Abstract: Boredom proneness has been previously shown to be associated
with higher levels of alexithymia, the inability to accurately label and
represent one’s affective states. One prominent model of affective regulation
suggests that we make use of interoceptive signals to predict the affective
outcomes of intended actions. Given recent neuroimaging work implicating the
anterior insular cortex in boredom, a region known to be critical for
interoceptive processing, we explored the relations between alexithymia,
interoception, and boredom proneness. Results showed strong relations with
boredom proneness and attention to interoceptive signals. There were hints that
the highly boredom prone also struggle to make sense of those interoceptive
signals, however these were not prominent predictors of boredom proneness in
regressions. We discuss the results and potential future experiments to explore
the relation between boredom proneness and interoceptive processing.
Keywords: Boredom, Interoception, Affective regulation, Alexithymia, Predictive
coding.
1.
Introduction
For most, boredom may
seem trivial, yet over half a century of research has consistently revealed
consequential facets to the experience. Behaviourally
characterized by a lack of engagement that is negatively valenced
and felt as unpleasant (Goldberg et al., 2011; Van
Tilburg and Igou, 2011), boredom is often associated with
feelings of agitation and restlessness (Danckert et
al., 2018). It is also linked to attentional
difficulties in daily life (e.g., lapses in attention such as pouring orange
juice on your cereal; Carriere et al., 2008; Cheyne
et al., 2006) and when performing tasks in the
lab (Hunter and Eastwood, 2018; Malkovsky
et al., 2012). Rampant in academic settings and
monotonous workplaces, it impairs learning and vigilance (Pattyn
et al., 2008; Tze et
al., 2016). Finally, boredom proneness – the tendency to
experience the state more frequently and intensely, has been consistently
associated with negative mental well-being (e.g., higher rates of depression
and anxiety; Goldberg et al., 2011). In short, boredom and boredom proneness have consequences
both for cognitive control (i.e., poor attention), mental well-being (e.g.,
higher rates of depression), and potentially maladaptive responding (e.g.,
problem gambling).
1.1. Boredom and the Insula
Previous neuroimaging
work has implicated the default mode network and anterior insular in supporting
the state of boredom (Dal Mas and Wittman, 2017; Danckert and Merrifield, 2018; Ulrich
et al., 2014; Wang et al., 2021). That is, midline regions of the default mode network including the
medial prefrontal cortex, posterior cingulate and precuneus, are consistently
activated when people are bored (Danckert and
Merrifield, 2018; Ulrich et al., 2014). The posterior midline regions of this network also show diminished
grey matter volume in those highly prone to boredom (Wang et al., 2021). Finally, the anterior insular cortex likely plays a key role in the
experience of boredom, downregulated when bored (Danckert
and Merrifield, 2018) and upregulated when seeking
remedies to boredom (Dal Mas and Wittman, 2017; see Drody et al., 2024, for a review of the neuroimaging
literature).
The insular cortex forms part of what is known as the
salience network – a system that detects behaviourally relevant stimuli in the
environment and uses this information to coordinate appropriate responses
(Menon and Uddin, 2010). The findings discussed above suggest that when
bored, the brain does not only activate the default mode – a network typically
associated with off-task thinking or internal thought processes (Buckner et
al., 2008), but rather shows signs of
attempting (i.e., salience searching) but failing to engage with the
environment.
The insular cortex is also critical for interoceptive
processing – how the brain perceives internal physiological signals and
integrates them with emotional, cognitive, and motivational cues (Craig, 2009). In other words, the insular cortex is involved in representing the
physiological perceptions or sensations that are tied to subjective feelings (Namkung et al., 2018).
Everyday examples of interoceptive sensations include hunger pangs, thirst,
urges to use the bathroom, butterflies in the stomach or a racing heart. One
prominent model of insular cortex functioning suggests that the
posterior-to-anterior axis represents interoceptive signals in increasingly
complex ways, with the anterior insular forming a representation of one’s
current conscious state (Craig, 2009). Menon and Uddin (2010) depict the anterior insula as a dynamic mediator of interactions
between brain networks involved in externally and internally oriented attention
to ultimately guide behaviour. Both models of the
insular cortex are relevant for boredom, a self-focussed,
in-the-moment feeling state (Eastwood et al., 2012) which
functions to guide exploratory behaviour (Danckert and Elpidorou, 2023; Elpidorou, 2014;).
