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Research focusing on perceptual-cognitive skill in sport is abundant. However, the existing qualitative syntheses of this research lack the quantitative detail necessary to determine the magnitude of differences between groups of varying levels of skills, thereby limiting the theoretical and practical contribution of this body of literature. We present a meta-analytic review focusing on perceptual-cognitive skill in sport (N = 42 studies, 388 effect sizes) with the primary aim of quantifying expertise differences. Effects were calculated for a variety of dependent measures (i.e., response accuracy, response time, number of visual fixations, visual fixation duration, and quiet eye period) using point-biserial correlation. Results indicated that experts are better than nonexperts in picking up perceptual cues, as revealed by measures of response accuracy and response time. Systematic differences in visual search behaviors were also observed, with experts using fewer fixations of longer duration, including prolonged quiet eye periods, compared with non-experts. Several factors (e.g., sport type, research paradigm employed, and stimulus presentation modality) significantly moderated the relationship between level of expertise and perceptual-cognitive skill. Practical and theoretical implications are presented and suggestions for empirical work are provided.
Content may be subject to copyright.
  457
Journal of Sport & Exercise Psychology, 2007, 29, 457-4 
© 2007 Human Kinetics, Inc.
Mann and Janelle are with the University of Florida, Williams is with Liverpool John Moores University
and Florida State University, and Ward is with Florida State University.
Perceptual-Cognitive Expertise in Sport:
A Meta-Analysis
Derek T.Y. Mann,1 A. Mark Williams,2,3 Paul Ward,3
and Christopher M. Janelle1
1University of Florida; 2Liverpool John Moores University; 
3Florida State University
Research focusing on perceptual-cognitive skill in sport is abundant. However, the
existing qualitative syntheses of this research lack the quantitative detail neces-
sary to determine the magnitude of differences between groups of varying levels
of skills, thereby limiting the theoretical and practical contribution of this body
of literature. We present a meta-analytic review focusing on perceptual-cognitive
skill in sport (N = 42 studies, 388 effect sizes) with the primary aim of quantifying
expertise differences. Effects were calculated for a variety of dependent measures
(i.e., response accuracy, response time, number of visual fixations, visual fixation
duration, and quiet eye period) using point-biserial correlation. Results indicated
that experts are better than nonexperts in picking up perceptual cues, as revealed
by measures of response accuracy and response time. Systematic differences in
visual search behaviors were also observed, with experts using fewer fixations of
longer duration, including prolonged quiet eye periods, compared with nonex-
perts. Several factors (e.g., sport type, research paradigm employed, and stimulus
presentation modality) significantly moderated the relationship between level of
expertise and perceptual-cognitive skill. Practical and theoretical implications are
presented and suggestions for empirical work are provided.
Key Words: expert, skill acquisition, anticipation, advance-cue usage, visual
search
Sport expertise has been defined as the ability to consistently demonstrate
superior athletic performance (Janelle & Hillman, 2003; Starkes, 1993). Although
superior performance is readily apparent on observation, the perceptual-cognitive
mechanisms that contribute to the expert advantage are less evident. Perceptual-
cognitive skill refers to the ability to identify and acquire environmental informa-
tion for integration with existing knowledge such that appropriate responses can
be selected and executed (Marteniuk, 1976). Knowing where and when to look is
crucial for successful sport performance, yet the visual display is vast and often
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458 Mann, Williams, Ward, and Janelle
saturated with information both relevant and irrelevant to the task. Sport perform-
ers must be able to identify the most information-rich areas of the display, direct
their attention appropriately, and extract meaning from these areas efficiently and
effectively (Williams, Davids, & Williams, 1999).
For nearly three decades, researchers have sought to better understand the
psychological factors that discriminate outstanding from less outstanding indi-
viduals in sport (Starkes & Ericsson, 2003). Researchers have demonstrated
that experts possess extensive procedural and declarative knowledge that enables
them to extrapolate important information from the environment to anticipate
and predict future events (French & Thomas, 1987; French, Spurgeon, & Nevett,
1995; McPherson, 1999, 2000). Experts are typically more proficient at making
decisions and possess an unparalleled ability to foreshadow or predict future events
and outcomes (Holyoak, 1991; Starkes & Allard, 1993; Williams et al., 1999).
Furthermore, expert performers possess enhanced perceptual-cognitive skills,
such as effective attention allocation and cue utilization, each of which have been
demonstrated across sporting and other domains. This has led to further inquiry
into the role of perceptual skill acquisition in the development of sport expertise
(Abernethy & Russell, 1987a, 1987b). Consequently, emphasis has been placed on
clarifying how experts learn to acquire perceptual cues, as well as understanding
the superior ability of experts to process task- and domain-specific information
(Abernethy, 1999).
Regardless of their individual attributes, all sport contexts require athletes to
focus attention on the most appropriate cues so as to perform effectively. It is not
surprising, therefore, that experts have been shown to differ from nonexperts on
sport-specific measures of attention allocation and information pickup. Despite
these empirical efforts, widely pervasive conceptual and methodological variability
has made it difficult to extract information that can clearly advance the science of
expertise while offering practical recommendations for training perceptual-cognitive
skills. Several issues worthy of consideration are briefly presented in the following
section and then revisited in the description of moderator variables given in the
Method section as they directly affect the ability to determine the magnitude of
the expert advantage.
Limitations of Extant Research
A multitude of research protocols (anticipation, decision making, recall, task per-
formance, spatial and temporal occlusion, and eye-movement registration) have
been used to elicit expertise differences in cognitive and perceptual skill. Although
valuable, such a rich and diverse research base has hindered the ability to compare
effects across different protocols. For example, although the occlusion paradigm
has been instrumental in identifying the importance of specific cues, research
employing this paradigm may not maintain ecological saliency on the perceptual
dimension (see Hoffman & Deffenbacher, 1993) or the essential characteristics of
the task to be captured in a holistic manner. As such, examination of the expertise
effects noticed in various paradigms is warranted.
A critical factor in the study of expert performance concerns the ability to create
experimental tasks and conditions that allow the expert advantage to emerge (Erics-
son & Smith, 1991). Detailed consideration of the experimental settings, whether
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Visual Search and Expertise    459
laboratory-based or otherwise, is paramount to expertise researchers in attempting
to reproduce this advantage. However, researchers have relied on a wide range of
stimulus presentation and task performance modalities. For example, the use of
video (film) and slide presentations is often employed in visual search investiga-
tions, potentially altering the perceptual and sensory experience (Isaacs & Finch,
1983). Although construct validity has repeatedly been demonstrated in dozens of
experiments, one could argue that two-dimensional stimulus presentations may not
adequately capture the dynamic nature of sport (Abernethy, Burgess-Limerick, &
Parks, 1994a). Few researchers have made explicit comparisons of presentation
modality in this regard.
