Journal of Boredom
Studies
Issue 1, 2023, pp. 1–26
https://doi.org/10.5281/zenodo.7612345
https://www.boredomsociety.com/jbs
Self-Focused but Lacking
Self-Knowledge: The Relation Between Boredom and Self-Perception
Veerpal Bambrah
York
University
Andrew B.
Moynihan
University of Limerick
John D.
Eastwood
York University
How to cite this paper: Bambrah, V., Moynihan, A. B.,
and Eastwood, J. D. (2023). Self-focused but Lacking Self-knowledge: The Relation
Between Boredom and Self-perception. Journal of Boredom Studies, 1. https://doi.org/10.5281/zenodo.7612345
Abstract: Existing research suggests that people prone to boredom may have high self-directed attention (i.e., the tendency to focus on one’s inner
experiences) but low self-knowledge (i.e., the tendency to possess knowledge of
one’s inner experiences), which are two distinct aspects of self-perception. We
empirically tested this proposal across multiple studies by examining the
relationships between indices of boredom, self-directed attention, and
self-knowledge. In Studies 1 and 2, we created a measure of state self-directed
attention that possesses good psychometric properties, reliability, and
convergent and construct validity. Additionally, we tested and confirmed the
hypothesis that experimentally manipulating self-directed attention has no
significant impact on boredom (Study 1), but that experimentally manipulating
boredom causes a significant increase in self-directed attention (Study 2). In
Study 3, we tested and confirmed the hypothesis that trait
self-directed attention, trait self-knowledge, and trait boredom are
correlated, but psychometrically distinct, dispositional constructs. We also
tested and confirmed the hypothesis that trait self-directed attention and
trait self-knowledge are uniquely associated with trait boredom (Study 3).
Implications and future directions related to furthering our understanding of
boredom and aspects of self-perception are discussed.
Keywords: boredom, self-perception, self-directed
attention, self-knowledge.
1. Introduction
Some of the most
rewarding moments in people’s lives are the result of partaking in engaging and
immersive activities. Based on his research with surgeons, athletes, composers,
artists, dancers, and chess players, Csikszentmihalyi (2002) developed the concept of flow—the state achieved when a person’s
attention is intensely absorbed in an intrinsically
interesting and optimally challenging activity. Indeed, to experience flow, a person needs to
partake in a challenging and engaging pursuit that is proportionate to their
ability level and that permits the development of new skills and knowledge.
Conversely, if an activity is not sufficiently challenging or engaging, a
person may experience boredom—“the
aversive experience of wanting, but being unable, to engage in satisfying
activity” (Eastwood et al., 2012, p. 482). Boredom
can be usefully conceptualized as the aversive emotional concomitant of ‘unused
cognitive potential’ (Eastwood and Gorelik, 2019), engendered by
suboptimal levels of challenge, monotony, constraint, and devalued activities
(Elpidorou, 2018).
Boredom is rooted in a ‘desire for desires’ or desire bind (Tolstoy, 1899), which
means that when bored, a person cannot find anything they want to do in their
current surroundings, but they desperately want to do something. The feeling of
boredom can be so distressing that people will engage in unhealthy activities
to alleviate it (e.g., self-harm, unhealthy eating; Havermans et al., 2015). As a
trait, the tendency to feel bored frequently and intensely is linked to many
psychosocial problems, such depression, substance use, and risky and impulsive
behaviors (e.g., Goldberg
et al., 2011; Kılıç et al., 2019; LePera, 2011), which underscores the negative
impact of both transient and chronic boredom.
1.1. Boredom and Self-Focus
In addition to challenge
and engagement, flow and boredom can be differentiated based on one’s
attention. During a state of flow, attention becomes so focused on the activity
or goal at hand that self-perception is
diminished (Csikszentmihalyi, 1990).
According to Csikszentmihalyi (1975), if
one’s attention is intensely absorbed in an interesting and challenging
activity, a flow experience occurs in which the individual is aware of his or
her own actions, but not of awareness itself. This is supported
empirically, with several qualitative studies indicating that flow is disrupted
when self-focus occurs during an immersive activity (e.g., skydiving, performance art; see Hardie-Bick
and Scott, 2017 for a review of studies).
Conversely, in keeping with the above-reviewed definition
and components of boredom, it follows that when a person feels bored, they are
thrown out of engagement with activity and their attention is directed onto themselves.
Indeed, some work has discussed self-related attentional processes within the
context of boredom. For example, the existential escape hypothesis of
boredom suggests that when people encounter the meaning threat associated with
boredom, they seek to engage in hedonic and interpersonal escape behaviors that
lower self-awareness and reduce feelings of meaninglessness (Moynihan et
al., 2021). In this manner of thinking,
boredom may be aversive precisely because it involves greater self-focus.
Indeed, work by Van Tilburg and colleagues (see Moynihan et al., 2021 for a review) has consistently identified the
lack of meaning as a critical part of the experience of boredom, and, by
extension, this work suggests that boredom involves a focus on the self—i.e.,
viewing one’s life/current situation as lacking meaning. Tam and colleagues (2021) propose that boredom occurs when there is a discrepancy between one’s
actual and desired level of engagement and that one’s attention will either
shift to an external stimulus that is unrelated to the source of boredom, shift
inward, or shift to the current boring situation in order to obtain adequate
attentional engagement.
A few studies have directly examined the link between
boredom and self-focus. However, this literature as a whole can be
characterized as disparate, as self-focus appears to be conceptualized and
operationalized differently within and across studies. Seib and Vodanovich (1998) and von Gemmingen et al. (2003) found
that trait boredom, as measured by the Boredom Proneness Scale (BPS; Farmer and
Sundberg, 1986), was positively correlated with ‘self-reflectiveness’
(i.e., attempts at self-understanding – e.g., reflecting a lot about oneself)
and negatively correlated with ‘internal state awareness’ (i.e., awareness of
and sensitivity to one’s thoughts and emotions – e.g., being alert to mood
changes), which are two components of Fenigstein et al.’s (1975) Private Self-Consciousness (PSC) subscale of
the Self-Consciousness Scale (SCS). Using the BPS, Gana et al. (2000) found that trait boredom was positively associated with Hansell et
al.’s (1986) Introspectiveness Scale (IS),
which assesses the tendency to devote attention inward on one’s thoughts,
feelings, etc. Harris (2000) found that the BPS was positively
related to “mood monitoring” (i.e., “the tendency to scrutinize and focus on
one’s mood states”) and negatively related to “mood labelling” (i.e., “the
ability to identify and give name to one’s mood states”), which are two factors
of the Mood Awareness Scale (MAS; Swinkels and Giuliano, 1995, p. 936). Eastwood et al. (2007) found that trait boredom, as measured by the
BPS and the Boredom Coping Scale (BCS), were positively correlated with the ‘difficulty
identifying feelings’ and ‘difficulty describing feelings’ factors of Bagby et
al.’s (1994) Toronto Alexithymia Scale (TAS-20).
Notably, Moynihan et
al. (2015, 2017) found
that trait self-focused attention (as measured by Fenigstein et al.’s 1975 PSC and by Govern and Marsch’s 2001 Private Self-Awareness subscale) enhanced the impact of boredom
on unhealthy eating and impulsivity.
1.2. Integrating Past Findings and
Clarifying Self-Related Concepts
As indicated by this
review of the literature, a large number of terms related to the ‘self’ have
been introduced in the scientific literature. Indeed, Morin (2017)
described this proliferation of self-related concepts, noting that several of
these terms are either ill-defined or synonymous, leading to confusion.
