Cerebral Cortex February 2015;25:374–383
doi:10.1093/cercor/bht233
Advance Access publication August 26, 2013
Lesion-Symptom Mapping of Self-Prioritization in Explicit Face Categorization:
Distinguishing Hypo- and Hyper-Self-Biases
Jie Sui1, Magdalena Chechlacz1, Pia Rotshtein2 and Glyn W. Humphreys1
1
Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, UK and 2School of Psychology,
University of Birmingham, Birmingham B15 2TT, UK
Address correspondence to Jie Sui. Email: [email protected]
People make faster familiarity decisions for their own face compared
with a familiar other. Lesion studies diverge on whether this selfface prioritization (SFP) effect is associated with functional processes isolated in the left or right hemispheres. To assess both
decreases (hypo-) and increases (hyper-) in SFP after brain lesion,
we asked patients with chronic deficits to perform familiarity judgments to images of their own face, a familiar other, or unfamiliar
faces. Of 30 patients, 7 showed hypo- and 6 showed hyper-self-bias
effects, comparing responses with their own faces versus responses
with a familiar other. Hyper-self-bias correlated with reduced executive control function and, at a neural level, this was associated with
lesions to the left prefrontal and superior temporal cortices. In contrast, reduced self-prioritization was associated with damage to the
right inferior temporal structures including the hippocampus and
extending to the fusiform gyrus. In addition, lesions affecting fibers
crossing the right temporal cortex, potentially disconnecting occipital–temporal from frontal regions, diminished the self-bias effect.
The data highlight that self-prioritized face processing is linked to
regions in the right hemisphere associated with face recognition
memory and it also calls on executive processes in the left hemisphere that normally modulate self-prioritized attention.
Keywords: facial self-awareness, hyper-self, hypo-self, neuropsychology,
self-face prioritization, voxel-based morphometry
Introduction
The ability to distinguish ourselves from other people is a core
human capacity found in infants as young as 4 months (Rochat
and Striano 2002; see also Keenan et al. 2003). An early ability
to recognize our own relative to other faces has been argued to
act as an index of the development of the self-concept (Gallup
1977) and self-face recognition may serve as a fundamental
building block for developing more complex concepts of the
self (e.g., Keenan et al. 2001, 2003; Conway et al. 1996).
However, the ability to recognize one owns face is itself insufficient for processing to be prioritized. For example, children
with autism can recognize themselves in a mirror (Neuman and
Hill 1978; Spiker and Ricks 1984; Uddin et al. 2008; Uddin
2011a), but, unlike typically developing children, autistic children pay less rather than more attention to their own image in a
mirror (Reddy et al. 2010). On the other hand, increased selffocus over and above basic self-recognition has been associated
with the development of depressive symptoms (Grimm et al.
2009). This suggests that the biased processing to the self goes
beyond mere recognition processes and taps into deeper
self-related processes that may be essential for our wellbeing.
Understanding the functional and neural bases of these selfprioritization processes is important to helping our understand
both normal self-related processes and abnormalities in self-face
© The Author 2013. Published by Oxford University Press. All rights reserved.
For Permissions, please e-mail: [email protected]
processing, and when they occur. This is the aim of our
study, which explores the neural correlates of hyper- and hypoself-biases following brain lesion.
Prioritized processing of our own faces is found in a variety
of face processing tasks (Tong and Nakayama 1999; Keenan
et al. 2001; Keyes and Brady 2010; Ma and Han 2010). For instance, familiarity decisions (for one’s own and other familiar
faces vs. the faces of unfamiliar others) are faster and more accurate for one’s own face as opposed to the familiar other
(Sugiura et al. 2005, 2006, 2008; Sui and Humphreys 2013).
Neuroimaging studies have shown that recognizing our own
face is associated with increased activation in an extended
bilateral network of brain regions, when compared with the
faces of personally close others or famous public faces (Uddin
et al. 2005; Northoff et al. 2006; Platek et al. 2006). The most
consistent finding is for enhanced self-face responses in the
inferior, middle, and medial frontal cortices, the inferior parietal cortices, and the inferior temporal cortices including the fusiform and medial temporal cortices, with increased activity
most evident in the right hemisphere (for reviews see Platek
et al. 2008; Devue and Bredart 2011, but see Kircher et al.
2001). For example, Ma and Han (2012) examined the brain
regions related to self-face processing using a procedure in
which self, familiar and other faces were morphed together.
Focusing on the fusiform face area (FFA), they reported that
there was increased activation in the left or right FFA dependent on whether the analysis emphasized processing of the
physical features or the identity of the self relative to other
faces.
However, one limitation of neuroimaging studies is their
correlative nature, preventing an inference on the necessary
role of a region for the cognitive process at hand (Price et al.
1999). Another problem is the low temporal resolution of functional magnetic resonance imaging, which makes it difficult to
isolate effects of a variable at any one stage of processing. For
example, the image of one’s own self may not only strongly
evokes perceptual memories, but also processes related to
heightened attention, retrieval of semantic/autobiographical
memory, self-evaluation processes, and so forth. Event-related
potential (ERP) studies show that self-faces modulate multiple
levels of processing. Viewing one’s own face affects the amplitude of the early N170 (the occipito-temporal face-specific
component; Keyes et al. 2010), the anterior N2 (the frontal–
central saliency component; Sui et al. 2009; Sui, Hong, et al.
2012), the N250 (the temporal familiarity component; Caharel
et al. 2002), and finally, the P3 (the posterior attentional component; Sui et al. 2006; Wang et al. 2011).
These limitations can be overcome by investigating how
brain lesions impact on self-recognition. For example, after
brain damage, the core tendency for prioritizing self-related
stimuli may be disrupted, and this may separate from effects
on face recognition more generally (e.g., Gallup et al. 2011).
Keenan et al. (2001) reported data from 5 patients undergoing
hemispheric-specific anesthesia (the WADA test). Participants
were required to remember a picture of their own face
morphed with a picture of a celebrity and subsequently, to
choose whether they had seen the picture of themselves or the
celebrity. Right hemisphere anaesthetization was associated
with a bias against memory for self-faces relative to the faces of
familiar others. On the other hand, Turk et al. (2002) reported
a left-hemisphere bias toward self-discrimination when faces
were selectively presented to each hemisphere in a split-brain
patient. The above studies and others (Sperry et al. 1979;
Breen et al. 2001; Feinberg and Roane 2005; Villarego et al.
2011; Van den Stock et al. 2012) provide contrasting results for
the lateralization of the self-bias effects. However, it is difficult
to draw clear conclusions due to the relatively small sample
sizes of patients tested and the differences in methodologies
across studies. Furthermore, the majority of the studies have
not examined the differential contribution of various structures
within each hemisphere to the self-effect. This was done here.
Sui, Chechlacz, et al. (2012) took a somewhat different approach. They explored lesion-symptom mapping of deficits in
self-face prioritization (SFP) using voxel-based morphometry
(VBM) analysis across the whole brain in a relatively large
group of patients. Behavioral measures were based on a task in
which patients were presented with 3 faces of the same gender
and age: Their own, a familiar other, and an unfamiliar other.
