BR A I N R ES E A RCH 1 4 18 ( 20 1 1 ) 8 3 –92
Available online at www.sciencedirect.com
www.elsevier.com/locate/brainres
Research Report
N400 and LPP in spontaneous trait inferences
Kris Baetens a,⁎, 1 , Laurens Van der Cruyssen a , Anja Achtziger b ,
Marie Vandekerckhove a , Frank Van Overwalle a,⁎
a
Vrije Universiteit Brussel, Belgium
Universität Konstanz, Germany
b
A R T I C LE I N FO
AB S T R A C T
Article history:
Past research on spontaneous trait inferences using event related potentials (ERPs) has
Accepted 29 August 2011
consistently reported increased late positive potential (LPP) amplitudes following social
Available online 2 September 2011
expectancy violations, but no N400 modulation. In the present study, participants read
scenarios describing behaviors of unknown actors. They entailed descriptions of several
Keywords:
positive trait implying behaviors, followed by a single final sentence describing behavior
ERP
that was either consistent or inconsistent with the previously implied trait. As in previous
N400
studies, we found significantly increased LPP amplitudes following inconsistent behaviors
LPP
at multiple frontal sites. Unlike in previous research, we also found increased N400
Trait inference
amplitudes at several centro-parietal sites. The divergence of these results is explained
World knowledge
from minor differences in the stimulus presentation procedure and possible overlap of
ERP components of opposite polarity. Temporal principal component analysis (PCA) confirmed the separate influence of concurrent LPP and N400 ERP modulations, and the source
of the largest factors was located using sLORETA. It is suggested that the increased N400 in
response to trait inconsistencies reflects difficulties in understanding unanticipated behavior, while the LPP effect might reflect evaluative incongruence.
© 2011 Elsevier B.V. All rights reserved.
1.
Introduction
Imagine a co-worker you have always known as a particularly
honest and ethical person. One day, you haphazardly stumble
upon him while he is prying money from the faculty's charity
donation box. This probably sets a number of psychological reactions in motion. Initially, you might have difficulties understanding what he is doing because you don't expect somebody
to do such a thing, especially not this person. Once you do realize
what he is doing, you might be shocked by the discrepancy between his past and present behavior. Finally, you may adjust
your expectations and associate him with a negative personality
trait like “greedy” or “dishonest”. Indeed, folk wisdom implores
us to use observations of past behavior to predict future behavior: “Fool me once, shame on you; fool me twice, shame on me.”
Stripped-down, abstracted versions of scenarios such as described above have been used to study event-related potentials
(ERPs) associated with trait inference processes (Bartholow et
al., 2001, 2003; Van Duynslaeger et al., 2007, 2008; Van Overwalle
et al., 2009). In these studies, participants read behavioral descriptions of (unknown) individuals which strongly implied a
certain personality trait. Subsequently, one or more critical sentences followed, either consistent or inconsistent with the previously implied trait. All of these studies report a positivity
⁎ Corresponding authors at: Faculty of Psychology and Educational Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium.
Fax: +32 2 629 24 89.
E-mail addresses: [email protected] (K. Baetens), [email protected] (F. Van Overwalle).
1
PhD fellow of the Research Foundation – Flanders (FWO).
0006-8993/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.brainres.2011.08.067
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BR A IN RE S E A RCH 1 4 18 ( 20 1 1 ) 8 3 –92
peaking around 500 ms post stimulus in the grand averages of
ERPs following the last (critical) word of inconsistent sentences
relative to consistent sentences. This peak was interpreted as a
late positive potential (LPP) or P300 component (Bartholow et al.,
2001), reflecting context-updating processes, and the latency of
this potential was considered an index of the process of trait inference (Van Duynslaeger et al., 2007).
The trait inconsistencies in the cited ERP studies contrasted
with respect to valence and induced affective responses with
the previously presented behavior descriptions. Therefore, one
might alternatively interpret these late positivities as a consequence of change in valence which triggers increased attention
due to the intrinsic motivational significance of stimuli containing emotional information (Holt et al., 2009). Research on
emotional word comprehension has established a general association of the processing of emotional information conveyed by
words with a late, extended, attention-modulated process
encoding their valence, expressed in the LPP component (for a
review, see Hacjak et al., 2010). Stimuli of negative valence generally attract more attention than positive stimuli, a tendency
termed the “negativity bias” (e.g., Ito et al., 1998). Furthermore,
research has shown that evaluative incongruence, a mismatch
between the valence of a category and a subsequent target stimulus, is associated with an increased positivity in the ERP that
peaks between 300 and 600 ms post stimulus (Cacioppo et al.,
1993, 1996).
