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Perceptual Load Influences Selective Attention Across Development
Jane W. Couperus
Hampshire College
Research suggests that visual selective attention develops across childhood. However, there is relatively
little understanding of the neurological changes that accompany this development, particularly in the
context of adult theories of selective attention, such as N. Lavie’s (1995) perceptual load theory of
attention. This study examined visual selective attention across development from 7 years of age to
adulthood. Specifically, the author examined if changes in processing as a function of selective attention
are similarly influenced by perceptual load across development. Participants were asked to complete a
task at either low or high perceptual load while processing of an unattended probe stimulus was examined
using event related potentials. Similar to adults, children and teens showed reduced processing of the
unattended stimulus as perceptual load increased at the P1 visual component. However, although there
were no qualitative differences in changes in processing, there were quantitative differences, with shorter
P1 latencies in teens and adults compared with children, suggesting increases in the speed of processing
across development. In addition, younger children did not need as high a perceptual load to achieve the
same difference in performance between low and high perceptual load as adults. Thus, this study
demonstrates that although there are developmental changes in visual selective attention, the mechanisms
by which visual selective attention is achieved in children may share similarities with adults.
Keywords: selective attention, event-related potential, P100, perceptual load, attention development
The ability to attend to the world around us is a multifaceted
process including orienting, sustaining attention, and selectively
attending to portions of the environment while simultaneously
ignoring others. Although it is clear that many of these aspects of
attention undergo development across the lifespan (e.g., Rid-
derinkhof and van der Stelt, 2000), the mechanisms they utilize are
not well understood. In particular, although there is a growing
body of research surrounding the development of auditory selec-
tive attention (e.g., Bartgis, Lilly, & Thomas, 2003; Coch, Sanders,
& Neville, 2005; Ma¨a¨tta¨, Pa¨a¨kkönen, Saavalainen, & Partanen,
2005; Oades, Dittmann-Balcar, & Zerbin, 1997; Sanders, Stevens,
Coch, & Neville, 2006; Satterfield, Schell, Nicholas, Satterfield, &
Freese, 1990; Stevens, Lauinger, & Neville, 2009), less is known
about visual selective attention and, in particular, the mechanisms
that produce visual selective attention in children.
Research examining visual selective attention in children has
focused on demonstrating functionality in early infancy (e.g.,
Amso & Johnson, 2006; Colombo, 2001) and general improve-
ments in speed, accuracy, and control of selection over time (e.g.,
Cowan, Fristoe, Elliot, Brunner, Saults, 2006; Perchet & Garcı´a-
Larrea, 2000, 2005; Ridderinkhof & van der Stelt, 2000; Rueda et
al., 2004). In addition, studies have examined this development in
relation to strategies used for selective attention, showing that
children may use different strategies in visual selection tasks, such
as visual search (e.g., Donnelly et al., 2007) or flanker tasks (e.g.,
McDermott, Pe´rez-Edgar, & Fox, 2007). However, although some
studies have begun to explore the neurological mechanisms behind
selective attention development (e.g., Ridderinkhof & van der
Stelt, 2000; Taylor & Khan, 2000; Taylor, Khan, & Malone, 1999;
van der Stelt, Kok, Smulders, Snel, & Gunning, 1998), this re-
search is in its infancy. These studies suggest that, across devel-
opment, there are changes in processing associated with visual
selective attention. For example, Taylor and Khan (2000) com-
pared parallel and serial visual search in children ages 7 to 12 years
using event-related potentials (ERPs). In addition to decreasing
latency of the P1 visual component across development, shorter
latencies were found during pop-out searches when a target was
present in the display compared with when it was not. This
suggests attention effects as early as the P1 in children. The
neurological correlates of changes in visual selective attention are
only beginning to be explored in the context of more general
theories of attention in adults (e.g., Perchet & Garcı´a-Larrea, 2000,
2005; Rueda et al., 2004) and, more specifically, theories of visual
selective attention (e.g., Harter & Anllo-Vento, 1991). Thus, the
purpose of this study is to begin filling in the gaps in knowledge
of the development of visual selective attention by focusing on one
of the current theories of visual selective attention in the adult
literature and examining how this theory can account for process-
ing related to visual selective attention across development.
Adult Theories of Visual Selective Attention
Research exploring the neural mechanisms of visual selective
attention in adults focuses on the neurological correlates of atten-
tional effects on stimulus processing, as well as structures and
This article was published Online First June 20, 2011.
