JemrJournal of Eye Movement Research1995-8692Bern Open PublishingBern, Switzerland10.16910/jemr.11.3.1Research ArticleHow prior experience, cognitive skills and practice are related with eye-hand span and performance in video gamingNivalaMarkusCichyAgnesGruberHansDepartment of Education, Communication and Learning, University of Gothenburg, SwedenDepartment of Educational Science, University of Regensburg, GermanyDepartment of Educational Science, University of Regensburg, GermanyDepartment of Teacher Education, University of Turku, Finland1152018201811310.16910/jemr.11.3.12018Nivala, Cichy and GruberThis work is licensed under a Creative Commons Attribution 4.0 International License,
(
https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use and redistribution provided that the original author and source are credited.
Research has shown that performance in visual domains depends on domain-specific cognitive and perceptual adaptations that result from extensive practice. However, less is known about processes and factors that underpin the acquisition of such adaptations. The present study investigated how prior experience, cognitive skills, task difficulty and practice effect eye-hand span (EHS) and performance in video gaming. Thirty-three participants played a platformer video game in a pre-test/practice/post-test experiment. Eye movements and keypresses were recorded. The results show that a short practice period improved performance but did not increase EHS. Instead, EHS was related to task difficulty. Furthermore, while EHS correlated with initial performance, this effect seemed to diminish after practice. Cognitive skills (concentration endurance, working memory, mental flexibility and executive functioning) predicted performance in some parts of the experiment. The study offers insights into the early development of visual adaptations and performance.
Research on expertise has shown that performance in
visual domains such as music, chess, sports, and
medicine depends on domain-specific cognitive and perceptual
adaptations (
7, 11, 16
). However,
less is known about processes that underpin the
acquisition of such adaptations and about how prior experience
and general perceptual and cognitive skills affect the
development of performance (
31
). Furthermore, due to the domain-specific
nature of these adaptations, comparisons across domains
are rare. Yet, as Ericsson and Smith (
8
)argue, “[t]he
most effective approach to organizing the results across
domains of expertise is to propose a small number of
learning mechanisms that can account for the
development of similar performance characteristics in different
domains” (p. 32). Therefore, the present study
investigated how prior experience, cognitive skills, task difficulty
and practice effect the novices’ eye-hand span (EHS) and
performance in a simple jump-and-run (platformer) video
game. The study explicitly tries to relate the findings to
findings from the domain of music in which eye-hand
span and its relation to performance has been studied
more extensively.
Research on musicians’ expertise in sight-reading
offers a particularly interesting parallel to the present study.
Both jump-and-run platformer games and sight-reading
require scanning of the incoming information and precise
timing of motor actions (
24
). Furthermore, in both domains, visual
information has to be processed with constant speed
(tempo) and is presented sequentially (information is
processed in order from left to right). However, the domains
also have considerable differences. Sight-reading relies
on canonical forms of visual information (notation) and
accomplished musicians have years of experience in
interpreting it. In contrast, video games in general offer a
multitude of highly varied visual and motor tasks.
Therefore, prior experience in video gaming is not directly
comparable to musicians’ prior experience in
sightreading.
In music, eye-hand span is commonly expressed
either in milliseconds, notes or beats between the eye
position and the currently played notes. For example, Truitt,
Clifton, Pollatsek, and Rayner (
30
) found that skilled
musicians have a larger EHS. On the other hand, Furneaux and Land (
10
)found that while professionals
showed less variation in EHS, both professionals and
amateurs had an EHS of approximately one second, but
that EHS varied with playing tempo. However, when
measured in notes (number of notes between the hand
and eye position), professionals had a larger EHS of
approximately four notes, whereas for amateurs it was
two. This led Furneaux and Land (
10
) to conclude that
while professionals have larger information buffers, EHS
is rather related to playing tempo than expertise.
Similarly, Penttinen, Huovinen, and Ylitalo (
22
) found that
EHS decreased when the participants encountered an
unexpected melodic alteration. In brief, EHS seems to be
related to the musicians’ skill level and task difficulty,
but the details of this relation are unclear.
In a recent study, Rosemann et al. (
24
)studied how
practice, playing tempo, musical complexity, and
cognitive skills influence the size of EHS of pianists. While not
a direct replication, the current study was originally
inspired by, and thus bears many similarities to, it.
