Visual perception in fencing: Do the eye movements of fencers represent their information pickup?

Abstract

The present study examined whether results of athletes' eye movements while they observe fencing attacks reflect their actual information pickup by comparing these results with others gained with temporal and spatial occlusion and cuing techniques. Fifteen top-ranking expert fencers, 15 advanced fencers, and 32 sport students predicted the target region of 405 fencing attacks on a computer monitor. Results of eye movement recordings showed a stronger foveal fixation on the opponent's trunk and weapon in the two fencer groups. Top-ranking expert fencers fixated particularly on the upper trunk. This matched their performance decrements in the spatial occlusion condition. However, when the upper trunk was occluded, participants also shifted eye movements to neighboring body regions. Adding cues to the video material had no positive effects on prediction performance. We conclude that gaze behavior does not necessarily represent information pickup, but that studies applying the spatial occlusion paradigm should also register eye movements to avoid underestimating the information contributed by occluded regions.

Full text

© 2010 The Psychonomic Society, Inc. 2204
Because fencing is such a fast-moving sport, athletes
are under enormous time pressure; as in racket sports,
the time interval for preparing their own motor responses
is so short that they have to anticipate their opponent’s
intentions (Azémar, 1999; Haase & Mayer, 1978). Fenc-
ing coaches hypothesize that rapid responses to the oppo-
nent’s actions are one of the major factors that determine
level of performance (Roi & Bianchedi, 2008). Because
even world-class fencers do not have faster than average
reaction times (see Di Russo, Taddei, Apnile, & Spinelli,
2006; Harmenberg, Ceci, Barvestad, Hjerpe, & Nyström,
1991), their performance advantage is thought to be due
not just to physiological components (Roi & Bianchedi,
2008) but also—and above all—to the ability to make bet-
ter predictions about the intended target of a fencing attack
by observing the opponent’s preparatory phase (Azémar,
1999; Haase & Mayer, 1978).
The goal of this study was to determine whether it is
possible to ascertain the information fencers use in this
anticipation process by analyzing eye movements. Ac-
cordingly, the fixation patterns of fencers observing fenc-
ing attacks were compared with results gathered in two
other paradigms frequently used to determine information
pickup in sports: the occlusion and cuing paradigms.
Anticipation in Interactive Sports
Early recognition of the action intentions of one’s op-
ponent leaves more time to prepare and carry out an ap-
propriate reaction (Williams, 2009). This is a crucial as-
pect of the perceptual–cognitive expertise in interactive
sports, as various temporal occlusion experiments have
confirmed, particularly in racket sports (Mann, Williams,
Ward, & Janelle, 2007; Williams & Ward, 2007). A typical
temporal occlusion experiment stops a videotaped action
Visual perception in fencing:
Do the eye movements of fencers
represent their information pickup?
No r b e r t Ha g e m a N N
University of Kassel, Kassel, Germany
Jö r g Sc H o r e r
Westfälische Wilhelms-University Münster, Münster, Germany
ro u w e N ca ñ a l -br u l a N d
VU University Amsterdam, Amsterdam, The Netherlands
Si m o N e lo t z
Leibniz University Hannover, Hannover, Germany
a N d
be r N d St r a u S S
Westfälische Wilhelms-University Münster, Münster, Germany
The present study examined whether results of athletes’ eye movements while they observe fencing attacks
reflect their actual information pickup by comparing these results with others gained with temporal and spatial
occlusion and cuing techniques. Fifteen top-ranking expert fencers, 15 advanced fencers, and 32 sport students
predicted the target region of 405 fencing attacks on a computer monitor. Results of eye movement recordings
showed a stronger foveal fixation on the opponent’s trunk and weapon in the two fencer groups. Top-ranking
expert fencers fixated particularly on the upper trunk. This matched their performance decrements in the spatial
occlusion condition. However, when the upper trunk was occluded, participants also shifted eye movements to
neighboring body regions. Adding cues to the video material had no positive effects on prediction performance.
We conclude that gaze behavior does not necessarily represent information pickup, but that studies applying
the spatial occlusion paradigm should also register eye movements to avoid underestimating the information
contributed by occluded regions.
Attention, Perception, & Psychophysics
2010, 72 (8), 2204-2214
doi:10.3758/APP.72.8.2204
N. Hagemann, n.hagemann@uni-kassel.de

In f o r m a t I o n PI c k u P 2205
actly the opposite pattern in a one-on-one situation in soc-
cer. In this task, experienced soccer players revealed more
fixations of shorter duration than did less experienced soc-
cer players (see also Bertrand & Thullier, 2009).
Bard, Guezennec, and Papin (1981) also replicated
this pattern in fencing. They used a portable NAC eye-
movement measurement system to study three groups of
fencers with different expertise levels in real-life fencing
situations (competitions and training). As in Williams and
Davids’s (1998) soccer study, fencing masters and experts
revealed shorter fixation durations than did novices. All
three groups fixated predominantly on the hand guard
(37.8%), followed by the forearm (21.4%). Among nov-
ices and experts, this was followed by the upper arm and,
among the fencing masters, the trunk (14.8%).
However, any interpretation of eye movement data must
face one fundamental problem: the missing link between
the registered fixation location and the extraction of infor-
mation from this region (“looking” is not the same as “see-
ing”; see Williams & Ericsson, 2005, p. 291). For example,
by shifting attention it is possible to fixate on one specific
region, while extracting information from neighboring
regions in the periphery (Posner, 1980; Williams et al.,
2004). Alongside such possible shifts of attention to pick
up information (Hoffman, Nelson, & Houck, 1983; Pos-
ner, 1980), it is also not yet known how much information
is picked up, not just through the fovea, but also through
parafoveal and peripheral regions of the retina (e.g., Ab-
ernethy, 1990b; Poulter, Jackson, Wann, & Berry, 2005;
Savelsbergh, Williams, van der Kamp, & Ward, 2002).
Gaze-contingent displays manipulating the size of the
visible region surrounding the fixation point may permit
quantitative estimates of this in future research (Schorer,
Hagemann, Cañal-Bruland, Lotz, & Strauss, 2010).
Spatial Occlusion Technique
Another way of ascertaining the regions from which
athletes extract information is the spatial or event occlu-
sion technique. This masks part of the information visible
during the presentation (by either covering over an area
or replacing it with the background). If the masking of a
particular region leads to a deterioration in a participant’s
prediction performance, it would seem to be a region pro-
viding important information. For example, Abernethy and
Russell (1987b) studied how precisely the landing point of
a shuttlecock could be predicted, on the basis of videotaped
badminton serves in which various regions were masked.
During the serves, either the arm and the racket, only the
racket, the head, the lower body, or background areas were
masked with a black spot, so that the participant could not
see them. Results showed that novices gained most of the
important information for their predictions from the racket
movement; top-ranking badminton players, in contrast,
particularly used the arm and racket movement, but also
fell back to some extent on head posture, lower body pos-
ture, and lower body motion. Interestingly, a comparison
of the occluded and the nonoccluded video clips revealed
no changes in eye movements. Further examples of the use
of the spatial occlusion technique can be found in cricket
(Müller et al., 2006), squash (Abernethy, 1990a), soccer
sequence at a certain point in time, and the participant
has to predict how it will continue. By presenting video
sequences of varying duration from the perspective of a
sport opponent, research has shown that experts can pre-
dict the action intentions of their opponents at an earlier
timepoint in the movement sequence than novices can.
