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Abstract

Alexithymia concerns a difficulty identifying and communicating one's own emotions, and a tendency towards externally-oriented thinking. Recent work argues that such alexithymic traits are due to altered arousal response and poor subjective awareness of "objective" arousal responses. Although there are individual differences within the general population in identifying and describing emotions, extant research has focused on highly alexithymic individuals. Here we investigated whether mean arousal and concordance between subjective and objective arousal underpin individual differences in alexithymic traits in a general population sample. Participants rated subjective arousal responses to 60 images from the International Affective Picture System whilst their skin conductance was recorded. The Autism Quotient was employed to control for autistic traits in the general population. Analysis using linear models demonstrated that mean arousal significantly predicted Toronto Alexithymia Scale scores above and beyond autistic traits, but concordance scores did not. This indicates that, whilst objective arousal is a useful predictor in populations that are both above and below the cutoff values for alexithymia, concordance scores between objective and subjective arousal do not predict variation in alexithymic traits in the general population.
Journal: Psychological Reports
Running head: Skin conductance and alexithymic traits
Word count: 4237 words
Submission date: 14/12/2020; Revision date: 25/02/2021
Skin conductance as an index of alexithymic traits in the general population
Lydia J Hickman1*, Connor T Keating1, Ambra Ferrari2, Jennifer L Cook1
1School of Psychology, University of Birmingham, UK
2Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The
Netherlands
*Corresponding author: LXH856@student.bham.ac.uk
School of Psychology,
University of Birmingham,
Edgbaston, Birmingham,
United Kingdom, B15 2TT
Acknowledgements
LH was supported by a BBSRC PhD studentship provided by the BBSRC Midlands
Integrative Biosciences Training Partnership [grant reference: BB/M01116X/1]. CK was
supported by an MRC PhD studentship [grant reference: MR/R015813/1]. AF was supported
by ERC-2012-StG Grant Agreement No. 20111109 (Multsens; Uta Noppeney PI). JC was
supported by the European Union’s Horizon 2020 Research and Innovation Programme
under ERC-2017-StG Grant Agreement No. 757583 (Brain2Bee; Jennifer Cook PI). The
authors would like to thank Dr Sebastian Gaigg for help with setting up the experimental
task.
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Abstract
Alexithymia concerns a difficulty identifying and communicating one’s own
emotions, and a tendency towards externally-oriented thinking. Recent work argues that such
alexithymic traits are due to altered arousal response and poor subjective awareness of
“objective” arousal responses. Although there are individual differences within the general
population in identifying and describing emotions, extant research has focused on highly
alexithymic individuals. Here we investigated whether mean arousal and concordance
between subjective and objective arousal underpin individual differences in alexithymic traits
in a general population sample. Participants rated subjective arousal responses to 60 images
from the International Affective Picture System whilst their skin conductance was recorded.
The Autism Quotient was employed to control for autistic traits in the general population.
Analysis using linear models demonstrated that mean arousal significantly predicted Toronto
Alexithymia Scale scores above and beyond autistic traits, but concordance scores did not.
This indicates that, whilst objective arousal is a useful predictor in populations that are both
above and below the cut-off values for alexithymia, concordance scores between objective
and subjective arousal do not predict variation in alexithymic traits in the general population.
Key words: alexithymia; physiological arousal; skin conductance, subjective arousal;
objective arousal
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Introduction
Alexithymia is defined as a difficulty in identifying and describing one’s own
emotions, and a tendency towards externally-oriented thinking (Bagby et al., 1994; Nemiah et
al., 1976). It is typically measured by self-report scales; a commonly used measure is the
Toronto Alexithymia Scale (TAS; Bagby et al., 1994), a questionnaire consisting of 20
statements such as “I am often confused about what emotion I am feeling” and “I find it hard
to describe how I feel about people”. With relationships emerging between alexithymia and
socio-cognitive processes (e.g., emotion recognition and production; empathy; Bird et al.,
2010; Brewer et al., 2015; Cook et al., 2013; Trevisan et al., 2016), and mental health more
broadly (Norman & Borrill, 2015; Ogrodniczuk et al., 2011), alexithymia is of increasing
importance for higher cognition and health respectively. A key issue, however, concerns the
underlying psychophysiological mechanisms that give rise to alexithymic difficulties in
identifying and describing emotions. Elucidating the underlying psychophysiological
mechanisms is not only important for gaining insight into the pathways that contribute to
such challenges, but also may result in the development of objective measures of alexithymia
which draw upon physiological markers of arousal. Indeed, a key problem for this field is that
the measurement of alexithymia relies almost exclusively on self-report questionnaires that
require participants to reflect on the difficulties they have in reflecting on their own emotions
(Vorst & Bermond, 2001). Consequently, an objective measure of alexithymia is much sought
after.
