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Short Communication
Intelligence and emotional disorders: Is the worrying and ruminating
mind a more intelligent mind?
Alexander M. Penney
⇑
, Victoria C. Miedema, Dwight Mazmanian
Department of Psychology, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada
article info
Article history:
Received 8 June 2014
Received in revised form 30 September
2014
Accepted 1 October 2014
Keywords:
Intelligence
Generalized anxiety disorder
Depression
Social anxiety disorder
Worry
Rumination
Post-event processing
abstract
Previous research has shown that anxiety and depression symptoms are negatively associated with
measures of intellige nce. However, this research has often not taken state distress and test anxiety into
account, and recent findings indicate possible positive relationships between generalized anxiety
disorder (GAD), worry, and intelligence. The present study examined the relationships between GAD,
depression, and social anxiety symptoms, as well as their underlying cognitive processes of worry,
rumination, and post-event processing, with verbal and non-verbal intelligence in an undergraduate
sample (N = 126). While the results indicate that verbal intelligence has positive relationships with
GAD and depression symptoms when test anxiety and state negative affect were taken into account,
these relationships became non-significant when overlapping variance was controlled for. However,
verbal intelligence was a unique positive predictor of worry and rumination severity. Non-verbal
intelligence was a unique negative predictor of post-event processing. The possible connections between
intelligence and the cognitive processes that underlie emotional disorders are discussed.
Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction
Intelligence has long been recognized as playing a key role in
human evolution. Adaptive emotional regulation is also considered
to be critically important for survival and reproduction (Darwin,
1872). More recently, some theorists have extrapolated the evolu-
tionary framework to encompass the maladaptive extremes of
emotions – the emotional disorders (e.g., Gilbert, 1998, 2001;
Marks & Nesse, 1994). In this view, experiencing the ‘‘right’’
emotion (e.g., anxiety, sadness, or happiness), with the optimal
intensity and duration, in the correct context or situation, would
clearly enhance an organism’s fitness. Emotional disorders, there-
fore, represent the extreme and non-adaptive tails of a normal dis-
tribution of individual variability in emotional reactions. For
example, given the adaptive value of an emotion like anxiety,
which would permit an individual to anticipate and plan for poten-
tial threats, it seems clear that anxiety might have co-evolved with
increased intelligence. Moreover, given the potentially fatal costs
of ‘‘false negatives’’ in decision-making about threats, selection
pressures may have favoured errors in the other direction, or ‘‘false
positives’’. From an evolutionary standpoint, there are fewer costs
associated with worrying about a threatening event that does not
occur than failing to anticipate, plan for, or avoid one that does.
Relevant research exploring these relationships has provided
mixed results, however. Researchers have often found a negative
relationship between intelligence and emotional disorders, across
a diverse range of samples (Feldhusen & Klausmeier, 1962;
Kerrick, 1955; McCandless & Castaneda, 1956). A recent meta-anal-
ysis indicated that gifted children are less likely to have anxiety
than non-gifted children (Martin, Burns, & Schonlau, 2010). Multi-
ple studies have also found that depressed individuals score lower
on measures of processing speed and visual–spatial reasoning than
they do on measures of verbal intelligence (Kluger & Goldberg,
1990; Zillmer, Ball, Fowler, Newman, & Stutts, 1991). However, it
is possible that the symptoms of acute depression might decrease
an individual’s ability to perform optimally on an intelligence test,
and that the individual may not have lower intelligence. Aligning
with this, Ruisel (2000) argued that state anxiety and test anxiety
should be taken into account when interpreting the relationship
between anxiety and intelligence, and Moutafi, Furnham, and
Tsaousis (2006) found that test anxiety mediated the relationship
between neuroticism and intelligence. This research suggests that
the negative relationship between emotional disorders and intelli-
gence may be an artifact of the testing itself.
http://dx.doi.org/10.1016/j.paid.2014.10.005
0191-8869/Ó 2014 Elsevier Ltd. All rights reserved.
⇑
Corresponding author at: Department of Psychology, MacEwan University, City
Centre Campus, 10700-104 Avenue, Edmonton, AB T5J 4S2, Canada. Tel.: +1 780 497
4165; fax: +1 780 497 5308.
