The development of attention skills in action video game players

Abstract

Previous research suggests that action video game play improves attentional resources, allowing gamers to better allocate their attention across both space and time. In order to further characterize the plastic changes resulting from playing these video games, we administered the Attentional Network Test (ANT) to action game players and non-playing controls aged between 7 and 22 years. By employing a mixture of cues and flankers, the ANT provides measures of how well attention is allocated to targets as a function of alerting and orienting cues, and to what extent observers are able to filter out the influence of task irrelevant information flanking those targets. The data suggest that action video game players of all ages have enhanced attentional skills that allow them to make faster correct responses to targets, and leaves additional processing resources that spill over to process distractors flanking the targets.

Full text

The development of attention skills in action video game players
M.W.G. Dye†, C.S. Green, and D. Bavelier
Department of Brain and Cognitive Sciences and Center for Visual Science University of Rochester,
Rochester, NY 14627
Abstract
Previous research suggests that action video game play improves attentional resources, allowing
gamers to better allocate their attention across both space and time. In order to further characterize
the plastic changes resulting from playing these video games, we administered the Attentional
Network Test (ANT) to action game players and non-playing controls aged between 7 and 22 years.
By employing a mixture of cues and flankers, the ANT provides measures of how well attention is
allocated to targets as a function of alerting and orienting cues, and to what extent observers are able
to filter out the influence of task irrelevant information flanking those targets. The data suggest that
action video game players of all ages have enhanced attentional skills that allow them to make faster
correct responses to targets, and leaves additional processing resources that spill over to process
distractors flanking the targets.
Attentional networks and their development in action video game players
Recently, we and others have shown that playing action video games alters some of the
fundamental aspects of visual attention (Bialystok, 2006; Castel et al., 2005; Green & Bavelier,
2003, 2006a, b; Greenfield et al., 1994; Trick et al., 2005). Expert action video game players
(VGPs) were found to outperform non-gamer controls (NVGPs) on tasks measuring the spatial
distribution and resolution of visual attention, the efficiency of visual attention over time and
the number of objects that can be attended simultaneously (Green and Bavelier, 2003). Training
studies demonstrating the causal effect of game playing on visual attention measures have led
to the proposal that action video game playing enhances attentional resources (Green &
Bavelier, 2003; Green & Bavelier, 2006a, b) and allows players of such games to better allocate
their attentional resources over a visual scene.
In the present study, we propose to further document the effect of video game playing on the
efficiency of attention allocation by comparing VGPs and NVGPs on the Attentional Network
Test (ANT). It has been suggested that the ANT provides a reliable measure of three
fundamental component processes of visual attention within one procedure: alerting,
orienting and executive control (Fan et al., 2002). Alerting is the ability to make use of a cue
which provides information about the onset time of a target stimulus, and thus trigger the
allocation of attention at a given point in time; this process appears mediated by right frontal
and parietal areas and to be linked to the release of noradrenalin (Coull et al., 1996; Witte &
Marrocco, 1997). Orienting is the ability to utilize an spatial cue to direct attention towards the
location of an imminent stimulus; a fronto-parietal network associated with the release of
acetylcholine has been associated with orienting (Corbetta & Shulman, 1998; Wilson et al.,
†Corresponding author: E-mail: mdye@bcs.rochester.edu; fax 585.442.9216.
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Neuropsychologia. 2009 July ; 47(8-9): 1780–1789. doi:10.1016/j.neuropsychologia.2009.02.002.
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2005). Finally, the executive control network, which serves to direct attention towards task-
relevant stimuli and inhibit the processing of distractor items, has been proposed to engage
areas in the prefrontal cortex and involve the release of dopamine (Badre & Wagner, 2004;
Casey et al., 2000; Diamond & Goldman-Rakic, 1989; Egner & Hirsch, 2005; Nelson et al.,
2003). Thus, the ANT provides a measure of how well attention can be both allocated to a
visual scene and used to filter irrelevant information within that scene. The task has also been
shown to be sensitive to developmental changes in all three networks (Rueda et al., 2004).
Procedurally, the ANT requires subjects to discriminate the orientation of a target (pointing
left or pointing right) that is presented either directly above or below a central fixation point.
The efficiency of the alerting network is measured by contrasting trials where the target is
uncued (no cue: location unknown and onset unknown) with trials where both possible
locations are cued simultaneously (double cue: location unknown and onset known). Orienting
efficiency is measured by comparing trials where a cue appears at the fixation point (center
cue: location unknown and onset known) with those where the location of the following target
is cued (spatial cue: location known and onset known). Finally, distractors also flank the target
on some trials. The flankers can be either congruent with target (point in the same direction)
or incongruent (point in the opposite direction). By contrasting trials with congruent and
incongruent flankers, Fan et al. (2002) suggest that a measure of the efficiency of ‘executive
control’ can be indexed. More specifically, this contrast appears to measure the efficiency of
filtering by computing a flanker interference effect, or how successfully flanker information
can be ignored and attention focused upon the task-relevant stimulus target. As discussed in
Callejas et al. (2004), Dye et al. (2007) and Fernandez-Duque & Posner (1997), the components
of the ANT can also be understood in terms of how it manipulates the allocation of attention
upon task-relevant stimuli in a visual scene. The cueing effects that the ANT measures are
henceforth given an alternative meaning. The alerting cue serves to shift the focus of attention
from a diffuse distribution across the display prior to the alerting cue, to a focus upon the display
area where targets are known to occur following the alerting cue (see Figure 1). The orienting
cue is a valid spatial cue that focuses attention even more sharply upon the actual location of
the upcoming target. This hypothesis explains the nature of the interactions between these types
of cue and the influence of distractors flanking the targets, as well as accounting for the changes
in those interactions as flanker eccentricity is manipulated (see Dye et al., 2007 for a more
detailed discussion). We therefore use the ANT here as a measure of how efficiently an observer
can use the cues to allocate their attention appropriately across a visual display and then
succesfully filter out stimuli that are task irrelevant. In accordance with this proposal, we will
refer to alerting, orienting and flanker compatibility effects, rather than to alerting, orienting
and executive control network efficiency.
