ArticlePDF Available

Perceptual Load Influences Selective Attention Across Development

Authors:

Abstract and Figures

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.
Content may be subject to copyright.
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
(n17, M8.06, SD 0.75), 10–12 years (n17, M10.77,
SD 0.75), 13–15 years (n12, M13.92, SD 0.79), 16–18
years (n14, M17.07, SD 0.62), and 19 years or older (n
14, M24.21, 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 M34.37, SD 4.98; ages 19 and
older M36.82, 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 19years
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 (13years).
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 19years
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.
References
Akhtar, N., & Enns, J. T. (1989). Relations between covert orienting and
filtering in the development of visual attention. Journal of Experimental
Child Psychology, 48, 315–334. doi:10.1016/0022-0965(89)90008-8
Amso, D., & Johnson, S. P. (2006). Learning by selection: Visual search
and object perception in young infants. Developmental Psychology, 42,
1236–1245. doi:10.1037/0012-1649.42.6.1236
Awh, E., Matsukura, M., & Serences, J. T. (2003). Top-down control over
biased competition during covert spatial orienting, Journal of Experi-
mental Psychology: Human Perception and Performance, 29, 52–63.
doi:10.1037/0096-1523.29.1.52
Barnhardt, J., Ritter, W., & Gomes, H. (2008). Perceptual load affects
spatial and nonspatial visual selection processes: An event-related brain
potential study. Neuropsychologia, 46, 2071–2078. doi:10.1016/
j.neuropsychologia.2008.02.007
Bartgis, J., Lilly, A. R., & Thomas, D. G. (2003). Event-related potential and
behavioral measures of attention in 5-, 7-, and 9-year-olds. Journal of
General Psychology, 130, 311–335. doi:10.1080/00221300309601162
Bjorklund, D. (1997). The role of immaturity in human development. Psycho-
logical Bulletin, 122, 153–169. doi:10.1037/0033-2909.122.2.153
Broadbent, D. (1958). Perception and communication. London, England:
Pergamon Press. doi:10.1037/10037-000
Caputo, G., & Guerra, S. (1998). Attentional selection by distractor sup-
pression. Vision Research, 38, 669689. doi:10.1016/S0042-
6989(97)00189-2
Coch, D., Sanders, L. D., & Neville, H. J. (2005). An event-related
potential study of selective auditory attention in children and adults.
Journal of Cognitive Neuroscience, 17, 605–622. doi:10.1162/
0898929053467631
Colombo, J. (2001). The development of visual attention in infancy.
Annual Review of Psychology, 52, 337–367. doi:10.1146/annurev-
.psych.52.1.337
Couperus, J. W. (2009). Implicit learning modulates selective attention at
sensory levels of perceptual processing. Attention, Perception, & Psy-
chophysics, 71, 342–351. doi:10.3758/APP.71.2.342
Couperus, J. W., & Mangun, G. R. (2010). Signal enhancement and
suppression during visual-spatial selective attention. Brain Research,
1359, 155–177. doi:10.1016/j.brainres.2010.08.076
Cowan, N., Fristoe, N. M., Elliott, E. M., Brunner, R. P., & Saults, J. S.
(2006). Scope of attention, control of attention, and intelligence in
children and adults. Memory & Cognition, 34, 1754–1768. doi:10.3758/
BF03195936
DeBoer, T., Scott, L. S., & Nelson, C. A. (2005). Event-related potentials
in developmental populations. In T. Handy (Ed.), Methodological hand-
book for research using event-related potentials (pp. 263–297). Cam-
bridge, MA: MIT Press.
Dell’Acqua, R., Pesciarelli, F., Jolicoeur, P., Eimer, M., & Peressotti, F.
