Early Emotion Word Processing:
Evidence from Event-Related Potentials
Graham G. Scott,a Patrick J. O’Donnell,a Hartmut Leuthold,a,b and Sara C. Serenoa,b,*
a
b
Department of Psychology, University of Glasgow
Centre for Cognitive Neuroimaging, Department of Psychology, University of Glasgow
* Corresponding author:
Dr. Sara C. Sereno
Department of Psychology
58 Hillhead Street
University of Glasgow
Glasgow G12 8QB
Scotland, UK
Running head: Early emotion word processing
phone: +44 (0)141 330-5089
fax: +44 (0)141 330-4606
email: s.sereno@psy.gla.ac.uk
Early emotion word processing
2
Abstract
Behavioral and electrophysiological responses were monitored to 80 controlled sets of
emotionally positive, negative, and neutral words presented randomly in a lexical decision
paradigm. Half of the words were low frequency and half were high frequency. Behavioral
results showed significant main effects of frequency and emotion as well as an interaction. Prior
research has demonstrated sensitivity to lexical processing in the N1 component of the eventrelated brain potential (ERP). In this study, the N1 (135-180 ms) showed a significant emotion
by frequency interaction. The P1 window (80-120 ms) preceding the N1 was examined as well
as post-N1 time windows, including the Early Posterior Negativity (200-300 ms) and P300 (300450 ms). The ERP data suggest an early identification of the emotional tone of words leading to
differential processing. Specifically, high frequency negative words seem to attract additional
cognitive resources.
The overall pattern of results is consistent with a time line of word
recognition in which semantic analysis, including the evaluation of emotional quality, occurs at
an early, lexical stage of processing.
Keywords: emotion words; event-related potentials, ERPs; lexical access; word frequency; P1;
N1; EPN; P300; N400; lexical decision
Early emotion word processing
3
How we process written emotion words is an important issue for word recognition as well
as affective neuroscience. Emotion words can either express an emotional state (angry, happy)
or elicit one (snake, puppy). Such words are characterized by having high arousal values and
either high (positive) or low (negative) valence.
behavioral
responses
(e.g.,
reaction
time),
Although most research has measured
more
recent
research
has
used
brain
electrophysiological and hemodynamic imaging methodologies to more precisely specify the
temporal and spatial loci, respectively, of emotion processing. Our focus was to determine
whether the emotionality of a word drives early lexical processes. Such evidence would indicate
that a word’s affective semantics is not a consequence of but, rather, a component of its lexical
activation. To this end, we not only manipulated the emotionality of words (positive vs. negative
vs. neutral), but also their frequency of occurrence (high vs. low frequency). As word frequency
effects are considered to be inextricably linked to the moment of lexical access (e.g., Balota,
1990; Sereno & Rayner, 2003), a significant interaction between emotion and frequency would
establish a lexical locus of emotional processing.
Despite the amount of research in emotion word processing, a clear picture has yet to
emerge. Two main points of concern across studies are inconsistencies in stimuli and task. Most
studies have selectively compared negative and neutral words; fewer have compared positive and
negative words, while still others have examined a particular emotional state. Second, while
most studies have employed a lexical decision task (LDT) or some version of an emotional
decision (or categorization) task, others have utilized recollection tasks (e.g., Van Strien &
Luipen, 1999), odd-ball paradigms (e.g., De Pascalis et al., 2004), forced-choice tasks (e.g.,
Kakolewski et al., 1999), and self-referential judgments (e.g., Lewis et al., 2007). Additionally,
studies often utilize experimental manipulations such as masking (e.g., Windmann et al., 2002),
priming (e.g., Wentura, 2000), mood induction (e.g., Olafson & Ferraro, 2001), lateralized
presentation (e.g. Kanske & Kotz, 2007; Windmann et al., 2002), stimulus repetition (e.g.,
Ortigue et al., 2004), and/or blocked presentation of each condition (e.g., Hamann & Mao, 2002).
Although these studies typically find emotion effects, their use of different methodologies –
Early emotion word processing
4
while clearly employed to investigate specific research questions – nevertheless make it difficult
to generalize across studies. For example, Tabert et al. (2001) showed better memory for
unpleasant versus neutral words; Kakolewski et al. (1999) showed increased right visual field
attention to euphoric versus dysphoric and neutral words; and Wurm et al. (2003) showed that
naming speed to heard words was related to their relative danger and usefulness.
Notably, to our knowledge, none of the behavioral or electrophysiological studies of
emotion word processing have manipulated word frequency. If emotion and frequency effects
are interactive, this may explain the mixed pattern of results across studies where word
frequency has not been explicitly examined.
The only study to date which has directly
manipulated frequency, using negative and neutral words, was an fMRI study by Nakic et al.
(2006) which also employed an LDT. Word stimuli were ‘high’ negative (highly unpleasant),
‘low’ negative (less unpleasant), or neutral words which were either high frequency (HF) or low
frequency (LF) words (40 words in each of the 6 conditions). Pseudowords were pronounceable
nonwords that differed by one letter from target words. Behaviorally, they found main effects of
frequency (LF<HF) and emotion (‘high’ negative < ‘low’ negative = neutral), but no interaction.
In terms of fMRI activation, LF ‘high’ and ‘low’ negative words elicited increased activation
relative to HF negative words in bilateral inferior frontal gyri. The frequency effect for neutral
words showed a different pattern, with greater activation for HF versus LF words in cingulate
and inferior parietal regions. For the emotion effect, ‘high’ negative words produced greater
activations than ‘low’ negative and neutral words in bilateral middle temporal gyri and in the
anterior and posterior cingulate gyri. Both ‘high’ and ‘low’ negative words showed greater
activation than neutral words in the right amygdala; only ‘high’ negative words showed greater
activation than neutral words in the left amygdala. Nakic et al. (2006) postulated that negative
words become conditioned stimuli which acquire a significant association with amygdala
activation; speeded responses to ‘high’ negative words were attributed to reciprocal feedback
from the amygdala. However, because the relative timing of these activations were not specified
Early emotion word processing
5
(because fMRI does not provide the required high temporal resolution), it is not clear whether
this feedback could directly guide early lexical processing.