1.2. Boredom Proneness
and Alexithymia
The insular cortex also
plays a role in affective regulation (Craig, 2009; Gu et
al., 2013). One recent model of affective regulation
suggests that one makes use of interoceptive signals to anticipate the
affective outcomes of intended actions (Barrett and Simmons, 2015). This fits well with the notion that the insula integrates external
and internal signals in the service of goal-oriented behaviour
(Uddin et al., 2015). Intriguingly, recent accounts of
boredom proneness suggest
that the trait can in part be characterized by a high degree of self-directed attention,
coupled with low self-knowledge (Bambrah et al., 2023). In addition, boredom proneness has been associated with high levels of
alexithymia—the inability to accurately label one’s emotions (Eastwood et al., 2007). In other words, people who get bored easily
have a harder time making sense of their emotions.
If the highly boredom prone struggle to make sense of
their emotions, this may be reflected in poor use of interoceptive signals,
those signals that play a critical role in affective regulation (Barrett and Simmons, 2015).
Hogeveen and colleagues (2016) examined the consequences of
damage to the insular cortex for the capacity to make sense of one’s emotions.
Their results showed that levels of alexithymia were highest in patients with
substantial insular damage (i.e., 15% or more of the anterior insula damaged [Hogeveen
et al., 2016]). As such, the insular cortex is likely critical
for accurate representation of affective states. Failure to represent
interoceptive signals may in part explain the struggle those high in boredom
proneness experience in launching into actions to resolve their boredom (Mugon et al., 2018). That is, if interoceptive signals
are not merely important for representing affective states, but also for
predicting the affective outcomes of action choices (Barrett and
Simmons, 2015), any failure to represent those
signals accurately will compound the challenge of choosing an engaging activity
to launch into.
1.3. Research Objectives
Here, we explored for
the first time the relation between boredom proneness and interoceptive
awareness and accuracy. Given prior
work showing that the boredom prone are highly self-focussed
but exhibit poor self-knowledge (Bambrah et al., 2023), we predicted that participants who report
high levels of boredom proneness would report a higher level of awareness of
interoceptive signals (i.e., high self-focus), coupled with difficulty in
making sense of those signals (i.e., poor interoceptive accuracy). The latter
would also be associated with higher levels of alexithymia.
1.4. Measuring Boredom Proneness
We acknowledge that the
boredom proneness scale (either the shortened version employed here or the
original, longer version) are not without controversy (Gana
et al., 2019; Gorelik and Eastwood, 2024). The scale does not capture all the variance inherent to the trait of
boredom proneness, with some suggesting that it is a better measure of how
people cope or respond to the experience (Gana et
al., 2019). Nevertheless, we chose to use the current
version for several reasons. First, much of the controversy regarding the
original scale (Farmer and
Sundberg, 1986) concerns the inconsistent factor
structure of the scale. As such, the shortened version, with no reverse-worded
items, has been shown to have a reliable single factor structure (Struk et al., 2017).
Second, this shortened version has rapidly become one of the most used metrics
of trait boredom proneness (cited more than 300 times since publication in
2017). Finally, given that we were asking participants to complete a relatively
large number of scales we wanted to be sure to avoid response fatigue (Meier et
al., 2024). Taken together, these pragmatic
justifications made the use of the shortened boredom proneness scale seem like
the optimal choice to us.
1.5. Measuring Interoception
Garfinkel and colleagues
(2015) proposed a tripartite model of
interoceptive ability comprising three distinct and dissociable dimensions: 1)
interoceptive accuracy, which relates to one’s performance on objective behavioural tests such as heartbeat detection tasks; 2)
interoceptive sensibility, which is the self-evaluated appraisal of subjective
interoception using questionnaires; and 3) interoceptive awareness, which
relates to the metacognitive awareness of interoceptive accuracy (i.e., how
confident one’s feels towards accurately perceiving one’s own bodily
sensations). Per these distinctions, a self-report instrument that purports to
probe interoceptive accuracy may actually be measuring
interoceptive sensibility or awareness. Given the fact that the current work
was exploratory we chose to use a broad swathe of metrics to encompass several
possible dimensions of interoception.
2. Method
2.1. Participants
Participants were adults recruited through the online crowdsourcing
platform Mechanical Turk. Responders who completed the surveys in under five
minutes (1 SD from the mean) were removed (n=55), as were any
respondents with duplicate IP addresses (n=41), abandoned surveys (n=8)
and univariate outliers (n=2). After exclusions, the final sample
consisted of 226 participants (112F, Mage=38.65, SDage=11.02).
2.2. Measures
A total of eight
surveys were administered. One scale measured boredom proneness and two scales
sought to replicate its known relationship with alexithymia (Eastwood et al., 2007) and self-control (Isacescu
and Danckert, 2018; Struk et al., 2017). Given
that this was an exploratory study we chose to include a wide range of scales
to examine potential relations between boredom proneness and interoceptive
processing.
