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de Leeuw, J R et al 2016 The Eects of Categorization on Perceptual Judgment are
Robust across Dierent Assessment Tasks.
Collabra,
2(1): 9, pp. 1–9, DOI: http://dx.doi.
org/10.1525/collabra.32
Learning to categorize stimuli in a new way can change
how those stimuli are perceived or judged. This phenom-
enon is called learned categorical perception (CP). There
are numerous kinds of reported CP effects (for a review,
see [1]). Learning that two stimuli belong to the same cat-
egory can increase their similarity or perceptual confus-
ability, an effect known as compression (e.g., [2]), while
learning that two stimuli belong to different categories
can have the opposite effect, often called expansion
(e.g., [3–4]). Categorizing stimuli based on particular fea-
tures may increase sensitivity to those features, regardless
of whether the stimuli belong to the same or different cat-
egories, or it may reduce sensitivity to features that are
not relevant to the categories (e.g., [3]).
Although there are these distinct possible conse-
quences of learning to categorize stimuli, most studies of
learned CP only report finding one of the possible effects,
even though different kinds of CP effects are not logically
mutually exclusive. Determining why categorization train-
ing leads to different CP effects in different experimental
contexts is an important step towards a thorough under-
standing of the mechanisms that cause CP, as the differ-
ent CP effects implicate different kinds of mechanisms.
For example, some models predict a change in overall
sensitivity to a dimension after categorization training
due to dimension-based attention weighting (e.g. [5–6]).
However, sensitization to a particular range of values
along a dimension requires different kinds of models
that allow for changes in perceptual sensitivity at a sub-
dimensional level (e.g. [7–8]).
Several studies have demonstrated that whether CP
is observed at all depends on various factors including
(1) the availability of verbal labels during perceptual
testing [9], (2) the particular kind of perceptual assess-
ment used to determine whether CP is present [10], and
(3) how stimulus morphspaces are created [11]. However,
few studies have reported differences in the kind of CP
effect observed based on experimental manipulations,
though there are exceptions. For example, Goldstone,
Lippa, and Shiffrin [12] and Livingston and Andrews [13]
both reported two qualitatively different CP effects exhib-
ited by the same subjects when they were tested in two
ORIGINAL RESEARCH REPORT
The Eects of Categorization on Perceptual Judgment
are Robust across Dierent Assessment Tasks
Joshua R. de Leeuw*
, Janet K. Andrews†, Kenneth R. Livingston† and Benjamin M. Chin‡
Learned visual categorical perception (CP) eects were assessed using three dierent measures (similarity
rating, same-dierent judgment, and an XAB task) and two sets of stimuli diering in discriminability
and varying on one category-relevant and one category-irrelevant dimension. Participant scores were
converted to a common scale to allow assessment method to serve as an independent variable. Two dif-
ferent analyses using the Bayes Factor approach produced patterns of results consistent with learned
CP eects: compared to a control group, participants trained on the category distinction could better
discriminate between-category pairs of stimuli and were more sensitive to the category-relevant dimen-
sion. In addition, performance was better in general for the more highly discriminable stimuli, but stimulus
discriminability did not inuence the pattern of observed CP eects. Furthermore, these results were
consistent regardless of how performance was assessed. This suggests that, for these methods at least,
learned CP eects are robust across substantially dierent performance measures. Four dierent kinds
of learned CP eects are reported in the literature singly or in combination: greater sensitivity between
categories, reduced sensitivity within categories, increased sensitivity to category-relevant dimensions,
and decreased sensitivity to category-irrelevant dimensions. The results of the current study suggest
that the cause of these dierent patterns of CP eects is not due to either stimulus discriminability or
assessment task. Other possible causes of the dierences in reported CP ndings are discussed.
Keywords: categorization; categorical perception; compression; expansion; learning; similarity; online
experiments; Bayes factor
* Indiana University, Bloomington, IN, USA
† Vassar College, Poughkeepsie, NY, USA
‡ University of Pennsylvania, Philadelphia, PA, USA
Corresponding author: Joshua R. de Leeuw (josh.deleeuw@gmail.com)
de Leeuw et al: Categorization Eects Don’t Depend on TaskArt. 9, page 2 of 9
different ways. Thus, one possible reason for the diversity
of CP effects is that different tasks used to assess CP are
sensitive to different aspects of CP or invoke different pro-
cesses, only some of which exhibit particular CP effects.