Related to the mode of stimulus presentation, response characteristics have
often been insufficiently considered in the experimental design. The expert advan-
tage may be disguised or even masked by an inability to link stimulus characteristics
to response selection and execution in contrived settings. For instance, a baseball
player who watches a video segment of a pitch and then responds with a button
press (e.g., Radlo, Janelle, Barba, & Frehlich, 2001) may rely upon a different
perception-action coupling than when facing an actual pitcher and swinging a
bat. Therefore, it is relevant to compare the magnitude of the expert/nonexpert
performance difference across tasks, including those in the laboratory and those
in the actual sport setting.
The Current Project
As described, a number of techniques, protocols, and measurement tools have
been used to index differences in expert sport performance. The inability to extract
definitive conclusions regarding the magnitude of the overall effects warrants a
quantitative synthesis of the extant literature. Pursuant to this goal, the purpose of
this project was to conduct a meta-analysis of sport expertise to assess the most
prevalent outcome measures identified in the literature concerning perceptual-cog-
nitive differences between expert and nonexpert athletes (Rosenthal & DiMatteo,
2001). These measures included response accuracy, response time, number of
fixations, fixation duration, and quiet eye period. Response accuracy represents the
participant’s frequency of producing appropriate responses according to objective
standards and in accord with environmental constraints and task demands. Response
time is defined as an objective measure of the elapsed time between stimulus onset
and the overt production of a response.
In addition to performance metrics, several indices of attentional allocation
differences between experts and nonexperts have been used by expertise research-
ers. During eye movement registration, both the number of fixations and fixation
duration index an individual’s point of interest and relative attention allocation. The
longer the eye remains fixated on a given target, the more information is thought
to be extracted from the display (albeit not necessarily from the locus of fixation),
permitting detailed information processing. Additionally, the number of visual fixa-
tions during a given period of time provides an index of the search characteristics
representative of the most pertinent cues extracted from the environment to facili-
tate the decision-making process. It should be noted, however, that corresponding
movements of 5° or less are often considered noise and statistically removed from
the calculation of fixation duration, which typically ranges from 150 ms up to
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460 Mann, Williams, Ward, and Janelle
600 ms (Irwin, 1992). Sport scientists have recorded fixations as short as 100 ms
and as long as 1,500 ms with corresponding movements of 1° or less (Williams et
al., 1999). Eye movements between successive fixations, known as saccades, are
believed to suppress information processing. In sport, given the typically dynamic
context, researchers have typically interpreted visual search strategies involving
fewer fixations of longer duration to be more representative of the expert than the
nonexpert performer, as this would allow more time for information extraction.
Finally, quiet eye is believed to be a period of time when task-relevant environmental
cues are processed and motor plans are coordinated for the successful completion
of an upcoming task (Vickers, 1996). Specifically, the quiet eye period represents
the elapsed time between the last visual fixation on a target and the initiation of
the motor response (Vickers, 1996).
Given the diverse approaches for examining the expert/nonexpert difference
put forth in the literature, coupled with the numerous dependent measures for quan-
tifying the expert/nonexpert difference, our aims were threefold. First, our primary
aim was to determine the overall effect of perceptual cue usage and visual search
behaviors on performance. More specifically, we sought to determine the extent
to which perceptual cue usage discriminates between experts and nonexperts. A
second aim was to evaluate the relationship between visual search strategies and
expertise. We were specifically interested in whether experts require fewer fixations
of longer duration in order to extract relevant information from the environment.
Furthermore, narrative reviews have failed to differentiate the impact of various
moderating variables. Therefore, our third aim was to assess the extent to which
the expert/nonexpert differences varied as a function of the research paradigm and
participant characteristics.
Hypotheses
Experts were expected to demonstrate superior response accuracy coupled with
faster response times, while executing fewer visual fixations of longer duration.
Furthermore, experts were hypothesized to exhibit a significantly longer quiet
eye period than the nonexpert comparison group. We also predicted that across
all dependent measures (a) the research paradigm employed would significantly
moderate the expert/nonexpert relationship, with more commensurate tasks on
both the perceptual and action dimensions evoking a greater expert advantage;
(b) a larger effect in favor of the experts would be evident for real-world tasks as
compared with film and static slide presentations; and (c) sport type would mod-
erate the expertise relationship only for response time, fixation duration, number
of fixations, and quiet eye, but not for response accuracy. Regardless of the sport
type, performance accuracy was expected to be superior for the experts across
comparisons with nonexpert performers.
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Visual Search and Expertise    461
Method
Literature Search
An exhaustive search of the expertise literature was conducted in an effort to
locate all relevant studies, including the ancestry and descendancy approach and a
computer-generated key word search of Dissertation Abstracts Online (1861–2004),
PsychINFO (1967–2004), and SPORTDiscus (1830–2004). The key words included
anticipation, cue use, expertise, decision-making, eye movement, eye-tracking,
information processing, occlusion, quiet-eye, sport, visual attention, and visual
search. In accord with the ancestry approach, the reference lists of all obtained
review articles and research studies were perused, followed by a manual search
of the following peer-reviewed journals: Canadian Journal of Sports Sciences,
Human Movement Science, International Journal of Sport Psychology, Journal
of Applied Sport Psychology, Journal of Sport & Exercise Psychology, Perceptual
and Motor Skills, Quest, Research Quarterly for Exercise and Sport, and Sport
Science Review. In accord with the descendancy approach, reference to several
seminal works were entered into a database (e.g., Social SciSearch, Get Cited)
in an effort to locate those studies referencing the early work used to compile the
working database from which this meta-analysis was derived.
Studies were considered for inclusion in this meta-analysis if they assessed
performance differences (response accuracy and response time) or visual search
characteristics (fixation duration, number of fixations, and quiet eye), if they
employed an expert/nonexpert paradigm, and if data (means and SD, t value, exact
p value, or a simple effect F ratio) were available to compute an effect size (point-
biserial correlation; Rosenthal, 1984) expressed as rpb (Cooper & Hedges, 1994).
Additionally, studies were retained for inclusion if the author failed to include the
requisite data to compute an effect size but clearly stated the direction and sig-
nificance of the expert/nonexpert relationship (i.e., no significant difference). The
Results section provides an elaborate discussion of this inclusion procedure. The
multidimensional search process resulted in approximately 240 related abstracts,
and research and review papers. Of the 180 articles retrieved, 42 met the inclusion
criteria, generating 388 effect sizes from studies involving 1,288 participants, with
45.6% (n = 588) classified as expert and 54.35% (n = 700) classified as nonexpert
performers.
Independent study ratings were conducted for each study included in this meta-
analysis to assess potential coder drift and study quality. Interrater reliabilities were
computed for a number of study characteristics, including skill level, paradigm,
sport type, presentation modality, and study quality. Rater agreement ranged from
0.83 to 0.95 across categories and was therefore deemed acceptable. In the case
of an interrater discrepancy, a consensus was met before inclusion into the study.
