 the corresponding entry of theadjacencymatrix is119860

119894119895= 1 otherwise119860

119894119895= 0The existence

of an edge is stated on the basis of its statistical significanceassessed by means of asymptotic statistic approach Thehigher reliability of the statistical approach for extracting theadjacency matrix has been demonstrated in [67] where adetailed comparison with the empirical methods is providedIn graph theory a path or a walk is a sequence of vertices inwhich from each of its vertices there is an edge to the nextvertex in the sequence Such adjacencymatrix can be used forthe extraction of salient information about the characteristicof the investigated network by defining several indexes basedon the elements of such matrix

Density Density is the fraction of present connections topossible connections Connection weights are ignored incalculations Density is defined as follows

119863 =

sum119873

119894=1sum119873

119895=1119860119894119895

119873(119873 minus 1)times 100 (10)

where 119860119894119895represents the entry 119894119895 of the adjacency matrix 119860

For each judgment (119881 119879 and 119863) we performed arepeated measure ANOVA with factor BAND (theta alphabeta and gamma) to compare the density value among bandsof interest

DegreeThedegree of a node is the number of links connecteddirectly to it In directed networks the indegree is the numberof inward links and the outdegree is the number of outwardlinks Connection weights are ignored in calculations [69] Itcan be defined as follows

119896119894= sum

119895isin119873

119860119894119895 (11)


Indegree and outdegree of the three judgments (119881119879 and119863) have been separately compared between valence (+ minus) foreach band of interest by performing multiple Studentrsquos 119905-teston ROIs FDR corrected

28 Autonomic Data Recording and Signal Processing Theautonomic activity both the Galvanic Skin Response (GSR)and the Heart Rate (HR) has been recorded by means of thePSYCHOLABVD13S system (SATEM Italy) with a samplingrate of 100Hz Skin conductancewas recorded by the constantvoltage method (05 V) Ag-AgCl electrodes (8mm diameterof active area) were attached to the palmar side of the middle

phalanges of the second and third fingers of the participantrsquosnon dominant hand by means of a velcro fastener The com-pany also provided disposable Ag-AgCl electrodes to acquirethe HR signal Before applying the sensors to the subjectsrsquoskin their surface has been cleaned following procedures andsuggestions published in the international literature [70ndash72]and GSR and HR signals have been continuously acquiredfor the entire duration of the stimulation and then filteredand segmented with in-house MATLAB software in order toanalyse the autonomic activity related to the observation ofthe politician faces

The GSR signal has been downsampled to 20Hz andsubsequently low pass filtered at 45Hz to filter out noise andsuppress artefacts caused by Ebbecke waves [70 73] In orderto split the phasic component of the electrodermal activity(skin conductance response (SCR)) from the tonic one (skinconductance level (SCL)) we acted as follows on the filteredGSR signal

(1) minima points detection within a 100 samples slidingwindow (5 seconds)

(2) linear interpolation of minima points(3) smoothing by means of a moving average (100 sam-

ples sliding window) This operation generates theSCL signal

(4) subtraction of the SCL signal from the filtered GSRThis operation generates the SCL signal

In such a way we split the GSR signal into a phasic (SCR)and a tonic (SCL) component and then segmented the tracesby taking into account 3 s long segments from the beginningof the face exposition Afterwards we generated the sametype of datasets as defined for the EEG analysis As far asconcerns the SCL we calculated the time average for eachsegment and experimental condition whereas for the SCRwetook into account the average peak number within the datasegment The results of these parameters will be showed inthe following section

The HR signal has been low pass filtered at 1Hz inorder to analyse the frequency components due to variationsof the sympathetic and parasympathetic nervous systemregardless the ones associated with thermoregulatory cycles[74 75] Hence this waveform has been segmented by takinginto account 3 s length segments from the beginning of theface exposition Afterwards we generated the correspondingdatasets defined for the EEG analysis From each segmentwe calculated the average beats per minute and the powerspectrum density (PSD) according to theWelchmethod [50]In this way we obtained a signal in the frequency domainfor the all experimental conditions and subjects Spectralcomponents were identified and then assigned on the basisof their frequency to one of two bands low frequency(LF) [004 015]Hz high frequency (HF) [015 06]Hz[37] The very low frequency (VLF) band located in thelowest part of the spectrum has been excluded from thepresent analysis since it is physiologically connected withlong-term regulation mechanisms [74 75] not of interestfor our purpose Several studies indicate that the LF bandcorresponds to baroreflex control of the heart rate and


reflects mixed sympathetic and parasympathetic modulationof heart rate variability (HRV) instead HF band correspondsto vagally mediated modulation of HRV associated withrespiration [37 74ndash76] For this reason some researchers [37]propose the ratio LFHF as index of the balance between thesympathetic and vagal activity

All autonomic variables of interest (SCL SCR HRand LFHF) and experimental datasets (119881+ 119881minus 119863+ 119863minus119879+ and 119879

minus) have been standardized by means of the 119911-score transformation by using the dataset referred to theneutral face (119873) as reference baseline Repeated-measuresanalyses of variance (ANOVA) were performed to assessdifferences in SCL SCR HR and LFHF Mauchlyrsquos testevaluated the sphericity assumption and where appropriatecorrection of the degrees of freedom was made accordingto the Greenhouse-Geisser procedure Bonferroni correctionwas applied to all post hoc tests (pairwise comparisons) Thestatistical analysis has been performed by the SPSS (v16)software

According to the presentedmethodology the intersubjectvariability has been taken into account by computing the 119911-score standardization of the experimental conditions datasets(observation of faces during judgments of vote dominanceand trustworthiness) and the mean and standard deviationrelated to the neutral condition (observation of neutralface) Moreover also the chosen statistical method (pairedStudentrsquos 119905-test) avoids intersubject variability Instead inorder to address the interstimuli variability according tothe illustrated experimental paradigm the single politiciansfaces have been observed and judged differently by eachparticipant In this way each participant has hisher ownpersonal datasets (119881+119881minus 119863+119863minus and 119879+119879minus) In fact theaim of the present analysis is to investigate the cerebralreaction due to personal judgments rather than objectivefeatures and expressions of singles candidatesHence data arecompared for a population analysis by means of the paired 119905-test

3 Results

31 Behavioural Results In order to asses significant dif-ferences among rates of correctly predicted races in thethree experimental conditions we employed a multivariaterepeated measures ANOVA design with judgment (votedominance and trustworthiness) as a factor and the per-centages of correctly predicted races as a dependent vari-able Average values of correct percentages are reported inFigure 2

The statistical analysis of correct percentages showeda significant difference for the factor judgment across thedifferent levels [vote = (4465plusmn704) dominance = (5115plusmn668) and trustworthiness = (4399 plusmn 748) 119865(382) =934 119875 lt 001] Hence the percentage of correct pre-diction depends on the judgment Specifically the pairwisecomparisons revealed significant differences for the contrastvote versus dominance (119875 lt 001) and dominance versustrustworthiness (119875 lt 001) whereas no difference has beenfound between vote and trustworthiness (119875 gt 005)

010203040506070

Vote Dominance Trustworthiness

()

Rates of correct predictionlowast lowast

Figure 2 Average rates of correct prediction for the three judgmentsof vote dominance and trustworthiness Error bars indicate stan-dard deviations Significant pairwise comparisons are highlightedwith asterisks

Subjectsrsquo judgment about vote dominance and trust-worthiness underwent a correlation analysis For each pairof judgments (votedominance votetrustworthiness anddominancetrustworthiness) we computed the Pearsonrsquos cor-relation among the number of subjects who expressed theirpreference for the real election winner

The results show that judgments concerning vote andtrustworthiness are positively correlated (119877 = 085 119875 lt 001)while the ones regarding vote anddominance (119877 = minus064119875 lt

001) and dominance and trustworthiness (119877 = minus060 119875 lt

001) are negatively correlated Figure 3 shows the correlationbetween vote and trustworthiness conditions

32 Cortical Patterns of Power Spectral Density The EEGsignals gathered during the observation of the politicianswere subjected to the estimation of the cortical power spectraldensity by using the techniques described in theMethods sec-tion In each subject the cortical PSDwas evaluated in the fre-quency bands adopted in this study and contrasted betweenexperimental conditions These cortical distributions of PSDobtained during the observation of politicians were thenorganized in six datasets (119881+119881minus 119863+119863minus and 119879+119879minus) TheStudentrsquos 119905-test has been performed between these corticalPSD distributions of homologous datasets (ie 119881+ versus119881minus) The resulting statistical spectral maps highlight cortical

areas in which the estimated PSD statistically differs betweentwo conditions In the following statistical spectral maps weshow only the results in the theta and alpha frequency bandsbecause they resulted the ones with most activations

Figures 4 5 and 6 present cortical maps in which thebrain is viewed from a frontal perspectiveThemaps are rela-tive to the contrast regarding the conditions vote dominanceand trustworthiness (ie 119881+ versus 119881minus) in the frequencybands of interest The colour scale on the cortex codesthe statistical significance where there are cortical areas inwhich the power spectrum does not differ between the twoconditions the grey colour is used The red colour presentsstatistically significant power spectral activity greater in thecondition 119881+(119863+ 119879+) with respect to 119881minus(119863minus 119879minus) while theblue colour codes the opposite situation


0

5

10

15

20

25

0 5 10 15 20

Vote

()

Trustworthiness ()

Correlation between judgments

R = 07173

(a)

0

5

10

15

20

0 5 10 15 20 25

Dom

inan

ce (

)

Vote ()

R = 04045


(b)

0

5

10

15

20

0 5 10 15 20

Trus

twor

thin

ess (

)

Dominance ()

R = 03618Correlation between judgments

(c)

Figure 3 Scatterplots of number of subjects expressing their preference for the real election winner related to judgments of trustworthiness(119909-axis) and vote (119910-axis) on (a) vote (119909-axis) and dominance (119910-axis) in (b) and dominance (119909-axis) and trustworthiness (119910-axis) on (c)Each dot represents a single election race with the corresponding number of subjects whomade their choice for the real winner of the electionIn each scatterplot there is the related Pearsonrsquos correlation coefficient all significant at 119875 lt 001

