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Category Learning is Shaped by the Multifaceted Development of Selective Attention


Abstract and Figures

Selective attention allows adults to preferentially exploit input relevant to their goals. One critical role of selective attention is in adult category learning: adults can simplify the entities they encounter into groups of entities that they can treat as equivalent by focusing on category-relevant attributes, while filtering out category-irrelevant attributes. However, much category learning takes place during development, when selective attention substantially matures. We designed two experiments to disentangle the contributions of the focusing and filtering aspects of selective attention to category learning over development. Experiment 1 provided evidence that learning simple categories was accompanied by selective attention in both four year-old and five year-old children and adults. Experiment 2 further provided evidence that only focusing contributed to selective attention in four year-olds, whereas both focusing and filtering contributed to selective attention in five year-olds and adults. Thus, category learning recruits different aspects of selective attention with development.
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Category Learning is Shaped by the Multifaceted Development of Selective
Layla Unger (
Department of Psychology, Ohio State University
Vladimir M Sloutsky (
Department of Psychology, Ohio State University
Selective attention allows adults to preferentially exploit input
relevant to their goals. One critical role of selective attention
is in adult category learning: adults can simplify the entities
they encounter into groups of entities that they can treat as
equivalent by focusing on category-relevant attributes, while
filtering out category-irrelevant attributes. However, much cat-
egory learning takes place during development, when selective
attention substantially matures. We designed two experiments
to disentangle the contributions of the focusing and filtering
aspects of selective attention to category learning over devel-
opment. Experiment 1 provided evidence that learning simple
categories was accompanied by selective attention in both four
year-old and five year-old children and adults. Experiment 2
further provided evidence that only focusing contributed to se-
lective attention in four year-olds, whereas both focusing and
filtering contributed to selective attention in five year-olds and
adults. Thus, category learning recruits different aspects of se-
lective attention with development.
Keywords: category learning; cognitive development; selec-
tive attention; focusing; filtering
One of the fundamental roles of cognition is to simplify and
distill the barrage of input available to our senses. For ex-
ample, if we encounter something that is furry, four-legged,
brown with white patches, about two feet tall, and has some
dead leaves caught in its fur, we can more readily determine
how to interact with it if we simplify this input by categoriz-
ing it as a dog. Adults simplify entities in the world into cate-
gories by learning to selectively attend to category-relevant
attributes that members of the same category share (e.g.,
shape), rather than irrelevant attributes that vary between
them (e.g., color and pattern) (Deng & Sloutsky, 2016; Gold-
stone & Steyvers, 2001; Rehder & Hoffman, 2005). How-
ever, much of the categories that populate adult knowledge
were originally learned during development, when selective
attention undergoes substantial maturation (Plude, Enns, &
Brodeur, 1994). How is category learning over the course of
development shaped by the development of selective atten-
Understanding how selective attention shapes the develop-
ment of category learning involves tackling the challenging
question of what selective attention is. Does learning to se-
lectively attend to relevant input consist of focusing (i.e., en-
hancing the processing of) relevant input, filtering out (i.e.,
or inhibiting/suppressing the processing of) input that is ir-
relevant, or does focusing on some input collaterally filter
out other input? Although researchers have grappled with
these questions for decades, neuroimaging research has pro-
vided mounting evidence that focusing and filtering may be
separate processes (Andersen & M¨
uller, 2010; Bridwell &
Srinivasan, 2012; Gazzaley, Cooney, McEvoy, Knight, &
D’esposito, 2005; Gulbinaite, Johnson, de Jong, Morey, &
van Rijn, 2014; Polk, Drake, Jonides, Smith, & Smith, 2008).
Moreover, as discussed below, these processes may follow
different developmental trajectories. However, little research
to date has investigated the development of focusing and fil-
tering when people must learn what to attend to, as they must
during category learning. In what follows, we review this re-
search, and present a series of studies designed to illuminate
how the development of focusing and filtering shapes the de-
velopment of category learning.
Selective Attention and its Development
By adulthood, a growing body of neuroimaging evidence sug-
gests that selective attention is supported by dissociable fo-
cusing and filtering processes. For example, an fMRI study
conducted by Gazzaley et al. (2005) found that relative to
neutral, passive viewing, the activity evoked by a stimulus
(such as a face) was both enhanced when participants were
instructed to attend to the stimulus (evidence of focusing),
and suppressed when participants were instructed to ignore
it (evidence of filtering; see also Andersen & M¨
uller, 2010;
Bridwell & Srinivasan, 2012; Polk et al., 2008). Moreover,
focusing may occur without filtering, and vice versa. For ex-
ample, using an innovative EEG paradigm, Gulbinaite et al.