This has been confirmed in badminton (Abernethy & Rus-
sell, 1987a, 1987b; Hagemann & Strauss, 2006), cricket
(Müller, Abernethy, & Farrow, 2006), soccer (Williams
& Burwitz, 1993), squash (Abernethy, 1990b), and ten-
nis (Jones & Miles, 1978). For example, Hagemann and
Strauss (2006) used this paradigm to show that first- and
second-division badminton players were already able to
derive information about the potential landing point of a
shuttlecock from the movement pattern of the stroke prep-
aration 160–80 msec before racket-shuttlecock contact
(see also Abernethy & Russell, 1987a, 1987b). Although
most findings based on the temporal occlusion technique
possess limited ecological validity (due to the visual dis-
play and the atypical motor reaction for the sport), they
match those from in situ experiments, in which the tech-
nique is implemented manually with liquid crystal occlu-
sion glasses (e.g., Farrow & Abernethy, 2003; Müller &
Abernethy, 2006; Müller et al., 2009).
However, little is known about which information is ac-
tually used to improve anticipation performance in each
type of sport (Williams & Ward, 2007). Alongside verbal
reports (e.g., McRobert, Williams, Ward, & Eccles, 2009),
point light displays (Ward, Williams, & Bennett, 2002),
and biomechanical motion analyses (e.g., Huys, Smeeton,
Hodges, Beek, & Williams, 2008), past research has been
based particularly on eye movement analyses and spatial
occlusion experiments. These will be described in more
detail below.
Eye Movement Analyses
An important indicator for describing visual information
pickup is eye movements. This has been confirmed not only
by numerous eye movement studies addressing a great va-
riety of applied fields (e.g., aviation, road traffic) but also,
and above all, by expert research in sport science. Generally,
eye movement studies use special cameras to record fixa-
tion patterns during visual search for specific features of a
presented scenario or image (the eye- tracking paradigm).
The fixated locations should then reflect the areas of inter-
est. Further quantitative measures such as the number of
fixations and their duration are then related to the other in-
formation gathered in a study (Williams & Ericsson, 2005).
Using these features, numerous studies (for an overview in
racket sports, see for example Cauraugh & Janelle, 2002)
have shown that experts use different visual search strate-
gies compared with novices. Their task-specific knowledge
structures seem to enable them to focus on more relevant
areas of interest (Henderson, 2003; for an example, see Wil-
liams, Ward, Knowles, & Smeeton, 2002). One frequent
observation is that experts tend to perform fewer fixations
of longer duration (for an overview, see Mann et al., 2007).
However, such quantitative differences depend strongly
on the task to be performed (Williams, Janelle, & Davids,
2004). For example, Williams and Davids (1998) found ex-

2206 Ha g e m a n n , Sc H o r e r , ca ñ a l -Br u l a n d , lo t z , a n d St r a u S S
Ogden, 1978) have demonstrated that precuing the loca-
tion at which a target stimulus is likely to appear facilitates
performance on visual search and signal detection tasks
(see Gottlob, Cheal, & Lyon, 1999; Whitehead, MacKen-
zie, Schliebner, & Bachorowski, 1997). Grant and Spivey
(2003) highlighted single features in a static visual image to
test whether this would help participants find the right an-
swer to a problem-solving task (Duncker’s radiation prob-
lem). They used pulsing to highlight the relevant features;
that is, the breadth of a feature (in this case, a circle) fluctu-
ated by one pixel three times a second. They concluded that
manipulating attention through “a subtle increase in per-
ceptual salience of a critical diagram component increased
the frequency of correct solutions” (p. 465).
The positive impact of attention cues has also been
confirmed in a perceptual learning study. Hagemann
et al. (2006) studied how an attention cue in video clips
influenced performance in predicting overhead serves in
badminton. The attention cue was a transparent red patch
that highlighted the main body regions (trunk, racket, and
arm). Participants trained with the attention-cuing video
clips showed a marked improvement in their predictions,
as compared with a group trained without them. Directing
attention toward the relevant body regions enabled nov-
ices to learn to anticipate the consequences of movement
patterns more quickly (see also Cañal-Bruland, 2009a,
2009b; Kirlik, Walker, Fisk, & Nagel, 1996).
Hence, at least for novices possessing little informa-
tion on the distribution of important information in sport
situations, highlighting relevant features with the cuing
technique may well lead to a general increase in perfor-
mance. Experts, in contrast, can be assumed to have al-
ready developed an optimal strategy for focusing attention
through many years of training (Nougier & Rossi, 1999).
Nonetheless, cuing nonrelevant features should elicit a
deterioration of prediction performance in both experts
and novices.
Research Question
Because movements in fencing are so fast (less than
300 msec for a complete attack; see Harmenberg et al.,
1991), one factor determining success is thought to be
early recognition of the target region of the opponent’s at-
tacks. A comparison of experts and novices in a temporal
occlusion experiment should confirm that experts can ex-
tract more information from the preparation for an attack.
To determine which information is crucial when predict-
ing the targets, we did not just analyze eye movements but
simultaneously applied the spatial occlusion paradigm and
the cuing technique to test whether the foveal fixations also
corresponded in terms of information extraction.
METHOD
Participants
A total of 62 participants with differing fencing experience took
part in the study. The expert group contained 15 athletes (12 male,
3 female) with a mean age of 20.36 years (SD 5 4.63) and an aver-
age of 12.20 years (SD 5 4.63) prior fencing experience. They were
among the 60 top fencers in Germany (including Olympic champi-
ons, world champions, and German champions). At the time of the
(Williams & Davids, 1998), and tennis (Jackson & Mogan,
2007; Shim, Carlton, & Kwon, 2006). By linking together
temporal and spatial masking, it is also possible to deter-
mine the course of information pickup (e.g., Hagemann,
Strauss, & Cañal-Bruland, 2006).
Because it is easy to manipulate, the spatial occlusion
technique has recently also been used with point light dis-
plays or stick figures (Abernethy & Zawi, 2007; Aber-
nethy, Zawi, & Jackson, 2008). Huys et al. (2009) have ap-
plied it to ascertain the use of local dynamic information
for anticipating tennis serves by manipulating the move-
ment patterns of single body segments (see also Williams,
Huys, Cañal-Bruland, & Hagemann, 2009).
One problem here and in all spatial occlusion experi-
ments is that the regions to be occluded are determined
a priori. Because producing the video sequences is a lot
of work and only a limited number of conditions can be
implemented, such experiments can assess the importance
of only a limited number of regions. Moreover, it is highly
likely that experts do not base their predictions just on
information from one isolated region, but tend to integrate
information from several regions (Huys et al., 2009; Wil-
liams & Ericsson, 2005).