Rosemann et al. (
24
)found that structural complexity and a
higher playing tempo decreased the pianists’ EHS.
However, a 30 minutes rehearsal period affected the pianists’
EHS only minimally. After the practice period, only a
trend for increased EHS was detected when measured in
milliseconds, but not when measured in beats. However,
EHS (in beats) also correlated positively with improved
performance after practice. Furthermore, EHS was related
to cognitive skills (visual scanning speed, concentration,
selective attention, visuomotor performance). In contrast
to the current study, Rosemann et al. (
24
) did not
operationalise prior experience as an independent variable. In
general, Rosemann et al. (
24
) concluded “that the EHS
is characteristic for each musician and developed over
years of practice” (p. 672). How these characteristics
develop, and how different factors affect this
development, has been studied considerably less (
21
).
Although expertise research since decades has built a
strong case for the domain-specificity of cognitive and
perceptual adaptations (
16, 31
), Dale and Green (
4
) argued in their recent
review that playing video games (and especially action
video games) is related to “global improvements in
perception, attention, memory, and executive functioning”
(p. 147). For example, action video game players were
shown to have a significantly better visual short-term
memory (
1, 19
)and a more
flexible working memory that allows them to actively
update and clear irrelevant information from it (
3
).
Furthermore, playing action video games seems to improve
perceptual processing speed (
6, 13, 17
), perceptual decision making (
15
) and the ability to track
multiple objects simultaneously (
29
). Interestingly, research on gaming also
demonstrated that action video players have a larger Useful Field of
View (UFOV) and that this effect can be found on
nonvideo game players after relatively short period of
training (
5, 9, 14, 32
). Therefore it
seems reasonable to expect that also eye-hand span, i.e.,
“the distance that the eyes are ahead of hand in playing”
position (
30
, p. 143) , is related to both
prior video gaming experience and performance in
platformer video games.
The aim of the present study was to investigate how
prior gaming experience, cognitive skills, task difficulty
and practice are related with eye-hand span and
performance in the context of video gaming and early skill
development. Moreover, as the design and research
questions are informed by earlier research in the domain of
music (most notably Rosemann et al.
24
), a second
aim is to study and discuss how the afore-mentioned
perceptual and cognitive skills manifest themselves in a
domain that has both notable differences and similarities
to music.
The main research questions are as follows:
(1) How do a short practice period and task
difficulty influence performance and eye-hand span in a
platformer video game?
(2) Does eye-hand span correlate with video game
performance?
(3) Are performance and eye-hand span related to
prior gaming experience and cognitive skills?
MethodsParticipants
The 33 voluntary participants (19 female, 14 male)
were young adults between 20 and 35 years of age (M =
24.44 years, SD = 3.04 years). However, one male
participant was excluded from the analysis due to the fact that
he had played the stimulus game extensively prior to the
experiment and was clearly an outlier both in terms of
performance and eye-hand span. Participants’ general,
self-reported, video game experience (see table 1) varied
between zero and 11520 hours (Md = 264 hours, M =
2350 hours, SD = 3447 hours). Informed consent was
obtained from each participant, and the participants were
free to withdraw from the study at any time.
Self-reported video game experience (hours)
N
%
Video game experience
0
7
21.9
< 2000
14
43.8
< 4000
3
9.4
< 6000
2
6.3
< 8000
2
6.3
< 10000
3
9.4
> 10000
1
3.1
Total
32
100.0
Materials
The study was conducted in the context of the video
game Geometry Dash (RobTop Games). The aim of the
game is to navigate a small square through a series of
obstacles by jumping over them (see figure 1). The only
control is tapping the Spacebar key, which causes the
square to jump vertically. Horizontally, the square stays
in the same constant location in relation to the screen. As
the game progresses, the obstacles become more complex
and difficult to get through and require exact timing and
multiple jumps in quick succession. In this experiment,
the participants played only the levels 1 and 2, which are
graded as easy by the game developer. However, the
game is relatively fast-paced (speed is constant in, and
between, each level). The oncoming obstacles move
approximately at the speed of 880 pixels (px), or 250
mm, per second. Consequently, each obstacle moves
across the whole screen in less than two seconds.
Geometry Dash video game. Player controls the green square.
