Professional athletes have extraordinary skills for rapidly learning complex and neutral dynamic visual scenes

Abstract

Evidence suggests that an athlete's sports-related perceptual-cognitive expertise is a crucial element of top-level competitive sports1. When directly assessing whether such experience-related abilities correspond to fundamental and non-specific cognitive laboratory measures such as processing speed and attention, studies have shown moderate effects leading to the conclusion that their special abilities are context-specific2. We trained 308 observers on a complex dynamic visual scene task void of context and motor control requirements3 and demonstrate that professionals as a group dramatically differ from high-level amateur athletes, who dramatically differ from non-athlete university students in their capacity to learn such stimuli. This demonstrates that a distinguishing factor explaining the capacities of professional athletes is their ability to learn how to process complex dynamic visual scenes. This gives us an insight as to what is so special about the elite athletes' mental abilities, which allows them to express great prowess in action.

Full text

Professional athletes have extraordinary
skills for rapidly learning complex and
neutral dynamic visual scenes
Jocelyn Faubert
NSERC-Essilor Industrial Research Chair, Visual Psychophysics and Perception Laboratory, School of Optometry, University of
Montreal.
Evidence suggests that an athlete’s sports-related perceptual-cognitive expertise is a crucial element of
top-level competitive sports
1
. When directly assessing whether such experience-related abilities correspond to
fundamental and non-specific cognitive laboratory measures such as processing speed and attention, studies
have shown moderate effects leading to the conclusion that their special abilities are context-specific
2
.We
trained 308 observers on a complex dynamic visual scene task void of context and motor control
requirements
3
and demonstrate that professionals as a group dramatically differ from high-level amateur
athletes, who dramatically differ from non-athlete university students in their capacity to learn such stimuli.
This demonstrates that a distinguishing factor explaining the capacities of professional athletes is their ability
to learn how to process complex dynamic visual scenes. This gives us an insight as to what is so special about
the elite athletes’ mental abilities, which allows them to express great prowess in action.
W
hat makes elite athletes so special? Do brains of athletes anatomically and functionally differ from
non-athletes and does this diff erence relate to performance level? A recent paper showed that
high-level athletes have increased cortical thickness in a few areas of the brain and that this
increased anatomical vol ume is correlated with the level of athletic training
4
. One of the areas identified
in the athlete brain as different from controls was the superior temporal sulcus (STS), which plays a
particular role in socially relevant stimuli
5
and biological motion perception
6
. Biologic al motion perception
involves the visual systems’ capacity to recognize complex human movements when they are presented as a
pattern of a few moving dots. This task is recognized as a critical and fundamen tal abil ity of social relev ance
7
,
and is a very strong dynamic cue that can be used for collision avoidance
8
and anti cipate opponents’
movements in sports
9,10
. Th is is further supported by a recen t study showing that athletes may be superior
to non-athletes for processing soci ally realistic multitasking crowd scenes involving pedestrians crossing
streets
11
. The superi or abilities of high-level athletes for sports specifi c and socially realistic scenes both
correspond to stimuli to whi ch athletes have been extensively exposed throu ghout their lifespan. We are still
lacking strong evidence that such abil ities represent fundamental perceptual-cognitive abilities that would be
expressed in laboratory measures void of social or contextual content
2
.
The 3-dimensional multiple-object-tracking speed threshold task (3D-MOT) was recently proposed as an
optimal training procedure for isolating critical mental abilities when processing dynamic scenes such as when
navigating in traffic or during sports activities
3
. The method relies on particular features suggested to be fun-
damental such as; 1) distributing attention among a number of moving targets among distractors, known in the
literature as Multiple Object Tracking
12,13
, 2) a large visual field 3) speed thresholds, and 4) binocular 3-dimen-
sional cues (3D) (i.e. stereoscopic vision). The rationale for using such conditions has been described in detail
elsewhere
3
. We tested a total of 308 individuals separated into three distinct groups based on their performance
levels in sports to determine whether the level of sports performance can distinguish the learning rate capacities
for this complex and neutral visual scene task.
Results
A total of 102 professional players (mean age 5 23,8 6 5,5 SD, median 22) from three different sports including
51 professional soccer players (English Premier League (EPL)), 21 professional ice hockey players (National
SUBJECT AREAS:
VISUAL SYSTEM
PSYCHOLOGY
COGNITIVE NEUROSCIENCE
LEARNING AND MEMORY
Received
10 December 2012
Accepted
7 January 2013
Published
31 January 2013
Correspondence and
requests for materials
should be addressed to
J.F. (jocelyn.faubert@
umontreal.ca)
SCIENTIFIC REPORTS | 3 : 1154 | DOI: 10.1038/srep01154 1