In addition to the task used to measure CP, it is possi-
ble that incidental differences in stimuli between experi-
ments are responsible for the different kinds of CP effects
observed. If subjects learn that stimuli with small differ-
ences belong in different categories, successful categori-
zation necessitates the ability to differentiate the stimuli,
which might naturally lead to expansion effects. However,
if the differences between stimuli are obvious prior to
training, then expansion might be less likely to occur.
Pevtzow and Harnad [14] used texture patterns and found
larger expansion effects for stimulus sets that were harder
to differentiate, which is consistent with this idea. This
would also explain why studies using near-JND stimulus
differences have tended to show expansion (e.g., [3–4])
while studies using stimulus differences well above JND
have tended to report compression effects instead (e.g., [2]).
However, in many cases, the difference in stimulus
discriminability across studies coincided with a difference
in dependent measure, complicating the interpretation.
In the experiment reported here, we directly tested the
influence of both stimulus discriminability and assess-
ment task on CP. We created an artificial stimulus set that
varied on two dimensions and selected two subsets of
stimuli from this set, one with only half as much varia-
tion between neighboring pairs as the other. We expected
that the stimulus set with less variation would be more
likely to produce expansion effects, while the stimulus
set with more variation would be more likely to produce
compression effects. Three commonly used tasks were
implemented to assess CP: a similarity rating task, a same-
different task, and an XAB forced-choice task. Different
tasks may not invite equivalent perceptual/cognitive strat-
egies, so this is a reasonable potential source of different
patterns of results between studies. For example, subjec-
tive rating tasks such as similarity judgments may invite
strategic responses (altering the rating based on the cat-
egory labels without warping of perceptual similarity) and
thus produce CP effects even when objective measures
such as same-different or XAB do not, especially in cases
where perceptual learning is not necessary for categori-
zation (i.e., when stimuli are highly discriminable). If this
is the case, assessment task and stimulus discriminability
should interact. We included both the XAB and same-
different tasks since both are frequently used in the literature,
though we expected them to produce similar results.
Method
Participants
The Vassar College Institutional Review Board approved
the procedures used in this study and consent was obtained
as part of the online data collection. We recruited 564
participants through Amazon Mechanical Turk (AMT).1
Eight subjects were excluded from the analysis because
of a bug that allowed them to complete more than one
condition of the experiment, leaving 556 participants.
Materials
The experiment was developed using jsPsych, a software
library for building online experiments [15]. Stimuli were
cell-like shapes that varied on two dimensions, shape and
tail length, created using Mathematica 8.0 (for details,
consult the stimulus creation script available in the pro-
ject repository). We generated two sets of stimuli: a high
discriminability (HD) set and low discriminability (LD) set.
The LD stimuli had half as much variation as correspond-
ing HD stimuli (see Figure 1). The stimulus space was
Figure 1: The two sets of stimuli used in the experiment. Shape varies along the X axis and tail length varies across the
Y axis.
de Leeuw et al: Categorization Eects Don’t Depend on Task Art. 9, page 3 of 9
6 (shape) by 6 (tail length) for both sets. We chose the val-
ues along each dimension by arbitrarily selecting the two
endpoints and creating equal numeric intervals between
neighboring items; however, we did not test whether the
psychological distance between neighbors was equivalent
throughout the space, nor do we expect that it is.
When shape was the category relevant dimension, the
category boundary was between the 3rd and 4th stimuli
on the shape dimension and the tail length dimension
was irrelevant. When tail length was the category relevant
dimension, the category boundary was between the 3rd
and 4th stimuli on the tail length dimension and the
shape dimension was irrelevant.
Procedure
Participants were randomly assigned to one of 24 con-
ditions: 2 training type (control v. category training) X 2
stimulus sets (HD v. LD) X 3 type of assessment (similarity
v. same-different v. XAB) X 2 category-relevant dimensions
(shape v. tail length). There were 18–28 participants per
condition after excluding subjects who failed the training
phase (see Results). The category-relevant dimension vari-
able was not of theoretical interest here but is included in
the analyses for completeness, as suggested by an anony-
mous reviewer.
