Ready to Learn: Predictive Exposure to Category-Relevant Regularities Facilitates Novel Category Learning

Abstract

Prior evidence suggests that category learning can occur implicitly by detecting regular co-occurrences of features within categories. Less studied is whether regularities wherein category membership predicts other events or actions also foster category learning. Moreover, we know little about whether, and to what degree, exposure to these regularities facilitates subsequent supervised learning. Here, participants were pre-exposed to exemplars from two categories during a cover task, while uninformed of their category membership. Pre-exposure occurred under conditions in which category membership did (Predictive Mapping) or did not (Mere Exposure) predict task events to which participants responded. Baseline participants completed the same task with category-irrelevant stimuli. Subsequently, all participants were taught the categories (using pre-exposure exemplars) under explicit supervision. Whereas neither Predictive Mapping nor Mere Exposure influenced cover task performance (vs. Baseline), Predictive Mapping substantially improved subsequent supervised category learning. These findings point to latent category learning given pre-exposure to Predictive Mapping regularities.

Full text

Ready to Learn: Predictive Exposure to Category-Relevant Regularities Facilitates
Novel Category Learning
Layla Unger (unger.114@osu.edu)
Ohio State University, Department of Psychology, 1835 Neil Avenue
Columbus, OH 43210 USA
Vladimir Sloutsky (sloutsky.1@osu.edu)
Ohio State University, Department of Psychology, 1835 Neil Avenue
Columbus, OH 43210 USA
Abstract
Prior evidence suggests that category learning can occur
implicitly by detecting regular co-occurrences of features
within categories. Less studied is whether regularities wherein
category membership predicts other events or actions also
foster category learning. Moreover, we know little about
whether, and to what degree, exposure to these regularities
facilitates subsequent supervised learning. Here, participants
were pre-exposed to exemplars from two categories during a
cover task, while uninformed of their category membership.
Pre-exposure occurred under conditions in which category
membership did (Predictive Mapping) or did not (Mere
Exposure) predict task events to which participants responded.
Baseline participants completed the same task with category-
irrelevant stimuli. Subsequently, all participants were taught
the categories (using pre-exposure exemplars) under explicit
supervision. Whereas neither Predictive Mapping nor Mere
Exposure influenced cover task performance (vs. Baseline),
Predictive Mapping substantially improved subsequent
supervised category learning. These findings point to latent
category learning given pre-exposure to Predictive Mapping
regularities.
Keywords: category learning, latent learning, implicit
learning, supervision
Introduction
Learning to group distinct perceptual experiences into
categories is a fundamental cognitive ability that manifests
itself in perception, memory, reasoning, and language. Much
of research on category learning conducted to date has
investigated how we accomplish this feat under explicit
supervision conditions (e.g., Ashby, Maddox, & Bohil, 2002;
Smith et al., 2014). For example, participants are often
informed that the stimuli they will experience belong to
categories whose composition they must try to determine,
often provided with category labels, and given explicit
feedback following their categorization decisions. During
real-world category learning, access to such explicit
information is often very limited. In the absence of explicit
supervision, how does implicit category learning unfold?
Moreover, does implicit learning (or even mere exposure to
stimuli) help learners capitalize on the relatively small
amount of explicit category information they may receive?
Here, we investigate how exposure to to-be-learned stimuli
influence category learning, with an emphasis on whether
such exposure heightens the readiness with which categories
may be learned under explicit supervision.
Category Membership and Perceptual Features
In comparison to research on category learning under
explicit, supervised conditions, the body of research
conducted on implicit or incidental category learning is
relatively small, and has focused primarily on learning that is
driven by sensitivity to regularities in the perceptual
characteristics of category members. Moreover, this research
has treated learning from such regularities as entirely separate
from learning from explicit category-relevant information.
Research in this field is motivated by the observation that,
in many naturalistic categories, members of the same
category often resemble each other more strongly than they
resemble members of other categories (Rosch, 1975). Such
family resemblances may be thought of as resulting from
reliable co-occurrences of perceptual features with each other
within exemplars from the same category, but not with
features that occur in exemplars from other categories. As an
example, consider the category of “birds”: In this category,
perceptual features such as feathers, wings, and beaks
reliably co-occur, and rarely occur with features
characteristic of other categories, such as fur.
