Neuropsychologia 180 (2023) 108479

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Neuropsychologia
journal homepage: www.elsevier.com/locate/neuropsychologia

Prosopagnosia does not abolish other-race effects
Pauline Schaller, Roberto Caldara, Anne-Raphaëlle Richoz *Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg, Fribourg, Switzerland

A R T I C L E I N F O

A B S T R A C T

Keywords:
Prosopagnosia
Other-race effects
Face processing
Fusiform face area
Occipital face area

Healthy observers recognize more accurately same-than other-race faces (i.e., the Same-Race Recognition
Advantage - SRRA) but categorize them by race more slowly than other-race faces (i.e., the Other-Race Cate­gorization Advantage - ORCA). Several fMRI studies reported discrepant bilateral activations in the Fusiform
Face Area (FFA) and Occipital Face Area (OFA) correlating with both effects. However, due to the very natureand limits of fMRI results, whether these face-sensitive regions play an unequivocal causal role in those otherrace effects remains to be clarified. To this aim, we tested PS, a well-studied pure case of acquired proso­
pagnosia with lesions encompassing the left FFA and the right OFA. PS, healthy age-matched and young adults
performed two recognition and three categorization by race tasks, respectively using Western Caucasian and East
Asian faces normalized for their low-level properties with and without-external features, as well as in naturalistic
settings. As expected, PS was slower and less accurate than the controls. Crucially, however, the magnitudes ofher SRRA and ORCA were comparable to the controls in all the tasks. Our data show that prosopagnosia does notabolish other-race effects, as an intact face system, the left FFA and/or right OFA are not critical for eliciting the
SRRA and ORCA. Race is a strong visual and social signal that is encoded in a large neural face-sensitive network,
robustly tuned for processing same-race faces.

1. Introduction
Humans are experts in recognizing faces, an ability that is critical for
adapted social interactions. With a single and rapid glance at a face, we
are able to extract a wide range of information, such as identity, age,physical attractiveness, gender, emotional state, or race. The processingof these biologically relevant social signals relies on the coordinatedactivity of an extended face cortical network (Duchaine and Yovel,
2015; Haxby et al., 2000; Ramon et al., 2015; Rossion, 2014). Whilesome authors suggest that this network is genetically and innatelyorganized to preferentially respond to faces (e.g., McKone et al., 2012),
others posit environmental factors and experience as being critical in
shaping face processing mechanisms (e.g., Arcaro et al., 2017). The
other-race effect is one of the most prominent examples illustrating how
experience drives face individuation and shapes our recognition abilities
to be tuned to the most familiar physiognomic variations of facial
morphologies present in our environment.
The other-race effect, also named same-race recognition advantage(SRRA), relates to the recognition advantage for same- (SR) compared toother-race (OR) faces. The SRRA has received extensive researchattention over the last forty years. This robust effect has been observedin a wide number of behavioral studies relying on a wide range of
methodologies, paradigms, databases of stimuli, and tasks involving
different populations with various cultural and ethnical backgrounds(Blais et al., 2008; Golby et al., 2001; Meissner and Brigham, 2001; for areview, see, Hugenberg et al., 2010) The developmental roots of the
SRRA seem to originate very early in infancy, with a preference for SR
faces being observed already at 3-months of age, although the effect canbe abolished at this age with an exposure to OR pictures (Anzures et al.,2010). The effect becomes more robust later in the development, at theage of 6- to 9- months. At this age, infants succeed at recognizing un­
familiar SR faces but fail with OR faces (Hayden et al., 2009; Kelly et al.,2007). The differential processing of SR and OR faces has been further
observed in early and late childhood (e.g., Pezdek et al., 2003),
adolescence (e.g., Goodman et al., 2007), and adulthood (e.g., Michel
et al., 2006a,b; Zhao et al., 2014). In adults, the SRRA has been typicallyinvestigated with old-new tasks, first familiarizing participants with asubset of faces and then asking them to identify old vs. new faces withina larger set of stimuli (for a review, see, Meissner and Brigham, 2001).
Interestingly some studies have shown that the SRRA can be prevented
or eliminated with increasing interracial social contacts (Gajewski et al.,2008), either when participants are intentionally exposed to pictures or* Corresponding author. Eye and Brain Mapping Laboratory (iBMLab), Department of Psychology, University of Fribourg Faucigny 2, 1700, Fribourg, Switzerland.
E-mail address: anne-raphaelle.richoz@unifr.ch (A.-R. Richoz).
https://doi.org/10.1016/j.neuropsychologia.2023.108479
Received 31 July 2022; Received in revised form 28 December 2022; Accepted 5 January 2023
Available online 7 January 2023
0028-3932/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Neuropsychologia 180 (2023) 108479

