ArticlePDF Available

Category learning is shaped by the multifaceted development of selective attention


Abstract and Figures

Categories are a fundamental building block of cognition that simplify the multitude of entities we encounter into equivalence classes. By simplifying this barrage of inputs, categories support reasoning about and interacting with their members. For example, despite differences in size, color, and other features, we can treat members of the category of dogs as equivalent, and thus generalize information about any given dog to other dogs. Simplifying entities into categories in adulthood is supported by selective attention, in which people focus on category-relevant attributes, while filtering out category-irrelevant attributes. However, much category learning takes place in infancy and early childhood, when selective attention undergoes substantial development. 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 4-and 5-year-old children and adults. Experiment 2 provided evidence that only focusing contributed to selective attention in 4-year-olds, whereas both focusing and filtering contributed to selective attention in 5-year-olds and adults. Thus, category learning may recruit different aspects of selective attention across development.
Content may be subject to copyright.
This is the accepted version of the article to appear in the Journal of Experimental Child Psychology.
Category learning is shaped by the multifaceted development of
selective attention
Layla Unger, and Vladimir M. Sloutsky
Department of Psychology, The Ohio State University
Categories are a fundamental building block of cognition that simplify the multitude
of entities we encounter into equivalence classes. By simplifying this barrage of inputs,
categories support reasoning about and interacting with their members. For example,
despite differences in size, color, and other features, we can treat members of the cat-
egory of dogs as equivalent, and thus generalize information about any given dog to
other dogs. Simplifying entities into categories in adulthood is supported by selective
attention, in which people focus on category-relevant attributes, while filtering out
category-irrelevant attributes. However, much category learning takes place in infancy
and early childhood, when selective attention undergoes substantial development. We
designed two experiments to disentangle the contributions of the focusing and filtering
aspects of selective attention to category learning over development. Experiment 1 pro-
vided evidence that learning simple categories was accompanied by selective attention
in both 4- and 5- year-old children and adults. Experiment 2 provided evidence that
only focusing contributed to selective attention in 4-year-olds, whereas both focusing
and filtering contributed to selective attention in 5-year-olds and adults. Thus, cate-
gory learning may recruit different aspects of selective attention across development.
Keywords: category learning; cognitive development; selective attention; focusing; fil-
tering; inhibition
One of the fundamental roles of cognition
is to simplify and distill the barrage of inputs
available to our senses. For example, if we en-
counter something that is furry, four-legged,
brown with white patches, about two feet tall,
and has some twigs caught in its fur, we can
more readily determine how to interact with it
if we simplify this input by categorizing it as
a dog. Adults simplify entities in the world
into categories by learning to selectively at-
tend to category-relevant attributes that mem-
bers of the same category share (e.g., shape),
rather than irrelevant attributes that vary be-
tween them (e.g., color and pattern) (Deng &
Sloutsky, 2016; Goldstone & Steyvers, 2001;
Rehder & Hoffman, 2005). However, many
of the categories that populate adult knowl-
edge were originally learned during infancy and
early childhood, when selective attention un-
dergoes substantial development (Plude, Enns,
& Brodeur, 1994). How is category learning
over the course of development shaped by the
development of selective attention?
Understanding how selective attention
shapes the development of category learning
involves tackling the challenging question of
what selective attention is. Does learning
to selectively attend to relevant input con-
sist of focusing on relevant input (i.e., enhanc-
ing its processing), or filtering out (i.e., sup-
pressing/inhibiting) irrelevant input? Alterna-
tively, is selective attention zero-sum, so that
focusing on some input collaterally filters out
other input? Although researchers have grap-
pled with these questions for decades, neu-
roimaging research has provided mounting ev-
idence that focusing and filtering may be sepa-
rate processes (Andersen & uller, 2010; Brid-
well & Srinivasan, 2012; Gazzaley, Cooney,
McEvoy, Knight, & D’esposito, 2005; Gulbi-
naite, 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 filtering when people must learn what to
attend to, as they must when they learn cat-
egories. In what follows, we review this re-
search, and present a set of studies designed
to illuminate how the development of focusing
and filtering shapes the development of cate-
gory learning.
Selective Attention and its Develop-
A growing body of neuroimaging evidence
suggests that by adulthood, selective attention
is supported by dissociable focusing and filter-
ing processes. For example, an fMRI study
conducted by Gazzaley et al. (2005) found that
relative to neutral (passive) viewing, the ac-
tivity evoked by a stimulus (such as a face)
was both enhanced when participants were in-
structed to attend to the stimulus (evidence
of focusing), and suppressed when participants
were instructed to ignore it (evidence of filter-
ing; see also Andersen & uller, 2010; Bridwell
& Srinivasan, 2012; Polk et al., 2008). More-
over, focusing may occur without filtering, and
vice versa. For example, 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 individuals 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 pro-
tracted development over infancy, childhood
and adolescence (Lane & Pearson, 1982; Plude
et al., 1994). However, only some of this
research has attempted to disentangle focus-
ing and filtering. Research that has explic-
itly investigated the development of focusing
has yielded evidence that it develops early,
starting in infancy. Much of this evidence
consists of findings that stimulus processing
is enhanced in infants when the stimulus ap-
pears in an attentionally cued location. For
example, when a stimulus appears in a loca-
tion that was recently cued, infants look at it
more quickly (Hood, Willen, & Driver, 1998;
Johnson, Posner, & Rothbart, 1994; Richards,
2000), show enhanced neural responses to it
(Richards, 2000), and show stronger subse-
quent evidence of recognizing it as familiar
(Reid & Striano, 2005; Reid, Striano, Kauf-
man, & Johnson, 2004). Similarly, stimulus
processing is enhanced when the infant is in an
attentive state (Kopp & Lindenberger, 2011;
Richards, 2003).