Given that study quality was deemed consistent across those studies retained for
inclusion, study quality was not assessed a potential moderator variable.
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462 Mann, Williams, Ward, and Janelle
Moderator Variables
The extent to which the magnitude and direction of the expert/nonexpert relation-
ship varied as a function of several moderator variables was examined. Based on
the limitations presented earlier, the (a) research paradigm employed, (b) mode
of stimulus presentation, and (c) type of sport, were identified and assessed as a
function of expertise.
Research Paradigm
Researchers have argued that perception and action are mutually interdependent,
cyclical processes that directly constrain and influence one another (Williams
et al., 1999). Although it has been well documented that the effective use of
relevant advance visual cues facilitates sport performance by means of anticipat-
ing opponents’ intentions (Williams & Davids, 1998; Williams et al., 1999), the
development of research protocols that permit relevant perception and action are
warranted. Furthermore, a comparison of the paradigms inherently restricting the
perception–action coupling (i.e., when individuals are asked to verbally or physi-
cally respond in a manner that is inconsistent with the way in which they would
typically perform the task) with those more representative paradigms (i.e., verbally
or physically performing the task in a manner that is consistent with the way in
which they would typically perform the task in the real world) may provide valuable
insight into the effects that the decoupling of perception and action may have on
performance, the visual search processes, and the corresponding expert/nonexpert
difference (Williams et al., 1999).
Researchers have made extensive use of the recall paradigm to assess the
degree to which the expert maintains a cognitive advantage over the lesser skilled
performer. The recall paradigm comprises both static and dynamic images, portray-
ing either a structured or unstructured task-specific display. In either case, upon
brief exposure to the image, the participant is required to recall the location of
each player present in the display. Performance is then ascertained as the level of
agreement between a priori–identified features in the actual display (e.g., player
positions) and the participant’s recall of those features (Williams & Davids, 1995).
Although expert/nonexpert differences have been reliably demonstrated across tasks,
the degree to which this task captures the essence of domain-specific performance
is questionable. Another concern in the task design is that it measures only the
accuracy of recall, neglecting the time taken to respond. Given the inherent time
constraints in sport, athletes must not only retrieve, encode, and respond accurately,
but also must respond under severe time pressure. Furthermore, the two-dimensional
representation of the sport context coupled with the frequent use of static images
may not truly capture expertise differences in sport given that movement may be
an integral component of the pattern recognition process (Williams et al., 1999).
As such, including the recall paradigm as a distinct level of a moderating variable
will help identify its utility on parsing the expert/nonexpert differences.
The occlusion paradigm, popularized by Jones and Miles (1978), was tradi-
tionally espoused as the paradigm of choice to probe the perceptual behaviors of
athletes. Both temporal and spatial occlusion techniques have been employed to
systematically demonstrate expert/nonexpert differences in the use of information
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Visual Search and Expertise    463
presented early in the visual display across a variety of sports, including tennis,
badminton, squash, cricket, baseball, and volleyball (Abernethy & Russell, 1987a,
1987b; Buckolz, Prapavessis, & Fairs, 1988; Starkes, Edwards, Dissanayake, &
Dunn, 1995). A summary of these experiments suggests that (1) experts are better
able to predict the direction and force of an opponent’s stroke based on kinematic
information that maintain subtle clues (such as the dominant arm of a tennis player)
(Abernethy, 1990b; Wright, Pleasants, & Gomez-Mesa, 1990) and (2) experts are
more adept than nonexperts at using early flight cues to predict the ball’s end loca-
tion. These findings have been relatively consistent, signifying the attunement of
expert-level performers to advance cues otherwise neglected by nonexpert perform-
ers (Abernethy & Russell, 1987a; Buckolz et al., 1988; Jones & Miles, 1978).
Although the utility of occlusion paradigms has been clearly confirmed, the
inherent limitations of this approach should be mentioned. First, both temporal and
spatial occlusion paradigms capture only a specific aspect of the task. When these
paradigms have omitted a physical or real-world response (e.g., Singer, Cauraugh,
Chen, Steinberg, & Frehlich, 1996; Williams & Burwitz, 1993), they may negate
the expert advantage, and may only partially capture specific elements of the deci-
sions made (Abernethy, Thomas, & Thomas, 1993). Second, the use of occlusion
techniques prohibits the connections of perceptual information (either temporal or
spatial) by restricting the sequential processing of subsequent perceptual cues and
therefore promotes the use of alternative cognitive strategies for decision making.
That is, rarely in sports are the athletes unable to view their opponents in their
entirety, yet occlusion paradigms inherently restrict the presentation of information.
From an information-processing approach, this may yoke very different connections
between perceptual stimuli and the declarative knowledge necessary to reach an
accurate problem solution (Abernethy et al., 1993; Williams et al., 1999).
A major point of contention thus far has been the lack of an ecologically
valid means for evaluating the expert/nonexpert difference. Therefore, studies
implementing sport-relevant tasks including the observation of actual performance
were isolated to in order to construct a “task performance classification” for sub-
sequent moderator analyses. As such, those investigations preserving the tendency
to contrive the environment by means of occlusion, static slide, video, and or other
artificial means of manipulation (Williams, Singer, & Frehlich, 2002) were excluded
from the task performance classification.
Several researchers (e.g., Bard & Fleury, 1987; Abernethy & Russell, 1987b)
have made extensive use of the frequency and duration of visual fixations in the
absence of other performance measures or dependent variables in an effort to
unveil expert/nonexpert differences (Petrakis, 1986). These studies were classified
independently.
Stimulus Presentation
Static slide presentations inherently fail to present the participant with the dynamic
attributes of the visual environment consistent within most sporting domains (Aber-
nethy et al., 1994a). The use of dynamic film or video may offer a more natural
perception of the scene when compared with static slides. However, both slides and
film or video presentations reduce a three-dimensional world into a two-dimensional
image, potentially changing the perceptual and sensory experience. Abernethy et al.
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464 Mann, Williams, Ward, and Janelle
(1993) suggested that tasks that take place in the real world should further discern
expert/nonexpert differences by exposing the participant to additional sources of
information not available in two-dimensional media, such as stereoscopic depth
information. Few explicit comparisons of these media have been made within a
single study. Therefore, a comparison of the effect sizes associated with film, slide,
and real-world stimulus presentations were examined as potential moderators.