(2007). The interdependence of spatial attention and lexical access as
revealed by early asymmetries in occipito-parietal ERP activity. Psy-
chophysiology, 44, 436443. doi:10.1111/j.1469-8986.2007.00514.x
Deutsch, J., & Deutsch, D. (1963). Attention: Some theoretical consider-
ations. Psychological Review, 70, 80–90. doi:10.1037/h0039515
Di Russo, F., Martinez, A., & Hillyard, S. A. (2003). Source analysis of
event-related cortical activity during visual-spatial attention. Cerebral
Cortex, 13, 486499. doi:10.1093/cercor/13.5.486
Donnelly, N., Cave, K., Greenway, R., Hadwin, J. A., Stevenson, J., &
Sonuga-Barke, E. (2007). Visual search in children and adults: Top-
down and bottom-up mechanisms. Quarterly Journal of Experimental
Psychology, 60, 120–136. doi:10.1080/17470210600625362
Handy, T. C., & Khoe, W. (2005). Attention and sensory gain control: A
peripheral visual process? Journal of Cognitive Neuroscience, 17, 1936
1949. doi:10.1162/089892905775008715
Handy, T. C., & Mangun, G. R. (2000). Attention and spatial selection:
Electrophysiological evidence for modulation by perceptual load. Per-
ception & Psychophysics, 62, 175–186. doi:10.3758/BF03212070
Handy, T. C., Soltani, M., & Mangun, G. R. (2001). Perceptual load and
visuocortical processing: Event-Related potentials reveal sensory-level
selection. Psychological Science, 12, 213–218. doi:10.1111/1467-
9280.00338
Harter, M. R., & Anllo-Vento, L. (1991). Visual–spatial attention: Prepa-
ration and selection in children and adults. In C. H. M. Brunia, G.
Mulder, & M. N. Verbaten (Eds.), Event-related potentials of the brain
(pp. 183–194). Amsterdam, the Netherlands: Elsevier.
Hillyard, S. A., Vogel, E. K., & Luck, S. J. (1998). Sensory grain control
(amplification) as a mechanism of selective attention: Electrophysiolog-
ical and neuroimaging evidence. Philosophical Transactions of the
Royal Society of London B: Biological Sciences, 353, 1257–1270. doi:
10.1098/rstb.1998.0281
Hopfinger, J. B., & Maxwell, J. S. (2005). Appearing and disappearing
stimuli trigger a reflexive modulation of visual cortical activity. Cogni-
tive Brain Research, 25, 48–56. doi:10.1016/j.cogbrainres.2005.04.010
Huang-Pollock, C. L., Carr, T. H., & Nigg, J. T. (2002). Development of
selective attention: Perceptual load influences early versus late atten-
tional selection in children and adults. Developmental Psychology, 38,
363–375. doi:10.1037/0012-1649.38.3.363
Lavie, N. (1995). Perceptual load as a necessary condition for selective
attention. Journal of Experimental Psychology: Human Perception and
Performance, 21, 451–468. doi:10.1037/0096-1523.21.3.451
Lavie, N., & Cox, S. (1997). On the efficiency of visual selective attention:
Efficient visual search leads to inefficient distractor rejection. Psycho-
logical Science, 8, 395–398. doi:10.1111/j.1467-9280.1997.tb00432.x
1438 COUPERUS
Lavie, N., & de Fockert, J. W. (2003). Contrasting effects of sensory limits
and capacity limits in visual selective attention. Perception & Psycho-
physics, 65, 202–212. doi:10.3758/BF03194795
Lavie, N., Hirst, A., de Fockert, J. W., & Viding, E. (2004). Load theory
of selective attention and cognitive control. Journal of Experimental
Psychology: General, 133, 339–354. doi:10.1037/0096-3445.133.3.339
Ma¨a¨tta¨, S., Pa¨a¨kkönen, A., Saavalainen, P., & Partanen, J. (2005). Selec-
tive attention event-related potential effects from auditory novel stimuli
in children and adults. Clinical Neurophysiology, 116, 129–141. doi:
10.1016/j.clinph.2004.07.023
Mangun, G. R., & Fannon, S. P. (2007). Attention: Control in the visual cortex.
Current Biology, 17, R170–R172. doi:10.1016/j.cub.2006.12.028
Mangun, G. R., & Hillyard, S. A. (1991). Modulations of sensory-evoked
brain potentials indicate changes in perceptual processing during visual-
spatial priming. Journal of Experimental Psychology: Human Percep-
tion and Performance, 17, 1057–1074. doi:10.1037/0096-1523
.17.4.1057
Mangun, G. R., & Hillyard, S. A. (1995). Mechanisms and models of
selective attention. In M. D. Rugg & M. G. H. Coles (Eds.), Electro-
physiology of mind: Event-related brain potentials and cognition (pp.
40–85). New York, NY: Oxford University Press.