Combining the Spatial Occlusion Technique
With Eye Movement Recordings
Despite frequent calls to combine eye movement studies
with, among others, the spatial occlusion technique (Wil-
liams & Ericsson, 2005; Williams & Ward, 2007), only
Abernethy and Russell (1987b) and Williams and Davids
(1998) have actually done this. Abernethy and Russell
(1987b), for example, showed that, although there were no
major differences in eye movements between experts and
novices, the former were able to extract far more informa-
tion from the movement patterns of badminton players.
Abernethy (1990b, p. 74) concluded, “Information pick-up
and visual search are clearly not identical.” This study also
showed that event occlusion manipulation had no impact
on eye movements. Abernethy and Russell (1987b, p. 305)
concluded, “This observation therefore supports in princi-
ple the capability of the event occlusion paradigm to make
controlled comparisons of cue usage without causing the
subject to elicit atypical or adaptive search patterns.”
In the one-on-one situation in soccer described above,
Williams and Davids (1998) studied not only eye move-
ments (Experiment 1B) but also the impact of four spatial
occlusion conditions on performance (Experiment 2B).
Here as well, the pattern of eye movements did not reflect
performance in the occlusion condition. However, because
Experiment 2 did not control eye movements, and because
the occlusion manipulation may have changed them, it is
hard to make statements on actual information pickup.
Cuing As a Further Option?
Another possible way to assess relevant movement fea-
tures that has received little attention in sport science up to
now is cuing. Attention cues can be used in visual displays
to direct participants’ attention toward certain relevant
features (Posner, 1980). Studies based on the cuing para-
digm by Posner (1980) and colleagues (Posner, Nissen, &

In f o r m a t I o n PI c k u P 2207
Apparatus and Procedure
All participants were seated approximately 50 cm in front of a
17-in. monitor (Iiyama VisionMaster Pro 510) with the seat height
adjusted so that their eyes were approximately midscreen. Partici-
pants were given a brief introduction to the eye-tracking system, and
they were fitted with a head-mounted Eyelink II system. This bin-
ocular eye-tracker recorded the eye movements at 500 Hz. The nine-
point calibration was followed by a validation of this calibration.
In a first trial, participants watched 45 videos. For this set, 90
video clips were allocated at random to two control blocks while
ensuring that variations in attack actions and temporal occlusion
condition were distributed equally across the two conditions. The
two conditions were counterbalanced across the participants. After
the end of each video, participants saw a written list of the five pos-
sible target regions on the monitor and had to click the anticipated
target region with the mouse.2
Experimental Conditions
Each participant had to complete one control block of 45 videos
and one experimental block containing four conditions (two occlu-
sion and two cuing conditions, see below) each containing 90 vid-
eos. This resulted in 360 experimental videos, so that participants
watched a total of 405 short video clips. The sequence of blocks was
balanced across participants.
Spatial occlusion
. The study took account of the different proce-
dures to be found in the spatial occlusion literature. To detect poten-
tial methodological differences, one condition masked the region in
the visual display with a black circular spot, whereas the other condi-
tion replaced it with the background. Drawing on Bard et al. (1981)
and further spatial occlusion experiments in other sports (e.g., Ab-
ernethy & Russell, 1987b), we selected the following regions: head,
trunk/attacking arm, upper legs, and lower legs/feet.
Spatial occlusion through masking
. In these spatial occlusion
tasks, a black circular spot was used to shadow the head, trunk/
attacking arm, upper legs, and lower legs/feet (see Figure 2). This
spot was synchronized with the fencer’s movements so that the re-
gion in question remained invisible throughout the trial. The size
of the circle was the same in all conditions. In order to guarantee
an equal distribution of the four body regions to the different vid-
eos, each of the 90 videos was provided with a black spot for each
body region. These 360 videos were then distributed equally across
four blocks while ensuring that the variations in attack actions and
temporal occlusion condition were distributed equally across the
four conditions. These four conditions were counterbalanced across
participants. All experimental conditions were processed with the
digital video processing program Adobe Premiere Pro 2.0.
Spatial occlusion by replacement with the background
. In this
spatial occlusion condition, the different body parts (head, trunk/
attacking arm, upper legs, and lower legs/feet) were erased and re-
placed with the background (see Figure 2). This condition also con-
tained 90 videos.
Cuing
. In this experimental condition, single body regions were
highlighted with a transparent red patch (see Hagemann et al., 2006;
Snowden, 2002). The position of the cue was synchronized with
the position of the fencer in order to highlight one of the four body
regions throughout the trial. As with the manipulations in the occlu-
sion conditions, two methods were taken into account here as well:
In one, regions were highlighted with a circular spot; in the other,
they were covered by a transparent patch that highlighted only the
body region in question and not other areas in the background (as
is unavoidable with a circular spot). When producing all transpar-
ent patches and spots, care was taken to ensure that the highlighted
regions remained highly visible (see Figure 2).
Cuing circle
. Cues were the same size as the circular spots in the
occlusion condition. They were transparent and accompanied the
fencer’s movement (see Figure 2). Ninety videos were presented in
this condition as well.
Cuing body
. This condition was identical to the occlusion condi-
tion, in which body regions were replaced by background, except that
experiment, this group was completing an average of 5.37 training
sessions per week (SD 5 3.07). The advanced group contained 15
athletes (9 male, 6 female) who participated regularly in regional
level competitions. Their mean age was 24.25 years (SD 5 7.18),
and they had an average of 12.09 years (SD 5 5.12) of fencing expe-
rience. At the time of the experiment, this group was completing an
average of 2.03 training sessions per week (SD 5 1.21). The novice
group contained 18 male and 14 female sport students. Their mean
age was 24.70 years (SD 5 2.66), and they had no prior experience
of fencing. All participants provided informed consent prior to the
study, which was conducted according to the ethical guidelines of
the American Psychological Association (APA).
Stimulus Materials
Participants had to watch temporally occluded fencing attacks on
a computer screen and predict the target region of the attacks. The
stimulus materials were recorded at the University of Heidelberg with
two right-handed males belonging to the top-60 épée fencers in Ger-
many.1 As Figure 1 illustrates, the background was black, whereas the
movement area was grayscale. The camera was positioned at normal
human eye level in order to create the most representational perspec-
tive possible. Additionally, another top-60 fencer stood right next
to the camera to act as the opponent in the filmed scenarios. Five
different attacks (direct attack, angulation attack, and fleche attack;
last two with sixte or quarte as defense response; see note 1) aimed
toward f ive different target regions (right hand, right arm, body, right
leg, and right foot) were recorded five times per fencer. These 250
video clips were then rated by an independent expert. The 30 video
clips with the highest scores on ecological validity and technical ap-
propriateness were chosen as the basis for each experimental con-
dition. To ensure an equal distribution, we chose 10 direct attacks,
10 angulation attacks, and 10 fleche attacks (5 from each fencer).
For the direct attack, each fencer performed an attack to the five tar-
get regions. In the last two conditions, the defense techniques were
equally distributed, so that a sixte and a quarte defense technique
(one by each fencer) were presented for each target region. In total,
there were 6 different attacks to each of the five different target areas.
These 30 clips were then temporally occluded at one (40 msec), two
(80 msec), or three (120 msec) frames before weapon impact. These
90 video clips were used in each experimental condition (see below).
