Proneness for psychological flow in everyday life: Associations with personality and intelligence

Abstract

Flow is an experience of enjoyment, concentration, and low self-awareness that occurs during active task performance. We investigated associations between the tendency to experience flow (flow proneness), Big Five personality traits and intelligence in two samples. We hypothesized a negative relation between flow proneness and neuroticism, since negative affect could interfere with the affective component of flow. Secondly, since sustained attention is a component of flow, we tested whether flow proneness is positively related to intelligence. Sample 1 included 137 individuals who completed tests for flow proneness, intelligence, and Big Five personality. In Sample 2 (all twins; n=2539), flow proneness and intelligence, but not personality, were measured. As hypothesized, we found a negative correlation between flow proneness and neuroticism in Sample 1. Additional exploratory analyses revealed a positive association between flow proneness and conscientiousness. There was no correlation between flow proneness and intelligence. Although significant for some comparisons, associations between intelligence and flow proneness were also very weak in Sample 2. We conclude that flow proneness is associated with personality rather than intelligence, and discuss that flow may be a state of effortless attention that relies on different mechanisms from those involved in attention during mental effort.

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Proneness for psychological flow in everyday life: Associations with personality
and intelligence
Fredrik Ullén
a,
⇑
, Örjan de Manzano
a
, Rita Almeida
c
, Patrik K.E. Magnusson
b
, Nancy L. Pedersen
b
,
Jeanne Nakamura
d
, Mihály Csíkszentmihályi
d
, Guy Madison
e
a
Dept. of Women’s and Children’s Health and Stockholm Brain Institute, Karolinska Institutet, Sweden
b
Dept. of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden
c
Dept. of Neuroscience, Karolinska Institutet, Sweden
d
Quality of Life Research Center, Claremont Graduate University, CA, USA
e
Dept. of Psychology, Umeå University, Sweden
article info
Article history:
Received 18 May 2011
Received in revised form 4 October 2011
Accepted 8 October 2011
Available online 8 November 2011
Keywords:
Neuroticism
Conscientiousness
IQ
Enjoyment
Motivation
Attention
Personality
Intelligence
Flow
Expertise
abstract
Flow is an experience of enjoyment, concentration, and low self-awareness that occurs during active task
performance. We investigated associations between the tendency to experience flow (flow proneness),
Big Five personality traits and intelligence in two samples. We hypothesized a negative relation between
flow proneness and neuroticism, since negative affect could interfere with the affective component of
flow. Secondly, since sustained attention is a component of flow, we tested whether flow proneness is
positively related to intelligence. Sample 1 included 137 individuals who completed tests for flow prone-
ness, intelligence, and Big Five personality. In Sample 2 (all twins; n= 2539), flow proneness and intelli-
gence, but not personality, were measured. As hypothesized, we found a negative correlation between
flow proneness and neuroticism in Sample 1. Additional exploratory analyses revealed a positive associ-
ation between flow proneness and conscientiousness. There was no correlation between flow proneness
and intelligence. Although significant for some comparisons, associations between intelligence and flow
proneness were also very weak in Sample 2. We conclude that flow proneness is associated with person-
ality rather than intelligence, and discuss that flow may be a state of effortless attention that relies on
different mechanisms from those involved in attention during mental effort.
Ó2011 Elsevier Ltd. All rights reserved.
1. Introduction
Flow is a state of concentration, low self-awareness and enjoy-
ment that typically occurs during activities that are challenging but
matched in difficulty to the person’s skill level. Several elements
recur in verbalizations of this state (Csikszentmihalyi &
Csikszentmihalyi, 1988). Actions feel effortless and automatic
although there is a subjective sense of high control and concentra-
tion, or even absorption in the task. Goals are clear and there is
unambiguous feedback on performance. Self-reflective thoughts
and fear of evaluation by others are low. Time perception may be
altered. Finally, flow is highly enjoyable, i.e. performance is accom-
panied by positive affect. Flow experiences can occur in a wide
range of activities, from chess playing to mountain climbing
(Csikszentmihalyi & Csikszentmihalyi, 1988). While there appear
to be considerable differences between individuals with regard to
the conditions and tasks that are conducive to flow, the state itself
is described in remarkably similar terms regardless of socioeco-
nomic status, age, culture and ethnicity (Asakawa, 2004, 2010; Bas-
si & Delle Fave, 2004; Csikszentmihalyi & Csikszentmihalyi, 1988;
Moneta, 2004).