This work was supported by the Foundation for Psychocultural
Research–Hampshire College Program in Culture, Brain, and Develop-
ment. Additional thanks to Brittany Alperin, who was a wonderful help in
collecting this data.
Correspondence concerning this article should be addressed to Jane W.
Couperus, School of Cognitive Science, Adele Simmons Hall, Hampshire
College, Amherst, MA 01002. E-mail: jcouperus@hampshire.edu
Developmental Psychology © 2011 American Psychological Association
2011, Vol. 47, No. 5, 1431–1439 0012-1649/11/$12.00 DOI: 10.1037/a0024027
1431
mechanisms that control attention effects. Research on the former
has shown that spatial attention relatively enhances processing of
stimuli at attended locations compared with unattended locations
(e.g., Di Russo, Martinez, & Hillyard, 2003; Handy & Khoe, 2005;
Hillyard, Vogel, & Luck, 1998; Mangun & Hillyard, 1991, 1995;
Mangun & Fannon, 2007). Moreover, this process of facilitation of
attended signals also appears to function in children (e.g., Harter &
Anllo-Vento, 1991; Rueda et al., 2004). For example, Harter and
Anllo-Vento (2001) demonstrated that young children, ages 6 to 8
years, show increased processing (as measured through the visual
P1 component) when attention was endogenously cued to a loca-
tion where a stimulus appeared. This parallels adult work and
suggests that the mechanism of selection at the level of stimulus
processing may occur in similar ways across development.
In contrast to these effects, the mechanisms by which spatial
attentional selection is directed and controlled are still under
debate. For example, one body of research concerns when in
processing attentional selection occurs. Research suggests that
selection can occur through stimulus driven (bottom-up), or en-
dogenously driven (top-down) processes (e.g., Hopfinger & Max-
well, 2005; Mangun & Hillyard, 1991, 1995; Mu¨ ller & Rabbitt,
1989; for review, see Posner, 2004). Moreover, these processes
can influence neural activity at early or late stages of processing
(e.g., Vogel, Woodman, & Luck, 2005). However, although there
are multiple theories that attempt to explain the conditions that
influence when in processing selection occurs (e.g., Broadbent,
1958; Lavie, 1995, and Treisman & Gelade, 1980), the specifics
are still contested.
Lavie’s perceptual load theory of selective attention (Lavie,
1995; Lavie, Hirst, Focker, & Viding, 2004) suggests that early
attentional selection is influenced by perceptual features and the
type of perceptual analysis required. This theory is based on earlier
work by Broadbent (1958), who suggested that selection is based
on a brief perceptual analysis of physical features. Broadbent
suggested that this is due to constraints early in processing. In
addition, this theory was based on the work of Deutsch and
Deutsch (1963), who conversely suggested that all stimuli are
processed in parallel. Thus, selection only occurs later in process-
ing when decisions and responses are required. Lavie (1995)
combined these two approaches to suggest that both processes
occur; however, which occurs is dependent on the processing
needed (i.e., the perceptual load of the stimulus). Lavie suggested
that perceptual load is determined by the perceptual features and
type of perceptual analysis required within an attended display.
Support for perceptual load theories of attentional selection can be
found in behavioral studies in which the addition of competing
stimuli facilitate attentional selection (Huang-Pollock, Carr, &
Nigg, 2002; Lavie & Cox, 1997; Lavie & de Fockert, 2003). For
example, Lavie and Cox (1997) used a central visual search task to
demonstrate that peripheral distractors were more distracting when
the central search task contained dissimilar nontargets (low per-
ceptual load) than when those nontargets were perceptually similar
(high perceptual load). Lavie and de Fockert (2003) argued that
increases in perceptual load are derived from increases in the in-
formation contained in the attended display, rather than changes in
factors such as stimulus discriminability. However, electrophysi-
ological studies have shown that changes in discriminability,
through changes in either stimulus characteristics or the duration
of presentation in relation to the duration of a patterned mask, can
function as changes in perceptual load (Barnhardt, Ritter, Gomes,
2008; Couperus, 2009; Handy & Mangun, 2000; Handy, Soltani,
& Mangun, 2001). The conflicting findings between Lavie and de
Fockert and electrophysiological studies may lie in the tasks them-
selves. Electrophysiological studies have all relied on a singular
central stimulus, whereas Lavie and de Fockert use a central search
task. It is possible that such a search task may require a different
attentional strategy for effective performance in the degradation
manipulation (i.e., high load perceptual discrimination) that results
in greater distractor interference.