However, is this the only component one should expect after
these trait inconsistencies? In what follows, we will propose
that modulation of another ERP component, the N400, is also to
be expected in paradigms involving disconfirmations of trait
inferences.
(Bartholow et al., 2001, 2003; Van Duynslaeger et al., 2007,
2008; Van Overwalle et al., 2009). After all, trait inconsistencies
could be considered a specific type of world knowledge violation
(e.g., you consider this person to be honest, but all of a sudden it
turns out he is very sneaky). Moreover, if a person is described
as behaving contrary to expectations, surely this information
seems more difficult to integrate with the context than if he
acts as usual, which should lead to a relative increase in N400
amplitude. Indeed, increased N400 amplitudes have been
found in response to violations of stereotypes, another type of
person schema (Van Berkum et al., 2008; White et al., 2009).
In contrast to these studies, an earlier study by Osterhout
et al. (1997) found no increased N400 amplitudes, but instead increased late positivities at about 600 ms (P600) in response to
gender stereotype violations (e.g., “The lumberjack sharpened
her axe.”). A possible explanation is that the participants interpreted these sentences as containing a grammatical error in applying the pronoun (reliably associated with the P600). The
authors went on to predict that “Anomalies involving social categories that are not marked in the grammar (e.g., race) should
not elicit the P600 effect but might elicit the N400 effect associated with semantic/pragmatic aspects of language.” (p. 282).
Following up on this interpretation, Bartholow et al. (2001,
2003) included a semantically inconsistent condition in order
to compare the effects induced by their trait inconsistencies
with a classic N400 effect. However, as noted earlier, they
found a very dissimilar P300/LPP following trait inconsistencies,
and a modulation of the N400 only for semantic inconsistencies. How can the consistent absence of any N400 effect in
the trait ERP literature be explained?
1.2.
1.1.
The absence of N400 effects
The N400 and expectancy violations
The N400 component is a negative deflection in the ERP about
400 ms post stimulus. It is a function of semantic inconsistency
(e.g., Kutas and Hillyard, 1980) and reflects the ease of integrating a stimulus with a given context, not only for verbal stimuli
(Kutas and Federmeier, 2000), but also for other modalities, for
example, the comprehension of visually presented real-world
events (Sitnikova et al., 2008). The degree of semantic consistency is not a stable quality of any given combination of stimuli, but
rather determined flexibly and under influence of the local context (Filik and Leuthold, 2008; Nieuwland and Van Berkum,
2006). For example, “The peanut is in love” will cause a larger
N400 than “The peanut is salted”, but the opposite is true if this
sentence is preceded by the title “The amorous peanut”. It has
been demonstrated that the N400 may also reflect the violation
of objective knowledge about the world (Hagoort et al., 2004).
Much of the extensive N400 literature can be conveniently organized around the concept of cloze-probability. This is the probability that people will use a given word to complete a
sentence, and it is inversely related to the N400 amplitude following the presentation of that word (Federmeier and Laszlo,
2009).
Personality traits can be conceptualized as a schema, a type
of world knowledge we construct and constantly adapt to anticipate or predict the behaviors of others (Kressel and Uleman,
2010; Read, 1987). Therefore, one might expect to find N400 effects in the aforementioned studies on trait inferences
An obvious reason why no previous ERP study on trait inferences
has reported modulation of the N400 might be that it was overshadowed by a large positive potential. All previous ERP studies
on trait inferences report a large LPP/P300 (Bartholow et al.,
2001, 2003; Van Duynslaeger et al., 2007, 2008; Van Overwalle et
al., 2009). Positivities occurring simultaneously with the N400
may obscure it (Franklin et al., 2007; Roehm et al., 2007). In such
cases, principal component analysis (PCA) may serve to disentangle the influence of simultaneously occurring ERP components (Franklin et al., 2007).