Other recent fMRI studies have also demonstrated amygdala involvement in processing
emotion words (Lewis et al., 2007; Strange et al., 2000).
Hamann and Mao (2002) had
participants emotionally evaluate blocks of same-affect words and found increased left amygdala
activity for negative as well as positive lexical affect versus a neutral condition. Tabert et al.
(2001) showed increased right amygdala activity for unpleasant versus neutral words during an
emotional decision task in which participants selected the most unpleasant (or neutral) word
from a set of three unpleasant (or neutral) words. However, no such activation difference
occurred during a recognition memory task. They suggested that a correlation of amygdala and
occipital cortex activity indicated that the amygdala mediates early visual processing. Although
fMRI studies have identified specific neural loci related to particular tasks, they cannot in
general capture the moment-to-moment temporal course of such activity. Using intracranial
recording, for example, Naccache et al. (2005) showed amygdala involvement during subliminal
presentation of emotion words, but only from 800 ms post-stimulus. Such activation is quite
delayed in relation to word recognition, estimated to take place within the first 200 ms (Sereno &
Rayner, 2003).
Electrophysiological studies are better suited to capture the real-time perceptual and
cognitive processes of emotion word recognition.
Several studies have examined different
components of the event-related potential (ERP) for emotionality effects (for a review, see
Kissler et al., 2006). Most, however, use many repetitions of the experimental materials (e.g.,
Bernat et al., 2001; Ortigue et al., 2004).
Repetition priming has known effects in word
recognition including, for example, greater facilitation for LF versus HF words. In an eventrelated fMRI study, Luo et al. (2004) used a masked repetition priming paradigm with positive,
negative, and neutral words in which participants judged whether the target appeared in normal
or italics font. They found behavioral repetition priming effects for positive and negative but not
neutral words. In terms of fMRI activation, they found greater repetition priming for positive
Early emotion word processing
6
compared to negative words in the left mid-fusiform gyrus.
Because word repetition can
produce differential effects depending on particular lexical characteristics, results of such studies
cannot be easily generalized to conditions in which words are normally identified (without
repetition).
Three recent ERP studies have found emotion word effects in the time range of 180 to
300 ms post-stimulus. All three studies, however, employ complex methodologies which may
limit their generalizability. Kanske and Kotz (2007) presented abstract and concrete words
which were affectively positive (N=60), negative (N=60), or neutral (N=120) for 200 ms either
to the left or right hemifield in an LDT. Each target was presented twice, once to each hemifield.
In addition, a blocked presentation was used with either positive and neutral or negative and
neutral words occurring in the same block. They found faster RTs in conjunction with larger P2s
(210-300 ms) for positive versus neutral words and for right- versus left-hemifield words. The
P2 positive emotion effect disappeared, however, when the LDT was changed to a go/no-go
(pseudoword/word) LDT. Herbert et al. (2006) presented pleasant, unpleasant, and neutral
adjectives (60 words of each type) for 5 sec each. Participants were instructed to emotionally
evaluate and memorize the words. They found a larger P2 (180-250 ms) for both pleasant and
unpleasant versus neutral adjectives which they attributed to conscious processing of affective
content. However, on one-third of the trials, a 90 dB acoustic probe was introduced (2.5-4 sec
after word onset) to induce a startle response.
This manipulation presumably increased
participants’ anxiety levels throughout the task (as with mood induction procedures). Finally,
Kissler et al. (2007) presented a set of 180 words – 60 pleasant, 60 unpleasant, and 60 neutral
words – 10 times to participants for passive viewing, 5 times at a rate of three words per second
(3 Hz) and 5 times at a rate of one word per second (1 Hz). They found increased negativity for
high arousal words (positive and negative) versus low arousal (neutral) words over posterior sites
from 200-300 ms post-stimulus for both presentation rates. Although words were repeated
across the 10 experimental blocks, the emotion effect was stable across blocks and hence cannot
be attributed to stimulus repetition (possibly because the task was passive viewing). Kissler et
Early emotion word processing
7
al. suggested the emotion effect reflects post-lexical feedback from the amygdala which
enhances the processing of a high arousal stimulus regardless of its valence. Although all three
studies found effects of emotionality, the use of elaborate experimental procedures (e.g.,
lateralized presentation, induced startle response, repetition with passive viewing) makes it more
difficult to generalize these findings to normal reading. Finally, since these studies did not use
word frequency as an experimental variable, they are less capable of interpreting emotion-related
influences on lexical access.
As mentioned above, lexical access can be indexed by the presence of word frequency
effects. A word frequency effect represents the differential response to commonly used HF
words versus LF words that occur much less often. Reliable word frequency effects have been
reported in the posterior N1 (~130-190 ms post-stimulus), with LF words eliciting a greater
amplitude than HF words (Sereno et al., 1998, 2003). Other ERP and MEG studies have
confirmed an early time course for lexical processing (Dien et al., 2003; Hauk & Pulvermüller,
2004; Neville et al., 1992; Nobre & McCarthy, 1994; Pulvermüller et al., 2001). Lexical effects
occurring within the exogenous components of the ERP are consistent with what has been
inferred from eye movement studies of normal reading. That is, because the duration of a single
fixation on a word (~225 ms) varies with the psycholinguistic complexity of that word (Rayner,
1998), a temporal window can be specified within which lexical processing must occur;
corresponding electrophysiological differences within this time period can both confirm and
pinpoint an early lexical time course (Sereno et al., 1998; Sereno & Rayner, 2003).
Our hope was to more clearly establish the early temporal dynamics of emotion word
processing by anchoring them to frequency effects. We used a 3 (Emotion: Positive, Negative,
Neutral) X 2 (Frequency: LF, HF) design (40 words of each type). Word stimuli and an equal
number of pseudowords were randomly presented in an LDT while ERPs were recorded.
Notably, we did not repeat stimuli, use lateralized presentation, blocking, priming, or masking,
nor make use of self-referential judgments or mood induction. Behaviorally, we expected to find
clear word frequency effects. We were less certain about emotion effects, as past research has
Early emotion word processing
8
demonstrated an advantage sometimes for negative words (e.g., Wurm et al., 2003) and other
times for positive words (e.g., Dahl, 2001). Likewise, we were uncertain whether to expect an
interaction. Nakic et al. (2006) did not find one, but they only used negative and neutral words.
