10490 • The Journal of Neuroscience, June 19, 2013 • 33(25):10490 –10502

Behavioral/Cognitive

A Critical Role for the Hippocampus and Perirhinal Cortex in
Perceptual Learning of Scenes and Faces: Complementary
Findings from Amnesia and fMRI
Matthew E. Mundy,1,2 Paul E. Downing,3 Dominic M. Dwyer,1 Robert C. Honey,1 and Kim S. Graham1
1

Wales Institute of Cognitive Neuroscience, School of Psychology, Cardiff University, Cardiff CF10 3AT, United Kingdom, 2School of Psychology and
Psychiatry, Clayton Campus, Monash University, Victoria 3800, Australia, and 3Wales Institute of Cognitive Neuroscience, School of Psychology, Bangor
University, Gwynedd LL57 2AS, United Kingdom

It is debated whether subregions within the medial temporal lobe (MTL), in particular the hippocampus (HC) and perirhinal cortex (PrC), play
domain-sensitive roles in learning. In the present study, two patients with differing degrees of MTL damage were first exposed to pairs of highly
similar scenes, faces, and dot patterns and then asked to make repeated same/different decisions to preexposed and nonexposed (novel) pairs
from the three categories (Experiment 1). We measured whether patients would show a benefit of prior exposure (preexposed ⬎ nonexposed)
and whether repetition of nonexposed (and preexposed) pairs at test would benefit discrimination accuracy. Although selective HC damage
impaired learning of scenes, but not faces and dot patterns, broader MTL damage involving the HC and PrC compromised discrimination
learning of scenes and faces but left dot pattern learning unaffected. In Experiment 2, a similar task was run in healthy young participants in the
MRI scanner. Functional region-of-interest analyses revealed that posterior HC and posterior parahippocampal gyrus showed greater activity
during scene pattern learning, but not face and dot pattern learning, whereas PrC, anterior HC, and posterior fusiform gyrus were recruited
during discrimination learning for faces, but not scenes and dot pattern learning. Critically, activity in posterior HC and PrC, but not the other
functionalregion-of-interestanalyses,wasmodulatedbyaccuracy(correct⬎incorrectwithinapreferredcategory).Therefore,bothapproaches
revealedakeyrolefortheHCandPrCindiscriminationlearning,whichisconsistentwithrepresentationalaccountsinwhichsubregionsinthese
MTL structures store complex spatial and object representations, respectively.

Introduction
Although it is undisputed that medial temporal lobe (MTL)
structures, including the hippocampus (HC) and perirhinal cortex (PrC), participate in memory, their exact role remains controversial (Burgess et al., 2001; Eichenbaum et al., 2007; Squire et
al., 2007; Brown et al., 2010; Graham et al., 2010; Montaldi and
Mayes, 2010; Ranganath, 2010). A current debate is whether these
regions, or subareas within them, differ in their contribution to
learning and memory for distinct categories of visual stimuli (Diana et al., 2008; Aly et al., 2010; Preston et al., 2010; Duarte et al.,
2011; Watson et al., 2012).
For example, recent neuropsychological studies have revealed
that selective bilateral HC damage impairs recognition memory
Received June 22, 2012; revised March 21, 2013; accepted April 10, 2013.
Author contributions: M.E.M., P.E.D., D.M.D., R.C.H., and K.S.G. designed research; M.E.M. performed research;
M.E.M., P.E.D., and K.S.G. analyzed data; M.E.M., P.E.D., D.M.D., R.C.H., and K.S.G. wrote the paper.
This work was supported by the Wales Institute of Cognitive Neuroscience and the BBSRC (Grant #BB/I007091/1).
The Wales Institute of Cognitive Neuroscience was set up by a cross-institution grant from the Welsh Government to
the Schools of Psychology at Cardiff, Bangor, and Swansea Universities. We thank our colleagues at the Cardiff
University Brain Research Imaging Centre, particularly John Evans and Martin Stuart, for help with the scanning
protocol and data collection, Andy Lee for providing Figure 1, and Chris Chambers, Andrew Lawrence, and Ed Wilding
for comments on the manuscript.
This article is freely available online through the J Neurosci Author Open Choice option.
Correspondence should be addressed to Prof. Kim Graham, School of Psychology, Cardiff University, Tower
Building, Cardiff, CF10 3AT, United Kingdom. E-mail: grahamks@cardiff.ac.uk.
DOI:10.1523/JNEUROSCI.2958-12.2013
Copyright © 2013 the authors 0270-6474/13/3310490-13$15.00/0

(Bird et al., 2007), discrimination learning (Graham et al., 2006), and
odd-one-out decisions (Lee et al., 2005a) for scene, but not face,
stimuli. In contrast, larger MTL lesions that encompass both the HC
and PrC result in poor long-term memory for scenes and faces (Taylor et al., 2007) and reduced discrimination accuracy for scenes,
faces, and objects (Barense et al., 2005, 2007; Lee et al., 2005b). Critically, however, these domain-specific patterns are not present in all
patients and there is disagreement over the exact anatomical locus of
such deficits (Levy et al., 2005; Shrager et al., 2006; Kim et al., 2011).
It has been suggested that concomitant involvement of domainsensitive regions in parahippocampal cortex and/or fusiform gyrus,
rather than damage to the HC and PrC per se, may underlie such
stimuli-dependent functional dissociations (Squire et al., 2006; Suzuki, 2009, 2010 but see Baxter, 2009; Graham et al., 2010; Jeneson
and Squire, 2012; Lee et al., 2012; Rudebeck et al., 2013).
We undertook complementary patient and fMRI experiments to
address this issue, investigating whether the HC and PrC would play
distinct domain-sensitive roles in a novel learning task in which performance on scenes and faces were compared directly alongside an
equally difficult visual control (dot patterns). In Experiment 1, two
amnesic patients (one with selective HC damage and another with
damage including the HC and PrC) and 12 matched controls were
preexposed to pairs of visually similar faces, scenes, and dots. Subsequently, they made repeated same/different judgements to both previously exposed and nonexposed pairs. This paradigm allowed us to
determine whether patients could learn to discriminate faces, scenes,

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J. Neurosci., June 19, 2013 • 33(25):10490 –10502 • 10491

and/or dots as measured by either an accuracy advantage for preexposed compared with nonexposed pairs (Analysis 1A) and/or by
increasingly better discrimination success as same/different discriminations were repeated during the test (Analysis 1B). In Experiment
2, we aimed to elucidate the unique contributions of the HC and
PrC, alongside the parahippocampal and fusiform areas, to perceptual learning. Young healthy participants performed a version of the
task used in Experiment 1. A functional region-of-interest (fROI)
approach was adopted, complemented by whole-brain analyses, to
determine which brain regions showed a difference between preexposed versus nonexposed scene, face, and/or dot pairs (Analysis 2A)
and how brain activity was modulated by decision accuracy (correct ⬎ incorrect, Analysis 2B).