(2014) found that responding to a target in the presence of
distractors was associated with focusing on the target in in-
dividuals with low working memory, and with filtering the
distractors in individuals with high working memory.
Extensive evidence attests that selective attention in adults
emerges following protracted development over infancy,
childhood and adolescence (Lane & Pearson, 1982; Plude
et al., 1994). However, only some of this research has at-
tempted to disentangle focusing and filtering. First, research
that has explicitly investigated the development of focusing
has yielded evidence that it develops early, starting in in-
fancy. For example, numerous findings suggest that stimulus
processing is enhanced in infants both: (1) when the stimu-
lus appears in an attentionally cued location (Johnson, 1994;
Johnson, Posner, & Rothbart, 1994), and (2) when the infant
is in an attentive state (Richards, 2003).
In contrast with research on focusing, research that has ex-
plicitly investigated the development of filtering has yielded
conflicting results. Conflicting findings are particularly no-
table in research spanning early childhood, from approxi-
mately 3 to 7 years of age. For example, equivocal results
have been found from the negative priming paradigm, in
which filtering is inferred when participants respond worse
to targets that were recently distractors (and that could thus
have been filtered) versus those that were not. This research
has yielded evidence for both the early development of filter-
ing (from at least age 3; Chevalier & Blaye, 2008; Pritchard
& Neumann, 2004), and substantial increases in filtering with
age (from age 7 to adulthood; Pritchard & Neumann, 2011;
Tipper, Bourque, Anderson, & Brehaut, 1989). Studies of the
development of filtering have also measured filtering from
retrieval induced forgetting (RIF). In this paradigm, partici-
pants first study category-item pairs such as FRUIT-banana
and FRUIT-apple, then retrieve memory for only a subset
of pairs for a category, such that filtering is measured from
worse memory for the non-retrieved pairs. Evidence from
this paradigm is also equivocal: Although some RIF studies
suggest that filtering changes little with development from at
least age 7 to adulthood (Zellner & B¨
auml, 2005), some stud-
ies have provided evidence that filtering increases across an
earlier span of development, from age 4 to 7 (Aslan & B¨
Taken together, research to date suggests that focusing de-
velops early, but provides conflicting evidence for the devel-
opment of filtering. Critically, much of this research does
not tackle the development of selective attention when people
must learn what to attend to. Illuminating this development
is important because people often must learn what input is
most relevant to their goals through experience, as is the case
when people learn categories. In the next section, we review
research that has investigated the development of selective at-
tention in category learning, and highlight how it does not
disentangle the contributions of focusing and filtering.
Role of Selective Attention in Category Learning
A handful of findings suggest that selective attention increas-
ingly contributes to category learning over development, par-
ticularly over the course of early childhood from approxi-
mately age 4 to 7 (Best, Yim, & Sloutsky, 2013; Deng &
Sloutsky, 2015, 2016). Most of these studies have used a
paradigm in which children and adults learn categories with
dimensions that vary in relevance to category membership,
such that one dimension is deterministically associated (i.e.,
perfectly correlated) with category membership, multiple di-
mensions are probabilistically associated (i.e., imperfectly
correlated) with category membership, and the remaining di-
mensions are irrelevant. The results of these studies suggest
that attention is distributed across deterministic and proba-
bilistic dimensions in young children (e.g., from age 4), and
becomes increasingly selectively oriented to just the deter-
ministic dimension with age (e.g., from age 6-7 to adulthood).
These findings suggest that, with age, people deploy increas-
ingly narrow selective attention to only dimensions that they
learn are most diagnostic of category membership. How-
ever, this research did not investigate attention to irrelevant
dimensions, and thus leaves open the possibility that even
young children may attend more to dimensions that are at
least somewhat relevant (i.e., both deterministic and proba-
bilistic) than to dimensions that are entirely irrelevant.
Critically, the research that has investigated the develop-
ment of selective attention during category learning has not
disentangled whether this developmental trajectory is driven
by changing contributions of focusing, filtering, or both. Dis-
entangling these contributions is challenging: When people
learn a specific category structure, any difference in atten-
tion to relevant versus irrelevant dimensions can be due to
focusing attention to relevant dimensions, filtering irrelevant
dimensions, or both. One way to overcome this challenge is
to introduce a “switch” in relevance at some point during cat-
egory learning, in which a dimension that was not previously
relevant becomes relevant. If category learning involves fo-
cusing, learners should always struggle to shift their focus
to any newly relevant dimension after the switch. Critically,
if category learning also involves filtering, learners should
find it more difficult to focus on a newly relevant dimension
that had been irrelevant (and thus filtered) prior to the switch,
versus a newly relevant dimension that was not present (and
thus could not be filtered) prior to the switch (Goldstone &
Steyvers, 2001; Hoffman & Rehder, 2010). Research that has
used this approach with adults has found evidence of both
focusing and filtering (Goldstone & Steyvers, 2001). As de-
scribed below, the present research used this approach to dis-
entangle the contributions of focusing and filtering in the de-
velopment of category learning.