E-mail addresses: apenney@lakeheadu.ca (A.M. Penney), vcmiedem@lakeheadu.
ca (V.C. Miedema), dwight.mazmanian@lakeheadu.ca (D. Mazmanian).
Personality and Individual Differences 74 (2015) 90–93
Contents lists available at ScienceDirect
Personality and Individual Differences
journal homepage: www.elsevier.com/locate/paid
Recent studies by Coplan et al. (2006, 2012) compared healthy
controls to individuals with generalized anxiety disorder (GAD),
and found that individuals with GAD had higher intelligence. Worry
severity also positively correlated with intelligence within the GAD
samples. Unfortunately, both studies had very small samples, and
the authors did not investigate the role of other cognitive processes.
While worry is the proposed cognitive process underlying GAD
(American Psychiatric Association, 2013), rumination and post-
event processing are thought to be the primary cognitive processes
involved in major depressive disorder and social anxiety disorder,
respectively (Clark & Wells, 1995; Nolen-Hoeksema, 2000).
This study sought to further examine the relationships between
emotional disorders and intelligence. Using a large undergraduate
sample, we examined the relationships of GAD, depression, and
social anxiety symptoms, as well as the relationships of worry,
rumination, and post-event processing, with verbal and non-verbal
intelligence while controlling for state negative affect and test
anxiety.
2. Materials and methods
2.1. Participants
A total of 126 undergraduate students participated. The sample
consisted primarily of Caucasian (85.7%), young adult (M
age = 20.46, SD = 4.53) women (77.0%). This study was reviewed
and approved by the university’s research ethics board.
2.2. Measures
2.2.1. Generalized Anxiety Disorder Questionnaire-IV (GADQ-IV;
Newman et al., 2002)
The GADQ-IV is a 9-item self-report measure, with higher
scores indicating a higher amount of GAD symptoms. The GADQ-
IV demonstrates strong convergent and divergent validity, as well
as good internal consistency.
2.2.2. Penn State Worry Questionnaire (PSWQ; Meyer, Miller, Metzger,
& Borkovec, 1990)
The PSWQ is a 16-item self-report questionnaire. The PSWQ has
been found to have high internal consistency, and high content
validity. Higher scores indicate more frequent worries.
2.2.3. Centre for Epidemiological Studies Depression Scale (CES-D;
Radloff, 1977)
The CES-D is a 20-item self-report measure. Higher scores indi-
cate more frequent depressive symptoms. The CES-D has high
internal consistency, high content validity, and moderate conver-
gent validity.
2.2.4. Ruminative Responses Scale-Brooding and Reflection (RRS-BR;
Treynor, Gonzalez, & Nolen-Hoeksema, 2003)
Higher scores on the RRS-BR indicate more frequent rumina-
tion. The RRS-BR is a 10-item self-report questionnaire, which
has been found to have high internal consistency, and strong con-
vergent validity.
2.2.5. Social Phobia Inventory (SPIN; Connor et al., 2000)
The SPIN is a 17-item self-report measure, with higher scores
corresponding to more intense social anxiety symptoms. The SPIN
has excellent internal consistency and good convergent validity.
2.2.6. Post-Event Processing Questionnaire-Revised (PEPQ-R; McEvoy
& Kingsep, 2006)
The PEPQ-R is an 8-item self-report questionnaire. The PEPQ-R
has been found to have high internal consistency and moderate
convergent validity. Higher scores indicate more frequent and
intense post-event processing.
2.2.7. Verbal Comprehension Index (VCI; Wechsler, 2008)
A VCI score was calculated for each participant using the Simi-
larities, Comprehension, and the Vocabulary subscales from the
Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV;
Wechsler, 2008). Raw scores on each of the three scales were con-
verted into scaled scores and transformed according to the rules
specified in the WAIS-IV manual. Higher scores on the VCI indicate
higher verbal intelligence. The subscales and the VCI have been
shown to have excellent psychometric properties.
