cognition and emotion - university of arizona...journal: cognition and emotion manuscript id cem-fa...
TRANSCRIPT
For Peer Review Only
The mental representation and social aspect of expressives
Journal: Cognition and Emotion
Manuscript ID CEM-FA 12.20.R2
Manuscript Type: Full Article
Date Submitted by the Author: n/a
Complete List of Authors: Donahoo, Stanley; University of Arizona, LinguisticsTzuyin Lai, Vicky; University of Arizona, Psychology
Keywords: Swearing, ERP, Emotion, Language, Taboo
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
For Peer Review Only
1
The mental representation and social aspect of expressives
Author Names and affiliations:
Stanley A. Donahooa,c, Vicky Tzuyin Laib,c
aDepartment of Linguistics, University of ArizonabDepartment of Psychology, University of ArizonacCognitive Science Program, University of Arizona
Data Availability Statement: Data available on request from the authors.
Page 1 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
2
AbstractDespite increased focus on emotional language, research lacks for the most emotional
language: Swearing. We used event-related potentials (ERPs) to investigate whether swear
words have content distinct from function words, and if so, whether this content is emotional
or social in nature. Stimuli included swear (e.g. shit, damn), negative but non-swear
(e.g. kill, sick), open-class neutral (e.g. wood, lend), and closed-class neutral words
(e.g. while, whom). Behaviourally, swears were recognised slower than valence- and arousal-
matched negative words, meaning that there is more to the expressive dimension than merely
a heightened emotional state. In ERPs, both swears and negative words elicited a larger
positivity (250-550 ms) than open-class neutral words. Later, swears elicited a larger late
positivity (550-750 ms) than negative words. We associate the earlier positivity with
attention due to negative valence, and the later positivity with pragmatics due to social
tabooness. Our findings suggest a view in which expressives are not merely function words
or emotional words. Rather, expressives are emotionally and socially significant. Swears are
more than what is indicated by valence ore arousal alone.
Keywords: Swearing, ERP, Emotion, Language, Taboo
Words: 7,942
Page 2 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
3
1. Introduction
Swear words are words such as damn, hell, and shit, and are often perceived as both
negative and highly arousing. How are swear words understood? Traditionally, swears have
been considered categorically as closed-class, function words, much like prepositions (Leech
et al., 1982; Segalowitz & Lane, 2000). However, swear words differ from function words in
that they give rise to physical effects. For instance, swearing increases tolerance for physical
pain (Stephens et al., 2009), and elicits stronger skin conductance responses than neutral
words (Bowers & Pleydell-Pearce, 2011). Recent theories consistent with such a lack-of-
content view propose that swear words have no propositional content themselves. Rather,
they exert influence on their neighbouring content words (Gutzmann, 2019; Potts, 2005b,
2007). Further, this lack of propositional content allows for great flexibility as to where the
swear is interpreted with respect to the surrounding lexical context (Frazier et al., 2015,
2017). Another rational way to understand swear words is to treat them as emotional words,
because swear words are oftentimes perceived as both negative and arousing. Following this
line of reasoning, is the category of swear words a special kind of negative words? In this
study, we propose that swears are neither just function words nor emotional words and that
there is some use-conditional, social content encoded in swear words. We use event-related
potentials (ERPs) to investigate whether swear words have content distinct from function
words, and if so, whether this content is emotional or social in nature.
According to linguistic theories that view swear words as lacking propositional
content, swearing is studied as expressive content (Gutzmann, 2019; Potts, 2007).
Expressives are thus considered not-at-issue content, which is difficult to isolate from the
utterance in which it occurs (Simons et al., 2010). For example, an infelicitous follow-up to
The damn dog is on the couch would be something like, That’s not true, Fido is a lovely dog,
demonstrating the difficulty an interlocutor has in trying to isolate the expressive content on
Page 3 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
4
its own. Given that the expressive content of the swear word damn is not what is at-issue in
an utterance, it has been argued that the expressive content represents a dimension of
meaning which is separate from the rest of the descriptive, truth-conditional content of the
utterance (Gutzmann, 2013; Potts, 2005a, 2007, see also Anderbois, Brasoveanu, &
Henderson, 2015; Barker, Bernardi, & Shan, 2010). Evidence in support of an additional
dimension of meaning are provided by both clinical and neurotypical populations.
Spontaneous usage of expressive content is an infamous trait of Tourette syndrome
(Robertson, 2000; Van Lancker & Cummings, 1999). In healthy populations, Dörre and
colleagues (Dörre et al., 2018) examined not-at-issue content using German modal particles
and found processing differences compared to their at-issue counterparts, suggesting
differences in meaning representations.
In emotion research, many view swear words as being negatively valenced and highly
arousing (Reilly et al., 2020). Emotional language researchers typically assumes a two-
dimensional model, comprised of valence and arousal (Citron, 2012; Citron et al., 2013;
Russell, 1980; Lang, Bradley, & Cuthbert, 1998). Valence represents a value measure where
higher scores represent a word that could be considered good or positive, whereas lower
values represent a word considered bad or negative. Arousal is an excitement measure,
where highly arousing words produce feelings of stimulation or excitement and low arousal
words produce feelings of relaxation or calmness (Russell & Barrett, 1999; Scott et al., 2018).
In what follows we briefly review some of the behavioural and electrophysiological literature
on emotional words relevant to swears (Citron, 2012).
Behavioural studies have found faster and more accurate recognition for words with
any emotional valence, be it negative or positive, in comparison with neutral words (Citron et
al., 2013; Kousta et al., 2009). This processing advantage is theorised to represent the
relevance of emotional stimuli in both survival generally and goal attainment specifically.
Page 4 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
5
While the directionality of the effects of word valence are mixed, (Algom et al., 2004;
Kanske & Kotz, 2007; Knickerbocker et al., 2015; Kuperman et al., 2014; Scott et al., 2014;
Wentura et al., 2000), overall, researchers have taken a theoretical stance which favours a
processing advantage for negative words as a reflection of survival often coinciding with
fleeing from negative situations (Fox et al., 2001; Knickerbocker et al., 2015). Generally,
emotional stimuli, regardless of valence directionality, capture attention because they are
motivationally significant (Lang et al., 1990, 1997). More recently, it has been argued that
prior studies on emotional language processing conflated one or more linguistic variables
(e.g. word frequency), and that, when all known variables are accounted for, we are left with
a U-shape model where more extreme valence values across the spectrum speed up lexical
decision times (Kuperman et al., 2014). Further, while arousal has been shown to facilitate
lexical decision times when valence is held constant (Hofmann et al., 2009), valence, rather
than arousal, has been demonstrated to be a more robust predictor in modelling factors of
lexical access (Bradley & Lang, 2000; Kousta et al., 2009; Kuperman et al., 2014; Vinson et
al., 2014). Specifically, valence is a significant predictor of lexical decision response
latencies, even when other factors, including arousal, are included in regression models
(Kousta et al., 2009). Finally, valence, unlike arousal, appears to remain a modulating factor
in tasks beyond the word level (Bayer et al., 2010).
Electrophysiological studies, with their superior temporal resolution, inform us that
the emotional content of a word is activated rapidly. Event Related Potential (ERP) studies of
emotional words have identified at least two neural correlates (Citron, 2012). One is the
early posterior negativity (EPN), which peaks roughly between 200 and 300 ms after stimulus
onset, and is larger for both positive and negative valenced words than for neutral words
(Herbert, Junghofer, & Kissler, 2008). It has been suggested that the EPN reflects automatic,
implicit processing of emotion (Palazova et al., 2011). However, studies which reported an
Page 5 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
6
EPN tend to employ tasks distinct from natural reading, like multiple repetitions of single
words (Kissler et al., 2007) or counting the number of times a word from a grammatical class
occurs (Kissler et al., 2009). A second neural correlate robustly associated with emotional
word processing is the late positive component (LPC), which peaks roughly between 500 and
750 ms (Bayer et al., 2010, 2012; Bayer & Schacht, 2014; Yao et al., 2016). The LPC is an
index of a more sustained, explicit, processing of emotional content; or, put another way, the
LPC indexes higher-order stimulus evaluation (Schacht & Sommer, 2009). Negative words
have been shown to increase the amplitude of the LPC (Kanske & Kotz, 2007; L. Wang &
Bastiaansen, 2014). Collapsing over positive and negative words, emotional words elicited a
larger LPC relative to neutral words, a finding in parallel with behavioural studies, where
valence tends to facilitate lexical access (Citron et al., 2013; Kousta et al., 2009). In general,
more valenced words produce larger ERP amplitudes, which is associated with emotional
processing (Citron et al., 2013; Kanske & Kotz, 2007).