Hockey League (NHL)) and 30 professional rugby players (French
Top 14 Rugby League (Top14)). We also tested a total of 173 elite
amateurs (mean age 5 23,5 6 5,8 SD, median 22) with 136 from the
NCAA university sports program in the US and 37 from a European
Olympic sport-training center. We have also tested 33 non-athlete
university students (mean age 5 23,8 6 5,0 SD, median 22) from the
Universite
´
de Montre
´
al.
We have previously reported that, given identical conditions, top
professional soccer, ice hockey or rugby teams generate very similar
sensitivity profiles
3
. For this reason the professionals are presented as
a single population group. Similarly, we obtained identical functions
for our two amateur cohorts (NCAA and Olympic training center)
studied here so again, we show the elite amateurs as one group.
Figure 1 shows the session-by-session geometrical mean graphs
for the three groups with the session number on the x-axis and the
3D-MOT speed thresholds on a log y-axis. The fits shown are log
regression functions and the R
2
corresponds to the amount of vari-
ance explained by the fit. The data clearly show that the professional
athlete group starts at higher speed values with a much steeper learn-
ing slope as a function of training session then the elite amateurs. In
turn, the elite-amateur group starts at the same level as the non-
athletes but the learning function rapidly distances itself from the
one obtained for thenon-athlete university group. To emphasize the
learning rate differences between the groups, the small graph on the
right shows the normalized data (Log(sessions score) – Log(initial
score)). One can see that the three learning rate functions are distinct
regardless of the initial starting point scores.
Discussion
The present results show a clear distinction between the level of
athletic performance and corresponding fundamental mental
capacities for learning an abstract and demanding dynamic scene
task. How would this exceptional ability translate to specific real-life
situations? For athletes, it is obviously related to their high levels of
competitive sport performance. But what actions can we predict are
enhanced by such a specialised ability for learning dynamic complex
scenes? It would make logical sense that high-level athletes should
be superior for achieving biological motion perception skills for
instance. This is supported by the fact that cortical thickness of
STS, an area known to process socially relevant cues and biological
motion perception
5
, is greater and linked to training experience in
athletes
4
. In other populations such as healthy older observers it has
been shown that training with the 3D-MOT results in a direct sub-
sequent transfer benefit to biological motion perception abilities at
distances critical for collision avoidance
14
. The 3D-MOT speed task
strongly engages several attention and mental skills that should carry
over to other functions. To achieve high levels on this task one
requires exquisite selective, dynamic, distributed and sustained
attention skills for brief yet intense periods. Such abilities are cer-
tainly necessary when engaged in activities requiring the integration
of simultaneous inputs such as when driving, crossing busy streets or
when engaged in sporting activities. We have previously shown that
the condition of testing can influence the learning curve
3
. This was
demonstrated by the fact that if the professional players were stand-
ing as opposed sitting down for the initial consolidation training, the
growth curve was reduced, which argues for shared resources. It
remains to be determined whether this is specific to professional
athletes or whether it can also be observed in other populations, as
there clearly is something special about professional athletes. They
appear to be able to hyper-focus for short periods of time resulting in
extraordinary learning functions for the 3D-MOT task. We cannot
determine here whether this superb ability to learn to process
Figure 1
|
Geometrical 3D-MOT speed threshold means for 308 individuals on a log scale separated into professional, elite-amateur and
non-athlete university students as a function of training sessions. The y values are arbitrary speed units. Only 14 sessions are shown for the amateurs
because the protocol for the Olympic training center athletes was pre-set to terminate at 14 sessions. Error bars represent SEM.
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SCIENTIFIC REPORTS | 3 : 1154 | DOI: 10.1038/srep01154 2

