B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

Ava i l ab l e on l i ne a t www.sc i enced i r ec t . com

www.e l sev i e r . com/ loca te /b ra i n res

Research Report

N400 and LPP in spontaneous trait inferences

Kris Baetensa,⁎, 1, Laurens Van der Cruyssena, Anja Achtzigerb,Marie Vandekerckhovea, Frank Van Overwallea,⁎aVrije Universiteit Brussel, BelgiumbUniversität Konstanz, Germany

A R T I C L E I N F O

⁎ Corresponding authors at: Faculty of PsychoFax: +32 2 629 24 89.

E-mail addresses: Kris.Baetens@vub.ac.be1 PhD fellow of the Research Foundation –

0006-8993/$ – see front matter © 2011 Elseviedoi:10.1016/j.brainres.2011.08.067

A B S T R A C T

Article history:Accepted 29 August 2011Available online 2 September 2011

Past research on spontaneous trait inferences using event related potentials (ERPs) hasconsistently reported increased late positive potential (LPP) amplitudes following socialexpectancy violations, but no N400 modulation. In the present study, participants readscenarios describing behaviors of unknown actors. They entailed descriptions of severalpositive trait implying behaviors, followed by a single final sentence describing behaviorthat was either consistent or inconsistent with the previously implied trait. As in previousstudies, we found significantly increased LPP amplitudes following inconsistent behaviorsat multiple frontal sites. Unlike in previous research, we also found increased N400amplitudes at several centro-parietal sites. The divergence of these results is explainedfrom minor differences in the stimulus presentation procedure and possible overlap ofERP components of opposite polarity. Temporal principal component analysis (PCA) con-firmed the separate influence of concurrent LPP and N400 ERP modulations, and the sourceof the largest factors was located using sLORETA. It is suggested that the increased N400 inresponse to trait inconsistencies reflects difficulties in understanding unanticipated behav-ior, while the LPP effect might reflect evaluative incongruence.

© 2011 Elsevier B.V. All rights reserved.

Keywords:ERPN400LPPTrait inferenceWorld knowledge

1. Introduction

Imagine a co-worker you have always known as a particularlyhonest and ethical person. One day, you haphazardly stumbleupon him while he is prying money from the faculty's charitydonation box. This probably sets a number of psychological re-actions in motion. Initially, you might have difficulties under-standing what he is doing because you don't expect somebodyto do sucha thing, especially not this person.Once you do realizewhat he is doing, you might be shocked by the discrepancy be-tween his past and present behavior. Finally, you may adjustyour expectations and associate himwith a negativepersonality

logy and Educational Scie

(K. Baetens), Frank.VanOFlanders (FWO).

r B.V. All rights reserved.

trait like “greedy” or “dishonest”. Indeed, folk wisdom imploresus to use observations of past behavior to predict future behav-ior: “Foolme once, shame on you; fool me twice, shame onme.”

Stripped-down, abstracted versions of scenarios such as de-scribed above have been used to study event-related potentials(ERPs) associated with trait inference processes (Bartholow etal., 2001, 2003; VanDuynslaeger et al., 2007, 2008; VanOverwalleet al., 2009). In these studies, participants read behavioral de-scriptions of (unknown) individuals which strongly implied acertain personality trait. Subsequently, one ormore critical sen-tences followed, either consistent or inconsistent with the pre-viously implied trait. All of these studies report a positivity

nces, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium.

verwalle@vub.ac.be (F. Van Overwalle).

84 B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

peaking around 500 ms post stimulus in the grand averages ofERPs following the last (critical) word of inconsistent sentencesrelative to consistent sentences. This peak was interpreted as alate positive potential (LPP) or P300 component (Bartholowet al.,2001), reflecting context-updating processes, and the latency ofthis potential was considered an index of the process of trait in-ference (Van Duynslaeger et al., 2007).

The trait inconsistencies in the cited ERP studies contrastedwith respect to valence and induced affective responses withthe previously presented behavior descriptions. Therefore, onemight alternatively interpret these late positivities as a conse-quence of change in valence which triggers increased attentiondue to the intrinsicmotivational significance of stimuli contain-ing emotional information (Holt et al., 2009). Research onemotionalword comprehension has established a general asso-ciation of the processing of emotional information conveyed bywords with a late, extended, attention-modulated processencoding their valence, expressed in the LPP component (for areview, see Hacjak et al., 2010). Stimuli of negative valence gen-erally attract more attention than positive stimuli, a tendencytermed the “negativity bias” (e.g., Ito et al., 1998). Furthermore,research has shown that evaluative incongruence, a mismatchbetween the valenceof a categoryand a subsequent target stim-ulus, is associated with an increased positivity in the ERP thatpeaks between 300 and 600ms post stimulus (Cacioppo et al.,1993, 1996).

However, is this the only component one should expect afterthese trait inconsistencies? In what follows, we will proposethat modulation of another ERP component, the N400, is also tobe expected in paradigms involving disconfirmations of traitinferences.