Underscoring the need for a standardized taxonomy for self-related terms, Morin
(2017) created a classification scheme that includes
the definitions and conceptually-related terms of multiple basic terms
associated with the process of self-perception, which is defined an
“overall process of self-awareness, self-knowledge acquisition and self-concept
formation; an awareness of the characteristics that constitute one’s self” (p.
2). He listed the term self-directed attention to refer to the “capacity
to become the object of one’s own attention; to focus one’s attention inward
toward the self” (Duval and Wicklund, 1972; Silvia and
Duval, 2001; as cited in Morin, 2017, p. 2) and the term self-knowledge to refer an “organized set of
accurate self-information; realistic self-concept; accurate introspection about
one’s own self” (Carlson, 2013; Gibbons, 1983; Wilson, 2009; as cited in Morin, 2017, p. 2). Morin’s distinction between self-directed attention and
self-knowledge can be usefully applied to organize the otherwise disorganized
above-reviewed findings on trait boredom and self-focus, leading to the
proposal that people who have higher self-directed attention and lower self-knowledge
(of one’s thoughts and feelings) are more prone to boredom. Rather than
focusing on the external environment, the boredom prone person focuses on
themselves and their inner experiences, for example noticing their feelings,
thoughts, and sensations, but they struggle to identify, describe, and
understand what these inner experiences are, for example identifying feelings
of sadness versus feelings of disappointment. We sought to empirically test
this proposal, doing so armed with a clear conceptualization of terms related
to self-perception and using up-to-date measures of trait boredom that possess
stronger psychometric properties and clearer conceptualizations of boredom than
the BPS (Vodanovich and Watt, 2016).
As boredom is a state, we also sought to examine how
fluctuations in boredom and self-directed attention impact one another. In a
state of flow, one’s attention
is intensely absorbed in an interesting and challenging activity
(Csikszentmihalyi, 1975, 1990). In contrast, we suggest that in a state of
boredom, one’s attention is directed towards oneself. We sought to empirically examine this proposal by experimentally manipulating
state self-directed attention and state boredom, exploring if manipulating one
state experience significantly alters the other state. We hypothesized that
manipulating state boredom causes an increase in state self-directed attention.
To the very best of our knowledge, only one existing scale, the Situational
Self-Awareness Scale (SSAS; Govern and Marsch, 2001),
measures spontaneously occurring fluctuations in private self-awareness—i.e.,
one’s attentiveness to the internal, personal aspects of oneself, such as
thoughts, feelings, memories. However, considering Morin’s recent review (2017), two of the three items for the Private Self-Awareness subscale are
ambiguously worded, with one item assessing the degree to which a person is
reflecting on their ‘life’, which could be interpreted to mean aspects of
oneself that are public to others (e.g., physical features), and the
second item assessing the degree to which a person is ‘conscious’ of their
inner feelings, which could be interpreted to mean being ‘self-conscious’ of
one’s inner feelings. Thus,
to clearly examine the relationship between state self-directed attention and
state boredom, we created a new measure of state self-directed attention.
1.3. Research Objectives
Across three studies, we sought to accomplish the following research
objectives:
1) Create and
validate a measure of state self-directed attention (Study 1);
2) Test the
hypothesis that manipulating state boredom causes an increase in state
self-directed attention (Study 2), but that manipulating state self-directed
attention does not alter state boredom (Study 1);
3) Test the
hypothesis that trait self-directed attention, trait self-knowledge, and trait
boredom are related, but conceptually and psychometrically distinct,
dispositional constructs (Study 3). Support for this third hypothesis would
permit us to:
4) Test the
hypothesis that trait self-directed attention and trait self-knowledge are each
uniquely associated with trait boredom (Study 3). Informed by the
above-reviewed literature on boredom and self-focus and Morin’s (2017) classification scheme, we hypothesized that trait self-directed attention
relates significantly and positively to trait boredom, over and above
self-knowledge, and that trait self-knowledge relates significantly and
negatively to trait boredom, over and above self-directed attention.
2. Study 1 and 2 Method: Exploring the
Relationship Between State Boredom and State Self-Directed Attention
2.1. Study 1 and 2 Participants
Participants in Studies 1 and 2 were undergraduate students recruited
from a research participant pool and from advertisements posted across social media
platforms. The final samples consisted of 112 participants in Study 1 (Mage=19.92,
SDage=3.23, rangeage=18–47, 66.96% Female) and 119
participants in Study 2 (Mage=22.62, SDage=6.40,
rangeage=18–47, 84.87% Female).
2.2. Study 1 and 2 Measures
In order to assess the convergent validity of our scale of state
self-directed attention, participants in Studies 1 and 2 completed three
trait-based measures of self-directed attention, based on the above-reviewed
boredom literature. More specifically, participants completed the 12-item
Introspectiveness Scale (IS; Hansell et al., 1986), which measures the tendency to devote attention
inward on oneself (i.e., one’s thoughts, feelings, etc.); each item was rated
using a 5-point scale (1=very little to 5=very much).
They also completed the 10-item Mood Awareness Scale (MAS;
Swinkels and Giuliano, 1995),
which measures ‘mood monitoring’ (and ‘mood labelling’); each item was rated
using a 5-point scale (1=disagree very much to 5=agree very much).
Finally, participants completed the 10-item Private
Self-Consciousness (PSC; Fenigstein et al., 1975) subscale of the Self-Consciousness Scale, which
measures ‘self-reflectiveness’ (and ‘internal state awareness’); each item was
rated using a 5-point scale (0=extremely uncharacteristic to 4=extremely
characteristic). Each PSC subscale was computed using the items proposed by
exploratory and confirmatory factor analyses conducted by Ben-Artzi (2003).
2.3. Study 1 Procedure: Manipulating Self-Directed Attention
Participants in Study 1 completed three state-based questionnaires
twice—once before and once after a manipulation of self-directed attention. The
measures were completed in the following fixed order each time: state boredom,
state self-directed attention, state affect.
2.3.1. Multidimensional State Boredom Scale
The short-form Multidimensional State Boredom Scale (MSBS-SF; Hunter et
al., 2015)
measured participant’s state boredom. From the 28-item MSBS (Fahlman et al., 2013), participants rated eight items
using a 7-point scale (1=strongly disagree to 7=strongly agree),
with a higher total score indicating greater boredom. Confirmatory factor
analyses conducted with undergraduate and community samples from other studies
in our lab find that a one-factor structure of a six-item MSBS fits the data
best and possesses good internal reliability (CFI range=.956–.981, RMSEA
range=.079–.107, SRMR range=.028–.042, and ω range=.79–.89). See Appendix A for
all six items of the MSBS-SF.
2.3.2. State Self-Directed Attention Scale
Five items were constructed to measure state self-directed attention—i.e.,
one’s ‘in-the-moment’ focus on their inner experiences (i.e., thoughts and
feelings; S-SDAS). See Table 1 for the items. We emphasized the situational
nature of the items: ‘Please respond to each question indicating HOW YOU
FEEL RIGHT NOW, even if it is different from how you usually feel’. The
items were phrased as declarative statements (e.g., ‘Right now, I am focused on
my thoughts’) and were rated using a 7-point scale (1=strongly disagree
to 7=strongly agree), with a higher total score indicating greater
self-directed attention.