Materials and Methods
Experiment 1: patients
Participants
Two patients with damage to the MTL (initially reported in Lee et al.,
2005b, as patients HC3 and MTL3) and 12 healthy participants matched
to the patients for age and education were included in Experiment 1. The
two patients were selected for the study based on definitive evidence of
circumscribed involvement of the MTL, neuropsychological confirmation of selective difficulties with episodic recall, and willingness to take
part in our study (Barense et al., 2005; Lee et al., 2005b; Graham et al.,
2006; Barense et al., 2007; Lee and Rudebeck, 2010a; Rudebeck et al.,
2013). Qualitative and quantitative measures of the patients’ brain damage,
as well as detailed neuropsychology, have been published previously (Lee et
al., 2005b; Lee and Rudebeck, 2010a). Patient HC3, a 50-year-old woman
with 10 years of education, has selective bilateral HC involvement after an
episode of carbon-monoxide-induced hypoxia. MTL3, a 64-year-old
woman with 10 years of education, has a larger bilateral lesion to the MTL
that includes damage to both the HC and PrC. Use of a standard functional
localizer in both patients confirmed activation in parahippocampal place
area (PPA), fusiform face area (FFA), and lateral occipital cortex, a profile
consistent with the pattern of structural integrity evident from volumetric
and connectivity analyses of the patients’ structural MRI scans (Lee and
Rudebeck, 2010a; Rudebeck et al., 2013; Figure 1).
On neuropsychological testing, the patients showed exceptionally
poor episodic recall of both verbal and visual material. For example, both
scored 4 of 50 on delayed recall of a prose passage (logical memory) and
were similarly poor at reproducing the Rey-Osterrieth Complex Figure
after a delay (HC3, 3 of 36; MTL3, 4.5 of 36) despite good initial drawings
(HC3, 35 of 36; MTL3, 30.5 of 36). Recognition memory in HC3, as
measured using the Warrington Recognition Memory Test, was within
the normal range for faces and scenes, but not words, whereas MTL3
showed impairment in all three recognition memory tasks. This pattern
is consistent with the patients’ performance on other experiments in
which we have investigated recognition memory performance across visual categories (Taylor et al., 2007). Other measures of cognition, including visual processing (as measured by the Visual Object and Space
Perception Battery, see also copy of the Rey-Osterrieth Figure described
earlier) and problem solving were preserved in both the patients (Lee et
al., 2005b), although MTL3 showed some difficulties with semantic
memory as evidenced by a mild deficit on category comprehension and
semantic association tasks (Lee and Rudebeck, 2010a).
Two groups of six neurologically healthy control participants (male
and female) age and education matched to the patients were recruited
from the Cardiff University School of Psychology Community Panel.
The controls for patient HC3 had a mean age of 53.3 ⫾ 2.9 years and
9.8 ⫾ 1.0 years of education; matched controls for MTL3 were on average
62.3 ⫾ 4.1 years of age with 10.0 ⫾ 0.6 years of education. Because
analyses of the visual discrimination data obtained from these two
groups revealed no significant differences in accuracy or reaction time
(RT) across any of the three experimental conditions (all F ⬍ 1), the two
groups were combined into a single group for comparison with the patients (age, 57.8 ⫾ 5.8 years; education, 9.9 ⫾ 8.2 years). There was no
significant difference between the patients and the larger control group

Figure 1. A, Coronal slices from structural T1 images obtained in patients HC3 and MTL3.
Detailed information regarding volumetric analysis of these scans is included in Lee and Rudebeck (2010a) (see also Rudebeck et al., 2013, for an examination of resting functional networks
and white matter connectivity in these cases). In brief, Lee and Rudebeck (2010a) measured 10
brain regions in each of the right and left hemispheres (temporopolar cortex, amygdala, entorhinal cortex, PrC, HC, parahippocampal cortex, anterior fusiform gyrus, posterior fusiform
gyrus, anterior lateral temporal cortex, and posterior lateral temporal cortex). Patient HC3
showed significantly reduced volume compared with healthy controls in the right and left HC
only, whereas patient MTL3 showed reduced volumes bilaterally in the amygdala, entorhinal
cortex, PrC, HC, and parahippocampal cortex. MTL3 also showed a significant reduction in brain
volume in the right anterior fusiform gyrus and anterior lateral temporal cortex. B–D, Activity in
parahippocampal and extrastriate areas associated with a functional localizer task in patients
HC3 and MTL3 (overlaid on T1-weighted images). B, Transverse slices showing PPA based on
scenes ⫺ (faces ⫹ objects). C, Transverse slices showing FFA based on faces ⫺ (scenes ⫹
objects). D, Coronal slices showing lateral occipital cortex based on objects ⫺ (scenes ⫹ faces).
(Permission to reproduce these images from Lee and Rudebeck, 2010a, was granted by A.C.H.
Lee).
in age or years of education (all t ⬍ 1.07, p ⬎ 0.30). Ethical approval was
obtained from the Cambridge National Health Service Research Ethics
Committee. All participants gave informed consent according to the
Declaration of Helsinki (1991) regarding involvement in the experiment.

Materials
Faces. Portrait photographs (grayscale) of two pairs of men and two pairs
of women with similar shaped faces and visual features were taken from