Electrophysiologically, our strategy was to examine effects in the N1 component, occurring
before the P2 and EPN components where differences have previously been reported (Herbert et
al., 2006; Kanske & Kotz, 2007; Kissler et al., 2007). Reliable word frequency effects have been
demonstrated in the N1, linking this component to the processes of lexical access (Hauk &
Pulvermüller, 2004; Sereno et al., 1998, 2003). We expected to find N1 frequency effects at
least for Neutral words (comparable to stimuli in the earlier studies). If the arousal and valence
of emotion words affect lexical access, differential effects should also be evident in the N1. In
addition to examining N1 effects, we were also interested in delineating a time course of
activation. To this end, we also examined the earlier P1 component and later, post-N1 time
windows.
Method
Participants
Twenty-six members of the University of Glasgow community (15 females, 11 males;
mean age 21, range: 17-24) were paid £10 for their participation. An additional four participants
were run in the experiment, but were not included in the analyses because of excessive EEG
artifacts which resulted in a data loss of more than 70% of the trials. All participants were native
English speakers, had not previously been diagnosed as dyslexic, and were strongly right-handed
(mean score 35.6, range: 33-36) as assessed by the 36-point Edinburgh Handedness Inventory
(Oldfield, 1971). In addition, all had normal or corrected-to-normal vision and were naïve
concerning the purpose of the experiment.
In accordance with the guidelines set by the
University’s ethics committee, written informed consent was obtained prior to experiment
participation.
Early emotion word processing
9
Materials and Design
A 3 (Emotion: Positive, Negative, Neutral) x 2 (Frequency: LF, HF) design was used.
Word stimuli varied in terms of arousal, valence, and word frequency. Arousal and valence
values were taken from Affective Norms for English Words (ANEW), a database of 1000 words
(Bradley & Lang, 1999). Each word in ANEW has associated ratings both for arousal, from 1
(low) to 9 (high), and for valence, from 1 (negative) to 9 (positive). The following criteria for
word selection were employed. Arousal values for Positive and Negative words were greater
than 6.00, while those for Neutral words were less than 5.45. Valence values were greater than
6.00 for Positive words, less than 4.00 for Negative words, and between those values for Neutral
words. Word frequencies were taken from the British National Corpus (BNC), a database
comprising 90 million written word tokens (http://www.natcorp.ox.ac.uk/).
A total of 80 sets of word triples (Positive, Negative, and Neutral) were generated with
words within each set matched for length and frequency. Forty sets were LF and 40 were HF.
All word stimuli are listed in the Appendix and their specifications are listed in Table 1.1 In
addition, three sets of 80 pseudowords were created, with each set length-matched to its
corresponding word set. The pseudowords were orthographically legal pronounceable nonwords
(e.g., blimble), and none were pseudohomophones (i.e., pseudowords matching in phonology to
real words).
Each participant was presented with all 480 items – 240 words and 240
pseudowords.
Insert Table 1 about here
Apparatus
Participants were tested in the Psychology Department in an electrically shielded booth
with low level ambient light. Experimental Run Time System (ERTS) software was used to
control stimulus presentation (cf. Dutta, 1995). Participants were seated at a viewing distance of
approximately 65 cm from the monitor, maintained throughout the experiment by means of a
chin rest. Stimuli were presented centrally in 20-point Helvetica font on a Sony 15” monitor in
white letters on a black background. Approximately three characters subtended 1o of visual
Early emotion word processing
angle.
10
A keypad registered word and nonword responses (right and left index fingers,
respectively) with millisecond accuracy. Keys were mounted about 35 cm apart on a board
aligned to the body’s midline.
Procedure
Before
the
experiment,
participants
were
informed
about
the
nature
of
electrophysiological recording and were given specific task instructions. They were told that
half of the stimuli were words and half were nonwords and that they should respond as quickly,
but as accurately, as possible. For each trial, the sequence of events was as follows. A central
fixation cross was presented for 750 ms followed by a blank interval of 500 ms. A letter string
was then presented centrally until response onset. After the response (or if no response occurred
within 2 sec of stimulus onset), a variable blank interval of 1.5 sec mean duration (range: 1.252.50 sec) followed. Experimental trials were presented in a different random order for each
participant.
Participants were first presented with a practice block of 24 trials to become accustomed
to the task. Experimental trials were then presented in 10 blocks with short rest periods in
between. Each experimental block consisted of 50 trials and lasted approximately 3.5 min. The
first two trials of each block were filler items and were not recorded.
After each block,
participants were given feedback about their performance.
Electrophysiological Recording
A BIOSEMI Active-Two amplifier system was used for continuous recording of
electroencephalographic (EEG) activity from 72 Ag/AgCl electrodes (see Figure 1): (a) midline
electrodes Fpz, AFz, Fz, FCz, Cz, CPz, Pz, POz, Oz, and Iz; (b) left hemisphere electrodes IO1,
Fp1, AF3, AF7, F1, F3, F5, F7, F9, FC1, FC3, FC5, FT7, C1, C3, C5, M1, T7, CP1, CP3, CP5,
TP7, P1, P3, P5, P7, PO3, PO7, O1, including two nonstandard positions PO9’ and O9’ (located
at 33% and 66% of the M1-Iz distance, respectively); and (c) homologous right hemisphere
Early emotion word processing
11
electrodes. Two additional electrodes (the Common Mode Sense (CMS) active electrode and
Driven Right Leg (DRL) passive electrode) were used as reference and ground electrodes,
respectively (cf. www.biosemi/faq/cms&drl.htm). EEG and EOG recordings were sampled at
256 Hz. All EEG channels were recalculated off-line to an average mastoid reference. Trials
containing blinks were corrected using a dipole approach (BESA, Version 5.1.8). The analysis
epoch started 200 ms prior to stimulus onset and lasted for a total duration of 1.5 sec.