videos of OR faces (Anzures et al., 2012) or when an adoption occurs in
an OR family (De Heering et al., 2010). These findings highlight themalleability of the SRRA, and show the strong influence of social
exposure and face individuation in shaping face processing mechanisms,
especially if social contacts occur in early childhood (McKone et al.,2019).
Another race-related effect, the other-race categorization advantage(ORCA), has also been observed in several behavioral studies. In contrast
to the SRRA, whereby SR faces are better recognized than OR faces, thisparadoxical phenomenon demonstrates that OR faces are more quickly
and accurately categorized than SR faces (Caharel et al., 2011; Caldara
et al., 2004; Contreras et al., 2013; Feng et al., 2011; Ge et al., 2009; Liet al., 2018; Liu et al., 2015; Wiese, 2013; Zhao and Bentin, 2008).
Interestingly, in a behavioral study concurrently examining both the
SRRA and ORCA, Ge et al. (2009) revealed a negative correlation interms of response time between both effects. The faster observers
recognized SR faces compared to OR faces, the slower they categorized SR
faces as opposed to OR faces. This antagonist interaction between faceindividuation and face categorization is due to the very nature of face
representation shaped by visual expertise underlying the same-racerecognition advantage and the other-race categorization advantage(Valentine, 1991). A very high level of expertise is necessary foradequate and rapid recognition of SR faces, yet the same level ofexpertise negatively impacts their categorization.
One of the most prominent theoretical frameworks accounting forboth other-race effects is the multidimensional face-space model pro­
posed by Valentine (1991; see also, Valentine and Endo, 1992). Ac­
cording to this psychological model, faces are stored in a
multidimensional space, shaped by social and visual experience. In thisface space, each location represents a specific identity, and the distancefrom a prototypical average face is located at the center, with themultidimensional space coding for facial features and the distance be­
tween points representing the similarity between faces. Since observers
are more frequently exposed to SR faces, the space dedicated to thosefaces results in a wider and more elaborated representations with faces
being located further away from the prototypical face according todistinctive facial features shared across exemplars (i.e., color of the eyes,
hair, etc. for Western Caucasian faces). However, such diagnostic in­formation is not effective for other-race faces (for instance, all East Asianfaces share dark hair and brown eyes). Fewer contacts with OR faces
engender a more rudimentary multidimensional face-space, as faces aregrouped together due to their high similarity. This dense cluster resultsin quicker categorization of OR faces as the distance is very small be­
tween the face exemplars, but slower and impaired recognition of thosefaces because of their lack of distinctiveness. Conversely, the high dis­
tance between SR faces leads to higher recognition judgments as faceexemplars do not overlap, but slower and poorer categorization per­formance. This theoretical multidimensional face-space model hasreceived support from single-cell recordings in monkeys (Leopold et al.,2006) and computational auto-associative neural networks (e.g., Cal­dara and Abdi, 2006).
In addition to this prominent theoretical explanation of the otherrace effects, several brain imaging studies have also investigated theneural underpinnings of these perceptual phenomena. Greater neuralactivation to SR vs. OR faces have been reported in functionally defined
face-selective areas of the bilateral ventral occipitotemporal cortex(VOT) during both face recognition (e.g., Golarai et al., 2020; Golby
et al., 2001; Natu et al., 2011) and categorization (e.g., Feng et al., 2011;
Ng et al., 2006; Wei et al., 2014) although these results are contrasted (e.
g., Brosch et al., 2013; Brown et al., 2017; Contreras et al., 2013).
More specifically, an ERP study investigating the neural correlates of
the SRRA with an adaptation paradigm revealed repetition suppression(RS) on the early face-sensitive N170 component only to SR, but not OR
faces when the same identities were shown. These findings suggest ahigher sensitivity of the face processing system to SR faces and a more
efficient coding of those faces compared to OR faces (Vizioli et al.,2010). Several fMRI studies have further evidenced broader and stron­
ger activations to the memorization and recognition of SR vs. OR faces inbilateral fusiform gyri, such as in the fusiform face area (FFA - Golarai
et al., 2020; Golby et al., 2001; Kim et al., 2006) and ventral lateral
occipital areas, where the OFA is located (Natu et al., 2011). For
instance, Golby et al. (2001) conducted a fMRI study to examine thebrain activity of African American, and Western Caucasian observers
asked to memorize and recognize SR vs. OR faces with an “old-new”
recognition task. Their findings reported stronger neural activations to
SR vs. OR faces in the right FFA but not in the left FFA. Interestingly,
although the authors failed to report a correlation between the neuralactivity in the right FFA and the magnitude of the SR recognition
advantage, a positive correlation between both measures was observedin the left FFA, suggesting that this region is related to face identity
memorization. More recently, in a cross-sectional study, Golarai et al.
(2020) investigated the developmental time-course of the SRRA in FFAs
responses in children, adolescents, and adults. Their results revealed
greater recognition and stronger FFAs activation for SR vs. OR faces inall age groups. Their data further revealed an age-related increase in themagnitude of the SRRA in both behavioral and FFAs responses. Inter­estingly, the authors also reported a positive correlation between the
SRRA in memory and the activity in both the left and right FFAs, sug­gesting that both hemispheres are involved in the development of theSRRA. Natu et al. (2011) further demonstrated that the discrimination of
SR vs. OR faces does not depend solely on bilateral FFA activations but
involves a broader range of cortical regions in the ventral temporal
cortex, including ventral lateral occipital areas – where the OFA is
located. Other studies, however reported more contrasted findings. For
example, Kim et al. (2006) reported larger neural responses of thebilateral FFAs to SR vs. OR faces, yet solely when the faces were unfa­
miliar to the participants. More recently, Brown et al. (2017) measured
overall similar SR vs. OR activity in the fusiform gyrus. Finally, Brosch
et al. (2013) used multi-voxel pattern analysis (MVPA) to examine theblood-oxygen-level-dependent (BOLD) patterns elicited by SR vs. OR
faces in early visual and face-selective regions. Their results revealed
that the BOLD activation patterns in the early visual cortex could predictthe race of the faces observed by the participants, while successful
predictions in higher-face processing regions (FFA) were limited toparticipants with a very high implicit pro-white bias. Intriguingly, theauthors did not replicate the univariate differences in FFAs activations
between SR vs. OR faces reported in previous studies (Golarai et al.,2020; Golby et al., 2001; Natu et al., 2011). Taken together, these
discrepant findings leave several issues open regarding the neural un­
derpinnings of the SRRA. It remains to be clarified whether both bilat­eral FFA activations are necessary to observe this effect and whatconsequences damage to the left FFA, highly sensitive to memory per­formance, could have.
The ORCA has also been examined by several fMRI (for a review, see,Bagnis et al., 2020; Feng et al., 2011) and electrophysiological studies (e.
g., Caharel et al., 2011; Caldara et al., 2004; Vizioli et al., 2010). For
example, in a study by Feng et al. (2011), whole-brain analyses revealed
that the categorization of SR faces elicited stronger activations in theright medial frontal cortex (rMFC) and right inferior frontal gyrus (rIFG)
compared to OR faces. Regions of Interest (ROI) analyses also high­lighted stronger bilateral FFAs and OFAs activations to SR vs. OR faces,
while brain-behavior correlation analyses revealed a negative correla­tion between SR face categorization accuracy and left FFA activation (i.
e., the higher the activity for SR vs. OR faces, the lower the categoriza­tion accuracy for SR faces). Finally, Psychophysiological Interaction(PPI) analyses revealed strong interactions between the posterior VOT
and frontal cortical areas for SR compared to OR faces. Feng et al. (2011)concluded that more cortical resources are engaged with SR comparedto OR faces due to observers’ extensive experience with those faces.
Broader and stronger activations might provide more in-depth pro­cessing of SR faces (i.e., at the individual level), which might, in turnparadoxically result in slower and less accurate categorization responses
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Neuropsychologia 180 (2023) 108479