Extant studies of the psychophysiological mechanisms underlying alexithymic traits
have drawn an important distinction between objective and subjective arousal (e.g., Gaigg et
al., 2017). The former concerns a bodily reaction to a contextual cue, whereas the latter
concerns a subjective judgement about one’s own arousal level. Whereas subjective arousal is
assessed by asking participants to reflect on their physiological state, objective arousal can be
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assessed using a variety of methods including heart rate recordings (Eastabrook et al., 2013;
Papciak et al., 1985; Pollatos et al., 2011; Stone & Nielson, 2001), blood pressure (Papciak et
al., 1985) and electromyography (Papciak et al., 1985). The most common method for
assessing objective arousal is to record skin conductance in response to arousing stimuli.
Skin becomes a better conductor of electricity when an individual is physiologically aroused
thus it is typically observed that when participants are exposed to high, compared to low,
arousal stimuli their skin conductance response reaches a higher maximum peak (e.g., Gaigg
et al., 2017), and/or maintains a higher average level of activity throughout the stimulus
presentation interval (e.g., Pollatos et al., 2011). By drawing this distinction between
objective and subjective arousal, the literature has made progress in understanding whether
the difficulties experienced by individuals with clinically significant levels of alexithymia are
due to broader impairments in mental state reasoning (Moriguchi et al., 2006) and/or atypical
physiological arousal.
Studies of objective and subjective arousal have highlighted at least two mechanisms
thought to contribute to emotion identification and communication problems in populations
with clinically significant levels of alexithymia: 1) altered levels of objective emotional
arousal, and 2) reduced awareness of otherwise preserved emotional arousal (see Vorst &
Bermond, 2001 for further discussion). A small but burgeoning literature has found evidence
consistent with both the former mechanism (note that there is evidence to suggest both hyper-
arousal (Eastabrook et al., 2013; Papciak et al., 1985; Stone & Nielson, 2001) and hypo-
arousal (e.g., Gaigg et al., 2017; Pollatos et al., 2011; Roedema & Simons, 1999)) and the
latter mechanism (Eastabrook et al., 2013; Gaigg et al., 2017; Papciak et al., 1985; Pollatos et
al., 2011; Stone & Nielson, 2001). A study by Gaigg et al. (2017) is particularly notable
because the design enabled the authors to calculate individual participant scores
corresponding to the two aforementioned mechanisms: objective arousal responses and the
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concordance between subjective and objective arousal. Thus, this facilitated investigation of
whether self-reported difficulties with emotion identification and description are predicted by
either mechanism or a combination of the two mechanisms. Specifically, Gaigg and
colleagues asked participants to rate their subjective arousal responses to images from the
International Affective Picture System (IAPS; a set of colour photographs with normative
emotion ratings; Lang et al., 2008). Concurrently, objective arousal responses to each image
were measured in terms of skin conductance responses. Gaigg and colleagues observed that
self-reported alexithymic traits were predicted by altered levels of objective arousal and
independently with a reduced correlation between subjective and objective arousal.
It is important to note that the majority of studies investigating these two mechanisms
in alexithymia have adopted the approach of assessing group differences between alexithymic
and non-alexithymic individuals (e.g., Eastabrook et al., 2013; Papciak et al., 1985; Pollatos
et al., 2011; Roedema & Simons, 1999; Stone & Nielson, 2001). This relies on a categorical
view of alexithymia and assumes a cut-off point for alexithymic traits. Conversely, the
method adopted by Gaigg et al. (2017) of creating individual objective arousal and
concordance scores allows for a continuous analysis approach. Here, relationships between
levels of alexithymic traits and the extent of psychophysiological differences can be
observed. This is of importance as such individual differences in identifying one’s own
emotions have been found to predict important functions such as sleep quality (Murphy et al.,
2018) and mental health (Norman & Borrill, 2015; Ogrodniczuk et al., 2011).