1.1. The N400 and expectancy violations

The N400 component is a negative deflection in the ERP about400 ms post stimulus. It is a function of semantic inconsistency(e.g., Kutas and Hillyard, 1980) and reflects the ease of integrat-ing a stimulus with a given context, not only for verbal stimuli(Kutas and Federmeier, 2000), but also for other modalities, forexample, the comprehension of visually presented real-worldevents (Sitnikova et al., 2008). Thedegreeof semantic consisten-cy isnot a stable quality of any given combinationof stimuli, butrather determined flexibly and under influence of the local con-text (Filik and Leuthold, 2008; Nieuwland and Van Berkum,2006). For example, “The peanut is in love” will cause a largerN400 than “The peanut is salted”, but the opposite is true if thissentence is preceded by the title “The amorous peanut”. It hasbeen demonstrated that the N400may also reflect the violationof objective knowledge about the world (Hagoort et al., 2004).Much of the extensiveN400 literature can be conveniently orga-nized around the concept of cloze-probability. This is the proba-bility that people will use a given word to complete asentence, and it is inversely related to the N400 amplitude fol-lowing the presentation of that word (Federmeier and Laszlo,2009).

Personality traits can be conceptualized as a schema, a typeofworld knowledgewe construct and constantly adapt to antic-ipate or predict the behaviors of others (Kressel and Uleman,2010; Read, 1987). Therefore, one might expect to find N400 ef-fects in the aforementioned studies on trait inferences

(Bartholow et al., 2001, 2003; Van Duynslaeger et al., 2007,2008; Van Overwalle et al., 2009). After all, trait inconsistenciescould be considereda specific type ofworldknowledgeviolation(e.g., you consider this person to be honest, but all of a sudden itturns out he is very sneaky). Moreover, if a person is describedas behaving contrary to expectations, surely this informationseems more difficult to integrate with the context than if heacts as usual, which should lead to a relative increase in N400amplitude. Indeed, increased N400 amplitudes have beenfound in response to violations of stereotypes, another type ofperson schema (Van Berkum et al., 2008; White et al., 2009).

In contrast to these studies, an earlier study by Osterhoutet al. (1997) foundno increasedN400 amplitudes, but instead in-creased late positivities at about 600ms (P600) in response togender stereotype violations (e.g., “The lumberjack sharpenedher axe.”). A possible explanation is that the participants inter-preted these sentences as containing a grammatical error in ap-plying the pronoun (reliably associated with the P600). Theauthorswent on to predict that “Anomalies involving social cat-egories that are not marked in the grammar (e.g., race) shouldnot elicit the P600 effect but might elicit the N400 effect associ-ated with semantic/pragmatic aspects of language.” (p. 282).Following up on this interpretation, Bartholow et al. (2001,2003) included a semantically inconsistent condition in orderto compare the effects induced by their trait inconsistencieswith a classic N400 effect. However, as noted earlier, theyfound a very dissimilar P300/LPP following trait inconsistencies,and a modulation of the N400 only for semantic inconsis-tencies. How can the consistent absence of any N400 effect inthe trait ERP literature be explained?

1.2. The absence of N400 effects

Anobvious reasonwhynoprevious ERP study on trait inferenceshas reported modulation of the N400 might be that it was over-shadowed by a large positive potential. All previous ERP studieson trait inferences report a large LPP/P300 (Bartholow et al.,2001, 2003; Van Duynslaeger et al., 2007, 2008; Van Overwalle etal., 2009). Positivities occurring simultaneously with the N400may obscure it (Franklin et al., 2007; Roehm et al., 2007). In suchcases, principal component analysis (PCA) may serve to disen-tangle the influence of simultaneously occurring ERP compo-nents (Franklin et al., 2007).

The stimuli presentation procedure of previous ERP studiesmight be anadditional factorworking againstN400modulation.In all but one of these studies (Bartholow et al., 2001, 2003; VanDuynslaeger et al., 2007, 2008; but see Van Overwalle et al.,2009) expectancy establishing sentences were followed by twocritical consistent and two critical inconsistent sentences, pre-sented in a random order. This yielded six possible sequences,as presented in Table 1. As can be seen, only 50% of the incon-sistent sentences were unambiguously inconsistent, in thatthey were not preceded by another inconsistent sentence.Thus, the remaining 50% of the inconsistent sentences wereambiguous (e.g., if John discloses a trusted secret after stealingcharitymoney, is this really inconsistent?). This could have sig-nificantly reduced N400 amplitudes for the inconsistent condi-tion, as it constitutes a form of priming (Federmeier andLaszlo, 2009), dramatically changing the local context (Filikand Leuthold, 2008; Nieuwland and Van Berkum, 2006). The

Table 1 – Possible sequences of two consistent (C) and inconsistent (I) sentences.

Sequence Unambiguous C Unambiguous I

1st 2nd 1st 2nd

C C I I Yes Yes Yes NoC I C I Yes No Yes NoC I I C Yes No Yes NoI C C I No No Yes NoI C I C No No Yes NoI I C C No No Yes No

4/12 (25%) 6/12 (50%)

85B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

case is even more problematic for consistent sentences, ofwhich only 25% were unambiguously consistent, that is, notpreceded by an inconsistent sentence. Therefore, on average75% of the consistent sentences were actually ambiguous (e.g.,John refusing to disclose the secret after stealing charitymoney). This may have significantly increased the N400 ampli-tudes for the consistent condition. Taken together, it is likelythat this way of presenting stimuli diminishes possible differ-ences in N400 amplitude between the conditions.

Of course, the same reasoning holds for the LPP in the previ-ously mentioned ERP studies on trait inferences. However, as-suming that the LPP reflects evaluative incongruence in theseparadigms, it is conceivable that this LPP would be less dimin-ished by this procedure. Whereas the expectations about thedescribed actor can be altered drastically by one inconsistentsentence, the valence of the behaviors is not much affected byit (e.g., when you catch John stealing charity money again, thisis less surprising but not less upsetting). As such, the evaluativeincongruence in the behavior descriptions associated with theLPP remains obvious and therefore more stable. After all, therewas always a small minority of one or two sentences that wasevaluatively incongruent with the established context and theother critical sentences, making it plausible that they shouldstill stand out.