In contrast, research on filtering has
yielded conflicting results. Conflicting find-
ings are particularly notable in research span-
ning early childhood, from approximately 3 to
7 years of age. For example, conflicting re-
sults have been found from the negative prim-
ing 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 emergence of filtering (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 the retrieval induced forgetting
(RIF) paradigm. In this paradigm, partici-
pants first study category-item pairs such as
FRUIT-banana and FRUIT-apple, then re-
trieve memory for only a subset of pairs for
a category. Filtering is then assessed based on
worse memory for the non-retrieved pairs. Evi-
dence from this paradigm is also equivocal: Al-
though some RIF studies suggest that filtering
changes little with development from at least
age 7 to adulthood (Zellner & auml, 2005),
other studies have provided evidence that fil-
tering increases across an earlier span of de-
velopment, from age 4 to 7 (Aslan & auml,
Taken together, research to date suggests
that focusing develops early. In contrast, prior
research provides conflicting evidence for the
development 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 develop-
ment 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. Specifically, attentional cue-
ing, negative priming, and other paradigms of-
ten used to study selective attention typically
involve some explicit cue or instruction that
directs participants’ attention to some char-
acteristic of their input, such as an instruc-
tion to make responses based on color. In
contrast, when a child encounters a dog, a
parent or other knowledgeable agent may la-
bel it as a “dog” while the child attends to
it, but may rarely highlight the characteristics
of dogs that are important for making them
dogs and not something else (Bergey, Mor-
ris, & Yurovsky, 2020; Ran, Kirby, Naigles, &
Rowe, 2022). Instead, characteristics relevant
to membership in the category of dogs must
be learned from experience, such as from con-
sistently encountering dogs while hearing the
label “dog” (Smith, Jones, Landau, Gershkoff-
Stowe, & Samuelson, 2002; Smith & Yu, 2008).
In the next section, we review research that
has investigated the development of selective
attention in category learning, and highlight
how it does not disentangle the contributions
of focusing and filtering.
Role of Selective Attention in Cate-
gory Learning
A handful of findings suggest that the
contribution of selective attention to category
learning increases over development, particu-
larly over the course of early childhood from
approximately 4 to 7 years of age (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 rele-
vance to category membership, such that one
dimension is deterministically associated with
(i.e., perfectly predictive of) category mem-
bership, multiple dimensions are probabilisti-
cally associated with (i.e., imperfectly predic-
tive of) with category membership, and the
remaining dimensions are irrelevant. The re-
sults of these studies suggest that attention
is distributed across deterministic and prob-
abilistic dimensions in young children (e.g.,
age 4), and becomes increasingly selectively
oriented to just the deterministic dimension
with age (e.g., from age 6-7 to adulthood).
These findings suggest that, with age, peo-
ple tend to increasingly orient selective atten-
tion primarily towards dimensions that they
learn to be most diagnostic of category mem-
bership. However, this research did not investi-
gate attention to completely irrelevant dimen-
sions, and thus leaves open the possibility that
even young children may attend more to di-
mensions that are at least somewhat relevant
(i.e., to both deterministic and probabilistic di-
mensions) than to dimensions that are entirely
Critically, the research that has investi-
gated the development of selective attention
during category learning has not disentan-
gled whether this developmental trajectory is
driven by changing contributions of focusing,
filtering, or both. Disentangling these contri-
butions is challenging: When people learn a
specific category structure, any difference in
attention to relevant versus irrelevant dimen-
sions can be due to focusing attention to rel-
evant dimensions, filtering irrelevant dimen-
sions, or both. One way to overcome this
challenge is to introduce a “switch” in rele-
vance at some point during category learning,
in which a dimension that was previously ir-
relevant becomes relevant. If category learn-
ing involves focusing, learners should always
struggle to shift their focus to any newly rele-
vant dimension after the switch. Critically, if
category learning also involves filtering, learn-
ers should find it more difficult to focus on a
newly relevant dimension that had been irrel-
evant (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 evi-
dence of both focusing and filtering (Goldstone
& Steyvers, 2001). As described below, the
present research used this approach to disen-
tangle the contributions of focusing and filter-
ing in the development of category learning.