Vote

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

V+gt V

minus

Vminusgt V

+

120599

120572

Figure 4 The picture presents four cortical 119905-test maps of PSD values for the vote condition in the theta (upper row) and alpha (lower row)bands As to the theta band the cortex model is seen from a front-left side (left) and from a frontal perspective (right) As to the alpha bandthe cortexmodel is seen from a front-right side (left) and from a frontal perspective (right) Colour bar indicates in red cortical areas in whichincreased statistically significant activity occurs in the 119881+ dataset when compared to the 119881minus dataset Blue colour is used when the activity isstatistically higher in the 119881minus than in the 119881+ condition (119905 values at 119875 lt 005 FDR corrected) Grey colour is used to map cortical areas wherethere are no significant differences between the cortical activity in the two experimental conditions


Trustworthiness

120599

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120572

T+gt T

minus

Tminusgt T

+

Figure 5 The picture presents four cortical 119905-test maps of PSD values for the trustworthiness condition in the theta (upper row) and alpha(lower row) band As to the theta band the cortex model is seen from a front-right side (left) and from a frontal perspective (right) As to thealpha band the cortex model is seen from a front-left side (left) and from a frontal perspective (right) Colour bar indicates in red corticalareas in which increased statistically significant activity occurs in the 119879+ dataset when compared to the 119879minus dataset Blue colour is used whenthe activity is statistically higher in the 119879minus than in the 119879+ condition (119905 values at 119875 lt 005 FDR corrected) Grey colour is used to map corticalareas where there are no significant differences between the cortical activity in the two experimental conditions

Figure 4 presents the four statistical cortical maps relatedto the comparisons of PSD values in the theta and alphabands for the vote condition These maps highlight a frontalasymmetry which discriminates 119881+ and 119881

minus conditions inboth theta and alpha bands Specifically it is possible toobserve an increase of PSD across frontal and central areasin the theta band for the 119881

+ condition Findings in thealpha band return a significant desynchronization for the 119881minuscondition in the frontal central and parietal cortical regionsof the right hemisphere In addition a smaller frontal regionof the left hemisphere accounts for a desynchronization forthe 119881+ condition

Figure 5 presents the contrast between the 119879+ and 119879minus

conditions in the theta and alpha bands As similarlyobserved for the vote condition the comparison between 119879+and 119879minus also revealed an asymmetrical pattern of activationIn this case most of the increase of PSD in the theta band isdue to the 119879minus condition involving left frontal cortical areasHowever a significant spot of activation for the 119879+ conditionis also visible around right frontal regions The significantdesynchronization of the frontal activity in the alpha bandis associated with the 119879+ condition From Figure 5 it is alsopossible to observe a sparse activation in left central andtemporal areas


conditions in the theta and alpha bands In this case thesignificant activations in the theta band show an involvementof the frontal midline regions for the 119863minus condition whereas

a significant desynchronization of the alpha rhythm is visibleacross left frontal areas for the opposite condition119863+

33 Analysis of Functional Connectivity Patterns and DegreeThe two-way ANOVA performed on the density index indi-cated significant difference for factor BAND for all the threeexperimental conditions [vote 119865(357) = 455 119875 lt 001dominance 119865(357) = 452 119875 lt 001 and trustworthiness119865(357) = 423 119875 lt 001] This result led us to consider onlytheta and alpha bands for the following analysis In fact bothbeta and gammapresent smaller density values than the lowerfrequency ranges and they are close to zero

The connectivity patterns have been estimated by partialdirected coherence (PDC) to the spatially averaged wave-forms related to different ROIs considered in the studyand statistically compared as described in the Methodssection Studentrsquos 119905-tests have been performed between PDCdistributions of homologous datasets (ie119881+ versus119881minus)Theresulting statistical connectivitymaps highlight cortical areasalongwith the related strength of the connection inwhich theestimated PDC statistically differs between two conditionsSimilarly the analysis of indegree and outdegree has beencomputed by Studentrsquos 119905-test for each ROI and experimentalcondition

Figures 7 8 and 9 present cortical maps as well as valuesof indegree and outdegree in which the brain is viewedfrom above The results are related to the contrast regarding


Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+

Figure 6The picture presents four cortical 119905-test maps of PSD values for the dominance condition in the theta (upper row) and alpha (lowerrow) bandsThe cortex model is seen from a front-left side (left) and from a frontal perspective (right) for both theta and alpha bands Colourbar indicates in red cortical areas in which increased statistically significant activity occurs in the119863+ dataset when compared to the119863minus datasetBlue colour is used when the activity is statistically higher in the 119863minus than in the 119863+ condition (119905 values at 119875 lt 005 FDR corrected) Greycolour is used tomap cortical areas where there are no significant differences between the cortical activity in the two experimental conditions

the conditions vote dominance and trustworthiness (ie119881+ versus 119881minus) in the activated frequency bands of interest

The colour scale on the cortex codes the ROIs and the greycolour is used otherwise The red colour presents statisticallysignificant connections in the condition 119881

+(119863+ 119879+) with

respect to 119881minus(119863minus 119879minus) while the blue colour codes the

opposite situationFigure 7 presents the two statistical connectivity maps

related to the comparisons of PDC values in the theta andalpha bands for the vote condition on the right side of thepicture On the left side the related comparison for valuesof in- and outdegrees are reported These results highlighta strong involvement of frontal and parietal right areas inboth theta and alpha bands Particularly in the theta bandthe largest amount of significant inter- and intrahemisphericconnections emerges in the left hemisphere related to the119881+ condition An interhemispheric flow between the BA10 L

and BA8 R is the strongest for the 119881minus condition From thestatistical comparison of in- and outdegrees we may observethat the only significant result highlights the BA19 R as theROIwith highest value of outdegree for the condition119881minusThestatistical pattern in the alpha band presents an involvementof inter- and intrahemispheric connection thickened in theright hemisphere in the119881+ condition However a significantconnection between BA37 R and BA946 R for the 119881

minus

condition appears These results are supported by in- andoutdegrees values which return the BA10 R as a source ofinformation in the119881+ condition whereas the BA37 R sourcein the 119881minus condition

Figure 8 presents the two statistical connectivity mapsrelated to the comparisons of PDC values in the theta andalpha bands for the trustworthiness condition on the right

side of the picture On the left side the related comparisonsfor values of in- and outdegrees are reported These resultshighlight an involvement of frontal and parietal left areas inboth theta and alpha bands although only a few significantconnections emerge Particularly in the theta band there isa parietal-frontal connection in the 119879+ condition whereastwo parietal BAs are connected in the 119879minus condition Fromthe statistical comparison of in- and outdegrees there areno ROIs resulting different between the two experimentalconditions in the theta band The statistical pattern in thealpha band shows the existence of two intrahemispheric con-nections related to the 119879minus between frontal and parietotem-poral regions There is only one significant link between theBA2122 L and BA5 R regarding the 119879

+ condition Fromthe statistical comparison of in- and outdegrees there areno ROIs resulting different between the two experimentalconditions in the alpha band

Figure 9 presents the two statistical connectivity mapsrelated to the comparisons of PDC values in the theta andalpha bands for the dominance condition on the right sideof the picture On the left side the related comparison forvalues of in- and outdegrees are reported These resultshighlight a strong involvement of frontal and parietal rightareas in both theta and alpha bands Particularly in thetheta band intrahemispheric parietofrontal connections arerelated to the119863minus condition whereas intrahemispheric frontalconnections regard the 119863+ condition From the statisticalcomparison of in- and outdegrees we may observe thatthe BA37 R and BA2122 R are source of information forthe 119863+ condition whereas BA8 R for the 119863minus condition isa sink The statistical pattern in the alpha band presentsan involvement of inter- and intrahemispheric connection


minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+

Figure 7 In- and outdegree values (left side) and PDC links (right side) in the theta (upper row) and alpha (lower row) bands for the votecondition The cortex model is seen from above for both theta and alpha bands Colour bar indicates PDC connections in which increasedstatistically significant activity occurs in the 119881+ dataset when compared to the 119881minus dataset in red Blue colour is used when the increase forthe119881minus activity is statistically higher than the one in the119881+ condition (119905 values at 119875 lt 005 FDR corrected)The arrows depict the statisticallysignificant connections estimated between the activities recorded in the vote conditionThe color and size of the arrows code for the strengthof the interaction as reported by the color bar on the right A color map highlights the ROIs whereas grey colour is used to map corticalareas that have not been used in the analysis Red circles in the left part of the figure highlight significant difference of in- and outdegrees forthe theta and alpha bands

thickened in the left hemisphere in both the 119863+ and 119863minus

conditions In particular the information flow is directedfrom right parietal to left and temporal frontal areas in 119863+condition and vice versa for119863minus condition From the statisticalcomparison of in- and outdegrees there are noROIs resultingdifferent between the two experimental conditions in thealpha band

34 Analysis of the SCL and SCR To assess the effect ofquestions on answers we performed a two-way repeated-measures ANOVA on SCL and on SCR with judgment (119863119879 and 119881) and valence (+ minus) as factors As far as the SCLis concerned we did not find significant difference for anyfactor [judgment 119865(236) = 177 119875 = 018 valence 119865(118) =004119875 = 083 judgment times valence119865(236) = 063119875 = 054]However we also performed Studentrsquos 119905-tests to comparepairs of judgmentsThese statistical analyses returned that theSCL values for the dominance condition are higher than those

in trustworthiness [119905(19) = 21 119875 lt 005] and no differencebetween the other conditions

As to the analysis of the SCR the two-way repeated-measures ANOVA did not highlighted any significant dif-ference for any factor [judgment 119865(236) = 021 119875 = 082valence 119865(118) = 198 119875 = 018 judgment times valence119865(236) = 018 119875 = 080] However there is a trend as to the119911-score of SCR values since for all the six conditions they arealways positive with SCR values related to the condition ldquo+rdquolarger than those related to the condition ldquominusrdquo