2.3.3. State Affect Items
State affect was measured with eight items. Eight feelings were
assessed: sad, happy, interested, disgust, frustrated, ashamed, guilty, and
pride. Participants rated each item using a 7-point scale (1=strongly
disagree to 7=strongly agree), with a higher score indicating
greater intensity of a particular feeling.
2.3.4. Self-Directed Attention Manipulation
Participants were randomly assigned to one of two conditions: a
self-novelty condition (N=49) or an externally-oriented (non-self-directed)
condition (N=63). After completing the MSBS-SF, S-SDAS, and State Affect
items the first time, participants in the self-novelty condition completed a
self-novelty writing task created by Silvia and Eichstaedt (2004), where they wrote about what
makes them unique: 1) ‘What is it about you that makes you different
from your family?’; 2) ‘What is it about you that makes you different from your
friends?’; 3) ‘What is it about you that makes you different from people in
general?’ Across multiple laboratory and internet experiments, the authors
found that the self-novelty manipulation significantly increases self-focus
(Silvia and Eichstaedt, 2004). Participants in the
externally-oriented condition completed a neutral writing task created by the
current authors, where they wrote about the typical features that can be found
in various settings: 1) ‘Please describe all of the features (items) of
the computer room you are sitting in’; 2) ‘What are the most important features
of a gas station?; 3) ‘What are the most important
features of a coffee shop?’ Across both conditions,
participants had three minutes to respond to each question before submitting
their responses, totalling nine minutes in each condition. After completing
either writing task, participants completed the MSBS-SF,
S-SDAS, and State Affect items a second time.
2.4. Study 2 Procedure: Manipulating Boredom
2.4.1. State Measures
Participants in Study 2 completed the S-SDAS and MSBS-SF twice—once
before and once after a manipulation of boredom. The measures were completed in
a fixed order (S-SDAS then MSBS-SF prior to the manipulation; MSBS-SF then S-SDAS
after the manipulation).
2.4.2. Boredom Manipulation
All participants were randomly assigned to one of two conditions: a
boredom condition (N=59) and a non-boredom condition (N=60).
After completing the S-SDAS and MSBS-SF the first time, participants in the
boredom condition watched a 5-minute video of a man talking about his
work at an office supply company in a monotone and ‘boring’ manner (Markey et
al., 2014).
Participants in the non-boredom condition watched a 5-minute clip of the
first episode of the comedy sitcom Brooklyn Nine-Nine (Goor et al., 2013). Prior work comparing these
two experimental groups shows that participants in the boredom condition report
significantly higher state boredom scores than participants in the non-boredom
condition (Hunter et al., 2015).
After watching either video, participants completed the MSBS-SF
and S-SDAS a second time.
3. Study 1 and 2 Results: Exploring the
Relationship Between State Boredom and State Self-Directed Attention
3.1. Reliability, Validity, and Distinct Measurement
Ability of the S-SDAS
We examined the reliability, validity, and distinct measurement ability
of the newly-created S-SDAS. Table 1 presents the inter-item and item-total
correlations for the measure’s pre-manipulation items (i.e., the items
participants completed before the self-directed attention or boredom
manipulation), collapsed across Studies 1 and 2 (N=231). First, the
inter-item correlations ranged from .32 to .61, and the item-total correlations
were above .45 for all items, which suggests that the items correlate
sufficiently with one another and the overall scale.
Table 1. Corrected Item-Total Correlations and
Inter-Item Correlations of the State Self-Directed Attention Scale in Studies 1
and 2 (N=231)
|
Item-Total Correlations |
Item 1. |
Item 2. |
Item 3. |
Item 4. |
Item 5. |
Item 1. |
.46 |
- |
|
|
|
|
Item 2. |
.68 |
.43 |
- |
|
|
|
Item 3. |
.60 |
.32 |
.51 |
- |
|
|
Item 4. |
.70 |
.35 |
.60 |
.58 |
- |
|
Item 5. |
.65 |
.39 |
.55 |
.47 |
.61 |
- |
Note: Items of the State
Self-Directed Attention Scale:
Item 1. Right now… I am noticing
changes in my mood.
Item 2. Right now… While doing
the activity at hand, I keep thinking about my feelings.
Item 3. Right now… I am focused
on my thoughts.
Item 4. Right now… My thoughts
are on my mind.
Item 5. Right now… I am focusing
my attention on my inner self.
Second, a confirmatory factor analysis assessed a one-factor model for
the five-item S-SDAS (see Figure 1). Maximum likelihood estimation was used to
estimate the fit of the obtained covariance matrix for the model. We
used the following criteria as an indication of ‘good’ model fit: comparative fit
index (CFI)>.95; root mean square error of approximation (RMSEA)<.06; and
standardized root mean square residual
(SRMR)<.08 (Hu and Bentler, 1999);
CFI values>.90 and RMSEA values between .05 and .10 suggest ‘acceptable’ fit
(Browne and Cudeck, 1992;
MacCallum et al., 1996;
McDonald and Ho, 2002;
as cited in Lai and Green, 2016). A
CFA confirmed that a one-factor structure of the five-item S-SDAS had a good
fit with the combined pre-manipulation data of Studies 1 and 2: χ2(5)=7.547,
p=.183, CFI=.993, RMSEA=.047 (RMSEA 90% CI:
.000, .111), SRMR=.024. The state self-directed attention latent
variable accounted for 24.60 to 65.50 percent of the variance in the five
S-SDAS items and it accounted for statistically
significant variance in all items (all p’s<.001 for the factor
loadings). The five-item S-SDAS also possessed good internal
reliability (ω=.82).
Third, the S-SDAS was moderately and positively
correlated with trait-based measures of self-directed attention that
participants had also completed in Studies 1 and 2, specifically the Introspectiveness
scale (r=.30, p<.001), the Mood Monitoring subscale of the
Mood Awareness Scale (r=.34, p<.001), and the
Self-Reflectiveness factor of the Private Self-Consciousness subscale of the
Self-Consciousness Scale (r=.26, p<.001), which underscores
the convergent validity of the S-SDAS.
Figure
1. One-Factor Model of the State Self-Directed Attention Scale (Studies 1 and
2)
Note: Unstandardized estimates (with standard errors in parentheses and
with the 95% bias-corrected CI in brackets) are shown to represent the
relationships of the state self-directed attention latent variable with its
respective observed items. The latent variable accounted for 24.60%, 56.60%,
46.10%, 65.50%, and 54.50% of the variance in items 1, 2, 3, 4, and 5, respectively.
All p’s < .001.
Item 1. Right now… I am noticing changes in my mood.
Item 2. Right now… While doing the activity at hand, I keep thinking
about my feelings.
Item 3. Right now… I am focused on my thoughts.
Item 4. Right now… My thoughts are on my mind.
Item 5. Right now… I am focusing my attention on my inner self.
Fourth, to examine the distinct measurement ability of the S-SDAS, we
hypothesized that state self-directed attention is a related, but
psychometrically distinct, construct from state boredom.[1]
Support for this hypothesis would be demonstrated by a two-factor CFA model
that provides a better fit to the data than a one-factor model, and it would
permit us to subsequently examine the causal relationships between state
self-directed attention and state boredom in Studies 1 and 2. We used the
above-noted criteria to evaluate the fit of the models. Results revealed that
the two-factor model (see Figure 2) provided a good fit with the combined
pre-manipulation data of Studies 1 and 2: χ2(43)=67.779,
p=.009, CFI=.970, RMSEA=.050 (RMSEA 90% CI:
.025, .072), SRMR=.051. In this model, state self-directed
attention and state boredom were moderately and positively related (.26, p<.001).