10492 • J. Neurosci., June 19, 2013 • 33(25):10490 –10502

Mundy et al. • Perceptual Learning of Scenes and Faces

an online yearbook. From these, four morphed
face pairs were created using the software package Morpheus 1.85 (ACD Systems; see Mundy
et al., 2007, for detailed information about the
procedure). In brief, a sequence of intermediate (blended) images were created from a pair
of exemplars by anchoring key feature points
such as nose, eyes, and mouth and changing the
distance between these points (Fig. 2A shows
example stimuli pairs). Two faces were then
selected from each male and female morph
continuums, one of which had 56.6% of the
features of original face 1 and 43.3% of original
face 2 and the other with 43.3% of face 1 and
56.6% of face 2.
Scenes. Four 3D virtual reality, computergenerated rooms were created. A new item was
then generated from each of these prototype
room layouts ensuring that within the pair
there were differences in the size, orientation,
and/or location of three of the features of the
room (e.g., a window, a staircase, and a wall Figure 2. Examples of discrimination pairs presented sequentially during learning used in both experiments: faces (A), scenes
cavity). In the example pair shown in Figure (B), and dots (C). D, Schematic and timings of a discrimination test trial.
2B, the two rooms differ in the location of the
pillar on the left, the orientation of the right
automatically after the completion of the response period (Fig. 2D is a
wall, and the angle of the center staircase. The rooms were created using
schematic of a test trial).
a commercially available computer game (Deus Ex; Ion Storm) and a
In each of the three separately run conditions (faces, scenes, and dots),
freeware software editor (Deus Ex Software Development Kit version
there were 64 test trials consisting of 16 trials for each of the stimulus
1112f).
pairs seen in the exposure phase, and 16 trials for each of the two pairs not
Dots. A computer program written in Visual Basic was used to generate
seen in the exposure phase. Half of the presentations of each item pair
four pairs of confusable dot patterns (Fig. 2C). The program was conwere “same” trials (e.g., either FP1 then FP1 or FP1⬘ then FP1⬘) and half
strained to create an initial random pattern of 11 dots of 0.5 cm radius. A
were
“different” trials (e.g., either FP1 then FP1⬘ or FP1⬘ then FP1). The
second confusable pattern was made for each initial dot pattern by makorder of same and different trials within a run was randomized, with the
ing random adjustments to the location of 3 dots in the original image
restriction that no more than two of each type of trial could occur in
within a range of 0.25 to 0.75 cm. All stimuli were 10.2 ⫻ 9.9 cm when
succession. Furthermore, the order of trials was randomized with the
presented on the computer screen.
constraint that there must be eight trials from each condition (preexposed or nonexposed) in every 16 trials. After every 16 trials, a fixation
Experimental procedure
cross was presented for 20 s to allow the participant to rest. At the
Stimuli were presented using Presentation (Neurobehavioural Systems)
completion of the test phase, participants were allowed to rest for 5
running on either a 17 inch laptop ( patients) or an IBM-compatible
min before moving on to the next exposure-test cycle with a different
desktop computer (controls), with the latter connected to a standard 17
type of stimulus. Patient HC3 was tested on dots, then faces, then
inch LCD monitor. Stimuli were shown at a resolution of 1024 ⫻ 786
scenes; her controls received the same sequence. Patient MTL3 was
pixels. Participants were seated ⬃60 cm from the computer screen. After
tested on dots, then scenes, then faces; her controls received the same
providing informed consent, the first of three exposure-test cycles began
sequence.
with the following instructions appearing on the computer screen:
“You will now see a series of images; some will be very similar. Please
Data analysis
pay close attention—the differences are very subtle. (Press the response
The data were analyzed in two ways. In Analysis 1A, we investigated
button to begin).”
whether participants would show any benefit of prior exposure to stimuli
Once the response key was pressed, the participants were presented
by comparing discrimination performance (both accuracy and RT) for
with an item for 2 s, followed by an empty black screen for 0.5 s (a single
preexposed pairs compared with nonexposed pairs. Analysis 1B investitrial). They were not required to make any response to these items. As in
gated whether the patients, compared with their controls, showed any
Mundy et al. (2006), an intermixed presentation schedule was used. For
evidence of learning across the nonexposed (and preexposed) pairs by
example, within Face Pair 1 (FP1), the two morphed faces (FP1 and FP1⬘)
looking for improvement in accuracy over four separate time blocks of
were presented in an intermixed manner one after each other (e.g., FP1,
the test phase (Block 1 to Block 4).
FP1⬘, FP1, FP1⬘. . .) until there had been 5 presentations of each exemplar
(a total of 10 individual trials). The participant was then presented with
the stimuli comprising FP2 in the same fashion. Participants then moved
Experiment 2: fMRI in healthy participants
on to the test phase.
Participants
At the start of the test phase, participants received the following inSixteen right-handed healthy participants (10 male) were scanned. The
structions on the computer screen:
ages of the participants ranged from 18 to 40 years (mean, 30) and all had
“You will now see a second series of images; some will be new. The
normal or corrected-to-normal vision. All participants gave written inimage will flash—please indicate whether you think the image has
formed consent for their participation in the study (according to the
changed. Left button ⫽ yes, right button ⫽ no. (Press the response butDeclaration of Helsinki, 1991). This work received ethical approval from
ton to begin).”
the Cardiff University School of Psychology Research Ethics Committee.
During each test trial, participants saw one stimulus for 500 ms, followed by a 300 ms interstimulus interval (which was filled by a highcontrast mask) and then a second stimulus for 500 ms, which was
Materials
Twelve face, 12 scene, and 12 dot pattern pairs were created using the
followed by a 4 s response period. Two mouse keys were used to record
procedure described above (see Materials, Experiment 1).
the participants’ “yes” and “no” responses. Subsequent trials proceeded

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Figure 3. Schematic showing a single run (from one participant) from the fMRI experiment. The top shows the two exposure (intermixed and blocked) conditions. The bottom shows the
discrimination test phase (including the timings of stimuli presentation).

Experimental design
The basic design of the fMRI experiment was similar to Experiment 1 in
that participants were exposed to pairs of stimuli at study before undertaking a same/different discrimination task with previously seen, but also
nonexposed pairs, at test. One difference, however, was the use of two
different preexposure conditions (intermixed and blocked), a manipulation designed to investigate the impact of exposure schedule (Mundy et
al., 2009). The full experimental procedure is described below (also see
the schematic in Fig. 3), but our statistical analyses were restricted to the
preexposed intermixed and nonexposed pairs only.
Stimuli were presented during scanning using Presentation software
running on an IBM-compatible desktop computer connected to a digital
projector (1024 ⫻ 786 pixels resolution). The latter projected onto a
white screen situated behind the participant scanner bed and this could
be seen via an angled mirror placed directly above the participant’s eyes
in the scanner. The on-screen dimensions of all images was identical to
those in Experiment 1, with stimuli covering 15 ⫻ 12 degrees of visual
angle (h ⫻ w).
Figure 3 shows the basic experimental design for one run (of two) for
a single participant. Within a run, the study phase for one type of stimulus was always followed by the test phase for the same type of stimulus
comprising preexposed pairs interspersed with nonexposed pairs from
the same category. All stimulus categories appeared equally often in each
serial position (presented first, second, or third) within each of the two
runs (balanced across participants).
Two preexposure schedules, intermixed and blocked, were used during the study phases. The intermixed preexposure condition was similar
to the study phase undertaken by the patients in which the two items
comprising a pair were alternately presented (e.g., FP1, FP1⬘, FP1,
FP1⬘. . . ) until each item in a pair had been viewed five times. The
blocked preexposure involved five repetitions of one item from the pair
before five presentations of the other item from the pair (FP1, FP1, FP1,

FP1, FP1, FP2. . . ). In both of these conditions, the timing was the same
as that in the patient study. Participants were not required to make any
response during these preexposure conditions.
During the discrimination test, participants were presented with preexposed pairs from the intermixed and blocked conditions and also nonexposed pairs of faces, scenes, or dots. They indicated whether these pairs
were the same or not by pressing the relevant key of a button box held in
the right hand. To ensure adequate jitter in trial timings, instead of the 4 s
response window used for the patient study, there was a random intertrial interval of between 4.5 and 12 s sampled from a Poisson distribution.
In the test phase, there were 64 preexposed and 32 nonexposed trials in
each run, resulting in a total of 64 trials per stimulus type per condition
(intermixed, blocked, and nonexposed) across the whole experiment.
Trials from pairs seen previously during preexposure were randomly
interspersed between nonexposed stimuli trials. Items from each visual
category were presented in blocks.

Data acquisition
Imaging was performed on a General Electric 3T HDx MRI system using
an eight-channel receive-only head coil at the Cardiff University Brain
Research Imaging Centre, School of Psychology, Cardiff University. For
functional imaging, a T2*-weighted gradient-echo, echoplanar imaging
(EPI) sequence with high-order shim (HOS) was used to image volumes
with BOLD contrast. Fifty slices were collected per image volume covering the whole brain, prescribed 30 degrees inclined from the AC-PC
plane (to maximize signal coverage in the MTL). Scanning parameters
were as follows: TR/TE, 3000/35 ms; flip angle, 90 degrees; slice thickness,
2.8 mm (1 mm gap); acquisition matrix GE-EPI, 64 ⫻ 64; in-plane field
of view, 22 cm; ASSET (acceleration factor), 2; and HOS. The HOS is a
procedure that allows the scanner to ( partially) correct for variations in
the magnetic field that arise once a participant is placed in the scanner by
adjusting shims inside the gradient coils according to a low-resolution

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10494 • J. Neurosci., June 19, 2013 • 33(25):10490 –10502

Figure 4. fMRI statistical analysis steps. Left: Identification of the five fROIs. Top right: Analyses 2A, effect of exposure. Middle right: Analysis 2B, discrimination accuracy. Bottom right:
Complementary whole-brain analyses.

magnetic field map. Additional high-resolution field maps were also acquired for every participant for the purpose of un-distorting the EPI
datasets during image preprocessing. For anatomic localization, a structural scan was obtained for each participant using a T1-weighted sequence (3D FSPGR). Scanning parameters were as follows: TR/TE 7.9/
3.0 ms; flip angle, 20 degrees; acquisition matrix, 256 ⫻ 256 ⫻ 176; field
of view, 256 ⫻ 256 ⫻ 176 mm; isotropic resolution, 1 mm.