The stimuli presentation procedure of previous ERP studies
might be an additional factor working against N400 modulation.
In all but one of these studies (Bartholow et al., 2001, 2003; Van
Duynslaeger et al., 2007, 2008; but see Van Overwalle et al.,
2009) expectancy establishing sentences were followed by two
critical consistent and two critical inconsistent sentences, presented in a random order. This yielded six possible sequences,
as presented in Table 1. As can be seen, only 50% of the inconsistent sentences were unambiguously inconsistent, in that
they were not preceded by another inconsistent sentence.
Thus, the remaining 50% of the inconsistent sentences were
ambiguous (e.g., if John discloses a trusted secret after stealing
charity money, is this really inconsistent?). This could have significantly reduced N400 amplitudes for the inconsistent condition, as it constitutes a form of priming (Federmeier and
Laszlo, 2009), dramatically changing the local context (Filik
and Leuthold, 2008; Nieuwland and Van Berkum, 2006). The
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BR A I N R ES E A RCH 1 4 18 ( 20 1 1 ) 8 3 –92
Table 1 – Possible sequences of two consistent (C) and inconsistent (I) sentences.
Sequence
C
C
C
I
I
I
C
I
I
C
C
I
Unambiguous C
I
C
I
C
I
C
I
I
C
I
C
C
case is even more problematic for consistent sentences, of
which only 25% were unambiguously consistent, that is, not
preceded by an inconsistent sentence. Therefore, on average
75% of the consistent sentences were actually ambiguous (e.g.,
John refusing to disclose the secret after stealing charity
money). This may have significantly increased the N400 amplitudes for the consistent condition. Taken together, it is likely
that this way of presenting stimuli diminishes possible differences in N400 amplitude between the conditions.
Of course, the same reasoning holds for the LPP in the previously mentioned ERP studies on trait inferences. However, assuming that the LPP reflects evaluative incongruence in these
paradigms, it is conceivable that this LPP would be less diminished by this procedure. Whereas the expectations about the
described actor can be altered drastically by one inconsistent
sentence, the valence of the behaviors is not much affected by
it (e.g., when you catch John stealing charity money again, this
is less surprising but not less upsetting). As such, the evaluative
incongruence in the behavior descriptions associated with the
LPP remains obvious and therefore more stable. After all, there
was always a small minority of one or two sentences that was
evaluatively incongruent with the established context and the
other critical sentences, making it plausible that they should
still stand out.
Note that the semantically incongruent sentences in the
work of Bartholow et al. (2001, 2003) were similarly randomly
shuffled 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 incongruent
with a preceding context. Therefore, one might expect their
meaning to be minimally affected by the context in which they
were presented.
To the best of our knowledge, there has been only one ERP
study on trait inferences in which the person description was
followed 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 peaks
were only compared in a 50–300 ms window) wasn't ideal for
capturing N400 effects, since they should occur later (Kutas
and Federmeier, 2000). Interestingly, these authors did report
an unexpected significant effect at the Pz site, where the positive peak amplitude was higher for consistent than for inconsistent trait-implying sentences in the 300–450 ms window, which
could reflect an N400 modulation. However, since this study
had a complex design (analyzing the joint effect of trait and
goal inferences) and effects were small, it is difficult to draw
clear conclusions from it.
Unambiguous I
1st
2nd
1st
2nd
Yes
Yes
Yes
No
No
No
4/12 (25%)
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
6/12 (50%)
No
No
No
No
No
No
1.3.
The present research
To avoid the methodological limitations of earlier trait inference
studies, we presented person descriptions that were each followed by one critical sentence that was either consistent (50%
of the trials) or inconsistent (50%) with it. The descriptions implied positive traits, and inconsistencies described behavior triggering a negative trait inference. This choice was based on the
finding that negative social behaviors produce stronger inconsistency effects (Bartholow et al., 2001; Van Duynslaeger et al., 2008).