Present Research
The present research sought to disentan-
gle the contributions of focusing and filtering
to selective attention in the development of
category learning. The research consisted of
two experiments designed to accomplish this
goal. Both experiments were designed to tar-
get the period of development that has yielded
conflicting evidence about the development of
filtering (Aslan & auml, 2010; Chevalier &
Blaye, 2008; Pritchard & Neumann, 2004; Tip-
per & McLaren, 1990). Thus, both experi-
ments investigated focusing 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 versus entirely irrelevant
dimensions during category learning. Adults
and children aged 4 and 5 learned simple cate-
gories of creatures that possessed one relevant
and one irrelevant dimension. To target selec-
tive attention when relevant dimensions must
be learned rather than externally cued, in con-
trast with some prior studies of category learn-
ing in development (Deng & Sloutsky, 2015,
2016), participants were not provided with any
initial cues about the relevance of different di-
mensions. For example, in Deng and Sloutsky
(2015, 2016), participants were given instruc-
tions identifying the relevant dimensions prior
to category learning, such as that “[all/most]
[members of this category] have this kind of
head”. In contrast, participants in the present
studies were not told which dimensions were
relevant. Thus, participants could only learn
by making categorization decisions and receiv-
ing feedback. Selective attention to the rel-
evant dimension was measured based on bet-
ter subsequent recognition memory for famil-
iar versus novel values of the relevant versus
irrelevant dimension (Deng & Sloutsky, 2016).
To anticipate the results, category learning oc-
curred in all age groups. Importantly, for these
simple categories, category learning was ac-
companied by greater learned selective atten-
tion to the relevant versus the irrelevant di-
mension even in the youngest children.
Experiment 2 used the “switch” approach
to investigate whether the contributions of fo-
cusing versus filtering to selective attention
change with age. Here, participants initially
learned categories like those used in Exper-
iment 1, but experienced a switch half-way
through category learning. In this switch, the
initially relevant dimension became irrelevant,
and the initially irrelevant dimension became
We anticipated that all participants would
find it challenging to learn the new category
structure following the switch. To isolate the
contributions of filtering to this challenge, we
assigned participants to one of two conditions.
In the “Visible” condition, the initially irrel-
evant dimension that became relevant post-
switch was visible prior to the switch. Thus,
in the Visible condition, the initially irrele-
vant dimension could be filtered prior to the
switch. In the “Hidden” condition, the initially
irrelevant dimension was occluded prior to the
switch. Thus, in the Hidden condition, the
initially irrelevant dimension could not be fil-
tered prior to the switch. Both conditions pose
learners with a challenge once the switch takes
place to shift their focus away from the dimen-
sion that was relevant prior to the switch, and
towards the newly relevant dimension. How-
ever, the newly relevant dimension should be
more challenging to attend to if it was filtered
(i.e., suppressed) prior to the switch. Given
that such filtering is only possible in the Visi-
ble condition, selective attention that involves
filtering should lead to worse performance af-
ter the switch in the Visible than in the Hid-
den condition. In contrast, selective attention
that does not involve filtering should lead to
similar performance after the switch in both
conditions. Using this approach, we investi-
gated two alternative hypotheses: (1) Selective
attention involves both focusing and filtering
even early in development, and (2) Selective at-
tention involves focusing early in development,
and the role of filtering increases with age.
Experiment 1
Participants were recruited in three age
groups: 4-year-olds (N = 33; M = 4 years,
5 months), 5-year-olds (N = 35; M = 5 years,
4 months), and adults (N = 53). The child
age groups were selected because they cap-
ture a period of development when the con-
tributions of filtering to selective attention are
disputed (Aslan & auml, 2010; Chevalier &
Blaye, 2008; Pritchard & Neumann, 2004; Tip-
per & McLaren, 1990).
The category stimuli were created based on
extensive 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 val-
ues on all other dimensions had to be irrele-
vant. This constraint was necessary for test-
ing selective attention to relevant versus ir-
relevant dimensions. Second, to target selec-
tive attention to dimensions that is evoked by
their learned relevance, it was of critical im-
portance 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
preceded category learning with explicit cues
to the dimensions relevant to category mem-
bership. These cues were used in these prior
studies because extensive piloting showed that
they were necessary for young children to learn
categories with several (e.g., seven) dimensions
(personal communication with authors). The
results of piloting for the present studies in-
dicated that in the absence of explicit rele-
vance cues, successful category learning only
occurred in the majority of 4-year-old children
with a simple category structure with two di-
mensions: One relevant, and one irrelevant.
Moreover, the values of the relevant dimen-
sion that were each associated with a different
category needed to be highly visually discrim-
Stimuli consisted of novel creatures with
two dimensions: flippers and tails. Each di-
mension was created by morphing between two
anchor images that were different in shape
(e.g., two different-shaped flippers). Values of
these dimensions 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 com-
binations of 4 possible values on each dimen-
sion (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”) dimen-
sion perfectly predicted category membership,
and the value of the other (“irrelevant”) di-
mension occurred equally often in both cate-
gories. When a dimension was relevant, val-
ues from near one extreme of the dimension
determined that the creature belonged to one
category, and values from near the other ex-
treme determined that the creature 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 re-
placed with a novel value, and four in which
the tail was replaced with a novel value (Fig-
ure 1B).
Design and Procedure
The experiment consisted of two phases:
Category learning, and recognition memory.