Data analysis: behavioral

Data preprocessing

We focused on two analyses complementary to the patient study (Fig. 4).
Both of these used an fROI approach to investigate how activity in key
regions sensitive to faces and scenes was modulated by exposure history
(Analysis 2A) and by discrimination accuracy (correct ⬎ incorrect, Analysis 2B). The latter was based on pairs presented in the nonexposed
condition, but similar findings were evident when we analyzed the intermixed (and blocked) preexposed pairs that were also presented at test.
The fROI analyses were complemented, where sensible, with wholebrain contrasts. The procedure for identifying the fROIs is described first,
before specific details about the two analyses.

Data preprocessing and statistical analysis of fMRI data were performed
using FEAT (fMRI Expert Analysis Tool) Version 5.63, part of the software library of the Oxford Centre for Functional MRI of the Brain (fMRIB) (www.fmrib.ox.ac.uk/fsl). The following prestatistics processing
was applied: motion correction using MCFLIRT (Jenkinson et al., 2002);
nonbrain removal using BET (Smith, 2002); spatial smoothing using a
Gaussian kernel of FWHM 4 mm; mean-based intensity normalization
of all volumes; high-pass temporal filtering (Gaussian-weighted leastsquares straight line fitting, with ␴ ⫽ 20.0 s); and un-distorting the EPI
data to correct for magnetic field distortions by means of individual field
maps. Time-series statistical analysis was performed using FILM with
local autocorrelation correction (Woolrich et al., 2001). Registration to
high-resolution 3D anatomical T1 scans ( per participant) and to a standard MNI template image (for group average) was performed using
FLIRT (Jenkinson and Smith, 2001; Jenkinson et al., 2002). Coordinates
reported here have been converted to Talairach and Tournoux (1988)
convention, where appropriate, for ease of comparison with existing
literature (Lacadie et al., 2008).

The primary measure of performance was response accuracy ( percentage
of correct discriminations) averaged over both scanning runs for each
stimulus type (dot patterns, faces, and scenes). RTs during test blocks
were also examined to assess whether preexposed compared with nonexposed discriminations were facilitated.

Data analysis: fMRI

fROI approach
To mirror procedures used in the visual perception literature, an fROI
localizer analysis was performed (Fig. 4, Steps 1 and 2). The following
procedures were first performed on individual participant data and then
pooled for group-level statistical analysis. To identify orthogonal fROIs
for our analysis, the first, completely novel, test trial involving each stimulus pair was used. The first test trial for each face pair was contrasted
with the first trial for each scene pair, giving voxel clusters particularly

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J. Neurosci., June 19, 2013 • 33(25):10490 –10502 • 10495

Table 1. Previously reported anatomical coordinates for the five fROIs used in
Analyses 2A and 2B
Previously reported coordinates
Left
x

Right
y

z

x

y

z

Reference

⫺38 ⫺46 ⫺16 41 ⫺47 ⫺17 Peelen and Downing, 2005 (see also
Kanwisher et al., 1997; Haxby
et al., 2001)
PostPG ⫺23 ⫺44 ⫺9 27 ⫺40 ⫺7 Peelen and Downing, 2005 (see also
Epstein and Kanwisher, 1998)
PrC
⫺26 ⫺9 ⫺18 29 ⫺9 ⫺18 Lee et al., 2008 (converted from
MNI space; see also Barense
et al., 2010a, 2011)
AntHC ⫺30 ⫺6 ⫺18 27 ⫺15 ⫺18 Barense et al., 2011 (see also Barense
et al., 2010a)
PostHC ⫺23 ⫺29
0 23 ⫺32
5 Lee et al., 2008 (converted from
MNI space; see also Lee and
Rudebeck, 2010b)

PFG

SDs are shown in parentheses.

activated by faces. The opposite contrast (first trial of each scene pair vs
first trial of each face pair) generated voxels particularly activated by
scenes. All other further analyses were performed on data from the subsequent trials (n ⫽ 15 per stimulus pair, a total of 60 per category) with
this first novel test trial removed so that localizer and test data were
independent, thus avoiding the problem of circularity (Kriegeskorte et
al., 2009).
The most significantly active voxel within each anatomical area of
interest [i.e., posterior fusiform gyrus (PFG), which includes FFA; posterior parahippocampal gyrus (PostPG), which encompasses PPA); PrC;
anterior HC (AntHC); posterior HC (PostHC)] was located in regions of
cortex that corresponded well with previously reported anatomical locations and visible anatomy (Tables 1, 2).
Two fROIs were defined for each of our five anatomical areas of interest: one containing any voxels active in the face minus scene localizer
contrast, and the other containing any voxels active in the scene minus
face localizer contrast. Therefore, each fROI was defined as the set of
contiguous voxels that were significantly activated within 12 mm in the
anterior/posterior, superior/inferior, and medial/lateral direction of the
peak anatomically constrained voxel in the contrast (Table 1, Table 2). To
ensure a liberal inclusion criterion for identification of all domainsensitive voxels involved in the task, a threshold of p ⬍ 0.05 (uncorrected) was used to isolate active voxels.
Analysis 2A: effect of preexposure on learning. fMRI time series data
were submitted to a (random effects) general linear model, with one
predictor that was convolved with a standard model of the hemodynamic
response function for each event type/condition. The regressors were
defined by the exposure history of each discrimination event (i.e., ‘intermixed dots,” “blocked dots,” “nonexposed dots,” “intermixed faces,”
“blocked faces,” “nonexposed faces,” “intermixed scenes,” “blocked
scenes,” and “nonexposed scenes”). The first nonexposed trial from each
stimulus type was excluded from this analysis because it had been used to
generate the independent fROI data; at this point, data from blocked
preexposure conditions were also discarded. Multiple linear regression
on the time courses resulted in one ␤-image for each event type per
participant. These parameter estimates were used in a higher-level
(group) FLAME analysis (fMRIB’s Local Analysis of Mixed Effects; Beckmann et al., 2003; Woolrich et al., 2004).
The parameter estimates within the 10 ROIs identified from our localizer (face-sensitive and scene-sensitive populations of voxels within PrC,
AntHC and PostHC, PFG and PostPG) were measured (using Featquery)
for intermixed preexposed versus nonexposed faces, scenes and dots (Fig.
4, Steps 3 and 4). A whole-brain contrast between the intermixed preexposed and nonexposed items was also performed for each stimulus category. FEAT’s group (Gaussianized) t-statistics were converted to
z-statistics and thresholded using clusters determined by z ⬎ 3 and a