In contrast to earlier research on trait inferences using a random
shuffling of consistent, inconsistent and irrelevant sentences, we
expect that by using single critical sentences, we will obtain an
increased N400 following inconsistencies, in line with the prediction of Osterhout et al. (1997). Specifically, we expect that the
mean amplitude for inconsistent endings will be more negative
than for consistent endings at centro-parietal sites in the 300–
450 ms interval. We additionally expect to find more positive amplitudes following inconsistent endings than following consistent endings in the later 450–1000 ms time interval reflecting
evaluative inconsistency, in line with previous trait studies that
reported an increased P300/LPP. To disentangle possible simultaneous influences of these components, we will employ principle
component analysis (PCA) (Dien et al., 2005). As a first step to
shed light on their functional correlates, we will roughly localize
the 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 midline
channels (Fz, Cz, Pz). Table 2 gives an overview of the mean amplitude and standard deviation for the time windows discussed
below for the consistent (C) and inconsistent (I) condition, as
well as the p-values of paired samples t-tests between the conditions per electrode.
2.1.
300–450 ms
In the N400 time window, we found a significant interaction
of Location and Consistency, F(2.63, 57.86) = 5.39, p < 0.005
(Greenhouse–Geisser corrected). Follow-up paired samples ttests revealed that the average amplitude in this interval was
significantly more negative following inconsistencies than following consistencies at CP1, CP2, P3, P4 and Pz (ps < 0.05), reflecting an increased N400 (which is usually maximal over centroparietal sites, Duncan et al., 2009).
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BR A IN RE S E A RCH 1 4 18 ( 20 1 1 ) 8 3 –92
Fz (µV)
-5
corrected). Follow-up paired samples t-tests revealed that at
Fz, FC2 and F4, the average amplitude was more positive for
trait inconsistencies than for trait consistencies (ps < 0.01),
reflecting an increased LPP. The largest difference was found
at Fz. At P3, P4, CP1, CP2 and Pz, the inverse was true, with less
positive amplitudes following inconsistencies compared to consistencies (ps < 0.05).
0
LPP
5
0
200
400
600
800
1000
ms
2.3.
Temporal PCA
Cz (µV)
-5
The data was decomposed into 14 temporal factors (TFs)
based on inspection of the scree plot, which indicates the
gain in explained variance of the grand average after adding
more factors (Dien, 2010). Of these factors, there were 8 factors
accounting for more than 5% of the variance in the grand averages. Of these 8, in turn, there were 4 that peaked after
250 ms, together accounting for 56% of the variance over the
entire 1.2 s trial. Fig. 2 displays the influence of these 4 largest
factors on the grand average at Cz. As can be seen, three of the
largest factors (TF1, TF3 and TF4) had a positive influence on
the amplitude at Cz, and this was true for all thirteen selected
channels. 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 displays
the influence of this factor on the grand average for this site as
a function of consistency. The negative influence of TF2 proved
to be greater for inconsistent than for consistent sentences in
the 300–450 ms window at several sites (C3, P3, CP1, Pz, CP2,
P4, ps < 0.05). For all selected sites, the average difference between consistent and inconsistent sentences for this factor in
the window 300–450 ms correlated significantly with the same
difference in the grand average in the window 300–450 ms
(mean r = 0.85, ps < 0.001). This indicates that this factor explains
the 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 depicts
which voxels were maximally involved in eliciting this factor.
As can be seen, the left temporo-parietal area, specifically the
0
5
0
200
400
600
800
1000
800
1000
ms
-5
Pz (µV)
N400
0
5
0
200
400
600
ms
Fig. 1 – Grand averages per condition for three midline sites.
Grand average waveforms for consistent (full lines) and
inconsistent (dotted lines) ending sentences at three midline
sites. Negativity is plotted upwards.
2.2.
450–1000 ms
Again, we found a significant interaction effect of Location and
Consistency, F(2.87, 63.06) = 9.65, p < 0.001 (Greenhouse–Geisser
Table 2 – Mean amplitude, standard deviation and p-value of the paired samples t-test per time window for the consistent
(C) and inconsistent (I) condition per electrode.
300–450 ms (N400)
Fz
Cz
Pz
F3
F4
FC1
FC2
C3
C4
CP1
CP2
P3
P4
Note.
⁎⁎ p < 0.01.
⁎ p < 0.05.