Insert Figure 1 about here
EEG Data Analysis
Trials with non-ocular artifacts (drifts, channel blockings, EEG activity exceeding ±75
μV) and incorrect responses were discarded, resulting in an average data loss of about 30% per
participant. After artifact rejection, there remained on average 28 trials of 40 (minimally 20) per
participant per condition. The signal at each electrode site was averaged separately for each of
the six experimental word conditions, time-locked to word onset, band-pass filtered (0.05-30 Hz,
6 dB/oct), and aligned to a 100-ms pre-stimulus baseline. In line with previous research, mean
amplitudes of specific ERP deflections were measured for the following time intervals: the P1
from 80-120 ms; the N1 from 135-180 ms; the Early Posterior Negativity (EPN; e.g., Schupp et
al., 2006) from 200-300 ms; and the P300 from 300-450 ms. For time windows occurring before
300 ms (P1, N1, and EPN), mean voltages were computed across four posterior electrodes over
right hemisphere (RH) sites (P6, P8, PO8, PO10’) and four homologous electrodes over left
hemisphere (LH) sites (P5, P7, PO7, PO9’) (see Figure 1). Mean P300 amplitude in a 300-450
ms time interval was measured across three parietal midline electrodes (CPz, Pz, POz). In
addition a computerized peak-picking program was used to measure P300 peak latency at Pz
(i.e., the time point, from 250-800 ms, when the voltage at Pz was maximally positive).
Results
The mean RT, mean ERP amplitude during the time intervals defined above, and P300
latency data are presented in Table 2. These data are also graphically depicted with standard
Early emotion word processing
12
error bars in Figure 2 (RT and P300 peak latency) and Figure 3 (P1, N1, EPN, and P300). ERP
waveforms from posterior electrodes PO7 (left) and PO8 (right), and from the midline electrode
Pz are shown in Figure 4. Scalp topographies of mean ERP amplitudes in each time window are
displayed in Figure 5.
Insert Table 2 and Figures 2 to 5 about here
RT data
Trials in which participants made errors were excluded from the RT data analysis
(4.92%). The RT data were subjected to two trimming procedures. Items with RTs less than 250
ms or greater than 1500 ms were excluded from further analysis. For each participant in each
condition, items with RTs beyond two standard deviations of that mean were also excluded.
These procedures resulted in an average data loss of 4.78%.
A two-way analysis of variance (ANOVA) was performed on the participant data. The
main effect of Emotion was significant [F(2,50)=11.53, p<.001]. Follow-up contrasts revealed
that responses to Positive words (525 ms) were only marginally faster than those to Negative
words (532 ms) [F(1,50)=3.83, p=.056]. However, responses to both Positive and Negative
words were significantly faster than those to Neutral words (541 ms) [Positive vs. Neutral:
F(1,50)=22.82, p<.001; Negative vs. Neutral: F(1,50)=7.95, p<.01].
The main effect of
Frequency was highly significant, with faster responses to HF versus LF words (511 vs. 555 ms)
[F(1,25)=86.20, p<.001]. The interaction was also significant [F(2,50)=4.10, p<.05]. For LF
words, both Positive and Negative words (which did not differ from each other) were faster than
Neutral words [Positive vs. Negative: F<1; Positive vs. Neutral: F(1,50)=22.29, p<.001; and
Negative vs. Neutral: F(1,50)=18.40, p<.001]. For HF words, Positive words were faster than
both Negative and Neutral words (which did not differ from each other) [Positive vs. Negative:
F(1,50)=7.40, p<.01; Positive vs. Neutral: F(1,50)=8.82, p<.01; and Negative vs. Neutral: F<1].
ERP data
We performed a 3 (Emotion: Positive, Negative, Neutral) x 2 (Frequency: LF, HF) x 2
(Hemisphere: RH, LH) repeated measures ANOVA, using the Huynh-Feldt correction, on the
Early emotion word processing
13
average voltage data in the P1, N1, and EPN windows. For the P300, we performed a similar
ANOVA but instead of Hemisphere as a factor, we used Electrode (comprising the three midline
electrodes CPz, Pz, and POz). Finally, we examined P300 peak latency at a single electrode (Pz)
using a two-way ANOVA.
P1 window (80-120 ms). As can be seen in Figure 5, mean P1 amplitude was larger over
right than left posterior electrodes (2.2 vs. 1.3 μV) [Hemisphere: F(1,25)=7.86, p<.01]. The
main effect of Emotion was significant [F(2,50)=4.41, p<.05] and tended to be modulated by
Frequency, as indicated by an Emotion x Frequency interaction [F(2,50)=2.61, p=.084]. Further
comparisons revealed that P1 amplitude was influenced by Emotion only for HF words
[F(2,50)=5.48, p<.01], and not for LF words [F<1]. As can be seen in Table 2 and Figure 3A,
P1 amplitude was smaller for HF Negative words (1.30 μV) than either HF Positive (1.93 μV) or
HF Neutral (1.94 μV) words [all Fs(1,25)>6.45, ps<.05]. Negative LF words tended to elicit a
larger P1 than Negative HF words (1.79 vs. 1.30 μV) [F(1,25)=4.08, p=.054]; frequency effects,
however, were not reliable for either Positive or Neutral words [all Fs(1,25)<1.48, ps>.20]. No
other effects were significant in the analysis of mean P1 amplitude [all Fs<1]. Overall, the
pattern of effects in the P1 seems to arise mainly from the very early effect of word frequency for
Negative words. The established, later N1 frequency effect comes from studies using, notably,
emotionally neutral words. Thus, it seems that HF Negative words are processed at an earlier
stage.
N1 window (135-180 ms). No main effects were significant, nor were most of the
interactions [for Emotion, Frequency, Hemisphere, and Frequency x Hemisphere: all Fs<1;
Emotion x Hemisphere: F(2,50)=1.92, p>.15; and Emotion x Frequency x Hemisphere:
F(2,50)=1.35, p>.25]. Only the Emotion x Frequency interaction was significant [F(2,50)=7.01,
p<.01].