(corrected) cluster significance threshold of p ⫽ 0.05 (Worsley et al.,
1992).
Analysis 2B: learning of nonexposed pairs over repetition. For Analysis
2B, we looked at the activity associated with behavioral performance on
scene and face nonexposed pairs during their presentation in the test
phase. Regressors (n ⫽ 32) were defined by the stimulus type of each
discrimination event and the time point of occurrence (e.g., scene stimuli, first trial; scene stimuli, second trial (i.e., first repeat) … scene stimuli, sixteenth trial). Each event was further categorized according to
behavioral outcome (correct or incorrect discrimination), resulting in
four additional regressors (correct scenes, incorrect scenes, correct faces,
and incorrect faces). Parameter estimates from the GLM were then combined in a higher-level (group) FLAME analysis (fMRIB’s Local Analysis
of Mixed Effects; Beckmann et al., 2003; Woolrich et al., 2004) that
allowed group-level contrasts.
Data were then submitted to a fROI analysis using the same localizer
coordinates used in Analysis 2A. To assess the effect of response accuracy
within each of the 10 fROIs, the remainder of the discrimination trials
(from the second to the sixteenth repeated trials) were separated according to correct versus incorrect discrimination response separately for face
and scene trials (Fig. 4, Steps 5 and 6). An average of 40 trials per category
per participant were classed as correct responses, with an average of 20
trials classed as incorrect.
For the purposes of the statistical analysis, the functional regions identified in the PostHC, AntHC, and PrC were grouped together as “MTL”
regions. Although parahippocampal cortex is anatomically associated
with the MTL (Witter, 2002) and necessary for some aspects of long-term
memory (Diana et al., 2007, 2010), it is also critical for representing the
spatial layout of visual environments (Epstein and Kanwisher, 1998).
This perceptual role seems to be functionally different from that played
by the HC in scene perception and memory (Epstein et al., 2007; Hartley
et al., 2007; Epstein, 2008; Mundy et al., 2012) and more similar to other
domain-sensitive areas located on the ventral surface of the temporal
lobe (Schwarzlose et al., 2008). Therefore, the domain-sensitive parahippocampal and fusiform fROIs were grouped together (for statistical purposes) as “extrastriate” regions.
For completeness, we also report standard whole-brain accuracy analyses at the end of the Results section.

Results
Patients: Analysis 1A (the effect of preexposure on learning)
Figure 5A shows the controls’ mean discrimination accuracy for
the three stimulus types (scenes, faces, and dots), represented as a
percentage difference between performance on pairs of stimuli
seen previously (exposed) compared with those not exposed to
participants at test. The greater the difference between these two
conditions, the larger the perceptual learning effect shown by the
controls (and, by extension, patients). Figure 5A indicates an
average improvement in discrimination accuracy between preexposed and nonexposed pairs of ⬃12% in controls, a difference
that was significant for all three stimulus types (smallest t(11) ⫽
18.28, p ⬍ 0.01). Furthermore, the graph revealed that control
performance was well matched across stimulus types, a conclusion supported by an ANOVA revealing no significant effect of
stimulus category (F(2,22) ⫽ 1.7, p ⫽ 0.193).
Like controls, patient HC3 showed clear evidence of a benefit
of preexposure on her discrimination decisions for dots and
faces, with an accuracy difference of 17% between preexposed
and nonexposed conditions. In contrast to the controls, however,
she was unable to learn any scene discriminations showing equivalent (chance) performance for both pairs of scenes seen previously and nonexposed scene pairs. Although MTL 3 showed a
small difference between the preexposed and nonexposed pairs of
scenes (2%) and faces (4%), both of these were well outside the
level of perceptual learning demonstrated in the controls, highlighting abnormal discrimination learning for these two stimulus

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Table 2. Talairach and Tournoux (1988) coordinates of the center voxel of each cluster used to produce the 10 fROIs from Analysis 2B
Faces ⬎ scenes

Scenes ⬎ faces

Left

PFG
PostPG
PrC
AntHC
PostHC

Right

Left

Right

x

y

z

x

y

z

Voxels

x

y

z

x

y

z

Voxels

⫺38 (5)
⫺21 (8)
⫺26 (7)
⫺30 (6)
⫺29 (8)

⫺46 (5)
⫺39 (8)
⫺8 (7)
⫺14 (7)
⫺31 (9)

⫺17 (5)
⫺6 (8)
⫺18 (6)
⫺16 (6)
⫺4 (9)

37 (6)
20 (8)
28 (7)
30 (7)
32 (8)

⫺45 (5)
⫺37 (9)
⫺7 (5)
⫺15 (7)
⫺30 (8)

⫺16 (6)
⫺6 (8)
⫺19 (5)
⫺17 (6)
⫺4 (8)

48 (7)
3 (2)
32 (5)
11 (3)
12 (3)

⫺41 (8)
⫺26 (6)
⫺20 (6)
⫺31 (6)
⫺28 (8)

⫺55 (9)
⫺42 (7)
⫺1 (7)
⫺20 (7)
⫺35 (7)

⫺10 (8)
⫺7 (5)
⫺24 (6)
⫺12 (8)
2 (6)

41 (8)
27 (5)
22 (5)
33 (7)
30 (5)

⫺53 (8)
⫺40 (7)
2 (5)
⫺19 (7)
⫺32 (7)

⫺12 (8)
⫺7 (5)
⫺25 (7)
⫺12 (6)
2 (5)

3 (2)
51 (8)
22 (5)
11 (3)
42 (6)

Data were acquired from the group average x, y, z (SD), along with the mean number of active voxels across both hemispheres.

types. MTL3 was not incapable of any
learning, however, as she showed a level of
perceptual learning similar to HC3 (and
numerically greater than the controls) for
dot patterns (16%). Crawford t test analyses (Crawford et al., 1998; Crawford and
Garthwaite, 2002) confirmed that the patients’ perceptual learning for dots was
not significantly different from controls
(all ts ⬍ 1), but that they had poor perceptual learning for scenes (HC3: t(11) ⫽ 5.3,
p ⬍ 0.01; MTL3: t(11) ⫽ 4.4, p ⬍ 0.01),
and, in the case of MTL3, deficient perceptual learning for faces (t(11) ⫽ 4.2, p ⬍
0.01; HC3: t(11) ⫽ 1.7, p ⫽ 0.1).
Similar stimulus-dependent patterns
(in patients) were also evident in the RTs
(Fig. 5B). To analyze these data, we collapsed across preexposed and nonexposed
trials because statistical analysis (ANOVA
for controls; paired one-way t tests for patients) confirmed that there were no significant differences between RTs for
preexposed versus nonexposed pairs in
controls (all Fs ⬍ 1) and that any differences in the RTs obtained for preexposed Figure 5. A, Mean discrimination scores for dots, faces, and scenes in patients HC3 and MTL3 and controls as measured by the
and nonexposed stimuli in patients were percentage difference in accuracy between preexposed compared with nonexposed discrimination trials. B, Mean RTs (in seconds)
not significantly greater than those seen in for dots, faces, and scenes in patients HC3 and MTL3 and controls as measured by the difference between average RT for preexposed
the individual controls (true of all condi- minus nonexposed discrimination trials. C, Learning profiles across test for the nonexposed discriminations presented at test. Test
tions). Although RTs were well matched trials were binned into four blocks and the percentage correct accuracy was calculated for each time bin. Patient HC3 is represented
across stimulus type in the controls (F ⬍ by the pink line, MTL3 by the blue line, and controls by the light gray line. Chance performance (50%) is highlighted by a horizontal
1), like the accuracy data, patient HC3 dashed gray line.
showed RTs that were similar to controls
Table 3. Behavioral discrimination accuracy (percentage correct) from the fMRI
for dot patterns and faces, but took almost double the time of
study for the exposed (intermixed) and nonexposed conditions
controls to respond to scene discriminations. Similarly, patient
Exposed (intermixed)
Nonexposed
MTL3 showed strikingly longer RTs for scene discriminations; in
addition, she was also much slower in her response to the face
Scenes
81.50 (0.221)
66.50 (0.237)
pair discriminations while responding as fast as controls to dot
Faces
83.00 (0.199)
68.25 (0.289)
patterns. Crawford t test analysis confirmed that the RTs obDots
82.25 (0.242)
66.25 (0.237)
tained for MTL3 in the face and scene conditions (faces: t(11) ⫽
SDs are shown in parentheses.
4.3, p ⬍ 0.01; scenes: t(11) ⫽ 5.5, p ⬍ 0.01) and in HC3 for scene
discriminations (t(11) ⫽ 8.3, p ⬍ 0.01) were significantly different
from those seen in controls. Critically, HC3 did not show a signonexposed pairs in the discrimination test phase (Fig. 5C).
nificantly different pattern in the face condition (t ⬍ 1), and
ANOVA confirmed that the controls’ discrimination perforneither patient was significantly slower than controls when remance to nonexposed stimulus pairs improved over repetition,
sponding to dots (all ts ⬍ 1).
but did not differ across stimulus type (e.g., there was a significant
main effect of block, F(3,9) ⫽ 27.63, p ⬍ 0.01, but no significant
effect of stimulus type or an interaction, F ⬍ 1). ANOVA also
Patients: Analysis 1B (learning of nonexposed pairs
confirmed significant linear trends in the controls’ learning
over repetition)
across all stimulus types (F(1,11) ⫽ 51.63, p ⬍ 0.01). To compare
Complementing these analyses, we also investigated whether the
the performance of the controls with the patients statistically,
patients showed any learning over repeated presentation of the