Each face was presented oriented toward the left or the right.
There were 2 tasks. In the first, patients had to indicate the
orientation of the face. In the second, a cross was overlaid on
the face and the task was to decide whether the horizontal or
the vertical line of the cross was longer. In the first case, the
face was the target for the task, while in the second it was
irrelevant and a potential distracter. Typically, participants
respond faster when judging the orientation of their own face
when compared with when the face is of a familiar other
person (Sui and Han 2007, Ma and Han 2010), and slower
when they need to ignore their own face (in the cross task;
Brédart et al. 2006). Sui, Chechlacz, et al. (2012) reported that
patients with damage to the left inferior parietal and superior
temporal cortices had both a reduced self-face advantage in
the orientation judgement task and reduced interference when
their own face was a distractor (in the cross task). This finding
is consistent with the argument that these brain regions are involved in controlling attention to self and socially relevant
stimuli (Saxe and Kanwisher 2003; Sui et al. 2013)—for
example, either in guiding attention to appropriate facial features to make the orientation judgement or in signaling the
presence of self-related cues in the background (in the cross
task). There were also effects selective to each task (orientation
judgements or judging the dimensions of the cross). Lesions to
the right superior frontal cortex, the right precuneus, and the
left anterior temporal cortices selectively impaired the SFP
effect for orientation judgements. Lesions to the left superior
temporal cortex, the cingulate, and the superior parietal cortex
selectively diminished interference effects from the self-face
when it was a distractor (in the cross task). The data suggests
that while some brain regions (left inferior parietal and
superior temporal cortices) support general self-prioritization
effects (across tasks where the self-face is a target and when it
is a distractor), other brain regions are recruited selectively
when self-faces are targets (in the orientation task) or distractors in a task (e.g., in the cross task).
However, while the results of Sui, Chechlacz, et al. (2012)
clarify the roles of some regions involved in self-prioritization
in face perception, neither of the tasks they examined directly
required self-other discrimination, and at best the tasks
indirectly assessed self-face perception. Indeed, it is possible
that the brain regions associated with self-face processing in
this study reflected control processes recruited either to
enhance task performance when the self-face was a target (in
the orientation judgement task) or to suppress attention to selffaces as distractors (in the cross-discrimination task).
Moreover, Sui, Chechlacz, et al. (2012) only evaluated decreases in SFP effects and they did not examine cases where
self-faces gain increased prioritization—though increased selfprioritization can be detrimental to the individual (Grimm
et al. 2009). In developmental research, it has been noted that
toddlers can exhibit strong self-biases in their performance—
for example, having difficulty in inhibiting their own perspective when they have to make judgments from another viewpoint
(e.g., Carlson and Moses 2001). It has been argued that is a
result of a failure to exert executive control processes, which
leads to an inability to inhibit responses to the self (Moses 2001;
Carlson et al. 2004; Sabbagh et al. 2006), though others have
hypothesized a failure to develop an adequate theory of mind
(Baron-Cohen et al. 1985; Leslie and Frith 1988; Moran et al.
2011). It is also possible that lesions to regions associated with
attentional control (e.g., dorsal prefrontal and parietal cortex;
Corbetta and Shulman 2002) and/or social cognition (e.g.,
medial frontal cortex, the temporo-parietal junction, the
superior temporal sulcus, and the temporal poles; see Northoff
and Bermpohl 2004; Amodio and Frith 2006; Mitchell et al.
2006; Northoff et al. 2006) affect the degree of self-biased behavior, and that damage to these regions may lead to hyperrather than hypo-self-biases in perception. This would fit with
arguments by Kahneman and Klein (2009) and Kahneman
(2011) that fast intuitive responses, operating outside of executive control, can be biased toward self-interest. The lesions
associated with hyper- and hypo-self-biases were assessed here.
In the current study, we directly tested self-bias effects on
face recognition, measuring both hyper- and hypo-self-biases in
a group of neurological patients. The task was to discriminate
familiar from unfamiliar faces. Two types of familiar faces were
presented—the patient’s own face and the face of a personally
familiar other. For each patient, we measured their SFP effect on
recognition by computing the difference between how quickly
judgements were made to the individual’s own faces compared
with the face of a personally familiar other. Importantly, all
patients could reliably and easily make accurate familiarity judgments to all 3 faces (their own, the familiar other, and the unfamiliar other) and showed no signs of prosopagnosia. In
addition, we measured the same behavior in gender and aged
matched healthy controls. The data from the healthy controls
were used as a reference to classify the patients into 3 groups: A
group with a normal self-bias effect, a hypo-self-bias group
(with a pathologically reduced self-bias), and a hyper-self-bias
group (with a pathologically increased self-bias effect). We also
explored whether performance on other cognitive tasks, such as
the ability to exert executive control over behavior, to sustain attention and to perform long-term recognition memory tasks
was associated with the self-bias effects. Finally, we used a VBM
analysis to directly compare between the 3 groups. Lesions
Cerebral Cortex February 2015, V 25 N 2 375
associated with a hypo-self-bias were dissected by measuring
reduced gray or white matter evident in the hypo-group compared with the intact and hyper-groups. Lesions associated with
a hyper-self-bias were identified by contrasting reduced gray
and white matter integrity in the hyper-group compared with
the intact and hypo-groups. We asked 3 questions: (1) What
lesions are associated hypo-self-bias? (2) what lesions are linked
to hyper-self-bias? And (3) are the lesions associated with
reduced self-bias in a direct recognition task (the familiarity judgement task used here) the same as those associated with abnormal self-face bias in implicit tasks (as in the orientation and
cross-judgement tasks used in Sui, Chechlacz, et al. 2012)?
Materials and Methods
Participants
Patients
The patients were randomly selected from the panel of neuropsychological volunteers at the School of Psychology, University of Birmingham. The patients mainly had acquired brain lesions from stroke,
though 1 had suffered herpes simplex encephalitis, 1 had cortico-basal
degeneration, and 3 carbon monoxide poisoning. [Excluding the nonstroke patients made little difference to the analysis (see also Chechlacz
et al. 2010).] All were at a chronic stage (>12 months postinjury). We
conducted a pretest to choose only nonprosopagnosic patients to participate. Patients were presented with central images and required to
discriminate their own faces, the faces of familiar others, and those of
unfamiliar people. Admission to the study was contingent on patients
obtaining 100% discrimination accuracy. Thirty patients were selected
who had no contraindications to MRI scanning. No other exclusion criteria were used. The age range was from 36 to 78 years (M = 64.97 ±
10.91 years). All patients provided written informed consent in agreement with ethics protocols at the School of Psychology and Birmingham University Imaging Centre (BUIC). All patients had undertaken
the Birmingham Cognitive Screen (BCoS) test battery (Humphreys
et al. 2012; www.BCoS.bham.ac.uk) to provide a background neuropsychological profile (for details see Table 1).