Importantly, for the ANT to measure the influence of cues and distractors on performance, the
difference between RTs across conditions is typically computed. This fact is particularly
critical because one of the best-documented changes induced by video game playing is that
VGPs have faster RTs than NVGPs (c.f. Bialystok, 2006; Castel et al., 2005; Greenfield et al.,
1994; Orosy-Fildes & Allan, 1989; Yuji, 1996; Dye, Green & Bavelier, submitted). Such
differences in between-groups baselines may produce interactions with within-subjects
measures such as those collected by the ANT that reflect the magnitude of the baseline RTs
rather than differences in processing per se (c.f. Faust et al., 1999; Madden et al., 1992,
1996). Consider two subjects, subject A who responds very rapidly across all task conditions
and subject B who makes much slower responses. In a hypothetical Posner cueing task (Posner,
1980), subject A responds to neutral cue trials in 300 ms and to valid cue trials in 200 ms, while
subject B responds to neutral cue trials in 600 ms and to valid cue trials in 400 ms. A standard
analysis would subtract the mean valid cue RT from the mean neutral cue RT and reach the
conclusion that subject B benefited twice as much from the valid cue as did subject A (200 ms
vs. 100 ms benefit). However, to state that subject A benefits less from the cue would be
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misleading – it is the difference in baseline RTs that has given rise to the apparent difference
in benefit. This problem is well known in the gerontology literature on generalized slowing
(Cerella, 1991, 1994; Salthouse, 2000, 2002), and would likely surface in any comparison of
VGPs and NVGPs as ‘generalized speeding’ – VGPs have significantly faster response times
compared to NVGPs (with equivalent accuracy) and thus VGPs will tend to have smaller RT
differences between task conditions irrespective of the particular task at hand (Dye et al.,
submitted). One aim of the current study was to carefully control for any such baseline RT
differences where they occurred.
As well as documenting possible additional effects of action video game play on visual
attention, a final aim of the study was to assess how those effects were modulated by the age
of the subjects who played the games. Based upon data from the ANT, Rueda et al. (2004)
report that attentional ‘alerting’ continues to develop until the age of 10 years, whereas
‘orienting’ is stable by age 7 years. The view that orienting networks are adult-like by the early
childhood is widely supported (Colombo, 2001; but see Schul et al., 2003); in contrast it has
been suggested that alerting networks may continue to develop past 10 years of age, well into
adolescence (Lin et al., 1999). The Rueda et al. (2004) study also reported stable ‘executive
control’ by 7 years of age as measured by the flanker compatibility effect in the ANT procedure.
Other authors, however, have suggested that there is an increasing ability to filter out irrelevant
information between 7 and 10 years of age (Enns and Akhtar, 1989; Enns and Cameron, 1997;
Goldberg et al., 2001; Ridderinkhof et al., 1997). There is some disagreement over the
mechanism by which filtering improves with age. The fact that the size of the filtering effect
(henceforth, flanker compatibility effect) is affected by the difficulty of the target task, the
location and saliency of the flankers and the amount of attentional resources available to the
subject (Green & Bavelier, 2006b; Miller, 1991; Lavie & Cox, 1997) may explain some of
these discrepancies, as may baseline differences in RTs at different ages (Kail, 1991; Kail &
Park, 1994).
In this study we administered a child-friendly version of the ANT (Rueda et al., 2004) to
subjects ranging in age from 7 to 22 years to evaluate the relative role of age and video game
expertise on attentional allocation. While using the same paradigm allows for easier
comparison with data from previous studies, it is important to note that in the ANT spatial cues
are always valid, the target remains visible until a response is made, and the cue-target SOA
is 500 milliseconds. Such a paradigm makes it unlikely that reflexive attentional processes are
being indexed by the ANT. Rather the paradigm allows one to measure how attention is focused
upon a target, possible through both attentional processes and eye movements. Our main aim
was to determine whether video game playing altered this allocation of attention indexed by
the ANT, whether it influenced the filtering of task irrelevant stimuli, and how any such effects
varied as a function of age.
No studies to date have examined the impact of video game playing upon attentional
development in children. In young adults, several studies have now shown that video game
playing enhances attentional resources leading to better performance on a number of
attentionally demanding visual tasks (Castel et al., 2005; Feng et al., 2007; Green and Bavelier,
2003, 2006a, b, 2007). One effect of this enhancement is increased processing of task-irrelevant
flankers – the argument is that on tasks where the decision to be made about a target is
straightforward and leaves attentional resources to spare, those resources ‘spill over’ to other
stimuli in the display (see Lavie, 1995). Here we ask whether juvenile gamers will show similar
increases in attentional resources, and importantly whether they will be able to harness this
extra capacity to benefit visual performance, as seen in adults. In the sample of school-aged
children and adults tested (7–22 years of age), we hypothesize that increased attentional
resources as a result of action video game experience will result in larger flanker compatibility
effects due to a ‘spill over’ of processing, as observed in our previous work in adults.