Evidence from research in infants (Quinn & Johnson,
2000; Younger & Cohen, 1983), children (Kloos & Sloutsky,
2008) and adults (Clapper & Bower, 1994; Nelson, 1984)
suggests that a sensitivity to these perceptual feature
regularities contributes to category learning from an early
age. Moreover, learners who are given implicit or incidental
exposure to category exemplars are more likely to learn
family resemblance categories, in contrast to learners given
explicit supervision, who are more likely to learn rule-based
categories. (Love, 2002; Nelson, 1984). Accordingly, in
many accounts of implicit category learning, sensitivity to
such regular co-occurrences of perceptual features plays a
key role (Quinn & Eimas, 1997; Sloutsky, 2010; Smith &
Heise, 1992). In prior empirical research, implicit,
unsupervised learning from perceptual feature regularities
has been treated as distinct from learning under explicit
supervision. The possibility that such implicit learning might
help learners take advantage of information about category
membership provided by explicit supervision remains largely

unexplored (though see Folstein, Gauthier, & Palmeri, 2010
for an investigation with equivocal results).
Category Membership and Prediction
Beyond regularities in the features of category exemplars
themselves, the environment may also convey regularities
with which category membership predicts other perceptual
experiences or goal-related actions. With respect to
perceptual experiences, an exemplar’s category membership
may predict the location in which it is seen (e.g., dogs
typically appear on the ground, whereas birds often appear in
the sky), the other entities with which it is seen (e.g., a dog is
often seen with a leash), the type of motion it will exhibit
(e.g., birds often fly, whereas dogs do not), the words that are
heard when it is seen (e.g., the words “dog”, “furry”, etc. are
more likely to be heard when seeing a dog than a bird, even
in the absence of explicit labeling events), and so on.
Similarly, with respect to goal-related actions, an exemplar’s
category membership may predict whether it is an entity to
approach or avoid, the appropriate way in which to interact
with it (e.g., a nail should be hammered, whereas a screw
should be turned with a screwdriver), and so on. Importantly,
these prediction regularities are often implicit: We typically
do not receive explicit feedback when we make a correct or
incorrect prediction. Instead, when predicting a perceptual
experience, we might see what we expect or be surprised, and
when predicting a goal-related action, we might accomplish
or fail at our goal. Both the possibility that such implicit
prediction regularities contribute to category learning, and
how category learning that is fostered by prediction
regularities unfolds, remain relatively unstudied.
To our knowledge, the only research in which implicit,
prediction-driven category learning has been studied to date
consists of a handful of studies conducted in the auditory
domain (e.g., Gabay, Dick, Zevin, & Holt, 2015). In these
studies, participants completed a task in which they made
different responses to visual stimuli that were different from
each other either in appearance, or in the location in which
they appeared. For example, in one version of this task
(Gabay et al., 2015), participants indicated in which of 4
possible locations an X appeared, but were not given
feedback about whether they responded correctly. Critically,
the different visual stimuli were each preceded by task-
irrelevant exemplars from the same number of acoustic or
speech sound categories. The absence of corrective feedback
during the task rendered the predictive mapping between
auditory category and perceptual events/visuomotor
responses implicit. Across studies, when the category
membership of the sounds predicted the location or identity
of the stimulus to which participants respond, participants
showed evidence of having learned both this predictive
mapping, and the relevant auditory categories. Moreover, the
category learning that took place in versions of this paradigm
generalized to novel category exemplars.
The success with which category learning took place in
these studies provides initial evidence that predictive
relationships between category membership and perceptual
events/goal-related actions fosters category learning.
However, this evidence consists of a small number of studies
conducted within the auditory domain, which recruits
different brain systems (Ley et al., 2012; Seger & Miller,
2010) and contains qualitatively different categories (e.g.,
ones that are temporally dynamic) from those in the otherwise
more extensively studied visual domain. Moreover, as in
research on implicit category learning from perceptual
feature regularities, this line of research has not investigated
whether implicit learning from prediction regularities
facilitates subsequent learning from explicit supervision.