If the not-at-issue content in swear words is primarily emotional, we would expect to
find the ERP correlates discussed in the above-mentioned literature. However, here we
suggest that the two dimensions of valence and arousal may not fully capture the use-
conditional content of swear words. In addition to emotionality, there are at least three
possibilities; the content is: (1) a pragmatic speech act, (2) tabooness, (3) a socially
threatening function. Frazier, Dillon, and Clifton (2015, 2017) argue for pragmatically
treating expressives as independent speech acts, independent of the speech act conveyed by
the rest of the utterance. As a result, swears thus have flexibility as to where they are applied
to the rest of the at-issue content. Frazier and colleagues found evidence for this claim using
offline sentence ratings. Participants read sentences such as The policeman gave me a damn
ticket and rated whether the speaker felt negative towards the ticket (object reading), the
policeman (subject reading), or the entire situation (situation reading) itself. Even when the
Page 6 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
7
expressive was in an object position, participants still preferred the situation response nearly
42% of the time. Similarly, damn presented in subject position yielded a situation
interpretation in 39% of ratings and damn in situation position (e.g. Damn. The policeman
gave me a ticket.) yielded a situation interpretation in only 63% of ratings, empirically
demonstrating the flexibility of not-at-issue swear.
Tabooness is another potential factor that may drive swear word recognition. Taboo
language generally produces slower reaction times across a broad variety of tasks, including
modified Stroop (Eilola & Havelka, 2011; Guillet & Arndt, 2009; Mackay et al., 2004;
Siegrist, 1995), attentional blink (Mathewson et al., 2008), and picture-word interference
(Dhooge & Hartsuiker, 2011). This slowdown is conversely associated with the taboo-
superiority effect: the phenomenon that memory is better for taboo words over neutral words
in a variety of experimental paradigms, for example in spontaneous recall tasks (Hadley &
MacKay, 2006; Mathewson et al., 2008). Better recall has also been demonstrated for taboo
words in word lists (Buchanan et al., 2006; Grosser & Walsh, 1966), and in repetition
priming (Thomas & LaBar, 2005). Madan, Shafer, Chan, & Singhal, (2017) reported that
factors other than valence, such as word frequency and tabooness, are the better predictors of
lexical decision latencies. It should be mentioned however, that stimuli in Madan et al.
(2017), while including words that could be considered offensive (e.g. skank), do not directly
feature any words that are commonly considered swears (e.g. damn, shit, hell).
Lastly, social threat has been associated with swear words. One study explored the
idea that social threat and physical threat are distinct (Wabnitz et al., 2012). Participants
passively read socially threatening negative words (swear words), physically threatening
negative words (e.g. bomb, explosion, wound), neutral, and positive words, while monitoring
for a dot presented in 15% of trials. The socially threatening negative words (swear words)
elicited an enhanced P1 (100-140 ms) relative to physically threatening negative words and
Page 7 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
8
positive words. The authors interpreted the P1 as a reflection of a fast-forward mechanism
for evolutionarily relevant cues. In their so-called N400 time window (450-580 ms), negative
and positive words elicited a larger negativity effect relative to neutral words, with no
difference between the positive and negative words. The authors interpreted the N400-like
effect as evidence that swear words receive no special semantic consideration relative to
other emotionally valenced words, at least not in a typical population. In people with social
anxiety disorder, swears did entertain semantic consideration in the N400 time window
(Wabnitz et al., 2016). A related study (Klein et al., 2019) reported LPC differences for
socially threatening and physically threatening words in adolescents with PTSD, but not in
healthy controls.
The present study uses event-related potentials (ERPs) to investigate the nature of the
content, if any, in swear words. Participants engaged in a lexical decision task while EEG
was simultaneously recorded on the scalp. We compared the response times and ERPs for
swear words, negatively valenced but non-swear words, neutral open class words, and neutral
closed-class function words. If swears are not as content heavy in their being not-at-issue,
they should behave similarly to function words. If swears have content and if the content is
emotional in nature, then swears should behave more like negative words. If swears have
content and if the content is something more than emotional in nature, then swears should be
distinct from negative words.
2. Material and methods2.1. Participants
Thirty-two right-handed native English speakers (15 female; age range 18-23)
participated in the experiment for course or extra credit. According to power analyses using
G*Power software (Faul et al., 2009), this was a sufficient sample size using a conventional
threshold of α = .05 for Type I error, a power of .80, and a medium effect size (partial
Page 8 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
9
η2 = .06). All participants had normal or corrected-to-normal vision, and had no known
language or neurological disorder. All gave informed consent before participation. For
institutional review board purposes, participants were verbally informed that stimuli
presented during the experiment included words that could be considered offensive; but, in
order to avoid any sort of priming effects, no examples of such words were provided before
the experiment began.
2.2. StimuliThe stimuli consisted of 28 swear words (e.g. shit, damn), 28 negatively valenced but
non-swear words (kill, sick), 28 open-class neutral words (e.g. wood, lend), 28 closed-class
neutral words (e.g. while, whom), and 112 pseudowords. The number of words is largely
constrained by the number of words considered swears. The words were generated by native-
speaking research assistants with reference to the swear literature (Jay, 1992; Wang, Chen,
Thirunarayan, & Sheth, 2014) and the literature on closed class words (Craig & Kinney,
2009). Two web-based norming surveys verified the manipulations of valence and
tabooness. Thirty-nine native-speaking participants rated the words one word at a time on a
9-point Likert scale. In the valence norming (“how positive or negative a word is”), 1 was
negative and 9 was positive. In the norming for tabooness (“how offensive the word is to
people in general”), 1 was not at all offensive and 9 was very offensive. Arousal values were
taken from existing corpus work (Warriner et al., 2013), supplemented by the web-based
norming survey for the closed-class words . Based on the results of the norming survey, one
stimulus item initially coded as a swear word, christ, was removed from the analysis, as
participants consistently rated the word as relatively positive (6.19) and not taboo (4.75).
Table 1 summarises the word properties. Both swears and negative words were rated more
negative than neutral words, (ts > 12, ps < .001), and swears and negative words were
similarly negative (n.s.), and similarly arousing (n.s.). Swears were rated more taboo than
Page 9 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
10
negative words (t(43) = 6.74 p < .001). In addition, the conditions were matched in terms of
log frequency (Brysbaert & New, 2009), word length, and number of morphemes, phonemes,
and syllables (Balota et al., 2007). There was no significant difference between these
conditions.
The pseudowords were constructed using the ARC Pseudoword Database (Rastle et
al., 2002), selecting only strings with orthographically existing onsets, and using an average
range of letter length (min: 4, max: 6), as well as orthographic (min: 4, max: 6) and
phonological (min: 12, max: 14) neighbourhood sizes that matched the target words in the
task.
2.3. ProcedurePreparation for the EEG recording lasted 30 minutes, in which participants were
measured and fitted with the electrode cap (ActiCap, Brain Products, GmbH). During the
setup, participants provided general language and demographic information and were briefed
as to the nature of the EEG technique. Participants then entered a dimly lit and sound
attenuated room. They were seated in a comfortable chair at a desk which featured a
computer screen 70-80 cm away from their eyes. The stimuli were presented visually in
white font (Font: Lucida Console; Font size: 20) against a black background via the E-prime
3.0 software (Psychology Software Tools, Inc.).