McDermott, J. M., Pe´rez-Edgar, K., & Fox, N. (2007). Variations of the
flanker paradigm: Assessing selective attention in young children. Be-
havior Research Methods, 39, 62–70. doi:10.3758/BF03192844
Mu¨ller, H. J., & Rabbitt, P. M. (1989). Reflexive and voluntary orienting
of visual attention: Time course of activation and resistance to interrup-
tion. Journal of Experimental Psychology: Human Perception and Per-
formance, 15, 315–330. doi:10.1037/0096-1523.15.2.315
Oades, R. D., Dittmann-Balcar, A., & Zerbin, D. (1997). Development and
topography of auditory event-related potentials (ERPs): Mismatch and
processing negativity in individuals 8–22 years of age. Psychophysiol-
ogy, 34, 677–693. doi:10.1111/j.1469-8986.1997.tb02143.x
Perchet, C., & Garcı´a-Larrea, L. (2000). Visuospatial attention and motor
reaction in children: An electrophysiological study of the “Posner”
paradigm. Psychophysiology, 37, 231–241. doi:10.1111/1469-
8986.3720231
Perchet, C., & Garcı´a-Larrea, L. (2005). Learning to react: Anticipatory
mechanisms in children and adults during a visuospatial attention task.
Clinical Neurophysiology, 116, 1906–1917. doi:10.1016/j.clinph.2005
.03.022
Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson,
Jr., R., . . . Taylor, M. J. (2000). Guidelines for using human event-
related potentials to study cognition: Recording standards and publica-
tion criteria. Psychophysiology, 37, 127–152. doi:10.1111/1469-
8986.3720127
Posner, M. I. (Ed.). (2004). Cognitive neuroscience of attention. New
York, NY: Guilford Press.
Posner, M. I., & Petersen, S. E. (1990). The attention system of the human
brain. Annual Review of Neuroscience, 13, 25–42. doi:10.1146/
annurev.ne.13.030190.000325
Ridderinkhof, K. R., & van der Stelt, O. (2000). Attention and selection in
the growing child: Views derived from developmental psychophysiol-
ogy. Biological Psychology, 54, 55–106. doi:10.1016/S0301-
0511(00)00053-3
Rueda, M. R., Fan, J., McCandliss, B. D., Halperin, J. D., Gruber, D. B.,
Pappert Lercari, L., & Posner, M. I. (2004). Development of attention
networks in childhood. Neuropsychologia, 42, 1029–1040. doi:10.1016/
j.neuropsychologia.2003.12.012
Sanders, L. D., Stevens, C., Coch, D., & Neville, H. J. (2006). Selective
auditory attention in 3- to 5-year-old children: An event-related potential
study. Neuropsychologia, 44, 2126–2138. doi:10.1016/j.neuropsycholo-
gia.2005.10.007
Satterfield, J. H., Schell, A. M., Nicholas, T. W., Satterfield, B. T., &
Freese, T. E. (1990). Ontogeny of selective attention effects on event-
related potentials in attention-deficit hyperactivity disorder and normal
boys. Biological Psychiatry, 28, 879–903. doi:10.1016/0006-
3223(90)90569-N
Serences, J. T., Yantis, S., Culberson, A., & Awh, E. (2004). Preparatory
activity in visual cortex indexes distractor suppression during covert
spatial orienting. Journal of Neurophysiology, 92, 3538–3545. doi:
10.1152/jn.00435.2004
Slotnick, S. D., Schwarzenbach, J., & Yantis, S. (2003). Attentional inhi-
bition of visual processing human striate and extrastriate cortex. Neu-
roImage, 19, 1602–1611. doi:10.1016/S1053-8119(03)00187-3
Stevens, C., Lauinger, B., & Neville, H. (2009). Differences in the neural
mechanisms of selective attention in children from different socioeco-
nomic backgrounds: An event-related brain potential study. Develop-
mental Science, 12, 634646. doi:10.1111/j.1467-7687.2009.00807.x
Taylor, M. J., & Khan, S. C. (2000). Top-down modulation of early
selective attention processes in children. International Journal of Psy-
chophysiology, 37, 135–147. doi:10.1016/S0167-8760(00)00084-2
Taylor, M. J., Khan, S. C., & Malone, M. A. (1999). Parallel and serial
attentional processes: A developmental ERP study. Developmental Neu-
ropsychology, 15, 351–358. doi:10.1080/87565649909540754
Tipper, S. P., Rafal, R., Reuter-Lorenz, P. A., Starrveldt, Y., Ro, T., Egly,
R., . . . Weaver, B. (1997). Object-based facilitation and inhibition from
visual orienting in the human split-brain. Journal of Experimental Psy-
chology: Human Perception and Performance, 23, 1522–1532. doi:
10.1037/0096-1523.23.5.1522
Treisman, A., & Gelade, G. (1980). A feature integration theory of atten-
tion. Cognitive Psychology, 12, 97–136. doi:10.1016/0010-
0285(80)90005-5
van der Stelt, O., Kok, A., Smulders, F. T. Y., Snel, J., & Gunning, W. B.