Note that the semantically incongruent sentences in thework of Bartholow et al. (2001, 2003) were similarly randomlyshuffled with the trait consistent and inconsistent sentences,and nevertheless followed by a clear increased N400. However,these sentences were semantically incongruent by themselves(e.g., “Gormak gave the stranger a rain”), rather than incongruentwith a preceding context. Therefore, one might expect theirmeaning to be minimally affected by the context in which theywere presented.

To the best of our knowledge, there has been only one ERPstudy on trait inferences in which the person description wasfollowed by a single final sentence (Van Overwalle et al., 2009).Again, the authors reported a P300 effect and no N400 effect.However, the statistical analysis employed (negative peakswere only compared in a 50–300ms window) wasn't ideal forcapturing N400 effects, since they should occur later (Kutasand Federmeier, 2000). Interestingly, these authors did reportan unexpected significant effect at the Pz site, where the posi-tive peak amplitudewas higher for consistent than for inconsis-tent trait-implying sentences in the 300–450 mswindow,whichcould reflect an N400 modulation. However, since this studyhad a complex design (analyzing the joint effect of trait andgoal inferences) and effects were small, it is difficult to drawclear conclusions from it.

1.3. The present research

To avoid the methodological limitations of earlier trait inferencestudies, we presented person descriptions that were each fol-lowed by one critical sentence that was either consistent (50%of the trials) or inconsistent (50%) with it. The descriptions im-plied positive traits, and inconsistencies described behavior trig-gering a negative trait inference. This choice was based on thefinding that negative social behaviors produce stronger inconsis-tency effects (Bartholowet al., 2001; VanDuynslaeger et al., 2008).In contrast to earlier research on trait inferences using a randomshuffling of consistent, inconsistent and irrelevant sentences,weexpect that by using single critical sentences, we will obtain anincreasedN400 following inconsistencies, in linewith the predic-tion of Osterhout et al. (1997). Specifically, we expect that themean amplitude for inconsistent endings will be more negativethan for consistent endings at centro-parietal sites in the 300–450ms interval.Weadditionally expect to findmorepositive am-plitudes following inconsistent endings than following consis-tent endings in the later 450–1000ms time interval reflectingevaluative inconsistency, in line with previous trait studies thatreported an increased P300/LPP. To disentangle possible simulta-neous influences of these components, we will employ principlecomponent analysis (PCA) (Dien et al., 2005). As a first step toshed light on their functional correlates, we will roughly localizethe neural sources of important components using sLORETA(Pascual-Marqui, 1999; Pascual-Marqui et al., 1994).

2. Results

Fig. 1 displays the grand averages for three selected midlinechannels (Fz, Cz, Pz). Table 2 gives an overview of themean am-plitude and standard deviation for the time windows discussedbelow for the consistent (C) and inconsistent (I) condition, aswell as the p-values of paired samples t-tests between the con-ditions per electrode.

2.1. 300–450 ms

In the N400 time window, we found a significant interactionof Location and Consistency, F(2.63, 57.86)=5.39, p<0.005(Greenhouse–Geisser corrected). Follow-up paired samples t-tests revealed that the average amplitude in this interval wassignificantly more negative following inconsistencies than fol-lowing consistencies at CP1, CP2, P3, P4 and Pz (ps<0.05), reflect-ing an increased N400 (which is usually maximal over centro-parietal sites, Duncan et al., 2009).

0 200 400 600 800 1000

-5

0

5

ms

Fz

(µV

)

0 200 400 600 800 1000

-5

0

5

ms

Cz

(µV

)

0 200 400 600 800 1000

-5

0

5

ms

Pz

(µV

)

N400

LPP

Fig. 1 – Grand averages per condition for three midline sites.Grand average waveforms for consistent (full lines) andinconsistent (dotted lines) ending sentences at three midlinesites. Negativity is plotted upwards.

86 B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

2.2. 450–1000 ms

Again, we found a significant interaction effect of Location andConsistency, F(2.87, 63.06)=9.65, p<0.001 (Greenhouse–Geisser

Table 2 –Mean amplitude, standard deviation and p-value of th(C) and inconsistent (I) condition per electrode.

300–450 ms (N400)

M(C) SD(C) M(I) SD(I) p

Fz −1.43 2.12 −0.98 3.04 0.247Cz −0.87 2.28 −1.27 2.43 0.202Pz −0.32 2.14 −1.24 2.49 0.007 ⁎

F3 −1.53 1.75 −1.16 2.50 0.274F4 −1.22 1.77 −1.12 2.47 0.776FC1 −1.32 1.95 −1.24 2.69 0.840FC2 −1.11 1.92 −1.08 2.40 0.916C3 −0.75 1.59 −1.11 1.92 0.117C4 −0.76 1.57 −1.04 1.99 0.288CP1 −0.35 1.89 −1.14 2.14 0.006 ⁎

CP2 −0.37 2.09 −1.18 2.25 0.009 ⁎

P3 0.64 1.34 0.05 1.40 0.017 ⁎

P4 0.40 1.74 −0.17 2.07 0.009 ⁎

Note.⁎⁎ p<0.01.⁎ p<0.05.

corrected). Follow-up paired samples t-tests revealed that atFz, FC2 and F4, the average amplitude was more positive fortrait inconsistencies than for trait consistencies (ps<0.01),reflecting an increased LPP. The largest difference was foundat Fz. At P3, P4, CP1, CP2 and Pz, the inverse was true, with lesspositive amplitudes following inconsistencies compared to con-sistencies (ps<0.05).