To date it is unclear whether the mechanisms that underpin emotion identification and
communication problems in alexithymia, also underpin variation in the general population. In
the sample studied by Gaigg et al. (2017), only 42% could be categorised as non-alexithymic
(according to Deborde et al. (2008) suggested cut-offs). Furthermore, 50% of Gaigg and
colleagues’ sample also had a co-morbid autism diagnosis. Co-occurring autism was a
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relevant feature of Gaigg et al’s design. Building on literatures that document atypically high
rates of alexthymia in autistic populations1 (50% of autistic individuals are alexithymic
compared to 5% of the general population (Kinnaird et al., 2019)), and which demonstrate
that alexithymia can account for atypicalities in emotional processing and empathy in autistic
individuals (Bird et al., 2011; Bird et al., 2010; Cook et al., 2013), Gaigg and colleagues
aimed to test the prediction that the co-occurrence of alexithymia in an autistic sample is
associated with an impairment in the subjective awareness of otherwise intact physiological
arousal responses (as reported by Ben Shalom et al., 2006). Gaigg et al.’s results, however,
contradicted their hypothesis: in their sample, alexithymia was associated with atypical
objective arousal and impaired subjective awareness. Indeed, these results align with a body
of literature documenting atypical objective arousal responses in autism (e.g., Dijkhuis et al.,
2019; Hirstein et al., 2001; Hubert et al., 2009; Mathersul et al., 2013). Consequently, whilst
Gaigg et al. addressed an important question that has advanced our understanding of the
interplay between autism and alexithymia, the sample they recruited is potentially biased
towards individuals who are more likely than other members of the general population to
exhibit atypicalities in arousal responses. Thus, to gain an unbiased understanding of whether
the mechanisms that underpin emotion identification and communication problems in
alexithymia also underpin variation in the general population, it is important to recruit a non-
clinical general population sample. Furthermore, since there is evidence that autistic
symptomatology is correlated with both alexithymic traits (Hobson et al., 2020) and atypical
physiological arousal (e.g., Dijkhuis et al., 2019; Hirstein et al., 2001; Hubert et al., 2009;
Mathersul et al., 2013), there is a risk that, if autistic traits are not controlled for, relationships
observed between objective arousal and alexithymic traits are mediated by autistic traits.
Consequently, to understand these relationships in the general population, it is not only
1 Identity-first language has been used instead of person-first language in accordance with Kenny et al., (2016).
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important to recruit a non-clinical general population sample, but autistic traits must also be
controlled for.
The current study investigated whether individual differences in emotion
identification and description in the general population are underpinned by variation in 1)
levels of objective emotional arousal and/or 2) subjective awareness of objective arousal
(“concordance scores”). To do so we employed the procedure developed by Gaigg and
colleagues which enabled us to assess individual differences in subjective and objective
arousal. Participants, who compromised a random sample of the general population,
completed an arousal estimation task based on Gaigg et al. (2017). Self-reported difficulties
identifying one’s own emotions were indexed with the Toronto Alexithymia Scale (TAS;
Bagby et al., 1994). Participants completed the Autism Quotient (AQ; Baron-Cohen et al.,
2006) to control for levels of autistic traits. Objective arousal was calculated as mean skin
conductance across all trials and concordance scores as the correlation between objective
arousal (skin conductance) and subjective arousal (self-reported arousal). Linear models were
employed to assess the extent to which alexithymic traits could be predicted by a) objective
arousal and b) concordance scores, whilst controlling for autistic traits. We predicted that, as
one would expect for a highly alexithymic sample, emotion identification difficulties would
be associated with altered objective arousal and a reduced correlation between subjective and
objective arousal.