of those faces at the behavioral level. Similar conclusions were drawn
from an ERP study by Caldara et al. (2004), revealing that the other-racecategorization advantage occurred at around 240 ms after stimulus
onset in a processing stage dedicated to semantic information. The
poorer experience observers have with OR faces generates fewer se­
mantic representations, which in turn accelerates the processing speedof those faces.
To the best of our knowledge, only one study concurrently examined
the neural correlates of the paradoxical behavioral effects of the SRRA
and ORCA. In this study, Liu et al. (2015) asked Chinese observers to
perform both face recognition and categorization by race tasks while
their brain activity was monitored with fMRI. Their data showed similarbilateral FFAs’, and OFAs’ activations to SR faces for both tasks, while
for OR faces, bilateral FFAs and OFAs activations were stronger for therecognition than the categorization by race task. These findings suggestthat SR faces are deeply individuated regardless of the task at hand. In
contrast, OR faces do not automatically benefit from in-depth process­ing, which occurs only during tasks that require face individuation.
Thus, although the same neural substrates underly the processing of SR
and OR faces, their activation is modulated by the race of the face, as
well as individuation constraints.
To sum up, the face processing network in the ventral occipito­
temporal cortex seems to be more, or at least equally activated by SR
compared to OR faces, with a critical role being played by the left FFA as
evidenced by significant brain-behavior correlations in this region (Fenget al., 2011; Golby et al., 2001). In fact, stronger responses in the left FFA
to SR faces enhanced identity recognition performance of SR faces
behaviorally (Golby et al., 2001), whereas for face categorization, theopposite pattern was true: increased activity in the left FFA impaired thecategorization of SR compared to OR faces (Feng et al., 2011). Whilethese left FFA-behavior correlational results could partially explain the
trade-off relationship between the recognition of SR vs. OR faces andtheir categorization by race (Feng et al., 2011), further studies arenecessary to provide direct proof of a causal engagement of the left FFA
in both effects. Crucially, although fMRI studies can reveal correlational
information between a particular task and the localization of its event inspecific brain regions, this neuroimaging technique cannot unequivo­
cally probe causal certainty about this relationship (Weber andThompson-Schill, 2010).
To tackle this issue, brain-damaged patients can be very informative,
as from their lesions and specific behavioral impairments it is possible toinfer the critical role played by the damaged regions in the healthy
operating system. Surprisingly, to the best of our knowledge, no study
has yet investigated the other-race effects in patients with lesions toface-selective regions. To address this literature gap, here we tested PS, awell-studied pure case of acquired prosopagnosia with bilateral occipi­
totemporal lesions, encompassing the right OFA, the left FFA, and asmall region in the right ATL. The main aim of the current study was toinvestigate whether face-sensitive regions play a causal role in bothother-race effects (SRRA and ORCA). PS, healthy age-matched andyoung adults performed two recognition and three categorization by
race tasks, with Western Caucasian and East Asian faces normalized fortheir frequency spectrum and contrast, with and without external fea­tures and faces presented in naturalistic settings. As expected, PS was
slower and less accurate than the controls. Crucially, however, themagnitude of her SRRA and ORCA were comparable to the controls in all
the tasks.

resulted at the age of 42 after she underwent a closed-head injury thatdamaged her left mid-ventral and right inferior occipital cortex,
encompassing the lFFA and rOFA respectively, as well as the right
middle temporal gyrus and the left posterior cerebellum to a lesser
extent (see, Sorger et al., 2007, for a precise anatomical description ofher lesions). After neuropsychological rehabilitation, patient PS wasable to work again as a kindergarten teacher. Despite a small left par­acentral scotoma, her low-level vision is preserved with good visualacuity. Her ability to perceive and recognize objects and to discriminate
faces surrounded by objects or complex scene backgrounds is within thenormal range (Rossion et al., 2003a). Yet, as a consequence of her le­sions, her ability to recognize the identity of faces (including the identityof her relatives and her own face) is severely impaired. She is further
impaired in categorizing the gender of faces for both accuracy andsensitivity. Patient PS shows also a marked impairment in categorizing
static facial expressions of emotion (Richoz et al., 2015), a deficit rooted
at the level of facial information use as she directs most of her fixations
toward the mouth, even when the eyes are the most informative (Fiset
et al., 2017, see also, Caldara et al., 2005, for facial information use withface identity). Interestingly, PS reaches maximum recognition accuracy
for all six basic expressions – except fear – when naturally evolving
dynamic expressions are shown (Richoz et al., 2015). These findings
reinforce the view of a distinct cortical route for the processing of dy­
namic face information, directly connecting early visual areas to thepSTS, a brain region anatomically spared in PS (see, Duchaine andYovel, 2015).
2.1.2. Control participants
Two control groups took voluntarily part in our experiments. The
first control group included 7 age-matched individuals from 59- to 76years old (M = 66.9, SD = 5.6, 2 women), while the second included
46 undergraduate students (35 women) from 18- to 28-years old (M =21.3, SD = 2.4) who obtained course credits for their participation. To
avoid an effect of culture on face recognition and categorization, we
ensured that all participants were Caucasian and had always been living
in a Caucasian country. All participants had a normal or corrected-tonormal vision, with no neurological or psychiatric history, and were
not taking any medication that could potentially slow down response
time. They all took part in both experiments. Prior to the experiments,
all participants signed a consent form that described the main goals of
our study. The ethical committee of the department of psychology of the
University of Fribourg (Switzerland) approved the study reported here.
2.2. Experiment 1 – Face recognition
We first assessed facial identity recognition accuracy of Caucasianand Asian faces with an Old-New face recognition paradigm and two
different databases of stimuli: a database of Asian and Caucasian faces
with external features and distinct facial expressions and a database ofneutral faces without external features.
2.2.1. Materials
2.2.1.1. Stimuli with external features. We first evaluated face recogni­tion performance using stimuli with external features (chin, outline offace, hair, ears). The stimuli were selected from the KDEF (Lundqvist
et al., 1998) and AFID (Bang et al., 2001) databases and were previously
used with a similar old-new face recognition paradigm in Blais et al.
(2008). The stimuli consisted of 56 Caucasian (28 females) and 56 Asian
identities displaying each happiness, disgust, and neutral expressions.
The face pictures were black and white and placed in the middle of agrey-colored background. They did not present any distinctive features
like glasses, clothes, jewelry, or scars, but the hair, chin, outline of theface, and ears were apparent. The males had all a shave. We cropped andresized the original images at the edges of the hair and neck to maintain

2. Methods
2.1. Participants
2.1.1. Patient PS’s case report
Patient PS was born in 1950, she is a Caucasian case of pure acquired
prosopagnosia with normal object recognition (for an exhaustive andrecent review, see, Rossion, 2022a, 2022b). Her cognitive disorder3

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the same visual angle across all stimuli (Fig. 1, Fig. 2). The resulting sizeof the images was 320 pixels in width and 361 pixels in height. They all
subtended a visual angle of 14◦ vertically from a distance of 57 cm,
corresponding to the size of a natural face during social interactions.
The stimuli were presented using the same old/new face recognition
paradigm as the one used in Blais et al. (2008). More specifically, the112 identities were divided into four blocks, two for each race (i.e.,
Asian and Caucasian). Each block was separated into two phases: alearning and a recognition phase. The learning phase consisted of 14
pictures (7 women), while the recognition phase consisted of the same
14 learned faces (old) and 14 new ones (Fig. 3). The identities presented
in both learning and recognition phases displayed distinct expressions ineach phase (e.g., disgust in the learning phase, happiness in the recog­
nition phase). All participants saw the same stimuli in each phase,
although the images were randomly presented. Each identity was
assigned to only one block. The race of the first block was counter­balanced across all participants. However, after seeing one race, a blockwith the other race was following.