2.2.1. Shortened Boredom Proneness
Scale (BPS-SF)
The eight-item Short
Boredom Proneness Scale (BPS-SF; Struk et al., 2017) measured boredom proneness—i.e., the propensity for an individual to
desire, but fail, to engage in satisfying activity (α = .91). This
scale was developed as a shorter, single-factor version of Farmer and
Sundberg’s Boredom Proneness Scale (1986).
Participants rated items such as ‘I don’t feel motivated by most things that I do’ using a 5-point scale (1=strongly disagree to
5=strongly agree), with a higher total score indicating greater boredom
proneness.
2.2.2. Toronto Alexithymia Scale
(TAS-20)
The 20-item Toronto
Alexithymia Scale-20 (TAS-20; Bagby et al., 1994) measured the difficulty identifying and describing feelings (α = .86). Participants rated items
such as ‘I have feelings that I can’t quite identify’ using a 5-point scale (1=strongly
disagree to 5=strongly agree), with a higher total score indicating
greater difficulties with identifying and describing one’s feelings.
2.2.3. Brief Self-Control Scale (BSCS)
The 13-item Brief
Self-Control Scale (BSCS, Tangney et al., 2004)
measured behaviours that involve self-control such as
restraint and self-discipline (α = .81). Participants rated items such as ‘I wish I had more
self-discipline’ using a 5-point Likert scale (1=not at all like me to
5=very much like me), with a higher score indicating higher levels of
self-control. Negatively phrased items were reverse-scored
to maintain scoring consistency.
2.2.4. Body Awareness Questionnaire
(BAQ)
The 18-item Body
Awareness Questionnaire (BAQ; Shields et al., 1989)
measured self-reported attentiveness to body processes such as sensitivity to
body cycles and small changes in normal functioning (α = .91). Participants rated items such as ‘I
notice specific bodily reactions to being over-hungry’ using a 7-point scale
(1=not at all true of me to 7=very true of me), with a higher
score indicating higher attentiveness to normal body processes.
2.2.5. Interoceptive Accuracy Scale
(IAS)
The 21-item
Interoceptive Accuracy Scale (IAS; Murphy et al., 2019)
measures perceived accuracy of representing internal states (α = .94). Participants rated items such as ‘I can always accurately
perceive when I am hungry’ using a 5-point scale (1=strongly disagree to 5=strongly agree),
with a higher score indicating greater self-reported interoceptive accuracy.
2.2.6. Multidimensional Assessment of Interoceptive
Awareness (MAIA-2)
The 37-item
Multidimensional Assessment of Interoceptive Awareness (MAIA-2; Mehling et al., 2018) comprises 8 subscales to evaluate various dimensions of interoception
(α = .85). Examples of
dimensions measured include noticing bodily signals (‘I notice changes in my
breathing, such as whether it slows down or speeds up’), worrying about
interoceptive cues (‘I start to worry that something is wrong if I feel any
discomfort’), and the ability to regulate distress by focusing attention on
bodily sensations (‘When I am caught up in thoughts, I can calm my mind by
focusing on my body/breathing’). Participants used a 5-point scale to rate how frequently each statement occurs
in their daily life (0=never and 5=always). Higher
scores indicate a greater ability to notice and process bodily sensations. Nine
items were reverse-scored to maintain scoring
consistency. Item 29 was missing from the survey in this sample due to an input
error on our part. We used the scores from the 28 remaining items and although
this meant the scale was not deployed as intended, results demonstrated that it
did not correlate with boredom proneness and is unlikely to do so with the
addition of the final item.
2.2.7. Self-Awareness Questionnaire
(SAQ)
The 28-item
Self-Awareness Questionnaire (SAQ; Longarzo et al., 2015) measured interoceptive awareness for commonly
felt bodily sensations (α
= .98). In contrast to the Interoceptive Accuracy Scale, this scale asks direct
questions about immediate experience (e.g., ‘I feel sudden thirst pangs’),
whereas the Interoceptive Accuracy Scale asks what could be considered
prospective questions (i.e., anticipating accuracy of experiences; ‘I
can always accurately perceive when I am thirsty’). Participants rated how
often they experience each statement using a 5‐point scale (1=never; 2=sometimes;
3=often; 4=very often; 5=always), with higher scores
indicating higher levels of self‐awareness related to bodily sensations.