Figure 1 shows an example of a trial. In each trial, a central fixation cross was
initially presented for 500ms, followed by a blank screen with a random duration of 500-1500
ms. The stimulus (either a legal letter string or illegal letter string) then appeared for a
duration determined by the number of letters in the string, ranging from 248-400 ms (37
ms/letter + 100 ms, max duration of 400 ms). Participants were instructed to decide whether
the letter string was a word or not as quickly and as accurately as possible by pressing one of
two buttons on a response pad. The order of the button press was counterbalanced with
Page 10 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
11
participant number. A trial timed out after 2000 ms, but this duration proved adequate to
garner a participant response.
There were 6 practice trials to familiarize participants with the task. Participants were
advised to refrain from moving or blinking during the trials, but encouraged to rest their eyes
at break intervals, occurring after every 8 trials. Each item was presented once, in one block
with a randomised presentation order. We decided against repeating items, as there is
evidence that emotional words in particular are susceptible to repetition priming effects, both
immediate and delayed (D. Zhang et al., 2019).
2.4. EEG acquisition and pre-processingContinuous EEG was recorded from 64 surface active electrodes using the actiCHamp
system (Brain Products, GmbH). Data were measured with respect to a vertex reference, with
the sampling rate of 500 Hz. Electrode impedances were below 10 kΩ throughout.
Data was analysed with Brain Vision Analyzer 2.0. Data was low-pass filtered at 30
Hz with a notch filter at 60 Hz to filter out line noise. Blinks were detected and corrected
offline via ocular artefact correction independent component analysis (ICA) using the
Infomax Restricted ICA algorithm. Artefacts were detected semi-automatically with Brain
Vision. Trials contaminated with artefacts due to body movements or peak deflection
exceeding ±100 µV were rejected. Individual bad channels were replaced through
interpolation of the average of the surrounding channels (Brain Vision User Manual) on a
participant-by-participant basis. Remaining artefacts were reduced by individual channel-
interpolation or the epochs were rejected. 1.5% of the channels were replaced across the
experiment. Data were re-referenced to the average of the left and right mastoids, and
segmented from 200 ms before the stimulus onset to 750 ms after, with baseline correction
from -200 ms to 0 ms preceding the onset of the target letter string. Finally, the ERP data
were averaged across participants for each condition.
Page 11 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
12
3. Results3.1. Behavioural
Mean behavioural accuracy and reaction times results for trials with correct responses
are displayed in Figures 2 and 3. Separate Repeated-Measures ANOVAs were conducted to
compare the effect of word condition (Swear, Negative, Neutral, Closed, Pseudoword) on
accuracy and on reaction times, respectively. Greenhouse-Geisser corrected p values are
reported when appropriate for all tests involving factors with more than two levels. In
addition, False Discovery Rate corrections were applied (Benjamini & Hochberg, 1995). In
terms of accuracy, there was a significant effect for condition, F(4,112)=) = 20.38, p < .001,
η2G = .259. Crucially, there were no differences for swear and neutral, F(1,28)=) = 1.70, p =
.303; negative and neutral, F(1,28)=.) = .15, p = .697; nor swear and negative, F(1,28)=) =
1.29, p = .319. There were significant differences between pseudoword and neutral
F(1,28)=) = 21.83, p < .002, η2G = .199; closed and neutral, F(1,28)=) = 53.65, p < .002, η2
G
= .312; and closed and swear, F(1,28)=) = 26.48, p < .002, η2G = .236.
In terms of reaction time, there was a significant effect for condition, F(4,112)=) =
47.58, p < .001, η2G = .139. Pairwise comparisons revealed that there were significant
differences between pseudoword and neutral, F(1,28)=) = 132.59, p < .001, η2G = .198; swear
and neutral, F(1,28)=) = 18.03, p < .001, η2G = .030; swear and negative, F(1,28)=) = 18.5, p
< .001, η2G = .056; closed and neutral, F(1,28)=) = 30.68, p < .001, η2
G = .054. Negative
compared to neutral was not significant, F(1,28)=) = 1.69, p=. = .204. Closed compared to
swear was not significant, F(1,28)=) = 1.90, p = .204.
3.2. ERPThe grand averaged ERPs are displayed in Figure 4, and the scalp distributions of the
effects, in Figure 5. Average trial counts for each condition were swear: 26.78; negative:
25.75; neutral: 25.97; closed: 26.38; pseudoword: 101.53. Descriptively, the overall
amplitude for swear was greater than the other conditions at early (250-550) and later (550-
Page 12 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
13
750) time windows: swear, M = 2.74 µV; negative, M = 2.12 µV; neutral, M = 1.17 µV;
closed, M = 1.33 µV; pseudoword, M = -.55 µV. 550-750: swear, M = 5.64 µV; negative, M
= 3.41 µV; neutral, M = 2.85 µV; closed, M = 3.87 µV; pseudoword, M = 1.95 µV.
Visual inspection indicated that all waveforms showed early components of N1 and
P2, which reflect visual processing and reassure data quality. The conditions diverged as
early as ~250 ms. Negative words elicited a larger positivity than neutral words from 250-550
ms, centrally and parietally distributed. Swears also elicited a larger positivity than neutral
content words from 250-550 ms, widespread in all locations, but showed more positive
amplitudes than negative words from 550-750 ms. Pseudowords elicited a larger negativity
than neutral open-class words starting at 250 ms, identified as N400. There was no EPN (See
supplementary materials).
Mean amplitudes were exported from two time windows of 250-550 ms and 550-750
ms at the frontal group (Afz, F1, F2, Fz), central group (FCz, C1, C2, Cz), and the posterior
group (CPz, P1, P2, Pz) along the midline. Greenhouse-Geisser corrected p values are
reported when appropriate for all tests involving factors with more than two levels. False
Discovery Rate corrections were also applied. The omnibus Repeated-Measures ANOVA
model of 5 condition (Swear, Negative, Neutral, Closed, Pseudoword) x 3 location (Front,
Central, Posterior) x 2 time (Early, Late) showed an interaction of condition x time x
location, F(8,248)=) = 5.19, p < .001, η2G = .001, and an interaction of condition x time
F(4,124)=) = 5.70, p < .001, η2G = .004. These interactions suggest that the patterns of results
in the two time windows differed. Thus separate Repeated-Measures ANOVAs of condition
x location were conducted within each time window.
3.2.1. 250-550 msThere was a condition x location interaction, F(8,248)=) = 3.36, p = .010, η2
G = .002,
a main effect of condition F(4,124)=) = 14.41, p < .001, η2G = .067, and a main effect of
Page 13 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
14
location F(2,62)=) = 40.48, p < .001, η2G = .200. Pseudoword elicited a larger and
widespread N400 than neutral words (Frontal: F(1,31)=) = 18.40, p < .004, η2G = .060;
Central: F(1,31)=) = 23.08, p < .004, η2G = .086; Posterior: F(1,31)=) = 9.57, p = .013),, η2
G
= .028), consistent with past ERP findings for pseudowords (Palazova et al., 2011; Schacht &
Sommer, 2009a). Figure 6 displays relevant ERPs and scalp topographies for these two
conditions.
Comparing negative and neutral words, negative words elicited a larger positivity
than neutral words, centrally and posteriorly distributed (Frontal: F(1,31)=) = 3.43, p = .074,
η2G = .009; Central: F(1,31)=) = 5.35, p = .033, η2
G = .018; Posterior: F(1,31)=) = 7.66, p =
.019, η2G = .020).
Comparing swear and neutral words, swear words elicited a larger positivity than
neutral words, at all locations (Frontal: F(1,31)=) = 9.04, p = .013, η2G = .037; Central:
F(1,31)=) = 5.89, p = .027, η2G = .030; Posterior: F(1,31)=) = 6.78, p = .020, η2
G = .025).