Mundy et al. • Perceptual Learning of Scenes and Faces

J. Neurosci., June 19, 2013 • 33(25):10490 –10502 • 10497

Figure 6. A, PFG (incorporating FFA) and PrC/AntHC activations identified from the fROI analysis. C, PostPG (incorporating PPA) and PostHC activations from the fROI analysis. B, D, Parameter
estimates obtained from the five fROIs (based on their preferred category) for each stimulus type (faces, scenes, and dots) plotted separately for nonexposed (NE) and preexposed (E) conditions in
PFG, AntHC, and PrC (face-sensitive voxels; B) and PostPG and PostHC (scene-sensitive voxels; D).

improvement was measured by calculating the gradient of the
linear trend (m) in learning for each stimulus type as follows: m ⫽
( y ⫺ c)/x, where c is the y-axis intercept, x-axis is the block
number, and y-axis is the percentage correct; values obtained for
m ⫽ 13.02 for dots (r 2 ⫽ 0.94), 12.5 (r 2 ⫽ 0.94) for faces, and
11.77 (r 2 ⫽ 0.99) for scenes. ANOVA confirmed that there were
no differences in these learning profiles (F ⬍ 1). Although patient
HC3 showed a similar learning profile to controls for dots and
faces (Crawford t ⬍ 1), the gradient of the linear trend for nonexposed scene stimuli was significantly weaker than that of controls (t(11) ⫽ 4.0, p ⬍ 0.01). Patient MTL3 also showed similar
performance to controls for dot patterns (t ⬍ 1), but her learning
profile for faces and scenes were significantly weaker than that
seen in the controls (faces: t(11) ⫽ 3.6, p ⬍ 0.01; scenes: t(11) ⫽ 4.0,
p ⬍ 0.01). It is reassuring to note that the same statistically significant patterns were evident over repetition of the preexposed
stimuli during the discrimination test phase, although the performance differences between patients and controls were exacerbated by the effect of preexposure itself.
Analysis 2A: fMRI (the effect of preexposure on learning)
Table 3 shows the discrimination scores obtained for the six experimental conditions. ANOVA revealed a main effect of exposure condition (F(1,15) ⫽ 45.86, p ⬍ 0.01), but no overall effect of
stimulus category (F ⬍ 1) and no interaction (F ⬍ 1).

Parameter estimates from preexposed (intermixed) and nonexposed face, scene, and dot trials were extracted from the 10
ROIs identified using the localizer. ANOVA revealed a significant
four-way interaction of: fROI (PFG, PostPG, PrC, PostHC, AntHC) ⫻ stimulus-sensitive voxels (face-sensitive/scene-sensitive) ⫻
stimulus type (faces/scenes/dots) ⫻ exposure (preexposed/nonexposed) (F(8,120) ⫽ 15.44, p ⬍ 0.01).
Further statistical exploration focusing separately on patterns
of activity within face-sensitive voxels and scene-sensitive voxels
in the five fROIs, revealed for face-sensitive voxels a significant
three-way interaction between stimulus type, exposure, and ROI
(F(4,60) ⫽ 25.01, p ⬍ 0.01; Fig. 6 A, B). Face-sensitive voxels in
PFG, AntHC, and PrC showed greater activity to preexposed face
pairs than to nonexposed faces (PFG: t(15) ⫽ 3.41, p ⬍ 0.01;
AntHC: t(15) ⫽ 2.99, p ⬍ 0.01; PrC: t(15) ⫽ 3.07, p ⬍ 0.01), but
there was no modulation of exposure in these three regions for
scene or dot pairs (t ⬍ 1). Face-sensitive voxels in PostPG and
PostHC showed no significant differences between preexposed
and nonexposed stimuli regardless of stimuli type (t ⬍ 1).
Turning to scene-sensitive voxels, ANOVA revealed a significant three-way interaction between stimulus type, exposure, and
ROI (F(2,30) ⫽ 19.83, p ⬍ 0.01; Fig. 6C,D). This interaction reflected greater activation in scene-sensitive voxels in PostPG and
PostHC for previously seen pairs of scenes compared with nonexposed pairs (PostPG: t(15) ⫽ 2.94, p ⬍ 0.01; PostHC: t(15) ⫽