35 Analysis of the HRV To assess the effect of judg-ments on the choices of subjects we performed a two-way repeated-measures ANOVA on HR and LFHF withjudgment (119863 119879 119881) and valence (+ minus) as factors

The two-way ANOVA on the HR parameter did notreturn any significant result for this comparison [judgments119865(236) = 199 119875 = 015 valence 119865(118) = 001 119875 = 095judgments times valence 119865(236) = 0184 119875 = 083] Picture


Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Figure 8 In- and outdegree values (left side) and PDC links (right side) in the theta (upper row) alpha (lower row) bands for the votecondition The cortex model is seen from above for both theta and alpha bands Colour bar indicates PDC connections in which increasedstatistically significant activity occurs in the 119879+ dataset when compared to the 119879minus dataset in red Blue colour is used when the increase forthe 119879minus activity is statistically higher than the one in the 119879+ condition (119905 values at 119875 lt 005 FDR corrected) The arrows depict the statisticallysignificant connections estimated between the activities recorded in the trustworthiness condition The color and size of the arrows code forthe strength of the interaction as reported by the color bar on the right A color map highlights the ROIs whereas grey colour is used to mapcortical areas that have not been used in the analysis

highlights that HR values related to the condition dominanceare both negative with respect to the baseline (neutral face)whereas the factor vote presents positive values As to thefactor trustworthiness the conditions ldquo+rdquo and ldquominusrdquo havea positive (001) and negative (minus003) values respectivelyHowever we also performed Studentrsquos 119905-tests to comparepairs of judgments These statistical analyses returned thatthe HR values for the vote condition are higher than those indominance [119905(19) = 211 119875 lt 005] and no difference betweenthe other conditions

The two-way ANOVA on the LFHF parameter did notreturn any significant result for this comparison [judgment119865(236) = 147 119875 = 024 valence 119865(118) = 066 119875 = 043judgment times valence 119865(236) = 012 119875 = 099] In eachcondition the average value of the sympathovagal balanceis negative This means that the LFHF values are alwayslarger during the observation of the neutral face comparedto the exposition of politicians Moreover the LFHF valuesrelated to the condition dominance are the smallest onesHowever we also performed Studentrsquos 119905-tests to compare

pairs of judgmentsThese statistical analyses returned that theHR values for the trustworthiness condition are lower thanthose in dominance [119905(19) = 21 119875 lt 005] and no differencebetween the other conditions

4 Discussion

41 Explicit Judgment The analysis conducted on the partic-ipantrsquos choice returned low percentages of correct predictionfor all the three possible judgments Two of them are belowthe 50 (vote 446 trustworthiness 439) while the onlyjudgment about dominance shows a percentage of 5115 Infact for our subjects the judgment on the dominance traitresulted the most predictive with respect to trustworthinessand preference of vote of the election outcome This resultcould appear contrasting the findings previously publishedin the literature [1 2] since they reported a goodness ofprediction around 70 in guessing the elections outcomeThis difference could be due to the exiguous number ofparticipants enrolled in our study (20) if compared to the


Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+

Figure 9 In- and outdegree values (left side) and PDC links (right side) in the theta (upper row) alpha (lower row) bands for the votecondition The cortex model is seen from above for both theta and alpha bands Colour bar indicates PDC connections in which increasedstatistically significant activity occurs in the 119863+ dataset when compared to the 119863minus dataset in red Blue colour is used when the increase forthe119863minus activity is statistically higher than the one in the119863+ condition (119905 values at 119875 lt 005 FDR corrected)The arrows depict the statisticallysignificant connections estimated between the activities recorded in the trustworthiness condition The color and size of the arrows code forthe strength of the interaction as reported by the color bar on the right A color map highlights the ROIs whereas grey colour is used tomap cortical areas that have not been used in the analysis Red circles in the left part of the figure highlight significant difference of in- andoutdegrees for the theta band

number of subjects utilized in their previous researches (morethan 100) In fact a more recent study performed with fMRI[5] enrolling 24 participants reported lower percentage injudging winners of real elections as more competent (55)This evidence could suggest a positive correlation betweenthe traits of dominance and competence employed in the twostudies Although Oosterhof and Todorov [40] establishedmean ratings for computer modelled faces on 14 trait dimen-sions showing a certain correlation among them the trait ofcompetence is not in the trait set used for the study Hencewe do not discuss about the degree of correlation betweenthe traits of competence and dominance However in thesame work traits of dominance and trustworthiness appearto be orthogonal Instead dominance appears to be correlatedwith aggressiveness Hence it was not surprising for us toget a similar result for real faces This kind of dependencecould explain the positive result in predicting winners asmore dominant and the negative result in prediction aboutthe trustworthiness judgment However it is important toremember that our participants were asked to give their

judgments according to a fast exposition of unknown faceof politicians Therefore they do not observe more than afew physical traits and know nothing related to their publicbehavior

The correlation analysis concerning answers about thethree judgments revealed that our experimental subjects tendto vote politicians appearing more trustworthy However weobserved that this judgment is not able to predict electionsoutcomes

42 Cortical Patterns of Power Spectral Density and FunctionalConnectivity Thehigh-resolution EEG analysis returned sta-tistically significant activations in all experimental conditionsof vote dominance and trustworthiness judgments Overallevidence highlights an asymmetrical cerebral activationmostly related to the frontal areas during the observationof politicians that will be judged trustworthy and dominantand for those who will get the vote in simulated electionsThus such neuroelectrical features seem to be able to predict


judgments of trustworthiness and dominance as well as thedecision of vote

Particularly the vote condition elicited the most signif-icant variations of the PSD in the alpha band The relatedcortical maps show a significant desynchronization of thealpha rhythm among frontal and parietal areas of the righthemisphere during the observation of politicians that havenot been voted in our simulated elections along with asmaller increase of PSD in left frontal areas for the oppositecondition Analogously the pattern of activations in the alphaband for the trustworthiness condition show a desynchro-nization of the left frontal regions associated with the obser-vation of candidates that will be judged more trustworthyInstead looking at the cortical maps in the theta band in bothexperimental conditions it is possible to appreciate almosta reverse behavior of the frontal regions Specifically leftfrontal areas are more involved during the observation ofcandidates that will be voted whereas right frontal areas aremore activated during the observation of politicians that willbe judged less trustworthy Hence the asymmetrical patternof activation in these particular tasks seems to concern boththeta and alpha bands In the dominance condition it ispossible to observe that the desynchronization of the leftfrontal regions is related to the observation of candidates thatwill be judged more dominant Adversely the activations inthe theta bandmostly regard the frontalmidlinewhich resultsactivated during the observation of politicians resulting lessdominant

Previous studies have shown that the frontal cortex (FC)is anatomically and functionally connected to structureslinked to the emotional processing activity in humans [77]Thus indirect variables of emotional processing could bealso gathered by tracking variations of these cerebral cor-tical frontal areas In fact although the frontal cortex isstructurally and functionally heterogeneous its role in thegeneration of the emotions is well recognized [78] EEGspectral power analyses indicate that the anterior cerebralhemispheres are differentially lateralized for approach andwithdrawal of motivational tendencies and emotions Specif-ically findings suggest that the left frontal and orbitofrontalcortex is an important brain area in a widespread circuit thatmediates appetitive approach while the right homologuesregions appear to form amajor component of a neural circuitthat instantiates defensive withdrawal [79 80]

Sutton and Davidson [81] found that greater left-sided activation predicted dispositional tendencies towardapproach whereas greater right-sided asymmetry predicteddispositional tendencies toward avoidance In contrast theirfrontal asymmetry measurement did not predict disposi-tional tendencies toward positive or negative emotions sug-gesting an association of frontal asymmetry with approachavoidance rather than with valence Other sources of dataconverge on a similar model of frontal asymmetry Of partic-ular importance are studies that link anger an unpleasant butapproach-related emotion to greater left-hemispheric activa-tion [82 83] Also tendencies toward worry thought to beapproach-motivated in the sense of being linked to problemsolving have been linked to relatively greater left frontal EEGactivity [84] Thus the emerging consensus appears to be

that frontal EEG asymmetry primarily reflects levels ofapproach motivation (left hemisphere) versus avoidancemotivation (right hemisphere) as also testified by previousstudies [85ndash88]

Hence the present results could be interpreted by tak-ing into account the approach-withdrawal theory [78ndash80]explaining that there exists an EEG asymmetry mostlyinvolving frontal regions discriminating appealing pleas-antness situations from unattractive and unpleasant onesa desynchronization of the alpha rhythm in left frontalareas is experienced when subjects are involved in positivelyjudged contexts whereas a desynchronization of same areasin the right hemisphere is often associated with rejectingexperiences In addition our experiment also highlights afrontal-hemispheric asymmetry involving the theta bandTherefore the faces of politicians that will be voted andjudged trustworthy arise feelings of approach whereas thosecandidates that will be judged less trustworthy and notadequate for winning the ballot generate emotions of detachand refusal This interpretation emerges from consideringthe activations in both theta and alpha bands and is inagreement with the behavioral results providing a high andsignificant correlation between the judgment of trustworthi-ness and the choice of vote When subjects expressed judg-ment on dominance trait politicians judged more dominantelicited an alpha desynchronization across left frontal andorbitofrontal areas As anger also the trait of dominancecould be considered as an approach-related emotion with anegative valence and high arousal in a tentative to reconcilethe concept of discrete emotions in terms of combinations ofmultiple dimensions [89 90] According to this perspectivethe alpha activity in the right hemisphere is in agreementwith the approach-withdrawal theory On the contrary lowdominance could lead to withdrawal-related emotions withpositive valence and low arousal Observation of politiciansjudged less dominant caused a significant activation of themedial frontal cortex (mFC) in the theta band Such signalscould be connected to the activity of the anterior cingulatecortex (ACC) In fact the circuit ACC-mFC is involved inprocessing emotions In particular Phan et al [91] reportedthat 60 of the studies they reviewed found activationin the medial frontal cortex (mFC) whereas Murphy etal [92] reported the strongest localization pattern in thesupracallosal ACC both related to sadness which is a lowarousal emotion Moreover the link between frontal midlineand the activity in the anterior cingulate cortex is also shownthrough listening to pleasant music since ACC is activated inmusical tasks [93 94]This evidence shows thatACCcould beinvolved in processing low arousal pleasant and withdrawal-related emotions Our result regarding the dominance traitand the activation of ACC is also in agreement with thestudy by Spezio et al [5] showing that this specific brainarea is a predictor of the simulated election loss In fact theobservation of politicians judged less dominant elicited anincrease of activity in the frontal midline which is negativelycorrelated with the lab vote share In addition the activationrelated to the dominance condition could reflect neuralprocessing to disgusted facial expressions and angry faceswhich exhibit involvement of the right putamen and the left


insula cortex enhancing activity in the posterior part of theright gyrus cinguli and the medial temporal gyrus of theleft hemisphere Fearful expressions also activate the rightfusiformgyrus and the left dorsolateral frontal cortex [95 96]

