RESEARCH ARTICLE
It’s a Matter of Mind! Cognitive Functioning
Predicts the Athletic Performance in Ultra-
Marathon Runners
Giorgia Cona1,2*, Annachiara Cavazzana1, Antonio Paoli3, Giuseppe Marcolin3,
Alessandro Grainer3, Patrizia Silvia Bisiacchi1,4
1 Department of General Psychology, University of Padua, Padua, Italy, 2 Department of Neuroscience,
University of Padua, Padua, Italy, 3 Department of Biomedical Science, University of Padua, Padua, Italy,
4 Center for Cognitive Neuroscience, University of Padua, Padua, Italy
* giorgia.cona@unipd.it
Abstract
The present study was aimed at exploring the influence of cognitive processes on perfor-
mance in ultra-marathon runners, providing an overview of the cognitive aspects that char-
acterize outstanding runners. Thirty runners were administered a battery of computerized
tests right before their participation in an ultra-marathon. Then, they were split according to
the race rank into two groups (i.e., faster runners and slower runners) and their cognitive
performance was compared. Faster runners outperformed slower runners in trials requiring
motor inhibition and were more effective at performing two tasks together, successfully
suppressing the activation of the information for one of the tasks when was not relevant.
Furthermore, slower runners took longer to remember to execute pre-defined actions asso-
ciated with emotional stimuli when such stimuli were presented. These findings suggest
that cognitive factors play a key role in running an ultra-marathon. Indeed, if compared with
slower runners, faster runners seem to have a better inhibitory control, showing superior
ability not only to inhibit motor response but also to suppress processing of irrelevant infor-
mation. Their cognitive performance also appears to be less influenced by emotional stimuli.
This research opens new directions towards understanding which kinds of cognitive and
emotional factors can discriminate talented runners from less outstanding runners.
Introduction
“I just run. I run in a void. Or maybe I should put it the other way: I run in order to acquire
a void. But as you might expect, an occasional thought will slip into this void. People’s
minds can’t be a complete blank. Human beings’ emotions are not strong or consistent
enough to sustain a vacuum. What I mean is, the kinds of thoughts and ideas that invade
my emotions as I run remain subordinate to that void.”
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OPEN ACCESS
Citation: Cona G, Cavazzana A, Paoli A, Marcolin G,
Grainer A, Bisiacchi PS (2015) It’s a Matter of Mind!
Cognitive Functioning Predicts the Athletic
Performance in Ultra-Marathon Runners. PLoS ONE
10(7): e0132943. doi:10.1371/journal.pone.0132943
Editor: Giuseppe di Pellegrino, University of
Bologna, ITALY
Received: March 3, 2015
Accepted: June 21, 2015
Published: July 14, 2015
Copyright: © 2015 Cona et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: Data of our experiment
are available upon request because of ethical
restriction in order to protect the privacy of research
participants. Readers can contact giorgia.
cona@unipd.it to request the data, which would be
given anonymously. Race ranking is freely available
on the web.
Funding: This study was supported by a grant from
the University of Padova (Strategic Grant NEURAT;
STPD11B8HM_004) to P.B. The funder did not have
any role in study, data collection or analysis, decision
to publish, or preparation of the manuscript.
Haruki Murakami—What I Talk About When I Talk About Running
All kinds of sports imply, to different extents, the application of cognitive, perceptual and
motor skill [1, 2]. Nevertheless, although superior performance is clearly evident on observa-
tion, the cognitive mechanisms that contribute to a successful performance are less clear. For
several decades researchers have sought to better understand the cognitive factors that are able
to discriminate between talents and less outstanding athletes [3]. Outstanding athletes were
shown to have enhanced declarative and procedural knowledge, and to be more able at making
decisions and at extrapolating relevant information from the environment to anticipate future
events and outcomes [4–6]. Experts seem also to have a more effective visuo-spatial processing
and greater selective attention [7–9]. In particular, the effect of focus of attention on athletic
skills has been extensively explored across sporting domains. Attentional focus is typically clas-
sified as internal or external, where the internal focus is meant to be directed toward the perfor-
mance of movements, whereas the external one is meant to direct attention toward the effects
of a movement [10] and/or to external environmental stimuli [11]. Overall, an external focus
of attention appears to be more beneficial for a successful sporting performance [12, 13].
