1. Introduction
Boredom is a common
affective state that is estimated to feature in approximately 2.8% of each
30-minute period of people’s waking hours (Chan et al., 2018)—the equivalent of 2 years in an 80-year long
life. It is an unpleasant experience (Smith and Ellsworth, 1985), and distinct in its phenomenology from correlated feelings such as
sadness, frustration, anger, apathy, and depression (Goldberg et al., 2011; Van Tilburg and Igou, 2012, 2016).
Boredom is often defined as “the aversive experience of wanting, but being
unable, to engage in satisfying activity” (Eastwood et al., 2012, p. 428) and features an inability to focus or
engage attention on an activity (Hunter and Eastwood, 2018; Tam et al., 2021). People who feel bored generally
report restlessness (Danckert et al., 2018a),
disinterest (Eastwood et al., 2012), and a lack of purpose and
challenge (Van Tilburg and Igou, 2012).
Researchers
propose that boredom serves a self-regulatory psychological function. By
signaling a lack of purpose, novelty, excitement, attentional, and cognitive
engagement, boredom may encourage the search for alternative activities that
redress this imbalance (Bench and Lench, 2019;
Danckert et al., 2018b; Elpidorou,
2014; Tam et al., 2021; Van
Tilburg and Igou, 2011; Westgate and Wilson, 2018). This self-regulatory pursuit may have
positive outcomes, such as sparking curiosity (Hunter et al., 2016), retrieving self-soothing nostalgic memories (Van Tilburg et al., 2013), and encouraging exploration (Danckert, 2019). Yet, most documented correlates and
consequences of boredom have focused on the negatives rather than the
positives, including excessive gambling (Mercer and Eastwood, 2010), substance use (LePera, 2011), and monetary risk-taking (Kiliç
et al., 2019) to name just a few (for reviews,
see Moynihan et al., 2021b; Van Tilburg et al., 2024; Vodanovich and Watt, 2016). Accordingly, despite its psychological
functionality and select positive outcomes, research has predominately focused
on negative consequences.
1.1. Boredom and Sexual
Arousal and Behavior
Sexual arousal is an important element of reproductive
behavior (Levin,
2005). There are several psychological and
situational predictors of sexual arousal. These include mood, attention, and
use of pornography or alcohol (De Jong, 2009; George and
Norris, 1991; Julien and Over, 1988; Mitchell et al., 1998), in addition to physical
attraction (Janssen et al., 2007). Among the various causes of
sexual arousal, boredom has recently gained increased attention.
Moynihan
and colleagues (2021b) proposed that boredom triggers a
desire for increasing physiological arousal, which sexual activities may offer
and thereby help reduce boredom. These researchers furthermore suggested that
sexual activities quench temporarily the awareness of one’s purposeless predicament
under boredom (see also Wisman, 2006). The acute focus on pleasure and
excitement that comes with sexual activities (Kor et al., 2014; Reid et al., 2008) helps people to disconnect from
undesirable feelings (Chaney and Chang, 2005; Reid et
al., 2009; Taubman Ben-Ari, 2004).
Research,
predominantly among men, shows that people who score high on trait boredom
express greater sexual sensation seeking, report a higher willingness to engage
in risky sexual behavior, are more likely to engage with pornography (Arnett, 1990; Bőthe et al., 2020; Miller
et al., 2014; Moynihan et al., 2021a, 2022), and
masturbate more (Gana et al., 2001). Further, trait boredom has been
alleged a cause of risky sexual behavior (e.g., Miller et al., 2014), and sex addiction (Chaney and Blalock, 2006). In a qualitative study by Jewkes and
colleagues (2010) on male rapists’ motives, feelings
of boredom were reported as reason for conducting rape in as many as one-third
of cases. In addition, a recent systematic review (de Oliveira and Carvalho, 2020) found evidence for a link between boredom
proneness and hypersexuality—an impulse control disorder featuring compulsive
sexual behavior. A study by Coleman and colleagues (2023), with a large sample of over 800 participants, suggested that this
link may be attributable to the poorer self-regulation of affect among those
high in boredom proneness, with sexual behavior being sought out by those high
in boredom proneness in the attempt to remedy negative moods and boredom.