The role of prefrontal cortex is also in agreement withthe experiment performed by Kato and colleagues [6] thatillustrate how stronger fMRI activation in the dorsolateralprefrontal cortex lowered ratings of candidates originallysupported more than did those with smaller fMRI signalchanges in the same region On the contrary the subjectsshowing stronger activation in the medial prefrontal cortextended to increase their ratings compared to subjects with lessactivation

The involvement of left ventral frontal cortex can be alsoassociated with neural response to facial emotions regardlessof the conscious mediation [97] In addition the activity ofthe temporal lobe has already been reported belonging tojudgments of trustworthiness trait [98]

Although it has not been investigated in the presentstudy it could be debated that the discussed patterns ofactivationmay vary according to the gender In fact previousstudies dealing with emotions processing report that femalesrespond strongly than males to emotional and negative facesjudged unpleasant and high arousing stimuli As reportedby Lithari et al [99] these differences involve mechanismslocalized across central and left brain regions Supportingthis evidence Prause et al [100] found that women eliciteda stronger frontal alpha asymmetry during the observationof sexually motivating films with a greater alpha power inthe left hemisphere Similar gender differences have been alsospecifically inspected during judgment of facial attractiveness[101] In fact Zhang and Deng report a delayed P1 andP3b latencies in response to attractive faces with slowerresponse times in men compared to women A further studyalso shows that women tend to prioritize the processing ofsocially relevant and negative emotional information [102]Overall such results summarize that emotion processingmostly involve central and left frontal regions both for menand women although the latter does it with a strongerintensity and smaller latency Instead an important func-tional gender differentiation could be investigated across theright hemisphere [103] In fact right-lateralized anticipatoryactivity selectively influenced the encoding of unpleasantpictures in women but not in men These findings indicatethat anticipatory processes influence theway inwhichwomenencode negative events into memory The selective use ofsuch activity may indicate that anticipatory activity is onemechanism by which individuals regulate their emotionsalso in face processing although this affirmation needs to beconfirmed by further experiments

43 Autonomic Variables As far as the results of the elec-trodermal activity are concerned we reported that the toniccomponent of the galvanic skin response is significantly lowerwhile participants observed politicians during the trustwor-thiness judgment when compared with the judgment ofdominance and vote Specifically SCL in the trustworthinesscondition is lower than the average value elicited during the

observation of a neutral face whereas SCL in dominanceand vote conditions is higher than that recorded in thesame baseline condition According to Critchley [35 104]the tonic level of the galvanic skin response decreases duringattentional tasks The SCL elicited in our experimental taskvaries according to the judgment to be expressed Since thejudgment about trustworthiness returned lower SCL valuesit seems that this task is more demanding if compared to thequestion of dominance and vote Instead as to the phasiccomponent of the galvanic skin response we did not findany significant difference from the statistical point of viewHowever by observing the average peak amplitude of theSCR values related to the choice of subjects appear higherthan values associated with politicians that have not beenchosen though not statistically significant Hence the obser-vation of politicians that will be later judged irrespective ofthe proposed trait could induce a stronger emotional statereflected in a variation of the autonomic nervous system

As to the analysis of the HRV statistical tests performedon average values of heart rate returned an increase ofthe heart rate during the observation of politicians to bevoted whereas the related values in the dominance andtrustworthiness judgments are negative when compared tothe observation of the neutral face Also the sympathovagalbalance returned negative values for all the experimentalconditions when compared to the baseline with an increasefor the dominance condition with respect to the trustwor-thiness Hence the activity of the parasympathetic nervoussystem seems to be prevailing during the observation ofpoliticians to be judged trustworthy There are also severalstudies reporting the increment of the vagal tone during statesof sustained attention [100] in agreement with the result ofthe skin conductance level reporting an increase of attentionfor the judgment of trustworthiness with respect to the traitof dominance In addition the modulation of the HRV isalso correlated with attentional engagement to fearful faces[101] and emotional face processing [102] which could be inline with the results related to the observation of politiciansduring the judgment of dominance

Overall autonomic results suggest that trait judgmentsof dominance and trustworthiness are related to visceralresponses in terms of skin conductance level and vagal tone

5 Conclusions

Theuse of the high-resolution EEG techniques [105ndash112] in anevaluation of the efficacy of trustworthy and dominant facesof political candidates has generated interesting results Suchfindings suggested the following answers to the questionselicited in introduction

(1) For the analysed population we found out that thejudgment on the trait of dominance after a rapidobservation of politiciansrsquo faces is themost predictivewith respect to the one of trustworthiness and prefer-ence of vote of the election outcome Preferences oftrustworthiness and vote are positively correlated

(2) By analysing the PSD cortical maps and the relatedPDC connectivity patterns the results highlighted


frontal asymmetrical activities characterizing all theexperimental conditions of vote trustworthiness anddominance These findings are in agreement withthe approach-avoidance theory and can predict thedecision of vote as well as the judgment of trustwor-thiness and dominance

(3) Despite not being completely statistically significantthe analysis of the autonomic parameters revealed adecrease of skin conductance level and sympathova-gal balance related to the observation of politicians tobe judged as trustworthy related to an increase of sus-tained attention These features could correlate withmodulation of attention and emotional engagementto faces

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

Giovanni Vecchiato would like to thank Dr Ana Susac forher precious support in the results discussion and duringthe writing of the final paper This work is cofinanced byEUROCONTROL on behalf of the SESAR Joint Undertakingin the context of SESAR Work Package E-NINA researchproject by the Italian Minister of Foreign Affairs with abilateral project between Italy andChinaNeuropredictor andby FILAS BrainTrained CUP F87I12002500007

References

[1] A Todorov A NMandisodza A Goren and C C Hall ldquoInfer-ences of competence from faces predict election outcomesrdquoScience vol 308 no 5728 pp 1623ndash1626 2005

[2] C C Ballew II and A Todorov ldquoPredicting political electionsfrom rapid and unreflective face judgmentsrdquo Proceedings of theNational Academy of Sciences of the United States of Americavol 104 no 46 pp 17948ndash17953 2007

[3] CNN ldquoRecord amount spent on campaign ads in 2006rdquo 2006httpeditioncnncomPOLITICSblogspoliticalticker200612record-amount-spent-on-campaign-ads-inhtml

[4] S Galdi L Arcuri and B Gawronski ldquoAutomatic mentalassociations predict future choices of undecided decision-makersrdquo Science vol 321 no 5892 pp 1100ndash1102 2008

[5] M L Spezio A Rangel R M Alvarez et al ldquoA neural basis forthe effect of candidate appearance on election outcomesrdquo SocialCognitive and Affective Neuroscience vol 3 no 4 pp 344ndash3522008

[6] J Kato H Ide I Kabashima H Kadota K Takano andK Kansaku ldquoNeural correlates of attitude change followingpositive and negative advertisementsrdquo Frontiers in BehavioralNeuroscience vol 3 article 6 2009

[7] J T Kaplan J Freedman and M Iacoboni ldquoUs versus thempolitical attitudes and party affiliation influence neural responseto faces of presidential candidatesrdquo Neuropsychologia vol 45no 1 pp 55ndash64 2007

[8] S Kernell ldquoPresidential popularity and negative votingrdquo Amer-ican Political Science Review vol 71 pp 44ndash66 1977

[9] R R Lau ldquoTwo explanations for negativity effects in politicalbehaviorrdquo American Journal of Political Science vol 29 pp 119ndash138 1985

[10] M P Fiorina and K A Shepsle ldquoIs negative voting an artifactrdquoAmerican Journal of Political Science vol 33 pp 423ndash439 1989

[11] A A Ioannides L Liu D Theofilou et al ldquoReal time pro-cessing of affective and cognitive stimuli in the human brainextracted from MEG signalsrdquo Brain Topography vol 13 no 1pp 11ndash16 2000

[12] B Guntekin and E Basar ldquoA review of brain oscillations inperception of faces and emotional picturesrdquo Neuropsychologiavol 58 pp 33ndash51 2014

[13] P L Nunez Neocortical Dynamics and Human EEG RhythmsOxford University Press New York NY USA 1995

[14] X Bai V L Towle E J He and B He ldquoEvaluation ofcortical current density imaging methods using intracranialelectrocorticograms and functional MRIrdquo NeuroImage vol 35no 2 pp 598ndash608 2007

[15] B He Y Wang and D Wu ldquoEstimating cortical potentialsfrom scalp EEGrsquos in a realistically shaped inhomogeneous headmodel by means of the boundary element methodrdquo IEEETransactions on Biomedical Engineering vol 46 no 10 pp1264ndash1268 1999

[16] A M Dale A K Liu B R Fischl et al ldquoDynamic statisticalparametric mapping combining fMRI and MEG for high-resolution imaging of cortical activityrdquo Neuron vol 26 no 1pp 55ndash67 2000

[17] F Babiloni F Cincotti C Babiloni et al ldquoEstimation of thecortical functional connectivity with the multimodal integra-tion of high-resolution EEG and fMRI data by directed transferfunctionrdquo NeuroImage vol 24 no 1 pp 118ndash131 2005

