fpsyg-07-02054 December 30, 2016 Time: 15:37 # 1
published: 06 January 2017
Tadhg Eoghan MacIntyre,
University of Limerick, Ireland
University of Hull, UK
Noel E. Brick,
Ulster University, Ireland
e Slapšinskait ˙
This article was submitted to
Movement Science and Sport
a section of the journal
Frontiers in Psychology
Received: 31 October 2016
Accepted: 19 December 2016
Published: 06 January 2017
e A, Hristovski R,
Razon S, Balagué N and
Tenenbaum G (2017) Metastable
Pain-Attention Dynamics during
Incremental Exhaustive Exercise.
Front. Psychol. 7:2054.
Metastable Pain-Attention Dynamics
during Incremental Exhaustive
e Slapšinskait ˙
e1*, Robert Hristovski2, Selen Razon3, Natàlia Balagué1and
1Complex Systems in Sport Research Group, INEFC Barcelona University, Barcelona, Spain, 2Saints Cyril and Methodius
University of Skopje, Skopje, Macedonia, 3West Chester University, West Chester, PA, USA, 4Florida State University,
Tallahassee, FL, USA
Background: Pain attracts attention on the bodily regions. Attentional allocation
toward pain results from the neural communication across the brain-wide network
“connectome” which consists of pain-attention related circuits. Connectome is
intrinsically dynamic and spontaneously ﬂuctuating on multiple time-scales. The present
study delineates the pain-attention dynamics during incremental cycling performed
until volitional exhaustion and investigates the potential presence of nested metastable
Method: Fifteen young and physically active adults completed a progressive
incremental cycling test and reported their discomfort and pain on a body map every
Results: The analyses revealed that the number of body locations with perceived pain
and discomfort increased throughout ﬁve temporal windows reaching an average of
4.26 ±0.59 locations per participant. A total of 37 different locations were reported and
marked as painful for all participants throughout the cycling task. Signiﬁcant differences
in entropy were observed between all temporal windows except the fourth and ﬁfth
windows. Transient dynamics of bodily locations with perceived discomfort and pain
were spanned by three principal components. The metastable dynamics of the body
pain locations groupings over time were discerned by three time scales: (1) the time
scale of shifts (15 s); (2) the time scale of metastable conﬁgurations (100 s), and (3) the
observational time scale (1000 s).
Conclusion: The results of this study indicate that body locations perceived as painful
increase throughout the incremental cycling task following a switching metastable and
nested dynamics. These ﬁndings support the view that human brain is intrinsically
organized into active, mutually interacting complex and nested functional networks,
and that subjective experiences inherent in pain perception depict identical dynamical
principles to the neural tissue in the brain.
Keywords: non-linear dynamics, pain, attention, accumulated effort, exercise, metastability
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e et al. Metastable Pain-Attention Dynamics
Humans have the capacity to distract from and also diﬀerentiate
between various sensations related to physical exercise such as
exercise-related eﬀort and pain (O’Connor and Cook, 2001;
Pageaux, 2016). Perception of eﬀort is deﬁned as “the conscious
sensation of how hard, heavy, and strenuous a physical task is”
(Marcora, 2010). Perception of pain on the other hand is deﬁned
as the perception of a distressing experience associated with
actual or potential tissue damage that entails sensory, cognitive,
emotional, and social components (Williams and Craig, 2016).
From a standpoint of measurement accuracy within self-report
settings, the instructions provided by the test administrator play
an important role for distinguishing between perception of eﬀort
and pain (Pageaux, 2016).
Of speciﬁc interest herein, perception of pain typically
requires attentional allocation. To that end, attention focus is
a key cognitive mechanism for increasing or decreasing the
perception of pain (Legrain et al., 2009;Slapsinskaite et al.,
2015). Even in the presence of unchanging nociceptive input
and regardless of on-going task demands the attentional state
seems waxing and waning spontaneously (Peters and Crombez,
2007). Consequently, shifting of attention could be due to the
pain-attention related processes that are intrinsically dynamic
and spontaneously ﬂuctuating on multiple time-scales (Bressler
and Kelso, 2001, 2016;Deco et al., 2013). Pervasiveness of
such attentional ﬂuctuations, their intrinsic nature, and their
relevance to subjective experience, such as pain, are further
supported by the evidence provided from studies of spontaneous
brain dynamics and the impact of pre-existing brain state
on subsequent perception (Kucyi and Davis, 2015). Within
such a framework, attentional ﬂuctuations away from non-
painful modalities and their neural mechanisms can be termed
“perception decoupling” or “disengagement of attention” from
perception (Schooler et al., 2011). Spontaneous attentional
ﬂuctuations toward and “away from pain” and individual
diﬀerences in this regard are represented in the very brain
network structure and dynamics.