2.2.8. Raven’s Standard Progressive Matrices (SPM; Raven, Raven, &
Court, 2000)
The SPM is a series of five matrices sets with a part missing. Par-
ticipants select a pattern that they believe completes the overall
design. The SPM has excellent psychometric properties and higher
scores indicate higher non-verbal intelligence.
2.2.9. Positive and Negative Affect Schedule – Negative Affect subscale
(PANAS-NA; Watson, Clark, & Tellegen, 1988)
The PANAS-NA is a 10-item self-report measure, with higher
scores indicating more intense state negative affect. The PANAS-
NA has demonstrated high internal consistency, convergent valid-
ity, discriminant validity, and construct validity.
2.2.10. Cognitive Test Anxiety Scale (CTAS; Cassady & Johnson, 2002)
The CTAS is a 27-item self-report measure. The CTAS demon-
strates high levels of internal consistency, stability, and predictive
validity. Higher scores indicate more severe test anxiety.
2.3. Procedure
After expressing interest in the study, potential participants
met individually with one of the primary researchers, or one of
seven research assistants. The primary researchers provided exten-
sive training to the research assistants on how to complete the
WAIS-IV subscales and the SPM. Participants were fully informed
of the nature of the study, and then completed a demographic
characteristics questionnaire, followed by the measures in the fol-
lowing order: the PEPQ-R, the CES-D, the WAIS-IV: Similarities, the
SPIN, the PSWQ, the WAIS-IV: Comprehension, the PANAS-NA,
the CTAS, the WAIS-IV: Vocabulary, the RRS-BR, the GADQ-IV,
and the SPM.
2.4. Statistical analyses
Partial correlations were first examined between the VCI and
SPM and the symptom measures, as well as between the VCI and
SPM and the cognitive process measures, controlling for scores
on the PANAS-NA and the CTAS. To examine if the associations
between the VCI and SPM and the measures of interest were
unique (i.e., not due to overlapping variance among measures),
hierarchical regression analyses were conducted.
3. Results and discussion
When controlling for test anxiety and state negative affect, the
VCI positively partially correlated (pr) with the GADQ-IV,
pr(122) = .18, p = .045, and with the CES-D, pr(122) = .20, p = .023.
The VCI also positively correlated with the PSWQ, pr(122) = .21,
A.M. Penney et al. / Personality and Individual Differences 74 (2015) 90–93
91
p = .018, and the RRS-BR, pr(122) = .24, p = .007. The VCI did not
correlate with the SPIN or the PEPQ-R, ps > .590. The SPM nega-
tively correlated with the PEPQ-R, pr(122) = .20, p = .027, but
did not correlate with any other measure, ps > .085. Table 1 reports
the results of the hierarchical regression analyses. The VCI was a
unique positive predictor of the PSWQ and RRS-BR, while the
SPM was a unique negative predictor of the PEPQ-R. The results
of this study indicate that verbal intelligence is positively associ-
ated with the tendency to worry and ruminate. Non-verbal intelli-
gence, on the other hand, is negatively associated with the
tendency to process past social events.
Overall, the results of this study support and extend the find-
ings of Coplan et al. (2006, 2012). While Coplan et al. (2006,
2012) found a positive association between worry and intelligence
only in a clinical sample, the current study extended this finding to
a non-clinical sample. The present findings have also revealed that
rumination is positively related to verbal intelligence. However,
post-event processing was negatively related to non-verbal intelli-
gence. It is possible that more verbally intelligent individuals are
able to consider past and future events in greater detail, leading
to more intense rumination and worry. Individuals with higher
non-verbal intelligence may be stronger at processing the non-ver-
bal signals from individuals they interact with in the moment,
leading to a decreased need to re-process past social encounters.
Previous studies in this area found a negative relationship
between intelligence and anxiety and depression (e.g., Feldhusen
& Klausmeier, 1962; Kluger & Goldberg, 1990). By controlling for
state distress and test anxiety, this study found positive correla-
tions between GAD and depression symptoms and verbal intelli-
gence, although the relationship was lost when controlling for
overlapping variance. No relationship was found between social
anxiety symptoms and verbal or non-verbal intelligence. These
findings indicate that while intelligence may be related to the
symptoms of emotional disorders, it is more strongly linked to
the cognitive processes that underlie these disorders.