[18] L Astolfi J Toppi F de Vico Fallani et al ldquoNeuroelectri-cal hyperscanning measures simultaneous brain activity inhumansrdquo Brain Topography vol 23 no 3 pp 243ndash256 2010

[19] L Astolfi J Toppi F De Vico Fallani et al ldquoImaging thesocial brain by simultaneous hyperscanning during subjectinteractionrdquo IEEE Intelligent Systems vol 26 no 5 pp 38ndash452011

[20] S Achard and E Bullmore ldquoEfficiency and cost of economicalbrain functional networksrdquo PLoS Computational Biology vol 3article 17 no 2 2007

[21] F Bartolomei I Bosma M Klein et al ldquoDisturbed functionalconnectivity in brain tumour patients evaluation by graphanalysis of synchronization matricesrdquo Clinical Neurophysiologyvol 117 no 9 pp 2039ndash2049 2006

[22] D S Bassett A Meyer-Lindenberg S Achard T Duke andE Bullmore ldquoAdaptive reconfiguration of fractal small-worldhuman brain functional networksrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 103 no51 pp 19518ndash19523 2006

[23] V M Eguıluz D R Chialvo G A Cecchi M Baliki and AV Apkarian ldquoScale-free brain functional networksrdquo PhysicalReview Letters vol 94 Article ID 018102 2005

[24] SMicheloyannis E Pachou C J StamM Vourkas S ErimakiandV Tsirka ldquoUsing graph theoretical analysis ofmulti channelEEG to evaluate the neural efficiency hypothesisrdquo NeuroscienceLetters vol 402 no 3 pp 273ndash277 2006

[25] R Salvador J SucklingMRColeman JD PickardDMenonandE Bullmore ldquoNeurophysiological architecture of functionalmagnetic resonance images of human brainrdquo Cerebral Cortexvol 15 no 9 pp 1332ndash2342 2005


[26] F De Vico Fallani L Astolfi F Cincotti et al ldquoStructure ofthe cortical networks during successful memory encoding inTV commercialsrdquo Clinical Neurophysiology vol 119 no 10 pp2231ndash2237 2008

[27] F D V Fallani L D F Costa F A Rodriguez et al ldquoA graph-theoretical approach in brain functional networks Possibleimplications in EEG studiesrdquoNonlinear Biomedical Physics vol4 no 1 article 8 2010

[28] C J Stam ldquoFunctional connectivity patterns of human magne-toencephalographic recordings a ffksmall-worldffk networkrdquoNeuroscience Letters vol 355 no 1-2 pp 25ndash28 2004

[29] C J Stam and J C Reijneveld ldquoGraph theoretical analysis ofcomplex networks in the brainrdquo Nonlinear Biomedical Physicsvol 1 article 3 2007

[30] O Sporns ldquoGraph theory methods for the analysis of neuralconnectivity patternsrdquo in Neuroscience Databases A PracticalGuide R Kotter Ed pp 171ndash186 Kluwer Academic PublishersBoston Mass USA 2002

[31] S H Strogatz ldquoExploring complex networksrdquo Nature vol 410no 6825 pp 268ndash276 2001

[32] A Urbano C Babiloni P Onorati and F Babiloni ldquoDynamicfunctional coupling of high resolution EEG potentials related tounilateral internally triggered one-digit movementsrdquo Electroen-cephalography and Clinical Neurophysiology vol 106 no 6 pp477ndash487 1998

[33] T Baumgartner M Esslen and L Jancke ldquoFrom emotionperception to emotion experience emotions evoked by picturesand classical musicrdquo International Journal of Psychophysiologyvol 60 no 1 pp 34ndash43 2006

[34] G Vecchiato L Astolfi F D V Fallani et al ldquoChanges in brainactivity during the observation of TV commercials by usingEEG GSR and HR measurementsrdquo Brain Topography vol 23no 2 pp 165ndash179 2010

[35] H D Critchley ldquoElectrodermal responses what happens in thebrainrdquo Neuroscientist vol 8 no 2 pp 132ndash142 2002

[36] Y Nagai H D Critchley E Featherstone P B C Fenwick MR Trimble and R J Dolan ldquoBrain activity relating to the con-tingent negative variation an fMRI investigationrdquo NeuroImagevol 21 no 4 pp 1232ndash1241 2004

[37] M Malik A J Camm J T Bigger Jr et al ldquoHeart rate vari-ability standards of measurement physiological interpretationand clinical userdquo European Heart Journal vol 17 no 3 pp 354ndash381 1996

[38] A Malliani ldquoHeart rate variability from bench to bedsiderdquoEuropean Journal of Internal Medicine vol 16 no 1 pp 12ndash202005

[39] N Montano A Porta C Cogliati et al ldquoHeart rate variabilityexplored in the frequency domain a tool to investigate the linkbetween heart and behaviorrdquo Neuroscience and BiobehavioralReviews vol 33 no 2 pp 71ndash80 2009

[40] N N Oosterhof and A Todorov ldquoThe functional basis of faceevaluationrdquo Proceedings of the National Academy of Sciences ofthe United States of America vol 105 no 32 pp 11087ndash110922008

[41] W Klimesch ldquoEEG alpha and theta oscillations reflect cogni-tive and memory performance a review and analysisrdquo BrainResearch Reviews vol 29 no 2-3 pp 169ndash195 1999

[42] F Babiloni C Babiloni L Locche F Cincotti P M Rossiniand F Carducci ldquoHigh-resolution electro-encephalogram

source estimates of Laplacian-transformed somatosensory-evoked potentials using a realistic subject head model con-structed frommagnetic resonance imagesrdquoMedical and Biolog-ical Engineering and Computing vol 38 no 5 pp 512ndash519 2000

[43] F De Vico Fallani L Astolfi F Cincotti et al ldquoCorticalfunctional connectivity networks in normal and spinal cordinjured patients evaluation by graph analysisrdquo Human BrainMapping vol 28 no 12 pp 1334ndash1346 2007

[44] L Ding Y Lai and B He ldquoLow resolution brain electro-magnetic tomography in a realistic geometry head model asimulation studyrdquo Physics in Medicine and Biology vol 50 no1 pp 45ndash56 2005

[45] L Astolfi F de Vico Fallani F Cincotti et al ldquoNeural basisfor brain responses to TV commercials a high-resolution EEGstudyrdquo IEEE Transactions on Neural Systems and RehabilitationEngineering vol 16 no 6 pp 522ndash531 2008

[46] L Astolfi F Cincotti D Mattia et al ldquoComparison of differ-ent cortical connectivity estimators for high-resolution EEGrecordingsrdquo Human Brain Mapping vol 28 no 2 pp 143ndash1572007

[47] L Astolfi F de Vico Fallani F Cincotti et al ldquoImagingfunctional brain connectivity patterns from high-resolutionEEG and fMRI via graph theoryrdquo Psychophysiology vol 44 no6 pp 880ndash893 2007

[48] L Astolfi G Vecchiato F de Vico Fallani et al ldquoThe track ofbrain activity during the observation of tv commercials with thehigh-resolution eeg technologyrdquoComputational Intelligence andNeuroscience vol 2009 Article ID 652078 7 pages 2009

[49] G Vecchiato F de Vico L Fallani et al ldquoThe issue of multipleunivariate comparisons in the context of neuroelectric brainmapping an application in a neuromarketing experimentrdquoJournal of Neuroscience Methods vol 191 no 2 pp 283ndash2892010

[50] P DWelch ldquoThe use of fast fourier transform for the estimationof power spectra a method based on time averaging overshortmodified periodogramsrdquo IEEETransactions onAudio andElectroacoustics vol 15 no 2 pp 70ndash73 1967

[51] S J van Albada and P A Robinson ldquoTransformation of arbi-trary distributions to the normal distribution with applicationto EEG test-retest reliabilityrdquo Journal of Neuroscience Methodsvol 161 no 2 pp 205ndash211 2007

[52] L A Baccala and K Sameshima ldquoPartial directed coherencea new concept in neural structure determinationrdquo BiologicalCybernetics vol 84 no 6 pp 463ndash474 2001

[53] C W J Granger ldquoInvestigating causal relations by econometricmodels and cross-spectral methodsrdquo Econometrica vol 37 pp424ndash438 1969

[54] H Akaike ldquoA new look at the statistical model identificationrdquoIEEE Transactions on Automatic Control vol 19 pp 716ndash7231974

[55] M J Kaminski and K J Blinowska ldquoA new method of thedescription of the information flow in the brain structuresrdquoBiological Cybernetics vol 65 no 3 pp 203ndash210 1991

[56] L Astolfi F Cincotti D Mattia et al ldquoAssessing cortical func-tional connectivity by partial directed coherence simulationsand application to real datardquo IEEE Transactions on BiomedicalEngineering vol 53 no 9 pp 1802ndash1812 2006

[57] K Sameshima and L A Baccala ldquoUsing partial directedcoherence to describe neuronal ensemble interactionsrdquo Journalof Neuroscience Methods vol 94 no 1 pp 93ndash103 1999


[58] B Gourevitch R le Bouquin-Jeannes and G Faucon ldquoLinearand nonlinear causality between signals methods examplesand neurophysiological applicationsrdquo Biological Cyberneticsvol 95 no 4 pp 349ndash369 2006

[59] D Y Takahashi L A Baccal and K Sameshima ldquoConnectivityinference between neural structures via partial directed coher-encerdquo Journal of Applied Statistics vol 34 no 10 pp 1259ndash12732007

[60] B Schelter M Winterhalder M Eichler et al ldquoTesting fordirected influences among neural signals using partial directedcoherencerdquo Journal of NeuroscienceMethods vol 152 no 1-2 pp210ndash219 2006

[61] M Kaminski M Ding W A Truccolo and S L BresslerldquoEvaluating causal relations in neural systems granger causalitydirected transfer function and statistical assessment of signifi-cancerdquo Biological Cybernetics vol 85 no 2 pp 145ndash157 2001

[62] J Theiler S Eubank A Longtin B Galdrikian and J DoyneFarmer ldquoTesting for nonlinearity in time series the method ofsurrogate datardquo Physica D Nonlinear Phenomena vol 58 no1-4 pp 77ndash94 1992