2.3. Temporal PCA

The data was decomposed into 14 temporal factors (TFs)based on inspection of the scree plot, which indicates thegain in explained variance of the grand average after addingmore factors (Dien, 2010). Of these factors, there were 8 factorsaccounting for more than 5% of the variance in the grand av-erages. Of these 8, in turn, there were 4 that peaked after250 ms, together accounting for 56% of the variance over theentire 1.2 s trial. Fig. 2 displays the influence of these 4 largestfactors on the grand average at Cz. As can be seen, three of thelargest factors (TF1, TF3 and TF4) had a positive influence onthe amplitude at Cz, and this was true for all thirteen selectedchannels. TF2 had a negative influence at all thirteen sites.

With respect to the negative factor, TF2 was maximal at Pz,peaking at approximately 386 ms post stimulus. Fig. 3 displaysthe influence of this factor on the grand average for this site asa function of consistency. The negative influence of TF2 provedto be greater for inconsistent than for consistent sentences inthe 300–450ms window at several sites (C3, P3, CP1, Pz, CP2,P4, ps<0.05). For all selected sites, the average difference be-tween consistent and inconsistent sentences for this factor inthe window 300–450ms correlated significantly with the samedifference in the grand average in the window 300–450ms(mean r=0.85, ps<0.001). This indicates that this factor explainsthe amplitude difference in this time interval to a large extent.We then located the source of this component using sLORETA(Pascual-Marqui, 1999; Pascual-Marqui et al., 1994). Fig. 4 depictswhich voxels were maximally involved in eliciting this factor.As can be seen, the left temporo-parietal area, specifically the

e paired samples t-test per time window for the consistent

450–1000 ms (LPP)

M(C) SD(C) M(I) SD(I) p

0.81 2.17 1.96 1.99 0.004 ⁎⁎

2.13 2.08 2.07 1.81 0.825⁎ 2.43 1.70 1.47 1.76 0.007 ⁎⁎

0.22 1.68 0.87 1.53 0.0640.54 1.83 1.26 1.62 0.010 ⁎

1.19 1.98 1.65 1.77 0.1891.03 2.22 1.87 1.97 0.009 ⁎⁎

1.22 1.44 0.92 1.11 0.2701.36 1.46 1.36 1.72 0.997

⁎ 2.31 1.63 1.50 1.51 0.010 ⁎⁎ 2.36 1.71 1.61 1.76 0.033 ⁎

1.69 1.24 1.08 1.29 0.011 ⁎⁎ 1.64 1.26 1.13 1.38 0.043 ⁎

0 200 400 600 800 1000

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

Cz

(µV

)

TF2

TF1

TF3

TF4

Fig. 2 – Projection of the four largest temporal factors to the Cz channel. Projection of the 4 largest temporal factors to the Czchannel. This image is representative for all analyzed channels as far as polarity of the peak of these factors goes.

87B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

superior temporal gyrus (BA22) was found to be most involvedin producing TF2 (MNI coordinates −60, −60, 20).

With respect to the positive factors, TF1 was maximal at Pz,while TF3 andTF4 both peakedat Fz. The combined influence of

0 200 40

-2

-1

0

1

Fz

(µV

)

0 200 40

-2

-1

0

1

Cz

(µV

)

0 200 40

-2

-1

0

1

Pz

(µV

)

Fig. 3 – Influence of the four largest temporal factors per conditiofactors on three midline channels. The influence of TF 2 is denotelines correspond to consistent ending sentences, dotted lines to

these three factors constituted a significantly larger positivityfor inconsistent than for consistent sentences at F3 and Fz(ps<0.05), while it was smaller for inconsistent sentences atP3, CP1, Pz, CP2 and P4 (p<0.01–0.05). Fig. 3 shows the combined

0 600 800 1000

0 600 800 1000

0 600 800 1000

n. Fig. 3 depicts the influence of the four largest temporald in red, the combined influence of TF 1, 3 and 4 in blue. Fullinconsistent endings. Negativity plotted upward.

TF

2

TF

1

TF

3

TF

4

Fig. 4 – Source localization of the four largest temporal factors. Estimated current source density for the voxelsmaximally activein producing the four largest temporal factors.

88 B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

influence of TF1, TF3 and TF4 on threemidline channels (Fz, Cz,Pz). Fig. 4 shows the sLORETA source localization of these threecomponents. Voxels in the left cuneus (−10,−100, 20; BA19),me-dial parietal lobe (5, −45, 71; BA 5) and left cuneus (−30, −95, −5;BA 18) were maximally involved in the generation of TF1, TF3and TF4 respectively.

3. Discussion

The aim of this study was to test whether trait inconsistenciesgenerate increased N400 amplitudes, reflecting violation of ex-pectations basedonprior behaviors of others, aswell as increased

89B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

LPPs, presumably reflecting evaluative inconsistency. Both hy-potheses were confirmed.

