The Role of the Prefrontal Cortex in Inductive Reasoning: An fNIRS Study
Layla Unger (LUnger@andrew.cmu.edu)
Carnegie Mellon University, Department of Psychology, 5000 Forbes Avenue
Pittsburgh, PA 15213 USA
Jaeah Kim (email@example.com)
Carnegie Mellon University, Department of Psychology, 5000 Forbes Avenue
Pittsburgh, PA 15213 USA
Theodore J. Huppert (firstname.lastname@example.org)
University of Pittsburgh, Center for the Neural Basis of Cognition, Clinical Science Translational Institute, Departments of
Radiology and Bioengineering
Pittsburgh, PA 15260 USA
Julia Badger (email@example.com)
University of Oxford, Department of Experimental Psychology, 9 South Parks Road
Oxford, UK OX1 3UD
Anna V. Fisher (Fisher49@andrew.cmu.edu)
Carnegie Mellon University, Department of Psychology, 5000 Forbes Avenue
Pittsburgh, PA 15213 USA
This study examined neural activity associated with inductive
inference using functional Near Infrared Spectroscopy
(fNIRS). Induction is a powerful way of generating new
knowledge by generalizing known information to novel items
or contexts. Two key bases for identifying targets for induction
are perceptual similarity, and rules that specify category-
relevant features. Similarity- and rule-based induction have
been argued to represent distinct mechanisms, such that only
rule-based induction requires executive function processes
associated with the prefrontal cortex (PFC), namely: active
maintenance of representations and inhibition of salient but
irrelevant features. Here, we address the lack of direct
empirical evidence supporting this possibility by recording
PFC activity using fNIRS while adult participants (n=24)
performed an inductive inference task. We found that PFC
activity during induction was greater when participants had
been taught a category-inclusion rule versus when participants
could only rely on overall similarity.
Keywords: inductive inference; fNIRS; PFC
Inductive inference is a powerful component of learning
because it allows us to use what we already know to derive
new information. For instance, knowledge that one’s cat has
a four-chambered heart can be generalized to other entities,
such as one’s dog. However, in order for inductive inference
to provide a useful source of information, targets for
generalization must be identified based on bearing some
relationship to the known entity.
Behavioral evidence from studies of adult cognition and
developmental research suggest a distinction between
similarity-based induction, in which targets chosen for
inductive inference are those that are globally similar to the
known entity, and rule-based induction, in which targets are
those that share specific critical features (Sloutsky, Kloos, &
Fisher, 2007; Yamauchi & Markman, 2000). This behavioral
distinction may emerge because these two forms of inference
involve qualitatively different processes. For instance,
similarity-based induction may involve a global assessment
of the degree to which known entities and potential targets
share features, whereas rule-based induction may involve
maintenance of the rule-relevant feature in memory and/or
inhibition of rule-irrelevant features (Sloutsky, 2010).
One critical implication of this proposal is that rule-based
induction should recruit prefrontal cortex (PFC) regions
associated with active memory maintenance and inhibition of
salient task-irrelevant information (Konishi, Kawazu, et al.,
1999; Konishi, Nakajima, et al., 1999) to a greater extent than
similarity-based induction. Indirect support for this
possibility comes from evidence that rule- and similarity-
based processes in other forms of reasoning recruit distinct
brain circuitry (Grossman et al., 2002; Koenig et al., 2005;
Nomura et al., 2007; Seger & Cincotta, 2002). However, no
studies have yet directly tested this possibility.
Here, we first review behavioral evidence for a qualitative
distinction between similarity- and rule-based induction, and
neuroimaging evidence for distinct patterns of brain activity
associated with similarity- and rule-based processes in other
forms of reasoning. Then, we present a study investigating
whether similarity- and rule-based induction is associated
with distinct patterns of neural activity.