Electrophysiological studies have shown changes in selective
attention as a function of perceptual load as early as sensory level
processing in the extrastriate cortex, as indexed by the P1 visual
component (Couperus, 2009; Handy & Mangun, 2000; Handy et
al., 2001). For example, when presented with a difficult perceptual
discrimination (i.e., high perceptual load), attention is more nar-
rowly focused, reducing processing of a parafoveal distractor
(Handy et al., 2001). This description of the narrowing of attention
can be understood as a spotlight or zoom lens (Posner & Peterson,
1990), whereby attention is more diffuse under conditions of low
perceptual load compared to high perceptual load.
Perceptual Load and Development of
Selective Attention
Although there is some evidence that children may also use
perceptual load to guide attentional selection (e.g., Huang-Pollock
et al., 2002), these studies have been behavioral in nature. For
example, Huang-Pollock et al. (2002) studied children between the
ages of 7 and 12 years, as well as adults in high and low perceptual
load tasks. In these tasks, children searched through a ring of
visual objects for a target object while a distractor appeared to the
side of the attended stimuli. Low perceptual load displays had
fewer attended objects than did high perceptual load displays.
Children showed greater interference from peripheral distractors
compared with adults at low perceptual loads. This difference is
thought to result from low perceptual load displays requiring
top-down or late attentional selection. In contrast, children showed
near adult performance at high perceptual loads, which is thought
to rely on early stimulus-driven selection. This suggests that earlier
developing systems involved in early selection (i.e., a posterior
network of brain structures including parietal and temporoparietal
cortex) that function under high levels of perceptual load may
allow for adult-like performance in children. At lower levels of
perceptual load, children may show reduced performance, because
performance relies more on late selection processes that utilize
neural substrates that are not fully developed (i.e., anterior cingu-
late and prefrontal cortex; Huang-Pollock et al., 2002). Moreover,
Huang-Pollock et al. suggested that children require less percep-
tual load than adults to produce early attentional selection. How-
ever, the neural correlates of this process have not been examined.
As noted previously, perceptual load may function in adults to
focus or narrow the scope of attention, thereby reducing the
processing of unattended stimuli (e.g., Handy & Mangun, 2000).
However, although behavioral findings, such as those by Huang-
Pollock et al. (2002), imply a similar mechanism in children, they
cannot demonstrate such effects. It is not clear if early selection in
children is facilitated by such a narrowing of focused attention or
if other mechanisms are responsible for these results. For example,
1432 COUPERUS
as noted previously, children may utilize difference strategies to
achieve visual selective attention (e.g., Donnelly et al., 2007),
perhaps selecting out the relevant information later in processing
than adults.
Thus, this study is designed to examine the neural correlates of
selective attention in relation to perceptual load. In particular, early
perceptual processing of unattended stimuli is explored in the
context of low and high perceptual load across development.
Based on Huang-Pollock et al.’s (2002) research, it is hypothesized
that children, like adults, will show reduced processing of an
unattended parafoveal stimulus at high perceptual load. However,
children will require a lower level of perceptual load to achieve
this selection compared with adults, reflecting quantitative rather
than qualitative differences across development. Moreover, these
quantitative differences will also be seen in the speed of process-
ing, reflected in shorter latencies for the P1 across development.
Method
Participants
Seventy-four participants, split into five age groups 7–9 years
(n⫽17, M⫽8.06, SD ⫽0.75), 10–12 years (n⫽17, M⫽10.77,
SD ⫽0.75), 13–15 years (n⫽12, M⫽13.92, SD ⫽0.79), 16–18
years (n⫽14, M⫽17.07, SD ⫽0.62), and 19 years or older (n⫽
14, M⫽24.21, SD ⫽7.02), participated in this study. Adult
participants were recruited from local colleges (Hampshire, Am-
herst, Smith, and Mount Holyoke Colleges, as well as the Univer-
sity of Massachusetts at Amherst) and child participants from the
local Pioneer Valley area of Massachusetts. Participants were paid
$10 to $20 for their participation. Thirty-three men and 41 women
participated, with 52 of the participants being Caucasian, 1 His-
panic, 11 Asian, 6 Asian/Caucasian, 1 Black/Caucasian, 1 Cauca-
sian/Native American, and 2 Hispanic/Native American. Partici-
pants were excluded from analysis if they were left-handed, had
noncorrectable visual impairments, were diagnosed with or sus-
pected of having learning disorders, were currently on psychotro-
pic medications, or were born prematurely (i.e., at less than 36
weeks) as noted by self or parent report. In addition, participants
were excluded following initial electroencephalogram (EEG) data
reduction if insufficient data for analysis remained (see Data
Analysis and Reduction). Although initially 88 participants were
recruited, after eliminating participants who met exclusionary cri-
teria or who had insufficient data, the 74 participants described
above remained and were used in data analysis.