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Method
Subjects
A total of 131 subjects participated – of these, 75 were classified as NVGPs and 56 as VGPs.
The frequency of video game play and the type of games played were assessed using a
background questionnaire that required children to list the ten games they had played the most
in the preceding 12 months, and to estimate how long they played each game in a typical session
along with how many sessions they played per month. A subject was classified post-hoc as a
VGP if they reported playing any action-based video game for any length of time in the 12
months prior to testing. As a result of parental constraints on video game playing, using the
more strict criterion employed in previous studies would have resulted in too few juvenile
VGPs for statistical analysis. All other subjects were classified as NVGPs (including those
who played other types of video game in the preceding 12 months).
Socio-economical status was not collected, but all subjects were recruited from the Brighton
Central School District and the University of Rochester, both in Rochester, NY. This school
district has an affluent and relatively homogeneous catchment area, with 63.2% of adults
having at least a bachelors degree (national average = 24.4%) according to the 2000 US Census
and low number of students claiming free school lunches (9.1%;
http://www.newsweek.com/id/39380). As a consequence, socioeconomic status is likely to be
relatively high in our sample.
Informed consent was obtained prior to participation, including permission from parents where
juvenile subjects were being tested. Children were given a $15 gift card for their participation,
and adults received $8 per hour of participation. The study was approved by the IRB at the
University of Rochester and by the Board of Governors at Brighton Central School District in
Rochester, NY. A breakdown of the sample by age group and gender is given in Table 1, along
with mean ages.
Design
All subjects were administered the child-friendly version of the ANT (see Rueda et al.,
2004), in which subjects are required to make a speeded decision to indicate the direction of a
central target (a fish) with a key press The experiment included two between-subjects factors
(age group – 7–10 yrs, 11–13 yrs, 14–17 yrs, 18–22 yrs; and video game experience – NVGP,
VGP) and two within-subjects factors (flanker type – incongruent, congruent, absent; and cue
type – absent, center, double, spatial).
Flankers were two fish presented horizontally aligned on either side of a central target fish (see
Figure 2). Flankers could either be incongruent with the target (fish pointing in the opposite
direction), congruent with the target (fish pointing in the same direction) or absent (target
presented in isolation). Each fish subtended 5.4 degree of visual angle, with the edges of
adjacent fish separated by 0.4 degrees of visual angle. The fish were presented 1.7 degrees of
visual angle above or below a central fixation point. The fish appeared above or below the
fixation point with equal probability.
The cue consisted of one or two asterisks presently briefly prior to the onset of the arrow(s).
The cue was either absent, central (presented at the fixation point), double (two cues presented
simultaneously above and below the fixation point at both possible target locations) or
spatial (a single cue presented above or below the fixation point and indicating the location of
the subsequent target).
There were a total of 48 experimental trials for each subject in each block, determined by the
combinations of flanker (3), cue type (4), target location (2) and target direction (2). Each
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subject participated in three blocks of experimental trials, with the first block preceded by 24
practice trials, resulting in a total of 168 trials overall.
The total duration of each trial was set to 4000 ms. A pre-stimulus fixation point appeared for
a variable duration of 400–1600 ms. This was then accompanied by a cue presented for 100
ms. After the offset of the cue, there was a 400 ms interval prior to the onset of the fish. The
fixation point was present at all times. Following the subject’s response, the fish were removed
from the display, leaving only the post-stimulus fixation point. The next trial was initiated after
3500 ms minus the duration of the pre-stimulus fixation point and minus the reaction time of
the subject (total duration = pre-stimulus fixation point + 100 + 400 + reaction time + 3500 −
pre-stimulus fixation point − reaction time).
Apparatus
Stimuli were presented on a 23-inch LCD display (Apple Computer, Inc.) with a 1024×5768
pixel resolution and a 60 Hz frame rate. A Java script was used to run the experiment (available
from http://www.sacklerinstitute.org/users/jin.fan/) under Mac OS X on a PowerBook G4
laptop computer (Apple Computer, Inc.). As a result the experimental details closely mirror
those reported in Rueda et al. (2004).
Procedure
Children were tested in a dimly-lighted room in their homes in the Rochester, NY area and
adults were tested in a laboratory at the University of Rochester. The experimental environment
and setup was the same for both VGPs and NVGPs. They were seated 40 cm from the centre
of the LCD display, and instructed to maintain fixation on the central fixation point (a crosshair)
at all times. The subjects were instructed to respond to the target fish by pressing a key
congruent with the direction of the fish as quickly and as accurately as possible. The practice
block took approximately 3 minutes, and each experimental block approximately 4 minutes,
for a total duration of 15 minutes.
Results
Reaction times from incorrect trials were excluded from analyses. Following this, on a subject-
by-subject basis, a mean RT was calculated for each of the twelve conditions (3 flanker types
by 4 cue types). Data were collapsed across target direction (left/right) and target location
(above/below). On the basis of unusually slow and error-prone responses (more than two
standard deviations beyond the mean for their age group and gaming status), data from six
NVGPs were excluded from analysis completely. For the remaining subjects, if the response
time for a trial was greater then two standard deviations from the mean for its condition, then
it was excluded as an outlier; neither RT nor accuracy data were analyzed for these outlier
trials. Median RTs were then calculated for each condition for each subject and submitted for
further analysis.