450–1000 ms (LPP)
M(C)
SD(C)
M(I)
SD(I)
p
M(C)
SD(C)
M(I)
SD(I)
p
−1.43
−0.87
−0.32
−1.53
−1.22
−1.32
−1.11
−0.75
−0.76
−0.35
−0.37
0.64
0.40
2.12
2.28
2.14
1.75
1.77
1.95
1.92
1.59
1.57
1.89
2.09
1.34
1.74
− 0.98
− 1.27
− 1.24
− 1.16
− 1.12
− 1.24
− 1.08
− 1.11
− 1.04
− 1.14
− 1.18
0.05
− 0.17
3.04
2.43
2.49
2.50
2.47
2.69
2.40
1.92
1.99
2.14
2.25
1.40
2.07
0.247
0.202
0.007 ⁎⁎
0.274
0.776
0.840
0.916
0.117
0.288
0.006 ⁎⁎
0.009 ⁎⁎
0.017 ⁎
0.009 ⁎⁎
0.81
2.13
2.43
0.22
0.54
1.19
1.03
1.22
1.36
2.31
2.36
1.69
1.64
2.17
2.08
1.70
1.68
1.83
1.98
2.22
1.44
1.46
1.63
1.71
1.24
1.26
1.96
2.07
1.47
0.87
1.26
1.65
1.87
0.92
1.36
1.50
1.61
1.08
1.13
1.99
1.81
1.76
1.53
1.62
1.77
1.97
1.11
1.72
1.51
1.76
1.29
1.38
0.004 ⁎⁎
0.825
0.007 ⁎⁎
0.064
0.010 ⁎
0.189
0.009 ⁎⁎
0.270
0.997
0.010 ⁎
0.033 ⁎
0.011 ⁎
0.043 ⁎
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-1.5
TF2
-1
-0.5
Cz (µV)
0
0.5
1
1.5
TF3
2
TF4
2.5
TF1
3
0
200
400
600
800
1000
Fig. 2 – Projection of the four largest temporal factors to the Cz channel. Projection of the 4 largest temporal factors to the Cz
channel. This image is representative for all analyzed channels as far as polarity of the peak of these factors goes.
superior temporal gyrus (BA22) was found to be most involved
in producing TF2 (MNI coordinates −60, −60, 20).
With respect to the positive factors, TF1 was maximal at Pz,
while TF3 and TF4 both peaked at Fz. The combined influence of
these three factors constituted a significantly larger positivity
for inconsistent than for consistent sentences at F3 and Fz
(ps < 0.05), while it was smaller for inconsistent sentences at
P3, CP1, Pz, CP2 and P4 (p < 0.01–0.05). Fig. 3 shows the combined
Fz (µV)
-2
-1
0
1
0
200
400
600
800
1000
0
200
400
600
800
1000
0
200
400
600
800
1000
Cz (µV)
-2
-1
0
1
Pz (µV)
-2
-1
0
1
Fig. 3 – Influence of the four largest temporal factors per condition. Fig. 3 depicts the influence of the four largest temporal
factors on three midline channels. The influence of TF 2 is denoted in red, the combined influence of TF 1, 3 and 4 in blue. Full
lines correspond to consistent ending sentences, dotted lines to inconsistent endings. Negativity plotted upward.
88
TF 4
TF 3
TF 2
TF 1
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Fig. 4 – Source localization of the four largest temporal factors. Estimated current source density for the voxels maximally active
in producing the four largest temporal factors.
influence of TF1, TF3 and TF4 on three midline channels (Fz, Cz,
Pz). Fig. 4 shows the sLORETA source localization of these three
components. Voxels in the left cuneus (−10, −100, 20; BA19), medial parietal lobe (5, −45, 71; BA 5) and left cuneus (−30, −95, −5;
BA 18) were maximally involved in the generation of TF1, TF3
and TF4 respectively.
3.
Discussion
The aim of this study was to test whether trait inconsistencies
generate increased N400 amplitudes, reflecting violation of expectations based on prior behaviors of others, as well as increased
BR A I N R ES E A RCH 1 4 18 ( 20 1 1 ) 8 3 –92
LPPs, presumably reflecting evaluative inconsistency. Both hypotheses were confirmed.
3.1.