(1998). Cerebral event-related potentials associated with selective atten-
tion to color: Developmental changes from childhood to adulthood.
Psychophysiology, 35, 227–239. doi:10.1111/1469-8986.3530227
Vogel, E. K., Woodman, G. F., & Luck, S. J. (2005). Pushing around the
locus of selection: Evidence for the flexible-selection hypothesis. Jour-
nal of Cognitive Neuroscience, 17, 1907–1922. doi:10.1162/
089892905775008599
Received August 10, 2010
Revision received April 5, 2011
Accepted April 13, 2011
1439
PERCEPTUAL LOAD AND SELECTIVE ATTENTION ACROSS DEVELOPMENT
... Lavie, 2005; for reviews) and children (Huang-Pollock, Carr & Nigg, 2002;Remington, Cartwright-Finch & Lavie, 2014). Couperus (2011) reported that the reduction of distractor processing under high load conditions did not appear to vary as a function of age in five groups of children between 7 and 18 years old and a group of young adults (mean age 24 years). ...
... This result was found consistently across the ages in our cohort (13-17), with no main effect of age or interactions with age for adolescent RTs. This result was unexpected, and contrasts with both our predictions and with the only other investigation into impacts of perceptual load in adolescents (Couperus, 2011), who found that a that the reduction of distractor processing under high load conditions did not vary as a function of age from childhood, through adolescence and into adulthood. I address one potential account for this finding in the 'Study 3-4 comparative analysis' section below, and another in Study 5. ...
... Forster and Lavie, 2008b;. Although the numerical trends for adolescents were also in the direction of load modulation when the distractor effects were calculated as proportional effects to address the load scaling effect of RTs, these were not significant, in contrast to the only previous investigation of perceptual load effects on adolescent distraction (Couperus, 2011), and adolescents also displayed a significantly smaller low load distractor effect in Study 4 than did adults in Study 3 (see Comparison analysis, Studies 3-4). This developmental difference could not be explained by an own age bias in adult participants (Study 5). ...
Conference Paper
This thesis examines whether laboratory measures of attention focus in the face of taskirrelevant distraction can predict or reduce real-world experiences of distraction henceforth ‘attention lapses’ (covering both external, and internal sources such as mindwandering), with a specific consideration of educational environments, the adolescence period, and relationships to mood. To establish a novel measure of attention focus in a variety of realworld environments, I conducted real-time sampling of distractibility, mindwandering and mood across a wide range of everyday environments and activities, e.g. reading in the park (Chapter 2). The results established a replicable single construct underlying everyday attention lapses, and also highlighted a negative correlation between attention lapses and mood, with greater levels of mindwandering and distraction from some external sources associated with reduced levels of mood. To assess whether daily-life attention lapses can be predicted from a laboratory attention-task measure, Chapters 3-4 employed a modified attention distractibility task and examined its relationship to distractibility reports in the realworld, specifically educational settings (at secondary school and university) in both adults and adolescents (aged 13-18). The results established that attention lapses in these settings can be predicted from distractor interference effects on task performance (across adults and adolescents) and that while distractibility levels did not differ between adolescents and adults, response variability was significantly higher in adolescence. In addition, perceptual load reduced distractibility in adults but not in adolescents. Chapter 5 subsequently examined whether an interpolated testing intervention was effective in reducing both mindwandering and external distraction during a university lesson. The results demonstrated that interpolated testing reduced mindwandering and increased recall but did not affect distractibility. This thesis thus extends our understanding of the relationship between mindwandering, external 3 distraction and mood in everyday settings, and of methods which might be used both to predict and mitigate such experiences.
... These results are consistent with the lack of effort required to process melodic components, as evidenced in a mismatch negativity study and its magnetic counterpart [62,84,85], as well as in an event-related potentials study [57]. Moreover, melody perception against melody-like distractors (CIT3) did not increase cognitive loads, indicating that intact selective attention (inhibitory attention control over irrelevant stimuli) is characteristic of healthy young adults [86][87][88]. ...