Videos were saved in AVI format (25 Hz) and had a resolution of
720 3 576 pixels. All videos were 1,800 msec long.
Figure 1. A still picture from the control condition. The partici-
pant sees the other fencer and the own weapon (on the right).

2208 Ha g e m a n n , Sc H o r e r , ca ñ a l -Br u l a n d , lo t z , a n d St r a u S S
guessing probability of 20%. Experts attained a mean of
52.6% correct responses, followed by the advanced group
with M 5 47.0%, and novices with M 5 40.1%. A mixed
3 (group) 3 3 (temporal occlusion point) 3 5 (target regions
of attack) ANOVA with repeated measurement revealed a
significant main effect of expertise level [F(2,59) 5 6.54,
p , .05, η2
p 5 .18]. A post hoc Scheffé test showed that
both experts and the advanced group performed better than
novices ( p , .05). There was also a main effect of tempo-
ral occlusion [F(2,118) 5 11.96, p , .01, η2
p 5 .17]. The
more participants saw of the attack sequence, the better they
could predict the target region of the attack (120 msec 5
41.4%; 80 msec 5 47.0%; 40 msec of occlusion before
weapon impact 5 51.4%). This effect was independent of
group [F(4,118) 5 .35, p . .10]. The target regions of the
attacks revealed an interaction with temporal occlusion
[F(8,472) 5 3.70, p , .01, η2
p 5 .06] (see Table 1). Table 1
shows that with the increasing length of the video sequence,
attacks on the legs and the feet in particular could be better
predicted. The main effect of target region was also signifi-
cant [F(4,236) 5 1.54, p , .01, η2
p 5 .25]. These effects
were independent of group membership.
Eye movements
. All 45 control videos from 21 ran-
domly selected participants (8 experts, 7 advanced, and
the regions were highlighted with a transparent patch instead of being
occluded (see Figure 2). Here as well, the regions were adjusted to fol-
low the movements of the fencer. Ninety videos were presented.
Data Analyses
The f irst step was to analyze response accuracy. The dependent
variable for all tasks was the percentage of correct predictions of the
hit region out of f ive given possibilities. The second step used the
eye-movement data to analyze viewing time on each region, relative
fixation duration in milliseconds, and the number of fixations dur-
ing the video clips. This used a frame-by-frame analysis, in which
the fixation was aligned with a specially constructed analysis tem-
plate for each single frame in the video material (see Figure 3). This
procedure should have ensured a clear classification of the body
regions. Finally, the fixation duration and the number of fixations
were recorded.
The mean percentages of correct responses for each participant in
each experimental condition and the eye movement data were sub-
jected to a mixed repeated ANOVA. Alpha was set at .05 and effect
sizes were calculated (η2
p).
RESULTS
Control Condition
Prediction performance
. Analysis of the 45 control
videos presented as a block before the experimental phase
showed that all three groups performed well above the
Figure 2. Sample frames from the four upper leg and lower leg manipulations. Top left: Spatial occlusion
by replacement with background. Bottom left: Spatial occlusion by masking. Top right: Body cuing. Bottom
right: Circular spot cuing.

In f o r m a t I o n PI c k u P 2209
ferences with a 3 (group) 3 10 (fixated body regions)
ANOVA with repeated measures revealed a significant
group 3 fixated body region interaction [F(20,180) 5
2.35, p , .05, η2
p 5 .21]. Experts f ixated longer than the
advanced group and the novices on the upper trunk region
(both ps , .05). Novices fixated significantly longer on
the upper legs than the either the experts or the advanced
group (both ps , .05; see Figure 4).
A quantitative analysis of the characteristics of eye
movements in all participants (univariate ANOVA) re-
vealed no group differences in either the number of fixa-
tions [F(2,56) 5 .50, p 5 .61 (experts, 4.73 msec; ad-
vanced, 4.33 msec; novices, 4.49 msec)], or their duration
[F(2,56) 5 0.67, p 5 .52 (experts, 522.85 msec; advanced,
568.16 msec; novices, 508.70 msec)].
Occlusion Condition
A 3 (group) 3 2 (occlusion technique) 3 4 (target area)
ANOVA with repeated measures revealed no differences in
prediction performance for the two occlusion techniques.
Occluding the region with a circular spot did not produce
any changes in response patterns, as compared with re-
placement by the background. Since there were no sig-
nificant main and interaction effects (all ps . .10), both
techniques were aggregated in further analyses.
Prediction performance
. The 180 videos with oc-
cluded body regions (head, trunk, upper legs, and feet)
also revealed a highly significant main effect of expertise
level [F(2,59) 5 15.34, p , .01, η2
p 5 .34]. The post hoc
Scheffé test showed that both experts (M 5 54.7%) and
the advanced group (M 5 52.6%) made more correct de-
cisions than did novices (M 5 40.8%; both ps , .05).
A comparison of the two fencer groups with the novice
group revealed a group 3 occluded region interaction
[F(2,180) 5 3.35, p , .05, η2
p 5 .05]. Figure 5 reveals
that occluding the trunk region led to a deterioration in
predictions only in the fencer groups. The novices ex-
hibited similar prediction performances for all occluded
regions. The sharp declines in the fencer groups also ex-
plained the main effect of occluded regions [F(3,177) 5
6.70, p , .05, η2
p 5 .10].
Eye movements
. To analyze eye movements in the oc-
clusion condition, all 90 videos of the background masking
condition for the above-mentioned 21 randomly selected
participants were subjected to a complete frame-by-frame
analysis. The 2 (group) 3 10 (f ixated body regions) 3
2 (condition: control vs. occlusion) revealed a change in
eye movements between the control condition and the oc-
clusion condition [F(10,160) 5 13.51, p , .05, η2
p 5 .46],
but no significant differences between groups. Whereas
no signif icant change in the regions observed could be
found when head, legs, or feet were occluded, participants
revealed different eye movements when upper and lower
trunk regions were occluded (see Table 2). When the trunk
was occluded, all groups shifted their gaze toward neigh-
boring body regions (particularly the upper legs). After
adjusting the α error,4 it could be seen that occluding the
trunk led participants to shift their gaze significantly more
to the upper legs [F(1,16) 5 11.60, p , .05, η2
p 5 .42].
At the same time, they looked significantly less at the op-
6 novices) were subjected to a frame-by-frame analysis.3
The percentage distribution of observed body regions re-
vealed a very strong focus on the upper body in all three
groups (see Figure 4). The longest viewing times were on
proximal regions, such as the lower trunk (M 5 30%) and
the opponent’s weapon (M 5 26%). Taken together, these
accounted for more than 50% of the observed body regions.
More distal regions, such as the lower legs (M , 1%), feet
(M , 1%), and head (M 5 1%), tended not to be the fo-
veal focus in all three groups. An examination of the dif-
Figure 3. Analysis template for gaze fixations (1 5 head; 2 5
upper trunk; 3 5 opponent’s weapon; 4 5 left hip; 5 5 upper
legs; 6 5 lower legs; 7 5 feet; 8 5 right hip; 9 5 lower trunk; and
10 5 own weapon).