expression).
Importantly, we normalized all stimuli in terms of luminance, con­trasts, and spatial frequency using the SHINE toolbox with the default
option (Willenbockel et al., 2010). The experiment was programmedwith Matlab (MATLAB, 2018) using the Psychophysics Toolbox (PTB-3;
Brainard, 1997; Kleiner et al., 2007). The faces were presented on a
13-inch MacBook Pro, version 10.13.6, with a resolution of 1440 × 900
pixels and a refresh rate of 60 Hz.
2.2.2. Procedure
Participants sat 57 cm away from a computer screen in a quiet roomat the University of Fribourg. They were told that they would see
different blocks (learning and recognition) of faces on a computer
screen, and their task would be to learn them as accurately as possible
during the learning phase and recognize them among other faces during
the recognition phase. After each learning phase, there was a 30s break.
During the recognition phase, participants were instructed to answer as
quickly and accurately as possible whether the face was familiar or not
by using computer keys labeled accordingly (“S” or “L” for familiar or
unfamiliar faces - counterbalanced across participants). The instructions
were given by the experimenter and written on the screen. Participants
were not informed about the ratio of old vs. new faces and no feedback
was provided.
Each trial began with a black fixation cross presented for 1000 ms forthe learning phase or randomly for 1000 or 2000 ms for the recognition
part, to control for anticipatory strategies. The faces were presented in arandom order for 3000 ms (learning phase) or until the participant
responded (recognition phase) (Fig. 5). The text instructions, fixation
cross, and the stimuli were all presented at the center of a grey screen.
The order of the tasks (with or without external features) was
counterbalanced across participants. Participants did the whole exper­
iment with both tasks in one session that lasted for approximately 15
min.

2.2.1.2. Stimuli without external features. In order to control for the
impact of facial expressions and external facial features on facial identity
recognition, we also evaluated face recognition performance with a
database of neutral faces without external features. This methodologicalchoice was also motivated by previous studies showing that increased
attention and fixations towards internal facial features result in superior
face recognition abilities (e.g., Fletcher et al., 2008, see also, Hills et al.,2014). The stimuli were selected from a database of Michel et al. (2007)
and consisted of 32 Asian and 32 Caucasian faces with the same numberof males and females. The differences in the stimuli number used acrossexperiments is simply due to their availability in the face databases wetook them from. The faces were presented in black and white on a grey
background and were cropped at the hairline to present only the internal
facial features (Fig. 4). They did not present any ornament (e.g., noglasses or jewelry) or other distinctive features (e.g., scars), and the men
had a shave. We cropped and resized the original images at the chin andforehead edges to maintain the same visual angle across all stimuli. The
resulting size of the images was 200 pixels in width and 252 pixels inheight. They all subtended a visual angle of 14◦ vertically from a 57
cm-screen distance.
The 64 stimuli were separated into two blocks, one for each race. As
before, each block was divided into 2 phases: a learning and a recog­
nition phase. More specifically, for each race, 16 (8 women) faces were
presented in the learning phase, and 32 (16 old and 16 new) appeared in
the recognition phase. The same stimuli were presented in each phase
for all participants but in random order. Note also that the facial ex­pressions were not changing, that is the exact same faces were presented
in both the learning and the recognition phase (same neutral

2.2.3. Data analysis
Data analysis was performed using RStudio 3.6.1. We merged thedata of healthy age-matched (AM) and young adult (YA) control subjects
as no statistically significant differences were observed between bothgroups. Before the analysis, we did not remove any participant for theSame-Race Recognition Advantage (SRRA) tasks, as no outlier wasobserved.
2.2.3.1. Recognition sensitivity. We computed d-prime (d’) scores foreach participant as a measure of recognition sensitivity. D-prime scores
are the subtraction of the z-score for false-alarm from the z-score for hits(d’ = ZH − ZFA). One-tailed paired-samples t-tests were computed on d’for the controls, with face race (OR (i.e., Other-Race, Asian faces) and SR(i.e., Same-Race, Caucasian faces)) as a within-subject factor for eachdatabase individually. We performed one-tailed modified t-tests, a sta­tistical test for single case studies (Crawford and Howell, 1998), tocompare the recognition sensitivity of PS with controls.
2.2.3.2. Normalized index of the SRRA. To determine the magnitude of
the SRRA in identity recognition, we calculated a normalized index of
the SRRA on the d’. More precisely, for each participant, we subtracted
the d’ for OR faces from the d’ for SR faces and divided the result by thesum of the d’ for SR and OR faces ((d’SR – d’OR)/(d’SR + d’OR)). Given thevery nature of the present study, we decided to use the normalized
instead of the absolute differences to compare the relative SR and OR face
performance across participants for all the experiments. The normali­zation makes easy to compare the patient single case performance withthose of the healthy controls. In fact, because of her brain lesions, PS isslower and less accurate than the controls and it is not straightforward tocompare her performance to the heathy controls when it comes to relatea difference for two face categories (SR and OR). For example, PS

Fig. 1. Example of a Face from the Database with External Features
Note. Example of a face (Caucasian woman expressing disgust) as it was cropped
in the experiment of Blais et al. (2008) (left) and in the present experi­
ment (right).
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Fig. 2. Faces from the database with external features.

showed a difference in response time of 692 ms during the categoriza­tion by race of natural SR and OR faces. This difference in patient PS
does not have the same weight or meaning than in the healthy control
population, as she is globally slower to achieve natural face categori­zation (2313-1621 ms vs. 846-741 ms for the controls). Therefore, anormalization step is necessary to put all the participants in the same
comparable scale, when computing ORE effects relating themself to adifference in performance between SR and OR faces. The obtained index
value indicates the difference in recognition sensitivity between bothraces (i.e., the greater the index, the greater the differences in recogni­tion sensitivity between both races). If the value of the index is positive,
it indicates an advantage for SR faces (i.e., better recognition of SR
faces), whereas if the value is negative, then OR faces are better
recognized. We performed two-tailed modified t-tests on the SRRA index
of the d’ to compare the magnitude of the other-race effect for PS and thecontrol participants.