... In a similar vein, pitch contours with similar timbres are considered similar. The timbre of the string and flute are similar in terms of temporal and spectral dimensions, whereas the piano is dissimilar to both the flute and string [85][86][87]. We, thus, performed an additional analysis to examine stimulus-and task-specific effects on behavioral performance. The items of CIT3 and CIT4 were recorded in relation to direction congruence and timbre similarity between target and target-like contours (Table 4). ...
Article
Full-text available
A key for earcon design in public environments is to incorporate an individual’s perceived level of cognitive load for better communication. This study aimed to examine the cognitive load changes required to perform a melodic contour identification task (CIT). While healthy college students (N = 16) were presented with five CITs, behavioral (reaction time and accuracy) and cerebral hemodynamic responses were measured using functional near-infrared spectroscopy. Our behavioral findings showed a gradual increase in cognitive load from CIT1 to CIT3 followed by an abrupt increase between CIT4 (i.e., listening to two concurrent melodic contours in an alternating manner and identifying the direction of the target contour, p < 0.001) and CIT5 (i.e., listening to two concurrent melodic contours in a divided manner and identifying the directions of both contours, p < 0.001). Cerebral hemodynamic responses showed a congruent trend with behavioral findings. Specific to the frontopolar area (Brodmann’s area 10), oxygenated hemoglobin increased significantly between CIT4 and CIT5 (p < 0.05) while the level of deoxygenated hemoglobin decreased. Altogether, the findings indicate that the cognitive threshold for young adults (CIT5) and appropriate tuning of the relationship between timbre and pitch contour can lower the perceived cognitive load and, thus, can be an effective design strategy for earcon in a public environment.
... Importantly, flanker-related neural activity within the DLPFC was related to the RT flanker effect, such that greater neural interference was associated with greater behavioral interference. The DLPFC is frequently implicated in high-order cognition and performance, especially during attention tasks (Amso and Scerif, 2015;Couperus, 2011;Pozuelos et al., 2014). ...
Article
Full-text available
Selective attention processes are critical to everyday functioning and are known to develop through at least young adulthood. Although numerous investigations have studied the maturation of attention systems in the brain, these studies have largely focused on the spatial configuration of these systems; there is a paucity of research on the neural oscillatory dynamics serving selective attention, particularly among youth. Herein, we examined the developmental trajectory of neural oscillatory activity serving selective attention in 53 typically developing youth age 9-to-16 years-old. Participants completed the classic arrow-based flanker task during magnetoencephalography, and the resulting data were imaged in the time-frequency domain. Flanker interference significantly modulated theta and alpha/beta oscillations within prefrontal, mid-cingulate, cuneus, and occipital regions. Interference-related neural activity also increased with age in the temporoparietal junction and the rostral anterior cingulate. Sex-specific effects indicated that females had greater theta interference activity in the anterior insula, whereas males showed differential effects in theta and alpha/beta oscillations across frontoparietal regions. Finally, males showed age-related changes in alpha/beta interference in the cuneus and middle frontal gyrus, which predicted improved behavioral performance. Taken together, these data suggest sexually-divergent developmental trajectories underlying selective attention in youth.
... Huang-Pollock et al. (2002) explain their findings as supporting the hypothesis that high load taps into earlier developing, automatic attentional systems, whilst, in contrast, because the low load condition is thought to require attentional control, this may develop later. In contrast with all previous developmental studies investigating load (Couperus, 2011;Huang-Pollock et al., 2002, 2005, we used an adapted load task, which used symbols instead of letters as target stimuli, which may be more appropriate for this age range (McDermott et al., 2007). ...
Article
Computational modelling can be used to precisely characterise the cognitive processes involved in attentional biases towards threat, yet so far has only been applied in the context of adult anxiety. Furthermore, studies investigating attentional biases in childhood anxiety have largely used tasks that conflate automatic and controlled attentional processes. By using a perceptual load paradigm, we separately investigate contributions from automatic and controlled processes to attentional biases towards negative stimuli and their association with pediatric anxiety. We also use computational modelling to investigate these mechanisms in children for the first time. In a sample of 60 children (aged 5‐11 years) we used a perceptual load task specifically adapted for children, in order to investigate attentional biases towards fearful (compared with happy and neutral) faces. Outcome measures were reaction time and percentage accuracy. We applied a drift diffusion model to investigate the precise cognitive mechanisms involved. The load effect was associated with significant differences in response time, accuracy and the diffusion modelling parameters drift rate and extra‐decisional time. Greater anxiety was associated with greater accuracy and the diffusion modelling parameter ‘drift rate’ on the fearful face trials. This was specific to the high load condition. These findings suggest that attentional biases towards fearful faces in childhood anxiety are driven by increased perceptual sensitivity towards fear in automatic attentional systems. Our findings from computational modelling suggest that current attention bias modification treatments should target perceptual encoding directly rather than processes occurring afterwards.