Flow has been studied using self-report questionnaires designed
to capture the major dimensions of the flow experience discussed
above (Csikszentmihalyi & Csikszentmihalyi, 1988; de Manzano,
Theorell, Harmat, & Ullén, 2010; Jackson & Eklund, 2004). Self-re-
port instruments have also been developed to measure the disposi-
tional tendency of an individual to have flow experiences, e.g.
Csikszentmihalyi’s Flow Questionnaire (Csikszentmihalyi, 1975;
Csikszentmihalyi & Schneider, 2000), with items on the frequency
of flow states in everyday life, and Jackson and Eklund’s Disposi-
tional Flow Scale-2 (DFS-2) (Jackson & Eklund, 2004), which mea-
sures the frequency of flow experiences during a particular type
of activity.
There are large individual differences in the frequency and
intensity of flow experiences (Asakawa, 2010; Csikszentmihalyi &
0191-8869/$ - see front matter Ó2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.paid.2011.10.003
⇑
Corresponding author. Address: Dept. of Women’s and Children’s Health and
Stockholm Brain Institute, Karolinska Institutet, Retzius v. 8, SE-171 77 Stockholm,
Sweden. Tel.: +46 8 524 832 68; fax: +46 8 517 773 49.
E-mail address: Fredrik.Ullen@ki.se (F. Ullén).
Personality and Individual Differences 52 (2012) 167–172
Contents lists available at SciVerse ScienceDirect
Personality and Individual Differences
journal homepage: www.elsevier.com/locate/paid

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Csikszentmihalyi, 1988; Csikszentmihalyi & Schneider, 2000; Mon-
eta, 2004). These differences are likely to depend both on individ-
ual traits and on situational variables. Flow proneness is positively
related to self-esteem, self-concept and perceived ability (Adlai-
Gail, 1994; Asakawa, 2010; Jackson, Kimiecik, Ford, & Marsh,
1998; Jackson, Thomas, Marsh, & Smethurst, 2001); life satisfaction
(Asakawa, 2010); intrinsic motivation (Jackson et al., 1998) and
enjoyment (Hamilton, Haier, & Buchsbaum, 1984); psychological
well-being (Asakawa, 2004, 2010; Ishimura & Kodama, 2006);
and a tendency to adopt active rather than passive coping strate-
gies (Asakawa, 2010). Negative relations have been reported be-
tween flow proneness and anxiety (Asakawa, 2010; Jackson et al.,
1998).
The aim of the present study was to investigate associations be-
tween flow proneness and the major dimensions of the standard
five-factor model of personality (McCrae & Costa, 1990), as well
as general intelligence. Specifically, we hypothesized a negative
relation between flow proneness and neuroticism. Several features
of neuroticism – in particular, a high reactivity to negative stimuli,
and a proneness to negative affect (Gray & McNaughton, 2000;
McCrae & Costa, 1990) – could plausibly interfere with flow states.
As mentioned, several studies have indeed found that flow prone-
ness is negatively related to trait anxiety, and positively related to
psychological well-being (Asakawa, 2004, 2010; Ishimura & Kod-
ama, 2006; Jackson et al., 1998). Associations between flow prone-
ness and the four other Big Five dimensions were investigated in
exploratory analysis. With regard to intelligence, performance on
tests of sustained attention show substantial positive associations
with psychometric general intelligence (Schweizer & Moosbrugger,
2004). Indeed, analyses of the cognitive processes utilized during
solving of the Raven Progressive Matrices test, which mainly mea-
sures general intelligence, have shown that individual differences
on the test are related to the ability to sustain problem-solving
goals in working memory (Carpenter, Just, & Shell, 1990). Notably,
however, the high concentration during flow states appears to dif-
fer from effortful attention both in terms of subjective experience
(Csikszentmihalyi & Nakamura, 2010) and physiological correlates
(de Manzano et al., 2010). A positive relation between flow prone-
ness and intelligence would support that effortful and effortless
attention nevertheless share mechanisms; if the relation were
weak or nil, it would rather suggest that the involved mechanisms
differ.