Sport Type
The current status of the perceptual-cognitive expertise literature suggests that the
perceptual strategies and corresponding decision-making processes of experts and
nonexperts is task dependent (Williams, Davids, Burwitz, & Williams, 1993; Wil-
liams et al., 1994; Williams & Davids, 1995). As such, the visual search behaviors
of expert and nonexpert players from one sport may be inconsistent with those
from another. For example, the contextual demands of anticipating a passing shot in
tennis may require different information-processing strategies when compared with
the underlying processes associated with anticipating a pass destination in a 3-on-3
soccer task. Therefore, sports were classified as interceptive (or coactive), strategic
(or interactive or invasive), and other (or independent or propulsive) to determine
the effect of sport type on expert/nonexpert comparisons. An interceptive sport
was defined as any sport that requires coordination between a participant’s body,
parts of the body or a held implement, and an object in the environment (Davids,
Savelsbergh, Bennett, & Van der Kamp, 2002; e.g., squash, badminton, tennis);
a strategic sport was operationalized as a sport that involves multiple teammates,
often resulting in tactical formations during offensive and defensive series, and
emphasizing the importance of allocating attention to both the projectile involved
and the diverse array of participants (i.e., field hockey, soccer); finally, a sport clas-
sified as other included such characteristics as being closed, self-paced, and aiming
at a target (e.g., billiards, golf, target shooting). As a result of the varied contextual
demands of sport, it is not altogether surprising to suspect mixed perceptual-cog-
nitive strategies across sport. Therefore, conducting a moderator analysis on the
expert/nonexpert difference across sport types is necessary to further our current
understanding of the role of task specificity on expertise.
Calculation of Effect Sizes and Statistical Analyses
Meta-analytic procedures and statistical techniques outlined and advocated by
Hedges and Olkin (1985), Cooper and Hedges (1994), Rosenthal (1984, 1995), and
Rosenthal and DiMatteo (2001) were used to conduct a fixed effects meta-analysis.
To clarify, a fixed effects analysis restricts significance testing to the total number
participants and not to the total number of studies. As such, a fixed effects approach
results in greater statistical power (Rosenthal, 1995). Effect size estimates, rpb,
and overall mean rpb were calculated for each dependent variable. Many studies in
the expertise literature have assessed multiple dependent measures relevant to this
research synthesis, including response time, response accuracy, number of visual
fixations, total fixation duration, and quiet eye duration. Although each dependent
measure provides important information furthering our understanding of expert and
nonexpert differences, including multiple dependent measures in one quantitative
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Visual Search and Expertise    465
synthesis inflates the sample size beyond the number of independent studies,
rendering it difficult to estimate the true error associated with the overall effect
size, while also inflating the Type I error rate (Wolf, 1986). Grouping the various
dependent variables into one quantitative synthesis perpetuates the “apples and
oranges” criticism of meta-analytic reviews. To avoid this pitfall, and in accordance
with Rosenthal (1984), each dependent variable was analyzed separately.
Estimates of effect size are subject to positive bias in small samples and there-
fore should be adjusted to account for the within-study sample size variability. Each
effect size was therefore weighted by the reciprocal of its variance by using Fisher’s
(1925) variance stabilizing z-transform. An overall weighted mean effect size and
an estimate of the associated variance was obtained. Subsequent analyses included
the calculation of the mean rpb, 95% confidence intervals (CI) around the mean to
determine whether effects were significantly different from zero, and comparisons
of the mean rpb between levels of moderator variables (Cooper & Hedges, 1994, pp.
265-268). Additionally, the omnibus test statistics Q, QBET, and Qw, were computed
to determine within-group and between-group sources of variation (Hedges & Olkin,
1985). Heterogeneity was calculated and indicated whether Q (the weighted total
sum of squares about the grand mean; Cooper & Hedges, 1994) exceeded the upper
tail critical value of χ2 at k − 1 degrees of freedom (Cooper & Hedges, 1994, p.
266). To test the between-group differences for each moderator variable, the QBET
was calculated (Cooper & Hedges, 1994). Furthermore, preplanned linear contrasts
were performed on each moderator variable to test the difference among levels of a
given moderator variable. As such, the 95% confidence interval and corresponding
χ2 value were calculated for each preplanned comparison (Cooper & Hedges, 1994).
In an effort to avoid the inflation of the Type I error rate, only the following linear
contrasts were computed for the moderator variable, paradigm: temporal – spatial,
anticipation – decision-making, task – anticipation, decision-making – task.
According to Rosenthal (1991), the probability of a meta-analyst accessing
all research, published and unpublished, is low, and furthermore the research is
unlikely to be a random sample of the existing research owing to publication bias.
To estimate the hypothetical effects of these limitations on the aggregated effect
size, a fail-safe n is necessary to estimate the number of studies averaging null
results needed to attenuate the observed effect and was thereby computed for each
dependent variable. Details for this calculation are provided by Rosenthal (1991).
Simply stated,
Fail-safe n = [(ΣZ)/1.96]2k
where Z is the sum of the standard normal deviates for k studies.
All qualifications of the magnitude and effects of the estimated effect size
reported here are based on the recommendations of Cohen (1977) for correlational
effect sizes, such that the values of .10, .30, and .50, represent small, medium, and
large effect size estimates, respectively. Furthermore, to facilitate the interpretation
and practical significance of the corresponding effect size, the results of a binomial
effect size display (BESD) will be presented for each dependent measure (Cooper
& Hedges, 1994). The BESD is a practical interpretation of the overall effect size
expressed as the difference in outcome rates between, in this case, the expert and
nonexpert groups for each of the dependent measures.
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466 Mann, Williams, Ward, and Janelle
Results
As mentioned previously, 388 effect sizes were calculated across the five dependent
measures: response accuracy, response time, fixation duration, number of fixations,
and quiet eye duration. Each dependent measure was analyzed separately (see
Rosenthal, 1984). A common finding across all but one dependent variable (i.e.,
quiet eye duration) was a significant test of heterogeneity.
The source of total variation around the grand mean can be divided into within
and between sources of variability (Cooper & Hedges, 1994). Repeated calculation
of Qi (which is identical to Q with the ith effect removed) indicated that the source
of heterogeneity was explained by those studies that reported a lack of statistical
significance and failed to provide sufficient data to allow the actual effect size to
be calculated. These studies were assigned an effect size of rpb = 0.00 and a corre-
sponding one-tailed p value of 0.50 (Rosenthal, 1995). Inclusion of this procedure
is conservative; simply ignoring such null findings would result in the inflation of
the overall observed effect size for each dependent measure. Therefore, despite
the heterogeneity around the mean rpb, and in accord with the recommendations of
Rosenthal (1995), an overall estimate of the mean rpb was computed and moderator
analyses were conducted. Therefore, the results of each dependent measure will
include the overall Q statistic, in addition to the Qremoved statistic, to account for the
aforementioned source of variability (i.e., studies claiming “no effects”).