3.1. N400 in spontaneous trait inferences

As expected, in the 300–450ms interval, an increased negativemean amplitude was observed in response to trait inconsis-tencies at centro-parietal sites. We interpreted this negativityas an N400. This was confirmed by a temporal PCA, whichrevealed that the second largest temporal factor was a sharpnegativity peaking at about 400ms. This factor hadanegative in-fluence on the amplitude at all analyzed electrode locations. Thedifference between consistency conditions in this time intervalcould to a great extent be explained by differences in this factor.The neural source that contributedmost to this component waslocated at the left superior temporal gyrus, more specifically inWernicke's area, which is one of the most consistently reportedgenerators of the N400 (for a review, see Van Petten and Luka,2006). Taken together, this clearly seems to support an interpre-tation of the differences in the 300–450ms as an increased N400in response to trait inconsistencies.

This is in line with previous ERP research on stereotypes(Van Berkum et al., 2008; White et al., 2009). In these studies, vi-olations of stereotypes, another type of person schema, wereassociated with increased N400 amplitudes. However, no suchmodulation of the N400 component has been reported beforein trait inference studies using paradigms similar to the presentresearch (Bartholow et al., 2001, 2003; Van Duynslaeger et al.,2007, 2008; Van Overwalle et al., 2009). This might be due to dif-ferences in the stimulus presentation procedure, as outlinedabove (see Section 1.2). To repeat briefly, we reasoned that byusingmultiple critical sentences per scenario, N400 amplitudesin response to trait inconsistencies might have been reduced inthe past, as this caused some inconsistencies to be followed byconsistencies, and some inconsistencies to be followed by in-consistencies as well, rendering the consistency ambiguous inthese cases. Moreover, the presence of a large LPP might havefurther obscured possible N400 modulations.

In line with a classic view of the N400, we propose that theincreased amplitude of this component following trait inconsis-tent behaviors reflects the increased effort required to under-stand these inconsistent sentences, as they are more difficultto integrate with the previous context (Federmeier and Laszlo,2009). It seems unlikely that the present modulation of theN400 would be a consequence of evaluative incongruence. Re-cent research (Holt et al., 2009) has shown that the N400 ampli-tude following emotional words in a neutral context was largerthan following neutral words when participants were explicitlyasked to evaluate their emotional content. However, no differ-ence was found between positive and negative words, and aninfluence of differences in cloze probability couldnot be entirelyexcluded. Moreover, a recent study by Herring et al. (2011) di-rectly addressed the possible influence of evaluative incongru-ence on the N400 and LPP. These authors found no evidencefor an influence of evaluative incongruence on the N400.

3.2. Late positivities in spontaneous trait inferences

In the later 450–1000 ms interval, increased positive ampli-tudes were observed at several frontal locations, in line with

many previous findings that trait inconsistencies evoke in-creased LPPs (Bartholow et al., 2001, 2003; Van Duynslaegeret al., 2007, 2008; Van Overwalle et al., 2009). The reversed ef-fect observed at posterior sites could be the result of the useof the average reference, possibly reinforced by the N400 ef-fect, which was larger for inconsistencies at the posteriorsites, thus possibly leading to an apparently smaller LPP. Inany case, the largest differences were observed at frontalsites. The temporal PCA derived three large late positive factorsin the 450–1000ms time window, all contributing to LPPs in thegrand average. In the recent literature, later positivities havebeen interpreted as reflecting increased attention due to the sa-lience of evaluatively incongruent stimuli. In a recent meta-analysis on the LPP and emotion, Hacjak et al. (2010) concludedthat “the LPP reflectsmultiple andoverlappingpositivities begin-ning in the time range of the classic P300, and that these positiv-ities reflect increased salience of the stimuli” (p. 147). Moreover,source localization indicated that the cuneus andmedial parietallobeweremost involved in generating these positivities. The im-portant role of these areas in generating the LPP has been con-firmed by fMRI research (for a review, see Sabatinelli et al.,2007), and interpreted as a consequence of the motivational rel-evance of emotional stimuli. To illustrate, it has been demon-strated that spider phobics show increased LPP amplitudes inresponse to pictures of spiders compared to healthy controlsand that both the cuneus and the medial parietal cortex weremore engaged by spider pictures in these patients (Scharmülleret al., 2011).

However, exactly which cognitive processes are reflected bythe increased LPP in response to trait inconsistencies in this andall previous trait studies, cannot be concluded based on the pre-sent data. Previous researchers (Bartholowet al., 2001, 2003; VanDuynslaeger et al., 2007, 2008; Van Overwalle et al., 2009) haveinterpreted it as a P300, reflecting context updating processes.The fact that the increased LPP, unlike the N400, occurs regard-less of the number of critical sentences, is compatible with analternative account, namely that it is a function of evaluativeincongruence.

It is important to note that the right frontal distribution ofthe present LPP effect is different than the posterior distribu-tion usually observed in response to evaluative incongruence(Cacioppo et al., 1996) and trait inconsistencies (Bartholowet al., 2003; Van Duynslaeger et al., 2008). There might be sev-eral reasons for this discrepancy. First, the difference in distri-bution could be due to the use of a different reference in thepresent study. Supporting this notion, Van Duynslaeger et al.(2007), who also employed an average reference like in thepresent research, found a centrally distributed LPP effect(and no significant differences at the Pz site). Second, Cun-ningham et al. (2005) showed that the valence of stimuli itselfmay exert an influence on the distribution of LPP amplitudes,independent of congruence. They found that negative stimulielicited larger LPPs at right frontal sites than positive stimuli,especially during evaluative judgment (albeit of single, non-trait words). The fact that all our inconsistent sentenceswere of strong negative valence may therefore be a tentativeexplanation for the right frontal distribution of the LPP in thepresent study.