Procedure
After completing the consent process, all adult participants
completed questionnaires addressing demographic information as
well as a brief psychological and medical history. Parents of child
participants completed the same questionnaires. Participants were
then set up for electrophysiological recordings and completed the
modified perceptual load task. Once the task was completed,
participants were debriefed and paid for their participation. The
entire session lasted from 1 hr for adult participants to a maximum
of 1.5 hr for children.
Modified perceptual load task. The paradigm was based on
a design used by Handy et al. (2001). On each task trial, a small
fixation was presented for 200 ms, followed by one of four letters
(A, B, C, D) or four numbers (1, 2, 3, 4; approximately 4.3 degrees
by 3.6 degrees visual angle; see Figure 1). Letters and numbers
were presented at either low or high perceptual load, and partici-
pants were asked to identify if a number or letter appeared using a
button press on a two-button mouse by holding the mouse in both
hands and using their thumbs to indicate the presence of a number
or letter. In the low-load condition, the letter was presented for 100
ms following fixation. In the high-load condition, the letter was
presented for a shorter duration (varying from 30 ms to 90 ms as
a function of performance) and was immediately followed by a
patterned mask for a time complementary to the stimulus duration
(a # sign; onset coincided with the offset of the target). The sum of
the duration of the letter and mask was 100 ms, and the ratio of the
duration of the target to the mask was varied across trials to
100 ms
..
.
A2
#
%
.
Low Perceptual Load Trial High Perceptual Load Trial
Probe Trial
100 ms
sm002
sm002
*30-90 ms
*70-10 ms
200 ms
*times vary as a function of task performance
Figure 1. Stimulus display.
1433
PERCEPTUAL LOAD AND SELECTIVE ATTENTION ACROSS DEVELOPMENT
maintain task difficulty using a step algorithm (correct answers
speeded the presentation by 10 ms; incorrect answers slowed
presentation by 10 ms within the constraints of a minimum of 30
ms or maximum of 90 ms). This methodology does not allow for
the examination of the differential contributions of stimulus and
mask durations on probe processing. However, it does allow for
the overall decrease in perceptual discriminability needed to
achieve a constant change between low and high perceptual load.
The study was split into two epochs: The first contained stimuli at
low perceptual load and the second at high perceptual load. Each
epoch contained eight blocks, each containing 100 trials, resulting
in 800 total trials.
Twenty percent of trials (80 total trials in each condition) were
replaced by a probe trial that consisted of a fixation and irrelevant
stimulus (a % symbol approximately 4.3 by 3.6 degrees) presented
for 100 ms at the top of the computer screen within the useful field
of view (approximately 11.4 degrees above the foveal target).
These target-free trials allowed measurement of stimulation within
the useful field of view as a function of perceptual load without
confounding foveal stimulation as has been done in previous
studies (Couperus, 2009; Handy et al., 2001). Probes were placed
randomly with the constraint that no two probe trials could be
placed consecutively or appear as the first trial of a given block.
Participants were asked to remain looking at the fixation during
probe trials and no response was required for probe trials.
Trials consisted of a 200-ms fixation followed by the appear-
ance of the stimulus (letter or number) or stimulus and mask for
100 ms (see Figure 1). Participants were then presented with a
fixation and given 4,000 ms to respond. The response ended the
trial and was followed by a random intertrial interval (range ⫽
800–1000 ms). Probe trials followed the same timing, (fixation for
200 ms, probe for 100 ms); however, instead of being given 4,000
ms to respond, as no response was required, a fixation appeared for
1,000 ms, followed by a random intertrial interval (range ⫽
800–1000 ms).
Electrophysiological methods. Scalp EEGs were recorded
from 32 tin electrodes sewn into a stretchy lycra cap (Electro-cap
International). Electrodes were referenced to linked mastoids (re-
referenced offline), and impedances were kept below 5 kohms for
all participants. The mastoid reference is preferred because it has
a smaller number of channels, and the average reference is not as
accurate with this number of electrodes (Handy & Khoe, 2005).