Similarity- versus Rule-Based Induction
Studies of both adult and developing cognition have yielded
evidence for a qualitative distinction between similarity- and
rule-based induction. For instance, in a number of
experiments with adults (e.g., Yamauchi & Markman, 2000)
researchers taught participants two artificial bug categories
that were each associated with typical anatomical features
and a category label, then investigated the basis on which
participants either inferred the value of a bug’s occluded
anatomical feature in the presence of its category label, or the
value of a bug’s label in the presence of all its anatomical
features. In the presence of a label, adults tended to make
inductive inferences consistent with the label regardless of
other anatomical features in a rule-based manner, whereas in
the absence of a label, adults made inferences consistent with
the degree to which an item’s anatomical features were
typical of each category in a similarity-based manner.
Developmentally, several studies suggest that rule- and
similarity-based induction emerge at different ages. For
instance, recent research has shown that although 4- to 5-
year-olds can learn a category-inclusion rule for a set of
unfamiliar items, children often fail to use it as the basis for
inductive inferences (Badger & Shapiro, 2012; Sloutsky,
Fisher, & Kloos, 2015; Sloutsky et al., 2007) (cf. Gelman &
Davidson, 2013). With age, children shift towards making
rule-based inductive inferences (Badger & Shapiro, 2012).
Therefore, whereas similarity-based inference is evident
from early childhood, rule-based induction appears to
Neural Distinction between Similarity- and Rule-
As previously noted, the behavioral distinctions between
similarity- and rule-based inductive inference may be
associated with a neural distinction between the brain
circuitry recruited during reasoning. Specifically, it has been
suggested that rule-based induction is associated with greater
recruitment of processes associated with the PFC than
similarity-based induction (Sloutsky, 2010). Indirect
evidence for this possibility comes from observations of
neural distinctions between rule- and similarity-based
processes in other forms of reasoning.
The primary source of indirect evidence for this distinction
comes from comparisons of brain activity observed across
different studies and tasks. For instance, tasks that require
rule learning, such as the Wisconsin Card Sorting Task, yield
significant PFC activity (e.g., Konishi, Kawazu, et al., 1999).
In contrast, similarity processing is associated with more
posterior brain regions (de Beeck, Torfs, & Wagemans, 2008;
Weber, Thompson-Schill, Osherson, Haxby, & Parsons,
2009). Similarly, tasks that require implicit extraction of a
category prototype from similarities between exemplars
involve visual regions that overlap with those associated with
similarity judgments (Reber, Stark, & Squire, 1998;
Zeithamova, Maddox, & Schnyer, 2008). Together, these
findings suggest that rule-based reasoning recruits PFC,
whereas processing visual similarity recruits visual cortex
regions. However, this contrast between rule- and similarity-
based processes is based on a comparison between studies
that used very different paradigms.
Indirect evidence from comparisons made within studies
comes from a smaller body of research that has primarily
focused on novel category learning. These studies have also
found neural distinctions between processes that are related
to, but do not directly map onto the rule- versus similarity-
based induction distinction of interest (Grossman et al., 2002;
Koenig et al., 2005; Nomura et al., 2007; Seger & Cincotta,
2002). Many such studies compare rule-based reasoning to
processes that do not involve similarity perception, such as
learning categories by integrating perceptual information
from multiple dimensions. The small subset of studies that
have compared rule- to similarity-based reasoning have done
so in domains that require additional processes, such as
retrieving previously experienced exemplars or semantic
knowledge from memory (Grossman et al., 2002; Koenig et
al., 2005). Accordingly, the nature of this distinction varied
as a result of the different processes evoked by different tasks.
Such distinctions support the possibility that rule- vs.
similarity-based induction recruit distinct brain regions. At
the same time, the fact that different tasks used across studies
yielded different neural distinctions suggests that the nature
of a potential neural distinction between similarity- and rule-
based induction cannot be inferred from those observed for
other forms of reasoning. Therefore, this review underscores
the importance of obtaining direct empirical evidence to test
the prediction that inductive reasoning, similar to other forms
of reasoning, relies on neurally distinct mechanisms
associated with rule-based and similarity-based processing.