Table 1
Mean Percentage of Correct Predictions Depending on
Temporal Occlusion Condition and Target Region
in the Control Condition
Right Right Right Right
Hand Arm Body Leg Foot
t-40 27.4 46.8 46.7 75.4 52.7
t-80 35.9 36.6 54.3 54.3 41.9
t-120 30.6 34.9 50.0 48.8 34.9

2210 Ha g e m a n n , Sc H o r e r , ca ñ a l -Br u l a n d , lo t z , a n d St r a u S S
The quantitative analysis of eye movement data (uni-
variate ANOVA) also revealed no significant group dif-
ferences for either the duration [F(2,56) 5 0.14, p 5 .87
(experts, 515.62 msec; advanced, 535.36 msec; novices,
539.01 msec)], or the number of fixations [F(2,56) 5
ponent’s weapon [F(1,16) 5 37.15, p , .05, η2
p 5 .70],
and at their own weapon [F(1,16) 5 33.16, p , .05, η2
p 5
.68]. However, after adjusting the level of α error, the in-
crease in looking at the head was no longer significant
[F(1,16) 5 6.09, p . .05, η2
p 5 .28].
Viewing Time (%)
0
5
10
15
20
25
30
35
40
Unclassified
Head
Upper Trunk
Opponent’s Weapon
Left Hip
Upper Legs
Lower Legs
Fixation Location
Feet
Right Hip
Lower Trunk
Own Weapon
Experts
Advanced
Novices
Figure 4. Mean viewing time (in % 6 SE) for the control videos in the three fencing groups.
Correct Predictions (%)
0
10
20
30
40
50
60
70
Head Trunk Upper Legs Lower Legs
Occluded Body Region
Experts
Advanced
Novices
Figure 5. Mean number of correct predictions (in % 6 SE) as a func-
tion of occluded body region and level of expertise.

In f o r m a t I o n PI c k u P 2211
well. The 2 (group) 3 10 (fixated body regions) 3 2 (con-
dition: control vs. cuing) ANOVA revealed that the cuing
conditions did not lead to changes in eye movements com-
pared with the control condition [F(10,160) 5 0.65, p .
.10]. This was the case for each comparison between the
control condition and the cuing conditions for head, trunk,
upper legs, or feet. There were also no group-specific dif-
ferences in changes in eye movements (see Table 2).
The quantitative analysis of eye movements (univariate
ANOVA) also revealed no signif icant differences between
groups in either the duration [F(2,56) 5 0.80, p 5 .92
(experts, 523.39 msec; advanced, 536.55 msec; novices,
521.20 msec), or the number of fixations [F(2,56) 5 0.17,
p 5 .84 (experts, 4.24 msec; advanced, 4.13 msec; nov-
ices, 4.28 msec).
DISCUSSION
This study was designed to ascertain which information
top-ranking fencers use to predict the direction of attacks.
This expert–advanced–novice comparison focused on
whether crucial information for this anticipation process
as determined by analyzing eye movements would match
results obtained with the occlusion and cuing techniques.
Because fencing movements are very fast (see, e.g., Har-
menberg et al., 1991), early recognition of the target area of
an opponent’s attack is expected to be a factor determining
performance (Azémar, 1999; Haase & Mayer, 1978; Roi
& Bianchedi, 2008). This was confirmed by the expert–
advanced–novice differences in all experimental condi-
tions. It was particularly top-ranking fencers who were able
to extract markedly more information from temporally oc-
cluded video sequences and use this to predict the direc-
0.33, p 5 .71 (experts, 4.47 msec; advanced, 4.18 msec;
novices, 4.42 msec)].
Cuing Condition
Similar to the spatial occlusion condition, data on the
two cuing conditions (body region patch and circular spot)
were aggregated. Although the 3 (group) 3 2 (cuing tech-
nique) 3 4 (target area) ANOVA with repeated measures
revealed a main effect of cuing condition [F(1,59) 5 5.31,
p , .05, η2
p 5 .08], indicating that cuing with a circle led
to a deterioration in predictions, no interaction could be
ascertained with the levels of expertise or the body regions
(all ps . .10).
Prediction performance
. The 180 videos in the cuing
conditions also showed the anticipated expertise effect
[F(2,59) 5 11.71, p , .05, η2
p 5 .28], and the post hoc
Scheffé test revealed that experts (M 5 52.2%) and the ad-
vanced group (M 5 48.6%) made significantly more cor-
rect decisions than novices (M 5 42.5%; both ps , .05).
Nonetheless, it should be noted that the percentage of cor-
rect responses was very low in general, and, although not
significant, below that found in the occlusion condition
(see Table 3).5
There was also a significant main effect of body region
[F(3,177) 5 3.34, p , .05, η2
p 5 .05]. In all three groups,
cuing the feet (Mfeet 5 44.5%) led to poorer prediction per-
formance than cuing the head region (Mhead 5 48.6%, p ,
.05) and trends toward poorer performance for the upper
body (Mupper body 5 46.0%) and the legs (Mlegs 5 46.7%).
Eye movements
. As in the occlusion condition, all 90
videos in the cuing body condition for the 21 randomly se-
lected participants were subjected to a complete frame-by-
frame analysis of eye movements in the cuing condition as
Table 3
Mean Percentage of Correct Predictions Depending on Condition
(Occlusion vs. Cuing) and Level of Expertise
Occlusion Cuing
Upper Lower Upper Lower
Head Trunk Legs Legs Head Trunk Legs Legs
Experts 57.2 50.0 57.1 54.5 55.2 51.4 52.5 49.7
Advanced 55.5 48.3 54.0 52.8 49.9 48.8 49.0 46.8
Novices 41.6 40.6 40.7 40.4 44.7 41.9 42.5 40.7
Table 2
Mean Percentage of Fixations on the Single Regions in the Experimental Conditions by All Three Groups
Experts Advanced Novices
Occlusion Cuing Occlusion Cuing Occlusion Cuing
CC H T U L H T U L CC H T U L H T U L CC H T U L H T U L
Unclassified 14 14 12 13 14 13 14 13 12 16 15 13 16 16 18 19 17 18 19 17 12 16 16 17 17 17 20
Head 1 14111112 1 35212112 1 714616657
Upper trunk 15 9 13 6 8 15 14 11 13 5 99466665 4 1012968986
Opponent’s weapon 25 24 8 23 25 27 26 29 28 29 20 8 27 28 24 26 25 25 23 21 8 24 29 19 20 20 18
Left hip 2 43233333 1 23113223 1 11021111
Upper legs 6 12 23 13 12 10 7 11 9 13 13 24 15 15 9 13 13 10 23 17 24 18 18 19 18 25 21
Lower legs 0 00100000 0 00010000 0 10101011
Feet 0 00000000 0 00000000 0 00001000
Right hip 1 11111111 2 24112112 1 10101111
Lower trunk 34 33 37 38 35 28 31 30 32 29 34 34 29 28 29 29 31 29 25 26 28 23 25 25 27 21 23
Own weapon 2 10122202 4 21445235 2 10322213
Note—CC, control condition; H, head; T, trunk; U, upper legs; L, lower legs.