In contrast, the alternative one-factor model resulted in a poorer fit: χ2(44)=416.581,
p<.001, CFI=.551, RMSEA=.191 (RMSEA 90% CI:
.175, .208), SRMR=.166. A likelihood ratio χ2 difference
test revealed that, in comparison to the two-factor model, constraining the
covariance between the state self-directed attention and state boredom latent
variables to estimate the one-factor model significantly deteriorated the fit
of the model (χ2difference (df)=348.802 (1), p<.001).
Overall, a model specifying two distinct, but related, constructs of state
self-directed attention and state boredom fit the data significantly better
than a model that specifies one latent construct.
Figure 2. Two-Factor Model of State
Self-Directed Attention and State Boredom – Distinct
Measurement Ability of S-SDAS (Studies 1 and 2)
Note: Unstandardized
estimates (with standard errors in parentheses and with the 95% bias-corrected
CI in brackets) are shown to represent the relationships of the state
self-directed attention and state boredom latent variable with their respective
observed items.
3.2. The Impact of Manipulating Self-Directed Attention on State Boredom
In Study 1, we examined the impact of manipulating self-directed
attention on participants’ subsequent state boredom. We conducted two multiple
regressions. In the first model (Table 2a), we regressed post-manipulation
S-SDAS scores on to condition (1=externally-oriented,
2=self-novelty), pre-manipulation S-SDAS scores, and pre-manipulation state
affect. Participants in the self-novelty condition endorsed significantly
higher post-manipulation self-directed attention than those in the externally-oriented
condition while pre-manipulation self-directed attention and state affect were
held constant, B=0.53, SE=.17, t=3.11, p=.001. This
result underscores the construct validity of the five-item S-SDAS as it was
sensitive to detecting experimentally manipulated variations in self-directed
attention. In the second model (Table 2b), we regressed
post-manipulation MSBS-SF scores on to condition,
pre-manipulation MSBS-SF scores, and pre-manipulation state affect.
Post-manipulation boredom scores did not significantly differ between
participants in the two conditions while statistically controlling for
pre-manipulation boredom and state affect, B=-0.13, SE=.14, t=-0.94,
p=.350. Together, these results suggest that manipulating self-directed
attention impacted state self-directed attention (i.e., those who wrote about
what makes them unique directed more attention to their inner experiences after
the task than those who wrote about the features of different settings) but had
no impact on people’s boredom.
Table 2a and
2b. Descriptive Statistics and Regression Models for S-SDAS and MSBS-SF in
Study 1 (N=112)
Study 1 |
Externally-Oriented Condition
(N=63) |
|
Self-Novelty Condition (N=49) |
||
|
Pre-Task |
Post-Task |
|
Pre-Task |
Post-Task |
S-SDAS |
4.37 (1.21) |
4.10 (1.31) |
|
4.60 (1.18) |
4.80 (1.12) |
MSBS-SF |
4.13 (1.12) |
4.20 (1.15) |
|
3.87 (1.25) |
3.71 (1.28) |
|
|||||
2a: S-SDAS
(Post-Task) |
B |
SE |
t |
p |
sr2 |
Condition |
0.53 |
.17 |
3.11 |
.001 |
.038 |
Pre-S-SDAS |
0.72 |
.07 |
10.06 |
<.001 |
.394 |
Pre-Sadness |
-0.04 |
.07 |
-0.52 |
.602 |
.001 |
Pre-Happy |
-0.01 |
.07 |
-0.07 |
.947 |
.000 |
Pre-Interested |
-0.03 |
.07 |
-0.51 |
.610 |
.001 |
Pre-Disgusted |
-0.05 |
.10 |
-0.44 |
.660 |
.001 |
Pre-Frustrated |
0.13 |
.06 |
2.34 |
.021 |
.021 |
Pre-Ashamed |
0.04 |
.09 |
0.49 |
.628 |
.001 |
Pre-Guilty |
-0.12 |
.08 |
-1.50 |
.136 |
.009 |
Pre-Pride |
0.12 |
.06 |
2.03 |
.045 |
.016 |
|
|||||
2b: MSBS-SF
(Post-Task) |
B |
SE |
t |
p |
sr2 |
Condition |
-0.13 |
.14 |
-0.94 |
.350 |
.003 |
Pre-MSBS-SF |
0.75 |
.07 |
11.09 |
<.001 |
.359 |
Pre-Sadness |
-0.02 |
.06 |
-0.30 |
.764 |
.000 |
Pre-Happy |
0.01 |
.06 |
0.12 |
.904 |
.000 |
Pre-Interested |
-0.21 |
.06 |
-3.86 |
<.001 |
.044 |
Pre-Disgusted |
-0.08 |
.09 |
-0.92 |
.358 |
.003 |
Pre-Frustrated |
0.02 |
.05 |
0.33 |
.746 |
.000 |
Pre-Ashamed |
-0.05 |
.07 |
-0.67 |
.506 |
.001 |
Pre-Guilty |
0.07 |
.07 |
1.07 |
.287 |
.003 |
Pre-Pride |
0.05 |
.05 |
1.14 |
.259 |
.004 |
Note: In both regression
models, the variance inflation factor was < 2.5 for each of the predictors.
3.3.
The Impact of Manipulating Boredom on State Self-directed Attention
In Study 2, we examined the impact of manipulating boredom on participants’
state self-directed attention. We conducted two multiple regressions. In the
first model (Table 3a), we regressed post-manipulation MSBS-SF scores on to
condition (1=non-boredom, 2=boredom) and pre-manipulation MSBS-SF
scores. Participants in the boredom condition endorsed significantly higher
post-manipulation boredom than those in the non-boredom condition while
pre-manipulation boredom was held constant, B=1.15, SE=.15, t=7.75,
p<.001, which suggests that the boredom manipulation successfully
altered people’s boredom. In the second model (Table 3b),
we regressed post-manipulation S-SDAS scores on to condition
and pre-manipulation S-SDAS scores. Participants in the
boredom condition endorsed significantly higher post-manipulation self-directed
attention than those in the non-boredom condition while statistically
controlling for pre-manipulation self-directed attention, B=0.40, SE=.18,
t=2.18, p=.015.
Table 3a and 3b. Descriptive
Statistics and Regression Models for MSBS-SF and S-SDAS in Study 2 (N=119)
Study 2 |
Non-Boredom Condition (N=60) |
|
Boredom Condition (N=59) |
||
|
Pre-Task |
Post-Task |
|
Pre-Task |
Post-Task |
MSBS-SF |
3.78 (1.45) |
3.12 (1.25) |
|
3.60 (1.43) |
4.13 (1.48) |
S-SDAS |
4.63 (1.30) |
4.23 (1.24) |
|
4.66 (1.25) |
4.64 (1.14) |
|
|||||
3a: MSBS-SF
(Post-Task) |
B |
SE |
t |
p |
sr2 |
Condition |
1.15 |
.15 |
7.75 |
<.001 |
.158 |
Pre-MSBS-SF |
0.77 |
.05 |
14.76 |
<.001 |
.572 |
|
|||||
3b: S-SDAS
(Post-Task) |
B |
SE |
t |
p |
sr2 |
Condition |
0.40 |
.18 |
2.18 |
.015 |
.028 |
Pre-S-SDAS |
0.50 |
.07 |
6.86 |
<.001 |
.280 |
Note: In both regression
models, the variance inflation factor was 1.00 for each of the predictors.