10498 • J. Neurosci., June 19, 2013 • 33(25):10490 –10502

Mundy et al. • Perceptual Learning of Scenes and Faces

3.19, p ⬍ 0.01). Activity associated with
preexposed and nonexposed face and dot
pairs was not significantly different (t ⬍
1). Similarly, scene-sensitive voxels in
PFG, AntHC, and PrC showed no evidence of significantly greater activation
for preexposed over nonexposed trials for
all stimuli (t ⬍ 1).
Whole-brain analyses in which the
preexposed and nonexposed conditions
for faces, scenes, and dots were contrasted
(separately by stimulus type) revealed
similar findings. For faces, a significant region of BOLD signal change was observed
in the lingual gyrus that extended into the
inferior occipital gyrus and (temporal/occipital) fusiform gyrus. The extent of this
activation likely included FFA. There was
also significant activity in the AntHC that
extended into the PrC bilaterally (L ⬎ R).
Previously reported anatomical locations
place the FFA bilaterally at ⫺38, ⫺46,
⫺16; 41, ⫺47, ⫺17 (Table 1), which is
close to the peak voxel coordinate in the
significant clusters highlighted here: ⫺36,
⫺47, ⫺15; 35, ⫺43, ⫺14. The (left) PrC
has previously been identified at ⫺26, ⫺9,
⫺18 (see MNI space ⫺27, ⫺7, ⫺25, Lee et
Figure 7. A, Percentage signal change in face-sensitive and scene-sensitive PostHC and PrC ROIs (left) and parahippocampal/
al., 2008), again, almost identical to the
fusiform ROIs (right) for correct (C, gray bars) and incorrect (I, white bars) discriminations from preferred categories (e.g., face trials
significant cluster obtained from our cur- in PrC and scene trials in PostHC). B, Percentage signal change in nonpreferred categories (scene trials in face-sensitive and face
rent analysis (⫺26, ⫺9, ⫺26).
trials in scene-sensitive areas) across all ROIs. Nonpreferred categories showed no evidence of significant accuracy effects (conIn the scenes contrast, activation was firmed by ANOVA; all Fs ⬍ 1).
evident in the PostPG (likely encompassing the PPA) and extending bilaterally
into PostHC. The PPA has been previously reported at ⫺23, ⫺44, ⫺9; 27, ⫺40,
⫺7 (Table 1) and the (left) PostHC at
⫺23, ⫺29, 0 (see MNI space ⫺24, ⫺29,
⫺4, Lee et al., 2008); both of these locations are close to the clusters found here
(PostPG: ⫺21, ⫺39, ⫺6; 25, ⫺37, ⫺7;
PostHC: ⫺26, ⫺33, ⫺4; ⫺28, ⫺32, ⫺7).
Perceptual learning of dot patterns revealed significant activation in the occipital pole extending into the medial inferior Figure 8. Activations from the whole-brain contrasts of correct nonexposed face discriminations versus incorrect nonexposed
occipital gyrus and lingual gyrus, but no face discriminations (PrC bilaterally, in blue) and correct nonexposed scene discriminations versus incorrect nonexposed scene
higher-order ventral visual or MTL areas. discriminations (PostHC bilaterally, in red).
Because no parahippocampal, fusiform,
“same” compared with “different” responses for any stimulus type
HC, or PrC activation was found in the dots perceptual learning
(largest t(15) ⫽ 1.73, p ⫽ 0.104).
contrast (preexposed vs nonexposed), this condition was exTurning to the analysis of the data obtained from the 10 fROIs
cluded from further analysis.
(face-sensitive and scene-sensitive populations of voxels within
PrC, AntHC and PostHC, PFG and PostPG), we found a signifiAnalysis 2B: fMRI (learning of nonexposed pairs
cant four-way interaction of area (extrastriate/MTL) ⫻ stimulusover repetition)
sensitive voxels (face-sensitive/scene-sensitive) ⫻ stimulus type
Participants showed no overall difference in their averaged dis(faces/scenes) ⫻ response accuracy (correct/incorrect) (F(1,15) ⫽
crimination accuracy between stimulus types for the nonexposed
15.78, p ⬍ 0.01). It is worth noting that although this accuracy
trials presented at test (mean faces accuracy: 66.25%, scenes acanalysis, like that of the patients in Experiment 1, focused on the
curacy: 68.55%, dots accuracy: 66.56%; F ⬍ 1). Similarly, there
nonexposed trials, an equivalent analysis can be performed using the
was no overall difference in RTs (mean faces RT: 1.40 s, scenes RT:
previously exposed intermixed stimuli, which also revealed a similar
1.43 s, dots RT: 1.40; F ⬍ 1). There was also no significant difference
significant four-way interaction (F(1,15) ⫽ 12.22, p ⬍ 0.01).
in participants’ use of “same” or “different” responses in any of the
Although extrastriate regions showed a main effect of stimustimulus types (t ⬍ 1), suggesting no response bias. Furthermore,
lus type (F(1,15) ⫽ 18.35, p ⬍ 0.01) qualified by an interaction between
there were no significant differences in the accuracy evident on

Mundy et al. • Perceptual Learning of Scenes and Faces

stimulus-sensitive subregion (PFG/PostPG) and stimulus type
(F(1,15) ⫽ 34.27, p ⬍ 0.01), there was no significant effect of
discrimination accuracy and no three-way interaction (all Fs ⬍ 1;
Fig. 7A, right). In contrast, the MTL showed a significant threeway interaction (F(2,30) ⫽ 18.94, p ⬍ 0.01; Fig. 7A, left). Facesensitive voxels in PrC were associated with discrimination
accuracy for faces (t(15) ⫽ 4.55, p ⬍ 0.01), but not scenes (t ⬍ 1),
but scene-sensitive PrC voxels did not show any significant
change in activity for either category of stimuli (t ⬍ 1.4). In
PostHC, scene-sensitive voxels were associated with discrimination accuracy for scenes (t(15) ⫽ 3.43, p ⬍ 0.01), but not for
faces (t ⬍ 1), whereas face-sensitive PostHC voxels were not
involved in successful discrimination performance for either
category (t ⬍ 1).
Voxels within the AntHC were not associated with accuracy
for either faces or scenes (t ⬍ 1) and thus will not be considered
further. Analyses of activity by accuracy for the nonpreferred
category in each voxel population revealed no statistically significant effects (all Fs ⬍ 1; Fig. 7B).
Consistent with our fROI analysis, whole-brain analysis also
revealed involvement of the PrC and PostHC in discrimination
accuracy for nonexposed faces and scenes, respectively (Fig. 8).
BOLD activity relating to accurate discrimination for faces alone
and scenes alone was defined by contrasting correct versus incorrect nonexposed trials. This contrast was performed with a
random-effects model and tested at an uncorrected threshold of
p ⬍ 0.001. When this analysis was conducted with face stimuli
(i.e., correct nonexposed faces vs incorrect nonexposed faces), it
revealed activation centered on PrC (⫺26, ⫺10, ⫺25; 25, ⫺12,
⫺25), with no further areas of significant BOLD activation. A
similar contrast of correct versus incorrect nonexposed scene
trials revealed activation in PostHC (⫺25, ⫺35, ⫺3; 27, ⫺34,
⫺5), with no further areas of significant activity. Furthermore,
equivalent patterns of domain-sensitive MTL activity were evident if preexposed stimuli were analyzed and when both nonexposed and preexposed were pooled together.
These findings were further complemented by a whole-brain
comparison in which we investigated whether there were any
brain areas showing a domain-general pattern as measured by a
significant difference in activity for correct discriminations compared with incorrect discriminations across all trial types (i.e.,
correct scenes ⫹ correct faces vs incorrect scenes ⫹ incorrect
faces). This contrast was performed with a random-effects model
and tested at an uncorrected threshold of p ⬍ 0.001. No areas in
the MTL, parahippocampal, or fusiform cortex showed a significant domain-general pattern of activation for correct compared
with incorrect discriminations. There was, however, a single cluster of activation revealed in lingual gyrus, corresponding to early
visual cortex. The results of this analysis do not change if dot trials
are included (e.g., correct faces ⫹ correct scenes ⫹ correct dots
minus incorrect faces ⫹ incorrect scenes ⫹ incorrect dots).

Discussion
In Experiment 1, HC damage resulted in impaired scene, but not
face or dot pattern, discrimination learning. Broader MTL involvement, including the HC and PrC, affected scene and face,
but spared dot learning. These distinct patterns were evident on
two measures: (1) a comparison of performance on preexposed
versus nonexposed discriminations and (2) learning of nonexposed (and preexposed) discriminations over repetition at test.
There was no hint that the patients’ preserved learning was abnormal; both patients performed as well as controls for accuracy
and their RTs were equivalent to controls when they showed