[63] J Toppi F Babiloni G Vecchiato et al ldquoTesting the asymptoticstatistic for the assessment of the significance of Partial DirectedCoherence connectivity patternsrdquo in Proceedings of the AnnualInternational Conference of the IEEE Engineering in Medicineand Biology Society (EMBC rsquo11) pp 5016ndash5019 2011

[64] T Nichols and S Hayasaka ldquoControlling the familywise errorrate in functional neuroimaging a comparative reviewrdquo Statis-tical Methods in Medical Research vol 12 no 5 pp 419ndash4462003

[65] Y Benjamini and Y Hochberg ldquoControlling the false discoveryrate a practical and powerful approach to multiple testingrdquoJournal of the Royal Statistical Society B Methodological vol 57no 1 pp 289ndash300 1995

[66] Y Benjamini andDYekutieli ldquoThe control of the false discoveryrate in multiple testing under dependencyrdquo The Annals ofStatistics vol 29 no 4 pp 1165ndash1188 2001

[67] J Toppi F De Vico Fallani G Vecchiato et al ldquoHow thestatistical validation of functional connectivity patterns canprevent erroneous definition of small-world properties of abrain connectivity networkrdquo Computational and MathematicalMethods inMedicine vol 2012 Article ID 130985 13 pages 2012

[68] J Toppi M Petti F De Vico Fallani et al ldquoDescribing relevantindices from the resting state electrophysiological networksrdquoin Proceedings of the IEEE Annual International Conference ofthe Engineering in Medicine and Biology Society (EMBC rsquo12) pp2547ndash2550 San Diego Calif USA 2012

[69] O Sporns D R Chialvo M Kaiser and C C HilgetagldquoOrganization development and function of complex brainnetworksrdquo Trends in Cognitive Sciences vol 8 no 9 pp 418ndash425 2004

[70] S Schmidt and H Walach ldquoElectrodermal activity (EDA)state-of-the-art measurement and techniques for parapsycho-logical purposesrdquo Journal of Parapsychology vol 64 no 2 pp139ndash163 2000

[71] D C Fowles M J Christie and R Edelberg ldquoPublication rec-ommendations for electrodermal measurementsrdquo Psychophysi-ology vol 18 no 3 pp 232ndash239 1981

[72] P H Venables ldquoAutonomic activityrdquo Annals of the New YorkAcademy of Sciences vol 620 pp 191ndash207 1991

[73] W Boucsein Electrodermal Activity Plenum Press New YorkNY USA 1992

[74] G G Berntson JThomas Bigger Jr D L Eckberg et al ldquoHeartrate variability origins methods and interpretive caveatsrdquoPsychophysiology vol 34 no 6 pp 623ndash648 1997

[75] MMendez AM Bianchi O Villantieri and S Cerutti ldquoTime-varying analysis of the heart rate variability during REM andnon REM sleep stagesrdquo in Proceedings of the IEEE Engineeringin Medicine and Biology Society vol 1 pp 3576ndash3579 2006

[76] S D Kreibig F H Wilhelm W T Roth and J J Gross ldquoCar-diovascular electrodermal and respiratory response patternsto fear- and sadness-inducing filmsrdquo Psychophysiology vol 44no 5 pp 787ndash806 2007

[77] R J Davidson and W Irwin ldquoThe functional neuroanatomy ofemotion and affective stylerdquo Trends in Cognitive Sciences vol 3no 1 pp 11ndash21 1999

[78] R J Davidson ldquoAnxiety and affective style role of prefrontalcortex and amygdalardquoBiological Psychiatry vol 51 no 1 pp 68ndash80 2002

[79] R J Davidson ldquoAffective style psychopathology and resiliencebrain mechanisms and plasticityrdquo The American Psychologistvol 55 no 11 pp 1196ndash1214 2000

[80] R J Davidson ldquoWhat does the prefrontal cortex ffkdoffkin affect perspectives on frontal EEG asymmetry researchrdquoBiological Psychology vol 67 no 1-2 pp 219ndash233 2004

[81] S K Sutton and R J Davidson ldquoPrefrontal brain asymmetrya biological substrate of the behavioral approach and inhibitionsystemsrdquo Psychological Science vol 8 no 3 pp 204ndash210 1997

[82] E Harmon-Jones and J J B Allen ldquoAnger and frontal brainactivity EEG asymmetry consistent with approach motivationdespite negative affective valencerdquo Journal of Personality andSocial Psychology vol 74 no 5 pp 1310ndash1316 1998

[83] W Heller J I Schmidtke J B Nitschke N S Koven and GA Miller ldquoStates traits and symptoms investigating the neuralcorrelates of emotion personality and psycho-pathologyrdquo inAdvances in Personality Science D Cervone and W MischelEds pp 106ndash126 Guilford Press New York NY USA 2002

[84] E Harmon-Jones L Lueck M Fearn and C Harmon-JonesldquoThe effect of personal relevance and approach-related actionexpectation on relative left frontal cortical activityrdquo Psychologi-cal Science vol 17 no 5 pp 434ndash440 2006

[85] G Vecchiato J Toppi L Astolfi et al ldquoSpectral EEG frontalasymmetries correlate with the experienced pleasantness of TVcommercial advertisementsrdquo Medical and Biological Engineer-ing and Computing vol 49 no 5 pp 579ndash583 2011

[86] G Vecchiato L Astolfi F de Vico Fallani et al ldquoOn the Use ofEEG or MEG brain imaging tools in neuromarketing researchrdquoComputational Intelligence and Neuroscience vol 2011 ArticleID 643489 12 pages 2011

[87] A B Usakli S Gurkan F Aloise G Vecchiato and F BabilonildquoA hybrid platform based on EOG and EEG signals to restorecommunication for patients afflicted with progressive motorneuron diseasesrdquo inProceedings of the 35thAnnual InternationalConference of the IEEE Engineering in Medicine and BiologySociety pp 543ndash546 2009

[88] G Borghini G Vecchiato J Toppi et al ldquoAssessment of mentalfatigue during car driving by using high resolution EEG activityand neurophysiologic indicesrdquo in Proceeding of the 34th AnnualInternational Conference of the IEEE Engineering in Medicineand Biology Society (EMBSrsquo12) pp 6442ndash6445 SanDiego CalifUSA September 2012

[89] C S Carver ldquoSelf-regulation of action and affectrdquo in Hand-book of Self-Regulation Research Theory and Applications


R F Baumeister and K D Vohs Eds pp 13ndash39 Guilford PressNew York NY USA 2004

[90] J Haidt and D Keltner ldquoCulture and facial expression open-ended methods find more expressions and a gradient of recog-nitionrdquo Cognition and Emotion vol 13 no 3 pp 225ndash266 1999

[91] K L Phan T Wager S F Taylor and I Liberzon ldquoFunctionalneuroanatomy of emotion a meta-analysis of emotion activa-tion studies in PET and fMRIrdquo NeuroImage vol 16 no 2 pp331ndash348 2002

[92] F C Murphy I Nimmo-Smith and A D Lawrence ldquoFunc-tional neuroanatomy of emotions a meta-analysisrdquo CognitiveAffective and Behavioral Neuroscience vol 3 no 3 pp 207ndash2332003

[93] A J Blood R J Zatorre P Bermudez and A C Evans ldquoEmo-tional responses to pleasant andunpleasantmusic correlatewithactivity in paralimbic brain regionsrdquo Nature Neuroscience vol2 no 4 pp 382ndash387 1999

[94] A J Blood and R J Zatorre ldquoIntensely pleasurable responsesto music correlate with activity in brain regions implicated inreward and emotionrdquo Proceedings of the National Academy ofSciences of theUnited States of America vol 98 no 20 pp 11818ndash11823 2001

[95] R Sprengelmeyer M Rausch U T Eysel and H PrzuntekldquoNeural structures associated with recognition of facial expres-sions of basic emotionsrdquo Proceedings of the Royal Society BBiological Sciences vol 265 no 1409 pp 1927ndash1931 1998

[96] R Sprengelmeyer A P Atkinson A Sprengelmeyer et al ldquoDis-gust and fear recognition in paraneoplastic limbic encephalitisrdquoCortex vol 46 no 5 pp 650ndash657 2010

[97] R J Dolan P Fletcher J Morris N Kapur J F W Deakinand C D Frith ldquoNeural activation during covert processing ofpositive emotional facial expressionsrdquoNeuroImage vol 4 no 3part 1 pp 194ndash200 1996

[98] J S Winston B A Strange J OrsquoDoherty and R J DolanldquoAutomatic and intentional brain responses during evaluationof trustworthiness of facesrdquo Nature Neuroscience vol 5 no 3pp 277ndash283 2002

[99] C Lithari C A Frantzidis C Papadelis et al ldquoAre femalesmoreresponsive to emotional stimuli A neurophysiological studyacross arousal and valence dimensionsrdquo Brain Topography vol23 no 1 pp 27ndash40 2010

[100] N Prause C Staley and V Roberts ldquoFrontal alpha asymmetryand sexually motivated statesrdquo Psychophysiology vol 51 no 3pp 226ndash235 2014

[101] Z Zhang and Z Deng ldquoGender facial attractiveness andearly and late event-related potential componentsrdquo Journal ofIntegrative Neuroscience vol 11 no 4 pp 477ndash487 2012

[102] Y Groen A A Wijers O Tucha and M Althaus ldquoAre theresex differences in ERPs related to processing empathy-evokingpicturesrdquo Neuropsychologia vol 51 no 1 pp 142ndash155 2013

[103] G Galli NWolpe and L J Otten ldquoSex differences in the use ofanticipatory brain activity to encode emotional eventsrdquo Journalof Neuroscience vol 31 no 34 pp 12364ndash12370 2011

[104] H D Critchley ldquoNeural mechanisms of autonomic affectiveand cognitive integrationrdquo Journal of Comparative Neurologyvol 493 no 1 pp 154ndash166 2005