Indeed, empirical evidence tentatively supports the notion that sexual content
may reduce boredom. Bergen and colleagues (2015) found
that levels of state boredom dropped after sexual online interactions with
others. Moreover, pornography use appears to be a coping mechanism among those
high in trait boredom, which in-itself increased overall sexual sensation
seeking (Moynihan et al., 2021a, 2022). With
boredom being commonly experienced throughout the population, and currently on
the rise in Western society (Chin et al., 2017),
understanding the link between boredom and sexual arousal is pressing.
Notably,
all these past studies relied on self-reports and with few exceptions measured
boredom not as state experience but rather as individual difference—i.e., some
people, more than others, feel bored frequently and see life in general as dull
(Tam et al., 2021; Van Tilburg, 2024). In sum, the previously alleged link between
boredom and men’s sexual arousal is exclusively inferred based on correlational
studies, by looking primarily at differences in boredom between persons rather
than within individuals, and by solely using subjective self-report measures of
their sexual arousal. Thus, despite the possible causal impact of boredom on
sexual arousal and its markers (e.g., sexual sensation seeking) in theory and
reviews (e.g., de Oliveira et al., 2021; Koukounas and McCabe, 1997;
Moynihan et al., 2021b), the current lack of experimental
methods and objective measures of sexual arousal render these interpretations
speculative. We sought to address this issue by investigating if state boredom
causes men’s arousal when presented with pornographic stimuli, using an experimental
methodology with an objective sexual arousal measure.
1.2. Current Study
Existing empirical and
theoretical work has linked boredom, especially among men, to various
self-reported sexual behaviors and sexual arousal measures in response to
preferred-sex others. We build on this prior work in several ways. We sought to
test if boredom increases men’s sexual arousal by measuring this directly,
using an objective measure of genital arousal, in addition to self-reports of
sexual arousal. Building on earlier theorizing, it is plausible that state
boredom may do so. Researchers have proposed that individuals who feel bored
may be attuned to remedies to their predicament available in the environment
(Moynihan et al., 2021a, 2022), and
may display a readiness to focus attend on sexual stimuli and become aroused.
We
employed a within-subjects experimental design in which we manipulated state
feelings of boredom. Different from prior work on the topic, this approach
offered us to evaluate the hitherto-unsubstantiated
but critical assumption that boredom causally raises sexual arousal. This
experimental approach also offered the opportunity to test whether the link
between boredom and sexual arousal occurs in response to boredom states, rather
than individual differences in boredom.[1]
2. Method
2.1. Participants
Participants were 48
male individuals recruited through advertisements in emails and social media
sites, and through the university research recruitment pool. Four participants
were excluded from analyses of genital arousal due to issues with the apparatus.
We sought to collect data from as many participants as practically and
financially feasible within a limited period of time,
which given the nature of the study can be particularly challenging. The study
received ethical approval by the Ethics Committee of the University of Essex
Research Governance team.
Sexual
orientation was not a key factor in this study (which was about the link of
sexual arousal with boredom), and therefore we did not actively search for men
of specific sexual orientations. For this reason, the majority (but not all)
men who took part in this research identified as heterosexual. Still, it was
important to collect data on sexual orientation, as the degree of sexual
arousal is dependent on which sex was preferred. We detail the procedures of
how to assess arousal to the preferred sex towards the end of Method section.
Participants
reported their sexual orientation using the Kinsey et al. (1948) 7-point Likert scale. Men self-identified as “exclusively
heterosexual” (n = 25), “mostly
heterosexual” (n = 14), “bisexual
leaning heterosexual” (n = 1),
“bisexual” (n = 2), or “bisexual
leaning gay” (n = 2). Averaged
genital arousal scores were highest for female targets for 36 (38) participants
in the high (low) boredom condition, with 8 (6) preferring male targets.
Averaged self-reported sexual arousal in the low boredom condition indicated
preference for female sexual targets among 42 participants and for male targets
in 2 participants, and the same frequencies in the high boredom condition.