Fig. 3. Schematic Representation of the Old/New Face Recognition Paradigm
Note. A schematic representation of the old/new face recognition paradigm forthe stimuli with external features. For the stimuli without external features, thisprocedure was repeated only once, and with 16 (instead of 14) old andnew faces.

Fig. 4. Faces from the database without external features.
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Fig. 5. The Paradigm of the Recognition Tasks
Note. Example of the procedure for the SRRA tasks (learning and recognition phases on the left and right, respectively) with the stimuli from the database without
external features.

2.3. Experiment 2 – Face categorization by racegroups, contextual features (i.e., beards, scarfs, hats, glasses, clothes, or
small pieces of jewelry), and natural backgrounds for each race (Asianand Caucasian) from Google images (Fig. 6).
More precisely, for each ethnical group, we selected a similar num­
ber of men and women (N = 18), front and side faces (N = 19), and faces
with the gaze directed away and towards the participant (N = 19). We
also controlled for the backgrounds of the pictures (plain, with clear or
blurred details), the age groups, the facial expressions (13 slightly
smiling and 23 neutral faces), and facial attractiveness to ensure that allthese characteristics were similar in both groups. To control for the
positions of the internal facial features (i.e., eyes, noses, mouths), we
used Matlab (MATLAB, 2018) to draw a triangle from the outer ends ofthe eyebrows to the upper two-thirds of the chin of each face (Fig. 7a).
We then summed the position of the different triangles, which showed asimilar pattern of face positions in both ethnical groups (Fig. 7b). All
faces were presented with their background and were 200 × 200 pixels.
They all subtended a visual angle of 14◦ vertically from a viewing dis­
tance of 57 cm. Each identity was presented once.
We again used a 13-inch MacBook Pro, version 10.13.6, with a res­olution of 1440 × 900 pixels and a refresh rate of 60 Hz for the three
categorization by race tasks. The tasks were also launched with the useof Matlab (MATLAB, 2018) and its Psychophysics Toolbox (PTB-3;
Brainard, 1997; Kleiner et al., 2007).

In Experiment 2, we assessed face categorization by race with acategorization paradigm and three different databases of stimuli. First,
we used the same databases as in Experiment 1, that is stimuli with andwithout external features. Then, to verify if the results are generalizable
to more ecological stimuli, we created a new database of faces collectedfrom the internet (for similar stimuli, see, Rossion et al., 2015). As forExperiment 1, we normalized all stimuli for their luminance, contrasts,
and spatial frequency using the SHINE toolbox with the default option(Willenbockel et al., 2010).
2.3.1. Material
2.3.1.1. Stimuli with external features. We used the same database of
stimuli with external features as in Experiment 1. For a description ofthis database, see above. We used each identity only once and presented
all of them with neutral facial expression.
2.3.1.2. Stimuli without external features. The same database without
external features as in Experiment 1 was employed (see above). Each
identity was presented once.
2.3.1.3. Natural stimuli. We created a new database of natural facepictures to assess whether the other race categorization advantage canbe generalized to more ecologically valid face stimuli. We selected 36
pictures of faces with various head positions, gaze directions, age

2.3.2. Procedure
Upon their arrival, participants sat 57 cm away from a computer
screen in a quiet room at the University of Fribourg. They were told that

Fig. 6. Example of faces from the natural database.
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Fig. 7. Example of Control of Internal Features Positions
Note. (a) Example of internal facial feature positions for two Asian (top) and two Caucasian (bottom) faces in front (center) or side view (left) view and (b) thesummation of the internal features for all faces of each race (Asian and Caucasian in red and blue, respectively).

faces would be presented on the screen and that they would have tocategorize each one according to its race (i.e., “Asian” or “Caucasian”) as
quickly and accurately as possible by using the labeled computer keys(“s” or “l” – semi-randomized across participants). The instructions wereorally explained by the experimenter and repeated on the screen at the
beginning of the experiment. The ratio of faces from both races was nottold to the participants, and they did not receive any feedback.
Each trial began with a fixation cross presented for a random dura­tion of 1000 or 2000 ms to control for anticipatory strategies (Fig. 8). A
randomly chosen face then appeared on the screen until the participant
answered. All faces of each database were presented, and each face waspresented only once. The instructions, fixation cross, and the stimuli
were all presented in the center of the screen on a grey background. All
texts were written in black.
The order of the tasks (with or without external features or natural
stimuli) was semi-randomized across participants. They participated in
the four categorization tasks in one session, which lasted for approxi­
mately 25 min.

2.3.3. Data analysis
As for the recognition tasks, data analysis was performed with theuse of RStudio 3.6.1. And as no statistical difference was found between
the performances of the AM and YA controls, we merged their data. For
the Other-Race Categorization Advantage (ORCA) tasks, we only
considered the trials with correct responses in our analyses and fromthose trials excluded one AM participant as his mean response time wasconsidered as an extreme outlier compared to the other AM participants.
2.3.3.1. Response time. For the ORCA tasks, only the response times forcorrect answers were taken into account in the analyses. For the con­trols, we performed one-tailed paired-samples t-tests on the responsetime (RT) with the race of the faces (OR and SR) as a within-subject
factor. We further performed one-tailed modified t-tests to compare
the differences in accuracy and response time between PS and thecontrols.
2.3.3.2. Normalized index of the ORCA. To determine the magnitude of
the ORCA, we computed a normalized index with the RT obtained foreach race. More precisely, for each participant, we subtracted the RT for
OR faces from the RT for SR faces and divided the result by the sum of
the RTs for SR and OR faces (i.e., (RTSR – RTOR)/(RTSR + RTOR). We
performed two-tailed modified t-tests on the ORCA index to compare themagnitude of the ORCA between PS and the controls.
3. Results
3.1. Experiment 1 – Face recognition
3.1.1. Recognition sensitivity
3.1.1.1. With external features. For controls, one-tailed paired-samples
t-tests revealed a significant main effect of race for the database withexternal features, t (52) = 11.35, p < .00, d = 1.56, 95% CI [0.53, ∞](bracket shows the lower and upper limits of the 95% confidence in­terval). Participants had a higher recognition sensitivity for SR (M = 1.4,
SD = 0.4) than OR (M = 0.7, SD = 0.4) faces.

Fig. 8. The Paradigm of the Categorization Tasks
Note. Example of the procedure for the categorization by race tasks with thestimuli from the database without external features.
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For PS, one-tailed modified t-tests indicated that she had a signifi­
cantly lower recognition sensitivity than 94.2% of the controls for SR
faces (M = 0.5), and that this difference was significant, t (52) = − 1.94,
p = .03 (Fig. 9, Fig. 10, see also, supplementary results, Table S1). For
OR faces, PS had lower d’ (M = 0.3) than 70.9% of the controls, but thisdifference was not significant, t (52) = − 1.07, p = .15. Bêta scores andconfusion matrices are also reported separately in the Supplementary
Fig. S1 and Supplementary Fig. S2, respectively.