2212 Ha g e m a n n , Sc H o r e r , ca ñ a l -Br u l a n d , lo t z , a n d St r a u S S
record eye movements, it was not possible to ascertain
whether participants tried to extract information on the
direction of an athlete’s moves from other regions (Wil-
liams & Ericsson, 2005). Williams and Davids (1998) also
suspected that their participants might have tried to do
this in the one-on-one situation in soccer reported above.
They reported that although the experienced players in
their Experiment 1B showed more foveal fixation on the
hips, occluding this region (in Experiment 2B) did not af-
fect their performance more than that of less experienced
players. However, because the two variables were assessed
separately, it was not possible to confirm this hypothesis.
Williams and Ericsson (2005) interpreted the possible
adaptation of information processing as an expression of
perceptual flexibility, which may well be a further char-
acteristic of top performance in sport. Huys et al. (2009)
have presented an approach may help resolve this issue:
a methodological modification of the spatial occlusion
experiment that purposefully manipulates the movement
pattern of single body segments instead of masking them
completely (see also Williams et al., 2009).
However, even with this modification, the a priori speci-
fication of the regions to be studied is still a problem. Partic-
ularly in less well-studied sports, any a priori specification
of potentially relevant regions is difficult. This applies to
both the number and the size of the regions to be occluded.
Moreover, it is necessary to consider quite generally that the
isolated examination of single regions may lead to the loss
of important information to be found at the transition to the
occluded region (Ward et al., 2002). One possible solution
might be to assess the additional utility of combining single
regions into larger units (e.g., Müller et al., 2006).
At this point, it is necessary to mention two problems
with the use of the occlusion technique in this study. First,
the trunk occlusion condition covered a relatively large area
that also contained the occlusion of the opponent’s weapon.
This obviously meant the loss of a great deal of relevant
information for predicting the direction of attack. A further
differentiation of the upper body regions into, for example,
attack arm and trunk or chest region may well have been
desirable. The occlusion of this large area may, in turn, have
favored the change in eye movement. On the other hand, it
is hard to decide on the basis of eye movements whether it
is the opponent’s weapon or the upper body that is currently
being fixated. This is made particularly difficult through
the continuous movement of the weapon and the way these
areas are located one in front of the other. This difficulty,
which is not found in other sports (e.g., “soccer penalty
kick”; see Savelsbergh, van der Kamp, Williams, & Ward,
2005), cannot be overcome in fencing. It must also have
been a problem in Bard et al.’s (1981) in situ study. Future
studies will have to search for ways to reduce the size of the
occluded region in crucial body parts.
Cuing
The main f inding from the cuing manipulation was that
using transparent cues in video material had no positive
effects on recognition performance. A comparison of the
cuing conditions with the occlusion conditions reveals
that although the average prediction performance did not
tion of the opponent’s attack. This is in line with not only
the models described in fencing literature (Azémar, 1999;
Haase & Mayer, 1978; Roi & Bianchedi, 2008), but also
with findings from temporal occlusion experiments in other
types of sport (Cauraugh & Janelle, 2002). The high effect
sizes in the expert–advanced–novice comparison suggest
that this is an essential feature of perceptual–cognitive ex-
pertise in fencers (Williams, 2009). Moreover, it also seems
to be a representative task with which to assess the underly-
ing perceptual–cognitive mechanisms of fencing expertise
in the laboratory (expert performance approach, see Wil-
liams & Ericsson, 2005). The present study also revealed
the typical findings from temporal occlusion experiments:
the more the participants saw of the movement sequence,
the better they could predict target regions. It is conspicu-
ous that attacks directed toward lower body parts seemed to
become recognizable only in longer video sequences.
Looking at the results of eye movements independently
from the experimental manipulations, it can be seen that
all three groups focused very strongly on the opponent’s
weapon and trunk. This corresponds to Bard et al.’s (1981)
findings obtained with a portable NAC eye movement
measurement system in real fencing situations. In the
present study as well, foveal f ixation was strong in the
central regions but weak in distal regions. However, Bard
et al. did not find the present study’s strong fixation on the
upper legs in novices. This may be due to, for example,
differences in the measurement techniques (type of eye-
tracker) or the task (video based vs. in situ).
Spatial Occlusion
Integrating the results of the occlusion conditions into
the analysis confirmed the importance of these regions
for predicting attack targets. When the opponent’s weapon
and the upper body were occluded, fencers’ prediction
performance declined compared with the other occlusion
conditions. In other words, it seems that important in-
formation was being extracted from the foveally fixated
regions. As the novices’ prediction performance did not
change, it can be concluded that experts extract impor-
tant information for predicting the attack target from the
movements of the arm and the weapon and from move-
ments of the upper body.
Occluding neighboring body regions might enable us
to make statements about the degree of peripheral infor-
mation processing. If a foveal fixation on the trunk re-
gion were to be accompanied by a deterioration due to the
occlusion of neighboring regions (legs), this would be a
sign of peripheral information processing. However, as
the fixation locations changed in this study, we can make
no statements about the peripheral information processing
posited in the literature (e.g., Abernethy, 1990b; Poulter
et al., 2005; Savelsbergh et al., 2002). Instead, the change
in gaze behavior found here indicates that the spatial oc-
clusion experiments reported in the literature have under-
estimated the importance of the occluded information. If
information is now extracted from neighboring regions
that would otherwise remain unused, this would result in a
smaller decline in prediction performance. Because most
studies (except for Abernethy & Russell, 1987b) did not

In f o r m a t I o n PI c k u P 2213
research should also take further variables into account
(e.g., one’s own motor experience or situational probabili-
ties) that could contribute to the eventual formulation of
a comprehensive model depicting the basic principles of
anticipation performance (e.g., Williams, 2009).
AUTHOR NOTE
This study was supported by a research grant from the German Re-
search Foundation, code number STR 490/9-1 (GZ). We thank the Ger-
man Olympic fencing centers (Olympiastützpunkt Fechten) in Tauber-
bischofsheim and Heidenheim for their support. Furthermore, we thank
Jutta Behr for helping to produce the stimulus material. Additionally,
we acknowledge Rebecca Rienhoff, Lennart Fischer, Nils Bender, Jan-
Micha Hoekstra, Helge Bräutigam, and Florian Loffing for helping to
prepare the stimuli and collect the data. Correspondence concerning this
article should be addressed to N. Hagemann, University of Kassel, Insti-
tute of Sports and Sport Science, Heinrich-Plett-Str. 40, 34109 Kassel,
Germany (e-mail: n.hagemann@uni-kassel.de).
REFERENCES
Abernethy, B. (1990a). Anticipation in squash: Differences in advance
cue utilization between expert and novice. Journal of Sports Sciences,
8, 17-34.
Abernethy, B. (1990b). Expertise, visual search, and information pick-
up in squash. Perception, 19, 63-77.
Abernethy, B., & Russell, D. G. (1987a). Expert–novice differences
in an applied selective attention task. Journal of Sport Psychology,
9, 326-345.
Abernethy, B., & Russell, D. G. (1987b). The relationship between
expertise and visual search strategy in a racquet sport. Human Move-
ment Science, 6, 283-319.