Healthy Controls for Lesion Identification
For the lesion identification protocol (see below), we acquired
T1-weighted images from 100 healthy controls (55 males and 45
females, mean age 54.5 years, range 20–87) with no history of stroke,
brain damage, or neurological disorders. These were used to normalize
and segment the patients’ MR images (see the section on Neuroimaging Data Acquisition and Preprocessing). All the controls provided
written informed consent in agreement with ethics protocols at the
School of Psychology and BUIC.
Cognitive Assessment
Self-Face Prioritization
SFP was measured in a face categorization task in which participants
were presented with self-faces, the faces of a gender-matched personally familiar other, and the faces of an unfamiliar person. The task was
to classify the face stimuli into 1 of 2 groups, familiar (self-faces and
personally familiar other faces) or unfamiliar (unfamiliar other faces).
We collected (1) 6 face images for each patient, (2) 6 face images of a
gender-matched individual who was highly personally familiar to each
patient, and (3) 6 face images of a gender-matched unfamiliar other.
The images showed 3 left and 3 right profiles of each face with a
neutral facial expression, depicted at angles ranging from 15° to 45° in
each direction. The images subtended about 5° × 5° of visual angle at a
viewing distance of 60 cm. Each patient completed 2 blocks of 72 trials
with equal numbers of images in the self, familiar, and unfamiliar face
conditions. Each trial began with the presentation of a white fixation
cross at the center of the screen for 500 ms. A face image was then
376 Lesion-Symptom Mapping of Self-Prioritization
•
Sui et al.
Table 1
Patients details: clinical and demographic data
ID
Age
Gender
Handed
Etiology
TPL
SFP deficit
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
54
65
74
76
50
69
72
77
73
69
40
72
63
64
63
73
57
78
56
78
63
77
62
36
74
74
55
67
54
64
M
F
F
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
F
M
M
M
M
M
M
M
R
L
L
R
R
L
R
R
R
R
R
R
R
R
R
R
R
R
R
R
L
R
R
R
R
R
R
L
R
R
HSE
S
S
S
S
S
S
S
S
S
CM
CM
S
CBD
S
S
S
S
CM
S
S
S
S
S
S
S
S
S
S
S
12
3
4
1
5
2
7
2
14
1
12
12
0.5
3
5
2
3
3
1
1
12
3
15
9
4
7
3
2
2
1
Hypo
Hypo
Hypo
Hypo
Hypo
Hypo
Hypo
Hyper
—
—
Hyper
—
—
—
—
—
—
—
Hyper
Hyper
—
—
—
Hyper
—
—
—
Hyper
—
—
CBD, cortico-basal degeneration; CM, carbon monoxide poisoning; F, female; HSE, herpes simplex
encephalitis; L, left; M, male; R, right; S, stroke; SFP, self-face prioritization; TPL, time post lesion
(year).
displayed at the center of the screen until the patients made a response.
The maximum duration of a face was 3000 ms, and this was followed
by feedback for 1000 ms.
We calculated SFP scores based on reaction times—the difference
between the familiar other and the self, divided by the sum of the 2
conditions, to take account of overall differences in response latency.
Control norms for the SFP scores were based on 25 healthy participants, with no history of neurological disease (9 males and 16 females,
age range 20–73). The controls showed self-prioritization effects in the
face categorization task, which is consistent with evidence from prior
studies in self-face processing (Tong and Nakayama 1999; Keyes and
Brady 2010; Sui and Humphreys 2013). The SFP scores for the controls
were used to define the SFP impairments for individual patients, which
were measured by subtracting the mean for the controls from that of
each patient and dividing by the standard deviation for the controls.
The cutoff to classify patients as impaired was based on them having a
mean level of self-prioritization effect either less or more than 2.5 SDs
from the control mean (defining, respectively, a hypo- or a hyper-SFP
deficit). (Note that these healthy controls were used to diagnose behavioral deficits in the patients, but they were not included in the imaging
analysis where the patients without behavioral SFP deficit served as
controls.)
Assessments of Other Cognition Tests
Several other cognitive tasks were also assessed: (1) An executive
control task (the Hayling and Brixton tests of executive functions;
Burgess and Shallice 1997); (2) immediate and delayed story recognition from the BCoS battery (Humphreys et al. 2012); (3) sustained attention and neglect (from BCoS; Humphreys et al. 2012). These
measures were of interest because (respectively): (1) Hyper-self-bias,
in particular, may reflect poor executive attentional control; (2) altered
self-bias may stem from general impairments in recognition memory;
and (3) variations in sustained attention and/or poor attention to the
left- or right-facing faces (in patients with neglect) could modulate the
effects of the self-face on performance. The story recognition memory
required the patients to listen to a story and then to make forced-choice
judgements either immediately after the story or after filled delayed of
about 10 min. The test of sustained attention required patients to
respond to 3 high-frequency, auditorily-presented target words (no,
hello, and please) and to ignore (not respond to) 3 high-frequency,
related distractors (yes, goodbye, and thanks). Sustained attention was
indexed by subtracting the number of correct responses in the last
block from those in the first block. Poor sustained attention was shown
when performance dropped across test blocks. The test of neglect required patients to cancel a set of complete line drawings of apples while
ignoring distractor apples that had a section missing on either the left or
right of the object. Targets and distractors were randomly positioned on
the page with the proviso that there were equal numbers of each type of
item within each of 5 columns across the page. This “Apples” test provides separate measures of “egocentric/spatial neglect” (where items are
missed according to their position on the page) and “allocentric/object
neglect” (based on false-positive responses to distractors according to
whether participants respond incorrectly to distractors with a gap on the
left or right of individual stimuli irrespective of the positions of the
stimuli on the page; see Chechlacz et al. 2010). Each patient’s behavioral
performance on these cognitive tasks was classified based on cutoffs
drawn from the BCoS and estimated on the dataset from healthy controls
(Humphreys et al. 2012).
Neuroimaging Data Acquisition and Preprocessing
Patients and the healthy controls used just for lesion identification (see
above) were scanned at BUIC on a 3-T Philips Achieva MRI system with
an 8-channel phased-array SENSE head coil. The anatomical scans were
acquired using a sagittal T1-weighted sequence (sagittal orientation,
time echo/time repetition = 3.8/8.4 ms, voxel size 1 × 1 × 1 mm3).
All T1 scans (both the 30 patients and the100 controls) were first
converted and reoriented using MRICro (Chris Rorden, Georgia Tech,
Atlanta, GA, USA). The preprocessing of all T1 scans was done using
SPM5 (Statistical Parametric Mapping, Welcome Department of Cognitive Neurology, London, UK). All brain scans were transformed into
the standard MNI space using the unified-segmentation procedure
(Ashburner and Friston 2005). The unified-segmentation procedure involves tissue classification based on the signal intensity in each voxel
and on a priori knowledge of the expected location of gray matter
(GM), white matter (WM), and cerebrospinal fluid in the brain. Furthermore, to improve tissue classification and spatial normalization of
lesioned brains, a modified segmentation procedure was used (see
Seghier et al. 2008, for details). The modified approach was used to
resolve misclassification of damaged tissue by including an additional
prior for an atypical tissue class (an added “extra” class) to account for
the “abnormal” voxels within lesions and thus, allowing classification
of the outlier voxels.