Comparing swear and negative words, the two behaved similarly in this time frame.
That is, swears elicited a larger positivity than negative words in the frontal group only
(Frontal: F(1,31)=) = 4.28, p = .050, η2G = .011; Central: F< < 1; Posterior: F< < 1).
Closed class words behaved similarly to neutral (content) words in this window (all
Fs < 1). But closed class words behaved differently compared to swears, with swear eliciting
a larger positivity (Frontal: F(1,31)=) = 12.46, p = .004, η2G = .032; Central: F(1,31)=) =
7.31, p = .019, η2G = .020; Posterior: F(1,31)=) = 7.1, p = .019, η2
G = .014).
3.2.2. 550-750 msThere was a main effect of condition, F(4,124)=) = 11.41, p < .001, η2
G = .053, but
condition did not interact with location, F< < 1. Thus in the following analyses we combined
Page 14 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
15
all electrode locations. Pseudoword elicited a marginally larger negativity than neutral words
(F(1,31)=) = 4.17, p = .07, η2G = .013), likely a continuation of the N400.
Swears elicited a larger positivity than neutral words, F(1,31)=) = 17.54, p < .003, η2G
= .078. Swear also elicited a larger positivity than negative words, F(1,31)=) = 12.75, p =
.003, η2G = .049. Negative and neutral words do not differ here (F(1,31)=) = 1.74, p = .197).
Closed class words behaved similarly to neutral (content) words in this time window,
F(1,31)=) = 2.76, p = .126. But closed class words behaved differently compared to swear
words, with swear words eliciting a larger positivity than the closed (F(1,31)=) = 9.96, p =
.008, η2G = .026).
Further, to explore task difficulty on ERP responses, using mean reaction times for
hits in the respective emotion conditions as a within-subject covariate, the ANCOVA
indicated that in the late positivity window (550-750 ms), the effect of swear words still
differs from the effect of negative words (F(1,62) = 7.97, p = .006). This suggests that after
taking task difficulty (approximated by reaction times) into consideration, swear words are
processed differently from the negative words in the late time window. In the early positivity
window (250-550 ms), the effect of swear words does not differ from the effect of negative
words (F(1,62) = .817, p = .369), suggesting that after taking task difficulty into
consideration, swear words and negative words are processed similarly in this earlier
positivity time window.
4. DiscussionThe present study investigated whether swear words have an additional dimension of
meaning, and if so, whether such meaning is functional, emotional, or social in nature.
Participants read swear words, negatively valenced but non-swear words, neutral open-class
words, neutral closed-class words, and pseudowords. They made word/non-word decisions
Page 15 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
16
while their EEG was recorded. The behavioural results showed that, while participants were
equally as accurate to respond to swears, negative, and neutral words, swear words were
recognised more slowly than neutral open-class words, and neutral open-class words were
recognised more slowly than negative words. Swears were recognized as quickly as the
closed-class neutral words. For ERPs, both swears and negative words elicited larger
positivity effects in the 250-550 ms time window relative to neutral words. In the 550-750
ms time window, the positivity effect in swears continued but the positivity effect in the
negative words condition converged with the neutral. Swears were also distinct from closed-
class neutral words in both time windows, in that swears elicited larger positivity effects than
closed-class neutral words.
4.1. Behavioural resultsThat swear words were recognised more slowly than neutral open-class words could
be interpreted as a taboo superiority effect, and that neutral open-class words were recognised
more slowly than negative words could be viewed as an example of well-attested negative
bias (Ito et al., 1998; L. Wang & Bastiaansen, 2014). Recent behavioural work in speech
production has argued that taboo effects are pre-lexical (Hansen et al., 2017). However, our
results suggest that taboo effects emerge later, around 250 ms—at which lexical access
already occurred (Dien, 2009; Kim & Lai, 2012). Looking at the issue of when emotional
meaning affects word processing specifically, Palazova and colleagues used lexical decision
simultaneously with EEG to conclude that emotionality has a post-lexical focus (Palazova et
al., 2011). While there were behavioural differencedifferences for open-class and closed-
class neutral words, with the closed-class words being identified more slowly and less
accurately, there were no ERP differences in the two, suggesting that the reaction time
differences are attributable to overall familiarity with the two conditions.
Page 16 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
17
In our opinion, ERPs and behaviours are measuring different aspects and timings of
the same process, and therefore behaviours might not always explain ERPs. Considering
reaction times and ERPs for swears and negative words together, a relatively consistent
picture emerges: The negative valence (present in both swears and negative words), or rather
the attention allocation to negative valence is processed relatively earlier, whereas the social
taboo aspect (present in swears and not in negative words) is processed later, after 550 ms.
4.2. Swears and early effectsOur results indicate that swears in isolation were perceived similarly to negative
words initially until 550 ms, and then continued to be processed for their pragmatic content.
Both swears and negative words elicited larger ERP positivity effects relative to neutral
words in the 250-550 ms window. We are confident in associating the positivity effect here
with negative valence, based on past literature (Hinojosa, Albert, López-Martín, & Carretié,
2014; Kanske & Kotz, 2007; L. Wang & Bastiaansen, 2014; Zhang, Ge, Kang, Guo, & Peng,
2018, but see Bayer & Schacht, 2014). Note that compared to those studies, our positivity
effect time window 250-550 ms seems earlier, e.g., 400-650 ms in Hinojosa et al. (2014). We
included an earlier portion because we considered effects in all conditions, including the
pseudoword effect which started at 250 ms. Such inclusion mathematically counteracted
statistical significance, yet we still found significance, pointing to the robustness of the
positivity effect. An additional post-hoc analysis using the 400-600 ms window verified the
significance of the larger positivity effect for negative than for neutral words (F(1,31)=) =
7.88, p = .009, η2G = .017).
We attribute the positivity effect in the 250-550 ms window to attention allocation
(Hinojosa et al., 2014). According to the Global Resource Theory (Mackay et al., 2004),
attentional capacities are finite. Emotional words demand more attentional resources and
incur a processing disadvantage, which possibly accounts for the delay we observed in lexical
Page 17 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
18
decisions times for participants responding to the swear condition. A related approach,
automatic vigilance (Erdelyi, 1974; Pratto & John, 1991; Wentura et al., 2000), suggests that
negative stimuli engage attention longer than neutral stimuli because humans preferentially
attend to negative stimuli, though it remains unclear at what stage of processing the effect
arises (Kuperman et al., 2014). Task type and relevance also appear to play a role in the
processing of negative compared to neutral stimuli (Trauer et al., 2012, 2015).
We did not find any sort of EPN for the swear words or the negative valence words,
replicating some previous work (M. Zhang et al., 2018; M. Zhang & Guo, 2014). Those
studies reporting an EPN typically found it for positive words or pictures (Hinojosa et al.,
2009; Schacht & Sommer, 2009b), and swears tend to be used in negative contexts. Future
work should examine whether swears used in a positive context (e.g. sheit is damn
prettyfucking awesome) would give rise to an EPN.
4.3. Swears and the late positivityOur primary finding is that there is more to swearing than negative affect, as reflected
by the late positivity effect in the 550-750 ms window which was more enhanced for swears
than for negative words. There are a few possible interpretations for this. The first, and
perhaps most parsimonious, interpretation is that the late positivity was merely a continuation
of the positivity effect (or a more extreme version of the effects). The effect was prolonged in
swears because some function in swears acted to amplify their emotional properties and made
the perceived emotionality more robust than what those properties actually were. This
interpretation is partially consistent with the not-at-issue view of swears. We say only
partially because the not-at-issue view suggests that swears impact words surrounding them,
and here as a single word study, there were no words neighbouring the swears.
A more likely interpretation is that the late positivity effect is a real effect in its own
right. Under this scenario, swears contribute their own dimension of meaning and the late
Page 18 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
19
positivity could reflect variables inherent to swears, such as: (1) a pragmatic speech act, (2)
social threat, (3) tabooness. Ultimately, we conclude that the tabooness element unique to
swears is the best candidate, but we take each of these in turn.