Data were recorded using a Synamps2 Amplifier with Scan 4.2
software and were digitized at the rate of 500 Hz using a bandpass
filter of 0.1 to 100 Hz. To ensure eye fixation, the electrooculo-
gram was recorded for both vertical (from an electrode inferior to
the left eye) and horizontal (from electrodes on both the right and
left outer canthus) eye movements through the entire trial.
Data analysis and reduction. EEGs were epoched offline to
examine event-related electrophysiological activity for all trials
(200 ms prestimulus to 1,000 ms poststimulus) and baseline cor-
rected using a baseline of 200 ms prestimulus. Trials were elimi-
nated if there were significant eye artifacts (defined as amplitudes
⫾50 volts for participants 13 years of age or older, ⫾75 for
participants 10–12 years of age, and ⫾100 for participants 7–9
years of age at vertical or horizontal eye electrodes, because
children may produce greater amplitude data; DeBoer, Scott, &
Nelson, 2005) or if visual inspection revealed that there was
significant alpha activity in more than three channels (i.e., 10% of
total channels). Channels that were consistently bad across the
experiment were marked as such and were not used in analyses.
Participants were eliminated from analyses if there were more than
three bad channels (Picton et al., 2000). The resulting ERPs were
used to produce grand-average waveforms for probe stimuli in
each of the two conditions used in statistical analysis: low percep-
tual load and high perceptual load. Statistical analysis of P1 data
was based on average amplitude measured over a 50-ms time
window, centered approximately on the peak amplitude of the
P100 (115–165 ms poststimulus in those 13 years of age or older,
125–175 for those 10–12, and 135–185 for those 7–9 years of
age)
1
seen in the grand-averaged waveforms of the occipital leads
O1, OZ, and O2. Latency data were measured by obtaining the
latency to peak during the same time windows used in amplitude
analysis. Differences in duration of stimulus presentation were
analyzed with a one-way analysis of variance (ANOVA) compar-
ing five age groups. Accuracy and reaction time data were ana-
lyzed using a two-way (perceptual load: high vs. low) repeated-
measures ANOVA with age group (7–9, 10–12, 13–15, 16–18, 19
years and up) as a between-subjects variable. The same analyses
were performed on P1 amplitude and P1 latency to be consistent
with the broader ERP literature. ERP analyses included an average
of 56.36 trials per condition (SD ⫽10.63). Although statistics
were also run for the N1 component, as there were no significant
differences as a function of perceptual load, the results are not
presented here. Effects at the N1 were likely not seen in this study,
because the low perceptual load condition was not sufficiently
difficult for the P1 to show maximal attentional effects. Thus,
additional attentional modulations at the N1 were not necessary at
high perceptual load.
Results
Statistical Analyses
Behavioral analysis. To produce perceptual load differences
in this task, the stimulus duration was varied for each individual to
maintain a steady difference in accuracy between high and low
perceptual load across development. As anticipated, a one-way
ANOVA comparing five age groups showed a significant main
effect of age on stimulus duration, with younger children requiring
longer stimulus durations to maintain the same level of relative
accuracy on high perceptual load trials than did adult participants,
F(1, 69) ⫽44.30, p⬍.001. Whereas children ages 7–9 years
required an average stimulus presentation time of 62.34 ms (SD ⫽
23.74), children 10–12 years of age required an average of only
41.84 ms (SD ⫽17.16). In contrast, teenagers 13 years of age and
older did not significantly differ from adults (ages 13–15 M⫽
38.03, SD ⫽9.59; ages 16–18 M⫽34.37, SD ⫽4.98; ages 19 and
older M⫽36.82, SD ⫽9.46).
Accuracy. To confirm that the perceptual load manipulation
was effective at producing a relatively constant difference in
accuracy between low and high perceptual load across develop-
ment, a 5 (Age Group) ⫻2 (Perceptual Load) repeated-measures
1
Different latency windows were used for different age groups, as
previous research suggested that latency decreases with age (DeBoer et al.,
2005; Taylor & Khan, 2000).