Interestingly, in a recent study, Leuthold et al. (2011) foundincreased N400 and LPP amplitudes in response to verbally

90 B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

described unanticipated emotional reactions (vs. predictablereactions) of unknown actors. Although it must be notedthat this study wasn't concerned with trait-based expecta-tions, the LPP reported by Leuthold et al. (2011) had a frontaldistribution similar to that in the present research.

3.3. Limitations

An important limitation of the present study is that it only in-cluded negative violations of positive traits. As such, we are un-able to investigate the influence of valence in detail, as thiswould require the inclusion of positive violations of negativetraits. It must be noted here that prior ERP research on sponta-neous trait inferences consistently failed to find significant dif-ferences between negative consistent and positive inconsistentending sentences following a negative trait-implying context,either in N400 or LPP amplitude (Bartholow et al., 2003; VanDuynslaeger et al., 2007, 2008). The results of Bartholow et al.(2001) are more difficult to interpret in this respect, because noseparate results were reported for negative and positive incon-sistencies. As in the present study, Van Overwalle et al. (2009)used only negative inconsistencies.

3.4. General conclusions

In sum, we found increases in both N400 and LPP amplitudefollowing behavior descriptions which were inconsistentwith a previously implied personality trait. We proposethat the increase in N400 amplitude following trait inconsis-tent behavior descriptions reflects increased effort requiredto understand these descriptions, as they clearly violate ex-pectations based on prior behaviors of the actor. We inter-preted the increased LPP as a consequence of evaluativeincongruence. However, based on the present data, it is the-oretically not possible to completely rule out that the N400might also reflect valence evaluation (but see Herring et al.,2011).

Further research could address the role of the LPP and N400in spontaneous trait inferences more precisely by systemati-cally comparing first and second violations of establishedtraits, by keeping the valence of inconsistent behaviors andprior descriptions constant, or by systematically manipulat-ing both valence and inconsistency. In any case, future re-search aimed at unveiling the cognitive processes underlyingtrait inferences should take into account that a trait entailsboth a valuation of past behavior and a prediction about fu-ture behavior.

4. Experimental procedures

4.1. Participants

25 students of the Vrije Universiteit Brussel who reported to beright-handed, participated in exchange for course credit. Nonehad a prior history of any neurological dysfunction. The partic-ipants comprised of 19 women and 6 men, with an age varyingbetween 18 and 23 (M=19.15, SD=1.17). 3 participantsmade useof reading glasses or lenses during the experiment.

4.2. Stimulus material

The ERP-design was a modification of the expectancy-violationparadigmapplied by Bartholowet al. (2001, 2003) andVanDuyn-slaeger et al. (2007, 2008). Participants read 120 sets ofDutch sen-tences, consisting of 2 or 3 positive trait-implying sentences andone critical sentence. Each sentence consisted of 4 to 7 words,andwas presentedword byword for 300mswith an interstimu-lus interval of 350ms. Fictional ‘Star Trek’ names were used toavoid association with an existing person (Hoffman and Hurst,1990). Examples of the trait-implying sentences (translatedfrom Dutch) are: “Diplaq says hello to everybody” and “Diplaq giveshis colleague a present” (implying that Diplaq is a friendly person).The last sentence was either trait-consistent (TC) or trait-inconsistent (TIC), and the degree of consistency was deter-mined by the last word of the sentence. TC-sentences describeda positive behavior that was consistent with the previously im-plied trait (e.g., “Diplaq gives his mother a hug”). TIC-sentences de-scribed a negative behavior that was inconsistent with thepreviously implied trait. (e.g. “Diplaq gives his mother a slap”). Con-sistent and inconsistent critical sentences contained the sameverbs, and were matched with respect to the amount of wordsand the number of syllables in the last word.

Part of the stimulus material was borrowed from VanDuynslaeger et al. (2007) and additional sentences were devel-oped. All sentences were pilot tested on college students(N=132)which rated the degree of consistency between each in-dividual sentence and the proposed trait on an 11-point scaleranging from 0=entirely inconsistent to 10=entirely consistent. Theparticipants also rated each sentence on valence on an 11-point scale going from 0=very negative to 10=very positive. Sen-tenceswith both amean consistency rating and ameanvalencerating higher than 7were kept as TC-sentences. Sentences withboth a mean inconsistency and a valence rating lower than 3were selected as TIC-sentences. The N400 amplitude followingthe presentation of words is inversely related to their frequencyof use in a given language (Federmeier and Laszlo, 2009). There-fore, we carried out an analysis of frequencyof use of the criticalend-words, based on the SUBTEX-NL database (Keuleers et al.,2010). This analysis revealed no statistically significant differ-ence in word frequency between conditions (WilcoxonW=30932, p=0.12). The absolute mean frequency was evenhigher for the inconsistent condition than for the consistentcondition,which should, if anything,work against the hypothe-ses regarding the N400.