In the normal mode of the game, the better one
performs, the longer each game lasts. Therefore,
performance was measured by calculating the mean time
(milliseconds) for all played games in each performance test
phase. However, the first two games of the pre-test were
excluded from the data analysis to give the participant
time to familiarize themselves with the simple game
mechanics.
Participants’ prior video gaming experience was
operationalised by asking the participants about the number
of years they have played video games and about the
average amount of hours they spend playing video games
per day/week/month (the participants were allowed to
choose the time period that was easiest for them to
assess). These numbers were then multiplied into a variable
“video game experience”.
Furthermore, the participants’ cognitive skills were
assessed by using the D2 Concentration Endurance Test (
2
) and
the Trail Making Test (
23
).
The D2 is a paper-and-pencil test of visual scanning
speed, concentration and selective attention. It consists of
14 lines, each comprised of 47 characters, for a total of
658 characters. The lines are made of characters “p” and
“d” with one to four vertical dashes around it. The
participants are given 20 seconds to scan each line and cross
out every letter "d" with two vertical dashes around it.
The test was scored for both accuracy and speed. TMT is
a paper-and-pencil test of visual attention and task
switching. It consists of two parts. In part A the
participant is instructed to connect 25 ascending numbers, and
in part B the participant is instructed to connect 25
numbers and alphabet letters in ascending, alternating order.
Both parts A and B are intended to be completed as
quickly and as accurately as possible. Whereas part A is
primarily a task of visuomotor performance (
26
) , part B primarily measures working
memory (
25
), mental flexibility (
27
), and executive
functioning (
28
). For each part of the test the
time the participants needed to complete the task was
measured with a stopwatch. Therefore, better test
performance is indicated by a lower value.
Procedure
Table 2 presents the experimental procedure in
chronological order. First, the participants were introduced to
the aims and to the procedure of the study. In addition to
the informed consent form, the participants filled in a
questionnaire including demographic questions and a
self-assessment of video gaming experience.
The experimental procedure in chronological order
Phase
Materials
Duration
Data
(1)
Demographic data and, assessment of prior gaming experience and of cognitive skills
Informed consent form, questionnaire, D2, TMT
10 min
Paper-and-pencil forms
(2)
Game demonstration
Game demo
2 min
(3)
Calibration
9-point calibration
30 sec
(4)
Pre-test
Level 1, normal mode
2 min
Eye movements, keypresses, screen recording (first two games excluded from the data)
(5)
Practice
Level 1, practice mode
2 min
(6)
Calibration
9-point calibration
30 sec
(7)
Post-test1
Level 1, normal mode
2 min
Eye movements, keypresses, screen recording
(8)
Calibration
9-point calibration
30 sec
(9)
Post-test2
Level 2, normal mode
2 min
Eye movements, keypresses, screen recording
After completing the paper-and-pencil part, the
participants were given a demonstration of the game,
introducing them to the aim and functionality of the game. The
eye-tracker was calibrated before each test phase. In the
pre-test (2 min), the participants played the game (Level
1) in a normal mode, i.e. after each failure
(playercontrolled square hits an obstacle) the game starts at the
same level from the beginning. During the practice phase
(2 min), the participants played the game in practice
mode which allows them to continue onwards from the
location of the failure. The practice phase was kept short
as the pilot data collections showed that longer practice
periods were frustrating to the participants. Finally,
posttest1 (2 min) and post-test2 (2 min) were played in the
normal mode with the difference that in the post-test2, the
participants played the Level 2, which they had not
played or seen before. Post-test2 was administered to
study how task difficulty affects performance and EHS.
Apparatus
Eye movements were recorded using a SMI 250Hz
red Eye Tracker with an accuracy of 0.5 degrees and
1680 X 1050 px monitor resolution and the related
Experiment CenterTM
software (SensoMotoric Instruments
GmbH, Teltow, Germany), which also recorded the
keypresses and screen activities. The head distance was set to
approximately 70 cm during each calibration, but no head
rest was used. Eye movements were analysed using
BegazeTM
(SensoMotoric Instruments GmbH, Teltow,
Germany) software. Eye-hand span (EHS) was defined and
measured for each keypress as the distance (in pixels)
between the player-controlled square and eye position.