In sum, across Studies 1
and 2, we developed a measure of state self-directed attention that: 1)
possesses good psychometric properties and reliability; 2) is distinct from
(yet moderately and positively related to) state boredom; and 3) demonstrates
good convergent and construct validity. Additionally, we confirmed our
hypothesis: manipulating boredom caused a significant increase in state
self-directed attention and we failed to find any evidence that manipulating self-directed attention
altered boredom.
4. Study 3 Method: Confirming the
Relationships Between Trait Boredom, Trait Self-Directed Attention, and Trait
Self-Knowledge
In Study 3, we sought to
confirm that trait boredom, trait self-directed attention (i.e., the propensity
to direct attention to one’s inner experiences), and trait self-knowledge
(i.e., the propensity to possess knowledge of one’s inner experiences) are
correlated, but distinct, dispositional constructs. Study 3 meaningfully builds
on the disparate research on boredom and self-focus and uses Morin’s (2017) classification of terms associated with self-perception to make the
important contribution of testing our proposal that self-directed attention is
uniquely and positively related to trait boredom, and that self-knowledge is
uniquely and negatively related to trait boredom.
4.1. Participants
Participants were
community adults recruited from Qualtrics’ Online Panels. Speedy responders (those who completed the
study in under five minutes; n=47), as well as univariate and
multivariate outliers (n=17), were removed, yielding a final sample of
686 participants (Mage=33.02, SDage=11.81,
rangeage=18–65, 60.50% Female).
4.2. Procedure and
Measures
Participants completed
trait-based measures in the below-described fixed order. Table 4 lists the
means, standard deviations, and coefficient omega estimates of each measure.
Table
4. Correlation Matrix and Descriptive Statistics
of Trait Measures in Study 3
|
1. |
2. |
3. |
4. |
5. |
6. |
7. |
Boredom: |
|
|
|
|
|
|
|
1. Short Boredom
Proneness |
- |
|
|
|
|
|
|
2. Trait Boredom
Scale |
.80*** |
- |
|
|
|
|
|
|
|||||||
Self-Directed Attention: |
|
|
|
|
|
|
|
3. Introspection Scale |
.25*** |
.23*** |
- |
|
|
|
|
4. MAS – Mood Monitoring |
.28*** |
.27*** |
.63*** |
- |
|
|
|
|
|||||||
Self-Knowledge: |
|
|
|
|
|
|
|
5. MAS – Mood Labelling |
-.55*** |
-.55*** |
.02 |
.06 |
- |
|
|
6. TAS Difficulty Identifying Feelings |
-.68*** |
-.63*** |
-.22*** |
-.24*** |
-.69*** |
- |
|
7. TAS Difficulty Describing Feelings |
-.56*** |
-.57*** |
-.13*** |
-.15*** |
-.76*** |
-.72*** |
- |
|
|
|
|
|
|
|
|
Mean |
4.06 |
4.36 |
3.69 |
4.17 |
3.13 |
2.77 |
3.05 |
Standard Deviation |
1.56 |
1.72 |
0.83 |
0.98 |
1.02 |
1.09 |
0.98 |
Coefficient Omega
(ω) |
.92 |
.94 |
.92 |
.79 |
.78 |
.90 |
.79 |
Note: Higher scores on the MAS
Mood Labelling subscale and on the TAS subscales are indicative of greater
self-knowledge (i.e., greater knowledge of one’s feelings).
* p < .05 ** p <
.01 *** p < .001
4.2.1. Short Boredom Proneness Scale
The eight-item, single-factor
Short Boredom Proneness Scale (SBPS) measured boredom proneness—i.e., the
tendency for an individual to want, but fail, to engage in sufficiently
satisfying activity (Struk et al., 2015a).
Participants rated each item using a 7-point scale (1=strongly disagree to 7=strongly agree),
with a higher total score indicating greater boredom proneness. The SBPS has a construct validity
that is comparable to the original BPS score
(Struk et al., 2015a). In
Study 3, the SBPS possessed excellent internal reliability (ω=.92).
4.2.2. Trait Boredom
Scale
The six-item Trait
Boredom Scale (TBS) is a newly developed measure of one’s experience of
boredom—i.e., the tendency to often feel bored because of diminished agency
(Gorelik and Eastwood, under review).
Participants rated each item (e.g., I often feel bored) using a 7-point scale
(1=strongly disagree to 7=strongly agree), with a higher total
score indicating higher trait boredom. See Appendix B for all items of the TBS.
The TBS possessed
excellent internal reliability (ω=.94) in Study
3.
4.2.3. Mood Awareness
Scale
See Studies 1 and 2 Method for a complete description of the MAS. In
Study 3, the MAS subscales possessed acceptable internal reliability (mood
monitoring ω=.79; mood labelling ω=.78).
4.2.4. Private Self-Consciousness Subscale
See Studies 1 and 2 Method for a complete description of the PSC. In
Study 3, the internal consistency for the self-reflectiveness subscale was poor
(ω=.53) and the internal consistency for the internal state
awareness subscale was questionable (ω=.69). As poor internal reliability can yield imprecise
parameter estimates in measurement and structural models, we excluded the PSC
from subsequent analyses.
4.2.5. Toronto Alexithymia Scale
The 20-item Toronto Alexithymia Scale-20 (TAS-20; Bagby et al., 1994) measured ‘difficulty
identifying feelings’, ‘difficulty describing feelings’, and ‘externally-oriented
thinking’. The current study analyzed the first two factors. Participants rated
each item using a 5-point scale (1=strongly disagree to 5=strongly
agree), with higher subscale scores indicating greater difficulties with
identifying and describing one’s feelings. These two subscales possessed
acceptable-to-excellent internal reliability (difficulty identifying feelings ω=.90; difficulty describing feelings ω=.79).
4.2.6. Introspectiveness Scale
See Studies 1 and 2 Method for a complete description of the IS. In the
current study, the IS possessed excellent internal reliability (ω=.92).
4.3. Latent Factors
Based on prior research,
as well as the internal reliability of the above-described measures, three
latent factors were specified. Boredom was measured by the total scores of a)
the Short Boredom Proneness Scale (SBPS) and b) the Trait Boredom Scale (TBS).
Self-directed attention was measured by a) the ‘mood monitoring’ subscale of
the MAS (MAS-MM) and b) the total score of Introspection Scale (IS).
Self-knowledge was measured by a) the ‘mood labelling’ subscale of the MAS
(MAS-ML), b) the ‘difficulties identifying feelings’ subscale of the TAS-20
(TAS-DIF), and c) the ‘difficulties describing feelings’ subscale of the TAS-20
(TAS-DDF). To simplify
the interpretation of the measurement model results and structural model
results, scores on the ‘mood labelling’ subscale of the MAS, and the ‘difficulty identifying feelings’ and ‘difficulty
describing feelings’ subscales of the TAS-20 were multiplied by -1 so that for these
measures, higher values indicate greater self-knowledge.
It was
predicted that trait boredom would be significantly related to, but distinct
from, trait self-directed attention and trait self-knowledge. Moreover, Study 3
sought to test the hypothesis that although the boredom prone person tends to
direct attention towards their inner experiences (self-directed attention),
they also tend to lack knowledge of their underlying feelings (poor
self-knowledge).
5. Study 3 Results: Confirming the
Relationships Between Trait Boredom, Trait Self-Directed Attention, and Trait
Self-Knowledge
Table 4 presents the correlation matrix of all trait variables from
Study 3 that were included in the measurement and structural models.