N400 in spontaneous trait inferences
As expected, in the 300–450 ms interval, an increased negative
mean amplitude was observed in response to trait inconsistencies at centro-parietal sites. We interpreted this negativity
as an N400. This was confirmed by a temporal PCA, which
revealed that the second largest temporal factor was a sharp
negativity peaking at about 400 ms. This factor had a negative influence on the amplitude at all analyzed electrode locations. The
difference between consistency conditions in this time interval
could to a great extent be explained by differences in this factor.
The neural source that contributed most to this component was
located at the left superior temporal gyrus, more specifically in
Wernicke's area, which is one of the most consistently reported
generators of the N400 (for a review, see Van Petten and Luka,
2006). Taken together, this clearly seems to support an interpretation of the differences in the 300–450 ms as an increased N400
in 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, violations of stereotypes, another type of person schema, were
associated with increased N400 amplitudes. However, no such
modulation of the N400 component has been reported before
in trait inference studies using paradigms similar to the present
research (Bartholow et al., 2001, 2003; Van Duynslaeger et al.,
2007, 2008; Van Overwalle et al., 2009). This might be due to differences in the stimulus presentation procedure, as outlined
above (see Section 1.2). To repeat briefly, we reasoned that by
using multiple critical sentences per scenario, N400 amplitudes
in response to trait inconsistencies might have been reduced in
the past, as this caused some inconsistencies to be followed by
consistencies, and some inconsistencies to be followed by inconsistencies as well, rendering the consistency ambiguous in
these cases. Moreover, the presence of a large LPP might have
further obscured possible N400 modulations.
In line with a classic view of the N400, we propose that the
increased amplitude of this component following trait inconsistent behaviors reflects the increased effort required to understand these inconsistent sentences, as they are more difficult
to integrate with the previous context (Federmeier and Laszlo,
2009). It seems unlikely that the present modulation of the
N400 would be a consequence of evaluative incongruence. Recent research (Holt et al., 2009) has shown that the N400 amplitude following emotional words in a neutral context was larger
than following neutral words when participants were explicitly
asked to evaluate their emotional content. However, no difference was found between positive and negative words, and an
influence of differences in cloze probability could not be entirely
excluded. Moreover, a recent study by Herring et al. (2011) directly addressed the possible influence of evaluative incongruence on the N400 and LPP. These authors found no evidence
for 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 amplitudes were observed at several frontal locations, in line with
89
many previous findings that trait inconsistencies evoke increased LPPs (Bartholow et al., 2001, 2003; Van Duynslaeger
et al., 2007, 2008; Van Overwalle et al., 2009). The reversed effect observed at posterior sites could be the result of the use
of the average reference, possibly reinforced by the N400 effect, which was larger for inconsistencies at the posterior
sites, thus possibly leading to an apparently smaller LPP. In
any case, the largest differences were observed at frontal
sites. The temporal PCA derived three large late positive factors
in the 450–1000 ms time window, all contributing to LPPs in the
grand average. In the recent literature, later positivities have
been interpreted as reflecting increased attention due to the salience of evaluatively incongruent stimuli. In a recent metaanalysis on the LPP and emotion, Hacjak et al. (2010) concluded
that “the LPP reflects multiple and overlapping positivities beginning in the time range of the classic P300, and that these positivities reflect increased salience of the stimuli” (p. 147). Moreover,
source localization indicated that the cuneus and medial parietal
lobe were most involved in generating these positivities. The important role of these areas in generating the LPP has been confirmed by fMRI research (for a review, see Sabatinelli et al.,
2007), and interpreted as a consequence of the motivational relevance of emotional stimuli. To illustrate, it has been demonstrated that spider phobics show increased LPP amplitudes in
response to pictures of spiders compared to healthy controls
and that both the cuneus and the medial parietal cortex were
more engaged by spider pictures in these patients (Scharmüller
et al., 2011).
However, exactly which cognitive processes are reflected by
the increased LPP in response to trait inconsistencies in this and
all previous trait studies, cannot be concluded based on the present data. Previous researchers (Bartholow et al., 2001, 2003; Van
Duynslaeger et al., 2007, 2008; Van Overwalle et al., 2009) have
interpreted it as a P300, reflecting context updating processes.