Present Experiment
As described above, category learning may be facilitated
implicitly by sensitivities both to the reliable co-occurrence
of perceptual features in category exemplars, and the
reliability with which category membership predicts
perceptual events or goal-directed actions. However, the
effects on category learning of the latter are comparatively
unstudied. Moreover, we know little about whether either
sensitivity improves the readiness with which categories are
learned under explicit supervision. These gaps in our current
understanding of category learning led to our two aims. First,
we aimed to compare category learning under conditions in
which participants were either only exposed to category-
relevant perceptual feature co-occurrences, or additionally
exposed to regularities in which category membership
predicted visual events and goal-related actions. Second, we
aimed to test whether learning under either condition
facilitated subsequent supervised category learning.
To accomplish these aims, we exposed participants to
stimuli that, unbeknownst to them, were members of two
categories within which perceptual features reliably co-
occurred. We accomplished this exposure in the context of a
speeded cover task, which we manipulated such that the
category membership of an exemplar did or did not predict
task events and appropriate responses. Specifically, in the
cover task, participants were given a short period of time
(<500ms) during which to indicate whether a stimulus that
first appeared in a central location had then “jumped” to the
left or right. In the “Predictive Mapping” condition, category
membership perfectly predicted the location to which stimuli
jumped, whereas in the “Mere Exposure” condition, category
membership and jump location were unrelated. As a control,
participants in a Baseline condition completed the same task
with stimuli unrelated to the categories in the former two
conditions. Importantly, participants were not asked to
predict where the stimulus jumped, nor were they given
explicit corrective feedback about whether their responses
were accurate (they were only informed when they had failed
to respond within the time allowance given). Accordingly,
the predictive regularities in the Predictive Mapping
condition were incidental to task performance. After
completing the cover task, all participants then were taught
the two categories under traditional explicit supervised
conditions with corrective feedback.

Using this approach, we investigated: 1) Whether
participants showed evidence of learning the Predictive
Mapping, as indexed by more rapid improvements in RT over
the course of the cover task, and 2) Whether Mere Exposure,
Predictive Mapping, or both facilitated subsequent
supervised category learning.
Method
Participants
Participants were 72 adults (Mage=34.21 SDage=11.89)
recruited from Amazon Mechanical Turk who received $1 for
participation for this ~15min study. Participants were
randomly assigned to one of three conditions (N=24):
Predictive Mapping, Mere Exposure, and Baseline.
Stimuli
Category Exemplars The primary stimuli used in this
experiment were exemplars from two categories: Flurps and
Jalets. These exemplars were colorful images of “creatures”
similar to those used in prior category learning research
conducted by Deng and Sloutsky (e.g., Deng & Sloutsky,
2012). Creatures were composed of 7 binary-valued features
including a head, antennae, body, hands, feet, tail, and button.
Category membership was based on a combination of
deterministic and probabilistic features, such that each
category had a family resemblance structure. Specifically,
one feature (antennae) was perfectly associated with category
membership, and therefore deterministic, whereas five were
associated with membership in a given category in 80% of
exemplars, and therefore probabilistic. The remaining feature
occurred equally often in exemplars from both categories,
and was therefore irrelevant to category membership. This
category structure is summarized in Table 1.
Baseline Exemplars An additional set of creatures dissimilar
in appearance from the Category Exemplars were adapted
from stimuli created by Badger and Shapiro (2012) for use in
the Baseline Condition only. Like the Category Exemplars,
these stimuli were composed of binary-valued features.
Unlike the Category Exemplars, the set of Baseline
Exemplars included all possible combinations of feature
values, and was not divided into categories.
Procedure
Participants followed a link on Amazon Mechanical Turk to
the experiment, which was presented using the Gorilla™
platform. During the experiment, participants proceeded
through three phases: A Practice Phase, an Exposure Phase,
and a Supervised Category Learning Phase.