The dynamical system theory (DST) is a sub ﬁeld of
mathematics that aims at understanding and describing the
dynamical changes that occur over time. Speciﬁcally, DST
establishes a series of principles that govern the system’s
dynamical changes. In the last few decades, DST has
demonstrated that painful experiences are emergent phenomena
resulting from self-organized processes, and that pain-attention
interaction can be understood as a virtue of such dynamics (Lutz
et al., 2008). To that end, it is known that non-linear dynamic
mechanisms are involved in the modulation of attentional
focus during physical activity (Balagué et al., 2012;Slapsinskaite
et al., 2016). Indeed, the DST framework that captures pain in
terms of spatiotemporal trajectories of neural activity emerging
from complex non-linear neural interactions, provides a novel
approach to the study of pain-attention dynamics (Freeman,
With regards to the perception of painful sensations also
known as nociception (Pfaﬀ, 2013), the brain is intrinsically
organized into active, mutually interacting complex and
functional networks. There is also a consensus that the pain
experience is both highly subjective and top-down modulated
(Garland, 2013). To that end, evidence indicates that non-linear
dynamical processes form the basis of a number of neural
(Izhikevich, 2010) and higher order processes. Speciﬁcally, non-
linear processes are deﬁned as those with non-proportionality
between the input and the output and with occasional reduction
to linear processes (Kelso, 1997).
Findings from research that focused on subjective experiences
have revealed ﬂuctuating and metastable dynamics inherent to
eﬀort perception and within diﬀerent types of exercise setting
(Balagué et al., 2012, 2015;Aragonés et al., 2013;Garcia et al.,
2015;Slapsinskaite et al., 2016). Metastability can be seen as a
property related to the existence of multiple separated timescales
(Bovier and Den Hollander, 2016). At short time-scales, the
system appears to be in equilibrium, but in fact, explores only a
limited part of its available state space. At longer timescales, it
undergoes transitions between numbers of metastable states.
From a broader standpoint, overall cognition is also facilitated
through the dynamical phenomenon of chunking (i.e., larger
sequence of information is managed into its smaller units).
Indeed, chunking has been shown to be involved in a range of
perception and cognition-related processes in humans (Gobet
et al., 2001;Rabinovich et al., 2014). Thus, the notion that mental
function is based on the dynamical and ongoing interaction of a
number of neural and bodily parts that produce complex patterns
has gained acceptance (Thompson and Varela, 2001;Rabinovich
et al., 2008;Rabinovich and Muezzinoglu, 2010).
To date, it is known that brain exhibit periods of stability and
instability in both behavioral and neural levels. The transition
from stable to unstable patterns comes as a response to the
changes in control parameters as those govern the system’s
properties (stability and instability) (Haken, 1987). The neural
dynamics of the brain, based upon metastability and dwelling
on diﬀerent time scales, ﬂexibly reorganize pain-attention on a
moment to moment basis. Consequently, the processes that are
more stable dwell over longer time scales and naturally tend to
correlate with the pain-attention conﬁgurations that emerge over
shorter time scales. These dynamics, in turn, are reﬂected in the
sequential switching in between the temporally and structurally
nested metastable states during trials (Rabinovich and Varona,
Of speciﬁc interest herein, the link among the dynamic
principles of spontaneous attention ﬂuctuations, brain networks
dynamics and the neural processing of pain observed through
subjective experiences of pain may be essential for the
understanding of attentional modulation and its involvement
in the perception of pain. To that end, exercise settings can
provide a particularly adequate context because during exercise
the sensory, cognitive, emotional, and physical conditions change
continuously. On a practical note, capturing pain-attention
interaction dynamics through subjective experiences can also
contribute to designing non-invasive approaches to ultimately
control pain during exercise or beyond. The purpose of this
study was to delineate the pain-attention dynamics during
incremental cycling performed until volitional exhaustion.
Speciﬁcally, drawing upon a DST approach, we hypothesized
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e et al. Metastable Pain-Attention Dynamics
that pain-attention relationship during exercise would display
hierarchically or nested metastable dynamics.