1434 COUPERUS
ANOVA was performed on mean accuracy both across the task as
well as for the final block of trials. As anticipated there were main
effects of both perceptual load and age, F(1, 69) ⫽44.30, p⬍
.001,
p
2
⫽.391; F(1, 69) ⫽26.72, p⬍.001,
p
2
⫽.279; and F(4,
69) ⫽3.49, p⫽.012,
p
2
⫽.169; F(4, 69) ⫽3.69, p⫽.009,
p
2
⫽
.176, respectively. However, it is important to note that there was
not a significant interaction between age and perceptual load (p⬎
.05). These findings confirm that the stimulus-duration manipula-
tion was effective in allowing general improvements in accuracy
with age while maintaining a difference in accuracy between low
and high perceptual load that remained similar across ages (low
perceptual load Macross all trials ⫽90.39, SD ⫽12.39; high
perceptual load Macross all trials ⫽81.19, SD ⫽12.16; average
difference across ages ⫽9.2, SD ⫽11.36; see Table 1). It is
important to note that performance did not reach chance during the
high perceptual load condition (as expected using a staircase
method of adjustment) because of both the lower limit of stimulus
presentation duration and time constraints of the study. However,
the study design was not dependent on reaching chance perfor-
mance, as the manipulation was designed to ensure that the high
perceptual load task remained at a similar level of relative diffi-
culty for all participants, regardless of age.
Reaction time. Similar to accuracy findings, a 5 (Age
Group) ⫻2 (Perceptual Load) repeated-measures ANOVA
showed a significant main effect of age, F(1, 69) ⫽10.03, p⬍
.001,
p
2
⫽.368. However, differences as a function of perceptual
load were not as strong, showing only a nonsignificant trend, F(1,
69) ⫽3.10, p⫽.083,
p
2
⫽.043. However, similar to accuracy,
there was no interaction between the effects of perceptual load and
age.
Electrophysiological analyses.
P100 amplitude. As anticipated, there was a significant de-
crease in processing of the parafoveal stimulus with increased
perceptual load. In addition, there were no differences in this effect
across development. This was confirmed by a significant main
effect of perceptual load—O1 F(1, 69) ⫽14.41, p⬍.001,
p
2
⫽
.173; OZ F(1, 69) ⫽12.76, p⫽.001,
p
2
⫽.156; O2 F(1, 69) ⫽
10.68, p⫽.002,
p
2
⫽.134—for all occipital leads. In addition,
although visual inspection suggests small changes in amplitude as
a function of age (see Figure 2), there were no significant main
effects of age (ps⬎.05,
p
2
s⬍.12) or interactions between age
and perceptual load (ps⬎.05,
p
2
s⬍.09), suggesting that the
effects of perceptual load on processing of an unattended parafo-
veal stimulus do not differ as a function of age.
P100 latency. In contrast to amplitude effects, there were no
effects of perceptual load on latency (ps⬎.05,
p
2
s⬍.025), but
main effects of age at all three occipital leads, O1 F(1, 69) ⫽
19.92, p⬍.001,
p
2
⫽.536; OZ F(1, 69) ⫽15.09, p⬍.001,
p
2
⫽.467; O2 F(1, 69) ⫽12.91, p⬍.001,
p
2
⫽.428. Post hoc
analysis using Fisher’s least significant difference suggest that
although there are significant or trend decreases between 7- to
9-year-olds and 10- to 12-year-olds (O1 Mdifference ⫽7.18, p⫽
.046; OZ Mdifference ⫽6.50, p⫽.067; O2 Mdifference ⫽6.32,
p⫽.063) and between 10- to 12-year-olds and 13- to 15-year-olds
(O1 Mdifference ⫽14.57, p⬍.001; OZ Mdifference ⫽11.61,
p⫽.004; O2 Mdifference ⫽8.25, p⫽.028), additional age did
not significantly reduce latency (ps⬎.05; see Table 2). In addi-
tion, as in findings with amplitude data, there were no significant
interactions between age and perceptual load (ps⬎.05,
p
2
s⬍
.06). Thus, the data suggest that although there are increases in
speed of processing across age, these do not alter the effects of
perceptual load.
Discussion
Studies of visual selective attention development have sug-
gested that while there are some aspects of processing that remain
similar across development (e.g., Harter & Anllo-Vento, 1991),
there may also be qualitative differences in how children and
adults approach selective attention tasks (e.g., Donnelly et al.,
2007). Data from the current study suggest a more quantitative
change in the mechanisms that facilitate visual selective attention.
Results suggest similar changes in processing as a function of
perceptual load, but differences in the level of perceptual load
needed for such effects to be seen. Moreover, changes in the speed
of processing were found across development.