2. Materials and methods
2.1. Participants
Sample 1 consisted of 137 individuals (83 females), aged 19–
49 years (mean = 25.6, SD = 5.0 years). The participants were re-
cruited through posters at Karolinska Institutet and Umeå Univer-
sity, and consisted of university students.
Sample 2 consisted of 2593 twin individuals (1342 females),
aged 51–68 years (mean = 58.6, SD = 4.6): 147 complete monozy-
gotic pairs, 218 complete dizygotic pairs, one complete pair with
unknown zygosity, and 1861 individuals from pairs for which only
one member of the pair participated. Data from Sample 2 was ac-
quired as part of a large wave of data collection (SALTY) coordi-
nated by the Swedish Twin Registry, from a cohort of twins born
between 1943 and 1958 (n= 25000). Sample 2 thus consists of
those individuals in that cohort who chose to participate in the
present web based collection of data on intelligence and flow
proneness.
Ethical approval for the study was given by the Regional Ethical
Review Board in Stockholm (Dnr 2008/1735-31/3) and the Ethical
Committe of Umeå University (Dnr 09-065 Ö).
2.2. Psychological tests
2.2.1. Flow proneness
Proneness to experience flow was measured using a newly
developed Swedish Flow Proneness Questionnaire (SFPQ). The
SFPQ was designed as a self-report measure of how frequently
the participant has flow experiences in three different situations
typical for division of activities in industrialized societies, i.e. work,
maintenance, and leisure time. The SFPQ has 22 items, 7 for each
domain and an initial branching question on whether the partici-
pant is professionally active, since the first 7 items on flow at work
are only answered by individuals that are employed. An English
translation is provided in Table 1. Each item has five response
alternatives ordered on a Likert scale: 1, ‘‘Never’’; 2, ‘‘Rarely’’; 3,
‘‘Sometimes’’; 4, ‘‘Often’’; 5, ‘‘Everyday, or almost everyday’’. The
items were chosen to capture the main dimensions of a flow expe-
rience (Csikszentmihalyi & Csikszentmihalyi, 1988), i.e. a subjec-
tive sense of concentration, balance between skills and the
challenge of a task, explicit goals, clear feedback, sense of control,
lack of a sense of boredom and enjoyment. Earlier factor analyses
of the DFS-2 flow scale have shown these dimensions to have the
highest loadings on a global flow factor (Jackson & Eklund, 2004).
For a confirmatory factor analysis of the SFPQ, see Supplementary
Data Mean scores of the 7 items in each domain were used to cal-
culate separate measures of flow proneness in professional life (FP-
Work), maintenance (FP-Maintenance), and leisure time (FP-Lei-
sure). The mean of all items was used as a measure of overall flow
proneness (FP-Total).
2.2.2. Personality
Personality was measured with the Swedish version (Bergman,
2003) of the Revised NEO Personality Inventory (NEO PI-R) (Costa
& McCrae, 1992). This is a 240-item inventory based on the five
factor model of personality (McCrae & Costa, 1990), and thus mea-
sures the higher-order personality factors Openness, Conscien-
tiousness, Extraversion, Agreeableness, and Neuroticism.
2.2.3. Intelligence
Intelligence in Sample 1 was measured either with the Raven
SPM Plus (n= 106) or the Wiener Matrizen Test (WMT; n= 31).
The reason that two tests were used was that these two groups
were also included in other studies on intelligence and temporal
accuracy of behavior, the results of which will be reported else-
where. In Sample 2, intelligence was measured using the WMT in
all participants. The SPM Plus is a 60-item version of the standard
Raven test. It is highly correlated with general fluid intelligence
(Gustafsson, 1984; Styles, Raven, & Raven, 1998), and requires
effortful attention (Carpenter et al., 1990). The WMT is a 24-item
test which is similar in construction to the Raven test, with which
it is highly correlated (r= .92) (Formann & Piswanger, 1979).
2.3. Testing procedure
In Sample 1, all tests were administered individually under
supervision. In accordance with the manuals, the SPM Plus was
untimed while a 25 min time limit was used for the WMT (For-
mann & Piswanger, 1979; Styles et al., 1998). Scores for both intel-
ligence tests were transformed into standard scores before pooling.