MATERIALS AND METHODS
Study Design and Participants
Fifteen young and physically active adults (10 women, 5 men,
Mage =22.5 years, SEM =0.43, age range: 20–25 years, and
BMI =22.84, SEM = ± 0.77) who engaged in a wide range of
aerobic activities (e.g., jogging, swimming, dancing) at least three
times a week, participated in this study. None of the participants
had previous history of chronic pain or musculoskeletal injuries
at the time of the study. Prior to the onset of the study,
participants completed a health history questionnaire, as well as
an informed consent form, which was approved by the Clinical
Research Ethics Committee of the Sports Administration of
Catalonia (registration number 072015CEICEGC). This study
was carried out in accordance with the Declaration of Helsinki.
Discomfort and Pain Monitoring
To detect pain dynamics and corresponding bodily regions, a
body map (see Figure 1) was verbally explained to participants
prior to the baseline test and experimental tasks. Using the
map, every 15 s during exercise, upon the researcher’s prompts,
participants reported bodily regions with discomfort and pain.
The instructions provided to the participants included the
“When prompted, we ask you to report the locations of discomfort
and pain (if you feel it, independently of its magnitude) using the
numbers on the body map placed in front of you.”
All participants were already familiar with cycle ergometer
testing. One week prior to the tests, they received instructions
on how to use the body map during the tests. To ensure
their competence, they practiced a submaximal version of the
incremental cycling test (see below) and using the body map,
reported bodily regions with pain every 15 s upon the researcher’s
prompts. All participants displayed adequate competence of the
study protocols following one single trial.
Incremental Cycling Test
Following a 2 min rest period, participants performed a
progressive incremental test on a cycle ergometer (Sport
Excalibur 925900) with saddle and handlebar speciﬁcations
adjusted to their preference. For the purposes of the test, they
were instructed to pedal at 60 rpm with an initial load of 30 W
and increases of 25 W/min for female and 30 W/min for male,
until they could no longer maintain the pedaling rate for ﬁve
consecutive seconds while in the sitting position. Participants
performed the test with no verbal communication except for
indicating bodily locations with pain after the researcher’s
prompts. Heart rate was continuously monitored (Polar RS 400)
to assure that participants reached at least 170 beats/min at the
point of exhaustion. Upon task completion, using an 11-point
FIGURE 1 | Body map. Head (areas 1, 2, 23, or 24); neck (areas 3 or 25), shoulders (areas 4, 5, 26, 27); arms (areas 6, 7, 8, 9, 28, 29, 30, or 31); hand (areas 10,
11, 32, 33); ribs or chest (areas 12 or 13); abdomen (areas 14 or 15), back (areas 34, 35, 36, 37), buttocks or hips (areas 38 or 39); genitalia (area 16), legs (areas
17, 18, 19, 20, 40, 41, 42, or 43); feet (areas 21, 22, 44, or 45). Adapted from Margolis et al. (1986).
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Likert-type scale with anchors ranging from 0 (not at all) to 10
(greatly), participants answered two questions to measure task
commitment: (a) “Have you pedaled as long as you can, achieving
your exhaustion point?” and, commitment to the protocol (b)
“Have you reported all the changes in your discomfort and pain-
locations when required?”
The reported number of locations with discomfort and pain
during the test were plotted for each participant. Each time
series was divided into ﬁve non-overlapping temporal windows
(time to volitional exhaustion of the participant/5). Mean value
of the number of locations with discomfort and pain in total
were computed for each time window. The changes of entropy
were computed within ﬁve temporal windows. A median was
calculated for each window from all participants’ mean value of
the number of locations with discomfort and pain frequencies of
painful bodily locations.
These collected data with painful locations obtained from the
body maps were then used to form 17 Boolean m×ndata
matrices where msigniﬁes the number of body locations and n
the number of time samples (Casari et al., 1995;Gogos et al.,
2000;Jolliﬀe, 2014). Visual observation of the structure of the
data matrices helped distinguish between two types of reported
locations: locations that were persistent throughout the entire
exercise bout (i.e., long-term and stable locations), and locations
that were inconsistent (i.e., short-term and unstable locations).
In other words, the data matrix either depicted long-term bodily
locations that were stable on the time scale of 10s of minutes, or
other short-term ones that were stable on the time scale of 10s of
seconds or minutes.
Collective variables were then determined by means of a
principal component analysis (PCA) (Jirsa et al., 1994). The
Kaiser-Guttmann criterion (eigenvalue λ≥1) was used to deﬁne
the number of salient PCs of the ﬁrst order (Yeomans and Golder,
1982). The hierarchical analysis of oblique principal components
(hPCA) (Fabrigar et al., 1999) was subsequently used to check for
the presence of collective variables of higher order, and to obtain
a maximal dimensional reduction of the data. For the purposes of
the hPCA analysis, the software package Statistica 5.0 was used.