Gender Differences in RT
Males are more likely than girls to play action video games, and this is reflected in an
asymmetric distribution of males and females across the NVGP and VGP categories (Table
1). Mezzacappa (2004) reported a small gender effect on RTs in the ANT in a study of 118
young girls and 131 young boys. An initial analysis of the overall RTs for males and females
in our NVGP group revealed no significant difference in RTs (F(1, 68)=1.45, p > .05, eta2p=.
02), suggesting that gender had no measurable impact on RT performance in the age range
tested.
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Controlling for Baseline Differences in RT
A four-way mixed ANOVA was performed on the median RT data with flanker type
(incongruent, congruent) and cue type (absent, center, double, spatial) as within subjects
factors, and age group (7–10 yrs, 11–13 yrs, 14–17 yrs, 18–22 yrs) and video game playing
(NVGP, VGP) as between subjects factors. This analysis revealed significant main effects of
age group (F (3, 117)=50.07, p<.001, eta2p=.56) and video game playing (F (1, 117)=8.68, p=.
004, eta2p=.07), suggesting baseline differences in RT as a function of both age and gaming
experience.
Before proceeding with any further analyses, these main effects of age group and video game
playing were addressed. The median RTs for older subjects were faster than those for younger
subjects (M7–10YRS = 678msec, M11–13YRS = 554msec, M14–17YRS = 496msec, M18–22YRS =
467msec). In addition, VGPs (MVGP = 525 msec) had faster median RTs than NVGPs
(MNVGP = 597 msec). As outlined previously, these baseline differences are of concern when
interpreting interactions (see Faust et al., 1999; Madden et al., 1992, 1996 for more discussion).
The next stage of the analysis sought to address these baseline differences before reanalyzing
the data.
In order to address baseline differences as a function of age group, the average median RT
(collapsed across all target-only conditions, i.e. using only those trials where no flankers
accompanied the target) was computed for each NVGP subject, and this was plotted against
their age in months. Following Cerella and Hale (1994), these data were fitted using an
exponential decay function (see Equation 1 and Figure 3A):
Equation 1
A goodness-of-fit metric for the non-linear function, analogous to R2, was computed using the
method provided by Haessel (1978). This revealed a good fit to the data: Cos2φ= 0.646. On
the basis of this function, a predicted RT score was computed for all NVGP and VGP subjects
and used to normalize their median RTs for each condition. For example, if a subject had an
age-predicted RT of 450msec and their performance within a condition was 400msec, then
their transformed RT would be 400/450 or 0.89. This age-normalized RT (RTage) was used for
all further analyses.
To control for RT differences resulting from video game experience – a categorical variable –
another procedure was employed; the RTage for each of the four target-only conditions were
computed for the NVGP and VGP groups. These were plotted against each other, and a linear
fit obtained (see Equation 2 and Figure 3B):
Equation 2
This linear function was used to transform the RTage for NVGPs in each of the other eight
experimental conditions formed by crossing flanker type (incongruent, congruent) with cue
type (absent, center, double, spatial). The resulting gamer-transformed age-normalized RTs
(henceforth, transformed normalized RTs – RTnormed) represent the extent to which RTs
deviate from what is expected given the age and video gaming experience of individual subjects
and thus provide a measure of the effects of flanker congruency and cue type that is not biased
by baseline differences in speed of response.
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Alerting, Orienting and Flanker Compatibility Effects
In line with previous studies using the ANT, we calculated ‘attentional network’ scores to
reflect the effects of alerting, orienting and flanker compatibility. These were computed using
these RTnormed values, and entered into two-way ANOVAs with age group (7–10 yrs, 11–13
yrs, 14–17 yrs, 18–22 yrs) and video game playing (NVGP, VGP) as between subjects factors.
Alerting effects – measuring the efficiency with which a temporal cue enhances processing of
the target – were computed by subtracting RTnormed values for the double cue conditions from
those for the no cue conditions for each subject. The main effect of age group was statistically
significant (F (3, 117)=2.68, p=.05, eta2p=.06) with younger children exhibiting larger alerting
effects than older children and adults (M7–10YRS=0.076, M11–13YRS=0.068, M14–17YRS=0.044,
M18–22YRS=0.053). A priori contrasts revealed significant differences between the alerting
scores of 7–10 year olds and 11–22 year olds (p=.027). The main effect of video gaming playing
(eta2p=.02) and the interaction between age group and video game playing (eta2p=.02) did not
approach statistical significance (see Figure 4A).
Orienting scores – measuring the efficiency with which a valid spatial cue enhances processing
of the target – were computed by subtracting RTnormed values for the spatial cue conditions
from those for the center cue conditions for each subject. The ANOVA revealed no significant
main effect of age group (F (3, 177)=0.17, p=.914, eta2p<.01) nor a significant interaction
between age group and video game playing (eta2p=.03). However, the analysis revealed a
significant main effect of video game playing on orienting effects (F (1, 117)=10.20, p=.002,
eta2p=.08), with VGPs (MVGP=0.060) exhibiting larger orienting effects than NVGPs
(MNVGP=0.038; see Figure 4B). This effect will be returned to in the ANOVA analysis reported
below.