In Sample 2, the data collection took place online through a test
web site. Each participant received a personal login and password
through ordinary mail. The online version of the WMT was imple-
mented using scanned versions of the original items, and the same
time limit (25 min) as the standard paper-and-pencil version.
168 F. Ullén et al. / Personality and Individual Differences 52 (2012) 167–172

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2.4. Statistical analyzes
The internal consistency and reliability of the SFPQ and WMT
were estimated using Cronbach’s alpha and the Spearman–Brown
split half coefficient, which also can be interpreted as a short-term
test–retest reliability indicator. We also compared the vectors of
the item difficulties (i.e. proportion of the sample that gave a cor-
rect answer to the item) of the WMT in the different samples, using
Spearman rank order correlations.
The aim of the analyses in Sample 2 was to investigate associa-
tions between intelligence and flow proneness in a larger sample,
given the null relation found in Sample 1 (see Section 3). To ac-
count for the relatedness of the twins we used a randomized
two-sample design, where the original sample was randomly split
into two independent subsamples, Sample 2a (n= 1296) and Sam-
ple 2b (n= 1297). The two members of complete pairs were always
assigned to different subsamples. Sample 2 was also used for a con-
firmatory factor analysis (CFA) of the SFPQ, the details of which are
presented as Supplementary Data.
3. Results
3.1. Construct validity, reliability and internal consistency of the SFPQ
and the WMT
The construct validity of the SFPQ was evaluated in Samples 2a
and 2b, using a confirmatory factor analysis (see Supplementary
Data). A model with the three flow proneness domains (work,
maintenance and leisure) the test was intended to measure, as well
as the seven main flow dimensions, as latent variables (see Supple-
mentary Fig 1), was found to fit the data well. The comparative fit
index (CFI) of this model was .955 for Sample 2a, and .96 for Sam-
ple 2b. The Root Mean Square Error of Approximation (RMSEA) was
.045 for Sample 2a and .04 for Sample 2b. A CFI above .9 and a
RMSEA below .05 are commonly considered to indicate good fit
of a model (Bentler, 1990; Browne & Cudeck, 1993; Hu & Bentler,
1999). We therefore assume that the SFPQ indeed is measuring
what it was intended to measure, i.e. proneness for flow experi-
ences in three domains of life.
Data on the internal consistency (Cronbach alpha) and the reli-
ability (Spearman–Brown split-half coefficient) of the SFPQ and the
WMT are summarized in Table 2. For the SFPQ these measures
were calculated for those participants who were employed and,
accordingly, responded to the items in all three subscales, FP-
Work, FP-Maintenance, and FP-Leisure (Table 2). In both super-
vised (Sample 1) and online (Samples 2a and 2b) administrations
of the SFPQ, values for Cronbach alpha and split-half reliability
were high (>.8) with weighted average values of .83 and .87,
respectively.
Good internal consistency and reliability were also found for the
WMT in all three samples (Table 2), with weighted means of .79
(Cronbach alpha) and .80 (split-half coefficient). The rank order
correlations of the item difficulty vectors of the WMT were close
to unity between the two twin samples (Spearman R= .999;
t(22) = 112.40; p< .00001), and highly correlated also between
Sample 1 and Sample 2a (R= .92; t(22) = 11.35; p< .00001) and be-
tween Sample 1 and Sample 2b (R= .92; t(22) = 11.20; p< .00001).
3.2. Descriptive statistics
Descriptive data on test scores are summarized in Table 3.In
Sample 1, flow proneness was lower and intelligence scores were
higher than in the twin Samples (2a and 2b). These differences
were significant for all flow dimensions and for the WMT (one-
way ANOVAs; pvalues < .00001).
Table 1
The Flow Proneness Questionnaire (SFPQ). English translation. Items 2–8 are only answered by participants with an employment.
1 Are you professionally active? (Yes/No)
(If No, go to item 9)
When you do something at work, how often does it happen that...
2...you feel bored?
3...it feels as if your ability to perform what you do completely matches how difficult it is?
4...you have a clear picture of what you want to achieve, and what you need to do to get there?