The null hypothesis of a constant median (with no signiﬁcant
diﬀerences) over time was tested using non-parametric repeated-
measures Friedman ANOVA. Eﬀect sizes (Cohen’s d) were
computed to demonstrate the magnitude of standardized
diﬀerences in medians where eﬀect sizes neared p<0.05 level.
During incremental cycling, the reached maximal load
corresponded to 228 ±17 and 240 ±30 W for females
and males, respectively. On average, participants’ heart rate
reached 180 ±9.5 bpm at the exhaustion point. The Friedman
ANOVA revealed a signiﬁcant eﬀect of time for the total number
of locations with discomfort and pain, χ2(15,4) =49.249,
p<0.001, during the incremental cycling test. Figure 2 depicts
the changes in the number of locations with discomfort and pain
throughout the ﬁve temporal windows. The number of locations
resulted in a signiﬁcant diﬀerence between temporal windows:
ﬁrst vs. third time intervals, Z= −2.97; p<0.05, d=1.59, 95%
CI [0.65, 2.04]); third vs. ﬁfth time intervals, Z= −3.26; p<0.05,
d=0.81, 95% CI [−0.35, 1.73]), and ﬁrst vs. ﬁfth time intervals,
Z= −2.17; p<0.05, d=2.27, 95% CI [1.11, 2.74]).
Figure 3 illustrates the frequencies of locations with
discomfort and pain during the incremental cycling test.
The number of locations and the probability of experiencing
FIGURE 2 | The number of locations with discomfort and pain throughout the ﬁve temporal windows.
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FIGURE 3 | Locations with pain and/or discomfort. The group pulled probabilities of locations with pain or discomfort during cycling tasks in ﬁve temporal
windows in a given sample (n= 15). As time on task increases (vertical axis) the number of locations and the probability of experiencing pain and discomfort at
selective locations also increase (darker shades of gray) on average. Legend: the probability of experiencing discomfort and pain.
discomfort and pain at select locations (depicted in darker shades
of gray), increased during the test until reaching 4.26 ±0.59 in the
ﬁfth temporal window. The dominant locations with discomfort
and pain at exhaustion included left and right quadriceps, lower
back and left ankle. Both the waxing and waning experience of
pain were also identiﬁed. Depicted in shades of gray in Figure 3,
exertive pain exhibited metastable dynamics, dwelling around
select bodily regions for some time to transition into another
one quickly after. A total of 37 diﬀerent areas were reported and
marked as painful for all participants throughout the cycling test.
The Friedman ANOVA revealed a signiﬁcant eﬀect of time for
the pain entropy, χ2(15,4) =49.77, p<0.001 in incremental
cycling (see Figure 4). The entropy of exertive pain showed
signiﬁcant diﬀerence between all temporal windows except the
fourth and ﬁfth windows. ﬁrst vs. second time intervals, Z=2.93;
p<0.05, d=1.04, 95% CI [1.02, 1.05]; ﬁrst vs. third time
intervals, Z=3.29; p<0.001, d=2.07, 95% CI [2.06, 2.08];
ﬁrst vs. fourth time intervals, Z=3.4; p<0.001, d=1.62, 95%
CI [1.61, 1.63); ﬁrst vs. ﬁfth time intervals, Z=3.4; p<0.001,
d=2.44, 95% CI [2.42, 2.45]; second vs. third time intervals,
Z=3.17; p<0.001, d=1.04, 95% CI [1.02, 1.05]; second vs.
fourth time intervals, Z=3.17; p<0.001, d=0.81, 95% CI
[0.8, 0.82]; second vs. ﬁfth time intervals, Z=3.26; p<0.001,
d=1.62, 95% CI [1.61, 1.63]; third vs. fourth time intervals,
Z=2.07; p<0.05, d=0.00, 95% CI [−0.01, 0.02]; and third
vs. ﬁfth time intervals, Z=3.26; p<0.05, d=0.81, 95% CI
[0.8, 0.82]. Kendall’s W was equal to 0.83 with an average rank
r=0.82. In general, relative to participants who started with low
entropy, participants with higher entropy kept and ended with
FIGURE 4 | The entropy of exertive pain throughout the ﬁve temporal windows.
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FIGURE 5 | Transient dynamics of bodily locations with perceived pain
spanned by three PCs.