4.2.1. Electrophysiological registration and analysisThe EEGwas recorded from 30 scalp sites according to the inter-national 10–20 electrode system, using sintered AgCl electrodesfixed in a Waveguard cap from Advanced Neuro Technology(ANT). The montage included six midline sites (Fpz, Fz, Cz, Pz,Poz, Oz) and twelve sites over each hemisphere (Fp1/Fp2, F3/F4, F7/F8, FC1/FC2, FC5/FC6, C3/C4, T7/T8, CP1/CP2, CP5/CP6,P3/P4, P7/P8, O1/O2), with the average of all EEG-channels as re-cording and off-line reference, as we failed to obtain goodmas-toid recordings due to technical difficulties. The groundelectrode (AFz) was located between the Fz and Cz electrodes.Electro-oculograms (EOGs) were recorded to measure the verti-cal and horizontal eye movements by means of electrodesplaced above and below the right eye and 1 cm external to the

91B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

outer canthus of each eye, respectively. Impedance was keptbelow 10 kΩ for each electrode. The EEG and EOGwere continu-ously recorded during the experiment at a digitizing rate of256 Hz with a DC amplifier. An offline 0.01- to 30-Hz 4th orderButterworth band pass was applied (Bartholow et al., 2001;2003). Epochs were extracted, composed of 1000ms followingthe onset of the last word of critical sentences as well as the200 ms preceding it, which served as sample-period forbaseline-correction.

Epochs containing ocular or muscular artifacts were re-moved, based on visual inspection of the data. Two participantswere excluded, based on the criterion ofmore than 33% rejectedtrials. For the remaining 23 participants, on average 20% of thetrials were rejected, resulting in 48 valid trials per condition onaverage.

The stimuli were presented on a TFT-screen with a refreshrate of 25ms at a distance of 50 cm. Stimuli were presentedwith E-prime, from Psychology Software Tools, Incorporated.EEG was recorded and processed with hardware (Cognitrace)and software (ASA, Eemagine) developed by ANT.

4.3. Procedure

Participants sat down in a comfortable chair, received informa-tion about the procedure, and signed the informed consent,which was approved by the medical ethical committee of theVrije Universiteit Brussel. They were informed that they wouldread sentences about a protagonist's behavior which would bepresented word by word on a computer screen. Every partici-pant was instructed to read the material attentively and to“try to familiarize yourself with thematerial of the experiment,”which is a typical instruction to elicit spontaneous trait infer-ences (see Todorov and Uleman, 2002). The participants re-ceived four practice runs prior to the actual experiment.

4.4. Data analysis

Weanalyzed the average amplitudes at thirteen centrally locat-ed electrodes (F3/4, Fz, FC1/2, C3/4, Cz, CP1/2, P3/4, Pz) using aseparate repeated measures ANOVA for two time windows,300–450 ms and 450–1000 ms post stimulus (Bartholow et al.,2001, 2003), with Location (each selected site) and Consistency(consistent versus inconsistent) as within-subject factors. Ifthe assumption of sphericity was not met, the Greenhouse–Geisser correction was applied in all analyses.

To disentangle ERP components of opposite polarity in thesame timewindow, we conducted a temporal principal compo-nent analysis (PCA) using the toolkit developed by Dien (2010),using the covariancematrix as the relationalmatrix, Promax ro-tationwith k=3 andKaiser correction. Finally, the portion of thegrand average accounted for by each factor was reconstructed(Dien et al., 2005).

R E F E R E N C E S

Bartholow, B.D., Fabiani, M., Gratton, G., Bettencourt, B.A., 2001. Apsychophysiological examination of cognitive processing of andaffective responses to social expectancy violations. Psychol. Sci.12, 197–204.

Bartholow, B.D., Pearson, M.A., Gratton, G., Fabiani, M., 2003. Effectsof alcohol on person perception: a social cognitive neuroscienceapproach. J. Personal. Soc. Psychol. 85, 627–638.

Cacioppo, J.T., Crites, S.L., Berntson, G.G., Coles, M.G.H., 1993. Ifattitudes affect how stimuli are processed, should they notaffect the event-related brain potential? Psychol. Sci. 4,108–112.

Cacioppo, J.T., Crites Jr., S.L., Gardner, W.L., 1996. Attitudes to theright: evaluative processing is associated with lateralized latepositive event-related brain potentials. Pers. Soc. Psychol. Bull.22, 1205–1219.

Cunningham, W.A., Espinet, S.D., DeYoung, C.G., Zelazo, P.D., 2005.Attitudes to the right- and left: frontal ERP asymmetriesassociated with stimulus valence and processing goals.NeuroImage 28, 827–834.

Dien, J., 2010. The ERP PCA Toolkit: an open source program foradvanced statistical analysis of event-related potential data. J.Neurosci. Meth. 187, 138–145.

Dien, J., Beal, D.J., Berg, P., 2005. Optimizing principal componentsanalysis of event-related potentials: matrix type, factor loadingweighting, extraction, and rotations. Clin. Neurophysiol. 116,1808–1825.

Duncan, C.C., Barry, R.J., Connolly, J.F., Fischer, C., Michie, P.T.,Näätänen, R., et al., 2009. Event-related potentials in clinicalresearch: guidelines for eliciting, recording, and quantifyingmismatch negativity, P300, and N400. Clin. Neurophysiol. 120,1883–1908.

Federmeier, K.D., Laszlo, S., 2009. Time for meaning:electrophysiology provides insights into the dynamics ofrepresentation and processing in semantic memory. In: Ross,B.H. (Ed.), Psychology of Learning and Motivation, Volume 51.Academic Press, Burlington, pp. 1–44.

Filik, R., Leuthold, H., 2008. Processing local pragmatic anomalies infictional contexts: evidence from theN400. Psychophysiology 45,554–558.

Franklin, M.S., Dien, J., Neely, J.H., Huber, E., Waterson, L.D., 2007.Semantic priming modulates the N400, N300, and N400RP.Clin. Neurophysiol. 118, 1053–1068.

Hacjak, G., MacNamara, A., Olvet, D.M., 2010. Event-relatedpotentials, emotion, and emotion regulation: an integrativereview. Dev. Neuropsychol. 35, 129–155.