Method
Participants
An a priori power analysis calculated with G*power (Erdfelder et al., 1996) using
data from Gaigg et al. (2017) (effect size = 0.46, alpha level = .05) determined that a
minimum of 32 participants were required to achieve a power level of 0.80. This power level,
convention in the field of psychology, was based on recommendations that the probability of
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Type 2 errors – beta – should not exceed four times the probability of Type 1 errors – alpha
(Cohen & Wolman, 1965). Thus, given the conventional alpha level of 0.05 and a consequent
recommended beta level of 0.2, a 0.8 power level was used (power = 1 – beta). We recruited
43 healthy participants via the University of Birmingham Research Participation Scheme. A
total of 35 participants (27 female, 7 male, 1 undisclosed) were included in the analysis due
to 8 participants not providing complete data for the arousal estimation task and
questionnaires. The sample had a mean age of 21 years (standard deviation [SD] = 2.57). All
participants gave fully informed consent and received either course credit or money (£8 per
hour). The experimental procedure was approved by the local Research Ethics Committee
(ERN 16-0281AP5).
Arousal Estimation Task
Stimuli. A total of 60 images from the International Affective Picture System (IAPS;
Lang et al., 2008) were selected for use in the arousal estimation task, each with pre-defined
arousal and valence ratings generated from ratings by 100 individuals made on 9-point rating
scales (Lang et al., 2008). The images covered a wide range of valence (mean[SD] =
4.93[2.23]) and arousal ratings (mean[SD] = 4.88[1.81]), aiming to elicit variation in
participants’ reactions during the task. In order to achieve systematic variation in the images
used, 20 images defined as positive (10 high arousal and 10 moderate arousal), 20 as negative
(10 high arousal and 10 moderate arousal) and 20 as neutral (all low arousal) were selected.
An additional 6 images representative of the images used in the experimental trials were
selected for use in the practice trials. See Appendices 1 and 2 for IAPS numbers, arousal
ratings and valence ratings for the images used in the practice and experimental trials
respectively.
Procedure. Following a practice of 6 trials, participants viewed 20 positive, 20
negative and 20 neutral IAPS images. During each trial, the stimulus was presented for 5
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seconds with a preceding fixation dot lasting for a duration of 2.5 seconds. Skin conductance
was recorded concurrently during the 5 second stimulus presentation window using a Biopac
MP36R, with disposable isotonic gel electrodes attached to the distal phalanges of
participants’ index and middle fingers on their non-dominant hand. After viewing each image,
participants were asked to rate how positive or negative it was on a sliding scale ranging from
‘very negative’, to ‘neutral’, to ‘very positive’ (valence). They were then asked to rate their
arousal level in response to the image on a sliding scale ranging from ‘calm’ to ‘moderate’ to
‘high arousal response’ (arousal). For each question, participants had 7.5 seconds to make a
response. The scale remained on the screen for the full 7.5 seconds irrespective of the
response time. The structure of each trial is displayed in Figure 1.
Figure 1. The structure of each trial within the arousal estimation task. Each trial involved a
fixation, stimulus presentation, valence judgement and arousal judgement. The image used in
the diagram is a placeholder and not an IAPS image presented to participants in the
experiment. ‘S’ denotes time in seconds of each period of the trial.
Data Processing. Responses to the valence and arousal questions were converted to
ratings out of 100 for each trial. Skin conductance (SC) data were analysed using
Acqknowledge Software. Various skin conductance indices have been used in the literature to
9
index arousal, generally focusing on the peak or magnitude of the responses during stimulus
presentation (e.g., Gaigg et al., 2017), or the average levels of activity during the interval in
which a stimulus is presented (e.g., Pollatos et al., 2011). In the current study, we created
indices of both peak (‘max’) and average (‘mean’) skin conductance levels in response to
stimulus presentation. Following the smoothing of the data using a 2Hz low pass filter to
remove noise, two SC values were calculated for each trial: the mean SC level within the 5
second stimulus presentation window (SC-mean) and maximum SC value observed within
the 5 second stimulus presentation window (SC-max). The former draws upon recent
suggestions that average skin conductance levels may be useful in distinguishing responses to
stimuli (e.g., Sugimine et al., 2020), and the latter reflects the peak value recorded during
stimulus presentation.
Questionnaires
Participants completed the 20-item TAS to index alexithymic traits, with individuals
categorised as non-alexithymic if their score fell below 51 (Bagby et al., 1994). The TAS has
good internal consistency and test-retest reliability (α ≥ 0.7; r ≥ 0.7; Bagby et al., 1994;
Taylor et al., 2003) and is the most commonly used measure of alexithymic traits. The 50-
item AQ (Baron-Cohen et al., 2006), a questionnaire with strong psychometric properties
including internal consistency and test-retest reliability (α ≥ 0.7; r ≥ 0.8; Stevenson & Hart,
2017), was employed to control for autistic traits in the general population. The order of the
arousal estimation task and questionnaires was counterbalanced.