Figure 5 depicts an example of transient dynamics of bodily
locations with perceived discomfort and pain in the space
spanned by three PCs. From a chunk sequence – trajectory
within three PCs a dwelling time around select region (e.g., PC1)
and a transitional trajectory to another state “temporal winner”
PC2 and a ﬁnal rapid switch to the next metastable state can
be identiﬁed. This example illustrates a metastability of sensory
interactions and integration of information from the painful
Figure 6 presents an integration-segregation picture of
exertive pain through three PCs joined hierarchically into one
second-order PC. Through hierarchical PC of time series of
exertive pain conﬁgurations chunks on diﬀerent time scales can
be detected. The trajectory dwells for some time in space then
wanes to ﬁnally dwell again. The persistent (longest dwell time)
painful locations over all time resulted in the emergence of super-
chunks. These group-clusters of persistent locations formed
a skeleton-like ﬁgure on which other less persistent (shorter
dwell time) motives join and dissolve following which further
short-lived (shortens dwell time) painful locations emerged. The
metastable dynamics of the body pain locations groupings over
time projected on the secondary PC. The system dwells for some
time in one conﬁguration state then quickly shifts to another one.
At least three time scales can be discerned: (1) the time scale
of shifts (15 s), (2) the time scale of metastable conﬁgurations
(100 s), and (3) the observational time scale (1000 s).
The present ﬁndings advance the understanding of generated
sequential switching nature of attention-pain dynamics, and
further support the view that human brain is intrinsically
organized into active, mutually interacting complex and
nested functional networks (Rabinovich and Varona, 2011).
Surprisingly, our results draw attention to a potential link
between pain-attention dynamic states and modeled sequences
of two psychological components of eﬀort, namely cognition and
emotion (Rabinovich et al., 2010, 2015), as well as the dynamical
signatures of several brain functions and mental diseases
(Rabinovich and Varona, 2011). This is an important ﬁnding
in that it demonstrates that the phenomenological subjective
experiences, so called qualia (Chalmers, 1996), are grounded in
identical dynamical principles to the neural tissue, i.e., the brain.
Consequently, perception of pain and attention to pain seem
to be multidimensional and interrelated through hierarchical
FIGURE 6 | The metastable dynamics of the conﬁguration of body pain locations over time projected on the secondary PC.
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dynamical processes that highly depend on sensory cues (i.e.,
painful locations) (Rabinovich et al., 2015). In the light of
these ﬁndings, it is important to note that attention does
not appear to be a static capacity but rather a process that
involves the attentional “reorienting” from one input (i.e., one
painful location) or modality (i.e., pain intensity or quality)
to another. Therefore, the perception process requires a short-
term integration between a number of continuously interplaying
components such as the environmental cues, the body and the
brain itself (Rabinovich et al., 2015).
The present study focused on the evolving interactions among
pain-attention dynamics, where perceived pain throughout
a cycling task was considered a bi-product of the mutual
interaction between attention and distinct psychophysiological
process, and not of select singular mechanisms. In fact,
disengagement of attention from perception within ongoing
dynamics of pain-attention or so called perceptual decoupling
could also be responsible for the metastable dynamics observed
in this study. Studies on neural mechanisms of spontaneous
attentional ﬂuctuations on multiple timescales and pain
variability have already underlined the importance of dynamics
of pain-attention interactions, and its mutual inﬂuence on each
other. Upcoming work must consider this phenomenon (Kucyi
and Davis, 2015).
The present ﬁndings also help detect and conﬁrm the
dynamical phenomenon of chunking that the biological-
cognitive system uses to manage larger sequence of information
into smaller units to facilitate information processing. Indeed, the
presence of interconnected pieces that are prevalent over long
periods of time supports the notion of a hierarchical organization
of neural processing, which is the basis for understanding
chunking dynamics (Rabinovich et al., 2014). Speciﬁcally, in the
present study we observed the produced hierarchical chunking
of locations with pain sequences, and to our knowledge for the
ﬁrst time, demonstrated how dynamics of mental hierarchies
may be established on component perceptions that dwell over
diﬀerent time scales. Our data suggest that basic functions,
such as focusing on the painful locations, and chunking of the
information evolve through dynamic and not static interactions.
That is, while forming a chunking network individuals tend to
transform the chain of metastable states along with transient
process to the chain of groups of such states. Therefore, within the
present framework, it was considered that the chunks operate on
an heteroclinic cycle of metastable states where each metastable
state itself is a heteroclinic cycle of basic information items
(Rabinovich et al., 2014). Altogether, the set of informational
items (i.e., painful locations) can be interpreted as sequences.
Consequently, conceptualizing pain and attention-related brain
and body network processes from the standpoint of a concurrent
activation of sensory cues emanating from the body and multiple
other sources within a distributed brain network can prove
Several limitations to our study should be noted. First, we
have not studied all the spatiotemporal mental ﬁelds (e.g.,
alternative facets of perception, cognition, emotion, mental
resources) and their dynamics within the exercise setting.