Abernethy, B., & Zawi, K. (2007). Pickup of essential kinematics un-
derpins expert perception of movement patterns. Journal of Motor
Behavior, 39, 353-367.
Abernethy, B., Zawi, K., & Jackson, R. C. (2008). Expertise and at-
tunement to kinematic constraints. Perception, 37, 931-948.
Azémar, G. (1999). Le controle du mouvement dans les duels sportifs
[Movement control in opposition sports]. Schweizerische Zeitschrift
für Sportmedizin und Sporttraumatologie, 47, 68-70.
Bard, C., Guezennec, Y., & Papin, J.-P. (1981). Escrime: Analyse de
l’exploration visuelle [Fencing: Analysis of visual exploration]. Mé-
decine du Sport, 55, 246-253.
Bertrand, C., & Thullier, F. (2009). Effects of player position task
complexity in visual exploration behavior in soccer. International
Journal of Sport Psychology, 40, 306-323.
Cañal-Bruland, R. (2009a). Guiding visual attention in decision mak-
ing—Verbal instructions versus flicker cueing. Research Quarterly for
Exercise & Sport, 80, 369-374.
Cañal-Bruland, R. (2009b). Visual cueing in sport-specific decision-
making. International Journal of Sport & Exercise Psychology, 7,
450-464.
Cauraugh, J. H., & Janelle, C. M. (2002). Visual search and cue utili-
sation in racket sports. In K. Davids, G. J. P. Savelsbergh, S. J. Bennett,
& J. van der Kamp (Eds.), Interceptive actions in sport ( pp. 64-89).
London: Routledge.
Di Russo, F., Taddei, F., Apnile, T., & Spinelli, D. (2006). Neural
correlates of fast stimulus discrimination and response selection in
top-level fencers. Neuroscience Letters, 408, 113-118.
Farrow, D., & Abernethy, B. (2003). Do expertise and the degree of
perception–action coupling affect natural anticipatory performance?
Perception, 32, 1127-1139.
Gottlob, L. R., Cheal, M., & Lyon, D. R. (1999). Attention operating
characteristics in a location-cuing task. Journal of General Psychol-
ogy, 126, 271-287.
Grant, E. R., & Spivey, M. J. (2003). Eye movements and problem
solving: Guiding attention guides thought. Psychological Science, 14,
462-466.
Haase, H., & Mayer, H. (1978). Optische Orientierungsstrategien von
Fechtern [Optical orientation strategies in fencers]. Leistungssport,
8, 191-200.
differ significantly, it was even lower than that in the oc-
clusion condition (see Table 3).
Looking at the results of the occlusion condition, it would
seem plausible that cuing the trunk region would lead to
better performance. This should be particularly the case
for novices, because they still do not possess any adequate
ideas as to which body regions are relevant. However, this
was not the case. In general, there was a trend toward bet-
ter performance when the head was cued, but this was still
inferior to performance in the occlusion condition. On the
other hand, the nonsignificant comparison with the control
condition also means that an occlusion of specif ic body
regions cannot be expected to lead to an improvement in
prediction performance. Hence, in all three groups, cuing
tended to be more of a distraction that impeded informa-
tion pickup. Either the change in coloring or the change
in contrast may mask relevant information (on the trunk).
Highlighting nonrelevant regions (as ascertained through
occlusion) may have led to a shift in information pickup
that has also led to a deterioration in performance. The
analysis of eye movements revealed no significant change
in the regions observed. This suggests that participants had
performed a covert shift (covert shift, Posner, 1980) in in-
formation pickup (Nougier, Stein, & Azémar, 1990). The
transparent patches may have had a distracting effect, par-
ticularly in the feet region, and impeded the processing of
information from the upper body region. This would also
fit Abernethy’s (1990b, p. 74) notion that “visual search
does not equal information extraction.” Hence, gaze be-
havior in this cuing condition is a further indication that
data from eye movement studies need to be interpreted
with caution. Further research will need to ascertain how
gaze behavior relates to information pickup in other con-
texts as well (e.g., tactile decision-making situations).
In sum, the f indings presented here reveal that cuing
is not an appropriate way to improve information pickup
in an anticipation task and is actually more of a distrac-
tor. Similar results have also been reported for tactical
decision- making situations with flicker cuing (Cañal-
Bruland, 2009a). It seems that cuing tends to distract in a
test situation, and it only helps to effectively focus atten-
tion on relevant movement information when it is part of a
long-term training program (e.g., Hagemann et al., 2006;
Janelle, Champenoy, Coombes, & Mousseau, 2003).
CONCLUSION
In summary, eye movement data and performance in
spatial occlusion experiments show that fencing experts
draw important movement information from the movement
of the opponent’s weapon or hand and trunk. The change
in eye movement data highlights the weaknesses of spatial
occlusion experiments and shows that when the spatial oc-
clusion paradigm is used to assess information pickup, it
is also desirable to record eye movements to be able to
ascertain changes in gaze behavior. Nonetheless, further
work needs to be directed toward ascertaining the relation
between gaze behavior and information pickup. This will
certainly also focus on the question of the role of periph-
eral information pickup (e.g., Schorer et al., 2010). Future

2214 Ha g e m a n n , Sc H o r e r , ca ñ a l -Br u l a n d , lo t z , a n d St r a u S S
Schorer, J., Hagemann, N., Cañal-Bruland, R., Lotz, S., &
Strauss, B. (2010). Contributions of foveal and peripheral vision to
action anticipation—Field of vision does not influence anticipation
training and retention, but transfer in fencing. Manuscript submitted
for publication.
Shim, J., Carlton, L. G., & Kwon, Y. H. (2006). Perception of kine-
matic characteristics of tennis strokes for anticipating stroke type and
direction. Research Quarterly for Exercise & Sport, 77, 326-339.
Snowden, R. J. (2002). Visual attention to color: Parvocellular guidance
of attentional resources? Psychological Science, 13, 180-184.
Ward, P., Williams, A. M., & Bennett, S. J. (2002). Visual search
and biological motion perception in tennis. Research Quarterly for
Exercise & Sport, 73, 107-112.
Whitehead, R., MacKenzie, T., Schliebner, S., & Bachorowski, J.
(1997). Effects of cue validity upon performance in the attention cue-
ing paradigm. Perceptual & Motor Skills, 84, 787-798.
Williams, A. M. (2009). Perceiving the intentions of others: How do
skilled performers make anticipation judgments? In M. Raab, J. G.
Johnson, & H. R. Heekeren (Eds.), Mind and motion: The bidirectional
link between thought and action (pp. 73-83). Amsterdam: Elsevier.
Williams, A. M., & Burwitz, K. (1993). Advance cue utilization in
soccer. In T. Reilly, J. Clarys, & A. Stibbe (Eds.), Science and foot-
ball II (pp. 239-244). London: Spon.
Williams, A. M., & Davids, K. (1998). Visual search strategy, selective
attention, and expertise in soccer. Research Quarterly for Exercise &
Sport, 69, 111-128.