First, the late positivity could reflect an extra step associated with processing a speech
act. This would support the view in which expressives represent their own distinct speech act
(Frazier et al., 2015, 2017). Theoretically, there are several factors to consider regarding any
one speech act (Bach, 1998). These extra steps come with processing costs, some of which
have been borne out experimentally for both direct and indirect speech acts (Coulson &
Lovett, 2010; Gísladóttir, Bögels, & Levinson, 2018; Gísladóttir, Chwilla, & Levinson,
2015). It is difficult to compare these ERP findings for multi-word speech acts to our lexical
decision task. Gísladóttir and colleagues reported a late negativity for speech acts in which
an interlocuter makes an indirect offer (by saying I have a credit card as a response to I don’t
have any money to pay for the ticket) as opposed to a direct answer (I have a credit card as a
response to How are you going to pay for the ticket?). Coulson and Lovett (2010) reported
LPC effects for differences in literal statements and indirect requests (My soup is too cold to
eat, said to either a husband or a restaurant waiter), with a larger LPC for indirect requests.
However, this positivity only emerged for words later in the request, much later than we see
for words presented in isolation. Still, negative and neutral words alone in isolation do not
represent a speech act in the way that a swear word might; for example, uttering shit after
spilling coffee on a computer desk can invoke a hearer to move the keyboard. However, it
remains an open question whether a swear word presented in a lexical decision task can be
construed as conveying communicative meaning in the way that a speech act typically does.
Second, the late positivity could reflect the processing of social threat, an idea
postulated for differences observed for taboo words in the two extant ERP studies directly
involving swearing (Wabnitz et al., 2012, 2016). Wabnitz and colleagues argued that swear
Page 19 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
20
words receive no special semantic consideration relative to other emotionally valenced words
while our findings suggest otherwise. One likely explanation for the differences in our ERPs
can be attributed to task differences. Wabnitz and colleagues relied on passive reading while
our study required that participants made an active decision about each letter string. Such
task differences are known to affect lexical processing for emotional words (Kissler et al.,
2009). Additionally, Wabnitz and colleagues employed a block design, meaning that
participants saw the words in each condition six times. Such repetition can significantly alter
ERPs (Van Petten et al., 1991). Finally, there is a potential confound in the design of both of
these studies regarding the label of swear word towards the stimuli used, however. Because
the research question was interested in navigating social threat, the swear stimuli used were
person-oriented, thus functioning more like an insult than a swear word specifically.
Categorically, there is a difference in fuck or fucking compared to motherfucker (Blakemore,
2015). The former are swears, whereas the latter is also an insult. All of the stimuli used by
Wabnitz and colleagues are person-oriented and interpreted as a noun. It remains an open
question as to whether these related but distinct conceptual categories are processed and
represented in similar fashion. In this vein, Klein and colleagues (2019) used the same
stimuli as Wabnitz and colleagues and found no difference in insults and physically
threatening words in healthy adolescents, whereas they did find differences for adolescents
with PTSD. In general, all of these words bear some social offense, but there are linguistic
distinctions. It is clear however, that expressives do receive special semantic consideration
relative to other words that are equally as emotionally valenced.
Third, the late positivity could reflect the processing of an additional dimension,
tabooness. That is, even though damn and death are both negatively valenced, only damn has
the additional use-conditional taboo dimension. Swearing is not merely the sum of extreme
values of valence and arousal. Instead, these words offer their own element of affect, namely
Page 20 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
21
tabooness, and contribute their own dimension of meaning, both of which are distinct from
other words that are just as extreme in their valence values. This interpretation is consistent
with recent experimental work were tabooness demands more attention in sentential contexts
(Christianson et al., 2017). In our lexical decision task, relative to both negative and neutral
words, swears elicited longer reaction times, a finding consistent with prior studies involving
a variety of tasks (Dhooge & Hartsuiker, 2011; Eilola & Havelka, 2011; Guillet & Arndt,
2009; Mathewson et al., 2008). Regarding our ERP findings, it is a challenge to situate the
positivity effect for swears relative to negative and neutral words, given that so few studies
have investigated these specific aspects of emotional language. Still, previous work shows
that negative words increase the amplitude of the LPC (Kanske & Kotz, 2007; Wang &
Bastiaansen, 2014).
The limitations of this study are as follows: (1) The trial counts are relatively
lowcomparatively lower (~26 each condition), which is due to the fact that there are not that
many swear words to begin with and that we had to reject trials contaminated by artefacts.
However, the data are still relatively reliable because first, within this dataset, early visual
components N1 and P2 are clearly present, which verifies data quality in general. Second,
beyond this dataset, otherOther studies have used comparable trial counts, e.g.,: 20 in
Hinojosa et al. (2009), 32 in Kanske et al. (2011), and 20 in Yao et al. (2016). Thus, while
we should keep this limitation in mind, the observed effects are relatively reliable.Note that
these studies repeated these items several times in blocks, however. We opted against using
repetition in our design, given the evidence that emotional words in particular are susceptible
to immediate and delayed repetition priming effects (D. Zhang et al., 2019). In addition, our
reported power analysis indicates sufficient sample size even with these lower item counts.
Future studies should employ higher more trials. (2) We matched multiple word properties
(Table 1), but we did not match part of speech, concreteness, or familiarity. What part of
Page 21 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
22
speech a swear word is cannot easily be determined. The majority of swear words can be
nouns (What is this shit?), verbs (He shit himself.), adjectives (That was a shit film), or
virtually any other part of speech. In this study, we compared swears with closed-class
function words to test the claim in the literature that swears act as closed-class words. In
some sense, it is part of the research question, as opposed to being a variable that must be
controlled.
Concreteness of swears is also hard to pin down and likely context dependent (e.g.
consider shit in This song is my shit and My dog took a shit).. For example, in This song is
my shit, shit is very abstract, but in My dog took a shit, shit is concrete and reflects literal
fecalfaecal matter. In Kanske and Kotz (2007), concreteness interacted with emotion in that
only concrete, negative words (e.g. bomb) showed the enhanced LPC, but not abstract
negative words (e.g. violence).
With respect to familiarity, we matched the word frequency, which according to some
school of thought (Tanaka-Ishii & Terada, 2011) can be viewed as an approximant of
familiarity. In addition This is especially so given that our stimuli have a relatively high
frequency, which typically have high familiarity. The familiarity effect matters most for low
frequency items. Finally, in our data, participants responded with over 90% accuracy to the
critical comparison conditions swear, negative, and neutral, suggesting that participants were
familiar with these stimuli. The reduced accuracy for the closed condition might possibly
suggest that familiarity played a role here, but these words were just as frequent as the other
conditions, suggesting that participants have encountered these items on a regular basis.
5. ConclusionThe current study found that swear words have content distinct from function and
from negative words, and that this content is emotional and social. Specifically, swears and
negative words elicited a larger positivity (250-550 ms) than open-class neutral words,
Page 22 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
23
associated with attention due to negative valence. Later on, swears elicited a larger late
positivity (550-750 ms) than negative words, associated with social tabooness. Our findings
suggest that a two-dimensional model of emotional language processing requires expansion
in order to fully capture all emotional language including swears. With these results, we
contribute a new data set for probing our understanding of one of the central properties of
language: conveying emotion.
ReferencesAlgom, D., Chajut, E., & Lev, S. (2004). A Rational Look at the Emotional Stroop
Phenomenon: A Generic Slowdown, Not a Stroop Effect. Journal of Experimental
Psychology: General, 133(3), 323–338. https://doi.org/10.1037/0096-3445.133.3.323
Anderbois, S., Brasoveanu, A., & Henderson, R. (2015). At-issue Proposals and Appositive
Impositions in Discourse. Journal of Semantics, 32(1), 93–138.
https://doi.org/10.1093/jos/fft014
Bach, K. (1998). Speech acts. In Routledge Encylopedia of Philosophy. Taylor and Francis.
https://www.rep.routledge.com/articles/thematic/speech-acts/v-1
Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., Neely, J.