Follow-up contrasts revealed a significant effect of Frequency for Neutral words,
replicating prior studies (e.g., Sereno et al., 1998; 2003), with LF Neutral words eliciting a larger
N1 than HF Neutral words (–2.50 vs. –2.03 μV) [F(1,25)=4.42, p<.05]. As can be seen in Table
2 and Figure 3B, the effect of Frequency was not significant for Positive words [F<1], but was
Early emotion word processing
14
significant in the opposite direction for Negative words, with HF Negative words eliciting a
larger N1 than LF Negative words (–2.61 vs. –2.06 μV) [F(1,25)=9.07, p<.01]. Within LF
words, similar to the pattern in the RT data, Neutral words tended to elicit a larger N1 than either
Positive or Negative words (which did not differ from each other) [Positive vs. Negative: F<1;
Positive vs. Neutral: F(1,25)=2.70, p=.113; and Negative vs. Neutral: F(1,25)=5.89, p<.05].
Within HF words, Negative words elicited a significantly larger N1 than either Positive or
Neutral words (which did not differ from each other) [Positive vs. Negative: F(1,25)=4.39,
p<.05; Positive vs. Neutral: F(1,25)=1.97, p>.15; and Negative vs. Neutral: F(1,25)=5.72,
p<.05]. In sum, the N1 frequency effect found in prior studies (which used emotionally neutral
words) was replicated with Neutral words. The different pattern of results for Positive and
Negative words seems to indicate that the frequency effect is influenced by a word’s arousal and
valence.
EPN window (200-300 ms). The main effect of Emotion was significant [F(2,50)=5.76,
p<.01]. Negative and Positive words showed larger EPN amplitudes (–0.96 and –0.94 μV) than
Neutral words (–0.47 μV).
[F(2,50)=10.75, p<.001].
The Emotion x Frequency interaction was also significant
Follow-up contrasts revealed a significant Frequency effect for
Neutral words, with LF Neutral words eliciting a larger EPN than HF Neutral words
[F(1,25)=12.11, p<.01]. As can be seen in Table 2 and Figure 3C, the Frequency effect was in
the opposite direction for Negative words, with HF Negative words eliciting a larger EPN
[F(1,25)=7.04, p<.05], and Positive words showed no effect [F(1,25)=1.31, p>.25]. Within LF
words, Emotion did not reliably influence EPN amplitude [Positive vs. Negative: F(1,25)=2.48,
p=.128; Positive vs. Neutral: F(1,25)=1.63, p>.20; and Negative vs. Neutral: F<1]. Within HF
words, Negative and Positive words (–1.27 and –0.81 μV) elicited a significantly larger EPN
than Neutral words (–0.16 μV), replicating Kissler et al. (2007) whose stimuli, notably, were HF
words; additionally, Negative words triggered a larger EPN than Positive words [Positive vs.
Negative: F(1,25)=9.00, p<.01; Positive vs. Neutral: F(1,25)=9.14, p<.01; and Negative vs.
Early emotion word processing
15
Neutral: F(1,25)=32.63, p<.001]. No other main effects or interactions were significant [all
Fs<1.41, ps >.20].
P300 window (300-450 ms).
To capture the P300’s centroparietal topography (see
Figure 5), we performed a 3 (Emotion: Positive, Negative, Neutral) x 2 (Frequency: LF, HF) x 3
(Electrode: CPz, Pz, POz) ANOVA on the average voltage data. There was a main effect of
Electrode, with Pz (7.20 μV) having greater amplitude than CPz and POz (5.24 and 6.13 μV)
[F(2,50)=4.50, p<.05]. As can be seen in Table 2 and Figure 3D, there was a main effect of
Frequency, with LF words eliciting a smaller P300 than HF words (5.71 vs. 6.68 μV)
[F(1,25)=10.93, p<.01]. This replicates prior research (e.g., Polich & Donchin, 1988). No other
effects were significant [all Fs<1.58, ps>.20].
P300 peak latency. The P300 peak latency from electrode Pz was analyzed by a two-way
ANOVA. Similar to the P300 pattern of results, there was only a main effect of Frequency,
indicating an earlier peak for HF versus LF words (534 vs. 566 ms) [F(1,25)= 9.00, p<.01]. This
effect also replicates Polich and Donchin’s (1988) findings. No other effects were significant
[all Fs<2.13, ps>.14].
Discussion
The purpose of the current experiment was to investigate the early time course of emotion
word processing. Positive, negative, and neutral words were presented randomly in an LDT
while brain electrophysiological responses were recorded. Unlike most prior studies, we did not
use masking, priming, mood induction, lateralized presentation, blocking, or repetition of
stimuli. Such manipulations make results difficult to generalize as they may produce second
order effects or induce strategic processing. Critically, we manipulated word frequency in order
to better determine the onset of lexical-semantic processing, and employed a 3 (Emotion:
Positive, Negative, Neutral) x 2 (Frequency: LF, HF) design.
Behaviorally, we found significant Emotion and Frequency effects as well as an
interaction. For LF words, both Positive and Negative words were responded to faster than
Early emotion word processing
16
Neutral words; for HF words, Positive words were responded to faster than Negative and Neutral
words. It is somewhat difficult to evaluate these findings with respect to the prior literature.
While past studies often match their stimuli (positive, negative, and/or neutral words) on word
frequency, with the exception of Nakic et al. (2006), none to our knowledge has directly
manipulated word frequency. Nakic et al. did not find an interaction, but they only used negative
and neutral words. It seems possible that the mixed nature of results across studies may arise
from the frequency profile of the stimuli used. Although the pattern of our behavioral findings
seems unambiguous, RTs from an LDT encompass a range of processes, from stimulus
registration and lexical evaluation to motor planning and execution. A precise time course of
lexical processing is difficult to infer from such data; one cannot simply align cognitive
components of the LDT with corresponding components of the ERP. Nevertheless, the presence
of an interaction in RT indicates that Frequency and Emotion influence at least one common
processing stage. By examining the electrophysiological record, the temporal dynamics of the
component processes of word recognition can be more effectively established.