Participants were randomly assigned to com-
plete one of two versions of the experiment that
counterbalanced the relevance of the two di-
mensions to category membership. Thus, for
a given participant, the values of one “Rele-
vant” 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 fol-
lowed instructions presented on the computer
screen and used the keyboard to make re-
sponses, whereas an experimenter read instruc-
tions aloud and recorded children’s verbal re-
sponses. 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 partici-
pants 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 creatures
were shown, participants were told that all
creatures had two body parts that come in dif-
ferent shapes: A flipper, and a tail. Each body
Figure 1. Panel A depicts creatures that illustrate values on the two extremes of each dimen-
sion. Panel B depicts the novel values for each dimension used in the recognition memory
task. Panel C depicts the creatures with dimensions hidden by bubbles, as used in Experi-
ment 2.
part was highlighted with a yellow outline
while the instructions or experimenter pointed
out its two different 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. These instruc-
tions were designed based on pilot testing to
increase the likelihood that even four-year-olds
could learn the categories, while still requiring
participants to learn the dimension relevant for
category membership for themselves.
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, partici-
pants 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, par-
ticipants 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 crea-
tures, 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 com-
pleted 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 de-
scribed in the Materials section above, half of
the New trials presented a creature in which
the relevant dimension 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 constructed using the rstan
package (Stan Development Team, 2020)
in the R environment for statistical com-
puting (R Core Team, 2021). We used
Bayesian analyses because both differences
and similarities between conditions (e.g.,
between memory for relevant versus irrele-
vant dimensions) were of interest. All data
and scripts have been made available on
OSF https://osf .io /8db62 /?view only =
Category Learning
Figure 2 depicts category learning in each
of the three age groups. Analyses assessed
whether participants in each age group learned
the categories. The outcome variable for these
analyses was trial-by-trial categorization ac-
curacy. Trial-by-trial accuracy was analyzed
using a Bayesian hierarchical model in which
it was predicted as the outcome of a logis-
tic regression, with an intercept and slope for
change in accuracy across trials for each par-
ticipant. Intercepts and slopes for participants
were each drawn from one of three higher-level
distributions (all given the same weak priors),
based on the participant’s age group. Fitting
this model to the data thus estimates the tra-
jectory of category learning both for each par-
ticipant, and for each age group. Rather than a
single estimate of the category learning trajec-
tory for a given participant or age group, the
Bayesian approach yields a posterior distribu-
tion of probable category learning trajectories.
We assessed category learning in each age
group using the model’s posterior distribu-
tions of categorization accuracy in the final
two blocks in each age group. Specifically,
for each sample from the posterior distribution
for each age group, we calculated the model’s
estimation of the mean accuracy on the final
two blocks of category learning. This calcula-
tion produced a posterior distribution of prob-
able accuracy on the final two blocks for each
age group. We then calculated the range of
most probable final accuracies for each age
group using Highest Density Intervals (HDIs).
This interval is the range of a distribution that
contains some specified percentage of probable
values. The interpretation of such intervals is
simply the probability that the “true” value
falls within the range. In all age groups, cate-
gorization accuracy was predicted to be above
chance (i.e., .5) in the final two blocks (4-year-
olds: Median = 0.62, 90% HDI = [0.60, 0.66];
5-year-olds: Median = 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
was slightly greater in 5- than in 4-year-olds
(Median = 0.06, 90% HDI = [0.02, 0.10]), and
substantially from 5-year-olds to adults (Me-
dian = 0.26, 90% HDI = [0.23, 0.29]).
Selective Attention
We assessed whether category learning was
accompanied by selective attention to the rele-
vant dimension based on whether participants
had better recognition memory for values of
Figure 2. Categorization accuracy across blocks in each age group. The dashed line depicts
chance. Error bars depict standard errors.
the relevant versus the irrelevant dimension.
We measured recognition memory for each di-
mension using d-prime scores, calculated from
hits for correctly identifying creatures with
an old value on the dimension as “old”, and
false alarms for incorrectly identifying new
creatures as “old”. For each participant, we
then calculated a “Relevant Attention” score
as their d-prime for the relevant dimension mi-
nus their d-prime for the irrelevant dimension.
Figure 3 shows d-prime and Relevant At-
tention scores by age, which suggest that all
age groups tended to attend more to the rel-
evant 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 hierarchi-
cal model in which each participant’s Relevant
Attention score was drawn from a normal dis-
tribution. For each participant, the mean of
this distribution was drawn from one of three
higher-level normal distributions (all given the
same weak priors), based on the participant’s
age. Thus, we assessed whether Relevant At-
tention 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 probability that Relevant Atten-
tion scores were greater than 0: 89% for four-
year-olds, 90% for five-year-olds, and 90% for
adults. Thus, category learning was accompa-
nied by similarly greater attention to relevant
versus irrelevant dimensions from age four to
Experiment 1 revealed that successful cat-
egory learning was accompanied by greater at-
tention to a category-relevant dimension than
to a category-irrelevant dimension. However,
this result does not disentangle whether selec-
tive attention only involves focusing on the rel-
evant dimension, or additionally involves filter-
ing the irrelevant dimension.