Three recordings (pre-test, post-test1, and post-test2)
from two different participants had unusually low eye
tracking ratios (below 80 %). After these were excluded
from the data, the mean tracking ratio for the remaining
data was 92 % (SD = 2.5, Min = 84, Max = 96). This was
considered reasonable as the ratio is for the whole
recording, including the short periods before and after each
experiment (10 – 15 seconds) when the experimenter was
starting and closing the game that was used in the
experiment.
Analysis
The statistical analysis was carried out using the IBM
SPSS Statistics 24 software. Based on the Shapiro-Wilk
test (p < .05) and visual inspection of Q-Q plots, many of
the variables deviated from the normal distribution
(Table 3). Therefore, non-parametric statistical analyses
were used. Differences in performance and EHS between
the pre-test and the post-tests were analysed with the
Friedman test, post hoc Wilcoxon signed-rank tests and
Independent-Samples Mann Whitney U-test. The
relationship between the eye-hand span, performance,
cognitive skills and prior gaming experience was analysed
using Spearman’s rho correlation. Bonferroni correction
was used for multiple correlations. However, as this is an
exploratory study, original p values are reported and
values that were not statistically significant after
Bonferroni correction are marked with #.
ResultsPractice, performance and EHS
Table 3 shows the descriptive data for all measured
variables. The performance difference between the
pretest, post-test1 and post-test2 was statistically significant,
X²(2, 30) = 48.07, p < .001.
Descriptive data
N
M
SD
Min
Max
Normally distributed
Performance (ms)
Pre-test
32
7,754
4,161
2,723
20,096
No
Post-test1
31
11,732
5,971
3,486
24,909
Yes
Post-test2
31
4,347
1,958
1,837
11,043
No
EHS (px)
Pre-test
31
186
78
45
421
No
Post-test1
29
192
84
51
396
Yes
Post-test2
30
147
62
34
271
Yes
Video game experience (hours)
32
2,350
3,447
0
11,520
No
D2
32
110
11
91
129
Yes
TMT
32
86
22
42
138
Yes
Wilcoxon post hoc tests showed that after the practice
phase, the average performance in the post-test1 (M =
11,732 ms) was significantly higher than in the pre-test
(M = 7,754 ms, p < .001), which, in turn, was higher than
in the post-test2 (M = 4,347 ms, p < .001). Considering
the fact that by post-test2 the participants already had
more experience playing the game than in the beginning,
it seems reasonable to conclude that, compared to Level
1, Level 2 (post-test2) was significantly more difficult.
The mean EHS differed between the three
measurement points, X²(2, 27) = 8.30, p = .016. However,
posthoc test revealed that the difference between pre-test (M
= 186 px) and post-test1 (M = 192 px) was not
statistically significant (p = .468). The mean EHS in post-test2 (M
= 147 px) was lower than in pre-test and post-test1 (p =
.003 and p = .002, respectively). The range of the mean
EHS from 147 px to 192 px corresponds to 41-54 mm
distance on screen, approximately 4 degrees viewing
angle and latency of 170-220 milliseconds.
In summary, performance and EHS follow a similar
pattern. Practice increases the mean length of played
games (performance) and EHS. However, for EHS this
increase was only marginal and not statistically
significant. In post-test2, both performance and EHS dropped
below the pre-test level.
The relationship between EHS and performance grew
weaker as the experiment advanced. The correlation
between EHS and performance was the strongest in the
pre-test (rs = .59, p < .001). After the practice period
(post-test1), the correlation was noticeably lower (rs =
.42, p = .025). Post-test2 did not have a statistically
significant correlation with the corresponding EHS.
Additionally, it is noteworthy that while both measures
(performance and EHS) had reasonably high correlations
between different phases of the experiment, the pattern is
somewhat different:
Performance measure correlations
Pre-test – Post-test1: rs = .67, p < .001
Pre-test – Post-test2: rs = .85, p < .001
Post-test1 – Post-test2: rs = .71, p < .001
EHS measure correlations
Pre-test – Post-test1: rs = .68, p < .001
Pre-test – Post-test2: rs = .54, p = .002
Post-test1 – Post-test2: rs = .42, p = .030 #
Pre-test performance had the strongest correlation with
post-test2, i.e., between measures where the participants
were faced with a new level. The high correlations
between performance measures indicate that performance
differences between the participants stayed relatively
stable. For EHS, the correlation was strongest between
pre-test and post-test1 in which participants played the
same level. Therefore, it seems that, compared to
performance, the EHS differences between test phases are less
stable and depend more on the task difficulty.