Training. Participants who were assigned to a training
condition completed an adaptive training protocol by iter-
ating through multiple blocks of training until category
assignments were learned for all stimuli. In each block of
the procedure, all 36 stimuli were shown one at a time
and categorized as either a ‘Tig’ or a ‘Bep’ by the partici-
pant. Participants were told that they would initially need
to guess which category a cell belonged to, but would
receive feedback indicating the correct category for each
cell. When a stimulus had been correctly categorized in
four consecutive blocks, the stimulus was removed from
the training set. If at any point fewer than five stimuli
were left in the training set, stimuli that had already been
learned were randomly included in the block (but these
stimuli were considered learned, even if an error was
made). Once all stimuli had been learned, the training
ended. If a participant got fewer than 60% of the items
correct in a round, then the round did not count towards
the four consecutive blocks (to prevent guessing strate-
gies). If a participant got fewer than 60% correct for 5 con-
secutive rounds, then training ended and the participant
was considered to have failed training.
Post-training categorization test. Once participants
completed the adaptive training, they categorized each of
the 36 stimuli without feedback in a single block. Stimuli
were presented in a random order and remained on screen
until the participant gave a response. A blank screen was
displayed for 1500ms between stimuli.2
Post-training assessment tasks. Participants com-
pleted one of three different assessment tasks. Those who
received category training completed the task immedi-
ately after the category learning test, while control partici-
pants only performed the assessment task.
Similarity. In the similarity task, participants saw two
stimuli sequentially. Each stimulus was visible for 750ms,
with a blank screen displayed for 1000ms between
stimuli. The participant dragged a movable slider to indi-
cate how similar the two stimuli were using a continuous
(no segmentation) scale anchored by the labels “most sim-
ilar” and “least similar”. There were 9 different pair types
that could be presented: (Tig-Tig pairs, Bep-Bep pairs, or
Tig-Bep pairs) X (1, 2, or 3 city block units of distance
between items in a pair). Each of the 9 different types was
selected exactly 4 times, but the particular exemplars that
made up each pair were selected at random from all pos-
sible pairs that satisfied the constraints.
Same-different. In the same-different task, partici-
pants saw two stimuli sequentially, each for 750ms, with
a blank screen displayed for 1000ms between stimuli.
They pressed one of two keys to indicate whether the
stimuli were the same or different. There were 4 blocks
of 54 pairs of stimuli. Each block consisted of 27 identi-
cal pairs and 27 non-identical pairs. The 27 non-identical
pairs were selected according to the same policy as used in
the similarity task, except that only 3 pairs per type were
chosen instead of 4 in order to limit the overall length of
the experiment.
XAB. In the XAB task, participants saw a target stimu-
lus (X) for 750ms, followed by a blank screen for 1000ms,
and then the simultaneous presentation of two stimuli (A
and B) for 750ms. Participants pressed a key to indicate
whether A or B was identical to X. There were 4 blocks of
36 pairs of stimuli, selected in the same manner as in the
similarity task.
Data and Analysis Files
Our data and analysis files are available in an Open Sci-
ence Framework repository at https://osf.io/54rve/.
Results
Training
A total of 39 participants failed training and were excluded
from the analysis. Of those 39 exclusions, 38 were for
participants for whom the less salient dimension of tail
length was the category-relevant dimension. Over all
training conditions, the mean number of trials required
to complete training was 220 with a standard deviation of
48. The minimum number of trials to complete training
by any subject was 149, and the maximum was 386.
CP Eects
Between-category versus within-category pairs. The
data set for analysis of between vs. within category pairs
was restricted to pairs that varied only on the category-rel-
evant dimension, with a maximum distance of 2 between
the items. This emphasizes the effect of category learning
on measures of CP. The pairs that were 3 units apart were
not used because all such pairs were between category
and have no within-category counterparts. The between-
category and within-category scores for each participant
consisted of mean rating for the similarity task and pro-
portion of correct responses for the same-different and
XAB tasks on the relevant subset of item pairs.