The segmented images (GM and WM maps) were smoothed with an
8-mm full-width at half-maximum (FWHM) Gaussian filter to accommodate the assumption of random field theory used in the statistical
analysis (Worsley 2003). The choice of intermediate smoothing of
8-mm FWHM was previously shown to be optimal for lesion detection
and further analysis of segmented images (Seghier et al. 2008; Leff
et al. 2009). The preprocessed GM and WM images were used in the
analyses to determine voxel-by-voxel relationships between brain
damage and SFP deficit.
Voxel-Based Morphometry
To delineate the anatomical structures involved in the SFP effect and to
examine contributions of white and gray matter changes, we applied a
voxel-wise statistical approach to assess the link between the cognitive
deficits in SFP and brain lesions using normalized and smoothed gray
and white matter images.
To assess the relationship between brain damage and loss of selfprioritization on a voxel-by-voxel basis, we used a VBM approach (Ashburner and Friston 2000) and conducted parametric statistics within the
framework of general linear modeling (Kiebel and Holmes 2003) with
SPM5. The analyses for gray and white matter were conducted separately. In the statistical model, we treated the hypo-, hyper-SFP and intact
SFP groups as 3 samples. Gender, age, handedness, etiology, and time
post lesion (year) were also included as covariates of no interest to
control potentially confounding factors. We then used t-contrasts to dissociate individuals with different forms of SFP deficit relative to the
other patients (e.g., testing for a change in voxel intensity that was
correlated with hypo- or hyper-SFP biases but not with other cognitive
deficits).
We report only results showing a significant effect at P < 0.001
cluster-level corrected for multiple comparisons based family wise
error correction with the amplitude of voxels surviving at P < 0.005 uncorrected across the whole brain and an extent threshold of 800 mm3
(>200 voxels). The brain coordinates are presented in MNI space. To
localize white matter lesions associated with SFP in relation to specific
white matter pathways, we used the Johns Hopkins University white
matter tractography (Hua et al. 2008), the Mori MRI Atlas of Human
White Matter (Mori et al. 2005), and SPM Anatomy Toolbox (Eickhoff
et al. 2005).
Results
Behavioral Data
The patients were divided into experimental and control
patient groups in terms of their SFP scores in relation to the
data of healthy controls. Seven of 30 patients were classified as
having a hypo-SFP deficit. Six of these patients had a brain
lesion within the right hemisphere and 1 had a bilateral lesion.
In contrast, 6 of the 30 patients had a hyper-SFP deficit, with
the damage in these cases either being unilateral left hemisphere or bilateral (see Table 2 for further details of individual
clinical profiles). Figure 1 shows the SFP effects for the hypo-,
hyper-, and control patients (i.e., patients with a SFP effect
within the normal range). Notably, there was a significant correlation between the magnitude of the SFP effect in the
patients and executive control performance (summed errors
on the Hayling and Brixton tests), r = 0.42, P = 0.04: The
weaker the executive control performance (the more executive
test errors that occurred), the greater the self-advantage
(Fig. 2). Also, while the hyper-SFP patients were more likely to
Table 2
Demographic and clinical details for patients with a SFP deficit and patient controls entered into
the VBM analyses
Age in years (SD)
Gender
Etiology
Handedness
Postlesion in years (SD)
Executive control (%)
Immediate verbal story recognition
deficits
Delayed verbal story recognition
deficits
Sustained attention deficit (%)
Spatial neglect (%)
Object neglect (%)
Hypo-SFC deficit
(n = 7)
Hyper-SFP
deficit (n = 6)
Control patients
(N = 17)
65.71 ± 10.08
2 women, 5
men
6 strokes, 1
HSEa
3 left-handed
4.86 ± 3.72
29
60% (n = 5)d
59.00 ± 18.15
6 men
66.76 ± 7.60
1 woman, 16 men
1 CBDb, 1 CMc,
4 strokes
1 left-handed
4.50 ± 4.76
83
50% (n = 4)
15 strokes, 2 CMc
1 left-handed
5.32 ± 4.82
24
55% (n = 11)
40% (n = 5)
50% (n = 4)
39% (n = 11)
0
17
33
0
29
14
24
29
35
Note: For the cognitive tasks, we report the percentages of patients within each group who were
classified as having a clinical deficit relative to age-appropriate control data.
a
Herpes simplex encephalitis.
b
Cortico-basal degeneration.
c
Carbon monoxide poisoning. The cognitive profile for the patients was extracted from the scores
on the BCoS test (see Humphreys et al. 2012 for detail).
d
It is based on patients who completed cognitive tests. For easier comparison, we present the
percentage of patients who have a deficit in any give measure.
Cerebral Cortex February 2015, V 25 N 2 377
Table 3
Gray matter and white matter lesions associated with a hypo-SFP deficit
Contrast
Cluster level
Size
Voxel level
Z-score
Coordinates
MNI (x, y, z)
Brain structure
Gray matter
4513*
White matter
5166*
5.38*
4.22
4.16
4.41
4.06
3.88
36, −12, −22
46, −6, −32
24, 6, −34
32, −10, −28
34, −22, −10
46, −34, −18
Right anterior HPCa and
PHGb extending to ITGc and
FGd
Right IFOFe and ILFf
a
Hippocampus.
Parahippocampal gyrus.
Inferior temporal gryus.
d
Fusiform gyrus.
e
Inferior fronto-occipital fasciculus.
f
Inferior lateral frontal fasciculus.
*FWE correction at P<0.01.
b
c
Figure 1. SFP scores for individuals across 3 patient groups and 1 control group:
Patients with the hypo-SFP deficit, with the hyper-SFP deficit, without SFP deficit, and
healthy controls. SFP scores were measured using reaction time by the difference
between the familiar and self-faces divided by the sum of the familiar and self-faces
and multiplying 100.
and posterior superior temporal sulcus in the left hemisphere
(Table 4 and Fig. 3B,C).
VBM analyses of white matter damage showed that the
hypo-SFP impairments were associated with right inferior temporal lesions along the inferior longitudinal fasciculus (ILF)
and the inferior fronto-occipito fasciculus (IFOF) in the white
matter adjacent to the right fusiform gyrus and parahippocampal gyrus (Table 3 and Fig. 4). These 2 fasciculi are major tracts
that project through the core fusiform region to the anterior
temporal and frontal cortices to provide a distributed cortical
network that subserves normal face processing (Thomas et al.
2009). White matter damage was not reliably associated with
the hyper-SFP deficit.
Discussion
Figure 2. The correlation between the magnitude of self-advantage and executive
test errors. Patients indicated in white are those classified as having a hyper-SFP
effect.
have an executive deficit than the other patient groups (the
hypo- and control patient groups; χ 2 = 7.03, P = 0.008), there
were no differences in the incidence of deficits in the other
cognitive tests across the patient groups (all P > 0.15).