The role of cognitive skills and of prior gaming
experience
Neither the prior video gaming experience, the D2
Concentration Endurance Test nor the Trail Making Test
(TMT) had any statistically significant correlations with
any of the EHS measures. However, prior video gaming
experience correlated with performance (pre-test rs = .61,
p < .001; post-test1 rs = .39, p = .031 #; post-test2 rs =
.54, p = .002). The relationship between prior gaming
experience and performance was strongest when the
participants were playing a level that was new to them.
Furthermore, D2 accuracy score correlated with
performance in post-test2 (rs = 39, p = .032 #) and TMT (part
B) correlated negatively with pre-test (rs = -.39, p = .026
#) and post-test2 (rs = -.45, p = .011) performance. The
correlation is negative as the TMT test is scored using the
time it took the participants to finish the tasks.
Finally, to study if the cognitive skills and prior
gaming experience are related to effectiveness of practice,
changes in EHS and performance between pre-test and
post-test1 were calculated by subtracting the post-test1
values from pre-test values (EHS and performance).
While there were no statistically significant correlations
between changes in performance (pre-test to post-test1),
cognitive skills and video game experience, prior video
game experience correlated negatively with changes in
EHS (pre-test to post-test1, rs = -.43, p = .020). This
indicates that for more experienced video game players, the
EHS was less likely to increase as a result of practice. A
further analysis was conducted by dividing the
participants into groups based on whether their EHS increased
(n = 14) or decreased (n = 15). Mann-Whitney U-test
showed that the groups did not differ in terms of video
game experience or increase in performance.
Discussion
The aim of the present study was to investigate the
relationship between EHS, performance, practice, task
difficulty, cognitive skills and prior experience in playing
a platformer video game. EHS predicted participants’
initial performance, but this effect diminished after
practice. Moreover, it seems that, instead of practice, EHS is
more strongly associated with task difficulty. In
comparison, practice had a strong effect on performance. Prior
gaming experience and cognitive skills were not related
to the size of EHS or changes in it. However, prior
gaming experience improved performance (especially) when
the participants were playing a level that was new to them
(pre-test and post-test2). Yet, the effect was smaller after
practice (post-test1). Concentration endurance (D2,
accuracy) was positively related to performance in the last
phase of the experiment and the Trail Making Test (TMT,
part B), that measures working memory, mental
flexibility and executive functioning, correlated with pre-test and
post-test2 performance.
In terms of EHS, the current results converge with
results obtained earlier in the domain of music (
24
). EHS seems to be characteristic for each
individual and is not effected considerably by a short
practice period. In addition, EHS is strongly related to
task difficulty. However, Rosemann et al. (
24
) studied
participants who had years of practice in the domain. The
present study extends these conclusions to novices who
encountered a task that was completely new to them.
Still, past research on EHS and its relation to competence
or experience and task difficulty is inconclusive (
30, 10, 22
).
Therefore, to conclude whether extended practice affects
EHS would require further longitudinal investigations
with longer practice periods (cf.
20
).
In addition to similarities, there are interesting
differences between the results of the current study and the
study conducted by Rosemann et al. (
24
). Despite their
main conclusion, Rosemann et al. found that the practice
period had a small effect on EHS (in milliseconds). In
contrast, the present study found no practice effect on
EHS. If individually characteristic EHS is a result of
extensive domain-specific practice, one would expect
that, in comparison to novices, EHS of participants who
have years of domain-specific experience would be more
resistant to change. Furthermore, while the musicians’
EHS (in beats) was related to better performance after
practice, in the current study EHS predicted, mainly, the
initial performance before practice. One possible
interpretation is that musicians who improved their
performance after practice were able to use their
domainspecific knowledge during the practice period to partly
memorize the musical structures and therefore to increase
their information buffers (
10
). Such
affordances are not available to novices.
The questions whether video gaming as an activity is
related to more general improvements in perceptual skills
and whether EHS is a valid measure of these skills
remain open. Regarding video gaming and general
perceptual skills, the current data showed no relationship
between prior video gaming experience and EHS (cf.