Measurement models were used to confirm that trait boredom, trait self-directed
attention, and trait self-knowledge are related, yet psychometrically distinct,
constructs. Support for this hypothesis would be demonstrated by a three-factor
model that provides a better fit to the data than all possible two- or
one-factor models. Second, structural models were used to explore the nature of
the relationships of trait self-directed attention and trait self-knowledge
with trait boredom. Correlated errors between the ‘mood monitoring’ and ‘mood
labelling’ subscales were specified in all models to account for the indicator
covariation resulting from a common measurement method (i.e., the MAS).
Maximum likelihood
estimation was used to estimate the fit of the obtained covariance matrix for
the measurement and structural models. The CFI, RMSEA (and its 90% confidence
interval), and SRMR were used to evaluate model fit. Cut-offs similar to those
described above were used for each index. Likelihood ratio χ2 difference tests compared the fit of
the model that estimates three latent factors with the fit of the models that
estimate two latent factors and one latent factor. Following
Flora and Bell’s (2021)
guidelines on the reporting of effect sizes in structural equation models, we
present the unstandardized parameter estimates to represent the relationships
of the latent variables with their respective indicators and we present
the standardized parameter estimates to represent the relationships between the
latent variables.
5.1. Measurement Models
Table 5 presents the above-noted fit indices of all models. Results
indicated that the three-factor solution (see Figure 3) provided the best fit
to the data: χ2(10)=132.419, p<.001; CFI=.961,
RMSEA=.134 (RMSEA 90% CI: .114, .154), SRMR=.051. In the three-factor model,
the three latent variables accounted for more than 50% of the variance in their
respective indicators and the latent variables accounted for statistically
significant variance in their respective indicators (all p’s<.001 for
the factor loadings), suggesting that each
indicator is important in defining its respective latent variable. In
contrast, the alternative two-factor and one-factor models resulted in poorer
fit indices relative to the three-factor model, as well as in non-positive
definite covariance matrices. Likelihood ratio χ2 difference tests revealed that, in
comparison to the three-factor model, constraining the covariance between two
of the three latent variables to estimate each two-factor model (Models
2-factor A to 2-factor C) and constraining the covariance between all three
latent variables to estimate the one-factor model significantly deteriorated
the fit of the model. These results suggest that a model specifying three
distinct, but related, dispositional constructs of boredom, self-directed
attention, and self-knowledge fits the data significantly better than models
that specify two or one latent constructs. The correlations between all three
latent factors were statistically significant (all p’s<.001).
5.2. Structural Model
Direct paths from trait
self-directed attention and trait self-knowledge were estimated to explore the
unique relation of each construct with trait boredom,
when the other construct is held constant (see Figure 3). Rather than model fit or factor loadings, which were evaluated in the
above-noted series of measurement models, of interest in this analysis is the
specific relations among the latent factors. Results showed that both
trait self-directed attention and trait self-knowledge were uniquely and
significantly related to trait boredom. Specifically, the path from
self-directed attention to boredom was modest and positive (.21, p<.001),
over and above self-knowledge. The path from self-knowledge to boredom was
strong and negative (-.69, p<.001), over and above self-directed
attention. Together, these results suggest that a person who tends to focus
greater attention on their inner experiences, but who tends to lack knowledge
of their feelings, is more prone to feeling bored.
Table 5. Fit Indices and Chi-Square Difference Tests
Comparing Nested Models to the Three-Factor Model in Study 3
Model |
3-factor |
2-factor A |
2-factor B |
2-factor C |
1-factor |
χ2
(df) |
132.419 (10) |
386.997 (11) |
373.898 (11) |
611.222 (11) |
855.151 (13) |
p value |
< .001 |
< .001 |
< .001 |
< .001 |
< .001 |
CFI |
.961 |
.879 |
.883 |
.807 |
.729 |
RMSEA |
.134 |
.223 |
.219 |
.282 |
.307 |
RMSEA 90% CI |
.114–.154 |
.204–.243 |
.201–.239 |
.263–.301 |
.290–.325 |
SRMR |
.051 |
.119 |
.114 |
.088 |
.129 |
|
|||||
χ2
difference (df) |
|
254.578 (1) |
241.479 (1) |
478.803 (1) |
722.732 (3) |
p value |
|
< .001 |
< .001 |
< .001 |
< .001 |
Note: In each of the
two-factor models, two of the latent factors were specified to measure the
same underlying construct by constraining the covariance between them to a
value of 1, as follows: 2-factor A = self-directed attention and
self-knowledge; 2-factor B = boredom and self-directed attention; and 2-factor
C = boredom and self-knowledge. In the one-factor model, all covariances were
constrained to a value of 1.
Figure 3. Three-Factor
Measurement Model (left) and Structural Model (right) of Boredom, Self-Directed
Attention, and Self-Knowledge (Study 3)
Note: Measurement model
(right): Unstandardized estimates (with standard errors in parenthesis and with
the 95% bias-corrected CI in brackets) are shown to represent the relationships
of each latent variable with its respective indicators. Structural model
(left): Standardized estimates are shown to represent the relationships between
the latent variables.
6. Discussion
6.1. Overview and Discussion of Findings
First, we created a measure of state self-directed
attention that possesses good psychometric properties, good internal
reliability, and convergent and construct validity. Our scale measures
spontaneously occurring fluctuations in self-directed attention—i.e., the
degree to which a person is directing their attention towards their inner
experiences (i.e., thoughts, feelings) at a given moment. Second, CFAs
collapsed across Studies 1 and 2 indicated that state self-directed attention
and state boredom are moderately related, but psychometrically distinct,
experiences, which permits researchers to explore how fluctuations in these
experiences influence each other. Indeed, regression results revealed that
manipulating boredom causes a significant increase in the self-directed
attention, but, as expected, manipulating self-directed attention does not
significantly influence boredom. Fourth, SEM measurement models in Study 3
indicated that trait boredom, trait self-directed attention, and trait
self-knowledge are related, but psychometrically distinct, dispositional
constructs. A follow-up structural model indicated that trait self-directed
attention and trait self-knowledge are uniquely associated with trait boredom,
such that self-directed attention is positively related to trait boredom and
self-knowledge is negatively related to trait boredom.
The
causal relationship between state boredom and state self-directed attention
across Studies 1 and 2 is a novel finding, indicating that when a person is made to feel more bored, they direct
more attention to their inner experiences, over and above other how
self-focused they previously were and the other feelings they experienced
prior. The positive associative relationship between trait self-directed
attention and trait boredom in Study 3 generally coincides with previously
noted positive associations between trait indices of self-directed attention
and boredom (Gana et al., 2000;
Harris, 2000;
Seib and Vodanovich, 1998;
von Gemmingen et al., 2003;
but see also Eastwood et al., 2007).
The notion that boredom
is associated with enhanced self-directed attention is consistent with
neurocognitive research. For example, mind-wandering and lapses in attention on
behavioral tasks—which are experiences associated with boredom—are associated
with increased activity in the default mode network (DMN; Buckner et al., 2008; Fox et al., 2015; Gusnard and Raichle, 2001; Mason et al., 2007), a set of interconnected brain regions that support internally focused
thought (e.g., thinking to oneself). Indeed, activity in DMN increases when
individuals are not engaged in any externally focused activity or task
(Andrews-Hanna, 2012; Buckner et
al., 2008; Mason et al., 2007), but decreases
when one is actively engaged in a task and their attention is externally
directed (Gusnard and Raichle, 2001; Ulrich et al.,
2014). Experimental work examining the state of boredom more directly (contrasted
with a resting state, an induction of interest/engagement, and a sustained
attention task) found that the posterior
components of the DMN are active during the boredom induction, as well as that
the anterior insular is anti-correlated with the DMN during the boredom
induction, which is indicative of a failure to activate executive network
regions that are necessary for engaging with the external world and information
at hand (Danckert and Isacescu, 2017; Danckert and
Merrifield, 2018).