Response Accuracy
The analysis of 214 effect sizes in which response accuracy was assessed revealed
a medium mean effect size of 0.31 (95% CI 0.29–0.34), which was significant (z =
13.83, p < .001). The fail-safe n was 1,386.9, indicating that approximately 1,400
studies averaging null results would be necessary to attenuate the significance of the
current effect size at the .05 level. The distribution of effect sizes was heterogeneous,
Q(213) = 331.95, p < .001. When the effect sizes derived from missing data were
removed, the results approached significance, Qremoved(195) = 226.60, p = .060. From
a practical perspective, it can be inferred that the experts were approximately 31%
more accurate across research studies as indexed by the BESD. Lastly, QBET was
calculated along with preplanned contrasts to test the difference between levels of
stimulus presentation, sport type, and study paradigm on the aggregated effect size
for response accuracy. A summary of these effect sizes is presented in Figure 1.
Sport Type. The type of sport performed was of primary interest as a potential
moderator to determine whether sport type (i.e., interceptive, strategic, and other)
influenced the skill-based performance difference. Although slight differences in
effect size magnitude were observed between the classifications of other (rpb = .37,
p < .001), interceptive (rpb = .32, p < .001), and strategic (rpb = .28, p < .001) sports,
these differences were not significant, QBET(2) = 2.53, p = .28.
Research Paradigm. As Figure 1 indicates, the overall estimate of the between-
group difference is significant, QBET(6) = 36.97, p < .001, suggesting that the
paradigm adopted to assess skill-based performance can yield variable effects.
Although significant, no statistical differences were found for the preplanned
comparisons of interest.
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Visual Search and Expertise    467
Stimulus Presentation. Researchers have questioned the degree to which various
stimulus presentation modalities adequately identify expert/nonexpert performance
differences in sport. A comparison of the presentation modalities yielded a sig-
nificant effect, QBET(2) = 7.60, p = .02. The field (rpb = .42, p < .001), video (rpb =
.31, p < .001), and static slide (rpb = .25, p < .001) stimulus presentations elicited
large-to-moderate effects with significant increases in the magnitude of effects as
the mode of stimulus presentation became progressively more representative of a
real-world task (i.e., static, video, field). Specifically, the preplanned comparison
between field and static (χ2 = 16.99, p < .001), field and video (χ2 = 5.19, p = .02),
and video and static were significant (χ2 = 7.27, p < .001).
Response Time
The analysis of 62 effect sizes in which response time was assessed revealed
that the aggregated effect was moderate, mean rpb = 0.35 (95% CI 0.30–0.40)
and significant (z = 11.90, p < .001). The fail-safe n was 198.13, indicating that
approximately 200 studies averaging null results would be necessary to attenuate
the significance of the current effect size at the .05 level. The distribution of effect
sizes was heterogeneous, Q(61) = 93.22, p < .001, and Qremoved(55) = 55.55, p =
.79). From a practical perspective, it can be inferred that the experts responded
approximately 35% faster across research studies as indexed by the BESD. Lastly,
QBET was calculated along with preplanned contrasts to test the difference among
levels of the aforementioned moderator variables. A summary of these findings is
presented in Figure 2.
Figure 1 — Summary of expertise difference for response accuracy.
PROOF
468 Mann, Williams, Ward, and Janelle
Sport Type. Although the expert was reportedly more accurate than the nonex-
pert across the various sport classifications, the magnitude of this difference was
relatively consistent, suggesting that RA was not moderated by the nature of the
sport. Conversely, response time significantly differed across sport type as a func-
tion of expertise, QBET(2) = 6.14, p = .05. Specifically, experts responded quicker
than their less skilled counterparts during interceptive sports (rpb = .37, p < .001),
strategic (rpb = .37, p < .001), and other (rpb = .15, p = .085) sports as evidenced by
the magnitude of the respective effect sizes across sport type, with notable sport
differences apparent between interceptive sports and those labeled other (e.g., bil-
liards; χ2 = 4.51, p = .033), and strategic sports and other (χ2 = 6.02, p = .014).
Research Paradigm. The paradigm adopted to assess response time signifi-
cantly moderates the expert/nonexpert relationship, QBET(3) = 13.55, p = .01. The
anticipation paradigm evoked the largest performance difference (rpb = .43, p <
.001), followed by spatial occlusion (rpb = .37, p < .001), decision-making (rpb =
.31, p < .001), and recognition (rpb = .25, p < .001) paradigms. The preplanned
comparison of the decision-making and anticipation paradigms was significant
(χ2 = 4.46, p = .034).
Stimulus Presentation. Although the experts evoked a quicker response than the
nonexpert performers during video presentation (rpb = .37, p < .001) as compared
with static slide presentations (rpb = .25, p < .001), this difference was not statisti-
cally significant, QBET(1) = 1.50, p = .22. An assessment of the field condition as a
moderator was not included as a result of insufficient data.
Figure 2 — Summary of expertise differences for response time.
PROOF
Visual Search and Expertise    469
Number of Fixations
A total of 58 effect sizes were calculated for number of fixations. The aggregated
effect was small to moderate, mean rpb = 0.26 (95% CI 0.20–0.32) and significant
(z = 9.21, p < .001). The fail-safe n was 94.09, indicating that approximately 94
studies reporting null results would be necessary to attenuate the significance of the
current effect size at the .05 level. The distribution of effect sizes was heterogeneous,
Q(57) = 117.11, p < .001, and Qremoved(49) = 63.72, p = .09. QBET was calculated
along with preplanned contrasts to test the difference between levels of stimulus
presentation, sport type, and study paradigm on the aggregated effect size for number
of fixations. A summary of these findings is presented in Figure 3.
Sport Type. As indicated in Figure 3, sport type—specifically strategic sports
(rpb = .49, p = .011)—exacerbate the expert/nonexpert visual search differences.
Experts executed fewer fixations as compared with the lesser skilled performers
when completing strategic tasks as compared with interceptive (rpb = .10, p = .435)
and other sports (rpb = .35, p = .197), QBET(2) = 37.66, p < .001. However, owing to
the presence of within-group heterogeneity, these differences are not significant.
Paradigm. Temporal and spatial occlusion paradigms were removed from the
analysis of number of fixations as a result of insufficient data. The results dem-
onstrated significant skill-based differences across research paradigms, QBET(3) =
29.01, p < .001. Accordingly, the preplanned comparison of the moderate effects
Figure 3 — Summary of expertise differences for number of fixations.
PROOF
470 Mann, Williams, Ward, and Janelle
for decision making (rpb = .44, p < .001) as compared with the small effects associ-
ated with anticipation (rpb = .12, p = .003) was significant (χ2 = 21.47, p < .001).
No other preplanned comparisons were significant; however, expertise differences
were evident, with the expert group demonstrating significantly fewer fixations
across paradigms, with the only exception being eye movement paradigm (rpb =
.05, p = .795).
Stimulus Presentation. Skill-based differences were observed across presenta-
tion modalities, QBET(2) = 10.99, p = .004: video (rpb = .19, p < .001) and static
slides (rpb = .41, p < .001), with the expert performers committing fewer fixations
as compared with the nonexpert performers.