Participants’
mean (SD) age was 26.04 (12.12).
Twenty were White, followed by 14 Asian, 5 Mixed, and 4 Black; 2 participants
selected the “other” labeled ethnicities.
2.2. Procedure,
Materials, and Measures
Before taking part,
participants gave informed consent and declared that they were at least 18
years old. They also completed an online survey on self-reported age, gender,
ethnicity, and sexual orientation.[2] On arrival in the lab, participants
were seated in a private, sound-proof booth. They received instructions on how
to put on the penile string gauge, which resembles a small flexible lasso and
captures the change in penile circumference. Genital
arousal in men is a strong indicator of sexual arousal to stimuli (Janssen, 2011; Janssen et al., 2002), and penile string gauges have
high validity and reliability for their intended use (e.g., Farkas et al., 1979; Janssen, 2012; Janssen et al., 1997), therefore it is the preferred
measure of male sexual arousal (Seto, 2004).
Genital
response was assessed every 5 milliseconds using a BIOPAC MP160 unit and AcqKnowledge software. Signals were acquired at a sampling
rate of 200 Hz, followed by low-pass filtering (10 Hz) and digitization (16
bits). Prior to participant testing, the gauge had been calibrated using a cone
in increments of 5mm, with calibration points at 80mm and 110mm intervals. We
checked the signal and accuracy of the genital device before commencing each
experimental session.
Next,
participants watched eight 3-minute pornographic videos. Four of these
displayed a man masturbating and four displayed a woman masturbating; all
actors were alone in a bedroom, with close ups on genitals and some women using
vibrators. All videos were edited using Shutter Encoder to be of similar
quality, ratio, and lighting. The chosen male and female models had been
previously rated as the most attractive from a pool of 200 videos and used in
past work on sexual arousal (Rieger et al., 2015).
Each
pornographic clip was preceded by a non-sexual stimulus. This was either one of
four 3-minute videos of a washing machine (high boredom condition) or one of
four comparatively neutral 3-minute clips from a nature documentary (low
boredom condition; Moynihan et al., 2015). These
videos allowed the participants to reach an unaroused state before and after
each sexual video, but also facilitated our
experimental boredom induction. Previous studies have
used neutral videos from 90s to 120s while remaining an adequate length to
reduce arousal to a baseline state (Gruia et al., 2022).
Pairings
of (high vs low) boredom videos with subsequent pornographic clips were
randomized in order across participants. However, to ensure that each
participant was exposed to both nature and boredom videos, pairings were
balanced such that the male clips were twice preceded by a high boredom video
and twice by a low boredom video; likewise, the female videos were twice
preceded by a high boredom video and twice by a low boredom video. After
watching the high or low boredom video, and before proceeding to the
pornographic video, participants reported felt boredom on three manipulation
check items, and participants also self-reported sexual arousal after watching
each of the pornographic videos (details below).
The
genital arousal data were processed using established procedures (Watts et al.,
2018): First, we averaged and then z-scored,
within participants, the genital arousal responses to each sexual video. We
also averaged and z-scored, within
participants, the arousal responses to the 10 seconds that preceded each sexual
video (these 10 seconds were our baseline measure for each video). Furthermore,
for each combination of stimulus sex and boredom condition, we created averages
to reflect each participant’s overall response to both males and females, and
this after high or low exposure to boredom.
We
then calculated participants’ sexual arousal to their preferred sex and
less-preferred sex (technically, “more-arousing sex” and “less-arousing sex”).
Specifically, we checked for each participant whether they were more aroused to
males or to females, on average; the higher one was considered the preferred
sex, and the lower one was considered the less-preferred sex, consistent with
past research (Raines et al., 2021). Doing so helped us accommodate
the fact that not all participants were heterosexual, and that we had a handful
of bisexual males for whom we would not a-priori decide to which sex they
should be more or less aroused to; measure of
preferred sex across sexual orientations was therefore more meaningful than a
measure to one sex or the other. This resulted in four averages for each
participant: genital arousal to the preferred sex for the high boredom
condition, genital arousal to the preferred sex for the low boredom condition,
genital arousal to the less-preferred sex for the high boredom condition, and
genital arousal to the less-preferred sex for the low boredom condition.