Finally, flanker compatibility effects – measuring the extent to which flankers interfere with
processing of the target – were computed by subtracting RTnormed values for the congruent
flanker conditions from those for the incongruent flanker conditions for each subject. The
ANOVA revealed no significant age group effect (F (3, 117)=1.08, p=.361, eta2p=.03) and no
interaction between age group and video game playing (eta2p=.02). There was, however, a
significant main effect of video game playing (F(1, 117)=19.71, p<.001, eta2p=.14), with VGPs
(MVGP=0.103) having larger flanker compatibility effects, or in other words experiencing more
interference from flankers, than NVGPs (MNVGP=0.070).
The data failed to reveal a significant interaction between age group and the size of flanker
compatibility effects. Although the data reported in Figure 5 suggest that such an interaction
may be present – with 7–13 year old gamers having disproportionately larger flanker
compatibility effects than their non-gaming peers – the effect appears to be driven by large
flanker compatibility effects for 7–10 year old gamers and small flanker compatibility effects
for 11–13 year old non-gamers. Therefore the data are inconclusive with respect to action video
gaming having greater effects for younger gamers.
The omnibus ANOVA below further addresses how changes in orienting and flanker
compatibility effects may be best understood in terms of attentional allocation, by looking at
changes to performance in which of the experimental conditions lead to the observed
differences.
Omnibus RTnormed ANOVA
The omnibus ANOVA was repeated using the RTnormed that were used to calculate the attention
effects. Importantly, the main effects of age group (F (3, 117) = 1.27, p = .289, eta2p = .03)
and videogame playing (F (1, 117) = 1.19, p = .277, eta2p = .01) did not approach significance,
nor did they interact significantly (F (3, 117) = 0.34, p = .771, eta2p = .01). With the applied
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corrections achieving their aims – there were no statistically significant baseline differences
in RT between groups – the analysis also revealed, as expected, significant main effects of
flanker type, due to slower RTnormed values in the presence of incongruent flankers (F (1, 117)
= 484.88, p < .001, eta2p = .81) and of cue type (F (3, 351) = 164.71, p < .001, eta2p = .59).
Two-way interactions between flanker type and cue type (F (3, 351) = 12.69, p < .001, eta2p
= .10; Figure 6), flanker type and video game playing (F (1, 117) = 19.71, p < .001, eta2p = .
14; Figure 7A) and cue type and video game playing (F (3, 351) = 4.96, p = .002, eta2p = .04;
Figure 7B) were statistically significant. There was also a statistically significant three-way
interaction between flanker type, cue type and video game playing (F (3, 351)=2.76, p=.042,
eta2p=.02).
Omnibus Error Analysis
A four-way mixed ANOVA was performed on the error data with flanker type (incongruent,
congruent) and cue type (absent, center, double, spatial) as within subjects factors, and age
group (7–10 yrs, 11–13 yrs, 14–17 yrs, 18–22 yrs) and video game playing (NVGP, VGP) as
between subjects factors. This analysis revealed significant main effects of flanker type (F (1,
117)=103.67, p<.001, eta2p=.47) and age group (F (3, 117)=8.46, p<.001, eta2p=.178). These
were qualified by a significant two-way interaction between flanker type and age group (F (3,
117)=5.06, p=.002, eta2p=.115). For incongruent flanker trials, younger subjects made more
errors than older subjects (M7–10YRS=7.7%, M11–13YRS=6.0%, M14–17YRS=4.6%,
M18–22YRS=2.4%). Error rates were equivalent for congruent flanker trials across the ages
tested (M7–10YRS=1.8%, M11–13YRS=1.1%, M14–17YRS=1.3%, M18–22YRS=0.8%), reflecting
the small impact of congruent flankers observed in RT measures. Importantly, the main effect
of video game playing on error rate was not statistically significant (F (1, 117)=2.74, p=.102,
eta2p=.02; MNVGP=2.84%, MVGP=2.91%), nor did it interact with any other factor (all Fs <
1).
Discussion
Effects of action video game experience on attention skills
Analysis of raw RT data revealed that VGPs responded more quickly than NVGPs, but did not
make more errors. Speeded processing of visual information without a concomitant decrease
in accuracy has now been reported by several groups in studies with adult subjects (e.g.
Bialystok, 2006; Castel et al., 2005; Clark et al., 1987; Dye, Green & Bavelier, submitted), and
here that finding is extended to children as young as 7 years of age. It suggests that across a
wide range of development VGPs are not more likely to make speed-accuracy trade-offs, but
are faster to respond accurately. It has been suggested that group baseline differences in RT in
mixed designs can lead to between and within subjects measures interacting, with the
interaction reflecting the baseline difference and not a group difference in processing on the
within subjects measure per se (Faust et al., 1999; Madden et al., 1992, 1996). Following
Madden et al., we transformed RT data to remove the difference in baseline across groups.
After doing so, and controlling for the effect of age on RT (see above), we found a significant
interaction between whether or not subjects played action video games and both flanker
congruency and the type of cue that preceded the target-flanker display.
The effect of video gaming on utilizing an alerting cue was not statistically significant, offering
no evidence for differences in alerting efficiency between NVGPs and VGPs. Effects of video
game playing were found on the orienting cue measure and the flanker compatibility measure,
with VGPs exhibiting greater benefit from an orienting cue and greater interference from
flankers. Although it is tempting to conclude that VGPs (i) lack the ability to spatially focus
their attention and (ii) have deficiencies in selecting task-relevant information, we argue below
that these effects are best understood in terms of enhanced attentional resources in VGPs.