We focused on differences in PFC activity between
similarity- and rule-based induction for two reasons. First, as
noted above, the qualitative behavioral distinction between
rule- and similarity-based induction may emerge because
rule-based induction uniquely requires processes such as
focusing on a specific feature to the exclusion of others and
maintaining rules in working memory (Badger & Shapiro,
2012; Sloutsky, 2010) that are associated with PFC activity
(Konishi, Kawazu, et al., 1999; Konishi, Nakajima, et al.,
1999). Second, the most consistent neural distinction
observed between rule- and similarity-based processes in
other forms of reasoning is that rule-based processing
involves PFC activity (Konishi, Kawazu, et al., 1999),
whereas similarity processing involves activity in more
posterior regions (de Beeck et al., 2008; Weber et al., 2009).
Accordingly, the present experiment tested whether rule-
based induction is associated with greater PFC activity than
similarity-based induction using functional Near Infrared
Spectroscopy (fNIRS), a neuroimaging technology that uses
cortical changes in infrared light absorption to measure brain
activity. To test this prediction, we recorded PFC activity
using fNIRS while adult participants completed one of two
versions of an inductive inference task modeled on a
paradigm introduced by Sloutsky et al. (2007) and updated
with natural kind-like stimuli by Badger and Shapiro (2012).
In this paradigm, participants are asked to infer which of two
“match” items shares a property attributed to a “target”. In
the “Rule-Given” version, participants were taught the
category inclusion rule, whereas in the “No-Rule” version,
participants were not taught the rule. We predicted that the
Rule-Given version would yield high rates of rule-consistent
match inference choices and significant PFC activity,
whereas the No-Rule version would yield high rates of
similarity-consistent match inference choices and no
significant PFC activity.
Participants were 24 adults (15 female, Mage=19 years)
recruited from the undergraduate community at a
Northeastern university who received partial course credit.
Materials and Apparatus
Stimuli were presented on a Dell computer screen with
physical dimensions 60 cm x 34 cm and pixel dimensions
1920 x 1080. Participants were seated at a desk facing the
screen with their heads about 2 feet away from the screen.
Neural activity was recorded using a continuous wave
(CW6) real-time fNIRS system (TechEn, Inc.), with 4 light
sources, each containing 690-nm (12 mW) and 830-nm (8
mW) laser light, and 8 detectors, to give oxygenation
measures in 10 channels on the prefrontal cortex. The sensors
were arranged in the layout depicted in Figure 1. Sensors
were snapped into a cap strip built from foam sheet and
plastic mesh, and connected to the fNIRS system by via optic
cables. For each participant, the cap strip was positioned on
the head, centered on position FpZ by the international 10-20
coordinate system standard, and extending over the
Brodmann area 10 (anterior prefrontal cortex) and area 46
(dorsolateral prefrontal cortex) bilaterally. The strip was
secured to the head using a neoprene scuba cap.
This task was presented using E-Prime (Psychology Software
Tools, 2012). Stimuli were modeled on those created by
Badger and Shapiro (2012), and consisted of bugs with five
anatomical features that could each take one of two values:
Head shape (pointy or round), body shape (triangular or
round), body color (purple or green), spot color (brown or
grey), and eye color (blue or orange). Of these features, body
shape and color took up a larger proportion of the stimuli than
the others. Following Badger and Shapiro, one of the smaller
features, head shape, was selected as the category rule-
inclusion feature. Specifically, bugs with pointy heads were
“Rockbugs”, and bugs with round heads were “Sandbugs”.
All other features were category-irrelevant.
We used these stimuli to create 16 induction trials, and 51
baseline trials. Each induction trial presented a triad of bugs
consisting of a Target, a Rule Match, and a Similarity Match,
arranged with the Target on top and the Matches on the
bottom to either the right or left (Figure 2).
In half of the induction trials, the Target was a Rockbug,
and in the other half, the Target was a Sandbug. The
assignment of Rule and Similarity Matches to the bottom
right or left locations was randomized separately for triads
with Rockbug and Sandbug Targets. In each triad, the Rule
Match belonged to the same category as the Target but
appeared dissimilar overall, whereas the Similarity match
belonged to a different category but appeared similar. To
accomplish this, the Rule Match had different values for all
features from the Target except head shape and one of the
smaller category-irrelevant anatomical features (eye or spot
color), whereas the Similarity match had the same values for
all features as the Target except head shape and one of the
smaller features (see Figure 2). Independently for triads with
Rockbugs and Sandbugs as Targets, we randomly assigned
whether the small feature shared by the Rule Match and not
the Similarity match was eye or spot color. All triads were
pseudo-randomized such that no more than two triads with a
Target bug from the same category or the Rule and Similarity
Matches in the same locations appeared consecutively.