We
also employed a subjective measure of sexual arousal using a three-item scale
presented after each pornographic video (“How sexually appealing is this person
to you?”, “How much would you like to have sex with this person?”, “How
sexually attracted are you to this person?”; 1 = not at all, 7 = very
much). A three-item boredom manipulation check (“How bored were you
watching this video?”, “How engaging was this video to you?” [reversed], “How
dull was this video?”; 1 = not at all, 7 = very much) featured after
each high or low boredom video, and before the ensuing pornographic clip. The
orders of items in subjective sexual arousal and the boredom manipulation check
were randomized. The subjective arousal measures were averaged and z-scored
using the same procedure as for the genital arousal measure.
3. Results
3.1. Boredom
Manipulation Check
We compared the
aggregated self-reported boredom reported after high boredom videos against
self-reported boredom after the low boredom videos using a Bayesian paired
samples t-test in JASP (JASP Team, 2024). We assumed an equal prior probability of the null-model and
alternative model, specifying the default Cauchy distribution with spread r = 0.707 (Van Doorn et al., 2021). On average, participants felt more bored
after watching the highly boring videos (M = 6.18, SD = 0.80;
Credibility Interval [CI95%] = [5.94;
6.41]) than the low boredom videos (M = 3.62, SD = 1.04, CI95% = [3.31; 3.92]). Given these
priors and data, the alternative model was estimated BF10 = 4.94×1016 (e = 2.30×10-20) times more likely than the null,
considered very strong evidence for it (Hoijtink et
al., 2019). Figure 1 displays prior and
posterior effect sizes, with positive values reflecting a greater standardized
difference between high and low boredom conditions. We conclude that the
manipulation was successful.
3.2 Genital Arousal
We hypothesized that
manipulated boredom increases genital arousal towards preferred-sex sexual
stimuli. We accordingly predicted that sexual response would be strongest for a
sexual video (vs baseline) when boredom was high and the target was preferred.
We tested this with a three-way interaction, using the following design: 2
(boredom condition: high vs low) × 2 (measurement: baseline vs video response)
× 2 (target: most preferred vs least preferred) Bayesian within-subjects ANOVA.
Figure 2 displays the means and credibility intervals.
Figure 1. Prior and Posterior Effect Sizes for the Difference
in Felt Boredom Across Boredom Conditions
![]()
Note: Higher values
indicate a higher felt boredom in the high boredom condition compared to the
low boredom condition.
Figure 2.
Genital Arousal Across Conditions
![]()
Note: Higher scores indicate higher genital
arousal. Error bars represent 95% credibility intervals.
We followed recommendations by Van den Bergh et al.
(2020) for
Bayesian ANOVA: given the large number of interaction terms (resulting in 19
possible unique models, including the null-model), we considered model-averaged
results for ‘matched’ models only; i.e., models with interaction terms were
compared only to other models that feature the same independent variables but
without the interaction term (as suggested by Sebastiaan Mathôt;
see Van den Bergh et al., 2020). This resulted in seven averaged models
whose predictors were each assigned a prior inclusion probability of P =
.263, with the exception of the triple interaction
model, which was assigned a prior inclusion probability of P = .053.[3]
Table 1 gives the results of this analysis, and
Table 2 reports corresponding posteriors. Inclusion Bayes factors provided
strong evidence for considering main effects of measurement and target,
alongside a measurement × target interaction. At the same time, the data
offered moderate evidence for the absence of any main or interaction effects of
boredom. Three pairwise comparisons within the measurement × target
interaction, using Bayesian paired sample t-tests, offered (1) strong
evidence for genital arousal increasing relative to baseline when watching the
most preferred target, BF10 = 3.68×1011 (e
= 8.14×10-15), (2) strong evidence for genital arousal being higher
for videos that featured most preferred vs least preferred targets, BF10
= 1.41×1010 (e = 1.02×10-13), and (3) moderate
evidence for no change in genital arousal between baseline and video when
watching least preferred targets, BF10 = .16×1011
(e = 1.40×10-5).