We tracked the time course of processing by examining the pattern of effects across
consecutive temporal windows. Taken together, these results reveal a transitory account of
emotion word processing. We were initially interested in the N1 (135-180 ms) because this is
when lexical effects (e.g., word frequency, contextual predictability) have been reliably
demonstrated (e.g., Hauk & Pulvermüller, 2004; Sereno et al., 1998; 2003). The P1 (80-120 ms)
window allowed us to determine whether there was prior evidence of such effects, in particular,
those related to stimulus arousal and valence. We then examined the EPN (200-300 ms), a
window in which Kissler et al. (2007) reported an initial differentiation between high arousal
(positive and negative) and neutral words. Finally, we examined the P300 (300-450 ms) time
window, because previous electrophysiological studies of word recognition have reported word
frequency effects in this late time range (e.g., Hauk & Pulvermüller, 2004; King & Kutas, 1998;
Polich & Donchin, 1988; Rugg, 1990).
Early emotion word processing
17
We had expected to find an N1 (135-180 ms) effect of word frequency. The N1,
however, only showed a significant Emotion x Frequency interaction. For LF words, Neutral
words generated higher amplitudes; for HF words, Negative words generated higher amplitudes.
Neutral words exhibited the established frequency effect, with LF words eliciting a larger N1
than HF words. This replicates the findings of past studies which, notably, have used LF and HF
emotionally neutral words (e.g., Sereno et al., 1998; 2003). Positive words, however, showed no
frequency effect, and Negative words showed a frequency effect in the opposite direction, with
HF words eliciting a larger N1 than LF words. Nevertheless, the presence of an interaction
suggests that frequency is modulated by arousal and valence.
Our analysis of the earlier P1 (80-120 ms) revealed that the only experimental condition
affecting P1 amplitude was the HF Negative word condition which elicited a smaller P1 than the
other conditions. Hauk and Pulvermüller (2004) demonstrated P1 sensitivity to word length.
Although LF Negative words were one character longer on average than HF Negative words in
our study, this LF-HF length disparity was identical for Positive and Neutral words (see Table 1)
where no such effect occurred. Thus, the P1 amplitude seemed to be selectively modulated by
the combined features of high frequency, high arousal, and negative valence (i.e., HF Negative
words).
Finally, we examined post-N1 time windows. In the EPN (200-300 ms), the N1 pattern
of frequency effects for Neutral, Positive, and Negative words was maintained. In addition,
while no significant differences emerged among LF words, for HF words, both Negative and
Positive words generated a larger EPN than Neutral words. This last finding replicates Kissler et
al. (2007) whose stimuli comprised HF words. The final P300 (300-450 ms) window only
exhibited significant effects of frequency, with LF words eliciting a smaller P300 than HF words.
Although this replicates the frequency effects of Polich and Donchin (1988), other ERP studies
have reported N400 word frequency effects in sentence and word list paradigms, with LF words
eliciting larger amplitudes than HF words (see Kutas, van Petten, & Kluender, 2006). These
apparently opposite effects both accurately describe the same waveform pattern (i.e., LF words
Early emotion word processing
18
elicit a smaller positive-going P300 or a larger negative-going N400 than HF words). On the
basis of our results, it is not possible to characterize these late word frequency effects as either
P300 or N400 effects. Given that the N400, rather than P300, is sensitive to variations in
language materials, it seems more plausible to assume that LF words elicited a larger N400
rather than a smaller P300. An N400 which overlays a more substantial P300 component could
also account for the P300 latency shift, with longer latencies to LF than HF words. Regardless
of the precise nomenclature of these later ERP components, it is clear that later stages of lexical
processing appear to behave differently than earlier ones in that effects of emotion have become
attenuated.
In the following, we describe a framework that allows us to interpret the current findings.
Our interpretations rely on two assumptions. First, we suggest that words that are highly salient
are easier to process. It is not disputed, for example, that HF words are easier to process than LF
words because they are more familiar. We extend this notion of salience to arousal. High
arousal words have stronger lexical representations and are more salient than low arousal
(neutral) words because of their emotionality. This should speed recognition. However, unlike
word frequency, arousal can have environmental consequences. Arousal that is positive or
negative in valence will lead to vastly different outcomes. In addition, arousal that is HF versus
LF may lead to consequences which have more or less environmental significance, respectively.
Our second assumption is that some version of a “perceptual defense” mechanism
operates on incoming stimuli. The idea of perceptual defense is not new; McGinnies (1949)
developed it from Freud and Rogers’ notion of unconscious denial. McGinnies presented taboo
and control words tachistoscopically and found that taboo words required longer exposures for
correct identification.
He suggested that perceptual defense insulated the observer from
(negative) emotion-provoking properties of stimuli.
While there have since been several
criticisms of this study, including accounts of why the results were artifactual (e.g., taboo words
had lower word frequencies; participants consciously withheld taboo word responses because of
social convention), the idea itself remains viable and has been resurrected in various forms. For
Early emotion word processing
19
example, Pratto and John’s (1991) Automatic Negligence Function proposes that negative words
require more cognitive resources and are processed longer than positive ones.
Similarly,
Taylor’s (1991) Mobilization-Minimization hypothesis states that if a stimulus is negative, there
are strong, rapid physiological and cognitive responses in the initial processing of that stimulus –
the mobilization stage.
In the subsequent minimization stage, additional physiological and
cognitive responses are employed to diminish the impact of the negative stimulus.
Our rendering of perceptual defense is that high arousal stimuli, in particular negativelyvalenced ones, are recognized more quickly and initiate an internal response because of their
environmental significance.
This internal response is manifest in the neural substrate as
increased processing and can be interpreted either as enhancement or disruption. When an
emotional stimulus is encountered, levels of high arousal are associated with that stimulus which
is internally registered (consciously or not).
In extreme cases, this awareness may occur
preattentively. This internal registration will occur more quickly for high arousal stimuli that are
more frequent. The valence of the arousal also plays a role, with a bias to process negative
stimuli more rapidly than positive stimuli because they engender unpleasant consequences.
The overall pattern of results supports the proposal that frequency, arousal, and valence
all contribute to the immediate processing of words. It remains less clear, however, precisely
how these factors interact over different stages of processing. For example, HF Negative words
are the most salient because of their frequency, arousal, and valence profile. The combination of
these features seems to result in a processing head start: HF Negative words produced lower
amplitudes than any other condition in the P1. According to our suppositions, early activation of
high arousal that is both negative in valence and highly frequent in occurrence, however, will
trigger an internal response which shows up as enhancement or disruption in the N1 window.