Hagoort, P., Hald, L., Bastiaansen, M., Peterson, K.M., 2004.Integration of wordmeaning and world knowledge in languagecomprehension. Science 304, 438–441.

Herring, D.R., Taylor, J.H., White, K.R., Crites Jr., S.L., 2011.Electrophysiological responses to evaluative priming: the LPPis sensitive to incongruity. Emotion. doi:10.1037/a0022804.

Hoffman, C., Hurst, N., 1990. Gender stereotypes: perception orrationalization? J. Personal. Soc. Psychol. 58, 197–208.

Holt, D.J., Lynn, S.K., Kuperberg, G.R., 2009. Neurophysiologicalcorrelates of comprehending emotional meaning in context. J.Cogn. Neurosci. 21, 2245–2262.

Ito, T.A., Larsen, J.T., Smith, N.K., Cacioppo, J.T., 1998. Negative informationweighsmore heavily on the brain: the negativity biasin evaluative categorizations. J. Personal. Soc. Psychol. 75,887–900.

Keuleers, E., Brysbaert, M., New, B., 2010. SUBTLEX-NL: a newfrequency measure for Dutch words based on film subtitles.Behav. Res. Meth. 42, 643–650.

Kressel, L.M., Uleman, J.S., 2010. Personality traits function ascausal concepts. J. Exp. Soc. Psychol. 46, 213–216.

Kutas, M., Federmeier, K.D., 2000. Electrophysiology revealssemantic memory use in language comprehension. TrendsCogn. Sci. 4, 463–470.

Kutas, M., Hillyard, S.A., 1980. Reading senseless sentences:brain potentials reflect semantic incongruity. Science 207,203–205.

Leuthold, H., Filik, R., Murphy, K., Mackenzie, I.G., 2011. The on-lineprocessing of socio-emotional information in prototypical

92 B R A I N R E S E A R C H 1 4 1 8 ( 2 0 1 1 ) 8 3 – 9 2

scenarios: inferences from brain potentials. Soc. Cogn. Affect.Neurosci. doi:10.1093/scan/nsr029.

Nieuwland, M.S., Van Berkum, J.J.A., 2006. When peanuts fall in love:N400 evidence for the power of discourse. J. Cogn. Neurosci. 18,1098–1111.

Osterhout, L., Bersick, M., McLaughlin, J., 1997. Brain potentialsreflect violations of gender stereotypes. Mem. Cogn. 25, 273–285.

Pascual-Marqui, R.D., 1999. Review of methods for solving the EEGinverse problem. Int. J. Bioelectromagn. 1, 75–86.

Pascual-Marqui, R.D., Michel, C.M., Lehmann, D., 1994. Lowresolution electromagnetic tomography: a new method forlocalizing electrical activity in the brain. Int. J. Psychophysiol. 18,49–65.

Read, S.J., 1987. Constructing causal scenarios: a knowledgestructure approach to causal reasoning. J. Personal. Soc.Psychol. 52, 288–302.

Roehm, D., Bornkessel-Schlesewsky, I., Rösler, F., Schlesewsky, M.,2007. To predict or not to predict: influences of task andstrategy on the processing of semantic relations. J. Cogn.Neurosci. 19, 1259–1274.

Sabatinelli, D., Lang, P.J., Keil, A., Bradley, M.M., 2007. Emotionalperception: correlation of functional MRI and event-relatedpotentials. Cereb. Cortex 17, 10985–11091.

Scharmüller, W., Leutgeb, V., Schäfer, A., Köchel, A., Schienle, A.,2011. Source localization of late electrocortical positivityduring symptom provocation in spider phobia: An sLORETAstudy. Brain Research 1397, 10–18.

Sitnikova, T., Holcomb, P.J., Kiyonaga, K.A., Kuperberg, G.R., 2008.Two neurocognitive mechanisms of semantic integrationduring the comprehension of visual real-world events. J. Cogn.Neurosci. 20, 2037–2057.

Todorov, A., Uleman, J.S., 2002. Spontaneous trait inferences arebound to actors: evidence from false recognition. J. Personal.Soc. Psychol. 83, 1051–1065.

VanBerkum, J.J.A., van denBrink, D., Tesink, C.M.J., Kos,M., Hagoort,P., 2008. The neural integration of speaker andmessage. J. Cogn.Neurosci. 20, 580–591.

Van Duynslaeger, M., Van Overwalle, F., Verstraeten, E., 2007.Electrophysiological time course and brain areas of spontaneousand intentional trait inferences. Soc. Cogn. Affect. Neurosci. 2,174–188.

Van Duynslaeger, M., Sterken, C., Van Overwalle, F., Verstraeten,E., 2008. EEG components of spontaneous trait inferences. Soc.Neurosci. 3, 164–177.

Van Overwalle, F., Van den Eede, S., Baetens, K., Vandekerckhove,M., 2009. Trait inferences in goal-directed behavior: ERP timingand localization under spontaneous and intentional processing.Soc. Cogn. Affect. Neurosci. 4, 177–190.

Van Petten, C., Luka, B.J., 2006. Neural localization of semanticcontext effects in electromagnetic and hemodynamic studies.Brain Lang. 97, 279–293.

White, K.R., Crites, S.L., Taylor, J.H., Corral, G., 2009. Wait, whatassessing stereotype incongruities using the N400 ERPcomponent. Soc. Cogn. Affect. Neurosci. 4, 191–198.