[105] C Babiloni F Babiloni F Carducci et al ldquoMapping of early andlate human somatosensory evoked brain potentials to phasicgalvanic painful stimulationrdquo Human Brain Mapping vol 12no 3 pp 168ndash179 2001

[106] C Babiloni F Babiloni F Carducci et al ldquoHuman brain oscil-latory activity phase-locked to painful electrical stimulations amulti-channel EEG studyrdquo Human Brain Mapping vol 15 no2 pp 112ndash123 2002

[107] C Babiloni F Babiloni F Carducci et al ldquoHuman corticalEEG rhythms during long-term episodic memory task A high-resolution EEG study of the HERAmodelrdquoNeuroImage vol 21no 4 pp 1576ndash1584 2004

[108] G Borghini L Astolfi G Vecchiato D Mattia and F BabilonildquoMeasuring neurophysiological signals in aircraft pilots andcar drivers for the assessment of mental workload fatigue anddrowsinessrdquo Neuroscience and Biobehavioral Reviews vol 44pp 58ndash75 2014

[109] F Cincotti D Mattia F Aloise et al ldquoHigh-resolution EEGtechniques for brain-computer interface applicationsrdquo Journalof Neuroscience Methods vol 167 no 1 pp 31ndash42 2008

[110] A B Usakli S Gurkan F Aloise G Vecchiato and F BabilonildquoOn the use of electrooculogram for efficient human computerinterfacesrdquo Computational Intelligence and Neuroscience vol2010 Article ID 135629 5 pages 2010

[111] M Zavaglia L Astolfi F Babiloni and M Ursino ldquoA neuralmass model for the simulation of cortical activity estimatedfrom high resolution EEG during cognitive or motor tasksrdquoJournal of Neuroscience Methods vol 157 no 2 pp 317ndash3292006

[112] J del R Millan J Mourino F Babiloni F Cincotti MVarsta and J Heikkonen ldquoLocal neural classifier for EEG-based recognition of mental tasksrdquo in Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks(IJCNN rsquo00) pp 632ndash636 July 2000

Submit your manuscripts athttpwwwhindawicom

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or negative ones (ie negative judgement about pleas-antness implicitly extended to the competence field) [4]The surprising result of this study was that an emotionallyldquonegativerdquo judgement towards a candidate (as being ldquolessappealingrdquo than the opponent) is a prevailing reason for hisdefeat even in a contest of simulated elections In additionthe cerebral activity generated from an emotional state ofldquorejectionrdquo of the candidate is completely different from theone generated from an emotional state of acceptation orsatisfaction [5] Neuropolitical studies have found that theactivation of emotion-related areas in the brain is linked topolitical preferences identifying the neural correlates in theprefrontal cortices of changes in political attitudes towardothers that are linked to social cognition [6] In particular ina study performed by functionalmagnetic resonance imaging(fMRI) Spezio and colleagues [5] revealed that activationin the insulaparainsula and anterior cingulate cortex (ACC)correlates with election loss in a simulated voting paradigm

Another study [7] investigated how political party affil-iation and political attitudes modulate neural activity whileviewing faces of presidential candidates They found thatviewing the candidate from the opposing political partyproduced signal changes in cognitive control circuitry inthe dorsolateral prefrontal cortex and anterior cingulate aswell as in emotional regions such as the insula and anteriortemporal poles

From the traditional political research that is performedwithout any neurophysiologicalmeasurements it was alreadyknown that the negative vote plays an important role in thefinal vote decision [8ndash10] In this context it is important tounderstand the role of peoplersquos emotional behaviour afterobserving a politician compared with the one of a wholeelectoral campaign

It is very well known that the hemodynamic measure-ments of the brain activity have the spatial resolution of theorder of cubic mm being capable of detecting activationsalso in deep brain structures such as amygdala and nucleusaccumbens However the lack of time resolution due to thedelay of the cerebral bloodflow increment after the expositionto the stimuli makes the fMRI unsuitable to follow thebrain dynamics For this reason other authors also adoptdifferent tools such asmagnetoencephalography (MEG)Thistechnique is sensitive to changes of magnetic fields that areinduced by the electrical brain activity and it is able to detectrapid changes of the neural activity on a temporal scale ofmilliseconds and on a spatial scale of centimetres [11]

It is worth noting that in the past several studies elec-troencephalography (EEG) was also used as brain imagingtool for the analysis of brain activity during the observationof faces and emotional stimuli (for a review see [12]) High-resolution EEG technology has been developed to enhancethe poor spatial information content of the EEG activity inorder to detect the brain activity with a spatial resolution ofa squared centimetre and the unsurpassed time resolution ofmilliseconds [13ndash17]

Recently it was realized that the functional connectivitynetworks [18 19] estimated frombrain-imaging data obtainedby EEG MEG (magnetoencephalography) and fMRI can beinvestigated using graph theory [20ndash29] Since a graph is

a mathematical representation of a network that has beenessentially reduced to nodes and connections between themthe use of a graph-theory approach is potentially relevantand useful to quantify and describe the degree and modalityof communication among different cerebral areas as firstdemonstrated on a set of anatomical brain networks [30 31]

The use of EEG allows following the brain activity on amillisecond base but it has the problem that the recordedEEG signals are mainly due to the activity generated inthe cortical structures of the brain In fact the electromag-netic activity elicited by deep structures advocated for thegeneration of emotional processing in humans is almostimpossible to gather from usual superficial EEG electrodes[13 32] It has been underlined that a positive or negativeemotional processing of the commercial advertisements is animportant factor for the formation of stable memory traces[6] Hence it became relevant to infer the emotional engageof the subject by using indirect signs for it In fact indirectsigns of emotional processing could be gathered by pickingvariations of the activity of the anatomical structures linkedto the emotional processing activity in humans such as theactivity of sweat glands on the hands andor the variation ofthe heart rate [33] In particular by monitoring autonomicactivity using devices able to record the variation of the skinconductivity (galvanic skin responses (GSR)) and the heartrate (HR) it is possible to assess the ldquointernalrdquo emotional stateof the subject [34] In fact galvanic skin response (GSR) activ-ity is actually viewed as a sensitive and convenientmeasure ofsympathetic arousal associated with emotion cognition andattention [35] Studies using functional imaging techniques[35 36] have related the generation and level of electrodermalactivity to specific brain areas These specific regions arethe ventromedial prefrontal cortex orbitofrontal cortex leftprimary motor cortex and the anterior and posterior cingu-late which have been shown to be associated with emotionaland motivational behaviours [35 36] Such findings indicatethe close association of peripheral and central measures ofarousal emphasising the close connections between electro-dermal activity arousal attention cognition and emotion Inaddition the link between the heart rate (HR) or the heart ratevariability (HRV) and the sympathovagal balance has alsobeen suggested [37ndash39]

In this study we were interested in analyzing the neuro-electrical and autonomic activity elicited during the obser-vation of real politiciansrsquo faces who actually participated inmunicipal and regional elections held in Italy in the 2004and 2008 The aim of the present research was to investigatethe EEG activity of a group of twenty healthy subjects whilethey were asked to both vote in simulated elections and givea judgment about personal traits of real politicians shownBy means of high-resolution EEG technique we mapped sta-tistical differences of cortical spectral activity elicited duringthe observation of candidates while subjects were asked togive a judgment on dominance trustworthiness traits anda preference of vote on the faces seen To measure boththe electrical brain activity and the autonomic responseswe used simultaneous EEG GSR and HR measurementsduring the whole experiment Moreover analysis of theelectrodermal activity and the heart rate variability related










QQQ

Question

Candidate 1

Candidate 2

Decision

Data analysis

Data analysis




= 1s

= 2 s

sim[2 3] s

Q

(1s)

(1s)




PR =

sum119873

119894=1120575 (119881119894 )

119873times 100 (1)








119885119881119863119879

=119883119881119863119879

minus 120583119873

120590119873

(2)

where 119885119881119863119879


119873and 120590

119873






119878 = [1199041 (119905) 1199042 (119905) 119904119873 (119905)]

119879 (3)


119901

sum

119896=0

Λ119896119878 (119905 minus 119896) = 119864 (119905) with Λ

0= 119868 (4)


119873(119905)]119879 is a vector




Λ (119891) 119878 (119891) = 119864 (119891) (5)

where

Λ (119891) =

119901

sum

119896=0

Λ119896119890minus1198952120587119891Δ119905119896

(6)


120587119894119895(119891) =

Λ119894119895(119891)

radicsum119873

119896=1Λ119896119895(119891)Λ

lowast

119896119895(119891)

(7)


119894119895(119891)





119894(119899)


119873

sum

119899=1

10038161003816100381610038161003816120587119899119895(119891)

10038161003816100381610038161003816

2

(8)





sPDC119894119895(119891) =

10038161003816100381610038161003816Λ119894119895(119891)

10038161003816100381610038161003816

2

sum119873

119898=1

1003816100381610038161003816Λ 119894119898 (119891)1003816100381610038161003816

2 (9)










119894119895= 1 otherwise119860




119863 =

sum119873

119894=1sum119873

119895=1119860119894119895

119873(119873 minus 1)times 100 (10)




119896119894= sum

119895isin119873

119860119894119895 (11)

















3 Results



010203040506070


()











0

5

10

15

20

25

0 5 10 15 20

Vote

()

Trustworthiness ()


R = 07173

(a)

0

5

10

15

20

0 5 10 15 20 25

Dom

inan

ce (

)

Vote ()

R = 04045


(b)

0

5

10

15

20

0 5 10 15 20

Trus

twor

thin

ess (

)

Dominance ()


(c)


Vote

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

V+gt V

minus

Vminusgt V

+

120599

120572



Trustworthiness

120599

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120572

T+gt T

minus

Tminusgt T

+














Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+




+(119863+ 119879+) with





minus







minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

























QQQ

Question

Candidate 1

Candidate 2

Decision

Data analysis

Data analysis




= 1s

= 2 s

sim[2 3] s

Q

(1s)

(1s)




PR =

sum119873

119894=1120575 (119881119894 )

119873times 100 (1)