The fact that the increased LPP, unlike the N400, occurs regardless of the number of critical sentences, is compatible with an
alternative account, namely that it is a function of evaluative
incongruence.
It is important to note that the right frontal distribution of
the present LPP effect is different than the posterior distribution usually observed in response to evaluative incongruence
(Cacioppo et al., 1996) and trait inconsistencies (Bartholow
et al., 2003; Van Duynslaeger et al., 2008). There might be several reasons for this discrepancy. First, the difference in distribution could be due to the use of a different reference in the
present study. Supporting this notion, Van Duynslaeger et al.
(2007), who also employed an average reference like in the
present research, found a centrally distributed LPP effect
(and no significant differences at the Pz site). Second, Cunningham et al. (2005) showed that the valence of stimuli itself
may exert an influence on the distribution of LPP amplitudes,
independent of congruence. They found that negative stimuli
elicited larger LPPs at right frontal sites than positive stimuli,
especially during evaluative judgment (albeit of single, nontrait words). The fact that all our inconsistent sentences
were of strong negative valence may therefore be a tentative
explanation for the right frontal distribution of the LPP in the
present study.
Interestingly, in a recent study, Leuthold et al. (2011) found
increased N400 and LPP amplitudes in response to verbally
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described unanticipated emotional reactions (vs. predictable
reactions) of unknown actors. Although it must be noted
that this study wasn't concerned with trait-based expectations, the LPP reported by Leuthold et al. (2011) had a frontal
distribution similar to that in the present research.
3.3.
Limitations
An important limitation of the present study is that it only included negative violations of positive traits. As such, we are unable to investigate the influence of valence in detail, as this
would require the inclusion of positive violations of negative
traits. It must be noted here that prior ERP research on spontaneous trait inferences consistently failed to find significant differences between negative consistent and positive inconsistent
ending sentences following a negative trait-implying context,
either in N400 or LPP amplitude (Bartholow et al., 2003; Van
Duynslaeger et al., 2007, 2008). The results of Bartholow et al.
(2001) are more difficult to interpret in this respect, because no
separate results were reported for negative and positive inconsistencies. 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 amplitude
following behavior descriptions which were inconsistent
with a previously implied personality trait. We propose
that the increase in N400 amplitude following trait inconsistent behavior descriptions reflects increased effort required
to understand these descriptions, as they clearly violate expectations based on prior behaviors of the actor. We interpreted the increased LPP as a consequence of evaluative
incongruence. However, based on the present data, it is theoretically not possible to completely rule out that the N400
might also reflect valence evaluation (but see Herring et al.,
2011).
Further research could address the role of the LPP and N400
in spontaneous trait inferences more precisely by systematically comparing first and second violations of established
traits, by keeping the valence of inconsistent behaviors and
prior descriptions constant, or by systematically manipulating both valence and inconsistency. In any case, future research aimed at unveiling the cognitive processes underlying
trait inferences should take into account that a trait entails
both a valuation of past behavior and a prediction about future behavior.
4.
Experimental procedures
4.1.
Participants
25 students of the Vrije Universiteit Brussel who reported to be
right-handed, participated in exchange for course credit. None
had a prior history of any neurological dysfunction. The participants comprised of 19 women and 6 men, with an age varying
between 18 and 23 (M = 19.15, SD= 1.17). 3 participants made use
of reading glasses or lenses during the experiment.
4.2.
Stimulus material
The ERP-design was a modification of the expectancy-violation
paradigm applied by Bartholow et al. (2001, 2003) and Van Duynslaeger et al. (2007, 2008). Participants read 120 sets of Dutch sentences, consisting of 2 or 3 positive trait-implying sentences and
one critical sentence. Each sentence consisted of 4 to 7 words,
and was presented word by word for 300 ms with an interstimulus interval of 350 ms. Fictional ‘Star Trek’ names were used to
avoid association with an existing person (Hoffman and Hurst,
1990). Examples of the trait-implying sentences (translated
from Dutch) are: “Diplaq says hello to everybody” and “Diplaq gives
his colleague a present” (implying that Diplaq is a friendly person).