Voxel-Based Morphometry
The VBM analysis for the hypo-SFP deficit revealed gray matter
damage within the right temporal lobe—there were changes in
the inferior temporal region, the hippocampus, and the parahippocampal gyrus partially extending to the fusiform gyrus
(Table 3 and Fig. 3A,C). In contrast, the VBM analysis for the
hyper-SFP deficit demonstrated that this pattern of performance was associated with damage to the superior frontal gyrus
378 Lesion-Symptom Mapping of Self-Prioritization
•
Sui et al.
The aim of the current study was to localize the brain region(s)
responsible for the behavioral advantage in explicit self-face categorization based on facial familiarity. We used brain-lesioned
patients who could recognize both their own and the faces of
personally familiar others. The VBM results revealed that
damage over the right inferior temporal–occipital subcortical
region including the hippocampus and parahippocampus
extending to the fusiform gyrus disrupted the behavioral
advantage for explicit self-face discrimination (generating a
hypo-SFP deficit). The involvement of the right hemisphere
in self-prioritization is consistent with previous behavioral
(Keenan et al. 1999; Platek and Gallup 2002), patient
(Preilowski 1977; Sperry et al. 1979; Keenan et al. 2001), and
neuroimaging studies (Platek et al. 2004, 2008; Uddin et al.
2005; Sui and Han 2007). Our data provide the first confirmation
of this with a relatively large group of patients. In particular, the
VBM analysis also pointed to the involvement of the ILF and
IFOF white matter fiber tracts in the right hemisphere as being
linked to self-prioritization in face familiarity judgements.
In addition to this, the data also revealed for the first time
that lesions in the left superior frontal gyrus and posterior
superior temporal sulcus were uniquely associated with an
increased self-advantage effect. The magnitude of the selfadvantage effect was also correlated with executive dysfunction across the patients, while the hyper-SFP group was
reliably more likely to have a deficit in executive dysfunction
than the other patient groups. These results are consistent with
the argument that, when fewer resources are available, people
Figure 3. (A) Gray matter damage associated with the hypo-SFP deficit over the right inferior temporal gyrus, hippocampus, and parahippocampal gyrus. (B) Gray matter damage
associated with the hyper-SFP deficit in the left superior frontal gyrus and right posterior superior temporal sulcus. (C) The plots illustrate the beta values with 90% confidence
interval in the left superior frontal gyrus, right hippocampus, and right FFA. Error bars represent standard errors.
Table 4
Gray matter lesions associated with a hyper-SFP deficit
Cluster level
Size
Voxel level
Z-Score
Coordinates
MNI (x, y, z)
Brain structure
242
336
238
4.53
3.95
3.58
−58, −54, −10
−24, 62 14
−40, −80, 28
Left IOGa/ITGb
Left SFGc
Left pSTSd
a
Inferior occipital gyrus.
Inferior temporal gyrus.
c
Superior frontal gyrus.
d
Posterior superior temporal sulcus.
b
have difficulty in inhibiting self-related biases (Carlson and
Moses 2001; Kahneman and Klein 2009; Kahneman 2011), and
this subsequently leads to a “super” self-benefit (Moses 2001;
Carlson et al. 2004; Sabbagh et al. 2006). In addition to this,
there is evidence that the left superior temporal sulcus moderates attentional responses to self-related cues (e.g., Sui et al.
2013). We propose that damage to this region may impair the
ability to implement attentional control signals originating in
frontal brain regions (e.g., the superior frontal gyrus), and this
makes self-faces more difficult to ignore. The frontal lesions
will also be associated with executive deficits, as we observed.
Uddin (2011b) suggested that “an intact corpus callosum enabling interhemispheric transfer is necessary for some, but not
all types of self-representation.” Some researchers have reported that, although both the left and right hemispheres are
able to recognize the images of the participant’s own face
(Sperry et al. 1979; Turk et al. 2002), the right hemisphere is
dominant (Preilowski 1977; Keenan et al. 2001). Researchers
have also reported some cases in which patients with right
hemisphere dysfunction fail to recognize their own faces, but
retain the ability to recognize the faces of other people (Breen
et al. 2001; Villarego et al. 2011; Van den Stock et al. 2012).
Consistent with this, behavioral studies with normal participants show an advantage for responding to self-faces when
judgments are made using the left hand (Keenan et al. 1999).
Other converging evidence comes from patients with delusional misidentification syndrome (DMS), who perceive some
alteration in familiar faces. Feinberg and Keenan (2005) reported that the frontal gyrus and the right hemisphere play a
crucial role in DMS disorders. For example, they discuss a
patient who had undergone surgical removal of a right frontal
subdural hematoma and, subsequently, reported that the
patient in the bed next to her was her husband (Ruff and Volpe
1981). This fits with evidence that the right prefrontal cortex is
crucial for self-awareness (Keenan et al. 2000) and responds to
the self-other distinction (Decety and Sommerville 2003).
Others have proposed that the sense of self-registered through
the right hemisphere is based on an emotion-related response
Cerebral Cortex February 2015, V 25 N 2 379
Figure 4. White matter damage linked to the hypo-SFP deficit over the right medial inferior temporal structures and connecting the ILF and the IFOF.
(Devinsky 2000) and on the role of the right hemisphere in
self-monitoring (Kaplan and Zaidel 2001). Self-other differences have also been reported in more posterior brain regions.
For example, neuroimaging studies show that the right extrastriate body area and the fusiform body area are activated differently by viewing one’s own relative to another person’s
body parts (Vocks et al. 2010). Decety and Chaminade (2003)
further propose that connectivity between the right inferior
parietal and prefrontal regions plays a crucial function in both
self-awareness and in relating the self to others. Our data fit
with these findings in showing both the involvement of posterior visually related processing areas as being necessary for
SFP (e.g., the temporal–occipital subcortical region—the parahippocampus and hippocampus, extending to the fusiform
area) and the critical role of posterior–anterior connectivity
(the ILF and IFOF). Our results are also consistent with prior
neuroimaging study (Ma and Han 2012), showing that the
right fusiform gyrus is engaged in self-identity judgements.
Similar evidence comes from an ERP study, demonstrating that
the face familiarity “drives” the N250 response in the temporal
occipital region (Caharel et al. 2002) and prominently in the
right inferior temporal region (Tanaka et al. 2006).
There is evidence indicating that processing of the self-face
is special in that configural responses to self-faces are not affected by inversion, while responses to the faces of familiar
others are (Keyes 2012). Sui, Chechlacz, et al. (2012) also reported dissociations in self-face recognition relative to the recognition of other faces when self, familiar other, and
unfamiliar other faces could be contrasted in a common task
(e.g., face orientation judgements), suggesting that self-related
processing may recruit brain circuits different to the processes
recruited for nonself-related stimuli (see also Sui et al. 2013).
380 Lesion-Symptom Mapping of Self-Prioritization
•
Sui et al.
The results in the current study further provide evidence that
the self is special in face recognition, given that all changes
were measured against responses to the face of a highly personably familiar other.