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The effect of videogame playing on the orienting effect is reflected in the interaction between
cue type and game playing experience. VGPs respond comparatively more slowly than NVGPs
unless a spatial cue is provided, in which case the two groups do not differ (Figure 5A). It
should first be definitively stressed that this does not mean that the VGPs took more time to
make their responses. In fact, in the raw (un-normed) RT data it is the case the VGPs responded
more quickly across all experimental conditions. Instead, what this means is that the VGPs
took more time than expected considering the RT advantages conferred by video game playing.
Second, the comparatively greater time taken by VGPs applies to the baseline condition used
to compute the orienting effect, but not to the spatial cue condition. Thus, a spatially informative
cue focuses attention equally well in VGPs and NVGPs, supporting the view that there is little
difference between NVGPs and VGPs in terms of how they use a valid spatial cue to allocate
their attention. This highlights the importance of examining RTs by condition in the ANT
paradigm, and not simply relying upon difference scores, in order to accurately interpret effects.
The effect of video game playing on flanker compatibility effect sizes is reflected in the
interaction between flanker type and gaming experience. Again, it is not the case that VGPs
were slower than NVGPs when incongruent flankers were presented; rather, they responded
comparatively more slowly to incongruent as compared to congruent flankers given what
would be expected considering the reaction time advantages conferred by video game playing.
This result is consistent with the proposal of greater attentional resources in VGPs, allowing
them to (unavoidably) devote more processing resources to flankers and thus to exhibit
comparatively greater flanker effects.
Thus we argue that the orienting and flanker compatibility effect differences noted between
VGPs and NVGPs are best understood in terms of changes in the spread of attention over the
visual scene. The notion of “spread of attention” has been used in previous studies to explain
the interaction between cue type and flanker type. The spatial cue, by focusing attention tightly
over the target area, diminishes the extent to which flankers are processed. This limits the
impact of flankers on target processing, and thus the size of the flanker compatibility effect.
On the other hand, by enhancing attention but only loosely restricting it in space, the center
and double cues result in efficient processing of both target and flankers, leading to greater
flanker compatibility effects and a cue by flanker interaction (see Callejas et al., 2004; Dye et
al., 2007; Fernandez-Duque & Posner, 1997). A similar account can also explain the double
and triple interactions of gaming experience with flanker type and cue type. Greater spread of
attentional resources in gamers will result in greater processing of the flankers and thus greater
interference from incongruent flankers (accounting for the gaming experience by flanker type
interaction), except when a spatial cue focuses attention over the target area (accounting for
both the gaming experience by cue type interaction and the triple interaction). The spatial cue
focuses attention tightly on the location of target, resulting in easier flanker exclusion, reducing
response conflict relative to conditions in which center and double cues are present (see Dye
et al., 2007). Thus, the presence of the spatial cue works against the spill-over of attentional
resources to the flankers and diminishes the gaming experience by flanker type interaction (see
Figure 8).
We note that the ANT flanker compatibility score cannot unambiguously resolve whether a
greater score is in fact due to enhanced attentional resources or due to poor attentional selection.
This score therefore needs to be considered in terms of RTs in each of the conditions used to
compute that measure of flanker compaitibility. In this respect, the findings that VGPs respond
faster in incompatible flanker conditions than NVGPs, and are no less accurate, support the
view that VGPs perform better even in the incompatible condition. In addition to providing
the most parsimonious account for the data, this explanation is in line with previous work
indicating greater attentional resources in action game players (Dye & Bavelier, 2004; Green
& Bavelier, 2003, 2006a, b).
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Effects of age on visual attention skills
Children made accurate responses more rapidly as they got older, in accord with many studies
that have demonstrated increases in speed of information processing through childhood (Kail,
1991; Kail and Park, 1994). We were concerned about these baseline differences in RT across
age groups when examining age group by within subjects factor interactions. As has been
discussed extensively elsewhere (Faust et al., 1999; Madden et al., 1992, 1996), these baseline
differences can produce ‘spurious’ interactions, where group differences on the within subjects
factor can be driven by a global speed of processing function rather than the experimental factor
of interest. Following Cerella and Hale (1994), we modeled the effect of age on RTs using an
exponential decay function. We showed that this correction adequately removed baseline RT
differences across groups.
Computing attentional effects, our study revealed a small effect of age group on the alerting
effect – 7–10 year olds obtained more benefit from a temporal cue than did older children. This
confirms the results of Rueda et al. (2004) who found improvements in alerting up until 10
years of age. Rueda et al. suggested possible greater inattention in younger children during
inter-trial intervals, a conclusion consistent with data from the current study. An additional
contribution of our study concerns changes in flanker compatibility effects over the course of
development. Although flanker compatibility effects as measured by RT did not vary with age,
error analyses revealed that as subjects got older they made fewer incorrect responses to targets
flanked by incongruent distractors, suggesting an increasing ability with age to filter out
distractors. Thus there is evidence that the presence of incongruent flankers did not slow down
the responses of the youngest children – in accordance with Rueda et al. – but rather made
them more prone to committing errors. This latter finding indicates that the filtering network
keeps maturing at least until 10 years of age, in agreement with the conclusions of Enns and
colleagues (Enns & Akhtar, 1989;Enns & Cameron, 1997). Flanker compatibility effects as a
function of action gaming experience were statistically equivalent across the age ranges tested.