Experiment 2 was designed to disentangle
these possibilities. To achieve this goal, Exper-
iment 2 introduced a “switch” in dimension rel-
evance halfway through category learning. Fol-
lowing the switch, the dimension that was ini-
tially relevant to category membership became
irrelevant, and a different dimension instead
became relevant. Critically, we manipulated
whether participants had the opportunity to
filter the dimension that became relevant after
the switch before it became relevant. Specifi-
cally, participants in the Visible condition saw
the dimension that only became relevant after
Figure 3. Top: dprime values for relevant and irrelevant dimensions in each age group. Er-
ror bars depict standard errors. Bottom: Relevant Attention scores in each age group. The
dashed line depicts no difference in attention to relevant versus irrelevant dimensions. Points
depict participants.
the switch, so had the opportunity to learn to
filter it while it was initially irrelevant. In con-
trast, participants in the Hidden condition did
not have the opportunity to learn to filter this
dimension because it was covered by bubbles
before the switch. Thus, it should be more dif-
ficult to learn to categorize on the basis of the
newly relevant dimension following the switch
in the Visible versus Hidden condition only if
selective attention involves filtering.
Experiment 2
Participants in Experiment 2 were assigned
to one of two between-subjects conditions: Vis-
ible or Hidden. Participants were recruited
from the same age groups as Experiment 1:
4-year-olds (Visible N = 32, M = 4 years,
5 months; Hidden N = 34, M = 4 years, 5
months), 5-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 4-year-olds and
six 5-year-olds were recruited but not analyzed
due to poor category learning (see Results).
This experiment used the same category
exemplars as Experiment 1, and a picture of
Design and Procedure
Participants were randomly assigned 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 counter-
balanced the relevance of the two dimensions
to category membership.
The procedure for Experiment 2 was sim-
ilar to Experiment 1, except that: (1) Cat-
egory learning instructions informed partici-
pants that sometimes one of the body parts
might be covered by bubbles, and (2) The rel-
evance of the two dimensions switched between
blocks 2 and 3, such that the dimension that
was relevant prior to the switch became irrel-
evant after the switch, and vice versa. In the
Visible condition, both dimensions were visi-
ble throughout category learning. In the Hid-
den condition, prior to the switch, the initially
irrelevant dimension was always occluded by
To investigate the effects of the Visible
versus Hidden conditions on post-switch cat-
egorization accuracy, it was important to fo-
cus on participants who were likely to have
learned the initial category structure over the
pre-switch blocks. Thus, we excluded partici-
pants who achieved low (.25) accuracy in the
final pre-switch block. This criterion excluded
four 4-year-olds, six 5-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 re-
sponding (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 in-
correct responses (Blanco & Sloutsky, 2021).
Figure 4 depicts category learning in three
age groups. Categorization 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 Ex-
periment 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 con-
ditions, with the exception of 5-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 bet-
ter in the Hidden than in the Visible condition
in each age group. For each posterior sam-
ple, we calculated accuracy in the post-switch
blocks in the Hidden condition minus the Visi-
ble condition, such that positive values indi-
cate better category learning post-switch in
the Hidden versus the Visible condition. Post-
switch category learning in 4-year-olds was
similar in the two conditions: There was only
a 61% probability that accuracy was better in
the Hidden condition. In contrast, in both 5-
year-olds and adults, there was a >99% prob-
ability that post-switch category learning was
better in the Hidden condition.
Experiment 2 was designed to disentangle
the contributions of filtering and focusing to
selective attention by introducing a “switch”
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
halfway through category learning. All partic-
ipants were expected to show a drop in accu-
racy following the switch because it imposes a
challenge for learners to shift their focus from
the dimension that was relevant prior to the
switch to the dimension that became newly rel-
evant following the switch. However, it should
be even more challenging to learn to catego-
rize based on the newly relevant dimension if
it was filtered prior to the switch. By manipu-
lating whether learners had the opportunity to
filter this dimension in the Visible versus Hid-
den conditions, we investigated whether partic-
ipants in the three age groups showed this ad-
ditional cost of filtering. The results revealed
evidence of filtering in 5-year-olds and adults.
In contrast, 4-year-olds performed similarly in
the Visible and Hidden conditions, and thus
did not show evidence of filtering.
General Discussion
In the two reported experiments, 4-year-
olds, 5-year-olds, and adults learned simple
categories with one relevant dimension that
perfectly predicted category membership, and
one entirely irrelevant dimension. Experi-
ment 1 demonstrated that learning these cat-
egories was accompanied by selective atten-
tion across age. To disentangle the contri-
butions 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. Criti-
cally, we manipulated whether learners had the
opportunity to filter the dimension that be-
came relevant after the switch. If filtering is
present in an age group, then learners should
have more trouble switching to the newly rel-
evant dimension when they had the opportu-
nity to filter it. Four year-olds only showed evi-
dence of focusing and not filtering. In contrast,
5- year-olds showed evidence of both. Thus,
these experiments provide evidence for devel-
opmental changes in the processes that under-
pin selective attention in category learning.
