This study used representational similarity analysis (RSA) to assess categorical representations in the right fusiform face area (FFA) and parahippocampal place area (PPA) using fMRI. Participants completed a multi-category localizer task and conceptual classification task while brain images were collected. RSA showed that the right FFA robustly differentiated faces from other categories and also distinguished animals. The right PPA robustly differentiated scenes from other categories and also distinguished landmarks. These results demonstrate that RSA can reveal categorical representations in visual cortex.

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• Multivoxel pattern analysis (MVPA) is a
multivariate statistical technique for analyzing
patterns of brain activity across voxels in
functional magnetic resonance imaging (fMRI).1
• Representational similarity analysis (RSA) is a
form of MVPA that computes correlations of
observed brain activity patterns within and
between stimulus categories.2
Objective
• Implement RSA on a large previously collected
multi-category fMRI dataset.
• Focus on well-established category-preferential
regions: the right fusiform face area (FFA)3
and
parahippocampal place area (PPA)4
to assess
the validity and reliability of RSA.
Hypothesis
• RSA will show the specificity of categorical
representations in the right FFA and PPA.
IntroductionIntroduction Data AnalysisData Analysis DiscussionDiscussion
ReferencesReferences
Multivoxel pattern analysis: An implementation ofMultivoxel pattern analysis: An implementation of
representational similarity analysis to assessrepresentational similarity analysis to assess
categorical representation in ventral visual cortexcategorical representation in ventral visual cortex
Carlo E. Iaboni, Lily Solomon-Harris, Naail Khan, & W. Dale StevensCarlo E. Iaboni, Lily Solomon-Harris, Naail Khan, & W. Dale Stevens
Department of Psychology, Faculty of Health, York University, TorontoDepartment of Psychology, Faculty of Health, York University, Toronto
ResultsResults
Day 1:
Blocked multi-category functional localizer task.
•10 runs: 14 categories over 14 blocks (20
stimuli/block; duration=20s) per run, interleaved
with fixation blocks (duration=10s)
•1-back repetition detection task
•FFA defined by contrast of faces > scenes in
univariate general linear model of activity.
•PPA defined by contrast of scenes > faces in
univariate general linear model of activity.
Day 2:
Event-related conceptual classification task.
•14 categories, 20 exemplars of each, presented
over 12 runs, with 6 repetitions of each stimulus
overall
•Natural vs. man-made judgment
•3 mm isotropic contiguous voxels; TR = 2 s
Data CollectionData Collection
• The right FFA and PPA robustly differentiate
faces and scenes, respectively, from other
categories of visual stimuli.
• FFA also distinguishes animals from other
categories, consistent with the role of the lateral
fusiform gyrus in processing animate entities.5
• PPA also distinguishes non-manipulable
objects, i.e. landmarks, from other categories,
consistent with its role in navigation.6, 7
• Results demonstrate that RSA shows
categorical representations in visual cortex.
• This proof of concept will allow us to ask further
questions about categorical representations
across the brain.
• This implementation can be expanded to
assess various categorical representations
across brain regions in both the current
experimental paradigm and future studies.
Similarity
matrices
for all
presented
stimuli
(280 x 280)
Similarity
matrices
averaged
across
subjects
(14 x 14)
Similarity
matrices
averaged
across
category
(14 x 14)
20 s 20 s20 s20 s 20 s
FACE
S
ANIMALS WORDS PSEUDOWORDSSCENES
++ + + ++
Legend
an=animals
bo=body parts
to=tools
sc=scenes
ob=real objects
fa=faces
ab=abstract objects
sb=scrambled words
aW=animal words
bW=body words
tW=tool words
sW=scene words
oW=object words
pW=pseudowords
AcknowledgmentsAcknowledgments
Project funded by the National Institute of Mental
Health (NIMH).
Average Average
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3
2
44
21
3
Right PPA Right FFA
Right PPA and FFA in a representative subject.
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3
2
4
1
3
2
4
Average Average