Practice Phase The purpose of the Practice Phase was to
accustom participants to the task in the subsequent Exposure
Phase (see below). The task in this phase was introduced as
the “Color Jump Game”. A schematic of this task as used in
both the Practice and Exposure Phases is provided in Fig. 1.
In the task, participants watched a star that initially
appeared on the center of the computer screen in between a
red panel on the left, and a blue panel on the right. The star
then disappeared, and reappeared on the left red panel or the
right blue panel. Participants were instructed to hit the “q”
key if the star reappeared on the left, and “p” if it reappeared
on the right. Participants were informed that they would have
only a short amount of time to respond, and that they would
receive feedback indicating whether they were correct,
incorrect, or too slow. (As noted below, corrective feedback
was not provided in the Exposure Phase version of this task.)
Participants then completed the task, which consisted of 20
trials. The star reappeared equally often on the left and right,
in a pseudorandomized order. The amount of time
Fig 1: Color Jump Game schematic. Dotted boxes denote
stimulus locations; “xxx” denotes feedback.
Table 1: Category Structure
Feature
Exemplar
D
P1
P2
P3
P4
P5
I
Flurps
E1
1
0
1
1
1
1
2
E2
1
0
1
1
1
1
3
E3
1
1
0
1
1
1
2
E4
1
1
0
1
1
1
3
E5
1
1
1
0
1
1
2
E6
1
1
1
0
1
1
3
E7
1
1
1
1
0
1
2
E8
1
1
1
1
0
1
3
E9
1
1
1
1
1
0
2
E10
1
1
1
1
1
0
3
Jalets
E1
0
1
0
0
0
0
2
E2
0
1
0
0
0
0
3
E3
0
0
1
0
0
0
2
E4
0
0
1
0
0
0
3
E5
0
0
0
1
0
0
2
E6
0
0
0
1
0
0
3
E7
0
0
0
0
1
0
2
E8
0
0
0
0
1
0
3
E9
0
0
0
0
0
1
2
E10
0
0
0
0
0
1
3
Note: The “D” feature is deterministic, “P”s 1-5 are
probabilistic, “I” is irrelevant.

participants had in which to respond after the star reappeared
started at 500ms, then decreased in 25ms increments every 5
trials such that the allowance for the last 5 trials was 425ms.
Exposure Phase In this phase, participants continued to play
the Color Jump Game. However, the star that appeared in the
Practice Phase was replaced by: 1) Category Exemplars
whose category membership perfectly predicted whether they
reappeared on the left red or right blue panel in (Predictive
Mapping Condition), 2) Category Exemplars whose category
membership was unrelated to their reappearance locations
(Mere Exposure Condition), or 3) Baseline Exemplars in the
Baseline Condition. Participation in each of these conditions
was randomly assigned between subjects. To keep any
predictive mapping between category membership and
reappearance location implicit, participants were only given
feedback when they responded too slowly, but were not told
whether their responses were correct or incorrect.
In all conditions, participants completed 80 trials (8 blocks
of 10 trials) of the Color Jump Game. To increase the
potential speed and accuracy benefits of detecting any
predictive mapping between category membership and
reappearance location, the difficulty of the task was increased
every two blocks by reducing the RT allowance. Specifically,
the RT allowance began at 425ms, then decreased by 25ms
every 2 blocks, such that the allowance was 350ms for the
final 2 blocks. Participants were alerted to this reduction in
RT allowance at the beginning of each block in which it
occurred. The outcome measure of interest in this phase is the
rate at which participants’ RTs for accurate trials improved.
Supervised Category Learning Phase The purpose of this
phase was to investigate how well participants in each of the
conditions learned to classify the Category Exemplars into
two categories under traditional supervised learning
conditions. In this phase, participants in all conditions
completed the same task with the same stimuli. First,
participants were informed that they would be learning about
two kinds of creatures: Flurps and Jalets. Participants were
told that for each creature, they should identify whether they
think it is a Flurp or Jalet using onscreen buttons, after which
they would receive corrective feedback.