For baseline trials, we used the bugs to create a simple
congruent Flanker task (Eriksen & Eriksen, 1974) (see
Procedure). Specifically, each baseline trial presented three
identical bugs that were all oriented to face either left or right.
We approximately equated the number of times all bugs faced
either left or right. Baseline trials were integrated with
induction trials such that three baseline trials followed each
induction trial, and one set of three baseline trials preceded
the first induction trial. This ratio was used to ensure that
there was a sufficient amount of baseline recording (i.e.,
approximately 5-10 s per each set of baseline trials, to mirror
the length of time on the Induction trials). Baseline trials were
pseudo-randomized such that, in each set of three baseline
trials, no more than two featured bugs from the same category
or bugs facing the same direction.
Figure 2. Example of induction trial. Top: Target, Bottom
Left: Rule Match, Bottom Right: Similarity Match.
Figure 1. Probe layout including 4 sources (S1-4) and 8
detectors (D1-8), overlaid on overhead view of a head.
Black lines represent source-detector channels. Source-
detector distance was 2.8 cm.
The Induction task was presented in two conditions: A
“Rule-Given”, condition and a “No-Rule” condition. The No-
Rule condition consisted of only the inductive inference and
baseline trials. The Rule-Given condition supplemented these
trials with two Rule Demonstration slides, and 16
Categorization trials. Each Rule Demonstration slide
depicted a pair of either Rockbugs or Sandbugs. Each pair of
bugs had opposite values for all non-head shape features.
Each Categorization trial presented a single Rockbug or
Sandbug, with equal numbers of trials for each category. We
assigned half of the Categorization trials for each category to
appear before and half to appear after the baseline and
induction trials. We pseudo-randomized each subset of
Categorization trials such that no more than two bugs from
the same category appeared consecutively.
Participants were tested in a quiet space. One experimenter
administered the Induction Task, and another managed
fNIRS data collection (see details below).
Induction Task Participants were randomly assigned to
complete either the Rule-Given or No-Rule condition of the
task. The procedure for participants assigned to each
condition was identical for Inductive Inference and Baseline
trials. During Induction trials, participants were told that the
Target possessed a novel biological property (e.g., “plaxium
hormone”, “tulvex nerve cells”), and asked to decide which
of the two Match items shared the property. Baseline trials
were modeled on the “congruent” version of the Eriksen
Flanker Task, in which participants respond to some
characteristic of a central stimulus in the presence of flanking
stimuli that possess the same characteristic. This task was
chosen as a baseline based on evidence that it elicits relatively
little frontal activity (Bunge, Hazeltine, Scanlon, Rosen, &
Gabrieli, 2002). In our version, participants were asked to
point in the direction that the middle bug was facing.
Only Inductive Inference and Baseline trials were
presented to participants in the No-Rule condition. In the
Rule-Given condition, Inductive Inference and Baseline trials
were presented in between an initial Rule Demonstration and
Categorization phase, and a final Categorization phase. To
demonstrate the rule, the experimenter showed the participant
the two Rule Demonstration Slides, and provided the
following descriptions of Rockbugs and Sandbugs: “These
two pictures are of [Rockbugs]/[Sandbugs]. [Rockbugs live
in rocks, and all have pointy heads that they use to dig in the
rocks]/[ Sandbugs live in sand, and all have round heads that
they use to burrow in the sand]. [Rockbugs][Sandbugs] come
in many different shapes and colors, but they all have [pointy
heads that they use to dig in the rocks]/[round heads that they
use to burrow in the sand].”