5...you are conscious of how well or poorly you perform what you are doing?
6...you feel completely concentrated?
7...you have a sense of complete control?
8...what you do feels extremely enjoyable to do?
When you are doing household work or other routine chores (e.g. cooking, cleaning, shopping) how often does it happen that...
9–15 Identical to items 2–8.
When you do something in your leisure time, how often does it happen that...
16–22 Identical to items 2–8.
Table 2
Internal consistency and split-half reliability of the SFPQ and the WMT in the different
samples.
Sample SFPQ WMT
n Cronbach
alpha
Split-half
coefficient
nCronbach
alpha
Split-half
coefficient
1 75 .85 .87 31 .74 .86
2a 1029 .83 .88 1296 .80 .80
2b 1004 .83 .87 1297 .79 .80
Weighted
mean
.83 .87 .79 .80
Table 3
Test scores in the different samples. Values are means with the standard deviation in
parentheses.
Sample 1 Sample 2a Sample 2b
Flow proneness
FP-Work 3.43 (.57) 3.98 (.47) 3.95 (.47)
FP-Maintenance 3.48 (.57) 3.73 (.51) 3.67 (.54)
FP-Leisure 3.69 (.51) 3.84 (.46) 3.81 (.47)
FP-Total 3.56 (.44) 3.84 (.46) 3.79 (.43)
Intelligence
SPM 45.8 (5.40) - -
WMT 15.7 (4.01) 10.9 (4.48) 11.0 (4.55)
Personality
Extraversion 53.7 (8.90) - -
Neuroticism 53.8 (10.96) - -
Conscientiousness 46.9 (12.76) - -
Openness 57.6 (9.58) - -
Agreeableness 47.5 (10.47) - -
F. Ullén et al. / Personality and Individual Differences 52 (2012) 167–172 169

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To explore differences between flow dimensions, we used re-
peated measures ANOVAs with SFPQ score as dependent variable
and flow domain as a within-subject factor with three levels (FP-
Work, FP-Leisure, and FP-Maintenance). In Sample 1, a significant
effect of flow domain was found [F(2, 148) = 8.53; p= .0003]. A
post-hoc Tukey HSD test showed that FP-Leisure scores were sig-
nificantly higher than both FP-Work (p= .0002) and FP-Mainte-
nance (p= .008) scores. There was no significant difference
between FP-Work and FP-Maintenance (p= .60). Significant effects
of flow domain were found also in Sample 2a [F(2, 1986) = 149.37;
p< .00001] and Sample 2b [F(2, 1948) = 162.48; p< .00001]. Post-
hoc tests (Tukey HSD) showed significant differences between all
domains in both samples (pvalues < .00001). Notably, in contrast
to in Sample 1, FP-Work scores were higher than both FP-Mainte-
nance and FP-Leisure scores in Samples 2a and 2b.
3.3. Flow proneness and personality
Big Five personality data was available only in Sample 1. To test
the hypothesis that flow proneness is negatively related to neurot-
icism, we investigated general linear models with the different
measures of flow proneness as dependent variable, neuroticism
as independent variable and sex and age as covariates of no inter-
est. In the model with FP-Total as dependent variable, a substantial
negative effect of neuroticism was found [b=.41; F(3,
133) = 25.23; p< .00001]. Negative relations were seen between
all three individual SFPQ scales and neuroticism: FP-Work
[b=.33; F(3, 71) = 8.65; p= .004], FP-Maintenance [b=.32;
F(3, 133) = 15.11; p= .0002], and FP-Leisure [b=.32; F(3,
133) = 14.02; p= .0003].
Secondly, we used forward stepwise regression to explore rela-
tions between flow proneness and all five NEO PI-R dimensions
(Openness, Conscientiousness, Extraversion, Agreeableness, and
Neuroticism) as well as intelligence. Since the previous analysis
did not demonstrate any major differences between the flow do-
mains in relations to neuroticism, we performed this analysis only
for FP-Total. In the final model, significant relations were found
with neuroticism [b=.28; F(2, 134) = 12.02; p= .0007] and con-
scientiousness [b= .30; F(2, 134) = 13.58; p= .0003]. Together, neu-
roticism and conscientiousness explained 22% of the total variance
in FP-Total. Adding all remaining personality dimensions and intel-
ligence scores to this model only explained an additional 3.3% of
the FP-Total variance.