Furthermore, motor response selection and inhibition processes were shown to be crucial,
for example, in fencing, baseball, tennis and soccer [14–17].
Despite the increasing evidence of the key role of cognitive factors across a wide range of
sporting domains, the contribution of such factors to endurance sports and, more specifically,
to running performance, is still poorly understood. The few studies that have addressed this
issue focused on the influence of cognitive strategies and focus of attention on quality and per-
formance of the run. Cognitive strategies are typically subdivided into associative strategies,
which imply directing of attention towards task-relevant stimuli and physiological sensations
experienced during exercise, and dissociative strategies, consisting in directing attention
toward distracting thoughts, as work, relationships, and other kinds of thoughts unrelated to
the experience of running [18]. Generally, these studies showed that runners adopting an asso-
ciative strategy ran faster than runners adopting a dissociative strategy [19; 18]. A recent study
also highlighted that having an external focus of attention increases running economy (mea-
sured as oxygen consumption at a set running speed), leading to a better performance as com-
pared with an internal focus of attention [20].
Since the contribution of the other cognitive aspects has been almost neglected, the present
study aimed to provide, for the first time, an overview of the impact of various cognitive func-
tions upon running performance. More specifically, the starting point questions were: Could
cognitive functioning contribute to running performance? And, if so, which cognitive processes
are the best mediators of running performance? To answer these questions we asked a group of
ultra-runners to execute a series of cognitive tasks immediately before the running race. Then,
we analysed the cognitive performance on these tasks comparing the runners who obtained a
batter rank in the race (i.e., faster runners) with those who obtained a worse rank (i.e., slower
runners).
We explored cognitive functioning by means of the modified versions of two computerized
tasks: The Inhibitory Control Task (ICT) and a dual-task paradigm with emotional stimuli,
which have been already utilized in our lab [21–25]. The ICT is composed of multiple types of
trials, thus allowed us to test distinct cognitive processes, including response speed, selective
attention, working memory updating and response inhibition [24]
To better explore the impact of executive functions on running, we utilized a dual-task para-
digm, in which two distinct tasks, heavily dependent on frontal executive processes, needed to
be executed simultaneously (Fig 1). The dual-task paradigm consists of an ongoing activity,
namely a working memory 2-back task, and a Prospective Memory (PM) task [25]. For the
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Competing Interests: The authors have declared
that no competing interests exist.
2-back task, participants were instructed to decide whether the picture occurring on the screen
was same or different from the picture presented two trials before by pressing one of two possi-
ble response keys (i.e., 2-back task). While executing the ongoing task, they had to remember
to complete a pre-specified intention (i.e., pressing a third key) when a pre-memorized picture,
Fig 1. Schematic illustration of the Dual-Task paradigm. The figure illustrates the pleasant PM session, in
which five pleasant PM cues needed to be encoded for later execution of the intention. The same tasks and
procedure were run for both unpleasant and neutral sessions. Although not displayed, a blank screen with a
fixation cross (lasting 1200, 1400, or 1600 ms) always occurred between two distinct stimuli. For the ongoing
task, participants had to press one of two keys with the right hand to decide whether the picture was same or
different from the picture presented two trials before. For the PM task, participants were required to remember
to press an additional key, with their left index finger, when they saw a picture presented during the encoding
phase. Note: The pictures displayed in the figure are not those used in the study, but are taken from Internet
only for illustration purposes.