While differences between VGPs and NVGPs appeared larger for 7–13 year old children than
for 14–22 year olds, the three-way interaction was not statistically significant.
This work further documents the enhanced attentional resources of action video gamers and
establishes faster reaction times in that population without a notable loss in accuracy. These
effects were seen throughout the age range studied suggesting similar effects of action game
playing from the early school years through to adulthood. While causality can only be inferred
with a training study, the findings are in accord with attentional changes that have been
previously trained in NVGPs using action video games (Feng et al., 2007; Green & Bavelier,
2003, 2006b).
Finally, this work calls for caution when interpreting ANT scores related to alerting, orienting
and executive control scores. First, differences in baselines across the groups compared need
to be taken into consideration. Otherwise, one may attribute processing differences to the
populations compared, when in fact they reflect a generalized baseline performance difference
rather than a specific processing difference. Second, greater or lower scores on the ANT may
not always readily associated with better or worse attentional control. Rather, the pattern of
interactions appear crucial in determining how cues alter attention allocation and thus the
efficiency with which targets and distractors will be processed.
Acknowledgements
We are grateful to the parents and children in Rochester NY who gave their time to facilitate the developmental aspects
of the work reported here. The research was made possible by a grant from the John F. Merck Foundation to DB.
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Appendix 1: Omnibus ANOVAs Using Raw RTs
Flanker Type F (1, 117) = 363.30, p < .001, pe2 = .76
Flanker Type * Age Group F (3, 117) = 5.75, p = .001, pe2 = .13
Flanker Type * Gaming F (1, 117) = 1.01, p = .316, pe2 = .01
Flanker Type * Age Group * Gaming F (3, 117) = 0.69, p = .559, pe2 = .02
Cue Type F (3, 351) = 124.69, p < .001, pe2 = .52
Cue Type * Age Group F (9, 351) = 3.95, p < .001, pe2 = .09
Cue Type * Gaming F (3, 351) = 0.52, p = .668, pe2 < .01
Cue Type * Age Group * Gaming F (9, 351) = 0.81, p = .611, p = .02
Flanker Type * Cue Type F (3, 351) = 9.16, p < .001, pe2 = .07
Flanker Type * Cue Type * Age Group F (9, 351) = 0.41, p = .931, pe2 = .01
Flanker Type * Cue Type * Gaming F (3, 351) = 1.40, p = .242, pe2 = .01
Flanker Type * Cue Type * Age Group * Gaming F (9, 351) = 0.31, p = .973, pe2 = .01
Age Group F (3, 117) = 50.07, p < .001, pe2 = .56
Gaming F (1, 117) = 8.68, p = .004, pe 2 = .07
Age Group * Gaming F (3, 117) = 1.09. p = .355, pe2 = .03
Appendix 2: Video Game Questionnaire
We are interested in how often you play video games, and what type of games you play. We
will use this information to examine the effects of video game playing on the development of
visual attention skills.
We want you to think of the 6 video games you played the most in the last year. For each game,
please write in the name of the game, the number of hours you play the game in a typical
session, and the number of times you play the game in a typical month. Also please indicate
the console used. We are interested only in the games you have played in the last year.
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Example 1. If you played Solitaire on your PC twice a week for about 30 minutes at a time,
then you would write in Solitaire (name of game), 1/2 (hours per session), 8 (2 ×4 = 8 sessions
per month) and PC (console).
Name of game Hours per session Sessions per month Console
Ex. 1 Solitaire 1/2 8 PC
Ex. 2 Final Fantasy XI 3 1 PS2
1
2
3
4
5
6
Example 2. If you played Final Fantasy XI on your Play Station 2 once a month, typically for
3 hours at a time, then you would write in Final Fantasy (name of game), 3 (hours per session),
1 (sessions per month) and PS2 (console).
Appendix 3: Video Game Classifications
Games Categorized As Action Video Games
007 – Everything or Nothing; 007 – Golden Eye; 007 – Goldfinger; 007 – Nightfire; Battlefield
1942; Bionicle; Counterstrike; Devil May Cry; Ghost Recon; Grand Theft Auto; Grand Theft
Auto – San Andreas; Grand Theft Auto – Vice City; Half Life 2; Halo; Halo 2; Hitman 2;
Medal of Honor; Metal Gear Solid; Metal Gear Solid 2; Quake III; Rainbow Six-Three; Splinter
Cell; Star Wars – Jedi Starfighter; Unreal Tournament; Viet Cong.
Games Categorized As Non-Action Video Games
Age of Empires; Age of Mythology; Angelica; ATV Crossroad Fury; Backyard Baseball;
Backyard Soccer; Barbie; Brute Force; Bubble Trouble; Burning Monkey Mahjong;
Civilization III; Crash Bandicoot; Cross Country USA; Dance Dance Revolution; Dave Mirra
Freestyle BMX; DBZ; Deimos Rising; Donkey Kong; Downhill Domination; Dr. Mario; Dr.