3.2. Experiment 2 – Face categorization by race
3.2.1. Response time
3.2.1.1. With external features. All participants were highly accurate for
the ORCA task, whether they had to categorize OR (MPS = 86%, MControls= 98%, SDControls = 4%) or SR (MPS = 90%, MControls = 98%, SDControls =3%) faces with external features. PS was however significantly less ac­
curate than more than 99% of the controls for both OR t (51) = − 2.9, p= .003, and SR faces, t (51) = − 3.31, p = .001, respectively, as high­lighted by the one-tailed modified t-tests.
For the response times, the one-tailed paired-samples t-tests indi­cated that the control participants were faster to categorize OR (M =681.4 ms, SD = 130.8) than SR (M = 710.9 ms, SD = 135.1) faces, t (51)
= 2.58, p = .006, d = 0.36, 95% CI [10.35, ∞].
Compared to the controls, the one-tailed modified t-tests indicatedthat PS was slower than 93.5% of the controls for OR faces (M = 930.5
ms, SD = 282.1) and 99.2% of the controls for SR ones (M = 1086 ms, SD= 236.9) (Fig. 12, see also, supplementary results, Table S2). Both dif­ferences were statistically significant, t (51) = 1.89, p = .032, and, t (51)
= 2.75, p = .004, for OR and SR faces, respectively.

3.1.1.2. Without external features. One-tailed paired-samples t-tests also
indicated a significant main effect of race for the database without
external features for the controls, t (52) = 11, p < .00, d = 1.51, 95% CI [0.73, ∞]. Participants were better at recognizing SR (M = 1.5, SD = 0.6)
than OR (M = 0.6, SD = 0.5) faces.
Although PS was lower than 84.1% of the controls for SR faces (M =0.7), the one-tailed modified t-tests indicated that this difference wasonly marginally significant, t (52) = − 1.43, p = .08 (Figs. 9 and 10, seealso, supplementary results, Table S1). PS had lower sensitivity than
61.8% of the controls for OR faces (M = 0.2), but the difference was notstatistically different, t (52) = − 0.88, p = .19. Bêta scores and confusion
matrices are also reported separately in the Supplementary Fig. S1 andSupplementary Fig. S2, respectively.

3.2.1.2. Without external features. For the faces without external fea­tures, the categorization performance of the controls were also high forboth OR (MPS = 94%, MControls = 98%, SDControls = 3%) and SR (MPS =88%, MControls = 97%, SDControls = 4%) faces. The one-tailed modified ttests indicated that PS was different from only 81.7% of the controls for
OR ones, t (51) = − 1.35, p = .09, but had a significantly lower accuracy
than 98.8% of the controls for SR faces, t (51) = − 2.61, p = .006.
One-tailed paired-samples t-tests further revealed that the controls
categorized OR faces (M = 684.8 ms, SD = 137.2) faster than SR ones (M= 792.9 ms, SD = 208.3), t (51) = 6.06, p < .00, d = 0.84, 95% CI [78.27,∞].
For PS, one-tailed modified t-tests showed that she was significantly
slower than 99.8% of the controls for OR faces (MOR = 1131.2 ms, SDOR
= 309.6), t (51) = 3.22, p = .001, as well as 91.4% of the controls for SR
faces (MSR = 1161.3 ms, SDSR = 327.7), t (51) = 1.75, p = .043 (Fig. 12,
see also, supplementary results, Table S2).

3.1.2. Normalized index of the SRRA
3.1.2.1. With external features. According to the two-tailed modified ttests, the magnitude of the same-race recognition advantage on the d’ forfaces with external features was similar for PS (M = 0.29) and thecontrols (M = 0.34, SD = 0.27), t (52) = − 0.18, p = .86 (Fig. 11, see also,
supplementary results, Table S3).
3.1.2.2. Without external features. Similarly, for faces without externalfeatures, the magnitude of the same-race recognition advantage on therecognition sensitivity was not significantly different for PS (M = 0.6)
and the controls (M = 0.49, SD = 0.39), t (52) = 0.28, p = .78 (Fig. 11,
see also, supplementary results, Table S3).

3.2.1.3. Natural. In a more natural context, the control participants
were also highly accurate to categorize OR (MControls = 97%, SDControls =3%) and SR (MControls = 97%, SDControls = 3%) faces by race. The onetailed modified t-tests indicated that PS was however less accurate
than 100% of the controls for both OR (M = 75%) and SR (M = 81%)faces, t (51) = − 6.44, p < .001, and, t (51) = − 5.32, p < .001,
respectively.
As indicated by the paired-samples t-tests, the controls were also
faster to categorize OR (M = 752.1 ms, SD = 148.7) than SR (M = 859.1
ms, SD = 229.5) individuals, t (51) = 4.84, p < .00, d = 0.67, 95% CI[69.93, ∞].
For PS, one-tailed modified t-tests further revealed that she wassignificantly slower from 100% of the controls for both OR (M = 1621.9
ms, SD = 647.5) and SR (M = 2313.8 ms, SD = 971.6) faces from thenatural database (Fig. 12, see also, supplementary results, Table S2). Her
scores are significantly different from the control participants, t (51) =5.79, p < .001, and, t (51) = 6.28, p < .001, for OR and SR faces,
respectively.
3.2.2. Normalized index of the ORCA
3.2.2.1. With external features. The magnitude of the other-race effecton the RT was similar for PS (M = 0.08) and the controls (M = 0.02, SD= 0.05) for the database with external features, t (51) = 1.06, p = .3
(Fig. 13, see also, supplementary results, Table S3).

Fig. 9. Mean d’ for the SRRA Tasks
Note. Mean d’ for each group of participants (YA, AM, and PS, in blue, orange
and red, respectively) according to each race (SR and OR) in each database(with and without external features, on the left and right, respectively).
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Neuropsychologia 180 (2023) 108479

Fig. 10. ROC Curves
Note. ROC curves for PS and the controls (in red and green, respectively), for the OR and SR faces (darker and lighter curves, respectively) with or without externalfeatures (left and right, respectively).

Fig. 11. Normalized SRRA index on the d’Note. Normalized SRRA index on the d’ for each type of participant (YA, AM, and PS, in blue, orange and red, respectively) according to the type of database (with orwithout external features, up and below, respectively). Values on the right side of the graph indicates greater SRRA, that is, better recognition of SR than OR faces.