Present Research
The present research sought to disentangle the contributions
of focusing and filtering to selective attention in the devel-
opment of category learning. The research consisted of two
experiments designed to accomplish this goal. To target the
period of development in which the development of filtering
in particular is contentious, both experiments investigated fo-
cusing and filtering in two age groups within this period (i.e.,
ages 4 and 5), and contrasted them with adults.
Experiment 1 investigated whether even young children
deploy some degree of selective attention to relevant ver-
sus entirely irrelevant dimensions during category learning.
Adults and children aged 4 and 5 learned highly simplified
categories of creatures that possessed one relevant and one
irrelevant dimension. To target selective attention when rele-
vant dimensions must be learned, in contrast with some prior
studies of category learning in development (Deng & Slout-
sky, 2016, 2015), participants were not provided with any ini-
tial cues about the relevance of different dimensions, and thus
could only learn by making categorization decisions and re-
ceiving feedback. Selective attention to the relevant dimen-
sion was measured based on better subsequent recognition
memory for familiar versus novel values of the relevant ver-
sus irrelevant dimension (Deng & Sloutsky, 2016). To antici-
pate the results, category learning occurred in all age groups,
and was accompanied by greater attention to the relevant ver-
sus the irrelevant dimension.
Experiment 2 investigated whether the contributions of fo-
cusing versus filtering to selective attention change with age.
Here, participants initially learned categories like those used
in Experiment 1, but experienced a switch half-way through
category learning. In this switch, the initially relevant dimen-
sion became irrelevant, and the initially irrelevant dimension
became relevant. We anticipated that all participants would
find it challenging to learn the new category structure fol-
lowing the switch. To isolate the contributions of filtering
to this challenge, we assigned participants to one of two con-
ditions. In the “Visible” condition, the initially irrelevant di-
mension that became relevant post-switch was visible prior to
the switch, and could thus be filtered. In the “Hidden” condi-
tion, the initially irrelevant dimension was occluded prior to
the switch, thus preventing it from being filtered. If selective
attention involves both focusing and filtering, participants in
the Visible condition should find it harder to learn the new
category structure post-switch, because they have the added
challenge of recovering attention to a filtered dimension. If
selective attention only involves focusing, then participants
should find it similarly hard to shift focus to a newly relevant
dimension in both conditions. Using this approach, we in-
vestigated two alternative hypotheses: (1) Selective attention
involves both focusing and filtering even early in develop-
ment, and (2) Selective attention involves focusing early in
development, and increasingly involves filtering with age.
Experiment 1
Participants. Participants were recruited in three age
groups: Four year-olds (N = 33; M = 4 years, 5 months),
five year-olds (N = 35; M = 5 years, 4 months), and adults
(N = 53). The child age groups were selected because they
capture a period of development when the contributions of
filtering to selective attention are disputed (Aslan & B¨
2010; Chevalier & Blaye, 2008; Pritchard & Neumann, 2004;
Tipper & McLaren, 1990).
Materials. The category stimuli were created based on ex-
tensive piloting designed to identify category structures that
even 4-year-old children could learn. This piloting had two
key constraints. First, values on only a single dimension
could be relevant to category membership, and values on all
other dimensions had to be irrelevant. This constraint was
necessary for testing selective attention to relevant versus ir-
relevant dimensions. Second, to target selective attention to
dimensions that is evoked by their learned relevance, it was of
critical importance to use categories that children could learn
purely from categorizing exemplars and receiving feedback.
This constraint contrasts with some prior studies of category
learning in children (Deng & Sloutsky, 2015, 2016) that pre-
Figure 1: Panel A depicts creatures that illustrate values on
the two extremes of each dimension. Panel B depicts the
novel values for each dimension used in the recognition mem-
ory task.
ceded category learning with explicit cues to the dimensions
relevant to category membership, which were necessary for
young children to learn categories with several (e.g., seven)
dimensions. The results of this piloting indicated that suc-
cessful category learning only occurred in the majority of 4-
year-old children with a highly simplified category structure
with two dimensions: One relevant, and one irrelevant. More-
over, the values of the relevant dimension that were each as-
sociated with a different category needed to be highly visually
Stimuli consisted of novel creatures with two dimensions:
Flippers and tails. Each dimension was created by morph-
ing between two anchor images that were different in shape
(e.g., two different-shaped flippers). Values of these dimen-
sions used in the stimuli were only taken from near one of the
two extremes of the morph dimension (shown in Figure 1A).