2.2.8. Interoceptive Sensory
Questionnaire (ISQ)
The Interoceptive Sensory Questionnaire (ISQ;
Fiene et al., 2018) is a 20-item self-report questionnaire intended to
measure interoceptive challenges (i.e., confusion in rating or labelling
interoceptive experiences) in autistic adults (α = .98). Participants rated items such as ‘Sometimes I don’t know how to
interpret sensations I feel within my body’ using a 7-point Likert scale (1=not true at all of
me, 7=very true of me), with higher scores indicating more
difficulty registering or interpreting interoceptive sensations. Three items
were reverse-scored to maintain scoring consistency.
3. Results
Table 1 presents
the correlation matrix and descriptive statistics of all scales.
Table 1.
Correlation Matrix and Descriptive Statistics of the Scales Administered
Results
replicated known relationships with boredom proneness. That is, there was a
significant, strong positive correlation between boredom proneness and
alexithymia, such that those high in boredom proneness also exhibit
difficulties in accurately labelling their emotions (Eastwood et al., 2007). Similarly, there was a strong, negative
correlation between boredom proneness and self-control, such that those high in
boredom proneness tended to exhibit lower levels of self-control (Isacescu et al., 2017).
There was a moderate positive correlation between boredom
proneness and bodily awareness indicating that those high in boredom proneness
also reported attending to their own body states. This questionnaire is not a
direct metric of interoceptive sensations, but a more general measure of how
one represents bodily experiences writ large. Contrary to our predictions,
boredom proneness also showed a small positive association with interoceptive
accuracy. There was no relation between boredom proneness and the Multidimensional
Assessment of Interoceptive Awareness Scale.
The strongest correlations with boredom proneness
observed in this sample were both positive relations, first with self-awareness
(r=0.81) and second with interoceptive confusion (as measured by the
Interoceptive Sensory Questionnaire; r=0.75; Table 1). This
suggests that those high in boredom proneness also exhibit a strong focus on
internal body states but may struggle to make sense of those states.
3.1. Forward Stepwise
Regression
First, we conducted a
forward stepwise regression to determine which of our interoceptive metrics may
best predict boredom proneness. From the null model, this type of regression
adds one predictor at a time, starting with the predictor with the largest correlation
with the dependant variable. Each predictor must
satisfy the criterion for entry. In this model, the criterion was a probability
of F 
.05 when testing the significance of
the group of variables. With each step, the next independent variable with the
largest partial correlation is considered next. The procedure adds and removes
predictors until the model is no longer improved. While there are limitations
to using stepwise selections to carry out regressions (Olusegun et al., 2015; Smith, 2018), our aim was to obtain a
comparator to our subsequent theory-informed hierarchical model. Table 2
presents the forward stepwise regression results for boredom proneness with all
seven predictors entered.
The
final forward stepwise
regression model
included five predictors and accounted for a significant amount of variance in
boredom proneness, F(1, 140) = 4.04, p
<.05, R2 = .73. The first predictor to be
included was self-awareness (SAQ) which accounted for 63% of the variance in
boredom proneness. The second step included alexithymia (TAS-20) which
explained an additional 7% of variance for this sample. The magnitude of the
variance accounted for by the rest of the predictors individually was more
modest. Each of the remaining predictors was entered one at a time in the
following order: self-control (BSCS), 
2 = .01, p<.01; body awareness (BAQ), 2 = .02, p<.01; and interoceptive confusion
(ISQ); 2 = .01, p<.05. Together these variables
explained an additional 4% of variance in boredom proneness. Notably, interoceptive
awareness (MAIA-2) and interoceptive accuracy (IAS) were not included in this
model indicating that they did not improve the fit based on the selection
criterion.
Table 2.
Forward Stepwise Regression Results for Boredom Proneness
3.2. Hierarchical
Regression
Next, we conducted a
hierarchical regression to explore the variance explained by our predictors of
interest while controlling for known relationships such as self-control and
alexithymia. To build a parsimonious model, we selected the variables based on
our hypothesis and on the findings reported in the correlation matrix
(Table 1). Given that: 1) the MAIA-2 did not correlate with boredom
proneness; 2) the IAS revealed only a small correlation (r=.24); and 3)
neither was included in the forward stepwise regression above, we elected to
exclude both scales from this model. Table 3 presents the hierarchical
regression results for boredom proneness.
Table 3.