The
positive relationship between boredom and self-directed attention additionally
coincides with theory that discusses the direction of attention
during an affective episode. Lambie and Marcel (2002) describe that emotional experience can take
different forms, depending on one’s direction of attention. The authors suggest
that attention can be directed towards the ‘world’ (to external objects of
perception or thought) or towards ‘oneself’ (to one’s body, one’s location in
space, etc.). Further, they describe that attention can be directed to how the
emotional object (the world or oneself) appears or to one’s ‘action/action
readiness’ (either perceived action targets in the world or strivings for
oneself). This is not to say that factors in the external world (e.g.,
constraint, monotony) are wholly unnoticed when one feels bored, as the
experience of boredom entails the
unfulfilled desire to be engaged in satisfying activity in one’s environment.
Using Lambie and Marcel’s (2002) explanations of emotional
experience and attention, the results of the current study
suggest that during an episode of boredom, one’s attention shifts away
from the external ‘world’ and ‘action/action readiness’ towards ‘oneself’.
That is, even if factors in the external world are salient to an individual,
one’s attention will shift to themselves. Self-directed attention and
externally-directed attention might best be thought of as existing on a
continuum (rather than a dichotomy), and future work could examine the extent
to which boredom engenders self-directed attention relative to externally-directed
attention in a person. Doing so can further elucidate the phenomenological experience
of boredom and accordingly how best to cope with this aversive state.
Relatedly, that
manipulating boredom caused an increase in state self-directed attention
underscores the theoretical and empirical work
on the types of emotions that people can experience. In particular, the
fact that manipulating boredom caused an increase in state
self-directed attention underscores the distinction between non-self-referential
and self-referential emotions. For a non-self-referential emotion, the ‘subject’
(a person) emotionally appraises or evaluates an intentional ‘object’ in the
environment as a whole (or a specific property of the object), which can then
engender an emotion (Zinck, 2008).
For example, a person might feel happy about the pink tulips in their garden as
such or specifically because of their property of being pink because the person
likes pink. For a self-referential emotion, the ‘subject’ emotionally appraises
him/herself (as a whole or in terms of a specific property)—that is, the ‘subject’
and intentional ‘object’ of the emotion are identical (Zinck, 2008). For example, a person might
feel proud of themselves because they are a helpful person, and because they
believe helpfulness to be a valuable character trait, or they may feel proud of
themselves as a whole without regarding any specific property that is relevant
and notable. As self-referential emotions involve the ‘subject’ him/herself,
they predispose self-consciousness (Darwin, 1965; as cited in Zinck, 2008). The results of the current study suggest that
boredom may be a self-referential emotion. In keeping with the above-reviewed
definition of boredom (Fahlman et al., 2013), we posit that when an external ‘object’ or
environment does not support cognitive engagement, a person is thrown out of
engagement with the external ‘object’/environment and back on to
themselves—focused on the aversive feeling of being cognitively unengaged.
Alternatively, the
positive causal relationship between state boredom and state self-directed
attention may simply reflect the fact that boredom is a negative affect.
Indeed, a number of empirical studies (e.g., Brockmeyer et al., 2015; Mor and
Winquist, 2002;
Mor et al., 2010)
suggest that, compared to positive affect, negative affect (e.g., depressed mood,
generalized anxiety, social anxiety) is associated with greater self-focus. As
boredom is an aversive feeling, it reasonably follows that this state is
associated with greater self-directed attention. Notably, in Study 1, both
pre-manipulation frustration (a negative emotion) and pride (a self-referential
emotion) significantly and positively predicted post-manipulation self-directed
attention.
As noted earlier, the existential
escape hypothesis of boredom suggests that when people feel bored, they
seek to engage in behaviors that lower self-focus and feelings of
meaninglessness (e.g., unhealthy eating; Moynihan et al., 2021). Our findings
highlight that increasing one’s boredom increases one’s focus on their inner
experiences in particular—i.e., thoughts and feelings. We did not examine the
impact of boredom on objective self-focus (i.e., perceiving oneself as an
object, observable and open to evaluation by others, see Duval and Wicklund, 1972) and on subsequent behaviors. It
may be useful for future research to explore the relation of boredom to both
objective and subjective self focus.
The negative
relationship between trait self-knowledge and trait boredom found in Study 3 is
consistent with prior work that has found a link between difficulties with
labelling and describing one’s feelings and boredom propensity (Eastwood et
al., 2007;
Gana et al., 2000;
Harris, 2000;
Seib and Vodanovich, 1998;
von Gemmingen et al., 2003).
That self-knowledge had a particularly robust relationship with trait boredom
not only coincides with other empirical work examining the link between
emotional awareness and boredom (Bambrah et al., 2023; Eastwood et al., 2007), but is consistent with
psychodynamic theories of boredom, which posit that boredom
stems from an inability to consciously decipher what one desires
(e.g., Wangh, 1975). Boredom is often thought of as being
engendered by monotony, inappropriate levels of challenge, lack of choice, and
devalued activities in one’s environment (Elpidorou, 2018), but our results offer an additional way of
thinking about boredom by underscoring potential internal and emotional underpinnings (i.e., self-knowledge) of this
aversive experience.
Other
theories suggests that boredom is linked to an appraised lack of meaning in
one’s present context or even life in general (Moynihan et al., 2021). That both
self-directed attention and self-knowledge were uniquely associated with
boredom, over and above one another, may further round out the role of meaning
in the experience of boredom. For example, when a person tends to focus on, but
does not know or understand, themselves and their inner experiences, they are
precluded from knowing and understanding a wide range of emotions, values, and
desires. It follows, then, that they would struggle to articulate and pursue
meaningful activities and thus, feel bored. Indeed, existential theories (e.g.,
Bargdill, 2000)
posit that boredom ensues when a person fails to articulate and
participate in activities that are consistent with their values.
6.2. Future Directions
Our work provides clarity and coherence to the disparate and
disconnected pre-existing literature on boredom and self-focus. Moreover, the
findings from the current studies set up the opportunity to ask additional
questions about the relationships between boredom with self-directed attention
and self-knowledge. First, it is important to emphasize that self-directed
attention is a complex phenomenon in that there are different types of
self-directed attention. For example, Fenigstein and colleagues (1975) posit that
self-consciousness can be divided into private and public, with the former
referring to the tendency to think about and attend to the most covert aspects
of oneself (e.g., thoughts/feelings) and the latter referring to the tendency
to think about overt self-aspects (e.g., behaviors and appearance). Prior work
suggests that private self-consciousness is more strongly associated with
depression and generalized anxiety, whereas public self-consciousness is more
strongly associated with social anxiety (e.g., Mor and Winquest, 2002). Trapnell and Campbell (1999) proposed another
model of self-focused attention, based on reflection and rumination as types of
self-focus, with the former referring to a self-attentiveness
motivated by curiosity or interest in oneself and the latter referring to a
self-attentiveness motivated by perceived losses, injustices, or threats to
oneself. Prior work suggests that reflection is positively associated with
various indicators of emotional wellbeing (e.g., purpose in life,
self-acceptance, personal growth, etc.; Harrington and Loffredo, 2010), whereas rumination is negatively associated with
these indicators. The current studies and existing work on the different types
of self-directed attention underscore the need to specify this concept
conceptually and operationally in order to elucidate how the manner in which
one pays attention to themselves relates to boredom.