... Huang-Pollock et al. (2002) explain their findings as supporting the hypothesis that high load taps into earlier developing, automatic attentional systems, whilst, in contrast, because the low load condition is thought to require attentional control, this may develop later. In contrast with all previous developmental studies investigating load (Couperus, 2011;Huang-Pollock et al., 2002, 2005, we used an adapted load task, which used symbols instead of letters as target stimuli, which may be more appropriate for this age range (McDermott et al., 2007). ...
Article
Full-text available
Computational modelling can be used to precisely characterise the cognitive processes involved in attentional biases towards threat, yet so far has only been applied in the context of adult anxiety. Furthermore, studies investigating attentional biases in childhood anxiety have largely used tasks that conflate automatic and controlled attentional processes. By using a perceptual load paradigm, we separately investigate contributions from automatic and controlled processes to attentional biases towards negative stimuli and their association with pediatric anxiety. We also use computational modelling to investigate these mechanisms in children for the first time. In a sample of 60 children (aged 5-11 years) we used a perceptual load task specifically adapted for children, in order to investigate attentional biases towards fearful (compared with happy and neutral) faces. Outcome measures were reaction time and percentage accuracy. We applied a drift diffusion model to investigate the precise cognitive mechanisms involved. The load effect was associated with significant differences in response time, accuracy and the diffusion modelling parameters drift rate and extra-decisional time. Greater anxiety was associated with greater accuracy and the diffusion modelling parameter ‘drift rate’ on the fearful face trials. This was specific to the high load condition. These findings suggest that attentional biases towards fearful faces in childhood anxiety are driven by increased perceptual sensitivity towards fear in automatic attentional systems. Our findings from computational modelling suggest that current attention bias modification treatments should target perceptual encoding directly rather than processes occurring afterwards.
... But unlike with reaction time, the magnitude of this decrease in the difference in error rate between compatible and incompatible trials did not reliably decrease with increasing age (i.e., the interaction between set size and age that was observed in response time was much less pronounced with accuracy). In all, these findings show that distractor interference in children was similar to that of adults at high target loads while being much greater than adults at low target loads (for convergent evidence of parallels between adults and children, using event-related potentials responsive to distractors, see Couperus, 2011). ...
Article
Full-text available
Attention is limited in terms of both capacity (i.e., amount of information attended) and selectivity (i.e., the degree to which non-attended information is nonetheless processed). One of the seminal theories in the field, load theory, predicts that these two aspects of attention interact in systematic ways. Specifically, load theory predicts that when the amount of information to attend is less than the available capacity, spare attention will naturally leak out to unattended items. While load theory has found a great deal of empirical support, the robustness of the findings has recently been called into question, in particular with respect to the extent to which the predictions are borne out across different tasks and populations. Here we report tests of perceptual load effects in two different tasks (change detection and enumeration) and in two populations (adults and 7- to 8-year-old children). Adults’ accuracies did not demonstrate the predicted interaction between the capacity and selection dimensions, whereas children’s performance, in addition to being overall worse than adults, did show the interaction. The overall lower accuracy of children was seen to be the result of a larger performance decrement in response to capacity demands, distracting information, and their interaction. Interestingly, while these results were seen at the level of the two tasks, there was no within-participants correlation across tasks. Overall, these results suggest that maturation-related changes attenuate the magnitude of distractor effects in attention, which in turn limits the evidence for interactions between capacity and selection in high-functioning populations.
... Participants are instructed to identify whenever the target stimuli is presented (e.g., Lavie, 2010). The results usually indicate that when targets and distractors are perceptually different, processing 3 of the latter increases as early attention selection is more influenced by their perceptual features (e.g., Couperus, 2011). ...