Fixation Duration
The analysis of 49 effect sizes in which response time was assessed revealed that
the aggregated effect was small to moderate, mean rpb = 0.23 (95% CI 0.16–0.30)
and significant (z = 6.68, p < .001). The fail-safe n was 11.9, indicating that
approximately 12 studies averaging null results would be necessary to attenuate
the significance of the current effect size at the .05 level. The distribution of effect
sizes was heterogeneous, Q(48) = 102.00, p < .001, and Qremoved(39) = 66.28, p <
.001. From a practical perspective, it can be inferred that the experts exhibited
fixation durations lasting approximately 23% longer across research studies as
indexed by the BESD. QBET was calculated along with preplanned contrasts to
test the difference between levels of stimulus presentation, sport type, and study
paradigm on the aggregated effect size for fixation duration. A summary of these
findings is presented in Figure 4.
Figure 4 Summary of expertise differences for fixation duration.
PROOF
Visual Search and Expertise    471
Sport Type. Sport type significantly moderated the expert/nonexpert fixation dura-
tion relationship. The effect sizes for each sport type were as follows: interceptive
(rpb = .14, p = .016), strategic (rpb = .23, p = .003), and other (rpb = .32, p < .001);
QBET(2) = 36.09, p < .001. Only the preplanned comparison of interceptive tasks
to other tasks was significant (χ2 = 5.06, p = .024).
Paradigm. Although slight differences were present among the varied paradigms
used to assess fixation duration—including temporal occlusion (rpb = .22, p = .041),
anticipation (rpb = .24, p < .001), decision making (rpb = .15, p = .011), task perfor-
mance (rpb = .40, p < .001), and eye movement (rpb = −.11, p = .542) paradigms;
QBET(4) = 35.66, p < .001—only the preplanned comparison between decision
making and task performance was significant (χ2 = 6.38, p = .011).
Stimulus Presentation. Not unlike the findings associated with the number of
fixations, presentation modalities significantly moderated the expert/nonexpert
relationship, with the expert performer committing longer fixations as compared
with the nonexpert group: video (rpb = .30, p < .001), static slides (rpb = −.36, p <
.001), and field (rpb = .32, p < .001); QBET(2) = 38.25, p < .001. However, contrary
to hypotheses, when viewing static slides, the nonexpert group engaged in longer
fixations than did the expert group. Specifically, the preplanned comparison between
video and static (χ2 = 35.97, p < .001) and static and field presentation modalities
were significant (χ2 = 30.40, p < .001).
Quiet Eye
The analysis of five effect sizes derived from 150 participants across three separate
laboratories in which the quiet eye duration was assessed revealed a moderate-to-
large mean rpb of 0.62, (95% CI 0.40–0.82) and significant (z = 5.76, p < .001)
aggregated effect. The fail-safe n was 0.38, indicating that 1 study reporting null
findings would be necessary to attenuate the significance of the current effect size
at the .05 level. The distribution of effect sizes was homogeneous, Q(4) = 6.158, p
= .189. As a result of the small sample, subsequent analyses of potential modera-
tors were not conducted. The magnitude and direction of the reported effect size
supported the hypothesis that experts exhibit longer quiet eye periods coupled with
superior performance as compared with their less skilled counterparts. Experts
maintain a quiet eye period that is approximately 62% longer in duration across
research studies as indexed by the BESD.
Discussion
The purpose of this investigation was to provide a quantitative synthesis of the
research on perceptual-cognitive expertise in sport and to assess the moderating
effects of a number of commonly employed research paradigms, participant charac-
teristics, and presentation modalities. Perceptual cue usage and visual gaze behaviors
were assessed using a number of dependent measures. These outcome measures
provided a natural framework from which this meta-analysis was constructed.
The analysis of performance measures confirmed expectations that experts were
more accurate in their decision making relative to their lesser skilled counterparts
PROOF
472 Mann, Williams, Ward, and Janelle
(mean rpb = .31, p < .001). Moreover, experts anticipated their opponents’ intentions
significantly quicker (mean rpb = .35, p < .018) than less skilled participants. These
results are consistent with the notion that the use of advance perceptual cues has been
demonstrated to facilitate sport performance by means of aiding in the anticipation
of opponent’s actions and decreasing overall response time (e.g., Goulet, Bard, &
Fluery, 1989; Helsen & Starkes, 1999). As Abernethy (1991) contends, decision
making in sport is the product of a sequence of events occurring well before overt
movement is required. For example, during racquet sports, an ordered sequencing
of events occurs, commencing with a range of reliable kinematic cues preceding
ball flight, which, when processed, can foretell the probability of a given outcome.
The ability of expert performers to extract perceptual cues can alleviate the temporal
constraints imposed by reaction time alone (Buckolz et al., 1988). The presumption
is that the experts possess qualitatively different cognitive mechanisms and strate-
gies that facilitate anticipation, permitting reduced response times and increased
response accuracy (e.g., Ericsson & Kintsch, 1995).
In addition to performance indices, we were able to quantify the functional
significance of the expert performers’ eye movement behaviors relative to their
nonexpert counterparts. Experts were characterized by fewer fixations (mean rpb =
.26, p < .001) of longer duration (mean rpb = .23, p < .001). These findings support
the interpretation that experts in sport extract more task-relevant information from
each fixation than do lesser skilled performers. Conversely, nonexperts typically
require more fixations of shorter duration to gather sufficient information to respond.
Because the ability to extract information from the display is reduced during sac-
cadic eye movements (Duchowski, 2002), one could argue that a strategy involving
more fixations of shorter duration is less efficient and effective than one involving
fewer fixations or longer duration (Williams et al., 1993). Without sufficient time
to process task-relevant cues, oversights and incorrect decisions are inevitable as
indicated by the inferior performance outcomes displayed by the novices.
In addition to typical fixations and fixation durations—although only few effects
were included in the analysis—the quiet eye period resulted in a large positive mean
effect size (rpb = .62, p < .001). Researchers have reliably demonstrated relatively
prolonged quiet eye periods as an effective marker for differentiating skilled and
lesser skilled athletes. Moreover, these findings have shown consistency across
domains as diverse as rifle shooting (Janelle et al., 2000) and billiards (Williams
et al., 2002), for tasks that require aiming at a target (e.g., billiards and shooting),
and those that require the individual to receive a projectile momentarily while
aiming and releasing it to a designated target (e.g., volleyball; Vickers & Adolphe,
1997).
Several possible moderating effects of these results were examined. Results
indicated that sport type is not a significant moderator of the expertise relationship
for response accuracy. Regardless of the type of sport performed, experts maintain
a perceptual advantage over their less skilled counterparts, facilitating response
accuracy. Response time, however, was influenced by sport type, with the largest
expert/nonexpert differences evident for interceptive sports (rpb = .37, p < .001), and
strategic sports (rpb = .37, p < .001), followed by other (rpb = .15, p = .085) sports.