H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English lexicon project.
Behavior Research Methods, 39(3), 445–459.
Barker, C., Bernardi, R., & Shan, C. (2010). Principles of interdimensional meaning
interaction. Semantics and Linguistic Theory, 20, 109–127.
Bayer, M., & Schacht, A. (2014). Event-related brain responses to emotional words, pictures,
and faces—A cross-domain comparison. Frontiers in Psychology, 5.
https://doi.org/10.3389/fpsyg.2014.01106
Page 23 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
24
Bayer, M., Sommer, W., & Schacht, A. (2010). Reading emotional words within sentences:
The impact of arousal and valence on event-related potentials. International Journal
of Psychophysiology, 78(3), 299–307. https://doi.org/10.1016/j.ijpsycho.2010.09.004
Bayer, M., Sommer, W., & Schacht, A. (2012). P1 and beyond: Functional separation of
multiple emotion effects in word recognition. Psychophysiology, 49(7), 959–969.
https://doi.org/10.1111/j.1469-8986.2012.01381.x
Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and
Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society:
Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-
6161.1995.tb02031.x
Blakemore, D. (2015). Slurs and expletives: A case against a general account of expressive
meaning. Language Sciences, 52, 22–35.
https://doi.org/10.1016/j.langsci.2014.06.018
Bowers, J. S., & Pleydell-Pearce, C. W. (2011). Swearing, Euphemisms, and Linguistic
Relativity. PLoS ONE, 6(7), e22341. https://doi.org/10.1371/journal.pone.0022341
Bradley, M. M., & Lang, P. J. (2000). Measuring Emotion: Behavior, Feeling, and
Physiology. In Cognitive Neuroscience of Emotion (p. 35). Oxford University Press.
Brysbaert, M., & New, B. (2009). Moving beyond Kučera and Francis: A critical evaluation
of current word frequency norms and the introduction of a new and improved word
frequency measure for American English. Behavior Research Methods, 41(4), 977–
990. https://doi.org/10.3758/BRM.41.4.977
Buchanan, T. W., Etzel, J. A., Adolphs, R., & Tranel, D. (2006). The influence of autonomic
arousal and semantic relatedness on memory for emotional words. International
Journal of Psychophysiology, 61(1), 26–33.
https://doi.org/10.1016/j.ijpsycho.2005.10.022
Page 24 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
25
Christianson, K., Zhou, P., Palmer, C., & Raizen, A. (2017). Effects of context and individual
differences on the processing of taboo words. Acta Psychologica, 178, 73–86.
https://doi.org/10.1016/j.actpsy.2017.05.012
Citron, F. M. M. (2012). Neural correlates of written emotion word processing: A review of
recent electrophysiological and hemodynamic neuroimaging studies. Brain and
Language, 122(3), 211–226. https://doi.org/10.1016/j.bandl.2011.12.007
Citron, F. M. M., Weekes, B. S., & Ferstl, E. C. (2013). Effects of valence and arousal on
written word recognition: Time course and ERP correlates. Neuroscience Letters, 533,
90–95. https://doi.org/10.1016/j.neulet.2012.10.054
Coulson, S., & Lovett, C. (2010). Comprehension of non-conventional indirect requests: An
event-related brain potential study. Italian Journal of Linguistics, 22(1), 107–124.
Craig, D. H., & Kinney, A. F. (2009). Shakespeare, computers, and the mystery of
authorship. Cambridge University Press.
Dhooge, E., & Hartsuiker, R. J. (2011). How Do Speakers Resist Distraction?: Evidence
From a Taboo Picture-Word Interference Task. Psychological Science, 22(7), 855–
859. https://doi.org/10.1177/0956797611410984
Dien, J. (2009). The neurocognitive basis of reading single words as seen through early
latency ERPs: A model of converging pathways. Biological Psychology, 80(1), 10–
22. https://doi.org/10.1016/j.biopsycho.2008.04.013
Dörre, L., Czypionka, A., Trotzke, A., & Bayer, J. (2018). The processing of German modal
particles and their counterparts. Linguistische Berichte, 255, 313–346.
Eilola, T. M., & Havelka, J. (2011). Behavioural and physiological responses to the
emotional and taboo Stroop tasks in native and non-native speakers of English.
International Journal of Bilingualism, 15(3), 353–369.
https://doi.org/10.1177/1367006910379263
Page 25 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
26
Erdelyi, M. H. (1974). A new look at the new look: Perceptual defense and vigilance.
Psychological Review, 81(1), 1–25. https://doi.org/10.1037/h0035852
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using
G*Power 3.1: Tests for correlation and regression analyses. Behavior Research
Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Fox, E., Russo, R., Bowles, R., & Dutton, K. (2001). Do Threatening Stimuli Draw or Hold
Visual Attention in Subclinical Anxiety? Journal of Experimental Psychology:
General, 130(4), 681–700.
Frazier, L., Dillon, B., & Clifton, C. (2015). A note on interpreting damn expressives:
Transferring the blame. Language and Cognition, 7(02), 291–304.
https://doi.org/10.1017/langcog.2014.31
Frazier, L., Dillon, B., & Clifton, C. (2017). Together They Stand: Interpreting Not-At-Issue
Content. Language and Speech, 61(2), 199–226.
Gisladottir, R. S., Bögels, S., & Levinson, S. C. (2018). Oscillatory Brain Responses Reflect
Anticipation during Comprehension of Speech Acts in Spoken Dialog. Frontiers in
Human Neuroscience, 12. https://doi.org/10.3389/fnhum.2018.00034
Gisladottir, R. S., Chwilla, D. J., & Levinson, S. C. (2015). Conversation Electrified: ERP
Correlates of Speech Act Recognition in Underspecified Utterances. PLOS ONE,
10(3), e0120068. https://doi.org/10.1371/journal.pone.0120068
Grosser, G. S., & Walsh, A. A. (1966). Sex Differences in the Differential Recall of Taboo
and Neutral Words. The Journal of Psychology, 63(2), 219–227.
https://doi.org/10.1080/00223980.1966.10543035
Guillet, R., & Arndt, J. (2009). Taboo words: The effect of emotion on memory for
peripheral information. Memory & Cognition, 37(6), 866–879.
https://doi.org/10.3758/MC.37.6.866
Page 26 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
27
Gutzmann, D. (2013). Expressives and beyond. An introduction to varieties of use-
conditional meaning. Beyond Expressives: Explorations in Use-Conditional Meaning,
1–58.
Gutzmann, D. (2019). The grammar of expressivity. Oxford University Press.
Hadley, C. B., & MacKay, D. G. (2006). Does emotion help or hinder immediate memory?
Arousal versus priority-binding mechanisms. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 32(1), 79–88. https://doi.org/10.1037/0278-
7393.32.1.79
Hansen, S. J., McMahon, K. L., Burt, J. S., & de Zubicaray, G. I. (2017). The locus of taboo
context effects in picture naming. The Quarterly Journal of Experimental Psychology,
70(1), 75–91. https://doi.org/10.1080/17470218.2015.1124895
Herbert, C., Junghofer, M., & Kissler, J. (2008). Event related potentials to emotional
adjectives during reading. Psychophysiology, 45(3), 487–498.
https://doi.org/10.1111/j.1469-8986.2007.00638.x
Hinojosa, J. A., Albert, J., López-Martín, S., & Carretié, L. (2014). Temporospatial analysis
of explicit and implicit processing of negative content during word comprehension.
Brain and Cognition, 87, 109–121. https://doi.org/10.1016/j.bandc.2014.03.008
Hinojosa, J. A., Carretié, L., Valcárcel, M. A., Méndez-Bértolo, C., & Pozo, M. A. (2009).