random and complex dynamic scenes has evolved by experience or
stems from an innate predisposition. Prospective outcomes of athlete
performance based on initial measures should prove very interesting
in the future. The 3D-MOT method has been used to profile athletes
for both the NHL and NFL combines where the best prospects for the
entry draft are evaluated on a series of test batteries. It will be inter-
esting to see whether these initial scores predict future performance
outcomes. It is clear that individual performances on this task will be
affected by many factors other than athletic skill including, sensory,
physical, and psychological makeup so we should not expect a direct
one to one relationship. It is clear that individual performances on
this task will be affected by many factors other than athletic skill
including, sensory, physical, and psychological makeup so we should
not expect a direct one to one relationship. Nevertheless, our results
do suggest that rapid learning in complex and unpredictable dyna-
mic contexts is one of the critical components for elite performance.
In conclusion, we have demonstrated that professional athletes as
a group have extraordinary skills for rapidly learning unpredictable,
complex dynamic visual scenes that are void of any specific context.
It is clear from these results that these remarkable mental processing
and learning abilities should be acknowledged as critical elements for
world-class performance in sport and potentially elite performance
abilities in other dynamic contexts.
Methods
The observers trained up to 15 sessions separated over a minimum of five different
days using the NeuroTracker
TM
CORE program distributed by CogniSens Athletics
Inc., which is the commercial equivalent of the laboratory 3D-MOT speed threshold
procedure that has been licenced by CogniSens Athletics Inc. from the Universite
´
de
Montre
´
al. Each session lasted around 8 minutes and the subjects were not allowed to
train for more then three sessions in a given day. The basic 3-D MOT trial sequence is
presented in Figure 2 and comprises of 5 steps (see legend).
The size of the 3D volume space was 46 degrees of visual angle at the level of the
screen. After a single trial (Figure 2), if the subject got all 4 indexed spheres correct the
speed went up for the next trial. If at least one sphere was missed the speed slowed
down on the next trial (1 up 1 down staircase) so on and so forth until a threshold was
achieved
3
. All subjects gave the answers verbally and an experimenter recorded the
answers on a keyboard. This study was approved by the ethics board of the Universite
´
de Montre
´
al.
1. Mann, D. T., Williams, A. M., Ward, P. & Janelle, C. M. Perceptual-cognitive
expertise in sport: A meta-analysis. J Sport Exercise Psy 29(4), 457–478 (2007).
2. Voss, M., Kramer, A. F., Prakash, R. S., Roberts, B. & Basak, C. Are expert athletes
‘‘expert’’ in the cognitive laboratory? A meta-analytic review of cognition and
sport expertise. Appl Cogn Psychol. 24, 812–26 (2009).
3. Faubert, J. & Sidebottom, L. Perceptual-cognitive training of athletes. J Clin Sport
Psy 6, 85–102 (2012).
4. Wei, G., Zhang, Y., Jian g, T. & Luo, J. Increased Cortical Thickness in Sports
Experts: A Comparison of Diving Players with the Controls. PLoS ONE 6(2),
(e17112 2011). doi:10.1371/journal.pone.0017112
5. Lahnakoski, J. M., Glerean, E., Salmi, J., Ja¨a¨skela¨inen, I. P., Sams, M., Hari, R. &
Nummenmaa, L. Naturalistic fMRI mapping reveals superior temporal sulcus as
the hub for the distributed brain network for social perception. Front. Hum.
Neurosci. 6, 233 (2012). doi: 10.3389/fnhum.00233
6. Grossman, E. D., Jardine, N. L. & Pyles, J. A. fMR-adaptation reveals invariant
coding of biological motion on the human STS. Front. Hum. Neurosci. 4,15
(2010). doi: 10.3389/neuro.09.015
7. Troje, N. F. Retrieving information from human movement patterns. In: Shipley,
T. F. and Zacks, J. M. editors. Understanding events: how humans see, represent,
and act on events. New York: Oxford University Press. 2008. pp. 308–334.
8. Ouellette, M., Chagnon, M. & Faubert, J. Evaluation of human behavior in
collision avoidance: A study inside immersive virtual reality. Cyberpsychol Behav
12(2), 215–218 (2009). doi:10.1089/cpb.2008.0089
9. Ward, P., Williams, A. M. & Bennett, S. J. Visual search and biological motion
perception in tennis. Res Q Exerc Sport. 73(1), 107–112 (2002).
10. Abernethy, B. & Zawi, K. Pickup of essential kinematics underpins expert
perception of movement patterns. J Mot Behav. 39(5), 353–367 (2007).
11. Chaddock, L., Neider, M. B., Voss, M. W., Gaspar, J. G. & Kramer, A. F. Do
Athletes Excel at Everyday Tasks? Med Sci Sports Exerc 43(10), 1920–1926 (2011).
12. Cavanagh, P. & Alvarez, G. A. Tracking multiple targets with multifocal attention.
Trends Cogn Sci 9(7), 349–354 (2005).
13. Pylyshyn, Z. W. & Storm, R. W. Tracking multiple independent targets: Evidence
for a parallel tracking mechanism. Spatial Vision 3(3), 179–197 (1988).
14. Legault, I. & Faubert, J. Perceptual-cognitive training improves biological motion
perception: evidence for transferability of training in healthy aging. Neuro Report
23, 469–473 (2012).
Acknowledgements
This work was supported by a Natural Sciences and Engineering Research Council of
Canada discovery grant. I would like to thank Dr. Leonard Zaichkowsky for helpful
discussions.
Author contributions
J.F. wrote the manuscript text, did the analysis and prepared the figures.
Additional information
Competitive financial interests: The author is director of the Visual Psychophysics and
Perception Laboratory at the University of Montreal and he is the Chief Science Officer of
CogniSens Athletics Inc. who produces the commercial version of the 3D-MOT used in this
study. In this capacity, he holds shares in the company.
License: This work is licensed under a Creative Commons Attribution 3.0 Unported
License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/
How to cite this article: Faubert, J. Professional athletes haveextraordinaryskills for rapidly
learning complex and neutral dynamic visualscenes. Sci. Rep. 3, 1154; DOI:10.1038/
srep01154 (2013).
Figure 2
|
Five steps of the 3D-MOT task (a) presentation phase where 8 spheres are shown in a 3D volume space, (b) indexing phase where 4 spheres
(targets) change colour (red) and are highlighted (hallo) for 1 second, (c) movement phase where the targets indexed in stage b return to their original
form and colour and all spheres move for 8 seconds crisscrossing and bouncing off of each other and the virtual 3D volume cube walls that are not
otherwise visible, (d) identification phase where the spheres come to a halt and the observer has to identify the 4 spheres originally indexed in phase (b).
The spheres are individually tagged with a number so the observer can give the number corresponding to the original targets, and (e) feedback phase where
the subject is given information on the correct targets.
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SCIENTIFIC REPORTS | 3 : 1154 | DOI: 10.1038/srep01154 3