3.2.2.2. Without external features. Similarly, for the faces without
external features, the magnitude of the ORCA of PS (M = 0.01) was not
significantly different from the controls (M = 0.07, SD = 0.07), t (51) =− 0.75, p = .46 (Fig. 13, see also, supplementary results, Table S3).
3.2.2.3. Natural. Finally, with natural faces, the magnitude of the
ORCA was similar between PS (M = 0.18) and the controls (M = 0.06,
SD = 0.08), t (51) = 1.37, p = .18 (Fig. 13, see also, supplementary
results, Table S3).
4. Discussion
The present study examined whether and how acquired proso­
pagnosia modulates the same-race recognition advantage (SRRA) andthe other-race categorization advantage (ORCA) to shed further light on
the neural substrates underlying both effects. Several brain imaging
studies previously reported the FFA and OFA as key regions for pro­cessing race in faces. Yet, it remained to be clarified whether these brain
regions play a causal role in those other-race effects. To this aim, we
tested patient PS, a pure case of acquired prosopagnosia with occipito­
temporal lesions to the right OFA, the left FFA, and a small part of theright anterior temporal lobe. We first assessed patient PS and healthy
controls’ ability to recognize SR and OR faces with the use of old-newtasks and then examined their response time when asked to categorize
SR and OR faces by race. To assess both other-race effects in patient PS,
we computed the magnitudes of the SRRA and ORCA in our healthy

Fig. 12. Mean Response Time for the ORCA Tasks
Note. Mean response time for each group of participants (YA, AM, and PS, inblue, orange and red, respectively) according to each race (SR and OR) in eachdatabase (without, with external features, and natural, from the left to the right,
respectively).

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Fig. 13. Normalized ORCA Index on the Response
Time
Note. Normalized ORCA index on the response timefor each type of participant (YA, AM, and PS, in blue,
orange and red, respectively) according to the type of
database (without, with external features and natural,from top to bottom, respectively). Values on the rightside of the graph indicates greater ORCA, that is,quicker categorization of OR than SR faces.

controls and compared the obtained results to the magnitudes of theeffects observed in patient PS.
As predicted, our findings revealed the typical behavioral other-race
effects in face recognition and categorization by race in our healthy
controls. Our participants were more accurate at recognizing SR than OR
faces, but categorized OR faces more rapidly than SR ones, replicating
the previously found other-race effects in memory (e.g., for reviews, see,
Hugenberg et al., 2010; Lee et al., 2011; Meissner and Brigham, 2001)
and categorization (Caharel et al., 2011; Caldara et al., 2004; Contreras
et al., 2013; Feng et al., 2011; Ge et al., 2009; Li et al., 2018; Liu et al.,2015; Wiese, 2013; Zhao and Bentin, 2008), thus corroborating theface-space model predictions (Valentine, 1991).
Without surprise, our results further revealed that PS was signifi­
cantly less accurate in recognizing SR but not OR faces and slower in
categorizing both SR and OR faces relative to healthy controls. These
results are consistent with her prosopagnosia (Rossion et al., 2003a;
Schiltz et al., 2006) and align with the idea that lesions to the FFA
and/or OFA affect SR face processing abilities (Dalrymple et al., 2011;
Pitcher et al., 2007; Rossion et al., 2003a,b; Steeves et al., 2006).
However, the comparison of PS’ performance for both races in therecognition and categorization tasks might seem discrepant at first sight.
Her lesions seem to affect both SR and OR faces in the categorization
tasks, but SR faces solely for the recognition tasks. In fact, PS and thehealthy controls were equally impaired during OR face recognition. Thisobservation is fully in line with a recent study showing that a higher rateof individuals meet the clinical criteria of face-blind when recognition istested with OR compared to SR faces (Wan et al., 2017). We observed
very similar results to those reported by Wan et al. (2017), as a high rateof healthy controls poorly performed during the OR face recognition
tasks in our study. This resulted in a non-significant difference between
PS and the healthy controls for OR face recognition. Interestingly,
however, the novelty of our work lies in the magnitudes of the other-race
effects observed for PS, as they were in the range of the healthy controls
despite her functional impairment and brain lesions. These data provide
unequivocal evidence that acquired prosopagnosia does not abolish theother-race effects, and overall support the claim that an intact facecortical network - with undamaged FFA and/or OFA - is not necessary toobserve these effects. These observations suggest that a common
mechanism outside those areas is impaired in processing OR compared
to SR faces in both PS and healthy observers.

participants (Feng et al., 2011; Kim et al., 2006). These results suggestthat the activity of the FFA and OFA is modulated by the perceived race.
In addition, previous work also reported brain-behavior correlations
between the other-race effects and the left FFA (Feng et al., 2011;
Golarai et al., 2020; Golby et al., 2001), with larger activation for SR
faces being associated with greater SR face recognition performance but
worst categorization abilities. Since strong correlations between
behavioral performance and the activations of the FFA and/or OFA haveoften been reported, one might posit that these face-selective areas arecausally engaged in the other-race effects. However, here, for the firsttime, we demonstrate that the lFFA and/or the rOFA are not causallynecessary to observe the SRRA and the ORCA, at least not in a qualitative(i.e., absolute) manner, as PS showed comparable magnitudes ofother-race effects as controls. Rather, our data suggest that an intact
face-sensitive neural network plays a quantitative role in face recognitionand categorization by race by boosting the accuracy and speed ofbehavioral performance. Future studies are necessary to directly
examine the specific mechanisms played by the FFA and the OFA thatenhance face recognition performance in the other-race effects.
4.2. An extended and robust neural network supports race processing
from faces
Our study reinforces previous evidence suggesting that the process­ing of face race does not solely depend on the FFA and OFA but involves
an extended race-sensitive network that goes well beyond occipito­
temporal areas. For instance, previous work reported distinct activationsfor SR relative to OR faces in frontal regions, such as in the right medial
frontal cortex (rMFC) (Feng et al., 2011), the inferior frontal gyrus (IFG)(Feng et al., 2011; Greer et al., 2012), the dorsolateral prefrontal cortex(DLPFC) (Cunningham et al., 2004; Richeson et al., 2003) or the anterior
cingulate cortex (ACC) (Cunningham et al., 2004; Greer et al., 2012;
Richeson et al., 2003). More posterior areas have also been reported to
be involved in race processing, with greater SRRA-related functional
connectivity between the fusiform cortex and the medial intraparietal
sulcus and distinct activations for SR relative to OR faces in frontopar­
ietal areas (Brown et al., 2017) or in the right STS (Earls et al., 2013; Liuet al., 2015). Natu et al. (2011) demonstrated that a large spatial map inthe VT cortex extending to the fusiform gyrus and ventral lateral oc­
cipital areas was involved in dissociating SR and OR faces. Componentsof the limbic system also demonstrated stronger activations in response
to SR relative to OR faces. Such results were observed for the amygdala(Cunningham et al., 2004; McCutcheon et al., 2018; Phelps et al., 2000;
Platek and Krill, 2009, but see also, Lieberman et al., 2005), hippo­
campal and parahippocampal areas (Cunningham et al., 2004; Greeret al., 2012) whose activations were also correlated with the SRRA
(Golby et al., 2001). As these above-mentioned frontal, frontoparietal
and limbic regions are involved in high-level processes, such as conflict
resolution, inhibition, memory, social, cognitive, or emotional control,
among others, their recruitment in race processing is likely a demon­
stration of the involvement of top-down processes in the perception offace race. Finally, some studies also reported successful race prediction
from the activity in the primary visual cortex (Brosch et al., 2013; Kaul
et al., 2014; Ratner et al., 2013). However, the stimuli used in these