To test rule retention, participants were asked to identify
the bug on each Categorization trial preceding and following
the Induction and Baseline trials.
fNIRS Recording fNIRS data was recorded for each
participant using custom data collection software described
in Abdelnour and Huppert (2009). The fNIRS cap was first
fitted on the head of the participant and the signal quality
checked and adjusted if needed to make sure the fNIRS fiber
optics made good contact with the scalp of the
participant. After initial setup, the fNIRS data was collected
at 20Hz at two wavelengths (690nm and 830nm). Following
signal quality checking, the experimenter started the
induction task. During the induction task, the timing of
stimulus onset and offset as presented in Eprime were synced
and marked in the fNIRS data by an automated analog signal
sent from the computer port (of the stimulus presentation
computer) to the fNIRS machine.
We first determined that all participants in the Rule-Given
condition successfully learned the category inclusion rule
(i.e., all Rule-Given participants were 100% accurate on the
initial and final Categorization Trials).Responses on the 16
Induction Trials were then analyzed to compare the degree to
which participants in the Rule-Given and No-Rule conditions
chose the Rule Match. The Rule Match was chosen
significantly more often by participants in the Rule-Given
condition (M=60.94%) than by than participants in the No-
Rule condition (M=10.42%) (t(22)=5.001, p<.0001).
Pre-Processing The raw fNIRS data at the two wavelengths
were converted into estimates of oxy- and deoxy-hemoglobin
using the modified Beer-Lambert law (Cope et al., 1988) with
a differential pathlength factor of 6 for both
wavelengths. The data was resampled to 4Hz for statistical
analysis using an autoregressively pre-whitened weighted
least-squares regression model (Barker, Aarabi, & Huppert,
2013). In brief, the stimulus timing of the induction trials are
used to construct a hypothesis of the timing of the expected
response based on a canonical hemodynamic response. This
model is then statistically compared against the data using a
general linear model and brain activity is inferred from the
statistical tests of the coefficients of this linear model. The
iteratively whitened weighted least-squares regression
described in Barker et al was used to solve this general linear
model and had been previously shown to have substantially
improved sensitivity and specificity and control of type-I
errors compared to other regression methods for fNIRS data
in the presence of physiological noise and potential motion-
artifacts from slippage of the head cap. This analysis is
similar to the general linear model and statistical parametric
modeling methods commonly used in functional magnetic
resonance imaging (fMRI) (e.g. SPM; Tak, Uga, Flandin,
Dan, and Penny (2016)).
fNIRS Analysis Processed fNIRS data were analyzed to first
compare activity during induction trials to baseline trials for
each condition, and then directly compare activity during
induction trials in each condition (see Figure 3). The
canonical general linear model for regression used in fMRI
analysis, based on convolving the neural responses with the
standard hemodynamic response function basis from SPM8,
was used for statistical testing of neural activity between
conditions (Friston et al., 1994). The comparison to baseline
analyses revealed that Induction Trials in the Rule-Given
condition were associated with significantly stronger activity
in channels S1-D1 (Source 1 to Detector 1; t(440)>4.249,
p<2.616e-05) and S4-D8 (t(440)>2.827, p<0.005), which
corresponds approximately to Brodmann area 46, bilaterally,
and channel S1-D2 (t(440)>2.419, p<0.05), which
corresponds approximately to right Brodmann area 10. In
contrast, no channels revealed significantly greater than
baseline activity during induction trials in the No-Rule
condition (all ts<1.031, all ps>.065). The direct comparison
between induction trial-activity in each condition revealed
significantly stronger activity in the Rule-Given than the No-
Rule condition in channel S1-D1 (t(440)>2.815, p<0.006),
which corresponds approximately to Brodmann area 46.
The purpose of this study was to use fNIRS to investigate the
possibility that rule-based induction recruits PFC, a brain
region associated with executive functions, to a greater extent
than similarity-based induction. Participants completed an
inductive inference task in which they could infer that a novel
property attributed to a Target item was shared by either a
similar looking item from a different rule-based category, or
a dissimilar looking item from the same rule-based category.