8 category exemplars were created from combinations of 4
possible values on each dimension (2 values from near one
extreme of the dimension, and 2 values from near the other
extreme). In the experiment (see Procedure), these stimuli
were divided into two categories, such that the values of one
(“relevant”) dimension perfectly predicted category member-
ship, and the value of the other (“irrelevant”) dimension oc-
curred equally often in both categories. When a dimension
was relevant, values from near one extreme of the dimension
determined that the creature belonged to one category, and
values from near the other extreme determined that the crea-
ture belonged to the other category. For a recognition mem-
ory task that followed category learning (see Procedure), we
additionally created eight novel creatures: Four in which the
flipper was replaced with a novel value, and four in which the
tail was replaced with a novel value (Figure 1B).
Design and Procedure. The experiment consisted of two
phases: Category learning, and recognition memory. Partic-
ipants were randomly assigned to complete one of two ver-
sions of the experiment that counterbalanced the relevance
of the two dimensions to category membership. Thus, for
a given participant, the values of one “Relevant” dimension
perfectly predicted category membership, and the values of
the “Irrelevant” dimension occurred equally often in both cat-
The procedure was similar for adults and children, with the
exception that adults followed instructions presented on the
computer screen and used the keyboard to make responses,
whereas an experimenter read instructions aloud and recorded
children’s verbal responses. At the start of category learning,
participants were instructed that they would see two kinds of
creatures: Zibbies and Tomas. They were then shown two
example creatures (Figure 1A). As in the creatures that par-
ticipants subsequently learned to categorize, each example
creature had a flipper and tail value taken from one of the
extremes of the flipper and tail dimensions. While these crea-
tures were shown, participants were told that all creatures had
two body parts that come in different shapes: A flipper, and
a tail. Each body part was highlighted with a yellow outline
while the instructions or experimenter pointed out its two dif-
ferent values. Then, participants were told that the shape of
one of the body parts is important for figuring out whether a
creature is a Zibbie or a Toma. Participants then completed
four category learning blocks that each contained one trial
for each of the 8 category exemplars described above. On
each trial, participants were asked to identify the creature as a
Zibbie or a Toma. Following the categorization decision, par-
ticipants received corrective feedback in which the creature
remained on the screen, and were told “[Correct!/Oops!] It’s
a [Zibbie!/Toma!]”.
After the final category learning block, participants began
the recognition memory task. Participants were told that they
would see some more creatures, some of which would be the
same as ones they saw during the first part of the experiment,
and some that would be new. They were further told that in
new creatures, one of the body parts would be a new shape,
and that their job was to figure out which creatures were ones
they saw before, and which ones were new. Participants then
completed 32 recognition memory trials in which they saw a
creature, and indicated whether it was “old” or “new”. These
trials consisted of: (1) 16 “Old” trials, across which each of
the 8 category exemplars was presented twice, and (2) 16
“New” trials, across which each of the 8 novel stimuli was
presented twice. As described under Stimuli above, half of
the New trials presented a creature in which the relevant di-
mension was replaced with its novel value, and half presented
a creature in which the irrelevant dimension was novel.
All analyses were conducted using Bayesian models con-
structed using the rstan package (Stan Development Team,
2020) in the R environment for statistical computing (R De-
velopment Core Team, 2008).
Category Learning. Figure 2 depicts category learning in
each of the three age groups. Analyses assessed whether par-
ticipants in each age group learned the categories. Trial-by-
trial categorization accuracy was analyzed using a Bayesian
hierarchical model in which accuracies were predicted as the
outcome of a logistic regression, with an intercept and slope
for change in accuracy across trials for each participant. Inter-
cepts and slopes for participants were each drawn from one
of three higher-level distributions (all given the same weak
Figure 2: Categorization accuracy across blocks in each age
group. The dashed line depicts chance. Error bars depict
standard errors.
priors), based on the participant’s age group. We assessed
whether successful category learning occurred in each age
group using the predicted categorization accuracies from this
model: For each posterior sample for each age group, we cal-
culated the mean accuracy on the final two blocks of category
learning predicted by the model. To capture the range of most
probable values for final mean accuracy, we calculated High-
est Density Intervals (HDIs) for the posterior distributions of
predicted final mean accuracies. This interval is the range
of a distribution that contains some specified percentage of
probable values. The interpretation of such intervals is sim-
ply the probability that the “true” value falls within the range.
In all age groups, categorization accuracy was predicted to be
above chance (i.e., .5) in the final two blocks (Four year-olds:
Median = 0.62, 90% HDI = [0.60, 0.66]; Five year-olds: Me-
dian = 0.69, 90% HDI = [0.66, 0.71]; Adults: Median = 0.96,
90% HDI = [0.94, 0.96]). Comparing the mean accuracy on
the final two blocks between age groups for each posterior
sample indicated that accuracy increased slightly from four to
five year-olds (Median = 0.06, 90% HDI = [0.02, 0.10]), and
substantially from five year-olds to adults (Median = 0.26,
90% HDI = [0.23, 0.29]).