Our data highlight a specific role in self-related familiarity
judgements for subcortical structures in the right hemisphere
including the hippocampus, the parahippocampus, and
related white mater fiber tracts (ILF and IFOF). Although some
previous self-recognition studies have reported that selfrecognition is associated with greater cortical activation in the
right hemisphere, especially around the right temporo-parietal
junction (Keenan et al. 2001; Uddin et al. 2005, 2007; Platek
et al. 2006; Heinisch et al. 2012), none of them have shown a
role for subcortical and white matter fiber tracts. The necessary
involvement of these subcortical structures and white matter
fiber tracts, shown by our lesion analysis, suggests a network
account of self-recognition in self–other face discrimination. In
particular, the inferior occipital temporal areas are associated
with the recognition of familiar faces and access to stored
visual memories, suggesting that the self-advantage arises out
of processes sensitive to face memory and their connection to
regions within the frontal cortex, which may include supramodal self-representations. Interestingly, the loss of SFP here does
not seem to reflect a general loss of familiarity. For example,
the patients were generally able to discriminate familiar from
unfamiliar faces. Most neuropsychological studies also indicate
that it is the most familiar objects that are retained after brain
lesion, so that extra familiarity makes representations robust to
neural degeneration (e.g., see Snowden et al. 1996). In contrast
to this, patients with hypo-SFP have a reduced response to
highly familiar stimuli. An alternative is that the SFP process
depends on rapid transmission of information from face
recognition areas in the right inferior occipital temporal gyrus to
frontal lobe regions concerned with self-representation (see Sui
et al. 2009; Sui, Hong, et al. 2012, for ERP evidence; Sui et al.
(2013) for evidence from brain connectivity analysis). The
current evidence indicates that white matter damage, affecting
temporal to frontal tracts, is associated with poor selfprioritization—consistent with this argument.
The present results show some contrasts with our own previous reported data using a VBM analysis with a similar patient
population, separating conditions where self-face information
would be beneficial or detrimental to performance (according
to whether the faces were targets or distractors; Sui, Chechlacz,
et al. 2012). In that study, right frontal damage was associated
with a reduced SFP effect when faces were targets (and selffaces were beneficial), while left-hemisphere lesions were
associated with a reduced ability for patients to ignore their
own face as a distractor (when the self-face was detrimental to
the task). The latter result could reflect control structures in the
left hemisphere involved in inhibiting salient distractors (see
Mevorach et al. 2010, for evidence), rather than self-face processing itself. The prior effect of right frontal damage can be
understood if performance previously was dependent on participants setting-up “templates” for the self-face when judging
face orientations. For example, the right frontal regions linked
to poor self-prioritization in Sui, Chechlacz, et al. (2012) have
been previously associated with participants holding templates
of stimuli in working memory (Soto et al. 2008). Such templates may have been more important for orientation judgements than for direct familiarity judgements, as required here.
The current task, requiring speeded familiarity judgements,
may depend less on explicit templates for stimuli and more on
directly generated familiarity responses to stimuli (Talyor et al.
2009; Seger et al. 2011; Immordino-Yang and Singh 2013). The
critical brain regions in our study, involving inferior occipitotemporal cortex, the hippocampus, and parahippocampus,
may support just such familiarity-based responses; weakening
such responses, through brain lesion, disrupts the usual advantage for self-faces even when explicit face recognition still
takes place.
Conclusions
There has been little prior evidence on the brain regions
necessary for SFP in explicit face discrimination tasks. The
present evidence points to cortical and subcortical structures
in the right hemisphere, and their connections to frontal lobe
regions, as being critical to show an advantage to self-faces,
with damage to these regions leading to a hypo-SFP effect due
to less efficient access to face memory. In contrast, damage to
the left superior frontal gyrus and posterior superior temporal
sulcus were associated with hyper-SFP effects, which we attribute to poor executive control and the modulation of social attention to self-faces.
Funding
This work was supported by a Royal Society Newton Fellowship to J.S., an ESRC grant to J.S. and G.W.H. (ES/J001597/1),
and a Stroke Association grant and an ERC Advanced Grant
(323883) to G.W.H.
Notes
Conflict of Interest: None declared.
References
Amodio DM, Frith CD. 2006. Meeting of minds: the medial frontal
cortex and social cognition. Nat Rev Neurosci. 7:268–277.
Ashburner J, Friston KJ. 2005. Unified segmentation. NeuroImage.
26:839–851.
Ashburner J, Friston KJ. 2000. Voxel-based morphometry—the
methods. Neuroimage. 11:805–821.
Baron-Cohen S, Leslie AM, Frith U. 1985. Does the autistic child have a
“theory of mind”? Cognition. 21:37–46.
Brédart S, Delchambre M, Laureys S. 2006. One’s own face is hard to
ignore. Q J Exp Psychol. 59:46–52.
Breen N, Caine D, Coltheart M. 2001. Mirrored-self misidentification:
two cases of focal onset dementia. Neurocase. 7:239–254.
Burgess P, Shallice T. 1997. The Hayling and Brixton Tests. Test
manual. Bury St Edmunds, UK: Thames Valley Test Company.
Caharel S, Poiroux S, Bernard C, Thibaut F, Lalonde R, Rebai M. 2002.
ERPs associated with familiarity and degrees of familiarity during
face recognition. Int J Neurosci. 112:1499–1512.
Carlson SM, Moses LJ. 2001. Individual differences in inhibitory
control and children’s theory of mind. Child Dev. 72:1032–1053.
Carlson SM, Moses LJ, Claxton LJ. 2004. Individual differences in executive functioning and theory of mind: an investigation of inhibitory control and planning ability. J Exp Child Psychol. 87:299–319.
Chechlacz M, Rotshtein P, Bickerton WL, Hansen PC, Deb S, Humphreys GW. 2010. Separating neural correlates of allocentric and
egocentric neglect: distinct cortical sites and common white matter
disconnections. Cogn Neuropsychol. 27:277–303.
Conway MA, Collins AF, Gathercole SE, Anderson SJ. 1996. Recollections of true and false autobiographical memories. J Exp Psychol
Gen. 125:69–95.
Corbetta M, Shulman GL. 2002. Control of goal-directed and stimulusdriven attention in the brain. Nat Rev Neurosci. 3:201–215.
Decety J, Chaminade T. 2003. When the self represents the other: a
new cognitive neuroscience view on psychological identification.
Conscious Cogn. 12:577–596.
Decety J, Sommerville JA. 2003. Shared representations between self
and others: a social cognitive neuroscience view. Trends Cogn Sci.
7:527–533.
Devinsky O. 2000. Right cerebral hemisphere dominance for a sense
of corporeal and emotional self. Epilepsy Behav. 1:60–73.
Devue C, Bredart S. 2011. The neural correlates of visual self-recognition. Conscious Cogn. 20:40–51.
Eickhoff SB, Stephan KE, Mohlberg H, Grefkes C, Fink GR, Amunts K,
Zilles K. 2005. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage.
25:1325–1335.
Feinberg TE, Keenan JP. 2005. Where in the brain is the self?. Conscious Cogn. 14:661–678.