The last sentence was either trait-consistent (TC) or traitinconsistent (TIC), and the degree of consistency was determined by the last word of the sentence. TC-sentences described
a positive behavior that was consistent with the previously implied trait (e.g., “Diplaq gives his mother a hug”). TIC-sentences described a negative behavior that was inconsistent with the
previously implied trait. (e.g. “Diplaq gives his mother a slap”). Consistent and inconsistent critical sentences contained the same
verbs, and were matched with respect to the amount of words
and the number of syllables in the last word.
Part of the stimulus material was borrowed from Van
Duynslaeger et al. (2007) and additional sentences were developed. All sentences were pilot tested on college students
(N = 132) which rated the degree of consistency between each individual sentence and the proposed trait on an 11-point scale
ranging from 0 = entirely inconsistent to 10 = entirely consistent. The
participants also rated each sentence on valence on an 11point scale going from 0 = very negative to 10= very positive. Sentences with both a mean consistency rating and a mean valence
rating higher than 7 were kept as TC-sentences. Sentences with
both a mean inconsistency and a valence rating lower than 3
were selected as TIC-sentences. The N400 amplitude following
the presentation of words is inversely related to their frequency
of use in a given language (Federmeier and Laszlo, 2009). Therefore, we carried out an analysis of frequency of use of the critical
end-words, based on the SUBTEX-NL database (Keuleers et al.,
2010). This analysis revealed no statistically significant difference in word frequency between conditions (Wilcoxon
W= 30932, p = 0.12). The absolute mean frequency was even
higher for the inconsistent condition than for the consistent
condition, which should, if anything, work against the hypotheses regarding the N400.
4.2.1.
Electrophysiological registration and analysis
The EEG was recorded from 30 scalp sites according to the international 10–20 electrode system, using sintered AgCl electrodes
fixed 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 recording and off-line reference, as we failed to obtain good mastoid recordings due to technical difficulties. The ground
electrode (AFz) was located between the Fz and Cz electrodes.
Electro-oculograms (EOGs) were recorded to measure the vertical and horizontal eye movements by means of electrodes
placed above and below the right eye and 1 cm external to the
BR A I N R ES E A RCH 1 4 18 ( 20 1 1 ) 8 3 –92
outer canthus of each eye, respectively. Impedance was kept
below 10 kΩ for each electrode. The EEG and EOG were continuously recorded during the experiment at a digitizing rate of
256 Hz with a DC amplifier. An offline 0.01- to 30-Hz 4th order
Butterworth band pass was applied (Bartholow et al., 2001;
2003). Epochs were extracted, composed of 1000 ms following
the onset of the last word of critical sentences as well as the
200 ms preceding it, which served as sample-period for
baseline-correction.
Epochs containing ocular or muscular artifacts were removed, based on visual inspection of the data. Two participants
were excluded, based on the criterion of more than 33% rejected
trials. For the remaining 23 participants, on average 20% of the
trials were rejected, resulting in 48 valid trials per condition on
average.
The stimuli were presented on a TFT-screen with a refresh
rate of 25 ms at a distance of 50 cm. Stimuli were presented
with 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 information about the procedure, and signed the informed consent,
which was approved by the medical ethical committee of the
Vrije Universiteit Brussel. They were informed that they would
read sentences about a protagonist's behavior which would be
presented word by word on a computer screen. Every participant was instructed to read the material attentively and to
“try to familiarize yourself with the material of the experiment,”
which is a typical instruction to elicit spontaneous trait inferences (see Todorov and Uleman, 2002). The participants received four practice runs prior to the actual experiment.
4.4.
Data analysis
We analyzed the average amplitudes at thirteen centrally located electrodes (F3/4, Fz, FC1/2, C3/4, Cz, CP1/2, P3/4, Pz) using a
separate 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. If
the assumption of sphericity was not met, the Greenhouse–
Geisser correction was applied in all analyses.
To disentangle ERP components of opposite polarity in the
same time window, we conducted a temporal principal component analysis (PCA) using the toolkit developed by Dien (2010),
using the covariance matrix as the relational matrix, Promax rotation with k = 3 and Kaiser correction. Finally, the portion of the
grand average accounted for by each factor was reconstructed
(Dien et al., 2005).
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