Selective Attention. We assessed whether category learn-
ing was accompanied by selective attention to the relevant di-
mension based on whether participants had better recognition
memory for values of the relevant versus the irrelevant dimen-
sion. We measured recognition memory for each dimension
using dprime scores, calculated from hits for correctly identi-
fying creatures with a new value on the dimension as “new”,
and false alarms for incorrectly identifying old creatures as
“new”. For each participant, we then calculated a “Relevant
Attention” score as their dprime for the relevant dimension
minus their dprime for the irrelevant dimension.
Figure 3 shows dprime and Relevant Attention scores by
age, which suggest that all age groups tended to attend more
to the relevant dimension. We analyzed these data to test
whether Relevant Attention scores tended to be above 0 in
each age group. For this analysis, we constructed a Bayesian
hierarchical model in which each participant’s Relevant At-
tention score was drawn from a normal distribution. For each
participant, the mean of this distribution was drawn from one
Figure 3: Top: dprime values for relevant and irrelevant di-
mensions in each age group. Error bars depict standard er-
rors. Bottom: Relevant Attention scores in each age group.
The dashed line depicts no difference in attention to relevant
versus irrelevant dimensions. Points depict participants.
of three higher-level normal distributions (all given the same
weak priors), based on the participant’s age. Thus, we as-
sessed whether Relevant Attention was greater than 0 based
on whether the mean of the distribution for each age group
tended to be greater than 0. All age groups had a high prob-
ability that Relevant Attention scores were greater than 0:
89% for four-year-olds, 90% for five-year-olds, and 90% for
adults. Thus, category learning was accompanied by simi-
larly greater attention to relevant versus irrelevant dimensions
from age four to adulthood.
Experiment 1 revealed that successful category learning was
accompanied by greater attention to a category-relevant di-
mension than to a category-irrelevant dimension. However,
this result does not disentangle whether selective attention
involves focusing on the relevant dimension, filtering the ir-
relevant dimension, or both. Experiment 2 was designed to
disentangle these possibilities.
Experiment 2
Participants. Participants in Experiment 2 were assigned
to one of two between-subjects conditions: Visible or Hid-
den. Participants were recruited from the same age groups as
Experiment 1: Four year-olds (Visible N = 32, M = 4 years,
5 months; Hidden N = 34, M = 4 years, 5 months), five year-
olds (Visible N = 32, M = 5 years, 4 months; Hidden N = 30,
M = 5 years, 4 months), and adults (Visible N = 58; Hidden N
= 53). An additional four four year-olds and six five year-olds
were recruited but not analyzed due to poor category learning
(see Results).
Materials. This experiment used the same category exem-
plars as Experiment 1, and a picture of bubbles.
Design and Procedure. Participants were randomly as-
signed to one of two between-subjects conditions: Visible or
Hidden. As in Experiment 1, participants were also randomly
assigned to complete one of two versions of each condition
that counterbalanced the relevance of the two dimensions to
category membership.
The procedure for Experiment 2 was similar to Experiment
1, except that: (1) Category learning instructions informed
participants that sometimes one of the body parts might be
covered by bubbles, and (2) The relevance of the two dimen-
sions switched between blocks 2 and 3, such that the dimen-
sion that was relevant prior to the switch became irrelevant
after the switch, and vice versa. In the Visible condition, both
dimensions were visible throughout category learning. In the
Hidden condition, prior to the switch, the initially irrelevant
dimension was always occluded by bubbles.
To investigate the effects of the Visible versus Hidden condi-
tions on post-switch categorization accuracy, it was important
to focus on participants who were likely to have learned the
initial category structure over the pre-switch blocks. Thus, we
excluded participants who achieved low (.25) accuracy in
the final pre-switch block. This criterion excluded four four
year-olds, six five year-olds, and no adults.It is important to
note that the sources of such low accuracy are ambiguous,
and could be due to a lack of learning coupled with chance
responding (i.e., .25 accuracy in the 8 trials could occur
by chance with a probability of .14), or successful learning in
children who are sometimes motivated to make intentionally
incorrect responses (Blanco & Sloutsky, 2021).