Dimension inuence. This analysis was restricted to
item pairs differing by 1 or 2 units on one dimension (and
de Leeuw et al: Categorization Eects Don’t Depend on TaskArt. 9, page 4 of 9
not differing on the other dimension). Each participant
received a score (mean similarity rating or mean propor-
tion correct) for the relevant dimension and the irrelevant
dimension.
Conversion to a common scale. In order to make
performance across the three different assessment tasks
comparable and to directly test whether assessment task
plays a role in producing learned CP effects, we took each
participant’s scores as explained above and transformed
them as follows: First, the similarity scores were reverse
coded so that higher scores represented greater differen-
tiation of items, to be comparable to the same-different
and XAB measures. For the between-category versus
within-category pairs analysis, we determined the mean
and standard deviation of all raw scores for a given assess-
ment task for the items used in that analysis. Each par-
ticipant’s score was transformed by subtracting that mean
and dividing by that standard deviation. The same thing
was done for the dimension influence data. Transforming
the data in this way provides a common scale for the three
assessment tasks, making it easier to incorporate assess-
ment task into the statistical analysis as an independent
variable. The nature of the transformation is such that
there is no possibility of a main effect of assessment task
on performance; however, that would not be possible to
determine using the raw scores either, and the transfor-
mation makes it possible to determine whether assess-
ment task interacts with other independent variables to
produce different patterns of CP effects, which is the pri-
mary goal.
Statistical analysis. We used the Bayes factor
approach for all statistical analyses because of the impor-
tance of being able to quantify relative evidence for the
null and alternative hypotheses concerning assessment
method, stimulus discriminability, and their interactions
with other independent variables. Statistical analyses were
done using the BayesFactor package [16] in R [17].
The BayesFactor package places a sensible default,
vaguely-informed prior on the standardized effect size
of the model predictors [18]. The only parameter for the
prior is a scaling factor, which controls how concentrated
the prior probability density is around the standardized
effect size of 0. We used a scaling factor of 0.5 on the prior,
which puts half of the prior probability on standardized
regression coefficients between -0.5 and 0.5. This reflects
an expectation of finding small-to-moderate effects.
Smaller scaling factors would generally increase the odds
in favor of the alternative models, while larger scaling fac-
tors would increase the odds in favor of the null.
Between-category versus within-category pairs. The
first analysis was designed to determine whether boundary
CP effects occurred and, if so, whether they varied accord-
ing to assessment task and/or stimulus discriminability.
Using the transformed pair type dependent measure,
we tested a set of linear models to quantify the support
for each model. First we tested just for main effects of
four of the five independent variables: stimulus set (HD
v. LD), training type (control v. category training), pair
type (between-category v. within-category), and category-
relevant dimension (shape v. tail length). We did not test
for a main effect of assessment type because the depend-
ent variable was normalized to remove any main effects
of assessment type (see previous section). For each effect,
we found the Bayes factor for a model that included the
effect and the random effect of subject against the model
that just included the random effect of subject. The results
are shown in the Appendix. There is very strong evidence
for models including a main effect for stimulus set, pair
type, and category-relevant dimension. The main effect of
stimulus set can be seen in the smaller graphs on the right
of Figure 2a, which show that HD performance consist-
ently tends to be above 0 and LD performance tends to be
below 0. The very strong main effect of category-relevant
dimension is presumably due to the boundary analysis
being restricted to item pairs differing only on the cate-
gory-relevant dimension and, as shown by the training
data, the categories were much harder to learn when they
were defined by tail length rather than shape.