4.1. An intact face cortical network is not necessary to observe the otherrace effects
Despite extensive lesions encompassing the right OFA and the left
FFA, the magnitudes of the SRRA and ORCA of patient PS are within therange of controls. These results question the interpretation of previously
reported brain imaging findings that have pointed towards theinvolvement of these brain regions in the SRRA and ORCA. For example,
several studies have consistently reported greater activation of thebilateral FFA and OFA when viewing SR than OR faces, whether theparticipants were asked to recognize (Golarai et al., 2020; Golby et al.,
2001; Kim et al., 2006) or categorize faces (Feng et al., 2011), and inboth Caucasian (Golarai et al., 2020; Golby et al., 2001) and Asian10

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Neuropsychologia 180 (2023) 108479

studies radically differed in terms of low-level features (e.g., skin color).
Consequently, the differential activations observed in the primary visualcortex in response to faces of different races might solely mirror an
efficient coding of low-level visual components.
To sum up, in addition to the FFA and OFA, previous studies havereported that a large number of frontal, frontoparietal, and limbic re­gions are involved in race processing. These areas are intact in patient PS(Sorger et al., 2007) and might have been recruited to efficiently process
facial race. In line with previous studies, our findings provide further
evidence that facial race is subtended by an extensive network involving
regions that are not exclusively dedicated to the processing of faces.
Future neuroimaging studies with PS are necessary to precisely map out
the brain regions dedicated to the processing of SR and OR faces in theabsence of the lFFA and rOFA during face recognition and categorization
by race. This would further sharpen our understanding of the brain re­gions engaged in these paradoxical other-race phenomena.

sensitivity to face race. Race seems, therefore, to be encoded in a large
face-sensitive network, going well beyond the face network in theventral occipitotemporal cortex. The biological and social importance ofthe race signal might also be rooted in the brain by an early develop­
mental tuning to familiar same-race faces. Altogether, our results pro­vide novel insights into the neuroanatomy of the other-race effects andreveal one of the rare, preserved face-related sensitivity in the proso­
pagnosic patient. But more importantly, they go well beyond the field offace perception, as they invite to caution when interpreting neuro­
imaging results and drawing conclusions on the sole correlational
involvement of brain regions in cognitive tasks acquired in the healthy
adult population. Confirmatory causal studies as the present study arenecessary for this scope.
Credit author statement
Pauline Schaller: Methodology, Software, Validation, Formal Anal­ysis, Investigation, Writing-Original draft preparation, WritingReviewing and Editing, Visualization, Project administration. Roberto
Caldara: Conceptualization, Resources, Writing-Reviewing and Editing,
Supervision, Funding acquisition. Anne-Raphaëlle Richoz: WritingReviewing and Editing, Supervision, Project administration.

4.3. Early tuning to same-race faces
The other-race effects seem to originate very early in infancy with abehavioral SRRA observed as soon as 3-months of age (Anzures et al.,2010; Hayden et al., 2009; Kelly et al., 2007, 2009). As early as 7 monthsof age, the eye movements of infants are also tuned to distinct cultural
facial features to process facial expressions of emotion in Western andEastern infants (Geangu et al., 2016). The neural substrates of race
processing undergo changes throughout development and tune to SR
faces with age. For instance, the FFA (Golarai et al., 2020), IFG (Dinget al., 2014), right middle frontal gyrus (Ding et al., 2014), and left
cuneus (Ding et al., 2014) experience developmental changes across age
in response to race. Note that some of these areas were also identified as
race-sensitive in adults in the previously mentioned studies of Feng et al.
(2011) or Greer et al. (2012). Previous studies also reported that thefunctional connectivity within occipital areas and between occipital andfrontal areas strengthens with age for both SR and OR faces (Ding et al.,2014; Zhou et al., 2016). Finally, Telzer et al. (2013) noticed that early
deprivation to OR faces was associated with enhanced amygdala acti­
vations for OR faces. These findings highlight an early tuning in the facesystem to race with connectivity with other brain regions changing andstrengthening with experience. PS might still also benefit from this earlytuning towards same-race faces, as the magnitude of her SRRA and
ORCA are comparable to the controls. This observation, coupled withprevious neuroimaging evidence in healthy adults, posits the view thatrace is a strong visual and social feature that is encoded in an extensive
brain network, going well beyond the face-sensitive regions.
In general, our results also invite to caution when interpreting neu­roimaging results and drawing conclusions on the functional involve­
ment of brain regions in cognitive tasks acquired on the sole adult
healthy population. Corroborative complementary causal Transcranial
Magnetic Stimulation (TMS) and patient studies are necessary to draw
solid functional theories.

Declaration of competing interest
None.
Data availability
Data will be made available on request.
Acknowledgements
We would like to express our deepest gratitude to PS for her patienceand her devoted participation in this study as well as to all our control
participants. Funding: This work was supported by a grant from the
Swiss National Science Foundation [n◦ 100019_189018] awarded toRoberto Caldara.
Appendix 2. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.neuropsychologia.2023.108479.
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5. Conclusions
Understanding the neural substrates of race processing is of societal
importance, as these biological social cues are automatically processed
and influence our everyday interactions and behaviors. Our results
confirm previous observations that individuals are markedly impaired inprocessing other-race faces while categorizing them by race faster thansame-race faces. We critically provide novel evidence that proso­
pagnosia does not abolish the other-race effects. Our results reveal
indeed that lesions in face-selective areas, specifically the lFFA and/or
the rOFA, are not critical to observe the SRRA and ORCA, since PS’s
lesions on those brain areas do not prevent the patient from showing an
advantage comparable to the healthy controls for both other-race ef­
fects. Other areas responding to race, such as frontal, frontoparietal, or
limbic regions, may have been recruited in patient PS to show similar
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