In
all, and in defiance of our hypothesis, the results evidenced moderately
strongly an absence of any experimental boredom effects. Instead, results
indicated that genital arousal generally increased when watching a pornography
video of a preferred-sex target.
|
Table 1. Matched
Model Averaged Results for Genital Arousal
|
|
Effects
|
P(inclusion)
|
P(inclusion
| data)
|
BFinclusion
|
|
Boredom
|
|
0.263
|
|
0.113
|
|
0.13
|
|
|
Measurement
|
|
0.263
|
|
2.820×10-25
|
|
8.00×1014
|
|
|
Target
|
|
0.263
|
|
2.825×10-25
|
|
3.79×1011
|
|
|
Boredom × Measurement
|
|
0.263
|
|
0.026
|
|
0.19
|
|
|
Boredom × Target
|
|
0.263
|
|
0.023
|
|
0.17
|
|
|
Measurement × Target
|
|
0.263
|
|
0.999
|
|
3.46×1024
|
|
|
Boredom × Measurement × Target
|
|
0.053
|
|
9.627×10-4
|
|
0.24
|
|
|
|
Note: Compares
models that contain the effect to equivalent models stripped of the effect.
Analysis suggested by Sebastiaan Mathôt.
|
3.3. Subjective Sexual
Arousal
We next tested our
hypothesis that manipulated boredom increases arousal towards preferred-sex
sexual stimuli in the context of subjective (self-reported) sexual arousal. We
specifically anticipated a two-way interaction, where subjective sexual arousal
was expected to be highest when boredom was high and the target was most
preferred (note that the baseline measurement for genital arousal does not
apply to subjective sexual arousal). We tested this with a 2 (boredom
condition: high vs low) × 2 (target: most preferred vs least preferred)
Bayesian within-subjects ANOVA. Figure 3 displays results for conditional means
and credibility intervals.
We
again followed recommendations by Van den Bergh et al. (2020), and considered model-averaged results
for ‘matched’ models only (as suggested by Sebastiaan Mathôt).
This resulted in three averaged models, whose predictors were each assigned a
prior inclusion probability of P =
.400, with the exception of the interaction model,
which was assigned a prior inclusion probability of P = .200.[4]
Figure 3. Subjective Sexual Arousal Across
Conditions
![]()
Note: Higher scores indicate higher subjective sexual arousal. Error
bars represent 95% credibility intervals.
Table
3 gives the results of this analysis, and Table 4 reports corresponding
posteriors. Inclusion Bayes factors provided strong evidence for a main effect
of target, moderate evidence for the absence of a boredom main effect,
and—against our prediction—moderate evidence for the absence of a measurement ×
target interaction. In all, the results indicated that people experienced more
subjective sexual arousal when watching a video with their most preferred
versus least preferred targets, and that this their subjective sexual arousal
was likely not influenced by induced boredom, consistent with the results for
genital arousal reported above.
|
Table 3.
Matched Model Averaged Results for Subjective
Sexual Arousal
|
|
Effects
|
P(inclusion)
|
P(inclusion | data)
|
BFinclusion
|
|
Boredom
|
|
0.400
|
|
0.188
|
|
0.25
|
|
Target
|
|
0.400
|
|
0.946
|
|
3.82×10+29
|
|
Boredom × Target
|
|
0.200
|
|
0.054
|
|
0.29
|
|
|
Note. Compares models that contain the effect to equivalent
models stripped of the effect. Analysis suggested by Sebastiaan Mathôt.