We then tested the model that includes all main effects,
the random subject effect, and the training type by pair
type interaction (which would constitute evidence for a
learned CP effect) against the model that contains only
the main effects and the random subject effect. The result
was strong support for the model that includes the inter-
action and is shown in Figure 2a; BF10 = 71.66
The next analysis tested whether assessment type
interacted with the CP effect. We tested all models that
included the assessment type by training type by pair
type interaction or other higher-order interactions of this
3-way interaction with the other independent variables,
against the model that included only the CP interaction,
the main effects, and the random subject effect. In other
words, we tested all of the models in which the CP inter-
action (training type by pair type) interacted with assess-
ment type. This set of model comparisons is a direct test
of whether the assessment type modulates the CP interac-
tion. None of the models that include interactions with
the CP interaction received more support than the model
with just the CP interaction. For all but one of the 15 tests,
the Bayes factor was better than 3:1 in favor of the CP-only
model (see Appendix for full results). A similar analysis
was performed to test all models that include interactions
between the CP interaction and the stimulus set and once
again, none of the models that include those interactions
received more support than the corresponding model
without the interaction. However, the Bayes factors were
generally equivocal between the CP-only model and the
interaction of stimulus set with CP. The three-way inter-
actions all had Bayes factors between 1 and 3 in favor of
the CP-only model. Only the higher order interactions and
models with multiple interactions had Bayes factors larger
than 3 in favor of the CP-only model. The larger Bayes
factors in favor of the CP-only model are to be expected
as a natural result of increasing the complexity of the
model when all the lower-order components of the model
slightly favor the simpler CP-only model.
Dimension inuence. The second analysis was designed
to determine whether dimensional sensitivity CP effects
occurred and, if so, whether they varied according to
assessment task and/or stimulus discriminability. The
de Leeuw et al: Categorization Eects Don’t Depend on Task Art. 9, page 5 of 9
Figure 2: (a) Performance on between-category versus within-category pairs by the control and training groups. (b) Performance on pairs differing only on the irrelevant versus
the relevant dimension by the control and training groups. In both panels, the large graph on the left shows the overall interaction across task type. The three graphs in the
middle show the interaction for each task, and the six smaller graphs on the right show the interaction for each task and each stimulus type. Higher scores on the Y axis represent
greater differentiation between items.
de Leeuw et al: Categorization Eects Don’t Depend on TaskArt. 9, page 6 of 9
exact same analyses described above were carried out on
the transformed dimensional influence dependent meas-
ure. There was very strong evidence for models including
a main effect for stimulus set and varying dimension (see
Appendix for results) and the varying dimension by train-
ing type interaction, which is shown in Figure 2b (BF10 =
166.43). For all models including interactions between
the CP-interaction and assessment type and/or stimulus
set, the Bayes factor was better than 4:1 for the CP-only
model (see Appendix for full results).
Bayes factor t-tests were also performed to determine
the specific nature of the CP effects shown in the interac-
tions with training type. We used the default Bayes factor
t-test method from the BayesFactor R package, with a scal-
ing factor of 0.5 on the prior, indicating a prior belief that
the standardized effect size will generally be small when
the null is false. The four types of CP effects imply that
the effect will be in a particular direction, e.g., compression
requires that trained subjects show less differentiation
on within-category judgments. We used alternative
priors that built in this one-sided constraint [19]. For the
boundary analysis, BF10 = 92.12 for the between-category
pairs and BF01 = 13.12 for the within-category pairs, clearly
demonstrating expansion but not compression. These
data strongly favor the null in the case of compression.
For the dimension analysis, BF10 = 11.16 for the relevant
dimension and BF10 = 6.15 for the irrelevant dimension,
suggesting stronger evidence of increased sensitivity to
the relevant dimension, but also support for decreased
sensitivity to the category-irrelevant dimension. We also
tested variations in the scaling factor to examine the
robustness of the BF t-tests to different prior beliefs. Even
with a scaling factor of 1.0, double the scaling factor we
used, the Bayes factors for expansion, acquired equiva-
lence, and acquired distinctiveness were still all better
than 3.5 to 1 in favor of the alternative. Scaling factors
less than 0.5 further increased the relative support for the
alternative models.
Discussion
This study was designed to use procedures typical of the
research literature on learned visual CP effects. Accord-
ingly, CP effects were examined in the context of success-
ful category training with an artificial stimulus set. Of the
four possible patterns of learned CP effects, we found
positive evidence for expansion (enhanced ability to dis-
tinguish between-category item pairs as a result of learn-
ing the categories), increased sensitivity to the category-
relevant dimension, and decreased sensitivity to the
category-irrelevant dimension. There was no evidence for
compression. In fact, the data indicated strong support for
the null that there was no compression effect.