Feinberg TE, Roane DM. 2005. Delusional misidentification. Psychiatr
Clin N Am. 28:665–683.
Gallup GG Jr. 1977. Self-recognition in primates: a comparative approach to the bidirectional properties of consciousness. Am
Psychol. 32:329–338.
Gallup GG, Anderson JR, Platek SM. 2011. Self-recognition. In: Gallagher S, editor. The oxford handbook of the self. New York: Oxford
University Press Inc. pp. 82–108.
Grimm S, Jutta E, Boesiger P, Schuepbach D, Daniel H, Boeker H,
Northoff G. 2009. Increased self-focusing major depressive disorder is related to neural abnormalities in subcortical–cortical
midline structures. Hum Brain Mapp. 30:2617–2627.
Heinisch C, Krüger MC, Brüne M. 2012. Repetitive transcranial magnetic stimulation over the temporoparietal junction influences distinction of self from famous but not unfamiliar others. Behav
Neurosci. 126:792–796.
Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, Calabresi PA, Pekar
JJ, van Zijl PC, Mori S. 2008. Tract probability maps in stereotaxic
Cerebral Cortex February 2015, V 25 N 2 381
spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage. 39:336–347.
Humphreys GW, Bickerton WL, Samson D, Riddoch MJ. 2012. The Birmingham Cognitive Screen (BCoS). London: Psychology Press.
Immordino-Yang MH, Singh V. 2013. Hippocampal contributions to
the processing of social emotions. Hum Brain Mapp. 34:945–955.
Kahneman D. 2011. Thinking, fast and slow. New York: Farrar, Straus
and Giroux.
Kahneman D, Klein G. 2009. Conditions for intuitive expertise: a
failure to disagree. Am Psychol. 64:515–526.
Kaplan JT, Zaidel E. 2001. Error monitoring in the hemispheres: the
effect of lateralized feedback on lexical decision. Cognition.
82:157–178.
Keenan JP, Gallup GG, Falk D. 2003. The face in the mirror: the search
for the origins of consciousness. New York: Harper Collins.
Keenan JP, McCutcheon B, Sanders G, Freund S, Gallup GG, PascualLeone A. 1999. Left hand advantage in a self-face recognition task.
Neuropsychologia. 37:1421–1425.
Keenan JP, Nelson AM, O’Connor M, Pascual-Leone A. 2001. Selfrecognition and the right hemisphere. Nature. 409:305.
Keenan JP, Wheeler MA, Gallup GG, Pascual-Leone A. 2000. Self recognition and the right prefrontal cortex. Trends Cogn Sci. 4:338–344.
Keyes H. 2012. Categorical perception effects for facial identity in robustly represented familiar and self-faces: the role of configural and
featural information. Q J Exp Psychol. 65:760–772.
Keyes H, Brady N. 2010. Self-face recognition is characterized by “bilateral gain” and by faster, more accurate performance which persists
when faces are inverted. Q J Exp Psychol. 63:840–847.
Keyes H, Brady N, Reilly RB, Foxe JJ. 2010. My face or yours?
Event-related potential correlates of self-face processing. Brain
Cogn. 72:244–254.
Kiebel S, Holmes A. 2003. The general linear model. In: Frackowiak
RSJ, Friston KJ, Frith C, Dolan R, Price CJ, Zeki S, Shburner J, Penny
WD, editors. Human brain function. 2nd ed. London: Academic
Press. pp. 725–760.
Kircher T, Senior C, Phillips M, Rabe-Hesketh S, Benson P, Bullmore E,
Brammer M, Simmons A, Bartels M, David A. 2001. Recognizing
one’s own face. Cognition. 78:B1–B15.
Leff AP, Schofield TM, Crinion JT, Seghier ML, Grogan A, Green DW,
Price CJ. 2009. The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension:
evidence from 210 patients with stroke. Brain. 132:3401–3410.
Leslie AM, Frith U. 1988. Autistic children’s understanding of seeing,
knowing and believing. B J Develop Psychol. 6:315–324.
Ma Y, Han S. 2010. Why respond faster to the self than others? An
implicit positive association theory of self advantage during
implicit face recognition. J Exp Psychol Human. 36:619–633.
Ma Y, Han S. 2012. Functional dissociation of the left and right fusiform
gyrus in self-face recognition. Hum Brain Mapp. 33:2255–2267.
Mevorach C, Hodsoll J, Allen HA, Shalev L, Humphreys GW. 2010.
Ignoring the elephant in the room: a neural circuit to down-regulate
salience. J Neurosci. 30:6072–6079.
Mitchell JP, Macrae CN, Banaji MR. 2006. Dissociable medial prefrontal
contributions to judgments of similar and dissimilar others.
Neuron. 50:655–663.
Moran JM, Young L, Saxe R, Lee SM, O’Young D, Mavros P, Gabrieli J.
2011. Impaired theory of mind for moral judgement in highfunctioning autism. Proc Natl Acad Sci USA. 108:2688–2692.
Mori S, Wakana S, Nagae-Poetscher LM, van Zijl PCM. 2005. MRI atlas
of human white matter. Amsterdam: Elsevier.
Moses LJ. 2001. Executive accounts of theory-of-mind development.
Child Dev. 72:688–690.
Neuman C, Hill S. 1978. Self-recognition and stimulus preference in autistic children. Dev Psychobiol. 11:571–578.
Northoff G, Bermpohl F. 2004. Cortical midline structures and the self.
Trends Cogn Sci. 8:102–107.
Northoff G, Heinzel A, de Greck M, Bermpohl F, Dobrowolny H,
Panksepp J. 2006. Self-referential processing in our brain—a metaanalysis of imaging studies on the self. Neuroimage. 31:440–457.
Platek SM, Gallup GG Jr. 2002. Self-face recognition is affected by
schizotypal personality traits. Schizophr Res. 57:311–315.
382 Lesion-Symptom Mapping of Self-Prioritization
•
Sui et al.
Platek SM, Keenan JP, Gallup GG Jr. 2004. Where am I? The
neurological correlates of self and other. Cogn Brain Res.
19:114–122.
Platek SM, Loughead JW, Gur RC, Busch S, Ruparel K, Phend N, Panyavin IS, Langleben DD. 2006. Neural substrates for functionally discriminating self-face from personally familiar faces. Hum Brain
Mapp. 27:91–98.
Platek SM, Wathne K, Tierney NG, Thomson JW. 2008. Neural correlates of self-face recognition: an effect-location meta-analysis. Brain
Res. 1232:173–184.
Preilowski B. 1977. Self-recognition as a test of consciousness in left
and right hemisphere of “split-brain” patients. Act Nerv Super
(Praha). 19(Suppl 2):343–344.
Price CJ, Mummery CJ, Moore CJ, Frakowiak RS, Friston KJ. 1999.
Delineating necessary and sufficient neural systems with functional
imaging studies of neuropsychological patients. J Cogn Neurosci.
11:371–382.
Reddy V, Williams E, Costantini C, Lan B. 2010. Engaging with the self:
mirror behaviour in autism, Down syndrome and typical development. Autism. 14:531–546.