doi:10.1371/journal.pone.0132943.g001
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namely the PM cue, occurred on the screen amid the ongoing trials (i.e., PM task). Therefore,
by using such paradigm we were able to test the runners’ ability: (i) to manage two tasks simul-
taneously; (ii) to monitor the external, ongoing, stimuli driven by an internal goal (i.e., the
identification of the PM cue), providing information about the runners’ attitude to adopt an
internal versus external focus of attention, as postulated in the PM context by the Attention to
Delayed Intention (AtoDI) model [26]; (iii) to remember to execute delayed intentions when
the appropriate cues occur; (iv) to process and react to emotional stimuli. In order to address
the fourth issue, we included pictures that were characterized by a specific emotional valence
(i.e., pleasant, unpleasant, or neutral) in both the ongoing and PM tasks. Cognitive-evaluative
reactions to emotional stimuli and situations were shown, indeed, to be pivotal for athletic per-
formance [27–29]. Thus, the inclusion of emotional stimuli in this paradigm was important to
illuminate the relationship between processing and reacting to emotional stimuli and the sub-
sequent running behaviour.
Materials and Methods
Running race and Participants
Data were collected on July 25th 2014, in occasion of the Trans d’Havet race. This competition
took place in the northeast of Italy and was part of the Ultra race of the European Skyrunning
championships. The track consisted of 80 km with a total elevation of 5500 mt and a maximum
altitude of 2238 mt. The race started on Saturday at 12.00 pm. The organizations guaranteed
medical stations and rest stops with drinks and food along the whole race. Each participant
had to pass pre-defined gate not exceeding a certain time to continue the race. Unexpectedly,
the race was interrupted because of weather conditions, and the race rank was obtained from
the order of arrival recorded at the last pre-defined gate passed before the interruption of the
race, which corresponded to the 30th km for all the participants.
Thirty ultra-marathon runners (M = 43 years, S.D. = 8.6) took part in this study. The deter-
mination of the sample size to detect a medium size effect (ηp² = .25) was based on a previous
study that used the same task (i.e., the ICT; [23]).
All participants were males, had normal, or corrected-to-normal, vision and no neurologi-
cal, psychiatric or psychological (including phobias) pathologies. Participants were in good
physical health, as proven by the medical certificate. All the runners were tested on cognitive
tasks right before their participation to the ultra-marathon (between the 7 pm and the 9 pm).
Then, a median split based on the ranking recorded at the last pre-defined gate before the inter-
ruption of the race was performed. This allowed us to create two groups, distinguishing
between faster runners and slower runners. The two groups did not differ either in age or edu-
cational level (Faster runners: Age 42.8 ± 9.6 yrs, Education 14.0 ± 4.1 yrs; Slower runners:
42.1 ± 7.7 yrs, Education 16.3 ± 2.5 yrs; all ps > .05).
Ethics Statement
The study was approved by the ethical committee of the Department of Biomedical Sciences
(University of Padua) and was conducted according to the principles of the Declaration of Hel-
sinki. All the participants were informed about the experimental procedure and signed a writ-
ten consent form.
Inhibitory Control Task
The ICT was adapted from the version used in our previous studies [23, 24]. Black letters were
presented, one after the other, for 500 ms without inter-stimulus interval, in the center of a
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white background computer screen. Interspersed within the other letters, the target letters X
and Y were presented. During the first session of the task, the participants were instructed to
respond by pressing the spacebar for every X and Y (detect trials). During the second session of
the task, participants were instructed to press the spacebar only when X and Y were alternating
(go trials) and to inhibit their response when X and Y were repeated (nogo trials). Two target
letters never occurred consecutively. The first session comprised 122 distracting letters and 30
target letters (detect trials). The second session was composed of 3 blocks, for a total of 567 dis-
tracting letters, 90 go trials and 18 nogo trials.