Electrophysiological differences in the processing of affective information in words
and pictures. Cognitive, Affective, & Behavioral Neuroscience, 9(2), 173–189.
https://doi.org/10.3758/CABN.9.2.173
Hofmann, M. J., Kuchinke, L., Tamm, S., Võ, M. L. H., & Jacobs, A. M. (2009). Affective
processing within 1/10th of a second: High arousal is necessary for early facilitative
processing of negative but not positive words. Cognitive, Affective, & Behavioral
Neuroscience, 9(4), 389–397. https://doi.org/10.3758/9.4.389
Page 27 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
28
Ito, T. A., Larsen, J. T., Smith, N. K., & Cacioppo, J. T. (1998). Negative information weighs
more heavily on the brain: The negativity bias in evaluative categorizations. Journal
of Personality and Social Psychology, 75(4), 887–900. https://doi.org/10.1037//0022-
3514.75.4.887
Jay, T. (1992). Cursing in America: A psycholinguistic study of dirty language in the courts,
in the movies, in the schoolyards, and on the streets. J. Benjamins Pub. Co.
Kanske, P., & Kotz, S. A. (2007). Concreteness in emotional words: ERP evidence from a
hemifield study. Brain Research, 1148, 138–148.
https://doi.org/10.1016/j.brainres.2007.02.044
Kanske, P., Plitschka, J., & Kotz, S. A. (2011). Attentional orienting towards emotion: P2 and
N400 ERP effects. Neuropsychologia, 49(11), 3121–3129.
https://doi.org/10.1016/j.neuropsychologia.2011.07.022
Kim, A., & Lai, V. (2012). Rapid Interactions between Lexical Semantic and Word Form
Analysis during Word Recognition in Context: Evidence from ERPs. Journal of
Cognitive Neuroscience, 24(5), 1104–1112. https://doi.org/10.1162/jocn_a_00148
Kissler, J., Herbert, C., Peyk, P., & Junghofer, M. (2007). Buzzwords: Early cortical
responses to emotional words during reading. Psychological Science, 18(6), 475–480.
Kissler, J., Herbert, C., Winkler, I., & Junghofer, M. (2009). Emotion and attention in visual
word processing—An ERP study. Biological Psychology, 80(1), 75–83.
https://doi.org/10.1016/j.biopsycho.2008.03.004
Klein, F., Schindler, S., Neuner, F., Rosner, R., Renneberg, B., Steil, R., & Iffland, B. (2019).
Processing of affective words in adolescent PTSD—Attentional bias toward social
threat. Psychophysiology, 56(11). https://doi.org/10.1111/psyp.13444
Page 28 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
29
Knickerbocker, H., Johnson, R. L., & Altarriba, J. (2015). Emotion effects during reading:
Influence of an emotion target word on eye movements and processing. Cognition
and Emotion, 29(5), 784–806. https://doi.org/10.1080/02699931.2014.938023
Kousta, S.-T., Vinson, D. P., & Vigliocco, G. (2009). Emotion words, regardless of polarity,
have a processing advantage over neutral words. Cognition, 112(3), 473–481.
https://doi.org/10.1016/j.cognition.2009.06.007
Kuperman, V., Estes, Z., Brysbaert, M., & Warriner, A. B. (2014). Emotion and language:
Valence and arousal affect word recognition. Journal of Experimental Psychology:
General, 143(3), 1065–1081. https://doi.org/10.1037/a0035669
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1990). Emotion, Attention, and the Startle
Reflex. Psychological Review, 97(3), 377–395.
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1997). Motivated attention: Affect, activation
and action. In Attention and orienting: Sensory and motivational processes. Lawrence
Erlbaum Associates.
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1998). Emotion, motivation, and anxiety:
Brain mechanisms and psychophysiology. Biological Psychiatry, 44(12), 1248–1263.
Leech, G. N., Deuchar, M., & Hoogenraad, R. (1982). English grammar for today: A new
introduction. London: Macmillan Press in conjunction with The English Association.
Mackay, D. G., Shafto, M., Taylor, J. K., Marian, D. E., Abrams, L., & Dyer, J. R. (2004).
Relations between emotion, memory, and attention: Evidence from taboo Stroop,
lexical decision, and immediate memory tasks. Memory & Cognition, 32(3), 474–488.
Madan, C. R., Shafer, A. T., Chan, M., & Singhal, A. (2017). Shock and awe: Distinct effects
of taboo words on lexical decision and free recall. The Quarterly Journal of
Experimental Psychology, 70(4), 793–810.
https://doi.org/10.1080/17470218.2016.1167925
Page 29 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
30
Mathewson, K. J., Arnell, K. M., & Mansfield, C. A. (2008). Capturing and holding attention:
The impact of emotional words in rapid serial visual presentation. Memory &
Cognition, 36(1), 182–200. https://doi.org/10.3758/MC.36.1.182
Palazova, M., Mantwill, K., Sommer, W., & Schacht, A. (2011). Are effects of emotion in
single words non-lexical? Evidence from event-related brain potentials.
Neuropsychologia, 49(9), 2766–2775.
https://doi.org/10.1016/j.neuropsychologia.2011.06.005
Potts, C. (2005a). Conventional implicatures, a distinguished class of meanings. In The
Oxford Handbook of Linguistic Handbooks.
Potts, C. (2005b). The logic of conventional implicatures. Oxford University Press.
Potts, C. (2007). The expressive dimension. Theoretical Linguistics, 33(2), 165–198.
Pratto, F., & John, O. P. (1991). Automatic vigilance: The attention-grabbing power of
negative social information. Journal of Personality and Social Psychology, 61(3),
380–391. https://doi.org/10.1037/0022-3514.61.3.380
Rastle, K., Harrington, J., & Coltheart, M. (2002). 358,534 nonwords: The ARC Nonword
Database. The Quarterly Journal of Experimental Psychology Section A, 55(4), 1339–
1362. https://doi.org/10.1080/02724980244000099
Reilly, J., Kelly, A., Zuckerman, B. M., Twigg, P. P., Wells, M., Jobson, K. R., & Flurie, M.
(2020). Building the perfect curse word: A psycholinguistic investigation of the form
and meaning of taboo words. Psychonomic Bulletin & Review, 27(1), 139–148.
https://doi.org/10.3758/s13423-019-01685-8
Robertson, M. M. (2000). Tourette syndrome, associated conditions and the complexities of
treatment. Brain, 123(3), 425–462. https://doi.org/10.1093/brain/123.3.425
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social
Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714
Page 30 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
31
Russell, J. A., & Barrett, L. F. (1999). Core Affect, Prototypical Emotional Episodes, and
Other Things Called Emotion: Dissecting the Elephant. Journal of Personality and
Social Psychology, 76(5), 805–819.
Schacht, A., & Sommer, W. (2009a). Time course and task dependence of emotion effects in
word processing. Cognitive, Affective, & Behavioral Neuroscience, 9(1), 28–43.
https://doi.org/10.3758/CABN.9.1.28
Schacht, A., & Sommer, W. (2009b). Emotions in word and face processing: Early and late
cortical responses. Brain and Cognition, 69(3), 538–550.
https://doi.org/10.1016/j.bandc.2008.11.005
Scott, G. G., Keitel, A., Becirspahic, M., Yao, B., & Sereno, S. C. (2018). The Glasgow
Norms: Ratings of 5,500 words on nine scales. Behavior Research Methods.
https://doi.org/10.3758/s13428-018-1099-3
Scott, G. G., O’Donnell, P. J., & Sereno, S. C. (2014). Emotion words and categories:
Evidence from lexical decision. Cognitive Processing, 15(2), 209–215.
https://doi.org/10.1007/s10339-013-0589-6
Segalowitz, S. J., & Lane, K. C. (2000). Lexical Access of Function versus Content Words.
Brain and Language, 75(3), 376–389. https://doi.org/10.1006/brln.2000.2361
Siegrist, M. (1995). Effects of taboo words on color-naming performance on a Stroop test.