The main purpose of this study was to explore two can-
didate reasons, stimulus discriminability and assessment
task, for why different studies on learned CP find differ-
ent patterns of CP effects. The stimulus discriminabil-
ity manipulation was effective in that performance was
clearly reduced for the LD stimuli relative to the HD stim-
uli. However, there was no strong evidence that the CP
effects were influenced by stimulus discriminability – the
analysis was generally equivocal about whether stimulus
discriminability interacted with boundary-based CP, and
the Bayes factor favored the CP-only model for the dimen-
sion effect – despite some prior evidence that expansion
is stronger when stimuli are more difficult to discrimi-
nate [14]. This result also suggests that the cause of expan-
sion as opposed to compression effects in the literature is
not due to stimulus discriminability.
Assessment task also did not influence the pattern of CP
effects obtained; the evidence was overwhelmingly in sup-
port of the null hypothesis for interactions of assessment
task with stimulus discriminability, category training, pair
type, and dimensional relevance. This is an important
result as no previously published study that we know
of has directly compared different methods of assessing
learned CP using the same stimuli and categories. This
suggests that the variations in CP effects reported in the
literature are not due to the use of different tasks, at least
among similarity rating, same-different judgments, and
XAB, which are the most common tasks used. It is reas-
suring that the CP effects obtained were so robust across
these very different tasks, and this also suggests that cur-
rent models of learned CP, which do not address the role
of assessment task, may well not need to do so.
This result can be reconciled with the few previous stud-
ies that suggested that assessment task might play a role.
Goldstone, Lippa, and Shiffrin [12] used morphed face
stimuli and a similarity rating task. They found expansion
using a traditional between-category versus within-cate-
gory pair analysis, but found compression when stimuli
were judged relative to a neutral, uncategorized face. Thus
their differing patterns of results were not due to different
assessment tasks but rather a different type of compari-
son stimulus. Livingston and Andrews [13] used artificial
“alien” figures and obtained expansion using a similarity
rating task and compression using a same-different task,
but without rendering data from the two tasks compara-
ble in the way that we did here and treating task as an
independent variable, we cannot but sure that task had
a significant effect on the pattern of learned CP results.
Even if it did, it would be important to determine whether
that result is replicable and genuinely conflicts with the
results of the current study.
The only other apparent counterexample of which we
are aware comes from Gerrits and Schouten [10], who
obtained CP effects under some conditions and not oth-
ers as a function of assessment task. These authors did
directly compare performance using two different assess-
ment tasks, but their study differed in many critical ways
from a typical visual learned CP study such as the one
reported here: the stimuli consisted of speech sounds and
no category training was involved. The fact that there was
no learning means that compression vs. expansion effects
cannot be compared as was done in the present study.
What Gerrits and Schouten [10] did observe was presence
of CP for vowels using one measure (2I2AFC or two inter-
val two alternative forced choice) but not when using a
variant of that measure (4I2AFC or four interval two alter-
native forced choice). They conclude that the disappear-
ance of the CP effect in the latter case is owing to features
de Leeuw et al: Categorization Eects Don’t Depend on Task Art. 9, page 7 of 9
of the task that render listeners “incapable of using availa-
ble phonetic information that would have improved their
performance” ([10], p. 375). This seems a limiting case in
which failure to process the information relevant for a
category distinction results in failure to show CP, which
is hardly surprising. The fact that there are measurement
tasks that can have this effect is interesting but is not in
itself inconsistent with the conclusion that assessment
task does not affect whether CP effects are the result of
compression vs. expansion when they do occur.
It is possible that different results would occur even for
visual learned CP and these same three tasks if the stimuli
and/or category structures were very different from those
used in the current study. In fact, if individually unique and
distinctive stimuli were used, a same-different or XAB task
would surely fail to show learned CP effects that a similar-
ity rating task might well show (as occurred, for example,
in a study by Livingston, Andrews, and Dwyer [20] that
produced compression using real tropical fish as stimuli
and similarity ratings as the assessment method). But very
few studies use such stimuli, and the current results sug-
gest that factors other than assessment task and discrimi-
nability are likely the cause of the variations in learned CP
effects reported in the literature.