With Positive words, the advantage that a moderate level of salience confers is seemingly offset
by an equally moderate level of enhancement/disruption caused by the automatic internal
response to high arousal stimuli. These opposing effects serve to mask the early frequency
effect. With Neutral words, unaccompanied by any effects of emotion, N1 frequency effects
Early emotion word processing
20
clearly emerge. The subsequent EPN shows increased amplitude for HF Negative and Positive
words versus HF Neutral words which could be interpreted either as continued disruption from
high levels of arousal or, as suggested by Kissler et al. (2007), enhancement of processing
emotion versus neutral words (their stimuli were HF words). By the P300/N400, emotion effects
have become attenuated and all that remains is the behaviorally more robust frequency effect.
The P300 peak latency shows the same pattern.
An alternative explanation of the early P1-N1 pattern of effects is one that relies instead
on attentional mechanisms (see, e.g., Mangun & Hillyard, 1991, 1995) which are selectively
responsive to HF arousal.
Such an effect could be expressed electrophysiologically as a
negative-going wave having an earlier onset and larger amplitude for HF Negative compared to
HF Positive words. As a result, for HF Negative words, the amplitude of the P1 is reduced and
the N1 greatly enhanced. For HF Positive words, because the attentional effect occurs later and
to a lesser degree, only the amplitude of the N1 is enhanced, thereby counteracting the standard
frequency effect. Whatever the explanation, the results clearly demonstrate an early effect of
arousal that is modulated by word frequency. This suggests that the differential activation to at
least a subset of high arousal words must engage brain mechanisms that are operative during
early visual processing. Accumulating evidence in visual object processing has demonstrated
that semantic analysis is more rapid than has been traditionally assumed (e.g., Thorpe et al.,
1996) and that top-down feedback can begin to affect processing in sensory areas after 80 ms
post-stimulus(e.g., Foxe & Simpson, 2002).
In sum, this experiment shows that the emotional tone of a word modulates its early
lexical processing. Past N1 effects of lexical processing, as indexed by the presence of word
frequency effects, were replicated. These effects were extended by demonstrating that such
effects were modulated by the emotional characteristics of the word stimuli. Effects of emotion
specifically for HF Negative words occurred as early as the P1. While emotion modulated early
processing, in general its effect was more transient than word frequency. HF emotion and
Early emotion word processing
21
neutral words were differentiated in the post-N1, EPN. However, by the P300/N400, only
effects of frequency were evident.
We recognize that the experiment has certain limitations. While the ERP methodology
provides a rich temporal record of events, the precise neural mechanisms involved are less
accessible. For example, it has often been suggested that the amygdala plays a role, having
reciprocal connections to visual cortical areas.
However, the timing of such activation is
estimated to occur well after lexical access. There is a need to explore temporally more plausible
determinants of emotional effects on word recognition. A second concern is that the presentation
of words in isolation using a specialized task involves the recruitment and application of
strategies not found in normal reading. Visual word recognition typically occurs in during
normal reading, where individual word meanings are activated and integrated on-line into a
developing discourse context. To this end, we have already begun investigation of processing
emotion words in normal reading while participants’ eye movements are monitored (Sereno et
al., 2008). Finally, although our contributions to theoretical notions of affective word processing
may be preliminary and speculative, we believe that our use of well-controlled stimuli and
temporally precise measures has produced results which will inform such endeavors.
Early emotion word processing
22
Acknowledgements
This research was supported by an ESRC postgraduate fellowship to G.G. Scott. Portions
of this research were presented at the Architectures and Mechanisms for Language Processing
(AMLaP) conference (August 2006, Nijmegen) and the joint Experimental Psychology Society
(EPS) / Psychonomic Society conference (July, 2007, Edinburgh).
Early emotion word processing
23
Footnote
1
In general, statistical analysis of our stimulus specifications (word length, frequency,
arousal, and valence) generally confirmed our classification. Although we attempted to match
word length across frequency class, HF words were approximately 1 character shorter on average
than LF words (6.9 vs. 5.8 characters, respectively) [F(1,39)=270,23, p<.001]. There is a natural
inverse relationship between word length and frequency in language which is often difficult to
counteract. In addition, although the difference was minimal, Positive words (6.6 characters)
were significantly longer than either Negative (6.3 characters) or Neutral (6.2 characters) words
[Positive vs. Negative: F(2,78)=7.19, p<.01; and Positive vs. Neutral: F(2,78)=13.92, p<.001].
For frequency, as intended, HF words were significantly higher in frequency than LF words
(59.5 vs. 7.1 per million) [F(1,39)=43.31, p<.001]. However, for HF words, Negative words
(49.7 per million) were significantly less frequent than either Positive (62.3 per million) or
Negative (66.6 per million) words [Negative vs. Positive: F(2,78)=12.79, p<.001; and Negative
vs. Neutral: F(2,78)=23.12, p<.001]. It is possible that HF Negative words are simply not as
well represented in the lexicon. Indeed, we had some difficulty finding enough HF Negative
words in the ANEW norms. For arousal, as intended, both Positive (6.7) and Negative (6.7)
words had significantly higher arousal values than Neutral (4.4) words [F(2,78)=519.10,
p<.001]. Finally, for valence, as intended, Positive words (7.7) had significantly higher valence
values than Neutral words (5.2) which, in turn, had significantly higher valence values than
Negative words (2.5) [Positive vs. Neutral: F(2,78)=584.15, p<.001; and Neutral vs. Negative:
F(2,78)=702.69, p<.001].