|
|
Table 4. Model Averaged Posteriors for Subjective
Sexual Arousal
|
|
95%CI
|
|
Variable
|
Level
|
M
|
SD
|
Lower
|
Upper
|
|
Intercept
|
|
.12
|
.015
|
.09
|
.14
|
|
Boredom
|
High
|
.00
|
.018
|
-.04
|
.04
|
|
Low
|
-.00
|
.018
|
-.04
|
.03
|
|
Target
|
Most preferred
|
.83
|
.028
|
.77
|
.88
|
|
Least preferred
|
-.83
|
.028
|
-.88
|
-.77
|
|
Boredom × Target
|
High & most preferred
|
.01
|
.012
|
-.02
|
.03
|
|
High & least preferred
|
-.01
|
.012
|
-.03
|
.01
|
|
Low & most preferred
|
-.01
|
.012
|
-.03
|
.02
|
|
Low & least preferred
|
.01
|
.012
|
-.02
|
.03
|
|
4. Discussion
We set out to test if state boredom causally increases genital and
self-reported sexual arousal among men in response to sexual stimuli. Findings
indicated that their genital and subjective arousal for preferred sex targets
did not vary as a function of boredom, despite the boredom manipulation being
highly effective. In fact, Bayes factors offered moderately strong evidence for
the absence of any influence of
boredom on sexual arousal, be that in the form of main effect or interactions.
Instead, we found strong evidence for genital and self-reported sexual arousal
to increase for preferred sex target relative to less-preferred sex targets.
Whereas
past work operationalized sexual behavior and attitudes in the forms of
(self-reported) sexual sensation seeking, pornography consumption, willingness
to engage in risky sexual behavior, and frequencies of masturbating (Arnett, 1990; Bőthe et al., 2020; Gana et
al., 2001; Miller et al., 2014;
Moynihan et al., 2021a, 2022), we are
the first to link boredom to direct measures of sexual arousal—operationalized
as genital arousal and self-reported sexual arousal. These extensions are
important, as prior theorizing has often assumed that links between boredom and
sexual behaviors or attitudes are in part rooted in the arousal processes
(e.g., sexual stimuli may alleviate low arousal under boredom by increasing
it). Our null-findings are therefore informative: they suggest that whatever
explains the link between boredom and sexual behavior at the level of
individual difference correlates may not involve changes in sexual arousal
changes in response to state boredom.
What,
then, might explain that boredom is linked to sexual behavior and motivations
in past findings but not ours? One possibility is that variables such as sexual
sensation seeking, pornography consumption, and willingness to engage in risky
sexual behavior are not elevated under (trait) boredom because they serve to increase
arousal, but rather than they help to engage attention. Trait boredom is
associated with failures to sustain attention (e.g., Isacescu
et al., 2017) and attempts to reengage it (Tam
et al., 2021). Indeed, Moynihan and colleagues (2021a, 2022) have
suggested that the links between trait boredom and sexual sensation seeking,
and between trait boredom and pornography consumption, reflect attempts to
distract oneself from a boring predicament, which speculatively may reflect the
pursuit of attentional reengagement with something else (i.e. sexual stimuli).
If this is the case, then one might expect state boredom not to increase sexual
arousal, but rather that it would increase attentional towards sexual stimuli
(e.g., pornographic imagery) relative to control. Following this line of
argumentation, a reason why boredom may be associated with sexual behavior
independently of sexual arousal is that it may simply offer people something to
do. Researchers have suggested that boredom serves as a call to action (Elpidorou, 2014), and
experiments show that boredom leads to the pursuit of novel activities
regardless of whether those stimuli are pleasant or not (Bench and Lench, 2019). Plausibly, sexual behaviors may offer bored individuals simply
something to do, although boredom itself does not affect their arousability.
Another
possibility for the putative divergence between our state boredom versus past
trait boredom findings is that the two are characterized by partly distinct
psychological mechanisms. Notably, state boredom has been proposed to help
regulate attention, arousal, meaning, and novelty pursuit, for example by
prompting disengagement with current (in)activity in favor of alternatives that
appear more satisfying (Bench and Lench, 2019;
Eastwood et al., 2012; Elpidorou,
2014; Van Tilburg and Igou, 2012). Trait boredom, on the other hand, has been
argued to involve an inability to pursue fulfilling activity effectively,
despite the desire to do so (Danckert, 2019; Elpidorou, 2014), with
Tam and colleagues (2021) proposing that trait boredom may
reflect the enduring failure to effectively cope with state boredom over time.