4
). There are at least two possible reasons
for this. First, it could be due to small sample size with
only few experienced video gamers. Second, it is
plausible that EHS does not capture the perceptual skills video
gaming, and its highly varied visual and motor tasks,
develop (cf.
1, 6, 5, 9, 13, 14, 17, 19, 29, 32
). Moreover, the relationship between EHS and
performance seemed to diminish with practice. In
comparison, prior video gaming experience correlated
especially with pre-test and post-test2. This indicates that as
one acquires experience, general perceptual skills become
less important than domain-specific experience (
16
). Yet, it is interesting that EHS was a strong
predictor of initial performance. Similarly, in studies that
contrasted non-action video game players to action video
game players, the instruments that were typically used to
measure participants’ general visual skills were
“contextfree”, i.e., they were not related to a task the participants
are familiar with and therefore completely new to the
participants (
4
). This may have
implications for research on how general perceptual skills
enable people to adapt to new situations.
Finally, the cognitive skills that were assessed in the
current study were not related to the participants’ EHS.
While Rosemann et al. (
24
) found a significant positive
correlation between EHS and the D2 Concentration
Endurance Test and a negative trend for the Trail Making
Test (TMT), in the current data the same tests correlated
with performance. As the results indicated, the
participants with better concentration endurance (D2, accuracy)
were able to keep their performance level higher in the
last phase of the experiment. Therefore, D2 seems to be a
valid predictor of performance in tasks that require
relatively long periods of intense concentration. Furthermore,
the correlations between performance in pre-test,
posttest2 and TMT (part B) indicate that performance in this
particular platformer game is related to working memory,
mental flexibility or executive functioning (cf.
25, 27, 28
). It is, however, plausible that these results
are only applicable to situations in which one is required
to adapt to a new visuo-motor task. For example, in their
study of pianists’ sight-reading skill, Kopiez and Lee (
18
) did not find a connection between working
memory and sight-reading performance. On the other
hand, this could be, as they point out, due to the fact that
their measure of working memory was not a
spatialfigural task and, thus, did not have close relevance to
sight-reading.
It should be noted that although the present study has
many similarities to the Rosemann et al. (
24
) study,
crucial differences exist concerning participants, task
(static versus dynamic or moving visual stimulus),
domain, and experiment. Therefore, the results are
complementary, not necessarily directly comparable. In the
present study, the participants were novices in playing this
particular game. While many of them had had fairly
extensive experience in playing video games, the task they
faced in the study was a new one to them. This limits the
interpretation of the present results to the early
development of skills and EHS. Yet, based on their self-reported
video game experience, some of the participants could
(almost) be considered as experts in video gaming.
Therefore, the fact that experienced video game players
were better at adapting to new situations (pre-test and
post-test2) begins to unravel how domain experience
affects learning in adjacent domains (
12
). Moreover, compared to
the Rosemann et al. (
24
) study, the task of playing a
platformer game is considerably different to playing
music. While in the present experiment the participants
were required to perform only a very simple motor output
(press key to jump), playing music is a much more
complex task which requires a higher level of domain-specific
knowledge and skills. Therefore, one should be careful in
generalising the results of the present study to domains
that require more complex expertise. Furthermore, in the
current study, the practice period in the experiment was
very short. Consequently, it is impossible to say how
EHS and performance would develop after a more
extensive training, and whether the correlations between EHS
and performance would remain at a similar pattern.
However, the short practice period did have a strong effect on
performance. If EHS and skill development are closely
connected, the effect should have also appeared for the
EHS. Additionally, compared to many studies that have
been conducted in the domain of music, the current study
had a considerably larger sample size (cf.
30
: eight participants;
10
: eight participants;
24
: nine participants),
which increases the ability to detect weaker signals and to
reduce the effects of individual variation.
The present study extends the research on EHS to a
new domain, video gaming, and offers insights into the
early development of EHS and performance and how
these are related practice, task difficulty, cognitive skills
and prior experience. The results highlight the need for
further studies on the dynamic nature of skill
development in visual domains.
Ethics and Conflict of Interest
The authors declare that the contents of the article are
in agreement with the ethics described in
http://biblio.unibe.ch/portale/elibrary/BOP/jemr/ethics.html
and that there is no conflict of interest regarding the
publication of this paper.
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