Participants who were taught the category rule prior to the
induction task both tended to choose the dissimilar looking
same-category item, and revealed significant PFC activity in
comparison to baseline. In contrast, participants who were
not taught the rule tended to choose the similar looking
different-category item, and did not reveal PFC activity
above baseline. Finally, participants in the Rule-Given
condition showed stronger PFC activity during induction
than participants in the No-Rule condition.
These results support the proposal that rule- and similarity-
based induction represent qualitatively distinct processes.
Specifically, rule-based induction may uniquely require
executive functions associated with PFC such as the active
maintenance of rules in memory, and/or inhibition of rule-
irrelevant input (Badger & Shapiro, 2012; Konishi, Kawazu,
et al., 1999; Konishi, Nakajima, et al., 1999; Sloutsky, 2010).
This distinction is consistent with similar distinctions
observed between rule- vs. similarity-based processes in
other forms of reasoning (Grossman et al., 2002; Koenig et
al., 2005; Nomura et al., 2007; Seger & Cincotta, 2002).
These results also support the proposal that the more
protracted development of rule- vs. similarity-based
induction implicates a greater role for the slow-maturing PFC
in rule-based than in similarity-based induction. The findings
presented here provide a foundation for further investigation
into currently unresolved questions about inductive inference
processes, as described below.
Limitations and Future Directions
The present study sets the stage for pursuing several
questions that follow on from the present findings and remain
currently unresolved. First, the past research that inspired the
prediction that PFC activity should be evoked to a greater
extent with rule- vs. similarity-based induction also predicts
that activity in posterior regions associated with visual
processing should be evoked to a greater extent with
similarity- vs. rule-based induction. The present study
investigated only a one-way dissociation; future research
should investigate the predicted double-dissociation to
provide further insight into the distinction between rule- and
similarity-based inductive inference.
Second, although participants in the Rule-Given condition
chose the category match more often than t in the No-Rule
condition, they did not always do so. This may reflect
variability in the degree to which different individuals
perform rule-based induction. Future research should
therefore test whether such variability both within and across
individuals is associated with differences in PFC activity.
Finally, the current work provides a foundation from which
to investigate the neural underpinnings of the previously
observed distinction between the developmental trajectories
of rule- and similarity-based induction. The present study was
inspired in part by the possibility that rule-based induction
emerges more gradually than similarity-based induction
because the former uniquely recruits brain circuitry involving
the slow-maturing PFC. However, no research has directly
tested this possibility. Because the present study used a child-
appropriate paradigm and imaging technology, the approach
used here could be used to study the development of the role
of the PFC in rule- versus similarity-based induction.
Figure 3. Group-level contrasts of oxy-hemoglobin
signals for No-Rule minus baseline, Rule-Given minus
baseline, and No-Rule minus Rule-Given. The color of
the line for each source-detector represents the contrast
t-statistic as marked on the color bar on the right. Solid
lines represent significant t-statistics.
This study investigated whether a proposed qualitative
distinction between rule- vs. similarity-based induction (in
which the former uniquely involves memory maintenance
and/or inhibition) corresponds with a neural distinction in
which rule-based induction uniquely recruits PFC. The
findings presented here are consistent with the proposed
neural distinction, and lay a foundation for further research
into the development of rule-based induction.
This work was supported by a grant from the Pennsylvania
Department of Health's Commonwealth Universal Research
Enhancement Program, a Graduate Training Grant awarded
to Carnegie Mellon University by the Department of
Education, Institute of Education Sciences (R305B040063),
and by the James S. McDonnell Foundation 21st Century
Science Initiative in Understanding Human Cognition–
Scholar Award (220020401) to the last author. We
additionally thank Francois Ban and Kevin Long for their
help collecting data.
Abdelnour, A. F., & Huppert, T. (2009). Real-time imaging
of human brain function by near-infrared spectroscopy
using an adaptive general linear model. NeuroImage, 46,
Badger, J. R., & Shapiro, L. R. (2012). Evidence of a
transition from perceptual to category induction in 3-to 9-
year-old children. Journal of experimental child
psychology, 113, 131-146.
Barker, J. W., Aarabi, A., & Huppert, T. J. (2013).