One such factor may well be category structure, but
very few studies have examined this directly. Reppa and
Pothos [21] used simple geometric stimuli and reported
differences in the pattern of learned CP results as a func-
tion of category structure. Categories with one relevant
and one irrelevant dimension of variation and non-linearly
separable categories both produced compression, while
a diagonal category structure in which both dimensions
were relevant produced expansion. Given that the cate-
gories in the study reported here were also based on one
dimension of variation yet produced expansion with the
same similarity rating task used by Reppa and Pothos, it
is clear that more than just category structure is involved.
Another possibility is that the variation in reported find-
ings is the result of statistical noise. Learned CP effects
caused by a relatively short period of training are gener-
ally not very large effects, and sampling noise combined
with low power could explain the variation in effects. Our
data illustrate this point quite well. Note that in Figure 2b
(central panel), the same-different and XAB tasks appear to
show opposite results, with increased sensitivity to the rel-
evant dimension in the same-different task and decreased
sensitivity to the irrelevant dimension in the XAB task.
If these tasks were analyzed separately, such as typically
occurs when comparing results across experiments, we
would wrongly conclude that task does matter because we
would observe a statistically significant acquired equiva-
lence effect in the XAB task and a statistically significant
acquired distinctiveness effect in the same-different task.
However, the full statistical model makes it quite clear
that these variations are simply noise and that the general
pattern is stable across task. An analogous situation may
be affecting the literature as a whole when comparisons
between the patterns of statistically significant results are
made without considering whether or not the differences
in the patterns themselves are statistically significant [22].
Comparing across different experiments in the literature
is therefore often misleading.
A thorough examination of the learned CP literature
shows that studies differ not only in the specific stimuli,
features or dimensions, and category structures used,
but also in the specific ways in which the effects of cat-
egory learning are measured. Some of these differences
are subtle but could be important for determining what
conditions create each of the four types of learned CP
effects identified at the outset. For example, for studies
that only analyze between-category versus within-cate-
gory pair data, it will be unknown whether dimensional
sensitivity effects also occurred. In addition, both types
of analyses may be influenced by the composition of
the item pairs used for collecting and/or analyzing the
data. Solid evidence that stimulus discriminability and
assessment task do not determine the observed pat-
tern of effects allows us to turn our attention to these
other factors in order to better understand the complex
phenomenon of learned CP.
Supplementary Files
The supplementary material for this article can be found
here: http://dx.doi.org/10.1525/collabra.32
Acknowledgments
This research is supported by a National Science Foun-
dation Graduate Research Fellowship to the first author
under Grant No. DGE-1342962. We thank the Vassar Col-
lege Phoebe H. Beadle Science Fund Endowment and
Research Committee for financial support, and Samuel
Schwamm and Calais Larson for assistance with the pro-
ject. Earlier versions of this work were originally reported
at the 35th and 36th Annual Meetings of the Cognitive
Science Society.
Competing Interests
The authors declare that they have no competing interests.
Notes
1
Samples from AMT tend to replicate laboratory find-
ings, though category-learning tasks on AMT have
produced mixed results [23]. Because the key meth-
odological consideration for our study is that all par-
ticipants in learning conditions have acquired the cat-
egory structure before doing the discrimination trials,
we used an adaptive training protocol that ensures
participants learned the category structure before pro-
gressing to the testing phase.
2
A data recording error resulted in missing data for
more than 50% of the participants for this particular
task, so we do not report any analysis of this data.
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de Leeuw et al: Categorization Eects Don’t Depend on Task Art. 9, page 9 of 9
How to cite this article: de Leeuw, J R, Andrews, J K, Livingston, K R and Chin, B M 2016 The Eects of Categorization on
Perceptual Judgment are Robust across Dierent Assessment Tasks.
Collabra,
2(1): 9, pp. 1–9, DOI: http://dx.doi.org/10.1525/
collabra.32
Submitted: 18 August 2015 Accepted: 27 May 2016 Published: 28 July 2016
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