Score Calculations
Following the work of Gaigg and colleagues, our primary measures were average SC
– as an index of objective arousal – and concordance score – as an index of the correlation
between subjective and objective arousal. Average SC (microsiemens) was calculated as the
mean of the SC data across all trials. Concordance scores were calculated as the Spearman’s
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correlation coefficient between participants’ self-reported arousal rating on each trial and SC
during the 5 second stimulus presentation windows. The two scores were calculated using
both SC-mean and SC-max data.
Considering evidence that stimulus valence can modulate arousal responses (Bradley
& Lang, 2000), concordance between arousal ratings and objective arousal responses may be
affected by the valence of the stimulus. Thus, we sought to create an index of concordance
which controlled for the impact of the valence of the stimuli in the relationship between
subjective and objective arousal. As a result, partial concordance scores were calculated as
the Spearman’s partial correlation coefficient between participants’ self-reported arousal
ratings and objectively measured SC on each trial controlling for participants’ self-reported
valence ratings on each trial. Again, separate indices were calculated using the SC-mean and
SC-max data.
TAS scores were calculated as a sum of participants’ responses, using reverse scoring
where appropriate, with a maximum possible score of 100 reflecting the highest level of
alexithymic traits. The AQ was scored as the sum of participants’ responses, using reverse
scoring where appropriate, with a maximum score of 50 reflecting the highest level of autistic
traits.
Analyses
To test our hypothesis that alexithymic traits would be associated with altered
objective arousal and a reduced correlation between subjective and objective arousal, we
employed two linear models with AQ score, average SC and concordance score as predictors
and TAS score as the dependent variable. All variables were z-scored. Model 1 used SC-mean
data, whilst Model 2 used SC-max data. Subsequently, linear models were implemented
which instead used the partial concordance score (which controls for the effect of stimulus
valence) in place of the standard concordance score. Again, one model used SC-mean data
11
(model 3) and another used SC-max data (model 4). Final models were then conducted,
predicting TAS score with the significant variables identified in models 1-4, with separate
models for SC-mean and SC-max data.
Results
Descriptive statistics for the 35 participants who completed all tasks were as follows:
TAS score (mean[SD] = 42.63[10.20]; 77% of the sample were categorised as non-
alexithymic), AQ score (mean[SD] = 14.74[7.04]), average SC-mean (mean[SD] =
5.03[0.30]), average SC-max (mean[SD] = 5.20[0.41]), SC-mean concordance score
(mean[SD] = 0.05[0.16]), SC-max concordance score (mean[SD] = 0.06[0.17]), SC-mean
partial concordance score (mean[SD] = 0.04[0.14]), SC-max partial concordance score
(mean[SD] = 0.05[0.16]).
To investigate whether variation in emotion identification and description was
predicted by 1) objective arousal and/or, 2) subjective awareness of objective arousal, linear
models predicting TAS score with average SC and concordance score were employed, using
either SC-mean data (model 1) or SC-max data (model 2). AQ score was included in the
model to control for variation associated with autistic traits. For both models, AQ score and
average SC were significant positive predictors of TAS score, whereas concordance score
was not a significant predictor (Table 1). Thus, increased alexithymic traits can be predicted
by increased autistic traits and increased objective arousal. Model 1 had an adjusted R2 value
of 0.39, meaning that 39% of the variance in TAS scores was accounted for. The addition of
average SC-mean – our index of objective arousal – in model 1 resulted in an R2 change of
0.19 (significant model improvement: F(1 , 31) = 10.53, p = .003) meaning that an additional
19% of the variance in TAS was accounted for by including SC-mean in the model. Model 2
had an adjusted R2 value of 0.32, meaning that 32% of the variance in TAS scores was
accounted for, and an R2 change of 0.13 (13% variance accounted for) resulting from the
12
addition of average SC-max (significant model improvement: F(1 , 31) = 6.61, p = .015).
Models 1 and 2 therefore demonstrate that, in a general population sample wherein autistic
traits are controlled for, objective arousal is a significant positive predictor of variation in
emotion identification and description as measured by the TAS.