Developmental Trajectory of Focus-
ing and Filtering
In the present study, 4-year-olds showed
evidence of focusing, but only 5-year-olds and
adults also showed evidence of filtering. These
findings are consistent with prior evidence that
filtering increases across early childhood (e.g.,
Aslan & auml, 2010). By the same token,
these findings are inconsistent with evidence
that filtering is already robust even early in de-
velopment (e.g., Pritchard & Neumann, 2004),
or only emerges even later in development
(e.g., Deng & Sloutsky, 2016). This variabil-
ity in findings highlights an important possi-
bility that the development of filtering (and of
selective attention generally) does not involve
a qualitative shift from absent to present. In-
stead, even immature selective attention may
involve filtering in favorable situations, such as
when there is a small amount of distracting in-
put that is consistent over time. With develop-
ment, filtering may become a more robust and
widespread aspect of selective attention. For
example, by using categories with just one rel-
evant and one irrelevant dimension, the present
study may have provided more favorable con-
ditions than prior studies that yielded evidence
for the later emergence of filtering, which pre-
sented either a larger number of less relevant
dimensions (e.g., Deng & Sloutsky, 2016; Ple-
banek & Sloutsky, 2017) or irrelevant input
that occupied the same space as relevant input
(e.g., Plebanek & Sloutsky, 2017). This possi-
bility raises testable predictions, such as that a
parametric manipulation of the complexity of
category structure would reveal that the role
of filtering emerges later as the complexity of
category structure increases.
It is additionally worth considering
whether alternative sources of developmental
change may have contributed to the appear-
ance of a developmental change in filtering.
Here, we consider whether floor effects in the
youngest age group may provide an alternative
explanation. Specifically, although four-year-
olds in Experiment 2 did show evidence of
learning the categories after the switch took
place, their learning was only modestly above
chance. Thus, four-year-olds might have failed
to show the difference in learning between the
Hidden and Visible conditions that we used to
diagnose filtering because learning was overall
too poor to detect such a difference. We eval-
uated this possibility by reanalyzing the data
from Experiment 2 after excluding participants
who may have contributed to floor effects by
performing at chance or below in the final
block (for full results, see Supplemental Ma-
terials). With this exclusion criterion, post-
switch performance was substantially above
chance even in 4-year-olds. Nonetheless, we
found the same pattern in which the difference
between the Hidden and Visible conditions
used to diagnose filtering transpired only in
5-year-olds and adults. Thus, the absence of
evidence for filtering in 4-year-olds is unlikely
to be due to floor effects, suggesting that its
emergence with age is a true developmental
Learning Relevance
Humans navigating real-world environ-
ments are often faced with the challenge of
learning what is relevant to their goals. In
contrast, in much of the prior research that
has investigated the development of selective
attention, relevance was explicitly specified
for participants. For example, participants
may be instructed to respond to stimuli in
a specific location (McDermott, Perez-Edgar,
& Fox, 2007; Pritchard & Neumann, 2004),
or respond to the location of specific stim-
uli (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 sug-
gest that learning what is relevant is increas-
ingly accompanied by filtering out input that
is learned to be irrelevant with age.
Open Questions
The present experiments represent a first
step towards disentangling the developmen-
tal contributions of focusing and filtering to
learning to attend selectively. These contribu-
tions thus merit further study in multiple di-
rections. Perhaps the most compelling of these
is to investigate what drives the different de-
velopmental trajectories of the different facets
of selective attention. One angle from which
to illuminate this question is to investigate
the aspects of brain maturation that under-
pin these developmental changes. For exam-
ple, as noted above, neuroimaging research in
adults has implicated different top-down net-
works connecting frontal to other cortical re-
gions in focusing versus filtering (Bridwell &
Srinivasan, 2012; Gazzaley et al., 2005; Gul-
binaite et al., 2014). Moreover, developmen-
tal neuroimaging research provides evidence
that top-down networks involved in filtering
out goal-irrelevant input undergo substantial
increases in connectivity throughout childhood
(Hwang, Velanova, & Luna, 2010). However,
we are not aware of research that has disen-
tangled this trajectory from the development
of focusing, or studied the networks involved in
learning what input is relevant to one’s goals.
Therefore, this prior evidence provides a foun-
dation from which to study the developmental
trajectories of learning to focus on relevant and
filter irrelevant input.
At the same time, brain maturation does
not unfold in a vacuum. For example, both
life experiences (Lawson, Hook, & Farah, 2018;
Sarsour et al., 2011) and behavioral interven-
tions (Diamond & Lee, 2011) influence chil-
dren’s ability to filter out goal-irrelevant input.
Therefore, a full understanding of the drivers
of the multifaceted development of selective at-
tention will require investigating the interplay
between childhood experiences and the devel-
opment of brain networks underpinning these
facets of selective attention.
In adults, learning to group entities into
categories is accompanied by selective atten-
tion to dimensions that are relevant to cat-
egory membership over dimensions that are
irrelevant. However, much of the categories
that populate adult knowledge are learned
over the course of childhood, when selective
attention undergoes substantial development.
The present studies investigated developmen-
tal changes in the contributions of selective at-
tention to category learning. Results revealed
that these contributions were multifaceted: In
younger children, category learning was ac-
companied by focusing on relevant dimensions,
but with age, category learning was also ac-
companied by inhibiting irrelevant dimensions.
Andersen, S., & uller, M. (2010).
Behavioral performance follows the
time course of neural facilitation and
suppression during cued shifts of
feature-selective attention.
Proceedings of the National Academy
of Sciences,107 (31), 13878-13882.