Figure 4 depicts category learning in three age groups. Cat-
egorization accuracy data were analyzed to compare post-
switch category learning in the Visible and Hidden conditions
for each age group. Accuracy data were fit using a model
similar to the model used in Experiment 1, with the exception
that accuracy was predicted separately for the pre-switch and
post-switch phases. Using the same approach as in analyses
for Experiment 1, we assessed category learning in the post-
switch blocks for the Visible and Hidden conditions in each
age group. 90% HDIs for post-switch accuracy were above
chance for all age groups and conditions, with the exception
of five year-olds in the Visible condition (Median = 0.54, 90%
HDI = [0.49, 0.56]).
We used post-switch accuracy to test whether post-switch
category learning was better in the Hidden than in the Vis-
ible condition in each age group. For each posterior sam-
ple, we calculated accuracy in the post-switch blocks in the
Hidden condition minus the Visible condition, such that pos-
itive values indicate better category learning post-switch in
the Hidden versus the Visible condition. Post-switch category
learning in four year-olds was similar in the two conditions:
There was only a 61% probability that accuracy was better
Figure 4: Categorization accuracy across blocks in each age group and condition, with the switch depicted as a gray bar. The
dashed line depicts chance. Error bars depict standard errors.
in the Hidden condition. In contrast, in both five year-olds
and adults, there was a >99% probability that post-switch
category learning was better in the Hidden condition.
General Discussion
Four year-olds, five year-olds, and adults learned simple cat-
egories with one relevant dimension that perfectly predicted
category membership, and one entirely irrelevant dimension.
Experiment 1 demonstrated that learning these categories was
accompanied by selective attention across age. To disentan-
gle the contributions of focusing and filtering, Experiment 2
introduced a switch in dimension relevance part-way through
category learning designed to cause all learners to struggle to
shift their focus to the newly relevant dimension. Critically,
we manipulated whether learners had the opportunity to fil-
ter the dimension that became relevant after the switch. If
filtering is present in an age group, then learners should have
more trouble switching to the newly relevant dimension when
learners had the opportunity to filter it. Four year-olds only
showed evidence of focusing and not filtering, whereas five
year-olds showed evidence of both. Thus, these experiments
provide evidence for developmental changes in the processes
that underpin selective attention in category learning.
Developmental Trajectory of Focusing and Filtering
In the present study, four year-olds showed evidence of focus-
ing, but only five year-olds and adults also showed evidence
of filtering. These findings are consistent with prior evidence
that filtering emerges from pre-school to school age (e.g.,
Aslan & B¨
auml, 2010), but inconsistent with evidence that fil-
tering is present even early in development (e.g., Pritchard &
Neumann, 2004), or only emerges even later in development
(e.g., Deng & Sloutsky, 2016). This variability highlights an
important possibility that the development of filtering (and
of selective attention generally) does not involve a qualitative
shift from absent to present. Instead, even immature selective
attention may involve filtering in favorable situations (e.g.,
when there is a small amount of distracting input that is con-
sistent over time). With development, filtering may become
a more robust and widespread aspect of selective attention.
This possibility raises testable predictions, such as that the
role of filtering in learning more complex category structures
will emerge later in development than observed in this study.
Learning Relevance
Humans navigating real-world environments are often faced
with the challenge of learning what is relevant to their goals.
In contrast, in much of the prior research that has investi-
gated the development of selective attention, relevance is ex-
plicitly specified for participants. For example, participants
may be instructed to respond stimuli in a specific location
(McDermott, Perez-Edgar, & Fox, 2007; Pritchard & Neu-
mann, 2004), or respond to the location of specific stimuli
(Tipper & McLaren, 1990). The present experiments move
beyond this prior research by investigating the development
of focusing and filtering processes when relevance must be
learned. Thus, the present findings suggest that learning what
is relevant is increasingly accompanied by filtering out input
that is learned to be irrelevant with age.
Andersen, S., & M¨
uller, M. (2010). Behavioral performance
follows the time course of neural facilitation and suppres-
sion during cued shifts of feature-selective attention. Pro-
ceedings of the National Academy of Sciences,107(31),
Aslan, A., & B¨
auml, K.-H. T. (2010). Retrieval-induced for-
getting in young children. Psychonomic Bulletin & Review,
17(5), 704–709.
Best, C. A., Yim, H., & Sloutsky, V. M. (2013). The cost
of selective attention in category learning: Developmental
differences between adults and infants. Journal of Experi-
mental Child Psychology,116(2), 105–119.
Blanco, N. J., & Sloutsky, V. M. (2021). Systematic explo-
ration and uncertainty dominate young children’s choices.
Developmental Science,24(2), e13026.
Bridwell, D. A., & Srinivasan, R. (2012). Distinct atten-
tion networks for feature enhancement and suppression in
vision. Psychological Science,23(10), 1151–1158.
Chevalier, N., & Blaye, A. (2008). Cognitive flexibility
in preschoolers: The role of representation activation and
maintenance. Developmental Science,11(3), 339–353.