Second, magnetic resonance imaging was not used to capture
the neurophysiological mechanisms behind the interaction of
attention-pain and this may have shed more light on the pain
dynamics. Third, pre-existing brain state was not measured and
ﬁnally, participants’ personal beliefs or expectations about pain
were not evaluated. Finally, prompting protocol used herein
may also present a limitation. It is plausible that, due to the
reporting task, participants’ attention focus was somewhat biased.
This protocol was implemented, however, to follow a systematic
and regularly imposed rating strategy. Traditionally, the data
of pain ratings were obtained in lower recording frequency,
for instance varying between 1 and 3 min intervals (Angius
et al., 2015), before and after physical activity (Choi et al.,
2013), or once per day (Burnett et al., 2010). Some researchers
have also recorded pain ratings at high frequencies with use
of 30 s (Cook et al., 1998) or 15 s (Slapsinskaite et al.,
2015). Intra-individual changes are, however, better captured
through short frequency recording of self-reports precisely
because participants may not be able to attend and report
all changes within lower recording frequency settings. To that
end, with regards to the present study, it is important to
note that the test administrator was frequently prompting the
self-report with no prompting of any particular pain location
To the best of our knowledge, this study remains a ﬁrst attempt to
illustrate and explain the pain-attention information processing
dynamics within an exercise setting. Finally, from a translational
standpoint greater knowledge into pain dynamics during exercise
can help practitioners design eﬀective strategies to cope with
painful sensations during eﬀort. This is important in that
within eﬀortful settings, somatic pain is associated with negative
aﬀective responses to exercise and eventual lack of exercise-
adherence (Ekkekakis et al., 2011). Consequently, strategies to
allow improved pain management during activity are likely to
facilitate exercise-related enjoyment, and help long term exercise-
engagement (Saanijoki et al., 2015).
Conceived and designed the experiments: NB, RH, AS; performed
the experiments: AS, NB; analyzed the data: AS, NB, RH, GT, SR;
contributed reagents/materials/analysis tools: RH, AS, NB, SR,
GT; wrote the paper: AS, SR, NB, RH.
This study was supported by the Institut Nacional d’Educacio
Física de Catalunya (INEFC), the Generalitat de Catalunya. AS
is the recipient of a predoctoral fellowship from the Institut
Nacional d’Educacio Física de Catalunya (INEFC). We would like
to thank TECNO SPORT for the technical support. The funders
had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
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e et al. Metastable Pain-Attention Dynamics
Angius, L., Hopker, J. G., Marcora, S. M., and Mauger, A. R. (2015). The eﬀect
of transcranial direct current stimulation of the motor cortex on exercise-
induced pain. Eur. J. Appl. Physiol. 115, 2311–2319. doi: 10.1007/s00421-015-
Aragonés, D., Balagué, N., Hristovski, R., Pol, R., and Tenenbaum, G. (2013).
Fluctuating dynamics of perceived exertion in constant-power exercise. Psychol.
Sport Exerc. 14, 796–803. doi: 10.1016/j.psychsport.2013.05.009
Balagué, N., Hristovski, R., Aragonés, D., and Tenenbaum, G. (2012). Nonlinear
model of attention focus during accumulated eﬀort. Psychol. Sport Exerc. 13,
591–597. doi: 10.1016/j.psychsport.2012.02.013
Balagué, N., Hristovski, R., García, S., Aguirre, C., Vázquez, P., Razon, S., et al.
(2015). Dynamics of perceived exertion in constant-power cycling: time-and
workload-dependent thresholds. Res. Q. Exerc. Sport 86, 371–378. doi: 10.1080/
Bovier, A., and Den Hollander, F. (2016). Metastability: A Potential-Theoretic
Approach. Cham: Springer.
Bressler, S. L., and Kelso, J. A. S. (2001). Cortical coordination dynamics and
cognition. Trends Cogn. Sci. 5, 26–36. doi: 10.1016/S1364-6613(00)01564-3
Bressler, S. L., and Kelso, S. (2016). Coordination dynamics in cognitive
neuroscience. Front. Syst. Neurosci. 10:397. doi: 10.3389/fnins.2016.00397
Burnett, D., Smith, K., Smeltzer, C., Young, K., and Burns, S. (2010). Perceived
muscle soreness in recreational female runners. Int. J. Exerc. Sci. 3, 108–116.