Aslan, A., & auml, K.-H. T. (2010).
Retrieval-induced forgetting in
young children. Psychonomic
Bulletin & Review,17 (5), 704-709.
Bergey, C., Morris, B., & Yurovsky, D.
(2020). Children hear more about
what is atypical than what is typical.
In Proceedings of the 42nd annual
conference of the cognitive science
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
Experimental Child Psychology ,
116 (2), 105-119.
Blanco, N. J., & Sloutsky, V. M. (2021).
Systematic exploration and
uncertainty dominate young
children’s choices. Developmental
Science,24 (2), e13026.
Bridwell, D. A., & Srinivasan, R. (2012).
Distinct attention 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
training on category representation.
Developmental Psychology,51 (3),
Deng, W., & Sloutsky, V. M. (2016).
Selective attention, diffused
attention, and the development of
categorization. Cognitive
Psychology,91 , 24-62.
Diamond, A., & Lee, K. (2011).
Interventions shown to aid executive
function development in children 4
to 12 years old. Science,333 (6045),
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),
Goldstone, R. L., & Steyvers, M. (2001).
The sensitization and differentiation
of dimensions during category
learning. 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 supervised classification:
The effect of learning task on
conceptual flexibility. Journal of
Experimental Psychology: General,
139 (2), 319.
Hood, B. M., Willen, J. D., & Driver, J.
(1998). Adult’s eyes trigger shifts of
visual attention in human infants.
Psychological Science,9(2), 131–134.
Hwang, K., Velanova, K., & Luna, B.
(2010). Strengthening of top-down
frontal cognitive control networks
underlying the development of
inhibitory control: a functional
magnetic resonance imaging effective
connectivity study. Journal of
Neuroscience ,30 (46), 15535–15545.
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.
Kopp, F., & Lindenberger, U. (2011).
Effects of joint attention on
long-term memory in 9-month-old
infants: an event-related potentials
study. Developmental Science,
14 (4), 660–672.
Lane, D. M., & Pearson, D. A. (1982).
The development of selective
attention. Merrill-Palmer Quarterly
(1982-), 317-337.
Lawson, G. M., Hook, C. J., & Farah,
M. J. (2018). A meta-analysis of the
relationship between socioeconomic
status and executive function
performance among children.
Developmental Science,21 (2).
McDermott, J. M., Perez-Edgar, K., &
Fox, N. A. (2007). Variations of the
flanker paradigm: Assessing selective
attention in young children.
Behavior Research Methods,39 (1),
Plebanek, D. J., & Sloutsky, V. M. (2017).
Costs of selective attention: When
children notice what adults miss.
Psychological Science,28 (6),
Plude, D. J., Enns, J. T., & Brodeur, D.
(1994). The development 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 processing of relevant features
and suppresses the processing of
irrelevant features in humans: a
functional magnetic resonance
imaging study of the stroop task.
Journal of Neuroscience ,28 (51),
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),
Pritchard, V. E., & Neumann, E. (2011).
Classic stroop negative priming
effects for children and adults
diverge with less-conflicting and
nonconflicting conditions. The
American Journal of Psychology,
124 (4), 405-419.
R Core Team. (2021). R: A language and
environment for statistical
computing [Computer software
manual]. Vienna, Austria. Retrieved
Ran, W., Kirby, A., Naigles, L. R., &
Rowe, M. (2022). Parents’ talk
about conceptual categories with
infants: stability, variability, and
implications for expressive language
development. Journal of Child
Language, 1–22.
Rehder, B., & Hoffman, A. B. (2005).
Eyetracking and selective attention
in category learning. Cognitive
Psychology,51 (1), 1-41.
Reid, V. M., & Striano, T. (2005). Adult
gaze influences infant attention and
object processing: Implications for
cognitive neuroscience. European
Journal of Neuroscience ,21 (6),
Reid, V. M., Striano, T., Kaufman, J., &
Johnson, M. H. (2004). Eye gaze
cueing facilitates neural processing of
objects in 4-month-old infants.
Neuroreport,15 (16), 2553–2555.
Richards, J. E. (2000). Localizing the
development of covert attention in
infants with scalp event-related
potentials. Developmental
psychology,36 (1), 91.
Richards, J. E. (2003). Attention affects
the recognition of briefly presented
visual stimuli in infants: An erp
study. Developmental Science,6(3),
Sarsour, K., Sheridan, M., Jutte, D.,
Nuru-Jeter, A., Hinshaw, S., &
Boyce, W. T. (2011). Family
socioeconomic status and child
executive functions: The roles of
language, home environment, and
single parenthood. Journal of the
International Neuropsychological
Society,17 (1), 120–132.
Smith, L. B., Jones, S. S., Landau, B.,
Gershkoff-Stowe, L., & Samuelson,
L. (2002). Object name learning
provides on-the-job training for
attention. Psychological Science,
13 (1), 13-19.
Smith, L. B., & Yu, C. (2008). Infants
rapidly learn word-referent mappings
via cross-situational statistics.
Cognition,106 (3), 1558-1568.
Stan Development Team. (2020). RStan:
the R interface to Stan. Retrieved
from (R
package version 2.21.2)
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), 353-378.