J. Neurosci., June 19, 2013 • 33(25):10490 –10502 • 10499

good perceptual learning. These findings complement Graham et
al. (2006), in which three patients with bilateral HC damage (including the patient reported here) showed slowed RTs (but normal accuracy) to scene categorization and learning. Our study
extends the conclusions from that study, however, by demonstrating a clear impact on accuracy as well as RTs, revealing a
PrC contribution to face perceptual learning, and showing normal dot pattern discrimination learning using an identical task.
The latter finding is important: normal perceptual learning in
amnesia is often demonstrated with a dot prototype learning paradigm (Knowlton and Squire, 1993; Kolodny, 1994; Squire and
Knowlton, 1995). Our patients showed normal learning on this
paradigm (Graham et al., 2006) and, as revealed here, for dot
discrimination learning using a different experimental task. Because the patients did not show evidence of normal perceptual
learning across all visual categories, however, our study reveals
that the type of information to be acquired is a key factor in
driving performance on perceptual learning tasks.
In Experiment 2, we obtained complementary evidence that
the PostHC and PrC were involved in discrimination learning for
scenes and faces, respectively. Activity within face-sensitive, but
not scene-sensitive, voxels in PrC and scene-sensitive, but not
face-sensitive, voxels in PostHC was modulated by discrimination accuracy (correct ⬎ incorrect) at test for both nonexposed
and previously exposed pairs. Whole-brain analysis also revealed
a similar domain-sensitive, accuracy-dependent pattern in the
PostHC and PrC. In contrast, activity in the parahippocampal
cortex and fusiform gyrus distinguished between preferred and
nonpreferred categories (scenes vs faces), but was not modulated
by discrimination accuracy (see also O’Neil et al., 2009, in which
fusiform gyrus showed more limited accuracy effects compared
with the PrC during recognition memory for face stimuli).
In our fMRI experiment, there was no difference in overall
accuracy across the three stimulus conditions; participants
started at the same baseline and showed the same degree of improvement in their learning of faces and scenes (and also dots).
Therefore, differences in the difficulty of learning about faces and
scenes cannot explain the fMRI findings, nor can they explain the
results of the patient study in which performance was similarly
matched. The fMRI results, therefore, imply that the PrC and
PostHC subregions that we identified encode face and scene representations (respectively) that are useful in supporting successful discrimination between the highly similar face and scene pairs
presented in our experiment. The lack of accuracy effects for
nonpreferred categories in the PostHC and PrC fROIs further
strengthens this contention.
The results reported here complement animal and human
neuropsychological studies highlighting stimulus-sensitive deficits for complex objects and scenes after damage to the MTL (e.g.,
Buckley et al., 2001; Bussey et al., 2002; Lee et al., 2005a; Saksida et
al., 2006; Barense et al., 2007; Bird et al., 2007; Taylor et al., 2007).
However, not all focal amnesic patients show such patterns (Levy
et al., 2005; Shrager et al., 2006), and there has been heated debate
regarding the locus of these cognitive difficulties, including suggestions that some patients have involvement of fusiform and/or
parahippocampal areas in addition to their HC and PrC damage
(Squire et al., 2006; Jeneson and Squire, 2012). This view is inconsistent with data showing that the two amnesic patients described here show domain-sensitive responding in PPA for
scenes, lateral occipital cortex for objects, and FFA for faces when
scanned during a functional localizer task (Lee and Rudebeck,
2010a; Fig. 1). Our neuropsychological and neuroimaging results
add weight to this finding, in particular the converging evidence

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10500 • J. Neurosci., June 19, 2013 • 33(25):10490 –10502

that the HC and PrC were the critical contributors to successful
discrimination learning. Therefore, it seems highly unlikely that
the deficits observed here, and in our patients on similar tasks
(Barense et al., 2005; Lee et al., 2005b), reflect fusiform and parahippocampal involvement. Instead, this developing body of evidence highlights that the requirement to process conjunctions of
visual and/or spatial features appears to be critical in eliciting
such impairments in patients (Graham et al., 2010). More explicitly, as argued by Barense et al. (2012), the PrC is necessary for
storing unique object representations (with individual object features dependent upon more posterior regions within the brain;
Mundy et al., 2012). In contrast, the HC stores the unique spatial
layouts of these objects in an environment and may be required
when there is repetition of object features, but also of the spatial
locations of objects themselves. It remains to be determined
whether the HC is also engaged by conjunctive spatial feature
changes within an object in the same way that it processes conjunctive spatial layout changes within a scene containing multiple
objects.
Our findings demonstrate that regions beyond visual cortex
(Mukai et al., 2007) contribute to short-term discrimination
learning, a finding not predicted by some human memory accounts (Diana et al., 2007; Squire et al., 2007; Brown et al., 2010;
Montaldi and Mayes, 2010; Ranganath, 2010). It is also controversial whether the role of MTL regions goes beyond long-term
memory to short-term memory (Ranganath and Blumenfeld,
2005; Hartley et al., 2007; Lee and Rudebeck, 2010b; Jeneson and
Squire, 2012) and even perceptual processing (Lee et al., 2005b;
Baxter, 2009; Suzuki, 2009; Barense et al., 2010a,b; Graham et al.,
2010; Lee et al., 2012). The domain-sensitive impairments observed in our amnesic subjects are also seen in trial-unique oddity
judgements that placed no explicit requirement on remembering
stimuli across trials. Patients were presented with different views
of the same item (e.g., face, object, or scene) alongside a completely different item and asked to indicate which item was the
odd one out (Lee et al., 2005a; Barense et al., 2007). Selective
damage to the HC affected scene oddity judgments, but not judgments on faces or objects, whereas larger lesions to the MTL,
including both the HC and PrC, impaired object, face, and scene,
but not color or size, oddity decisions (see also Lee et al., 2006b,
for similar findings in dementia and Buckley et al., 2001, for
equivalent impairments in nonhuman primates).
FMRI studies using variations of these oddity judgment tasks
activate similar regions to those elicited by our visual discrimination paradigm (Lee et al., 2006a, 2008; Devlin and Price, 2007;
O’Neil et al., 2009; Barense et al., 2010a, 2011), revealing complementary patterns of domain-sensitive responding in the HC and
PrC across different tasks with varying degrees of mnemonic demand. It is also worth highlighting recent findings from fMRI
studies in nonhuman primates revealing multiple temporal lobe
brain regions that respond relatively selectively to discrete object
categories, including an anterior face patch (Tsao et al., 2003;
Pinsk et al., 2005; Rajimehr et al., 2009). The precise functional
roles of these more anterior regions in animals have not yet been
elucidated, but given the striking convergence between findings
from human and nonhuman primate neuropsychological lesion studies (for review, see Saksida and Bussey, 2010), it is
possible that the anterior face patch, if analogous to PrC in
humans, may also include complex conjunctive face and/or
object representations.
There is accruing evidence that anatomically separate
domain-sensitive HC and PrC regions represent complex conjunctive stimuli necessary for multiple aspects of human mem-

ory, including—as demonstrated here—success on tasks that
require learning to make perceptual discriminations between
highly visually similar exemplars. Models that focus on a specific
role for the HC in spatial information processing (Hassabis and
Maguire, 2009; Bird et al., 2012), as well as accounts that place
these findings in an evolutionary context (Murray and Wise,
2010), provide a potential framework within which to understand these domain-sensitive contributions. The challenge for
these and related theories is to determine the following: (1) what
types of representations are being stored within these domainsensitive subareas and whether these are the only regions that
drive such effects, (2) when these regions are necessary (or not)
for learning and memory, and (3) how any domain-sensitive
parts of the HC and PrC may be anatomically and functionally
connected with areas involved in binding information across different modalities (Eichenbaum et al., 2007; Graham et al., 2010;
Montaldi and Mayes, 2010; Ranganath, 2010). A further issue
that requires resolution is the “anatomical” association of PHC
with the MTL (Witter, 2002) in the context of a “functional”
profile similar to other extrastriate areas (Schwarzlose et al., 2008;
Mundy et al., 2012). Consideration of possible anatomical/functional dissociations between anterior and posterior areas of the
PHC and HC might help to address this issue.

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