Casari, G., Sander, C., and Valencia, A. (1995). A method to predict
functional residues in proteins. Nat. Struct. Biol. 2:171. doi: 10.1038/
Chalmers, D. J. (1996). The Conscious Mind: In Search of a Fundamental Theory.
Oxford: Oxford University Press.
Choi, J. C., Min, S., Kim, Y. K., Choi, J. H., Seo, S. M., and Chang, S. J. (2013).
Changes in pain perception and hormones pre- and post-kumdo competition.
Horm. Behav. 64, 618–623. doi: 10.1016/j.yhbeh.2013.08.013
Cook, D. B., O’Connor, P. J., Oliver, S. E., and Lee, Y. (1998). Sex diﬀerences
in naturally occurring leg muscle pain and exertion during maximal
cycle ergometry. Int. J. Neurosci. 95, 183–202. doi: 10.3109/0020745980
Deco, G., Jirsa, V. K., and McIntosh, A. R. (2013). Resting brains never rest:
computational insights into potential cognitive architectures. Trends Neurosci.
36, 268–274. doi: 10.1016/j.tins.2013.03.001
Ekkekakis, P., Parﬁtt, G., and Petruzzello, S. J. (2011). The pleasure and displeasure
people feel when they exercise at diﬀerent intensities. Sport Med. 41, 641–671.
doi: 10.2165/11590680-000000000- 00000
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., and Strahan, E. J. (1999).
Evaluating the use of exploratory factor analysis in psychological research.
Psychol. Methods 4:272. doi: 10.1037/1082-989X.4.3.272
Freeman, W. J. (1992). Tutorial on neurobiology: from single neurons to brain
chaos. Int. J. Bifurc. Chaos 2, 451–482. doi: 10.1142/S0218127492000653
Garcia, S., Balagué, N., Razon, S., Hristovski, R., and Tenenbaum, G. (2015).
Dynamic stability of task-related thoughts in trained runners. Sport Psychol. 29,
302–309. doi: 10.1123/tsp.2014-0094
Garland, E. (2013). Pain processing the human nervous system: a selective review
of nocicetive and biobehavioral pathway. Prim. Care 39, 561–571. doi: 10.1016/
Gobet, F., Lane, P. C. R., Croker, S., Cheng, P. C. H., Jones, G., Oliver, I., et al.
(2001). Chunking mechanisms in human learning. Trends Cogn. Sci. 5, 236–243.
doi: 10.1016/S1364-6613(00)01662- 4
Gogos, A., Jantz, D., Sentürker, S., Richardson, D., Dizdaroglu, M., and Clarke,
N. D. (2000). Assignment of enzyme substrate speciﬁcity by principal
component analysis of aligned protein sequences: an experimental test using
DNA glycosylase homologs. Proteins 40, 98–105. doi: 10.1002/(SICI)1097-
0134(20000701)40:1<98::AID- PROT110$>$3.0.CO;2- S
Haken, H. (1987). “An approach to self-organization,” in Self-Organizing Systems:
The Emergence of Order, ed. F. Yates (New York, NY: Plenum Press), 417–437.
Izhikevich, E. M. (2010). Hybrid spiking models. Phil. Trans. R. Soc. A 368,
5061–5070. doi: 10.1098/rsta.2010.0130
Jirsa, V. K., Friedrich, R., Haken, H., and Kelso, J. A. S. (1994). A theoretical
model of phase transitions in the human brain. Biol. Cybern. 71, 27–35. doi:
Jolliﬀe, I. (2014). “Principal component analysis,” in Wiley StatsRef: Statistics
Reference Online, eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch,
F. Ruggeri, and J. L. Teugels (Hoboken, NJ: John Wiley & Sons, Ltd), doi:
Kelso, J. A. S. (1997). Dynamic Patterns: The Self-Organization of Brain and
Behavior. Cambridge: MIT press.
Kucyi, A., and Davis, K. D. (2015). The dynamic pain connectome. Trends
Neurosci. 38, 86–95. doi: 10.1016/j.tins.2014.11.006
Legrain, V., Van Damme, S., Eccleston, C., Davis, K. D., Seminowicz, D. A., and
Crombez, G. (2009). A neurocognitive model of attention to pain: behavioral
and neuroimaging evidence. Pain 144, 230–232. doi: 10.1016/j.pain.2009.
Lutz, A., Slagter, H. A., Dunne, J. D., and Davidson, R. J. (2008). Attention
regulation and monitoring in meditation. Trends Cogn. Sci. 12, 163–169. doi:
Marcora, S. M. (2010). “Eﬀort: perception of,” in Encyclopedia of Perception, ed.
