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5Seeing Faces in the Brain

Alumit Ishai

“It is with our faces that we face the world, from the moment of

birth to the moment of death.

Our age and our gender are printed on our faces. Our emotions,

the open and instinctive emotions that Darwin wrote about, as

well as the hidden or repressed ones that Freud wrote about, are dis-

played on our faces along with our thoughts and intentions. Though

we may admire arms and legs, breasts and buttocks, it is the face,

first and last, that is judged “beautiful” in an aesthetic sense, “fine”

or ‘distinguished” in a moral or intellectual sense. And, crucially, it

is by our faces that we can be recognized as individuals. Our faces

bear the stamp of our experiences and our character; at forty, it is

said, a man has the face he deserves.”

Oliver Sacks, “Face-Blind,” The New Yorker, August 30, 2010.

a distributed cortical network for face perception

Face recognition is a highly developed skill in humans. We are able to

identify thousands of faces individually, or to easily pick out familiar

faces in a crowd, distinctions that require a special expertise (Gauthier

and Nelson 2001). The cognitive development of face perception sug-

gests a special status for face processing. Shortly after birth, infants

prefer to look at faces longer than at other objects (Morton and John-

son 1991). The predilection of infants to imitate facial expressions at a

very early age (Meltzoff and Moore, 1977) further suggests that face per-

ception plays a central role in developing social interaction skills and

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language. It is therefore not surprising that functional brain imaging

studies have localized a specialized neural system for face perception

in the human brain.

The recognition of facial identity is based on invariant facial features,

whereas animated aspects of the face, such as speech-related movement

and expression, contribute to social communication. When looking at

faces, we rapidly perceive the gender, expression, age, and mood of the

individual. Processing information gleaned from the faces of others

requires the integration of activity across a network of cortical regions.

Converging empirical evidence suggests that face perception is medi-

ated by a distributed neural system (Sergent et al. 1992; Haxby et al.

2000; Ishai et al. 2004, 2005). The cortical network for face perception

includes the inferior occipital gyrus (IOG) and lateral fusiform gyrus

(FG), extrastriate regions that process the identification of individuals

(Kanwisher et al. 1997; Ishai et al. 2000a; Grill-Spector et al. 2004; Rot-

shtein et al. 2005); the superior temporal sulcus (STS), where gaze direc-

tion and speech-related movements are processed (Calder et al. 2007;

Hoffman and Haxby, 2000; Puce et al. 1998); the amygdala and insula,

where facial expressions are processed (Breiter et al. 1996; Morris et al.

1996; Phillips et al. 1997; Vuilleumier et al. 2001; Ishai et al. 2004); the

inferior frontal gyrus (IFG), where semantic aspects are processed (Leve-

roni et al. 2000; Ishai et al. 2000b; 2002), and regions of the reward cir-

cuitry, namely the nucleus accumbens and orbitofrontal cortex (OFC),

where facial beauty and sexual relevance are assessed (Aharon et al.

2001; O’Doherty et al. 2003; Kranz and Ishai, 2006; Ishai, 2007). The

existence of multiple face-selective regions in the human brain is also

corroborated by intracranial recordings in epileptic patients undergo-

ing brain surgery. Face-selective potentials were found in several sites

along ventral occipitotemporal and lateral temporal cortices (Allison et

al. 1999; McCarthy et al. 1999; Puce et al. 1999; Barbeau et al. 2008), as

well as the amygdala and prefrontal structures (Halgren et al. 1994a;,

1994b). It has been suggested that the face network includes “core” extra-

striate regions (IOG, FG, STS) that process the invariant facial features,

and “extended” limbic and prefrontal regions that process changeable

aspects of faces (Haxby et al. 2000).

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119

Interestingly, gender discrimination, an automatic and effort-

less task, seems to be a distributed feature. A recent decoding study

has shown that it is possible to predict with above chance accuracies,

based on the fMRI time series in face-responsive regions, whether

subjects viewed male or female faces. The decoding accuracies were

independent of the subject’s gender (man or woman) or sexual pref-

erence (hetero- or homosexual) (Kaul et al. 2011). Given the evolution-

ary importance of gender discrimination and its fundamental nature

in face processing, it is plausible that there is no “gender-detection

region” in the human brain, but rather gender information is a dis-

tributed attribute that depends on integration of information across

cortical regions.