119885119881119863119879

=119883119881119863119879

minus 120583119873

120590119873

(2)

where 119885119881119863119879


119873and 120590

119873






119878 = [1199041 (119905) 1199042 (119905) 119904119873 (119905)]

119879 (3)


119901

sum

119896=0

Λ119896119878 (119905 minus 119896) = 119864 (119905) with Λ

0= 119868 (4)


119873(119905)]119879 is a vector




Λ (119891) 119878 (119891) = 119864 (119891) (5)

where

Λ (119891) =

119901

sum

119896=0

Λ119896119890minus1198952120587119891Δ119905119896

(6)


120587119894119895(119891) =

Λ119894119895(119891)

radicsum119873

119896=1Λ119896119895(119891)Λ

lowast

119896119895(119891)

(7)


119894119895(119891)





119894(119899)


119873

sum

119899=1

10038161003816100381610038161003816120587119899119895(119891)

10038161003816100381610038161003816

2

(8)





sPDC119894119895(119891) =

10038161003816100381610038161003816Λ119894119895(119891)

10038161003816100381610038161003816

2

sum119873

119898=1

1003816100381610038161003816Λ 119894119898 (119891)1003816100381610038161003816

2 (9)










119894119895= 1 otherwise119860




119863 =

sum119873

119894=1sum119873

119895=1119860119894119895

119873(119873 minus 1)times 100 (10)




119896119894= sum

119895isin119873

119860119894119895 (11)

















3 Results



010203040506070


()











0

5

10

15

20

25

0 5 10 15 20

Vote

()

Trustworthiness ()


R = 07173

(a)

0

5

10

15

20

0 5 10 15 20 25

Dom

inan

ce (

)

Vote ()

R = 04045


(b)

0

5

10

15

20

0 5 10 15 20

Trus

twor

thin

ess (

)

Dominance ()


(c)


Vote

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

V+gt V

minus

Vminusgt V

+

120599

120572



Trustworthiness

120599

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120572

T+gt T

minus

Tminusgt T

+














Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+




+(119863+ 119879+) with





minus







minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of





















119885119881119863119879

=119883119881119863119879

minus 120583119873

120590119873

(2)

where 119885119881119863119879


119873and 120590

119873






119878 = [1199041 (119905) 1199042 (119905) 119904119873 (119905)]

119879 (3)


119901

sum

119896=0

Λ119896119878 (119905 minus 119896) = 119864 (119905) with Λ

0= 119868 (4)


119873(119905)]119879 is a vector




Λ (119891) 119878 (119891) = 119864 (119891) (5)

where

Λ (119891) =

119901

sum

119896=0

Λ119896119890minus1198952120587119891Δ119905119896

(6)


120587119894119895(119891) =

Λ119894119895(119891)

radicsum119873

119896=1Λ119896119895(119891)Λ

lowast

119896119895(119891)

(7)


119894119895(119891)





119894(119899)


119873

sum

119899=1

10038161003816100381610038161003816120587119899119895(119891)

10038161003816100381610038161003816

2

(8)





sPDC119894119895(119891) =

10038161003816100381610038161003816Λ119894119895(119891)

10038161003816100381610038161003816

2

sum119873

119898=1

1003816100381610038161003816Λ 119894119898 (119891)1003816100381610038161003816

2 (9)










119894119895= 1 otherwise119860




119863 =

sum119873

119894=1sum119873

119895=1119860119894119895

119873(119873 minus 1)times 100 (10)




119896119894= sum

119895isin119873

119860119894119895 (11)

















3 Results



010203040506070


()











0

5

10

15

20

25

0 5 10 15 20

Vote

()

Trustworthiness ()


R = 07173

(a)

0

5

10

15

20

0 5 10 15 20 25

Dom

inan

ce (

)

Vote ()

R = 04045


(b)

0

5

10

15

20

0 5 10 15 20

Trus

twor

thin

ess (

)

Dominance ()


(c)


Vote

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

V+gt V

minus

Vminusgt V

+

120599

120572



Trustworthiness

120599

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120572

T+gt T

minus

Tminusgt T

+














Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+




+(119863+ 119879+) with





minus







minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of


















119878 = [1199041 (119905) 1199042 (119905) 119904119873 (119905)]

119879 (3)


119901

sum

119896=0

Λ119896119878 (119905 minus 119896) = 119864 (119905) with Λ

0= 119868 (4)


119873(119905)]119879 is a vector




Λ (119891) 119878 (119891) = 119864 (119891) (5)

where

Λ (119891) =

119901

sum

119896=0

Λ119896119890minus1198952120587119891Δ119905119896

(6)


120587119894119895(119891) =

Λ119894119895(119891)

radicsum119873

119896=1Λ119896119895(119891)Λ

lowast

119896119895(119891)

(7)


119894119895(119891)





119894(119899)


119873

sum

119899=1

10038161003816100381610038161003816120587119899119895(119891)

10038161003816100381610038161003816

2

(8)





sPDC119894119895(119891) =

10038161003816100381610038161003816Λ119894119895(119891)

10038161003816100381610038161003816

2

sum119873

119898=1

1003816100381610038161003816Λ 119894119898 (119891)1003816100381610038161003816

2 (9)










119894119895= 1 otherwise119860




119863 =

sum119873

119894=1sum119873

119895=1119860119894119895

119873(119873 minus 1)times 100 (10)




119896119894= sum

119895isin119873

119860119894119895 (11)

















3 Results



010203040506070


()











0

5

10

15

20

25

0 5 10 15 20

Vote

()

Trustworthiness ()


R = 07173

(a)

0

5

10

15

20

0 5 10 15 20 25

Dom

inan

ce (

)

Vote ()

R = 04045


(b)

0

5

10

15

20

0 5 10 15 20

Trus

twor

thin

ess (

)

Dominance ()


(c)


Vote

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

V+gt V

minus

Vminusgt V

+

120599

120572



Trustworthiness

120599

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120572

T+gt T

minus

Tminusgt T

+














Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+




+(119863+ 119879+) with





minus







minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of



















119894119895= 1 otherwise119860




119863 =

sum119873

119894=1sum119873

119895=1119860119894119895

119873(119873 minus 1)times 100 (10)




119896119894= sum

119895isin119873

119860119894119895 (11)

















3 Results



010203040506070


()











0

5

10

15

20

25

0 5 10 15 20

Vote

()

Trustworthiness ()


R = 07173

(a)

0

5

10

15

20

0 5 10 15 20 25

Dom

inan

ce (

)

Vote ()

R = 04045


(b)

0

5

10

15

20

0 5 10 15 20

Trus

twor

thin

ess (

)

Dominance ()


(c)


Vote

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

V+gt V

minus

Vminusgt V

+

120599

120572



Trustworthiness

120599

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120572

T+gt T

minus

Tminusgt T

+














Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+




+(119863+ 119879+) with





minus







minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of





















3 Results



010203040506070


()











0

5

10

15

20

25

0 5 10 15 20

Vote

()

Trustworthiness ()


R = 07173

(a)

0

5

10

15

20

0 5 10 15 20 25

Dom

inan

ce (

)

Vote ()

R = 04045


(b)

0

5

10

15

20

0 5 10 15 20

Trus

twor

thin

ess (

)

Dominance ()


(c)


Vote

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

V+gt V

minus

Vminusgt V

+

120599

120572



Trustworthiness

120599

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120572

T+gt T

minus

Tminusgt T

+














Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+




+(119863+ 119879+) with





minus







minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















0

5

10

15

20

25

0 5 10 15 20

Vote

()

Trustworthiness ()


R = 07173

(a)

0

5

10

15

20

0 5 10 15 20 25

Dom

inan

ce (

)

Vote ()

R = 04045


(b)

0

5

10

15

20

0 5 10 15 20

Trus

twor

thin

ess (

)

Dominance ()


(c)


Vote

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

V+gt V

minus

Vminusgt V

+

120599

120572



Trustworthiness

120599

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120572

T+gt T

minus

Tminusgt T

+














Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+




+(119863+ 119879+) with





minus







minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















Trustworthiness

120599

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120572

T+gt T

minus

Tminusgt T

+














Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+




+(119863+ 119879+) with





minus







minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















Dominance

4

3

2

1

0

minus1

minus2

minus3

minus4

tva

lues

120599

120572

D+gt D

minus

Dminusgt D

+




+(119863+ 119879+) with





minus







minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















minus3

minus3

minus25

minus15

minus05

05

15

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R

Vote

8L

8R 5L

5R 7L

7R

minus2

minus2

minus1

minus1

1

1

2

2

3

0

0

tva

lues

tva

lues

IndegreeOutdegree

V+

gt Vminus

Vminus

gt V+

120599

120572

3

2

1

0

minus1

minus2

minus3

tva

lues10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

V+gt V

minus

Vminusgt V

+

V+gt V

minus

Vminusgt V

+










Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















Trustworthiness

3

2

1

0

minus1

minus2

minus3

tva

lues

tva

lues

tva

lues

IndegreeOutdegree

minus25

minus15

minus05

05

15

minus2

minus1

1

0

minus15

minus05

05

15

25

minus1

0

2

1

120599

120572

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

T+gt T

minus

Tminusgt T

+

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19R

946

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R





4 Discussion



Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















Dominance

3

2

1

0

minus1

minus2

minus3

tva

lues

minus3

minus2

minus1

1

2

3

0

tva

lues

120572

minus15

minus05

05

15

25

minus2

minus1

1

2

0

tva

lues

IndegreeOutdegree

120599D+

gt Dminus

Dminusgt D+

D+gt D

minus

D+gt D

minus

Dminusgt D

+

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

10

L10

R

37

L37

R19

L19

R9

46

L9

46

R21

22

L21

22

R41

42

L41

42

R8L

8R 5L

5R 7L

7R

Dminusgt D

+






















5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
































5 Conclusions









Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of





















Acknowledgments


References



























































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















































































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















Neuroelectrical Correlates of Trustworthiness and Dominance Judgments Related to the Observation of...

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Transcript of Neuroelectrical Correlates of Trustworthiness and Dominance Judgments Related to the Observation of...