To probe whether a relationship between alexithymic traits and subjective awareness
of objective arousal emerges when controlling for effects on arousal of the valence of the
stimuli, two linear models were employed whereby partial concordance score was used in
place of the standard concordance score. The two models used SC-mean data (model 3) and
SC-max data (model 4) respectively. Again, AQ and average SC were significant positive
predictors of TAS score in both models, and partial concordance score did not significantly
predict TAS score (Table 1). Model 3 had an adjusted R2 value of 0.39; thus, the model
accounted for 39% of the variance in TAS scores. The addition of average SC-mean in the
model resulted in an R2 change of 0.19 (19% variance accounted for; significant model
improvement: F(1 , 31) = 10.80, p = .003). Model 4 had an adjusted R2 value of 0.33,
meaning that 33% of the variance in TAS scores was accounted for, and an R2 change of 0.13
(13% variance accounted for) resulting from the addition of average SC-max (significant
model improvement: F(1 , 31) = 6.80, p = .014). These models demonstrate that, even after
controlling for the valence of the stimuli, concordance between subjective and objective
arousal was not associated with alexithymic traits; objective arousal remained a significant
predictor.
Two final linear models were conducted wherein predictors that were non-significant
in models 1-4 were dropped; this resulted in two models predicting TAS from AQ score and
SC-mean (model 5), and AQ score and SC-max (model 6), respectively. These models
enabled us to quantify the amount of variance in TAS explained by objective arousal when
AQ is controlled for. All variables were significant positive predictors (Table 1). Models 5
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and 6 accounted for 37% and 32% of the variance in TAS scores respectively (model 5: R2 =
0.37; model 6: R2 = 0.32). The addition of average SC-mean in model 5 explained 18% of
variance in TAS scores (R2 change = 0.18; F(1 , 32) = 9.90, p = .004), and the addition of
average SC-max in model 6 explained 14% of variance (R2 change = 0.14; F(1 , 32) = 6.924,
p = .013). Prior to adding either average SC-mean or average SC-max, AQ accounted for
20% of variance in TAS scores (R2 = 0.20). These analyses demonstrate that AQ scores and
average SC are significant positive predictors of TAS scores when concordance scores are not
included in the model, and that average SC explains variance above and beyond autistic traits.
Table 1
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The results of six linear models in which TAS score was predicted by AQ score, average SC,
and concordance score (models 1 and 2), AQ score, average SC, and partial concordance
score (models 3 and 4), or AQ score and average SC (models 5 and 6). SC-mean data were
used for models 1,3 and 5, and SC-max data were used for models 2, 4 and 6.
Mode
l
Variable Estimate Standard error t value p value
1 AQ score 0.62 0.14 4.40 <.001
Average SC-mean 0.45 0.14 3.25 .003
SC-mean concordance score 0.19 0.14 1.41 .170
2 AQ score 0.60 0.15 4.06 <.001
Average SC-max 0.38 0.15 2.57 .015
SC-max concordance score 0.15 0.14 1.05 .300
3 AQ score 0.62 0.14 4.42 <.001
Average SC-mean 0.45 0.14 3.29 .003
SC-mean partial concordance score 0.21 0.13 1.53 .136
4 AQ score 0.60 0.15 4.08 <.001
Average SC-max 0.38 0.15 2.61 .014
SC-max partial concordance score 0.16 0.14 1.13 .269
5 AQ score 0.59 0.14 4.16 <.001
Average SC-mean 0.44 0.14 3.15 .004
6 AQ score 0.58 0.15 3.94 <.001
Average SC-max 0.39 0.15 2.63 .013
Discussion
This study investigated whether alexithymic traits in the general population (as
indexed by the TAS) are associated with altered objective arousal and a reduced correlation
between subjective and objective arousal. Results from four linear models demonstrated that
objective arousal (as indexed by average SC) was a significant positive predictor of TAS
score. That is, individuals with a higher TAS score had greater levels of physiological arousal
in response to the IAPS stimuli. This result is consistent with findings associating alexithymia
with hyper-arousal (Eastabrook et al., 2013; Papciak et al., 1985; Stone & Nielson, 2001) and
with predictions from the stress-alexithymia hypothesis (Martin & Pihl, 1985), which
15
proposes that individuals with alexithymia “lack the affective awareness which would permit
identification of a particular situation as stressful” and consequently experience stressful
events more frequently and for longer periods of time. Here, we demonstrate that this
particular mechanism (objective arousal) is likely to contribute to alexithymic traits in the
general population, something that previous studies taking a group differences approach or
recruiting highly alexithymic samples have not been able to show. In addition, average SC
significantly predicted TAS scores regardless of whether SC-mean or SC-max was used,
accounting for an additional 19% and 13%2 of the variance in TAS scores respectively; this
demonstrates the utility of both indices in predicting alexithymic traits. These results pave the
way for the development of objective measures of alexithymia which draw upon
physiological markers of arousal in place of self-report. However, given that our best estimate
is that 19% of the variance in alexithymic traits can be accounted for by skin conductance,
future work may seek to combine multiple objective measures in order to more accurately
predict alexithymic traits.