One additional implication that arises from this study is that
future researchers who wish to examine the relationship between
intelligence and various psychological variables, such as psychopa-
thology, cognitive processes, or personality, should consider the
role of state negative affect (i.e., current emotional state) and test
anxiety. This study illustrates that these variables may play a
significant role in the observed strength and direction of the
relationships higher-level constructs have with intelligence.
The present study was not without limitations. The sample for
this study was undergraduate students, with few individuals over
the age of 30 participating. It is difficult to be certain how the
results obtained from this sample would generalize to older popu-
lations. However, this did not restrict the range of VCI scores,
which ranged from 74 to 130. Similarly the range of the emotional
disorder symptoms varied considerably, with the mean scores for
the sample at or above the empirical cut-offs for the GADQ-IV,
SPIN, and CES-D. Yet, without clinical interviews it is unclear
how many participants would have met diagnostic criteria for
the emotional disorders examined in this study.
The present study examined the relationships between verbal
and non-verbal intelligence and the symptoms and cognitions of
emotional disorders. Although only small positive correlations
between verbal intelligence and the symptoms of GAD and depres-
sion were found, positive associations between verbal intelligence
and worry and rumination and a negative association between
non-verbal intelligence and post-event processing emerged. Future
studies are needed to provide a thorough explanation and interpre-
tation of the relationships between these cognitive processes and
intelligence. However, these preliminary results indicate that a
worrying and ruminating mind is a more verbally intelligent mind;
a socially ruminative mind, however, might be less able to process
non-verbal information.
Authors’ note
We would like to acknowledge Stephanie Cottrell for her
assistance with preparation of study materials, data collection,
and data entry. We would also like to thank Alyssa Mervin for
preparation of study materials and data collection, and Kimberly
Ongaro, Amy Killen, Sarah Kaukinen, Dylan Antoniazzi, and
Matthew Nordlund for assisting with data collection.
Declaration of conflicting interests
The authors declare no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
This study was not funded by any external funding source.
Table 1
Hierarchical regression analyses.
Variable R Adjusted R
2
R
2
change tpr
Dependent variable: GADQ-IV
Step 1 .60 .35 .36
**
PANAS-NA 3.63
**
.31
**
CTAS 6.36
**
.50
**
Step 2 .67 .43 .09
**
SPIN 1.00 .09
CES-D 4.21
**
.36
**
Step 3 .68 .43 .01
VCI 1.26 .11
Dependent variable: CES-D
Step 1 .53 .26 .28
*
PANAS-NA 4.96
**
.41
**
CTAS 3.39
**
.29
**
Step 2 .61 .35 .10
**
SPIN 0.61 .05
GADQ-IV 4.21
**
.36
**
Step 3 .62 .36 .01
VCI 1.67 .15
Dependent variable: PSWQ
Step 1 .61 .36 .36
**
PANAS-NA 4.28
**
.36
**
CTAS 6.07
**
.48
**
Step 2 .68 .44 .09
**
PEPQ-R 3.65
**
.31
**
RRS-BR 1.28 .11
Step 3 .69 .46 .02
*
VCI 2.17
*
.19
*
Dependent variable: RRS-BR
Step 1 .42 .16 .18
**
PANAS-NA 4.16
**
.35
**
CTAS 1.94 .17
Step 2 .51 .24 .09
**
PEPQ-R 2.85
*
.25
*
PSWQ 1.28 .11
Step 3 .55 .27 .04
*
VCI 2.52
*
.22
*
Dependent variable: PEPQ-R
Step 1 .43 .17 .18
PANAS-NA 2.80
*
.24
*
CTAS 3.63
**
.31
**
Step 2 .58 .31 .15
PSWQ 3.65
**
.31
**
RRS-BR 2.85
*
.25
*
Step 3 .61 .35 .04
SPM 2.79
*
.25
*
*
p < .05.
**
p < .001.
92 A.M. Penney et al. / Personality and Individual Differences 74 (2015) 90–93
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