Perceptual and Motor Skills, 81(3 suppl), 1119–1122.
Simons, M., Tonhauser, J., Beaver, D., & Roberts, C. (2010). What projects and why.
Semantics and Linguistic Theory, 20, 309. https://doi.org/10.3765/salt.v20i0.2584
Stephens, R., Atkins, J., & Kingston, A. (2009). Swearing as a response to pain:
NeuroReport, 1. https://doi.org/10.1097/WNR.0b013e32832e64b1
Page 31 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
32
Tanaka-Ishii, K., & Terada, H. (2011). Word familiarity and frequency: Word familiarity and
frequency. Studia Linguistica, 65(1), 96–116. https://doi.org/10.1111/j.1467-
9582.2010.01176.x
Thomas, L., & LaBar, K. (2005). Emotional arousal enhances word repetition priming.
Cognition & Emotion, 19(7), 1027–1047.
https://doi.org/10.1080/02699930500172440
Trauer, S. M., Andersen, S. K., Kotz, S. A., & Müller, M. M. (2012). Capture of lexical but
not visual resources by task-irrelevant emotional words: A combined ERP and steady-
state visual evoked potential study. NeuroImage, 60(1), 130–138.
https://doi.org/10.1016/j.neuroimage.2011.12.016
Trauer, S. M., Kotz, S. A., & Müller, M. M. (2015). Emotional words facilitate lexical but not
early visual processing. BMC Neuroscience, 16(1). https://doi.org/10.1186/s12868-
015-0225-8
Van Lancker, D., & Cummings, J. L. (1999). Expletives: Neurolinguistic and
neurobehavioral perspectives on swearing. Brain Research Reviews, 31(1), 83–104.
Van Petten, C., Kutas, M., Kluender, R., Mitchiner, M., & McIsaac, H. (1991). Fractionating
the Word Repetition Effect with Event-Related Potentials. Journal of Cognitive
Neuroscience, 3(2), 131–150. https://doi.org/10.1162/jocn.1991.3.2.131
Vinson, D., Ponari, M., & Vigliocco, G. (2014). How does emotional content affect lexical
processing? Cognition and Emotion, 28(4), 737–746.
https://doi.org/10.1080/02699931.2013.851068
Wabnitz, P., Martens, U., & Neuner, F. (2012). Cortical reactions to verbal abuse: Event-
related brain potentials reflecting the processing of socially threatening words.
NeuroReport, 23(13), 774–779. https://doi.org/10.1097/WNR.0b013e328356f7a6
Page 32 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
33
Wabnitz, P., Martens, U., & Neuner, F. (2016). Written threat: Electrophysiological evidence
for an attention bias to affective words in social anxiety disorder. Cognition and
Emotion, 30(3), 516–538. https://doi.org/10.1080/02699931.2015.1019837
Wang, L., & Bastiaansen, M. (2014). Oscillatory brain dynamics associated with the
automatic processing of emotion in words. Brain and Language, 137, 120–129.
https://doi.org/10.1016/j.bandl.2014.07.011
Wang, W., Chen, L., Thirunarayan, K., & Sheth, A. P. (2014). Cursing in English on twitter.
Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work
& Social Computing - CSCW ’14, 415–425. https://doi.org/10.1145/2531602.2531734
Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and
dominance for 13,915 English lemmas. Behavior Research Methods, 45(4), 1191–
1207. https://doi.org/10.3758/s13428-012-0314-x
Wentura, D., Rothermund, K., & Bak, P. (2000). Automatic vigilance: The attention-grabbing
power of approach- and avoidance-related social information. Journal of Personality
and Social Psychology, 78(6), 1024–1037. https://doi.org/10.1037/0022-
3514.78.6.1024
Yao, Z., Yu, D., Wang, L., Zhu, X., Guo, J., & Wang, Z. (2016). Effects of valence and
arousal on emotional word processing are modulated by concreteness: Behavioral and
ERP evidence from a lexical decision task. International Journal of
Psychophysiology, 110, 231–242. https://doi.org/10.1016/j.ijpsycho.2016.07.499
Zhang, D., Nie, A., Wang, Z., & Li, M. (2019). Influence of lag length on repetition priming
in emotional stimuli: ERP evidence. Journal of Clinical Laboratory Analysis, 33(1),
e22639. https://doi.org/10.1002/jcla.22639
Page 33 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
34
Zhang, M., Ge, Y., Kang, C., Guo, T., & Peng, D. (2018). ERP evidence for the contribution
of meaning complexity underlying emotional word processing. Journal of
Neurolinguistics, 45, 110–118. https://doi.org/10.1016/j.jneuroling.2016.07.002
Zhang, M., & Guo, T. (2014). Event-related brain potentials differentiate three types of
emotional words categorized from linguistic perspective. Journal of Neurolinguistics,
31, 17–27. https://doi.org/10.1016/j.jneuroling.2014.06.001
Page 34 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
35
Tables
Table 1. Mean ratings/values with standard deviations in parentheses of word properties for each condition.
Word Properties
Swear Negative Open Class Neutral
Closed Class
Valence 2.46 (.92) 2.04 (.65) 5.9 (1.01)
4.95 (.90)
Arousal 5.57 (.94) 5.44 (.78) 3.81 (.81)
3.43 (.69)
Tabooness 6.82 (.84) 5.03 (.90) 2.95 (.37)
3.21 (.31)
Log Subtlex Word Freq
3.31 (.68) 3.58 (.4) 3.25 (.52)
3.08 (.57)
Letter Length
4.85 (1.32)
4.85 (.80) 5 (.85) 4.93 (.98)
NumMorphemes
1.27 (.44) 1.22 (.42) 1.11 (.31)
1.14 (.34)
Num Phonemes
3.81(1.04) 4.11 (.87) 4.04 (.78)
3.83 (1.15)
Num Syllables
1.27 (.44) 1.52 (.5) 1.5 (.5) 1.48 (.5)
Page 35 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
36
Figures
Page 36 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
37
Page 37 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
38
Page 38 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
39
Page 39 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
40
Page 40 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
41
Figure 1. Jittered sequence of one trial of the lexical decision task. Duration of each target depends on letter length.
Figure 2. Proportion of correct responses for lexical decision task experimental conditions.
Figure 3. Mean reaction time for experimental conditions.
Figure 4. Grand averaged ERP waveforms for experimental conditions at the frontal, central, and posterior locations.
Figure 5. Scalp topographies at for earlier (250-550) and LPC (550-750) time windows. All voltage differences are significant unless otherwise noted.
Figure 6. ERP waveforms and scalp topographies for pseudoword and open-class neutral words.
Page 41 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
Page 42 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
Figure 1. Jittered sequence of one trial of the lexical decision task. Duration of each target depends on letter length.
338x190mm (96 x 96 DPI)
Page 43 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
Figure 2. Proportion of correct responses for lexical decision task experimental conditions.
338x190mm (96 x 96 DPI)
Page 44 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
Figure 3. Mean reaction time for experimental conditions.
338x190mm (96 x 96 DPI)
Page 45 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
Figure 4. Grand averaged ERP waveforms for experimental conditions at the frontal, central, and posterior locations.
338x190mm (96 x 96 DPI)
Page 46 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
Figure 5. Scalp topographies at for earlier (250-550) and LPC (550-750) time windows. All voltage differences are significant unless otherwise noted.
338x190mm (96 x 96 DPI)
Page 47 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
Figure 5. Scalp topographies at for earlier (250-550) and LPC (550-750) time windows. All voltage differences are significant unless otherwise noted.
338x190mm (96 x 96 DPI)
Page 48 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
For Peer Review Only
Figure 6. ERP waveforms and scalp topographies for pseudoword and open-class neutral words.
338x190mm (96 x 96 DPI)
Page 49 of 49
URL: http://mc.manuscriptcentral.com/pcem Email: [email protected]
Cognition and Emotion
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960