Deng, W., & Sloutsky, V. M. (2015). The development of
categorization: Effects of classification and inference train-
ing on category representation. Developmental Psychology,
51(3), 392.
Deng, W., & Sloutsky, V. M. (2016). Selective attention,
diffused attention, and the development of categorization.
Cognitive Psychology,91, 24–62.
Gazzaley, A., Cooney, J. W., McEvoy, K., Knight, R. T.,
& D’esposito, M. (2005). Top-down enhancement and
suppression of the magnitude and speed of neural activity.
Journal of Cognitive Neuroscience,17(3), 507–517.
Goldstone, R. L., & Steyvers, M. (2001). The sensitization
and differentiation of dimensions during category learn-
ing. Journal of Experimental Psychology: General,130(1),
Gulbinaite, R., Johnson, A., de Jong, R., Morey, C. C., &
van Rijn, H. (2014). Dissociable mechanisms underlying
individual differences in visual working memory capacity.
Neuroimage,99, 197–206.
Hoffman, A. B., & Rehder, B. (2010). The costs of super-
vised classification: The effect of learning task on concep-
tual flexibility. Journal of Experimental Psychology: Gen-
eral,139(2), 319.
Johnson, M. H. (1994). Visual attention and the control of eye
movements in early infancy. Attention and Performance
Xv: Conscious and Nonconscious Information Processing,
15, 291–310.
Johnson, M. H., Posner, M. I., & Rothbart, M. K. (1994).
Facilitation of saccades toward a covertly attended location
in early infancy. Psychological Science,5(2), 90–93.
Lane, D. M., & Pearson, D. A. (1982). The development of
selective attention. Merrill-palmer Quarterly, 317–337.
McDermott, J. M., Perez-Edgar, K., & Fox, N. A. (2007).
Variations of the flanker paradigm: Assessing selective at-
tention in young children. Behavior Research Methods,
39(1), 62–70.
Plude, D. J., Enns, J. T., & Brodeur, D. (1994). The devel-
opment of selective attention: A life-span overview. Acta
Psychologica,86(2-3), 227–272.
Polk, T. A., Drake, R. M., Jonides, J. J., Smith, M. R., &
Smith, E. E. (2008). Attention enhances the neural pro-
cessing of relevant features and suppresses the processing
of irrelevant features in humans: a functional magnetic res-
onance imaging study of the stroop task. Journal of Neuro-
science,28(51), 13786–13792.
Pritchard, V. E., & Neumann, E. (2004). Negative priming
effects in children engaged in nonspatial tasks: Evidence
for early development of an intact inhibitory mechanism.
Developmental Psychology,40(2), 191.
Pritchard, V. E., & Neumann, E. (2011). Classic stroop neg-
ative priming effects for children and adults diverge with
less-conflicting and nonconflicting conditions. The Ameri-
can Journal of Psychology,124(4), 405–419.
R Development Core Team. (2008). R: A language and envi-
ronment for statistical computing. R Foundation for Statis-
tical Computing.
Rehder, B., & Hoffman, A. B. (2005). Eyetracking and selec-
tive attention in category learning. Cognitive Psychology,
51(1), 1–41.
Richards, J. E. (2003). Attention affects the recognition of
briefly presented visual stimuli in infants: An erp study.
Developmental Science,6(3), 312–328.
Stan Development Team. (2020). Rstan: the r interface to
stan. Retrieved from
Tipper, S. P., Bourque, T. A., Anderson, S. H., & Brehaut,
J. C. (1989). Mechanisms of attention: A developmental
study. Journal of Experimental Child Psychology,48(3),
Tipper, S. P., & McLaren, J. (1990). Evidence for efficient
visual selectivity in children. In Advances in psychology
(Vol. 69, pp. 197–210). Elsevier.
Zellner, M., & B¨
auml, K.-H. (2005). Intact retrieval inhi-
bition in children’s episodic recall. Memory & Cognition,
33(3), 396–404.
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Organisms need to constantly balance the competing demands of gathering information and using previously acquired information to obtain rewarding outcomes (i.e., the “exploration‐exploitation” dilemma). Exploration is critical to obtain information to discover how the world works, which should be particularly important for young children. While studies have shown that young children explore in response to surprising events, little is known about how they balance exploration and exploitation across multiple decisions or about how this process changes with development. In this study we compare decision‐making patterns of children and adults and evaluate the relative influences of reward‐seeking, random exploration, and systematic switching (which approximates uncertainty‐directed exploration). In a second experiment we directly test the effect of uncertainty on children’s choices. Influential models of decision‐making generally describe systematic exploration as a computationally refined capacity that relies on top‐down cognitive control. We demonstrate that (1) systematic patterns dominate young children’s behavior (facilitating exploration), despite protracted development of cognitive control, and (2) that uncertainty plays a major, but complicated, role in determining children’s choices. We conclude that while young children’s immature top‐down control should hinder adult‐like systematic exploration, other mechanisms may pick up the slack, facilitating broad information gathering in a systematic fashion to build a foundation of knowledge for use later in life.