3.4. Flow proneness and intelligence
To investigate whether flow proneness is related to intelligence,
we used general linear models with measures on the three SFPQ
scales as well as FP-Total as dependent variables, intelligence
scores as independent variable, and sex and age as covariates of
no interest. Results of these analyses are summarized in Table 4.
In Sample 1, no significant associations were found. Since this
sample had a limited size (n= 137) and since, furthermore, two dif-
ferent intelligence tests were employed (WMT, n= 31; and Raven
SPM Plus, n= 106), we investigated the intelligence-flow
proneness relation in the larger twin cohort (Sample 2, n= 2593),
where intelligence was measured with the WMT for all partici-
pants. To handle dependence between observations because of re-
lated subjects this sample was split into two independent
subsamples, so that twins from the same pair were sorted ran-
domly into different subsamples (Sample 2a and 2b; see Methods).
Relations between intelligence and flow proneness were very weak
and inconsistent for all SFPQ measures in both subsamples,
although some of the effects were still significant due to the large
sample sizes (Table 4). The weighted average of the bcoefficient for
intelligence, across samples, was .11 for FP-Work and even smaller
for the other scales and FP-Total, suggesting that intelligence and
flow proneness are essentially unrelated traits.
4. Discussion
4.1. Flow proneness and personality
A main finding of the study is that flow proneness is associated
with major personality dimensions (neuroticism and conscien-
tiousness) but essentially unrelated to intelligence. Specifically,
the hypothesis that flow proneness is negatively associated with
neuroticism was confirmed. A negative relation was found for all
SFPQ dimensions, i.e. during work, maintenance and leisure, sug-
gesting that a high level of neuroticism is detrimental for flow
experiences across a wide range of situations. This in turn suggests
that neuroticism affects cognitive and emotional processes that are
of general importance for entering and sustaining flow, regardless
of the task. Several mechanisms could underlie the association.
First, neuroticism is characterized by a tendency to experience
negative affect (Gray & McNaughton, 2000). This could directly
interfere with the affective component of a flow state, i.e. enjoy-
ment, which presumably is important for the subjective experience
of flow as attention occurring without effort (Csikszentmihalyi &
Csikszentmihalyi, 1988; de Manzano et al., 2010). Secondly, a sali-
ent feature of neuroticism is emotional (Eid & Diener, 1999) and
cognitive (Flehmig, Steinborn, Langner, & Westhoff, 2007) state
instability, which is also seen as high variability even in simple
behaviors, such as reaction time (Flehmig et al., 2007; Robinson
& Tamir, 2005) and rhythmic motor tasks (Forsman, Madison, &
Ullén, 2009). Such fluctuations in performance could conceivably
affect both cognitive and emotional aspects of flow, causing an in-
creased risk for attentional lapses and a reduced sense of control
and skill. Thirdly, relations between neuroticism and flow prone-
ness could be complex, and mediated by other variables that influ-
ence an individual’s tendency to participate in situations and
activities that are conducive to flow. Kommaraju and colleagues
(Komarraju, Karau, & Schmeck, 2009) found that neuroticism is
positively associated with the amotivation factor in Deci and
Ryan’s self-determination theory of motivation (Ryan & Deci,
2000). Amotivation reflects a lack of motivation to become in-
volved in activities and a sense of futility in engagement. Intrinsic
enjoyment is positively related to flow proneness (Hamilton et al.,
1984), and to internal locus of control, a trait which in turn is neg-
atively related to neuroticism (Clarke, 2004). Finally, Asakawa
Table 4
Associations between SFPQ dimensions and intelligence in the different samples. Effect sizes (beta coefficients) and their corresponding p values are shown, controlling for sex
and age.
Sample FP-Work FP-Maintenance FP-Leisure FP-total
nbnbnbnb
175.08 (n.s.) 137 .08 (n.s.) 137 .08 (n.s.) 137 .12 (n.s.)
2a 1029 .17 (p< .00001) 1281 .077 (p= .007) 1252 .075 (p= . 009) 1296 .13 (p< .00001)
2b 1004 .068 (p= .03) 1279 .016 (n.s.) 1255 .079 (p= .006) 1297 .052 (n.s.)