It should be noted that objective arousal significantly improved the prediction of TAS
scores above and beyond autistic traits as measured by the AQ. This was demonstrated
through the inclusion of AQ scores within the six linear models predicting TAS scores. More
specifically, AQ accounted for 20% of the variance in alexithymic traits in our population and
adding objective arousal indices to the model enabled us to account for an additional 18%
and 14% of variance for SC-mean and SC-max respectively (see models 5 and 6). The
importance of this aspect of the analyses is highlighted by previous studies which have shown
correlations between alexithymic traits and autistic symptomatology (Hobson et al., 2020),
and atypical objective arousal in autism (e.g., Dijkhuis et al., 2019; Hirstein et al., 2001;
Hubert et al., 2009; Mathersul et al., 2013). Thus, there is a risk that correlations observed
between objective arousal and TAS are mediated by autistic traits. Including AQ in our linear
2 over and above the contributions of AQ and concordance scores
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model enabled us to observe a significant relationship between average SC and TAS scores
after removing variance associated with autistic traits. These results further strengthen the
conclusion that average SC is a significant predictor of TAS scores in the general population.
In contrast to our prediction, TAS scores were not predicted by concordance scores
between objective and subjective arousal, nor partial concordance scores between objective
and subjective arousal which controlled for valence. Though reduced subjective awareness of
objective arousal is a putative mechanism underpinning emotion identification and
communication issues in the alexithymic population (Gaigg et al., 2017; Vorst & Bermond,
2001), we failed to find any evidence that this mechanism underpins variation in such traits in
the general population. Thus, concordance scores do not appear to be a useful predictor of
alexithymic traits in the general population. This result raises the possibility that concordance
scores should not be viewed as a continuous marker but rather as a binary index separating
alexithymic and non-alexithymic populations. Indeed, previous studies employing a group
differences approach to concordance have presented evidence for intact concordance in the
non-alexithymic group (Eastabrook et al., 2013; Papciak et al., 1985; Pollatos et al., 2011;
Stone & Nielson, 2001). This leads us to consider that, whilst variation in alexithymic traits
in the general population is correlated with objective arousal responses, the inability to reflect
upon one’s own emotions may be viewed as a binary metric which highlights clinically
significant levels of alexithymia.
Taken together, our findings raise doubts as to whether all psychophysiological
mechanisms underpinning emotion identification and communication problems in
alexithymic individuals also underpin such issues in the general population. Whilst objective
arousal appears to be a useful predictor in populations that are both above and below the cut-
off values for alexithymia, concordance scores between objective and subjective arousal do
not predict variation in emotion identification in the general population. Such findings are
17
important given the growing emphasis on individual differences in alexithymic traits and
their associations with mental health (e.g., Murphy et al., 2018; Norman & Borrill, 2015;
Ogrodniczuk et al., 2011).
Disclosure of Interest
The authors report no conflict of interest.
Data Availability
The data and analysis scripts that support the findings of this study are openly available on
the Open Science Framework at https://osf.io/9huq8/.