Williams, A. M., & Ericsson, K. A. (2005). Perceptual–cognitive ex-
pertise in sport: Some considerations when applying the expert perfor-
mance approach. Human Movement Science, 24, 287-307.
Williams, A. M., Huys, R., Cañal-Bruland, R., & Hagemann, N.
(2009). The dynamical information underpinning anticipation skill.
Human Movement Science, 28, 362-370.
Williams, A. M., Janelle, C. M., & Davids, K. (2004). Constraints
on the search for visual information in sport. International Journal of
Sport & Exercise Psychology, 2, 301-318.
Williams, A. M., & Ward, P. (2007). Anticipation and decision making:
Exploring new horizons. In G. Tenenbaum & R. C. Eklund (Eds.),
Handbook of sport psychology (3rd ed., pp. 203-223). Hoboken, NJ:
Wiley.
Williams, A. M., Ward, P., Knowles, J. M., & Smeeton, N. J. (2002).
Anticipation skill in a real-world task: Measurement, training, and
transfer in tennis. Journal of Experimental Psychology: Applied, 8,
259-270.
NOTES
1. Fencing terms for épée fencing: Three types of weapon are used in
fencing: épée, foil, and saber. Compared to foil and saber, the target area
in épée fencing is the entire body. Direct attack: a rapid attack directed
toward a target area that ends as it begins. Angulation attack: a type of
attack deviating around the defending weapon. Fleche attack: a surprise
attack advancing on the opponent as rapidly as possible. Sixte and quarte
defense response: one of the eight parries in the classical systems of
épée fencing.
2. After answering the question on the target region, a second option
field was presented in which participants had to select a motor action
in response to this attack. Because this data did not relate to the present
analysis, it is not reported here.
3. Participants were randomly selected from all three groups because
of the large number of videos and the time-consuming nature of frame-
by-frame analysis.
4. The α error of .05 was divided by the number of tests performed
(N 5 11), resulting in an adjusted α error of .0045.
5. An additionally computed 3 (group) 3 3 (experimental condi-
tion: control, occlusion, and cuing) analysis revealed a main effect for
the between-subjects factor group [F(2,59) 5 20.48, p , .01, η2
p 5
.41]. However, no effect could be confirmed for either the interaction
[F(4,118) 5 0.89, p . .10] or the experimental condition [F(2,118) 5
1.64, p . .10].
(Manuscript received November 20, 2009;
revision accepted for publication June 15, 2010.)
Hagemann, N., & Strauss, B. (2006). Perzeptive Expertise von Bad-
mintonspielern [Perceptual expertise in badminton players]. Zeitschrift
für Psychologie, 214, 37-47.
Hagemann, N., Strauss, B., & Cañal-Bruland, R. (2006). Training
perceptual skill by orienting visual attention. Journal of Sport & Exer-
cise Psychology, 28, 143-158.
Harmenberg, J., Ceci, R., Barvestad, P., Hjerpe, K., & Nyström, J.
(1991). Comparison of different tests of fencing performance. Interna-
tional Journal of Sports Medicine, 12, 573-576.
Henderson, J. M. (2003). Human gaze control during real-world scene
perception. Trends in Cognitive Sciences, 7, 498-504.
Hoffman, J. E., Nelson, B., & Houck, M. R. (1983). The role of at-
tentional resources in automatic detection. Cognitive Psychology, 51,
379-410.
Huys, R., Cañal-Bruland, R., Hagemann, N., Beek, P. J., Smee-
ton, N. J., & Williams, A. M. (2009). Global information pickup un-
derpins anticipation of tennis shot direction. Journal of Motor Behavior,
41, 158-170.
Huys, R., Smeeton, N. J., Hodges, N. J., Beek, P. J., & Williams, A. M.
(2008). On the dynamic information underlying visual anticipation
skill. Perception & Psychophysics, 70, 1217-1234.
Jackson, R. C., & Mogan, P. (2007). Advance visual information, aware-
ness, and anticipation skill. Journal of Motor Behavior, 39, 341-351.
Janelle, C. M., Champenoy, J. D., Coombes, S. A., & Mousseau, M. B.
(2003). Mechanisms of attentional cueing during observational learn-
ing to facilitate motor skill acquisition. Journal of Sport Sciences, 21,
825-838.
Jones, C. M., & Miles, T. R. (1978). Use of advance cues in predicting
the flight of a lawn tennis ball. Journal of Human Movement Studies,
4, 231-235.
Kirlik, A., Walker, N., Fisk, A. D., & Nagel, K. (1996). Supporting
perception in the service of dynamic decision making. Human Factors,
38, 288-299.
Mann, D. Y., Williams, A. M., Ward, P., & Janelle, C. M. (2007).
Perceptual– cognitive expertise in sport: A meta-analysis. Journal of
Sport & Exercise Psychology, 29, 457-478.
McRobert, A. P., Williams, A. M., Ward, P., & Eccles, D. W. (2009).
Tracing the process of expertise in a simulated anticipation task. Ergo-
nomics, 52, 474-483.
Müller, S., & Abernethy, B. (2006). Batting with occluded vision: An
in situ examination of the information pick-up and interceptive skills of
high- and low-skilled cricket batsmen. Journal of Science & Medicine
in Sport, 9, 446-458.
Müller, S., Abernethy, B., & Farrow, D. (2006). How do world-class
cricket batsmen anticipate a bowler’s intention? Quarterly Journal of
Experimental Psychology, 59, 2162-2186.
Müller, S., Abernethy, B., Reece, J., Rose, M., Eid, M., McBean, R.,
et al. (2009). An in-situ examination of the timing of information pick-
up for interception by cricket batsmen of different skill levels. Psychol-
ogy of Sport & Exercise, 10, 644-652
Nougier, V., & Rossi, B. (1999). The development of expertise in the ori-
enting of attention. International Journal of Psychology, 30, 246-260.
Nougier, V., Stein, J. F., & Azémar, G. (1990). Covert orienting of atten-
tion and motor preparation processes as a factor of success in fencing.
Journal of Human Movement Studies, 19, 251-272.
Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experi-
mental Psychology, 32, 3-25.
Posner, M. I., Nissen, M. J., & Ogden, W. C. (1978). Attended and unat-
tended processing modes: The role of set for spatial location. In H. L.
Pick & I. J. Saltzman (Eds.), Modes of perceiving and processing infor-
mation. Hillsdale, NJ: Erlbaum.
Poulter, D. R., Jackson, R. C., Wann, J. P., & Berry, D. C. (2005). The
effect of learning condition on perceptual anticipation, awareness, and
visual search. Human Movement Science, 24, 345-361.
Roi, G. S., & Bianchedi, D. (2008). The science of fencing: Implications
for performance and injury prevention. Sports Medicine, 38, 465-481.
Savelsbergh, G. J. P., van der Kamp, J., Williams, A. M., & Ward, P.
(2005). Anticipation and visual search behaviour in expert soccer
goalkeepers. Ergonomics, 48, 1686-1697.
Savelsbergh, G. J. P., Williams, A. M., van der Kamp, J., & Ward, P.
(2002). Visual search, anticipation and expertise in soccer goalkeep-
ers. Journal of Sports Sciences, 20, 279-287.