Article
Full-text available
Adolescence is a developmental period characterized by a complex maturation process of various cognitive abilities. Cognitive control, which includes response inhibition and working memory, is one of them. A typical study on response inhibition to visual stimuli presents distractors and targets on the same display (e.g., the computer screen). However, in most daily activities, the potential for distraction exists in the individuals’ surrounding environment. This study proposes an alternative experimental paradigm to investigate whether a high- vs. a low-load visual surrounding environment influences adolescents’ visuospatial performance. Sixty-four adolescents (aged 13–17 years) participated in two experimental sessions (one in a high-load and the other in a low-load visual surrounding environment) in which they responded to four visuospatial cognitive tasks (attention and memory). Overall, the results revealed lower performance when the tasks were performed in the high-load environment (e.g., fewer hits and correct responses, and more false alarms and errors). These results suggest that more attention should be devoted to the potential effect of the external environment in adolescents’ everyday activities. We discuss various areas to which these data might be of relevance and make suggestions for future directions of the proposed procedure.
... Importantly, even the 7-to 8-year-old participants could focus attention sufficiently to restrict search to the smaller subset when conditions were appropriate. Nevertheless, our study, together with several previous studies (Couperus, 2011;Couperus, Hunt, Nelson, & Thomas, 2011;Yang & Merrill, 2014 indicate that young children still exhibit numerous inefficiencies when they deploy attention in support of visual search. Further, the transition to exhibiting adult-like efficiency happens rapidly, with 10to 11-year-old children performing remarkably similarly to college students with much less experimenter-provided support. ...
Article
Four experiments are reported in which 60 younger children (7–8 years old), 60 older children (10–11 years old), and 60 young adults (18–25 years old) performed a conjunctive visual search task (15 per group in each experiment). The number of distractors of each feature type was unbalanced across displays to evaluate participants’ ability to restrict search to the smaller subset of features. The use of top-down attention processes to restrict search was encouraged by providing external aids for identifying and maintaining attention on the smaller set. In Experiment 1, no external assistance was provided. In Experiment 2, precues and instructions were provided to focus attention on that subset. In Experiment 3, trials in which the smaller subset was represented by the same feature were presented in alternating blocks to eliminate the need to switch attention between features from trial to trial. In Experiment 4, consecutive blocks of the same subset features were presented in the first or second half of the experiment, providing additional consistency. All groups benefited from external support of top-down attention, although the pattern of improvement varied across experiments. The younger children benefited most from precues and instruction, using the subset search strategy when instructed. Furthermore, younger children benefited from blocking trials only when blocks of the same features did not alternate. Older participants benefited from the blocking of trials in both Experiments 3 and 4, but not from precues and instructions. Hence, our results revealed both malleability and limits of children’s top-down control of attention.
... One area where the issue of perceptual load may have a significant impact is in the case of child witnesses. Research has shown that young children demonstrate reduced memory accuracy and increased susceptibility to leading questions relative to adults (Goodman and Reed, 1986;Bruck and Ceci, 1999) and have a much smaller perceptual capacity that increases with age (Huang-Pollock et al., 2002;Couperus, 2011). The effect of perceptual load on attention may therefore have a disproportionate effect on children, and should be taken into consideration in future child memory studies. ...
Article
Full-text available
Load Theory (Lavie, 1995, 2005) states that the level of perceptual load in a task (i.e., the amount of information involved in processing task-relevant stimuli) determines the efficiency of selective attention. There is evidence that perceptual load affects distractor processing, with increased inattentional blindness under high load. Given that high load can result in individuals failing to report seeing obvious objects, it is conceivable that load may also impair memory for the scene. The current study is the first to assess the effect of perceptual load on eyewitness memory. Across three experiments (two video-based and one in a driving simulator), the effect of perceptual load on eyewitness memory was assessed. The results showed that eyewitnesses were less accurate under high load, in particular for peripheral details. For example, memory for the central character in the video was not affected by load but memory for a witness who passed by the window at the edge of the scene was significantly worse under high load. High load memories were also more open to suggestion, showing increased susceptibility to leading questions. High visual perceptual load also affected recall for auditory information, illustrating a possible cross-modal perceptual load effect on memory accuracy. These results have implications for eyewitness memory researchers and forensic professionals.
Article
Full-text available
A variety of studies tried to examine the fundamental question of whether specific processing is “automatic,” that is, occurs without attention, by manipulating attention toward stimuli via the set-size manipulation of perceptual load. The present paper invites re-extermination of this common methodology of altering the perceptual load of a relevant task to manipulate attention toward peripheral stimuli. Four main arguments that propose alternative interpretations to the notion of automaticity in this line of studies are discussed, suggesting that automaticity cannot be verified utilizing manipulation of load, and outlining a plan for moving forward.