Autoregressive model based algorithm for correcting
motion and serially correlated errors in fNIRS. Biomedical
optics express, 4, 1366-1379.
Bunge, S. A., Hazeltine, E., Scanlon, M. D., Rosen, A. C., &
Gabrieli, J. (2002). Dissociable contributions of prefrontal
and parietal cortices to response selection. NeuroImage,
Cope, M., Delpy, D., Reynolds, E., Wray, S., Wyatt, J., &
Van der Zee, P. (1988). Methods of quantitating cerebral
near infrared spectroscopy data Oxygen Transport to
Tissue X (pp. 183-189): Springer.
de Beeck, H. P. O., Torfs, K., & Wagemans, J. (2008).
Perceived shape similarity among unfamiliar objects and
the organization of the human object vision pathway. The
Journal of Neuroscience, 28, 10111-10123.
Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise
letters upon the identification of a target letter in a
nonsearch task. Perception & psychophysics, 16, 143-149.
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J., Frith,
C. D., & Frackowiak, R. S. (1994). Statistical parametric
maps in functional imaging: a general linear approach.
Human brain mapping, 2, 189-210.
Gelman, S. A., & Davidson, N. S. (2013). Conceptual
influences on category-based induction. Cognitive
psychology, 66, 327-353.
Grossman, M., Smith, E. E., Koenig, P., Glosser, G., DeVita,
C., Moore, P., & McMillan, C. (2002). The neural basis for
categorization in semantic memory. NeuroImage, 17,
Koenig, P., Smith, E. E., Glosser, G., DeVita, C., Moore, P.,
McMillan, C., . . . Grossman, M. (2005). The neural basis
for novel semantic categorization. NeuroImage, 24, 369-
Konishi, S., Kawazu, M., Uchida, I., Kikyo, H., Asakura, I.,
& Miyashita, Y. (1999). Contribution of working memory
to transient activation in human inferior prefrontal cortex
during performance of the Wisconsin Card Sorting Test.
Cerebral Cortex, 9, 745-753.
Konishi, S., Nakajima, K., Uchida, I., Kikyo, H., Kameyama,
M., & Miyashita, Y. (1999). Common inhibitory
mechanism in human inferior prefrontal cortex revealed by
event-related functional MRI. Brain, 122, 981-991.
Nomura, E., Maddox, W., Filoteo, J., Ing, A., Gitelman, D.,
Parrish, T., . . . Reber, P. (2007). Neural correlates of rule-
based and information-integration visual category
learning. Cerebral Cortex, 17, 37-43.
Psychology Software Tools, I. (2012). E-Prime 2.0.
Reber, P., Stark, C., & Squire, L. (1998). Cortical areas
supporting category learning identified using functional
MRI. Proceedings of the National Academy of Sciences,
Seger, C. A., & Cincotta, C. M. (2002). Striatal activity in
concept learning. Cognitive, affective, & behavioral
neuroscience, 2, 149-161.
Sloutsky, V. M. (2010). From perceptual categories to
concepts: What develops? Cognitive science, 34, 1244-
Sloutsky, V. M., Fisher, A. V., & Kloos, H. (2015).
Conceptual influences on induction: A case for a late onset.
Cognitive psychology, 82, 1-31.
Sloutsky, V. M., Kloos, H., & Fisher, A. V. (2007). When
looks are everything Appearance similarity versus kind
information in early induction. Psychological Science, 18,
Tak, S., Uga, M., Flandin, G., Dan, I., & Penny, W. (2016).
Sensor space group analysis for fNIRS data. Journal of
neuroscience methods, 264, 103-112.
Weber, M., Thompson-Schill, S. L., Osherson, D., Haxby, J.,
& Parsons, L. (2009). Predicting judged similarity of
natural categories from their neural representations.
Neuropsychologia, 47, 859-868.
Yamauchi, T., & Markman, A. B. (2000). Inference using
categories. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 26, 776.
Zeithamova, D., Maddox, W. T., & Schnyer, D. M. (2008).
Dissociable prototype learning systems: evidence from
brain imaging and behavior. The Journal of Neuroscience,