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Appendices
[Appendix 1] IAPS images used in the practice trials of the arousal estimation task
Valence
Group
Arousal
Group IAPS
number
Valence
mean
Valence
standard
deviatio
n
Arousal
mean
Arousal
standard
deviatio
n
Negativ
e
High
3130 1.58 1.24 6.97 2.07
Negativ
e
Moderate
9181 2.26 1.85 5.39 2.41
Neutral Low 7010 4.94 1.07 1.76 1.48
Neutral Low 7161 4.98 1.02 2.98 1.99
23
Positive High 4659 6.87 1.99 6.93 2.07
Positive Moderate 2398 7.48 1.32 4.74 2.11
[Appendix 2] IAPS images used in the experimental trials of the arousal estimation task
Valence
Group
Arousal
Group IAPS
number
Valence
mean
Valence
standard
deviatio
n
Arousal
mean
Arousal
standard
deviatio
n
Negativ
e
High 9635.1 1.9 1.31 6.54 2.27
3102 1.4 1.14 6.58 2.69
9413 1.76 1.08 6.81 2.09
3400 2.35 1.9 6.91 2.22
6260 2.44 1.54 6.93 1.93
6550 2.73 2.38 7.09 1.98
3170 1.46 1.01 7.21 1.99
3080 1.48 0.95 7.22 1.97
3010 1.79 1.28 7.26 1.86
3000 1.59 1.35 7.34 2.27
Moderate 2205 1.95 1.58 4.53 2.23
2301 2.78 1.38 4.57 1.96
9561 2.68 1.92 4.79 2.29
9830 2.54 1.75 4.86 2.63
2141 2.44 1.64 5 2.03
2799 2.42 1.41 5.02 1.99
2053 2.47 1.87 5.25 2.46
2710 2.52 1.69 5.46 2.29
3185 2.81 1.52 5.48 2.18
9043 2.52 1.42 5.5 2.41
Neutral Low 7025 4.63 1.17 2.71 2.2
7150 4.72 1 2.61 1.76
7217 4.82 0.99 2.43 1.64
2393 4.87 1.06 2.93 1.88
7175 4.87 1 1.72 1.26
2840 4.91 1.52 2.43 1.82
7059 4.93 0.81 2.73 1.88
7235 4.96 1.18 2.83 2
24
7041 4.99 1.12 2.6 1.78
7004 5.04 0.6 2 1.66
7179 5.06 1.05 2.88 1.97
2038 5.09 1.35 2.94 1.93
7233 5.09 1.46 2.77 1.92
7090 5.19 1.46 2.61 2.03
5740 5.21 1.38 2.59 1.99
2850 5.22 1.39 3 1.94
7100 5.24 1.2 2.89 1.7
7026 5.38 1.26 2.63 1.93
5731 5.39 1.58 2.74 1.95
7140 5.5 1.42 2.92 2.38
Positive High 4660 7.4 1.36 6.58 1.88
8180 7.12 1.88 6.59 2.12
4698 6.5 1.67 6.72 1.72
8370 7.77 1.29 6.73 2.24
8186 7.01 1.57 6.84 2.01
4668 6.67 1.69 7.13 1.62
4220 8.02 1.93 7.17 2.69
8185 7.57 1.52 7.27 2.08
8492 7.21 2.26 7.31 1.64
8030 7.33 1.76 7.35 2.02
Moderate 2091 7.68 1.43 4.51 2.28
2070 8.17 1.46 4.51 2.74
1440 8.19 1.53 4.61 2.54
2550 7.77 1.43 4.68 2.43
2340 8.03 1.26 4.9 2.2
7330 7.69 1.84 5.14 2.58
8540 7.48 1.51 5.16 2.37
1710 8.34 1.12 5.41 2.34
4623 7.13 1.8 5.44 2.23
5270 7.26 1.57 5.49 2.54
25
... Whilst 89% of studies comparing the emotional self-awareness of autistic and non-autistic participants use self-report measures (and 62% use the TAS-20; Huggins et al., 2020), some authors (e.g., Leising et al., 2009;Marchesi et al., 2014) have questioned their utility as "people with alexithymia, by definition, should not be able to report their psychological state" (Marchesi et al., 2014). However, endeavours to develop objective measures of alexithymia are in their infancy and early attempts are yet to be replicated (e.g., Gaigg et al., 2018;Hickman et al., 2021) and thus self-report measures are necessary. Whilst the TAS-20 has long been the goldstandard tool for assessing alexithymia, there are some concerns that it might actually be a measure of psychopathology symptoms or current levels of psychological distress (see Badura, 2003;Helmes et al., 2008;Leising et al., 2009;Marchesi et al., 2014;Preece et al., 2020;Rief et al., 1996). ...
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