Perhaps, sexual behavior and motivations investigated in past work on trait
boredom represent self-regulation failures
rather than self-regulation attempts, which may be expected to occur in the
face of trait boredom, but not necessarily to result of state boredom. Indeed,
Lin and colleagues (2023) found that trait, but not state
boredom, correlated with solitary sexual activity during the COVID-19 pandemic,
which hints at the importance of differentiating state and trait boredom
mechanisms in context of sexual behavior and motivations.
Placing the current findings in the
literature on affect-dependent sexual arousal more broadly, the current
null-findings may hint that the link between affect and genital arousal is more
complex than we anticipated. Indeed, research shows that the impact of positive
and negative mood states in genital arousal are
probably not straightforward. For example, Mitchell and colleagues (1998) found that happy music (e.g.,
Mozart’s Eine Kleine Nacht Musik)
increased genital arousal responses to sexual stimuli in 24 men compared to
control, and that sad music (e.g., Albinoni’s Adagio in G Minor) reduced it; a mostly similar pattern of findings
was obtained by Ter Kuile et al. (2010) among a group of 32 women.
Strikingly, the ‘neutral’ control condition activity in Mitchell el al. (1998), which produced intermediate
levels of genital arousal, seems comparatively boring: press a button for each ‘t’
in an audio sequence of letters, for five minutes. An earlier study on genital
arousal in 15 men, by Meisler and Carey (1991), found that positive (vs negative)
mood inductions did not alter genital arousal in response to erotica, but did
increase (vs decrease) subjective arousal after a delay. Work by Carvalho and colleagues
(2017) in a larger sample of 52 men and 73 women found, instead, that
subjective sexual arousal in response to pornography was unaffected by positive
and negative mood inductions (vs control) induced using non-sexual videos.
Taken together, there seems to be considerable divergence in genital and
self-reported sexual arousal findings from experiments that induced positive
and negative forms of affect, with the current study further adding to this
discrepancy.
4.1. Limitations and
Future Directions
In keeping with most of
the previous work on the boredom-sexual behavior link, we focused only on men.
Women’s sexual arousal has been less studied than men’s (Chivers,
2017) and often their arousal does not reflect
their subjective sexual preferences (Rieger et al., 2016), which would make it more
difficult to make predictions of boredom’s impact (or indeed lack thereof) on
their genital arousal. Other research has furthermore found that men are, on average, more
boredom-prone (Polly et al., 1993; Watt and Vodanovich, 1999) and more sexually bored (Watt and Ewing, 1996) than
women. That being said, genital arousal (and boredom)
can be successfully measured in women (Suschinsky et
al., 2015). Future research should therefore
consider women and look into potential gender
differences in the link between boredom and sexual arousal.
While
the present study did not purposefully target specific sexual orientations, it
is worthwhile to examine further if there are differences by sexual orientation
in responses to boredom. McCoul and Haslam (2001) found
that sexual sensation seeking and impulsivity were associated with risky sexual
behaviors among heterosexual men, but that this was not the same for gay men.
Other research found that gay men engaged in more non-committal sexual
behaviors in comparison to heterosexual and bisexual men (Schmitt, 2007). Given the links that boredom has with impulsiveness and sensation
seeking (Dahlen et al., 2004; Moynihan et al., 2017), it is possible that there are differences in
the impact of boredom depending on sexual orientation.
The present study may have
benefitted from a mixed factorial design with boredom state as a between
participants variable to reduce possible carry over effects from prior videos.
Future studies should consider using this design to eliminate any effects caused
by the switching of boredom and neutral stimuli throughout the experiment.
Having said that, the boredom manipulation proved highly effective with the
current within-person design.
5. Conclusion
For the first time we
experimentally tested the causal effect of state boredom on sexual arousal in
men. Our findings show that experimentally induced boredom unlikely increases
sexual arousal—objectively assessed with a genital arousal measure and subjectively
assessed using self-reports—with the data providing moderately strong support
for the absence of a role for boredom. These findings provide much needed
insight into the links between boredom and sexual arousal.
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