Muto; Dracula; Duck Hunt; Emperor’s New Groove; Empires – Dawn of the Modern World;
ESPN NBA Basketball 2004; Extreme Ghostbusters; FIFA 2001; Final Fantasy VII; Formula
One 2001; Free Cell; Frogger; Gauntlet – Dark Legacy; Harry Potter and the Chamber of
Secrets; Harry Potter and the Sorceror’s Stone; High Heat 2003; Home Run King; Illusion of
Gaia; Jedi Knights; Karaoke Revolution; King’s Quest VII; Kingdom Hearts; Lego Island 2;
Lego Racers; Lord of the Rings – Return of the King; Lord of the Rings – The Two Towers;
Mad Dash; Mario Kart; Mario Kart Double Dash; Mario Party 3; Mario Super Party; Master
Matt’s Kung Fu Rama; Masters of Orion III; Mickey Mouse Kitchen; Mine Sweeper; MLB
Slugfest; MVP Baseball 2004; MX Unleashed; Myst; Mystery of the Monkey Kingdom; NBA
Live 2004; Need 4 Speed – Porsche Unleashed; Need for Speed – Underground; Neo Pets;
NFL Blitz; NFL Fever 2002; NFL Madden 2004; NHL 1999; NHL 2004l; Pokemon; Postopia;
Quad – Desert Fury; Railroad Tycoon; Scooby Doo – Night of 100 Frights; Shrek; Sim City;
Skateboarder Tycoon; Sly Cooper; Snood; Solitaire; Soul Caliber II; Spiderman; Spy Fox –
Operation Ozone; SSX 3; SSX Tricky 2; Star Craft; Star Fox; Star Wars – Knights of the Old
Republic; Super Mario Land; Super Mario RPG; Supersmash Brothers Melee; Tetris; Text
Twist; The Sims; The Sims –Bustin’ Out; Tiger Woods Golf; Tony Hawke Pro Skater 4; Tony
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Hawke’s Underground; Treasure Mountain; Vieautiful Joe; Virtual Pinball; Where In The
World Is Carmen Sandiego?; World of Warcraft; World Tour Soccer 3; Yoshi’s Island; Zelda
– Ocarina of Time; Zelda –Wind Waker; Zoo Tycoon; Zoombinis.
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Figure 1.
A. By default, visual attention is spread over the entire visual display. B. An alerting (double)
cue serves to focus attention in on the center of the display, but the attentional spotlight still
encompasses flankers that are proximal to the potential target locations. C. A valid orienting
(spatial) cue further restricts the spotlight to the impending target’s spatial location. Thus, an
alerting cue does not provide as much assistance to the observer when flankers are incongruent
– conflict resolution between the competing responses elicited by target and flanker arrows is
still required. However, an orienting cue provides a large benefit in such conditions, by focusing
upon the target at the expense of the competing flanker information.
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Figure 2.
Children’s version of the Attentional Network Test.
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Figure 3.
(A) Using the function outlined by Cerella and Hale (1994), RT was fitted as a function of
NVGP subject age using an exponential decay function. (B) After controlling for age
differences in RT, normalized RTs from the flanker absent conditions were used to fit NVGP
group RTs against RTs obtained from the VGP group. The linear fit from the resulting linear
regression was used to control for baseline differences in RT between gamers and non-gamers.
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Figure 4.
A. Alerting effects were computed by contrasting the no cue and double cue conditions. A
significant effect of age was observed, with 7–10 year olds having larger alerting effects than
older children and adults. These larger alerting effects possibly reflect higher levels of
inattention in young children that are alleviated by presenting a temporally informative cue.
The effect of video game playing was not statistically significant. B. In contrast, orienting
effects – computed by comparing center cue and spatial cue conditions – did not vary as a
function of age. However, there was a main effect of video game playing with VGPs having
larger orienting effects than NVGPs, suggesting that action video game players may be better
able to use a spatial cue to orient their attention to a target.
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Figure 5.
Incongruent flankers slowed down the responses of video game players more than it did those
of non-video game players, given the RTs expected as a function of both age and video game
experience. Higher flanker compatibility effects provide an index of the extent to which task-
irrelevant flankers were processed.
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Figure 6.
The effects of incongruent flankers were more pronounced – relative to those of congruent
flankers – following center or double cues that provided only temporal information about target
onset.
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Figure 7.
(A) Incongruent flankers slowed down the responses of VGPs more than it did those of NVGPs,
given the RTs expected as a function of both age and video game experience; (B) there were
differential effects of cue as a function of video game experience, with VGPs responding
comparatively more slowly than NVGPs in the absence of cues providing valid spatial
information (again, given the RTs expected as a function of both age and video game
experience).
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Figure 8.
The cues serve to focus attention more narrowly upon the target arrow. The ovals represent the
attentional window resulting from the preceding cue (c.f. Figure 2). Enhanced attentional
resources in VGPs result in a larger spill over of attention to distractors flanking the target, and
thus greater flanker compatibility effects. We propose that the spatial cue serves to focus
attention so tightly on the target arrow that the spill over of attention is attenuated, resulting in
comparable flanker compatibility effects for NVGPs and VGPs following spatial cues, despite
larger flanker compatibility yeffects following non-spatial (alerting) cues that do not convey
information about target location.
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Table 1
Subject characteristics of NVGPs and VGPs included in data analyses.
7–10 years 11–13 years 14–17 years 18–22 years
NVGP VGP NVGP VGP NVGP VGP NVGP VGP
N 30 13 13 15 12 15 14 13
Mean (SD) 8;11 9;5 12;1 12;9 15;10 14;11 20;4 19;9
Age (1;2) (1;1) (0;11) (0;7) (1;1) (0;8) (1;4) (1;3)
# Males 11 9 4 12 0 14 13 7
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