Early emotion word processing
24
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Appendix: Low- and high-frequency (LF, HF) positive, negative, and neutral words
Positive
nude
lust
sexy
fame
alert
champ
thrill
cheer
glory
flirt
dazzle
casino
riches
erotic
dancer
aroused
dollar
elated
miracle
admired
orgasm
terrific
intimate
reunion
ecstasy
treasure
sunlight
festive
graduate
fireworks
adventure
athletics
affection
valentine
intercourse
infatuation
astonished
triumphant
millionaire
rollercoaster
LF
Negative
demon
shark
rude
rage
toxic
venom
slap
snake
devil
annoy
detest
tumour
betray
sinful
insult
scared
killer
leprosy
poison
hatred
pervert
wicked
destroy
intruder
outrage
torture
hostile
disloyal
terrified
assassin
ambulance
slaughter
nightmare
humiliate
jealousy
cockroach
distressed
unfaithful
hurricane
suffocate
Neutral
hawk
cane
muddy
truck
lump
swamp
boxer
trunk
alien
rattle
limber
mystic
salute
clumsy
vanity
spray
invest
icebox
insect
hammer
ketchup
coarse
custom
trumpet
radiator
highway
whistle
repentant
privacy
scissors
nursery
pamphlet
nonsense
appliance
sheltered
sceptical
sentiment
nonchalant
thermometer
lighthouse
Positive
fun
joy
car
win
joke
gift
sex
cash
kiss
brave
plane
song
happy
heart
talent
quick
lucky
couple
rescue
engaged
pretty
loved
travel
leader
passion
desire
holiday
inspired
memories
progress
success
laughter
birthday
romantic
exercise
promotion
surprised
beautiful
confident
excitement
HF
Negative
mad
gun
war
fire
rape
evil
fight
pain
crash
hate
bomb
anger
angry
fear
burn
abuse
victim
afraid
horror
tense
bloody
guilty
cancer
trouble
panic
surgery
danger
tragedy
assault
stress
pressure
confused
divorce
violent
accident
disaster
rejected
nervous
suspicious
controlling
Neutral
odd
iron
book
army
tool
bowl
wine
rock
hide
stiff
clock
rough
cold
hotel
bench
coast
paint
watch
fabric
excuse
yellow
patient
manner
glass
journal
writer
detail
gender
shadow
teacher
market
avenue
medicine
contents
village
passage
context
material
reserved
concentrate
Early emotion word processing
28
Table 1
Specifications of Word Stimuli
Condition
N
Length
Frequency
Arousal
Valence
Positive
LF
HF
40
40
7 (2.2)
6 (2.0)
8 (5.3)
62 (48.3)
6.7 (0.6)
6.6 (0.5)
7.6 (0.6)
7.8 (0.5)
Negative
LF
HF
40
40
7 (1.7)
6 (1.8)
8 (5.8)
50 (48.2)
6.6 (0.6)
6.7 (0.5)
2.4 (0.5)
2.6 (0.7)
40
40
7 (1.8)
6 (1.6)
7 (4.8)
66 (62.3)
4.5 (0.5)
4.3 (0.4)
5.2 (0.6)
5.2 (0.5)
Neutral
LF
HF
Note. Mean values with standard deviations in parentheses are shown. Units of measurement
are as follows: Length in number of letters; Frequency in occurrences per million; Arousal
rating range is 1 (low) to 9 (high); and Valence rating range is 1 (negative) to 9 (positive). LF =
low frequency and HF = high frequency.
Early emotion word processing
29
Table 2
Mean RT (ms), P300 Peak Latency (ms), and Voltage Amplitudes (µV)
across ERP Windows for LF and HF Negative, Neutral, and Positive Words
Latency (ms)
RT
P300 peak
Voltage (µV)
P1
N1
EPN
P300
Negative
LF
Neutral
Positive
Negative
HF
Neutral
Positive
549
574
567
570
547
554
514
523
516
534
503
545
1.79
–2.06
–0.65
5.95
1.70
–2.50
–0.78
5.32
1.98
–2.16
–1.07
5.85
1.30
–2.61
–1.27
6.94
1.94
–2.03
–0.16
6.47
1.93
–2.27
–0.81
6.61
Note. LF = low frequency and HF = high frequency. For the P1, N1, and EPN
windows, electrode areas comprise posterior sites shown in Figure 1. For the
P300 window, midline electrodes were used. The P300 peak latency was
calculated from electrode Pz.
Early emotion word processing
30
Figure Captions
Figure 1. Electrode arrangement. Homologous left and right posterior electrodes are indicated
for analysis of average voltage amplitude in the P1, N1, and EPN windows. Midline
electrodes are indicated for analysis of average voltage amplitude in the P300 window.
Figure 2. (A) Mean RT (ms) with standard error bars indicated for LF and HF Negative, Neutral,
and Positive words. (B) Mean P300 peak latency (ms) from electrode Pz with standard
error bars for LF and HF Negative, Neutral, and Positive words.
Figure 3. Mean voltage amplitude (μV) with standard error bars for LF and HF Negative,
Neutral, and Positive words for the following time windows: (A) P1 (80-120 ms); (B) N1
(135-180 ms); (C) EPN (200-300 ms); and (D) P300 (300-450 ms).
Figure 4. ERP waveforms from posterior electrodes PO7 (left) and PO8 (right), and from the
midline electrode Pz. Each plot depicts the grand average ERPs to Positive, Neutral, and
Negative word conditions for either HF or LF words.
Figure 5: Spline-interpolated topographic maps for P1, N1, EPN, and P300 time intervals.
Isopotential line spacing is 0.5 μV.
Early emotion word processing
Figure1
31
Early emotion word processing
32
Figure 2
600
A
LF
HF
Reaction Time [ms]
575
550
525
500
475
450
425
600
Negative
Neutral
Positive
Negative
Neutral
Positive
B
P300 Latency [ms]
575
550
525
500
475
450
425
Word Condition
Early emotion word processing
33
Figure 3
P1 (80-120 ms)
A
LF
HF
3.0
-3.5
N1 Amplitude [V]
P1 Amplitude [V]
3.5
2.5
2.0
1.5
1.0
0.5
Neutral
-2.5
-2.0
-1.5
-1.0
Positive
Negative
EPN (200-300 ms)
C
8.0
P300 Amplitude [V]
EPN Amplitude [V]
-3.0
-0.5
Negative
-3.5
N1 (135-180 ms)
B
-3.0
-2.5
-2.0
-1.5
-1.0
Neutral
Positive
P300 (300-450 ms)
D
7.5
7.0
6.5
6.0
5.5
5.0
-0.5
Negative
Neutral
Word Condition
Positive
Negative
Neutral
Word Condition
Positive
Early emotion word processing
Figure 4
34
Early emotion word processing
Figure 5
35