Perceptual load theories of selective attention suggest that as the
level of perceptual load increases, selection occurs earlier in pro-
cessing (Lavie, 1995). Moreover, these changes may reduce pro-
cessing of unattended distractors by narrowing the focus of atten-
tion (e.g., Handy et al., 2001). Results of this study demonstrate
that processing of an unattended parafoveal stimulus was influ-
enced by the perceptual load of the attended stimulus. In particular,
as perceptual load increased, processing of the parafoveal distrac-
tor decreased. Moreover, results show this was true for children 7
years of age through adulthood. Although a secondary manipula-
tion check examining interference of a distractor at high perceptual
load was not included in this study because of children’s limited
attention spans, findings parallel those of Handy et al. (2001). In
addition, Handy et al. (2001) used a similar methodology and did
do such a manipulation check, finding increased distractor inter-
ference at low perceptual load compared with high perceptual load.
Thus, findings of this study provide preliminary evidence for
extending Lavie’s (1995) perceptual load theory of attentional
selection to children, suggesting that with increased perceptual
Table 1
Mean Accuracy Across Age
Perceptual load
7–9 years 10–12 years 13–15 years 16–18 years 19⫹years
M SD M SD M SD M SD M SD
Low 86.24 19.2 87.82 12.9 89.71 10.3 94.50 3.8 95.04 5.1
High 74.06 14.3 77.62 10.5 82.33 9.9 86.93 11.6 87.46 7.9
Difference 12.18 10.20 7.38 7.57 7.58
1435
PERCEPTUAL LOAD AND SELECTIVE ATTENTION ACROSS DEVELOPMENT
Figure 2. P1 to probes following low and high perceptual load stimuli.
1436 COUPERUS
demands within an attended stimulus display, selection occurs
earlier in processing.
According to Huang-Pollock et al. (2002), selective attention in
children may involve both facilitation of the attended stimulus and
suppression of an unattended stimulus, often referred to as selec-
tion and filtering in the developmental literature (e.g., Akhtar &
Enns, 1989; Ridderinkhof & van der Stelt, 2000). Recent studies of
the neural underpinnings of selective attention in adults support
this model, suggesting that selective attention involves both rela-
tive enhancement of the signal of the attended stimulus (e.g., Di
Russo et al., 2003; Handy & Khoe, 2005; Hillyard et al., 1998;
Mangun & Hillyard, 1991, 1995; Mangun & Fannon, 2007) and
relative suppression of the unattended stimulus (e.g., Awh, Mat-
sukura, & Serences, 2003; Caputo & Guerra, 1998; Dell’Acqua,
Pesciarelli, Jolicoeur, Eimer, Peressotti, 2007; Serences, Yantis,
Culberson, & Awh, 2004). Moreover, recent research has sug-
gested that these may be separable processes (e.g., Couperus &
Mangun, 2010; Slotnick, Schwarzenback, & Yantis, 2003; Tipper
et al., 1997). Huang-Pollock et al. (2002) suggested that children
may experience more interference at low perceptual load, because
both the attended and unattended stimulus (in their study, the target
and distractor) are processed. Thus, the unattended stimuli may
require inhibition at a higher level of processing. For example, the
unattended stimulus may require the inhibition of a motor response
or inhibition of the identification of that stimulus. Moreover, this
inhibition may be immature in children compared with adults. In
contrast, at high perceptual load, no such inhibition is required
because of early selection based on perceptual features. The ERP
data in this study are consistent with such an interpretation, as
unattended stimuli presented during high perceptual load epochs
produced less activity at the P1 compared with unattended stimuli
presented during low perceptual load epochs. However, although
they are consistent with early selection, the data presented here do
not exclude top-down contributions to early selection prior to
stimulus onset. Moreover, studies of perceptual load in adults have
suggested that top-down processes may be involved in biasing
processing prior to stimulus onset during both early (e.g., Coupe-
rus, 2009) and late selection (e.g., Lavie, 1995; Vogel et al., 2005).
Thus, future research should examine contributions of these pro-
cesses across development.
In addition to changes in activity as a function of perceptual
load, the data also suggest that a lower level of perceptual load was
required to reduce performance in younger children compared with
teens and adults. This was demonstrated by the necessity of longer
stimulus durations to achieve the same change in performance in
children (7–12 years) compared with teens and adults (13⫹years).