Full-text available
Attention biases sensory processing toward neurons containing information about behaviorally relevant events. These attentional biases apparently reflect the combined influence of feature enhancement and suppression. We examined the separate influence of enhancement and suppression in visual processing by determining whether responses to an unattended flicker were modulated when the flicker features matched target features at the attended location, competed with those features, or were neutral. We found that suppression primarily modulated parietal networks with a preferred frequency in the lower alpha band (f(2) = 8 Hz), and enhancement primarily influenced parietal networks with a preferred frequency in the upper alpha band (f(2) = 12 Hz). These responses were coupled with perception, with large responses to the unattended flicker leading to subsequently detected targets when the target features matched the flicker features (i.e., during enhancement). Our results suggest that enhancement and suppression are two distinct processes that work together to shape visual perception.
How do people learn categories and what changes with development? The current study attempts to address these questions by focusing on the role of attention in the development of categorization. In Experiment 1, participants (adults, 7-year-olds, and 4-year-olds) were trained with novel categories consisting of deterministic and probabilistic features, and their categorization and memory for features were tested. In Experiment 2, participants’ attention was directed to the deterministic feature, and in Experiment 3 it was directed to the probabilistic features. Attentional cueing affected categorization and memory in adults and 7-year-olds: these participants relied on the cued features in their categorization and exhibited better memory of cued than of non-cued features. In contrast, in 4-year-olds attentional cueing affected only categorization, but not memory: these participants exhibited equally good memory for both cued and non-cued features. Furthermore, across the experiments, 4-year-olds remembered non-cued features better than adults. These results coupled with computational simulations provide novel evidence (1) pointing to differences in category representation and mechanisms of categorization across development, (2) elucidating the role of attention in the development of categorization, and (3) suggesting an important distinction between representation and decision factors in categorization early in development. These issues are discussed with respect to theories of categorization and its development.
It appears to be a ubiquitous finding that children's selectivity is less efficient than that of adults. Much of this evidence arises from filtering tasks where a target is selected by a physical property, such as location, and action is determined by identity (e.g. naming the object). We suggest that many interactions with the environment are in fact opposite to this, in that targets are selected by identity and action is controlled by spatial position. An experiment suggests that when this latter more "ecologically valid" situation is examined, developmental differences in selectivity are no longer observed.
Does category representation change in the course of development? And if so, how and why? The current study attempted to answer these questions by examining category learning and category representation. In Experiment 1, 4-year-olds, 6-year-olds, and adults were trained with either a classification task or an inference task and their categorization performance and memory for items were tested. Adults and 6-year-olds exhibited an important asymmetry: they relied on a single deterministic feature during classification training, but not during inference training. In contrast, regardless of the training condition, 4-year-olds relied on multiple probabilistic features. In Experiment 2, 4-year-olds were presented with classification training and their attention was explicitly directed to the deterministic feature. Under this condition, their categorization performance was similar to that of older participants in Experiment 1, yet their memory performance pointed to a similarity-based representation, which was similar to that of 4-year-olds in Experiment 1. These results are discussed in relation to theories of categorization and the role of selective attention in the development of category learning. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
Covert shifts of visual attention may be demonstrated in both adult and infant subjects by facilitation of reaction times to make a saccade to a previously cued location However, this facilitation may be interpreted in terms of a direct effect on the eye movement system In the present experiment, we attempted to train 4-month-old infants to make a saccade in the location opposite from that in which a cue appeared Following such training, we examined the reaction time to occasional probe targets that appeared in the same location as the cue Infants were faster to respond to a target in this location than they were to respond to it either in the training (expected) location or in baseline trials We interpret the results as providing further evidence for covert shifts of attention in 4-month-old infants, and suggest that the effects of covert shifts of attention on the eye movement system are independent of those from sequence learning
Developmental differences in the ability to ignore irrelevant information have been assessed in terms of same/different judgments, speeded classification, selective listening, and incidental learning. Recent research suggests the presence of both quantitative and qualitative age differences in the operation of selective attention. Each of these paradigms has provided evidence for a progressive improvement with age in the ability to attend selectively to specific stimuli. Qualitative differences between older and younger children are manifested in the way each group perceives and organizes incoming information. It is suggested that more emphasis be given to understanding the basis of interference from irrelevant stimuli when it occurs. This would facilitate the understanding of developmental changes in the degree to which irrelevant stimuli interfere with performance. (47 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)