Building on the causal relationship
between state boredom and state self-directed attention, future research could
explore the relationship between enhanced self-directed attention during an
instance of boredom and subsequent boredom—that is, examining the
predictive role of self-directed attention on protracted boredom experiences
versus brief boredom experiences, as well as what dispositional indices may
moderate this path.
Our measurement
and structural models in Study 3 show psychometric distinction between trait
boredom, trait self-directed attention, and trait self-knowledge, as well as
highlight the unique associative relations between these variables. This work
provides much-needed conceptual and psychometric clarity amidst the
proliferation of self-related concepts. Equally importantly, this work sets the
stage for future research to theorize and empirically test the causal pathways
between trait boredom, trait self-directed attention, and trait self-knowledge,
to elucidate the mechanisms underlying the relationships between these
variables.
For example, as trait
self-directed attention was positively associated with trait boredom, future
research that theorizes and examines a causal pathway between trait
self-directed attention and trait boredom could explore the underlying
mechanism(s) that mediates this link. In keeping with the ‘unused cognitive
potential’ that is associated with boredom, self-regulatory styles
that diminish one’s pursuit of goals could mediate the relationship between
self-directed attention and boredom. Two studies, for example, found that
boredom proneness is positively correlated with the ‘Assessment’ mode of
self-regulation, whereby a person evaluates all possible goals and procedures
needed to attain specific goals (‘do the right thing’), and is negatively
correlated with the ‘Locomotion’ mode of self-regulation, whereby a person
takes action and adheres to a procedure or action until the goal is reached (‘just
do it’; Mugon et al., 2018;
Struk et al., 2015b).
A person high in the ‘Assessment’ orientation tends to rigorously reflect and
evaluate different possible pathways and goals. This type of person often
evaluates both her/his personality and behaviors. In contrast, people who are
high in the Locomotion orientation tend to focus primarily on achieving goals
and moving forward. It is possible that those more prone to directing attention
to themselves may tend to engage in a ruminative style of self-evaluation that
is characteristic of ‘Assessment’ orientation; and in turn, this propensity to
ruminatively stay in one’s mind, as opposed to shift into action, may predict a
greater propensity towards boredom.
Similarly, future
research could seek to further elucidate the mechanism underlying the
relationship between self-knowledge and trait boredom—that is, what it is about
poor self-knowledge that might pave the way for greater boredom. In keeping
with the ‘desire bind’ that is associated with boredom, when a person possesses
poor self-knowledge, they are alienated from their passions and desires—thus,
unable to articulate actionable desires. We theorize that the inability
to identify what one thinks and feels, and by extension desires, prevents
people from being able to articulate and pursue their goals; and, in turn, this
may predict greater boredom. Moreover, given the critical role of meaning in
boredom (Moynihan et al., 2021),
future research could examine if the relation between self-knowledge and
boredom involves or is independent from meaning. For example, it could be that
the lack of meaning mediates the relationship between poor self-knowledge and
boredom.
Furthermore, that
self-knowledge was negatively related to trait boredom introduces questions
about how other experiences of self-perception and affect may relate to trait
boredom. For example, experiential avoidance, defined as the unwillingness to remain in contact with uncomfortable
private events (e.g., thoughts, emotions, sensations, memories, urges) by
avoiding or escaping these experiences (Hayes et al., 1996), is positively associated with trait boredom (Mercer-Lynn et al., 2011). Some work additionally suggests that experiential avoidance is
positively related to chronic difficulties with identifying and describing
feelings (e.g., Panayiotou et al., 2015; Venta et al., 2012). In keeping with this literature, it is plausible that the tendency to escape or avoid
unwanted thoughts and feelings (experiential avoidance) may predict boredom
through one’s inability to identify, describe, and label these
experiences (poor self-knowledge).
From a methodological
standpoint, our sample sizes in Studies 1 and 2 were sufficient for adequate
estimation of the regression coefficients and standards errors of the
predictors in the regressions (see Austin and Steenberg, 2015; and Wilson Van Voorhis and
Morgan, 2007),
however future research should endeavor to explore our research questions and
variables of interest with a larger sample. Further, in Study 1,
participants in the self-novelty condition (who wrote about what makes them
unique from other people) endorsed higher state self-directed attention than
participants in the externally-oriented condition (who wrote about the external
features of various settings). This manipulation did not impact participants’
state boredom. The self-novelty task is a well-validated induction of
self-directed attention that has precedence in the literature (see Silvia and Eichstaedt, 2004) and we included a comparison
condition where the person is externally-directed. However, there are
potential differences unaccounted for between the self-novelty and
externally-oriented conditions, in that the former condition involves social
comparison whereas the latter condition does not, as well as that the former
condition may feel more interesting to participants than the latter condition.
Of note, there were no differences in Study 1 between conditions on post-manipulation
interest (B = -.11, SE = .24, t=-0.47 p = .640),
while holding participants’ pre-manipulation interest constant. Nevertheless,
future experimental research should aim to select manipulations of
self-directed attention that are closely matched in order eliminate potential
confounds.
7.
Conclusions
Boredom is the aversive experience of ‘unused cognitive potential’ and the current studies highlight
how aspects of self-perception relate to this experience. When one is made to
feel bored, they are more self-focused, which is consistent with research and
theories of emotion. Trait boredom is a related, but distinct, dispositional
construct from trait self-directed attention and trait self-knowledge. To the
degree that one tends to focus on their inner experiences, but tends to lack
knowledge of their inner experiences, they are more prone to boredom, which
underscores the internal self-related underpinnings of boredom. Further
research to deepen our understanding of the underlying mechanisms that link
boredom, self-directed attention, self-knowledge, and other components of
self-perception will enhance our understanding of boredom. Considering that
both state and trait boredom are associated with wide-ranging psychosocial
difficulties, the current work sparks directions for future work on
self-perception and boredom that may subsequently inform the development of
boredom interventions that can help mitigate the deleterious impact of boredom
on people’s functioning and wellbeing.
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Appendix A:
Six-item MSBS-SF Items in Studies 1 and 2
1. I seem to be forced to do
things that have no value to me.
2. I feel bored.
3. I am wasting time that
would be better spent on something else.
4. I feel like I’m sitting
around waiting for something to happen.
5. I am easily distracted.
6. Time is passing by slower
than usual.
Appendix B: Trait Boredom Scale Items in Study 3
1. I often feel bored.
2. I often do not know what I want to do.
3. I often feel like there is nothing fun to do.
4. I often feel like I am wasting time that would be better spent on
something else.
5. I often feel like I’m sitting around waiting for something to happen.
6. It is difficult for me to stay interested in what I’m doing.
[1] A one-factor structure of the six-item MSBS-SF had an ‘acceptable-to-good’
fit with the combined data of Studies 1 and 2: χ2(9)=17.931, p=.036,
CFI=.973, RMSEA=.074 (RMSEA 90% CI: .018, .124), SRMR=.045. The MSBS-SF
possessed good internal reliability (ω=.82).