Participants then proceeded through 30 trials of this task (3
blocks of 10 trials each). On each trial, participants were
presented with a Category Exemplar, and asked whether it
was a Flurp or Jalet. After responding, participants received
a message saying “That’s a [Flurp/Jalet]! preceded by a green
checkmark if they had responded correctly, or a red X if they
had responded incorrectly. The outcome measure of interest
in this phase was participants’ accuracy at categorizing
Category Exemplars over the course of the 3 blocks.
Results
Analyses were conducted in the R environment (R
Development Core Team, 2008) using functions in base R,
1
Analysis of accuracy also yielded no condition differences.
the lmer function for mixed effects regression from the lme4
package (Bates, Maechler, Bolker, & Walker, 2015), and the
Anova function for deriving F-statistics and p-values for
regression models from the car package (Fox & Weisberg,
2011).
Exposure Phase Overall, RT decreased over the Exposure
Phase. Specifically, in a mixed regression model with RT on
accurate trials as the dependent variable, Trial Number as a
fixed effect, and participant as a random effect, Trial Number
had a significant, negative coefficient (β = -0.65 ms, SE =
0.03, p < .0001).
To test whether exposure to Category Exemplars in either
the Predictive Mapping or Mere Exposure conditions
concurrently influenced performance on the Color Jump
Game during the Exposure phase, we fit regression models
for each participant in which Trial Number predicted RT on
accurate trials. We then used the regression coefficient for
Trial Number as a measure of the rate of change in RT over
the course of this phase. Finally, we analyzed whether rate of
change in RT varied across the three conditions. To conduct
this analysis, we fit a regression model in which Condition
predicted rate of RT change. This model revealed no
significant relationship between Condition and rate of RT
change (F(2,69)=0.10, p=.90)
1
(Fig. 2).
Supervised Category Learning Phase The purpose of this
phase was to test whether the success with which participants
learned to categorize the Category Exemplars given explicit
supervision varied according their preceding experience in
the Exposure Phase. We therefore first tested whether
participants in each condition were able to learn the
categories under explicit supervision. We found that
participants in the Predictive Mapping and Mere Exposure
conditions achieved above-chance performance in Block 1 of
the Supervised Category Learning Phase (both ps < .01); by
Block 2, participants in all three conditions achieved above-
chance performance (all ps < .01).
To investigate whether experience in the Exposure Phase
influenced the degree to which participants successfully
learned the categories, we took as our outcome variable the
accuracy with which participants categorized Category
Exemplars in each of the 30 trials in this phase, and tested
whether accuracy varied with Condition (Predictive
Mapping, Mere Exposure, and Baseline). Because the 30
trials were organized into three blocks, over which
categorization accuracy may improve, we also tested whether
accuracy varied across blocks. Specifically we fit an omnibus
mixed regression model with accuracy as the dependent
variable, Condition, Block, and their interaction as fixed
effects, and participant and trial number within a block as
random effects. We then derived F-statistics and p-values for
each of the fixed effects. This analysis revealed significant
main effects of both Condition (F(2,69)=5.22, p=.008), and
Block (F(1,2076)=20.82, p<.0001), which did not interact
(p=.16) (see Fig. 2).

To further investigate these main effects, we compared
each pair of Blocks or Conditions using three fixed effects
models. These models included the same components as the
omnibus model, but each used data from only one pair of
Blocks or Conditions. The pairwise comparisons of Blocks
revealed that Block 1 accuracy was significantly lower than
in Blocks 2 or 3 (both ps < .01), but that accuracies in Blocks
2 and 3 did not significantly differ (p=.22). More importantly,
the pairwise comparisons of Conditions revealed that the
Predictive Mapping condition exceeded both the Baseline
(F(1,46)=8.51, p=.005) and Mere Exposure (F(1, 46)=6.55,
p=.014) conditions. In contrast, accuracies in the Mere
Exposure and Baseline conditions did not significantly differ
(F(1,46)=.118, p=.733). In sum, participants in the Predictive
Mapping condition learned the categories more successfully
than either participants in the Mere Exposure or those in the
Baseline conditions, whereas the success of category learning
for participants in the latter two conditions did not differ.