Dual-Task paradigm
Following our previous study [25], the dual-task paradigm consisted of an ongoing working
memory task and a PM task, simultaneously executed (Fig 1). The ongoing task was a 2-back
task comprising pleasant, neutral and unpleasant pictures. Pictures were selected from the
International Affective Picture System ([30]; see [25], for more details on the features of the sti-
muli selected). Participants were instructed to decide whether the picture occurring on the
screen was same or different from the picture occurring two trials before by pressing one of
two possible response keys on the keyboard with the index or middle finger of their right hand
(‘N’ or ‘M’ keys). On each trial, the stimulus remained on the screen for 2000 ms or until a
response was made, and was followed by a black screen with a fixation cross that pseudo-ran-
domly lasted 1200, 1400, or 1600 ms. Simultaneously with the ongoing task, individuals were
instructed to remember to accomplish a PM task, which consisted in pressing the ‘Z’ key, with
their left index finger, when particular pictures (i.e., PM cues) occurred on the screen. The par-
adigm was composed of three PM sessions, which differ for the emotional valence of the PM
cue (pleasant, unpleasant, neutral). Each session was preceded by an encoding phase, during
which the PM cues were presented, one after the other, in the center of the screen and partici-
pants were required to memorize them. Within a PM session, pleasant, neutral and unpleasant
ongoing pictures were pseudo-randomly presented, whereas the valence of the PM cues was
constant. The order of the PM sessions was counterbalanced across participants. Each of the
PM sessions comprised 55 ongoing stimuli and 5 PM cues each. A PM cue was never also a
‘same’ 2-back trial. Before the PM sessions, a practice block comprising 39 ongoing trials was
given.
Data Analysis
We compared the ICT performance between faster runners versus slower runners by analyzing
the mean accuracy for the three ICT types of trials (detect, go, nogo trials) and the RTs for the
detect and go trials by means of two separate ANOVAs.
In order to investigate the effect of monitoring for emotional PM cues on the ongoing per-
formance, the mean RTs and the proportion of correct responses to the 2-back task were ana-
lyzed in two separate ANOVAs including one between-subject factor (i.e., runners group:
faster versus slower runners) and three within-subject factors: Stimulus type (same 2-back trial,
different 2-back trial), PM cue valence (unpleasant, neutral, pleasant) and Ongoing stimulus
valence (unpleasant, neutral, pleasant). Indeed, previous studies showed that the allocation of
attentional resources towards ongoing stimuli to monitor for PM cue were reflected in an
increase of RTs and were greater when there was a match between the valence of the PM cues
and the valence of the ongoing stimuli, revealing the Stimulus Specific Interference Effect
(SSIE; [25]).
The mean RTs and accuracy in the PM task were entered into two ANOVAs with the Run-
ners group and the Valence of the PM cues as factors.
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For all the analyses, post hoc comparisons were conduced applying the Fisher's LSD (Least
Significant Difference) correction. We estimated effect sizes using partial eta squared (ηp²).
Results
Inhibitory Control Task
The analysis of mean accuracy in the ICT revealed a significant main effect of the Runners
group [F(1,28) = 4.81, p < .05, ηp² = .15], of the Type of trial [F(2,56) = 40.99, p < .001, ηp² = .54],
as well as a significant interaction between the two variables [F(2,56) = 8.57, p < .001, ηp² = .23].
As can be also seen in Fig 2, post hoc comparisons revealed that faster runners outperformed
slower runners selectively in the nogo trials (p < .001), whereas did not differ from slower run-
ners in the detect and go trials (p > .05).
The same analysis performed on RTs showed no significant difference between the two
groups of runners [F(1,28) = 0.80, p > .05, ηp² = .02, Faster runners: M = 455 ms, standard error,
SE = 16.97; Slower runners: M = 438 ms, SE = 8.90]. It however revealed a main effect of the
Type of trial, revealing that the RTs were slower in go trials (M = 476 ms, SE = 12.47) than in the
detect trials (M = 417 ms, SE = 8.46) [F(1,28) = 4.81, p < .001, ηp² = .57], for both groups.