E. B. Goldstein (Thousaand Oaks, CA: Sage), 380–383.
Margolis, R. B., Tait, R. C., and Krause, S. J. (1986). A rating system for use with
patient pain drawings. Pain 24, 57–65. doi: 10.1016/0304-3959(86)90026-6
O’Connor, P. J., and Cook, D. B. (2001). Moderate-intensity muscle pain can
be produced and sustained during cycle ergometry. Med. Sci. Sport Exec. 33,
1046–1051. doi: 10.1097/00005768-200106000- 00026
Pageaux, B. (2016). Perception of eﬀort in exercise science: deﬁnition,
measurement and perspectives. Eur. J. Sport Sci. 16, 1–10. doi: 10.1080/
Peters, M. L., and Crombez, G. (2007). Assessment of attention to pain using
handheld computer diaries. Pain Med. 8, 110–120. doi: 10.1111/j.1526-4637.
Pfaﬀ, D. W. (2013). Neuroscience in the 21st Century: From Basic to Clinical.
New York, NY: Springer, doi: 10.1007/978-1- 4614-1997-6
Rabinovich, M. I., Huerta, R., Varona, P., and Afraimovich, V. S. (2008). Transient
cognitive dynamics, metastability, and decision making. PLoS Comput. Biol.
4:e1000072. doi: 10.1371/journal.pcbi.1000072
Rabinovich, M. I., and Muezzinoglu, M. K. (2010). Nonlinear dynamics of the
brain: emotion and cognition. Physics Uspekhi 53, 357–372. doi: 10.3367/UFNe.
Rabinovich, M. I., Muezzinoglu, M. K., Strigo, I., and Bystritsky, A. (2010).
Dynamical principles of emotion-cognition interaction: mathematical
images of mental disorders. PLoS ONE 5:e12547. doi: 10.1371/journal.pone.
Rabinovich, M. I., Tristan, I., and Varona, P. (2015). Hierarchical nonlinear
dynamics of human attention. Neurosci. Biobehav. Rev. 55, 18–35. doi: 10.1016/
Rabinovich, M. I., and Varona, P. (2011). Robust transient dynamics and brain
functions. Front. Comput. Neurosci. 5:24. doi: 10.3389/fncom.2011.00024
Rabinovich, M. I., Varona, P., Tristan, I., and Afraimovich, V. S. (2014). Chunking
dynamics: heteroclinics in mind. Front. Comput. Neurosci. 8:22. doi: 10.3389/
Saanijoki, T., Nummenmaa, L., Eskelinen, J.-J., Savolainen, A. M., Vahlberg, T.,
Kalliokoski, K. K., et al. (2015). Aﬀective responses to repeated sessions of high-
intensity interval training. Med. Sci. Sport Exerc. 47, 2604–2611. doi: 10.1249/
Schooler, J. W., Smallwood, J., Christoﬀ, K., Handy, T. C., Reichle, E. D.,
and Sayette, M. A. (2011). Meta-awareness, perceptual decoupling and the
wandering mind. Trends Cogn. Sci. 15, 319–326. doi: 10.1016/j.tics.2011.
Slapsinskaite, A., García, S., Razon, S., Balagué, N., Hristovski, R., and
Tenenbaum, G. (2016). Cycling outdoors facilitates external thoughts and
endurance. Psychol. Sport Exerc. 27, 78–84. doi: 10.1016/j.psychsport.2016.08.
Slapsinskaite, A., Razon, S., Balagué Serre, N., Hristovski, R., and Tenenbaum, G.
(2015). Local pain dynamics during constant exhaustive exercise. PLoS ONE
10:e0137895. doi: 10.1371/journal.pone.0137895
Thompson, E., and Varela, F. J. (2001). Radical embodiment: neural dynamics
and consciousness. Trends Cogn. Sci. 5, 418–425. doi: 10.1016/S1364-6613(00)
Williams, A. C., and Craig, K. D. (2016). Updating the deﬁnition of pain. Pain
157:1. doi: 10.1097/j.pain.0000000000000613
Frontiers in Psychology | www.frontiersin.org 8January 2017 | Volume 7 | Article 2054
fpsyg-07-02054 December 30, 2016 Time: 15:37 # 9
e et al. Metastable Pain-Attention Dynamics
Yeomans, K. A., and Golder, P. A. (1982). The Guttman-Kaiser criterion as
a predictor of the number of common factors. J. R. Stat. Soc. Ser. B 31,
Conﬂict of Interest Statement: The authors declare that the research was
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