Article
Full-text available
To study the mechanisms underlying covert orienting of attention in visual space, subjects were given advance cues indicating the probable locations of targets that they had to discriminate and localize. Direct peripheral cues (brightening of one of four boxes in peripheral vision) and symbolic central cues (an arrow at the fixation point indicating a probable peripheral box) were compared. Peripheral and central cues are believed to activate different reflexive and voluntary modes of orienting (Jonides, 1981; Posner, 1980). Experiment 1 showed that the time courses of facilitation and inhibition from peripheral and central cues were characteristic and different. Experiment 2 showed that voluntary orienting in response to symbolic central cues is interrupted by reflexive orienting to random peripheral flashes. Experiment 3 showed that irrelevant peripheral flashes also compete with relevant peripheral cues. The amount of interference varied systematically with the interval between the onset of the relevant cue and of the distracting flash (cue-flash onset asynchrony) and with the cuing condition. Taken together, these effects support a model for spatial attention with distinct but interacting reflexive and voluntary orienting mechanisms.
Article
Full-text available
examines briefly the history of the concept of attention within psychology and neuroscience / various methods used . . . to link cognitive approaches to attention to underlying brain systems are reviewed / 3 anatomical networks that subserve the processes of orienting, higher-level attention, and alertness are discussed / the overview ends with a summary of possible future development and application of this knowledge to attentional pathologies and other attentional states (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
Larger benefits of spatial attention are observed when distractor interference is prevalent, supporting the view that spatial selection facilitates visual processing by suppressing distractor interference. The present work shows that cuing effects with identical visual displays can grow substantially as the probability of distractor interference increases. The probability of interference had no impact on spatial cuing effects in the absence of distractors, suggesting that the enlarged cuing effects were not caused by changes in signal enhancement or in the spatial distribution of attention. These findings suggest that attentional control settings determine more than where spatial attention is directed; top-down settings also influence how attention affects visual processing, with increased levels of distractor exclusion when distractor interference is likely.
Article
Recordings of event-related potentials (ERPs) were combined with structural and functional magnetic resonance imaging (fMRI) to study the spatio-temporal patterns of cortical activity that underlie visual–spatial attention. Small checkerboard stimuli were flashed in random order to the four quadrants of the visual field at a rapid rate while subjects attended to stimuli in one quadrant at a time. Attended stimuli elicited enhanced ERP components in the latency range 80–200 ms that were co-localized with fMRI activations in multiple extrastriate cortical regions. The earliest ERP component (C1 at 50–90 ms) was unaffected by attention and was localized by dipole modeling to calcarine cortex. A longer latency deflection in the 150–225 ms range that was accounted for by this same calcarine source, however, did show consistent modulation with attention. This late attention effect, like the C1, inverted in polarity for upper versus lower field stimuli, consistent with a neural generator in primary visual cortex (area V1). These results provide support to current hypotheses that spatial attention in humans is associated with delayed feedback to area V1 from higher extrastriate areas that may have the function of improving the salience of stimuli at attended locations.
Article
The ability to ignore irrelevant peripheral distractors was assessed as a function of the efficiency in visual search for a target at the center of a display. Efficient target search, among dissimilar nomargen, led to greater distraction than inefficient search, among similar nontargets. This seemingly paradoxical result is predicted by the recent proposal (Lavie, 1995a) that irrelevant processing can be prevented only by increasing the load for relevant processing. Varying the set size of similar items in the central search task demonstrated that interference from irrelevant distractors was eliminated only with more than four relevant items. These results demonstrate how capacity limits determine the efficiency of selective attention, and raise questions about some standard assumptions of most visual search models.
Article
We studied the development of attentional processes in parallel and serial visual search tasks. Event‐related potentials (ERPs) were recorded for children (n = 40, age = 7–12 years) from 25 electrodes. Pop‐out paradigms were used in the 2 parallel processing tasks; the standard stimuli were small blue rectangles. In the color parallel task the pop‐out stimuli were small red rectangles. In the size task the targets were large blue squares. In the serial task the targets were a conjunction of features from the parallel tasks (i.e., large red squares). RTs varied with age and task. There were decreases in P3 latency with age, task, and an Age × Task interaction due to slower age‐related changes in the size compared to the color task. The data suggest that developmental changes in visual selective attention are tied more closely to the features of the target stimuli than to the parallel‐serial distinction, results consistent with the guided search model of attention.