Discussion
Prior evidence has revealed that exposure to regularities in
which perceptual features co-occur in category exemplars can
implicitly facilitate category learning. However, we know
little about whether exposure to regularities in which
category membership predicts other perceptual events or
goal-directed actions can similarly facilitate category
learning. Moreover, we know little about how either form of
exposure may allow us to more readily learn categories under
explicit supervision.
In this experiment, we exposed participants to exemplars
of two categories that possessed distinct perceptual feature
co-occurrence regularities. This exposure took place within a
cover task, under conditions in which category membership
did (Predictive Mapping) or did not (Mere Exposure) predict
task events to which participants responded. Importantly, this
exposure was implicit: Participants were neither informed
that the stimuli they viewed were members of two categories,
nor alerted of any relationship between stimulus appearance
and task events, or given corrective feedback about their
responses to task events. For comparison, Baseline
participants completed this task with other, non-categorized
stimuli. Subsequently, all participants were taught the two
categories under traditional, explicitly supervised conditions.
Although we found no variation across exposure
conditions in performance on the cover task, participants who
were exposed to the category exemplars under Predictive
Mapping subsequently learned the categories under explicit
supervision substantially more successfully than participants
in either the Mere Exposure or Baseline conditions. In
contrast, participants in the Mere Exposure condition were
not measurably more accurate than those in the Baseline
condition. These findings suggest that exposure to category
exemplars under conditions in which category membership
predicts other perceptual events and goal-directed actions
improves the readiness with which categories are learned
given explicit supervision. Exposure to predictive regularities
may therefore promote latent learning (e.g., Kimble &
BreMiller, 1981), in which such exposure promotes the
formation of mental representations that render learners more
receptive to explicit instruction.
Open Questions
The present experiment sets a foundation for further
investigation into both the influence on category learning of
category-relevant prediction regularities, and how exposure
to category-relevant regularities may facilitate subsequent
supervised category learning.
One question to explore in future research is raised by the
superiority of the Predictive Mapping condition during
supervised category learning, despite the lack of condition
effects during the Exposure Phase. Specifically, the
superiority of supervised category learning in the Predictive
Mapping condition suggests that participants were sensitive
to the regularity with which category membership predicted
Exposure Phase task events and appropriate responses. Such
sensitivity could, in principle, have allowed participants to
increasingly anticipate events in the Exposure Phase task, and
therefore improve more rapidly than participants in the other
conditions. However, we observed no such effect, as
evidenced by comparable RT decreases across conditions in
the Exposure Phase (the Color Jump Game). We therefore do
Fig 2: RTs during Exposure Phase (top) and Accuracies
during Supervised Category Learning Phase (bottom)

not have evidence of sensitivity to predictive regularities
while they were being experienced. One possible explanation
for the lack of condition differences during the Exposure
Phase is that the rate at which we shortened the RT allowance
in the Exposure Phase task pushed participants to the limit of
the rate at which they were able to speed up. Future research
might address this issue in multiple ways. For example, a
future version of this experiment might include “guess” trials
interspersed within the Exposure Phase, in which participants
are asked to predict task events. Sensitivity to predictive
regularities could therefore manifest as the gradual
achievement of above-chance performance on guess trials in
the Predictive Mapping, but not the Mere Exposure
condition.
Another key direction for future research is to illuminate
the nature of what is learned via implicit sensitivity to
category-relevant regularities. For example, does exposure to
these regularities increase attention to category-relevant
features, and away from those that are irrelevant?
Conclusion
In this experiment, we found that supervised category
learning was facilitated when preceded by implicit exposure
to exemplars from family resemblance categories under
conditions in which category membership predicted other
perceptual events and goal-directed actions. This facilitation
occurred above and beyond the effect of mere exposure to the
exemplars themselves. These findings point to implicit
learning due to exposure to category-relevant predictive
regularities that in turn helps learners capitalize on explicit
information about category membership.
Acknowledgements
This work was supported by National Institutes of Health
Grants R01HD078545 and P01HD080679 to Vladimir
Sloutsky.
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