These findings are also consistent with previous studies suggesting
that younger children may require reduced levels of perceptual
load to shift selection to earlier in processing (Huang-Pollock et
al., 2002). This difference has been interpreted by Huang-Pollock
et al. (2002) as resulting from children’s need to compensate for
poorer efficiency in the anterior system involved in cognitive
control. Moreover, it provides a mechanism for simplifying the
perceptual world by engaging early selection at a lower level of
perceptual load. This has been theorized by Bjorklund (1997) to be
beneficial for development by limiting the amount of information
that needs to be processed. However, the goal of the perceptual
load manipulation used in this study was to maintain a difference
in performance that was similar across development. To maintain
this difference, the perceptual load of the stimulus was changed for
each participant. Because this ensured that all participants experi-
enced the same change in perceptual load, it allowed for the
examination of the effects of changes in perceptual load on stim-
ulus processing across development. If a standard level of percep-
tual load had been used for the high perceptual load displays, the
task may have been either too difficult for children or too easy for
adults, both of which may have obscured changes as a function of
perceptual load at P1. A limitation of this perceptual load manip-
ulation is that it may be too insensitive to effectively see differ-
ences in processing at high perceptual load across development.
Because the primary aim of the manipulation was to maintain a
similar difference in performance between high and low perceptual
load, accuracy for high-load stimuli did change across develop-
ment. If accuracy for high perceptual load stimuli had been main-
tained across development, it is possible that an interaction be-
tween processing of high perceptual load stimuli and development
may have been found. Moreover, it also means that changes in the
level of perceptual load required to attain the same level of
performance difference is a proxy for changes in perceptual load
that are more typically examined by including multiple levels of
perceptual load for each individual (e.g. Huang-Pollock et al.,
2002). In these studies, perceptual load is manipulated by changing
the number of attended stimuli for each participant. This allows for
the examination of changes in the sensitivity to perceptual load
within individuals across development. Thus, although this study
cannot show at what level of perceptual load selection shifts from
Table 2
P1 Latency Across Development
Perceptual load
7–9 years 10–12 years 13–15 years 16–18 years 19⫹years
MSDMSD M SD M SD M SD
Low
O1 161.06 10.41 154.29 9.27 139.75 14.16 134.86 13.82 133.64 8.57
OZ 162.12 10.35 154.76 9.83 143.50 12.16 140.21 15.09 135.43 8.14
O2 159.71 10.80 149.94 11.83 141.67 10.47 137.00 13.06 136.00 8.06
High
O1 159.35 13.32 151.76 8.71 137.17 12.55 136.86 14.41 134.14 7.94
OZ 158.18 13.18 152.53 10.89 140.58 12.03 138.79 13.56 137.86 7.68
O2 152.76 11.52 149.88 9.94 141.67 13.01 134.86 15.28 136.43 9.07
Average latency 158.86 152.20 140.72 137.10 135.58
1437
PERCEPTUAL LOAD AND SELECTIVE ATTENTION ACROSS DEVELOPMENT
early to late, it does provide additional evidence that children may
shift from early to late selection as a function of perceptual load.
Moreover, it is consistent with studies that suggest that children
require lower levels of perceptual load for this shift.
Finally, in addition to effects of perceptual load, this study
provided evidence that there are changes in the speed of processing
across development. As previous studies have suggested, differ-
ences in latency can be found as a function of age (e.g., Taylor &
Khan, 2000). In the present study, changes in latency for the P1
were found in younger children, but changes were only significant
between 7 and 13 years of age. Although different tasks may show
a more protracted development, the changes in latency seen in this
study are consistent with previous studies (Taylor & Khan, 2000),
suggesting that there are changes in the speed of processing in
early stimulus processing at least until the early teen years. In
contrast, no significant changes in the topography of the P1 were
seen across development. However, visual inspection of the to-
pography suggests that there may be broader activation in younger
children compared with adolescents and adults. Given the low
spatial resolution of electrodes over the occipital cortex in this
study, future studies should explore these topographical changes,
as it is possible that such differences may exist.
In sum, this study demonstrates that although there are devel-
opmental changes in visual selective attention, the mechanisms by
which visual selective attention is achieved in children may share
similarities with those of adults. Children in this task showed
similar processing changes as a function of perceptual load, sup-
porting Lavie’s (1995) perceptual load theory of selective atten-
tion. However, as few studies have examined the development of
the mechanisms involved in visual selective attention, the current
research serves only as a starting point to filling in the gaps in
understanding of this process.
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Received August 10, 2010
Revision received April 5, 2011
Accepted April 13, 2011 䡲
1439
PERCEPTUAL LOAD AND SELECTIVE ATTENTION ACROSS DEVELOPMENT