Dual-Task Paradigm
The analysis of the RTs in the ongoing task revealed a significant Runners group × PM cue
valence interaction [F(2,56) = 3.48, p < .05, ηp² = .11]. Post hoc comparisons showed that, as
compared with faster runners, slower runners tended to have increased RTs in ongoing trials
when they had to monitor for pleasant and unpleasant PM cues (both ps = .05), whereas they
did not differ from faster runners when they had to monitor for neutral PM cues.
Fig 2. Mean Accuracy in the Inhibitory Control Task (ICT) trials for the faster and slower runners.
Faster runners outperform slower runners selectively in the nogo trials, whereas they did not differ from
slower runners in the detect and go trials. Vertical bars represent standard error.
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The three-way and the four-way interactions were both significant. To better investigate
the pattern of results in ongoing performance, the highest-level interaction [F(4,112) = 3.31,
p < .01, ηp² = .10] was split in two ANOVAs, separately for the ‘same’ and the ‘different’ 2-back
stimuli (Fig 3). Indeed, while there were not significant group differences in ‘different’ 2-back sti-
muli (all ps > .05), the effect of the Runners group was shown to be significant in the ANOVA
performed on the ‘same’ stimuli (i.e., pictures that were also presented two stimuli before). More
specifically, this analysis revealed a significant Runners group × PM cue valence × Ongoing stim-
ulus valence interaction [F(4,112) = 4.23, p < .01, ηp² = .13]. As can be seen in Fig 3, if compared
with the faster runners, the slower runners showed an increase in the RTs especially when the
valence of the ongoing trials matched the valence of the PM cue to be monitored for in that ses-
sion, thus revealing a higher SSIE. Indeed, the slower runners had slower RTs for pleasant ongo-
ing pictures when monitoring for pleasant PM cues (p < .05), and for neutral ongoing pictures
when monitoring for neutral PM cues (p < .01). The pattern of results in the unpleasant session
was instead less clear, as slower runners showed increased RTs especially for pleasant ongoing sti-
muli (p < .01).
The analysis of the accuracy in the ongoing task did not reveal any significant effect (all
ps > .05).
The analysis of the RTs in the PM task showed a significant interaction between Runners
group and PM cue valence factors [F(2,56) = 3.37, p < .05, ηp² = .11]. Faster runners responded
more quickly than slower runners to both pleasant PM cues (Faster runners: mean = 308 ms,
SE = 32.97; Slower runners: M = 463 ms, SE = 49.49; p < .05) and unpleasant PM cues (Faster
runners: M = 359 ms, SE = 39.25; Slower runners: mean = 496 ms, SE = 51.99; p < .05), whereas
they did not differ between each other in responding to neutral PM cues (Faster runners:
M = 414 ms, SE = 55.30; Slower runners: M = 422 ms, SE = 46.68; p > .05).
The analysis of the accuracy in the PM task did not show any significant effect (all ps > .05).
Discussion
What are the factors that make an outstanding athlete? In the last decades it appeared clear
that there is a combination of multiple factors, many of these are not strictly related to physical
skills but concern other individual aspects, such as cognitive abilities. The present study cor-
roborates this view, showing that some of the cognitive measures seem to be predictive of the
quality of running performance.