Weighted mean .11 .025 .069 .080
170 F. Ullén et al. / Personality and Individual Differences 52 (2012) 167–172

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found a positive association between flow proneness and the ten-
dency to adopt active problem-solving strategies when facing
everyday problems (Asakawa, 2010), while neuroticism is associ-
ated with an avoidance style, i.e. passivity, dependency, and pro-
crastination (D’Zurilla, Maydeu-Olivares, & Gallardo-Pujol, 2011).
Flow proneness was also associated with conscientiousness.
The contributions of Neuroticism (b=.28) and Conscientiousness
(b= .30) were comparable in magnitude in a model with both pre-
dictors included. Although this analysis was exploratory, a positive
relation between conscientiousness and flow proneness appears
reasonable in light of earlier literature. Conscientiousness is posi-
tively related to variables that also show positive associations with
flow proneness, i.e. active problem coping (D’Zurilla et al., 2011);
life satisfaction, subjective happiness and positive affect (Marrero
Quevedo & Carballeira Abella, 2011); and both intrinsic and extrin-
sic motivation (Komarraju et al., 2009). It is negatively related to
avoidance strategies in problem coping (D’Zurilla et al., 2011), neg-
ative affect (Marrero Quevedo & Carballeira Abella, 2011), and
amotivation (Komarraju et al., 2009). It seems likely that high con-
scientiousness involves emotional and motivational mechanisms
that make an individual engage in flow promoting activities. Fur-
thermore, flow appears to require not only a balance between task
difficulty and skills, but also that the challenges of the task is suf-
ficiently high in absolute terms (Csikszentmihalyi & Csikszentmih-
alyi, 1988). Highly conscientious individuals are presumably more
likely to spend enough time on deliberate practice to master more
challenging tasks (Kappe & van der Flier, 2010).
4.2. Flow proneness and intelligence
We found no significant relations between any of the SFPQ do-
mains and intelligence in Sample 1. Associations were also very
weak or non-significant in the larger twin samples: mean effects
(b) were lower than .1 for all SFPQ domains except for FP-Work,
where intelligence had a mean bcoefficient of .11. This association
could possibly reflect that higher intelligence increases the long-
term probability of getting a more stimulating and challenging
job that includes more flow-promoting tasks (Gottfredson, 2003).
Notably, work was more flow promoting than both leisure and
maintenance in the twin samples. In contrast, the younger stu-
dents in Sample 1 presumably often had relatively simple jobs as
extra sources of income alongside their studies. Indeed, work
was the least flow-promoting domain in this sample. At any rate,
the overall pattern of associations (see Table 4) makes it unlikely
that biological factors affecting intelligence would be of impor-
tance for flow. If that were the case, one would expect a consistent
association across flow domains. The intelligence tests employed
here mainly measure general fluid intelligence (Gf) which in turn
is close to unity correlated with general psychometric intelligence
(g)(Gustafsson, 1984). Further studies will be required to test
whether flow proneness is related to other ability factors, e.g. crys-
tallized intelligence (Gc), which reflects knowledge and skills ac-
quired through acculturation and investment of other abilities
during education (McGrew, 2009).
The present results suggest that flow proneness is related to
personality but not to cognitive ability. We have previously found
that physiological correlates of flow differ from what is typically
seen during mental effort, in terms of respiratory pattern and emo-
tion-related activity in facial musculature (de Manzano et al.,
2010). Flow may thus be a state of subjectively effortless attention
that occurs during skilled performance and has different underly-
ing mechanisms from attention during mental effort (Ullén, de
Manzano, Theorell, & Harmat, 2010). Dietrich has suggested that
the flow state, in contrast to effortful attention, could involve tran-
sient hypofrontality (Dietrich, 2003). Another interesting specula-
tion is that flow has commonalities with states of high
concentration experienced during meditation, and that the ante-
rior cingulate cortex is important for cognitive control in such
states (Posner, Rothbart, Rueda, & Tang, 2010). An important chal-
lenge for further studies on the neuropsychology of flow will cer-
tainly be to identify the neural correlates of the flow experience
itself.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.paid.2011.10.003.
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