Tipper, S. P., & McLaren, J. (1990).
Evidence for efficient visual
selectivity in children. In Advances
in psychology (Vol. 69, p. 197-210).
Zellner, M., & auml, K.-H. (2005).
Intact retrieval inhibition in
children’s episodic recall. Memory &
Cognition,33 (3), 396-404.
Full-text available
Although ever-growing literature has documented that a multisensory environment influences individuals’ learning, there is less agreement as to whether it facilitates or hinders and why. In this work, we addressed this issue with a systematic review and meta-analysis and examined the multisensory facilitation and interference of learning. The present study reviewed the literature on audiovisual learning and quantitatively synthesized a total of 29 studies with 198 comparisons to (1) assess the effect of multisensory context by examining the role of congruency, (2) examine study-level factors—modality, age, and familiarity—that may moderate the effects observed across studies, and (3) discuss possible explanations and implications for theories of attention in learning. The results indicated that the multisensory effect was constrained by the congruency between multimodal information. Compared to a unisensory presentation, a multisensory context enhanced the task performance in the congruent conditions whereas it hindered the performance in the incongruent conditions. Furthermore, the effect of congruency was modulated by the modality of the target stimulus and the age of participants, and the multisensory effect was limited for familiar multimodal associations under incongruent conditions. Importantly, interference was found from the auditory distractors to the visual targets but not from the visual distractors to the auditory targets, in child participants but not in adults, and for the familiar multisensory associations but not for the unfamiliar ones. These results point to a critical role of selective attention in multisensory learning and imply roles of attentional focusing and filtering in processing congruent and incongruent multimodal information.
Full-text available
Children’s exposure to talk about conceptual categories plays a powerful role in shaping their conceptual development. However, it remains unclear when parents begin to talk about categories with young children and whether such talk relates to children’s language skills. This study examines relations between parents’ talk about conceptual categories and infants’ expressive language development. Forty-seven parent-infant dyads were videotaped playing together at child age 10, 12, 14, and 16 months. Transcripts of interactions were analyzed to identify parents’ talk about conceptual categories. Children’s expressive language development was assessed at 18 months. Findings indicate that parents indeed talked about conceptual categories with infants and that talk was stable across time, with college-educated parents producing more than non-college-educated parents. Further, parents’ talk about conceptual categories between 10 and 16 months predicted children’s 18-month expressive language. This study sheds new light on mechanisms through which early experiences may support children’s language development.
Full-text available
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.
How do children learn the typical features of objects in the world? For many objects, this information must come from the language they hear. However, language does not veridically reflect the world: People are more likely to talk about atypical features (e.g., "purple carrot") than typical features (e.g., "orange carrot"). Does the speech children hear from their parents also overrepresent atypical features? We examined the typicality of adjectives produced by parents in a large, longitudinal corpus of parent-child interaction. Across nearly 2000 unique adjective–noun pairs, we found parents’ adjectives predominantly mark atypical features of objects, although parents of very young children are relatively more likely to comment on typical features as well. We then used vector space models to show that learning the typical features of common categories from linguistic input alone is challenging even with sophisticated statistical inference techniques.
The relationship between childhood socioeconomic status (SES) and executive function (EF) has recently attracted attention within psychology, following reports of substantial SES disparities in children's EF. Adding to the importance of this relationship, EF has been proposed as a mediator of socioeconomic disparities in lifelong achievement and health. However, evidence about the relationship between childhood SES and EF is mixed, and there has been no systematic attempt to evaluate this relationship across studies. This meta-analysis systematically reviewed the literature for studies in which samples of children varying in SES were evaluated on EF, including studies with and without primary hypotheses about SES. The analysis included 8760 children between the ages of 2 and 18 gathered from 25 independent samples. Analyses showed a small but statistically significant correlation between SES and EF across all studies (rrandom = .16, 95% CI [.12, .21]) without correcting for attenuation owing to range restriction or measurement unreliability. Substantial heterogeneity was observed among studies, and a number of factors, including the amount of SES variability in the sample and the number of EF measures used, emerged as moderators. Using only the 15 studies with meaningful SES variability in the sample, the average correlation between SES and EF was small-to-medium in size (rrandom = .22, 95% CI [.17, .27]). Using only the six studies with multiple measures of EF, the relationship was medium in size (rrandom = .28, 95% CI [.18, .37]). In sum, this meta-analysis supports the presence of SES disparities in EF and suggests that they are between small and medium in size, depending on the methods used to measure them.
One of the lawlike regularities of psychological science is that of developmental progression-an increase in sensorimotor, cognitive, and social functioning from childhood to adulthood. Here, we report a rare violation of this law, a developmental reversal in attention. In Experiment 1, 4- to 5-year-olds ( n = 34) and adults ( n = 35) performed a change-detection task that included externally cued and uncued shapes. Whereas the adults outperformed the children on the cued shapes, the children outperformed the adults on the uncued shapes. In Experiment 2, the same participants completed a visual search task, and their memory for search-relevant and search-irrelevant information was tested. The young children outperformed the adults with respect to search-irrelevant features. This demonstration of a paradoxical property of early attention deepens current understanding of the development of attention. It also has implications for understanding early learning and cognitive development more broadly.
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).