The inherent temporal constraints associated with interceptive sports (e.g., tennis,
squash) render this finding intuitively appealing. Strategic sports (e.g., soccer, field
hockey), in contrast, typically consist of a more elaborate sequencing of events,
PROOF
Visual Search and Expertise    473
which may reduce the impending temporal pressures necessary to perform at a
superior level. However, the source of the greatest difference lies between inter-
ceptive sports and with those tasks classified as other (e.g., billiards, golf), which
are rarely faced with temporal constraints. Thus, although experts’ responses were
quicker, the speed with which this response occurs is at least partly constrained by
the nature of the task.
Similarly, the number of fixations employed varied across sport type, with the
smallest margin of expert/nonexpert difference evident across interceptive sports
(rpb = .10). Clearly, this finding is attributable to the temporal constraints of task
duration. For example, an anticipation task in tennis, in which the service duration
from ball toss to racket contact may take no more than 300 ms (Abernethy, 1991),
is substantially shorter than a similar anticipation task in soccer, which may take
upwards of 9,000 ms (Williams et al., 1994b). In reality, however, the latter reflects
the time taken from the onset of a trial, whereas the anticipation response to some
critical event within that trial is likely to be more equivalent to that observed in
racket sports. Therefore, task duration alone will permit more fixations in the soccer
task than is possible in a tennis task designed to assess the same ability. However,
the duration of each corresponding fixation did not differ across sports, support-
ing the contention that experts seek the most information-dense areas of a display
while extracting task-relevant cues (Williams et al., 1993).
As predicted, the expert’s superior attunement to perceptual cues was moder-
ated by the research paradigm employed. For example, the effect sizes for response
time and response accuracy differed across paradigms, with the smallest expertise
difference noted with recall and recognition paradigms (Figures 1 and 2), that is,
tasks associated with the simple encoding and retrieval of sport specific informa-
tion. This finding questions whether performance on these tasks is predictive of
skilled performance (see also Ward & Williams, 2003). More likely, the primary
differentiation between expertise levels occurs when confronted with more complex
operations that occur rapidly, lack regularity, and are unpredictable. The remain-
ing protocols require the participant to not only encode and retrieve perceptual
information, but also to apply that information to a task that is skill dependent (i.e.,
anticipation and decision making).
The manner by which the testing stimulus was delivered to participants (i.e.,
video, static slide, and field presentations) revealed a difference for response
accuracy and fixation duration. The largest effect was reported in the field stud-
ies, followed by video, and static slides (Figures 1 and 4, respectively), suggest-
ing that there is a greater likelihood of finding an expert advantage when skilled
participants are asked to perform in ecologically valid environments. Although a
number of the video-based paradigms have appropriately captured the essence of
expertise during task performance, other researchers have asked participants to
respond in an alternate manner or have changed the nature of the task such that
the expert advantage is diminished. Expert decision making was facilitated under
field conditions, suggesting that the more realistic the paradigm, the greater the
measurement sensitivity.
Our quantitative findings support the early intuition of Jones and Miles (1978),
who discuss the inherent sterility of the laboratory and the inability of the laboratory
setting and task to accurately elicit comparable performance states. Such limitations
may confound the empirical estimates of perceived expert/nonexpert differences
PROOF
474 Mann, Williams, Ward, and Janelle
garnered from such paradigms (see Abernethy et al., 1993). Although the argument
proposed by Abernethy and colleagues would appear to have merit, when field-based
approaches are not permissible, video is a superior means of stimulus presentation
than static slides. Without question, field-based approaches have elicited the great-
est differences; however, the real crux of the matter is whether the paradigm or
mode of presentation used accurately captures the superior performance of experts.
Moreover, one has to pay particular attention to the level of experimental control
achieved when testing in the naturalistic environment. Although effects sizes are
largest in the field, it is difficult to ascertain whether participants are responding to
different stimuli, rendering reliable comparison highly problematic.
To summarize, in this meta-analysis we have synthesized and quantified a
conceptually intricate body of expertise research. The locus of expert/nonexpert
difference has been a difficult phenomenon to capture, given the diverse research
paradigms and varied experimental control, coupled with the wide-ranging opera-
tional definitions, techniques, and sampling characteristics. However, our quantita-
tive analysis has provided a means to objectively evaluate commonly held beliefs
concerning expertise in sport, confirming that sports experts are typically more
accurate and quicker in their responses and generally employ fewer fixations of
longer duration. More importantly, however, several factors (i.e., sport type, research
paradigm, and stimulus presentation modality) have been found to significantly
moderate the various relationships between level of sport expertise and perceptual-
cognitive skill that should be used to guide expertise research in future years.
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... A relevant mechanism underlying high levels of motor performance has been the efficient use of visual information available in dynamic and changing environments. For example, expert athletes usually observe the sport environment with an economical gaze pattern characterised by fewer fixations of longer duration, compared to other groups of low-skill levels [19]. Therefore, experts seem to be better at selectively allocating their attention and ignoring irrelevant areas. ...
... In this context, the data are collected in more ecological experiments because the players observe specific stimuli available in the environment and act with maximum mobility, rather than in video-based laboratory scenarios. This is a relevant question because previous studies have found differences in the visual and motor behaviours of athletes when performing in controlled laboratory settings compared to "real-life" situations [19,29]. Specifically, the portable head-mounted system utilised in this study captured the natural visual behaviours of padel players when interacting with their opponents on court. ...
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Eye-tracking research has allowed the characterisation of gaze behaviours in some racket sports (e.g., tennis, badminton), both in controlled laboratory settings and in real-world scenarios. However, there are no studies about visual patterns displayed by athletes in padel. Method: The aim of this exploratory case study was to address the visual behaviours of eight young expert padel athletes when playing match games on a padel court. Specifically, their gaze behaviours were examined with an in situ approach while returned trays/smashes, serves, and volleys were performed by their counterparts. Gaze patterns were registered with an SMI Eye Tracking Glasses 2 Wireless. Results: The participants’ gaze was mainly focused on the ball-flight trajectory and on the upper body of the opponents because they were the two visual locations with a larger number of fixations and longer fixation time. No differences were found in these variables for each type of visual location when the three return situations were compared, or independently of them. Conclusions: Padel players displayed a similar gaze behaviour during different representative return situations. This visual pattern was characterised by fixating at the ball and some opponents’ upper kinematics (head, shoulders, trunk, and the region of arm–hand–racket) to perform real interceptive actions while playing against them on a padel court. Keywords: eye-tracking; gaze behaviour; real-world scenarios; padel. Status: Website online. Full text available at: https://www.mdpi.com/1424-8220/23/3/1438/htm
... TTAs use the racket similarly to their hand, as the functional area of the racket is near the palm. To tease apart the effect of tool familiarity, we also recruited badminton athletes (BAs), who utilize a racket proficiently at the same interceptive sport level as TTAs (Mann et al., 2007). However, the functional area of the badminton racket is much further away from the hand than that of the table tennis racket. ...