Hierarchical Regression Results for Boredom Proneness
We
proceeded by entering alexithymia and self-control as known relations with
boredom proneness in step 1. Alexythimia and
self-control positively predicted boredom proneness and explained 61% of
variance (Table 3). In the second step, we added body awareness which
accounted for another 7% of the variance in boredom proneness. Lastly,
self-awareness and the ISQ were added to the last step and together explained
an additional 5% of the variance in boredom proneness. Overall, the results showed that the hierarchical
model was significant. Interestingly, the ISQ was not a significant predictor
of boredom proneness despite the magnitude of its correlation with the trait (r=0.75; Table 1).
4. Discussion
Aside from replicating
known correlations linking boredom proneness to alexithymia and self-control,
our exploratory sample revealed intriguing relationships between boredom proneness and interoception. The
strong positive correlations that linked self-awareness (r=.81, p<.001;
SAQ) and interoceptive confusion (r=75, p<.001; ISQ;
Table 1) support recent accounts of boredom proneness that suggest the
trait is characterized by high self-focus yet poor self-knowledge (Bambrah et al., 2023). That is, our own
sample shows a high focus on internal states (the Sensory Awareness
Questionnaire) coupled with a struggle to make sense of those states as
measured by the Interoceptive Sensory Questionnaire, which has been used to
explore interoceptive confusion in adults with autism (Fiene et al, 2018).
By examining measures of
interoceptive ability such as awareness and accuracy in the context of boredom,
our study has exposed several questions regarding the definition of these
constructs in the literature and how various scales might be used to measure
them. Indeed, the sample yielded a significant, albeit modest, positive
correlation between boredom proneness and interoceptive accuracy (IAS, r=.24,
p<.001; Table 1). At first glance, this finding seems to contradict our
predictions. However, a closer look at the format of the questions used in this
scale and at a prevalent theoretical account of distinct dimensions of
interoceptive ability, may partially explain these results. As mentioned
earlier, Garfinkel and
colleagues (2015)
proposed a tripartite model of interoceptive ability comprising three distinct and
dissociable dimensions: 1) interoceptive accuracy (i.e., one’s performance
on objective behavioural tests such as heartbeat
detection tasks; 2) interoceptive sensibility (i.e., the self-evaluated
appraisal of subjective interoception using questionnaires); and 3)
interoceptive awareness (i.e., how confident one’s feels towards accurately
perceiving bodily sensations). According
to Garfinkel and colleagues’ model, a self-report scale that intends to
measure interoceptive accuracy may actually be probing
for interoceptive sensibility or awareness. For instance, the Interoceptive
Accuracy Questionnaire (IAS) includes a common stem to all questions: ‘I can
always accurately perceive [insert interoceptive signal]’ (we
have added the emphasis here to indicate the prospective nature of this stem).
This structure likely blurs the lines between asking participants to evaluate
how well they can notice their internal bodily signals (i.e., evaluate their
sensibility) and rating how confidently (1=strongly
disagree to 5=strongly agree) they feel they can perceive
those bodily sensations with accuracy per se. In other words, the IAS may more
concisely be tapping into metacognitive awareness rather than accuracy when
representing interoceptive states.
Nonetheless, the
strongest correlations in our findings (SAQ, r=.81, and ISQ, r=0.75, both
p<.001; Table 1) suggest that boredom prone individuals may be
hyperaware of their interoceptive cues but struggle to make meaningful sense of
those signals. The exclusion of the predictors of interoceptive awareness
(MAIA-2) and interoceptive accuracy (IAS) in the forward stepwise model
(Table 2) underlines the need for further examination of the different
dimensions and items used by the instruments available to evaluate the
relationship between interoception and boredom proneness. Given that the
Interoceptive Sensory Questionnaire (ISQ) did not function as a significant
predictor of boredom proneness despite being strongly correlated with the state
highlights the need for direct testing of the connection between
interoceptive ability and both state and trait boredom.
5. Future Work
First and foremost is the need to replicate the current findings in
another sample. Second, future work should employ a variety of interoceptive
accuracy tasks from the common heart rate counting task (Hickman et al., 2020), to more
sophisticated heart rate phase adjustment tasks (Plans et al., 2021) to directly examine
the association between boredom proneness and interoceptive accuracy. One
further potential avenue would involve inducing changes in interoceptive
signals. For instance, using exercise to modulate heart rate, we could examine
the capacity of the highly boredom prone to detect changes in
interoceptive signals. Lastly, paradigms could test
predictive coding accounts to explore whether boredom prone individuals fail to
accurately anticipate both changes in interoception and the affective outcomes
associated with those changes. What emerged from the current work is that the
highly boredom prone seem to attend to their body states assiduously but may
struggle to use what they perceive in adaptive ways. We expect the present
work to spark new research efforts to test the proposed hypothesis.
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