More specifically, the findings indicate that, as compared with slower runners, faster run-
ners had a better accuracy in nogo trials of the ICT, in which it was required to promptly
inhibit a dominant, but inappropriate, response. Thus, our study showed enhanced motor inhi-
bition in faster runners, suggesting that such cognitive function might be essential for success-
ful running performance. Importantly, it extends the results of previous studies, which found
comparable results on motor inhibition in soccer, baseball, tennis and volleyball players [15–
17] so revealing that motor inhibition is crucial not only in team sports but also in endurance
sports. As hypothesized by Verburgh et al. [31], motor inhibition might have a key role in
some physical skills, as agility. Agility has been indeed defined as ‘‘a rapid whole-body move-
ment with change of velocity or direction in response to a stimulus” [32]. We can speculate
that the ability to re-direct a movement in response to a stimulus might be particularly impor-
tant in a mountain ultra-marathon consisting in running and walking uphill and downhill in
pebbly and stony terrains. Future studies might be useful to investigate whether motor inhibi-
tion has the same influence also on marathons that are performed on flat city roads. Con-
versely, no group difference was found for selective attention and working memory, which
were evaluated in detect and go trials of the ICT. These abilities might be more required in
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Fig 3. Mean reaction times (RTs) in the ongoing 2-back task, separately for each type of PM cue valence and ongoing stimulus valence. Runners
group differences were observed in the ‘same’ trials, especially when participants had to monitor for unpleasant and pleasant PM cues and when the valence
of the PM cue matched the valence of the ongoing stimuli.
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other kinds of sports, as in soccer, baseball, or volley, which rely more upon strategic abilities
as well as upon the execution of rapid actions towards stimuli.
Finally, the executive functions were evaluated in more depth by exploring the results on a
particular kind of dual-task paradigm. In general, the performance to the 2-back working
memory task did not vary according to the runners group. This would corroborate the findings
obtained with the ICT in indicating that working memory has not a great importance on run-
ning. However, investigating the RTs in the 2-back trials depending on the valence of the PM
cue to monitor for provided information about the interference derived from checking the
presence of an emotional PM cue on ongoing task. To this regard, we observed that the inter-
ference on RTs due to the addition of the PM task was greater for the slower runners than the
faster runners. More specifically, such greater interference shown by slower runners was
observed in the RTs for the 2-back ‘same’ stimuli (i.e., stimuli that were same to those pre-
sented two trials before) and it was displayed in particular when individuals had to monitor for
emotional PM cues. Notably, slower runners tended to have a greater Stimulus Specific Inter-
ference Effect (SSIE), which consisted of the increase in RTs when the valence of the PM cue
matched the valence of the ongoing stimulus [25]. Therefore, a possible explanation is that
slower runners were less able to suppress/inhibit the interfering representation of the PM cue,
especially when such cue was emotional and when the task was more demanding (as in the
‘same’ trials). The increased SSIE for slower runners also supports this view, indicating that the
group difference was observed mainly when the PM cue valence matched the ongoing stimulus
valence, thus when the degree of interference between the internal representation of the PM
cue and the external ongoing stimulus was higher given their similar valence. Following the
Attention to Delayed Intention (AtoDI) model [26], our hypothesis is that faster runners
tended to be more focused on external, ongoing stimuli, and were more effective at inhibiting
the internal interfering PM cue representation. In this sense, outperforming runners seem to
have a better inhibitory control not only over motor responses, but also over interfering dis-
tracting information. By contrast, slower runners tended to be more focused on the internal
representation of the PM cue, which was less effectively inhibited. This finding is in agreement
with the literature on the focus of attention, which highlighted that adopting an external focus
of attention was associated with a better sporting performance and an increase in running
economy [11–13; 20]. This was probably the experience that the writer Haruki Murakami
meant to describe in the sentences that we reported at the beginning of the present manuscript.
When he wrote “. . .the kinds of thoughts and ideas that invade my emotions as I run remain
subordinate to that void” he might indeed refer to the tendency to focus on the ongoing activity
as, in this case, on running, without being distracted by internal, momentaneously not relevant,
thoughts.
An alternative hypothesis is that slower runners were more motivated to perform success-
fully the PM task, thus they were more engaged in monitoring for the presence of the PM cue
amid ongoing stimuli. However, this would not explain why group difference was observed
only in the 2-back ‘same’ stimuli and not in the 2-back ‘different’ stimuli. Furthermore, as com-
pared with the faster runner, the slower runners took more time in executing the intention
when the PM cue was emotional (i.e., pleasant and unpleasant). This seems to indicate that the
emotional content of information had a greater impact on the motor responses in the slower
runners, leading to the suggestion that the different way to process and react to emotional sti-
muli might contribute to account for differences in running performance [28–29;33].