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Peri-hand space (PHS) can be extended to space near tools used in everyday life, indicating that the space near the functional area of a tool acquires more spatial attention, which may be affected by tool experience. In previous studies, effects of extensive experience in tool use on the allocation of spatial attention near a tool have not been investigated. We aimed to remedy this by examining spatial attention allocation among table tennis athletes experienced in using a racket. Hands — the body parts that directly link the body and a tool — are vital for tool utilization; yet, few studies have explored whether holding a highly familiar tool in hand biases spatial attention toward it. Using an attentional cuing paradigm, we examined attention allocation in peri-hand space by comparing three hand-held conditions: table tennis racket, short brush and free hand. The performance of table tennis athletes was compared to that of badminton athletes and non-athletes. All three groups demonstrated attentional enhancement in PHS. More importantly, only table tennis athletes differentiated the space near the palm and dorsal side: attentional bias was greater for the palm side. We suggest that attentional enhancement at the palm side may be due to their frequent use of the forehand stroke. We further argue that as the functional area of the table tennis racket is very close to the hand, it is feasible that the attentional advantage related to the peri-hand space may be strengthened by extensive tool use.
... This is also confirmed by Milic et al. [28], in which experienced fencers had a faster reaction time than beginners. Moreover, the most significant differences between experienced and novice athletes in terms of reaction time were observed in disciplines where coordination between a competitor's body parts and the held utensil is required, such as American football or basketball [29]. Similarly, in motorcycle speedway racing, motor coordination is vital [8]. ...
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The study aimed to determine whether the reaction time (RT) to the starting signal has an impact on the points scored by elite male motorcycle speedway riders, or whether it depends on the starting position (gate). Differences among junior and senior riders, and how it changes during a single match (15 heats) and in the subsequent phases of the competitive season (the main and knockout phases) were investigated. The database of reaction times to the starting signal obtained by motorcycle speedway riders was collected from a mobile application called PGE Ekstraliga ver. 1.0.66 (PGE Ekstraliga, Warsaw, Poland). The database included 1.261 results obtained by 65 male riders (age 25.9 ±7.6 years), competing in the highest league in Poland (PGE Speedway Ekstraliga) in the 2021 competitive season. Reaction time was measured using the Pegasus Speedway © telemetry system (Black Burst, Warsaw, PL). Riders scoring 3 points during a heat had the fastest reaction time (F(3,1257) = 8.90, p<0.001, η2 = 0.02), but RT did not influence the final result of the match (p<0.130). The times differ depending on the occupied starting position (F(3,1257) = 6.89, p<0.001, η2 = 0.02), with the fastest RT in the inner position-A compared to the B (p<0.05) and C (p <0.001) positions. Senior riders showed significantly faster RT (0.246s) compared to junior ones (0.258s) (p<0.001). The width of the starting line affects the reaction time (F(3,1257) = 7.94, p<0.001, η2 = 0.02). In the last (15th) heat of the match, RT was the fastest. The fast reaction time during the start affects the scoring of more points in a heat but depends on riders' experience, the starting position and the straight width of the motorcycle speedway stadium. Coaches should pay attention to these factors when programming training measures.
... (Kam et al., 2016) 한 잡파를 제거하였다 (Gallicchio & Ring, 2020 (Vickers, 1996b). 자기 조절 과제와 같 은 골프 퍼팅은 QE를 훈련함으로써 그 기능을 향상 시킬 수 있다 (Mann et al., 2007). 이러한 결과는 Kim & Yeo(2017) (Romei et al., 2010;Romai et al., 2008;Vanni et al., 1997 ...
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PURPOSE The quiet eye (QE) is defined as the final fixation time that is a specific target prior to initiating movement. This study aimed to identify the cause of QE in golf putting and to present an efficient practice method for improving putting skills. METHODS Thirty participants were randomly assigned to one of three groups. Each group practiced golf putting in different ways for two days. RESULTS The QE group showed a significant difference in putting scores, which was higher than that of the control group. The visual-occlusion group showed no difference compared to the other groups in terms of putting scores. The QE group showed a significant difference in terms of QE in the retention and competition tests compared to the pretest. The control group tended to have a slightly longer QE in competition tests compared to the pretest. The visualocclusion group showed no statistically significant difference in QE based on the period. All three groups had significantly longer swing times over the selected period. There was no significant difference in terms of the alpha power of the occipital lobe based on group and period. CONCLUSIONS The position of the visual-occlusion group became stable. However, the QE did not lengthen. The QE group had a longer QE. Furthermore, the control group that practiced with their eyes open tended to have longer QE. Therefore, QE may be related to visual-based cognitive processing rather than posturalkinematics. Finally, this study proved that QE practice is a more efficient method for novices in golf putting.
... they appear when adults are in states of alertness, are involved in decision-making, are making judgments, and are solving problems. Cognitive activities such as attention, anticipation, and decision-making are inseparable from the participation of visual networks, and efficient visual search behaviors are crucial to athletes' perceptual-cognitive abilities during the competition (Mann et al., 2007). ...
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Introduction Various approaches have been used to explore different aspects of the regulation of brain activity by acute exercise, but few studies have been conducted on the effects of acute exercise fatigue on large-scale brain functional networks. Therefore, the present study aimed to explore the effects of acute exercise fatigue on resting-state electroencephalogram (EEG) microstates and large-scale brain network rhythm energy. Methods The Bruce protocol was used as the experimental exercise model with a self-controlled experimental design. Thirty males performed incremental load exercise tests on treadmill until exhaustion. EEG signal acquisition was completed before and after exercise. EEG microstates and resting-state cortical rhythm techniques were used to analyze the EEG signal. Results The microstate results showed that the duration, occurrence, and contribution of Microstate C were significantly higher after exhaustive exercise ( p’s < 0.01). There was a significantly lower contribution of Microstate D ( p < 0.05), a significant increase in transition probabilities between Microstate A and C ( p < 0.05), and a significant decrease in transition probabilities between Microstate B and D ( p < 0.05). The results of EEG rhythm energy on the large-scale brain network showed that the energy in the high-frequency β band was significantly higher in the visual network ( p < 0.05). Discussion Our results suggest that frequently Microstate C associated with the convexity network are important for the organism to respond to internal and external information stimuli and thus regulate motor behavior in time to protect organism integrity. The decreases in Microstate D parameters, associated with the attentional network, are an important neural mechanism explaining the decrease in attention-related cognitive or behavioral performance due to acute exercise fatigue. The high energy in the high-frequency β band on the visual network can be explained in the sense of the neural efficiency hypothesis, which indicates a decrease in neural efficiency.
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