Rochat P, Striano T. 2002. Who’s in the mirror? Self-other discrimination in specular images by four- and nine-month-old infants.
Child Dev. 73:35–46.
Ruff RL, Volpe BT. 1981. Environmental reduplication associated with
right frontal and parietal lobe injury. Neurol Neurosurg Psychiatry.
44:382–386.
Sabbagh MA, Xu F, Carlson SM, Moses LJ, Lee K. 2006. The development of executive functioning and theory of mind: a comparison of
Chinese and US preschoolers. Psychol Sci. 17:74–81.
Saxe R, Kanwisher N. 2003. People thinking about thinking people.
The role of the temporo-parietal junction in “theory of mind”. Neuroimage. 19:1835–1842.
Seger CA, Dennison CS, Lopez-Paniagua D, Peterson EJ, Roark AA.
2011. Dissociating hippocampal and basal ganglia contributions to
category learning using stimulus novelty and subjective judgments.
Neuroimage. 55:1739–1753.
Seghier ML, Ramlackhansingh A, Crinion J, Leff AP, Price CJ. 2008.
Lesion identification using unified segmentation-normalisation
models and fuzzy clustering. Neuroimage. 41:1253–1266.
Snowden JS, Griffiths HL, Neary D. 1996. Semantic-episodic memory
interactions in semantic dementia: implications for retrograde
memory function. Cogn Neuropsychol. 13:1101–1137.
Soto D, Hodsoll J, Rotshtein P, Humphreys GW. 2008. Automatic guidance of attention from working memory. Trends Cogn Sci.
23:342–348.
Sperry RW, Zaidel E, Zaidel D. 1979. Self recognition and social awareness in the deconnected minor hemisphere. Neuropsychologia.
17:153–166.
Spiker D, Ricks M. 1984. Visual self-recognition in autistic children:
developmental relationships. Child Dev. 55:214–225.
Sugiura M, Sassa Y, Jeong H, Horie K, Sato S, Kawashima R. 2008. Facespecific and domain-general characteristics of cortical responses
during self-recognition. Neuroimage. 42:414–422.
Sugiura M, Sassa Y, Jeongb H, Miurab N, Akitsukib Y, Horied K, Satod
S, Kawashima R. 2006. Multiple brain networks for visual selfrecognition with different sensitivity for motion and body part.
Neuroimage. 32:1905–1917.
Sugiura M, Watanabe J, Maeda Y, Matsue Y, Fukuda H, Kawashima R.
2005. Cortical mechanisms of visual self-recognition. Neuroimage.
24:143–149.
Sui J, Chechlacz M, Humphreys GW. 2012. Dividing the self: distinct
neural substrates of task-based and automatic self-prioritization
after brain damage. Cognition. 122:150–162.
Sui J, Han S. 2007. Self-construal priming modulates neural substrates
of self-awareness. Psychol Sci. 18:861–866.
Sui J, Hong Y-Y, Liu CH, Humphreys GW, Han S. 2012. Dynamic cultural modulation of neural responses to one’s own and friend’s faces.
Soc Cogn Affect Neurosci. 8:326–332.
Sui J, Humphreys GW. 2013. The boundaries of self-face perception:
response time distributions, perceptual categories and decision
weighting. Vis Cogn. 21:415–445.
Sui J, Liu CH, Han S. 2009. Cultural difference in neural mechanisms of
self-recognition. Soc Neurosci. 4:402–411.
Sui J, Rotshtein P, Humphreys GW. 2013. Coupling social attention to
the self forms a network for personal significance. Proc Natl Acad
Sci USA. 110:7607–7612.
Sui J, Zhu Y, Han S. 2006. Self-face recognition in attended and unattended conditions: an ERP study. NeuroReport. 17:423–427.
Talyor MJ, Arsalidou M, Bayless SJ, Morris D, Evans JW, Barbeau EJ.
2009. Neural correlates of personally familiar faces: parents,
partner and own faces. Hum Brain Mapp. 30:2008–2020.
Tanaka JW, Curran T, Porterfield AL, Collins D. 2006. Activation of
preexisting and acquired face representations: the N250 eventrelated potential as an index of face familiarity. J Cogn Neurosci.
18:1488–1497.
Thomas C, Avidan G, Humphreys K, Jung KJ, Gao F, Behrmann M.
2009. Reduced structural connectivity in ventral visual cortex in
congenital prosopagnosia. Nat Neurosci. 12:29–31.
Tong F, Nakayama K. 1999. Robust representations for faces:
evidence from visual search. J Exp Psychol Human. 25:
1016–1035.
Turk DJ, Heartherton TF, Kelley WM, Funnell MG, Gazzaniga MS,
Macrae CN. 2002. Mike or me? Self-recognition in a split-brain
patient. Nat Neurosci. 5:841–842.
Uddin LQ. 2011b. Brain connectivity and the self: The case of cerebral
disconnection. Conscious Cogn. 20:94–98.
Uddin LQ. 2011a. The self in autism: an emerging view from neuroimaging. Neurocase. 17:201–208.
Uddin LQ, Davies MS, Scott AA, Zaidel E, Bookheimer SY, Lacoboni M,
Dapretto M. 2008. Neural basis of self and other representation in
autism: an fMRI study of self-face recognition. PLoS One. 3:e3526.
Uddin LQ, Kaplan JT, Molnar-Szakacs I, Zaidel E, Iacoboni M. 2005. Selfface recognition activates a frontoparietal ‘mirror’ network in the right
hemisphere: an event-related fMRI study. Neuroimage. 25:926–935.
Uddin LQ, Lacoboni M, Lange C, Keenan JP. 2007. The self and social
cognition: the role of cortical midline structures and mirror
neurons. Trends Cogn Sci. 11:153–157.
Van den Stock J, de Gelder B, De Winter FL, Van Laere K, Vandenbulcke M. 2012. A strange face in the mirror. Face-selective selfmisidentification in a patient with right lateralized occipitotemporal hypo-metabolism. Cortex. 48:1088–1090.
Villarego A, Martin VP, Moreno-Ramos T, Camacho-Salas A, PortaEtessam J, Bermejo-Pareja F. 2011. Mirrored-self misidentification
in a patient without dementia: evidence for right hemispheric and
bifrontal damage. Neurocase. 17:276–284.
Vocks S, Busch M, Gronemeyer D, Schulte D, Herpertz S, Suchan B.
2010. Differential neuronal responses to the self and others in the
extrastriate body area and the fusiform body area. Cogn Affect
Behav Neurosci. 10:422–429.
Wang J, Kitayama S, Han S. 2011. Sex difference in the processing of
task-relevant and task-irrelevant social information: an event-related
potential study of familiar face recognition. Brain Res. 1408:41–51.
Worsley KJ. 2003. Developments in random field theory. In: Frackowiak RSJ, Friston KJ, Frith C, editors. Human brain function. Cambridge: Academic Press. pp. 881–886.
Cerebral Cortex February 2015, V 25 N 2 383