A limitation of this research is that the race has been interrupted, thus one might wonder
whether the rank at the intermediate gate would have been confirmed by the final rank. Basi-
cally, would this ranking have been somehow the same also at the end of the competition?
Although we cannot answer this question, we sought to clarify this issue by analyzing the rank
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obtained by each runner in the last race that was characterized by a similar running distance,
which was freely available on the web. Then, we compared the mean rank between faster run-
ners and slower runners. We found that also for that race, the faster runners had a better run-
ning performance compared to the slower runners (Mean ranking score: Faster
runners = 47.2 ± 40.5; Slower runners = 116.9 ± 87.5; p < .01). This suggests that the interme-
diate ranking of the Trans d’Havet race was likely to reflect the final ranking, so it was a good
index to differentiate runners.
Finally, another question that can arise from this research concerns the role of cardiorespi-
ratory fitness in modulating the cognitive performance of ultra-runners. It might be possible
that faster runners had a better cognitive performance compared to slower runners since they
were characterized by higher cardiorespiratory fitness. The relationship between cardiorespira-
tory fitness and cognitive efficiency has been indeed increasingly explored over the past
decades, especially in relation to age-related cognitive differences [34–36]. Higher cardiorespi-
ratory fitness was found to be associated with increases in white and grey matter volume in the
prefrontal, parietal, anterior cingulate and temporal cortices and in the hippocampus, leading
to improvements in multiple cognitive functions, such as attention, control and memory [34–
38]. In the present experiment, we could not collect and assess physiological parameters, as
maximal oxygen uptake (VO2max) and Running Economy (RE) because of logistic reasons
(participants came from all over Italy and stayed at the experiment’s location only for the dura-
tion of the race). Nevertheless, we suppose that cardiorespiratory fitness played a minor role in
accounting for the cognitive differences highlighted by the present research, for two main rea-
sons. First, several studies showed that the VO2max is not a good performance predictor in
homogeneous groups [39]—as our sample is—since it does not vary with great extent within
such kind of groups. Second, we found that the cognitive differences between faster and slower
runners involved selectively some functions (e.g., inhibition but not working memory and
selective attention) rather than consisting in a global difference in cognitive functioning, which
would be instead the expected result of variations in cardiorespiratory fitness. However, our
hypotheses are still speculative, hence they need to be tested in future studies.
Summarizing, this is the first study to highlight that cognitive functioning seems to be pre-
dictive of the quality of running performance in ultra-trail. Indeed, as compared with slower
runners, outperforming runners have a better inhibitory control, showing superior ability not
only to inhibit motor responses but also to suppress processing of irrelevant distracting infor-
mation. Their cognitive performance also seems to be less influenced by emotional stimuli.
This research might open new directions toward understanding what cognitive and emotional
factors characterize talented runners.
Acknowledgments
The authors wish to thank Vincenza Tarantino, Davide Cappon, Claudia Pellegrino and Ric-
cardo Tronca for their help in data collection. The authors wish also to thank the organisers
and the participants of the “Trans d’Havet” and the municipality of Valdagno for the logistic
support.
Author Contributions
Conceived and designed the experiments: GC PB AP GM AG. Performed the experiments: GC
AC. Analyzed the data: GC. Contributed reagents/materials/analysis tools: GC AC. Wrote the
paper: GC AC GM PB.
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Cognitive Factors in Running Performance
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It's a Matter of Mind! Cognitive Functioning Predicts the Athletic Performance in Ultra-Marathon Runners.
Cona, Giorgia,Cavazzana, Annachiara,Paoli, Antonio,Marcolin, Giuseppe,Grainer, Alessandro,Bisiacchi, Patrizia Silvia