In many functional Magnetic Resonance Imaging (fMRI) studies of

face perception, a localizer is used to identify the face-selective region

in the fusiform gyrus, the “FFA,” based on stronger responses to faces

than to assorted common objects (Kanwisher et al. 1997). Although

the FFA also responds significantly to other objects (Ishai et al. 1999;

2000a; Haxby et al. 2001), it is commonly believed that the FFA is a

face-selective “module,” namely a cortical region dedicated to the

visual analysis of face stimuli. But is the FFA sufficient or even nec-

essary for face perception? Functional MRI studies in which neural

activity is not manifested by perceptual awareness provide evidence

against sufficiency, whereas studies in which perceptual awareness

is not caused by neural activity provide evidence against necessity.

When activation elicited by face stimuli is compared with activation

evoked by scrambled faces, a distributed neural system of multiple,

bilateral regions is revealed (Figure 1). The activation within visual,

limbic, and prefrontal face-selective regions is stimulus- (e.g. unfa-

miliar, famous, neutral, and emotional faces) and task- (e.g. passive

viewing, attractiveness rating) independent (Ishai et al. 2005; Kranz

and Ishai 2006). These consistent and replicable distributed patterns

of activation are what make faces special “stimuli”: the neural signa-

ture of face perception is not manifested by activation solely in the

“FFA,” but rather by activation within multiple regions that comprise

a network.

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120

Figure 1

Top: Viewing faces elicits activation within a distributed cortical network that includes visual, limbic and prefrontal regions. Coronal sections, taken from a representative subject, illustrate activation within the core (IOG, FG, STS) and extended (AMG, IFG, OFC) systems. Coordinates are in the Talaraich space.

Bottom: A model for face perception. Neural coupling among face-selective regions is stimulus-and task-dependent. The model assumes reciprocal connec-tions between all visual, limbic and prefrontal face-selective regions (although the strength of the connections may not be symmetrical.) Viewing emotional faces increases the effective connectivity between the FG and the AMG (yellow), whereas viewing famous, attractive faces increases the coupling between the FG and the OFC (blue). New predictions are shown in dashed arrows: Attention to gaze direction would increase the coupling between the STS and the FG (orange); Viewing animated faces would increase the coupling between the STS and the IFG/OFC (green); Viewing indeterminate, low-spatial frequency faces would result in increased effective connectivity from the OFC to the FG (red).

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Consistent with the human studies of face perception, electrophysio-

logical studies in non-human primates revealed face-selective neurons

not only in the temporal cortex (e.g. Bruce et al. 1981; Perrett et al. 1982)

but also in the orbitofrontal (Thorpe et al. 1983) and prefrontal (Wil-

son et al. 1993) cortices. Furthermore, recent fMRI studies in behaving

monkeys found activation in multiple face-selective regions in visual

(Pinsk et al. 2005; Tsao et al. 2006) as well as in limbic and prefrontal cor-

tices (Hadj-Bouziane et al. 2008). The innovative technical development

of fMRI-guided electrophysiology (e.g., Tsao et al. 2006) will enable not

only the identification and functional characterization of all face-re-

sponsive regions in the macaque brain, but also the exploration of the

homology between the face networks in monkey and man (see Tsao et

al. 2008).

face perception and cortical connectivity

With the identification and localization of multiple face-responsive

regions, the effective connectivity within the “face network” can be

quantified. In a pioneer study, conventional statistical parametric map-

ping (SPM) analysis (Friston et al. 1995) was combined with Dynamic

Causal Modeling (DCM, Friston et al. 2003) to investigate the neural cou-

pling and functional organization between and within the core (IOG,

FG, STS) and extended (amygdala, IFG, OFC) systems. It has been found

that during face viewing, the core system is functionally organized in

a hierarchical, feed-forward architecture, with the IOG exerting influ-

ences on both the FG and STS. Moreover, the FG exerted a strong causal

influence on the extended system, namely the amygdala, IFG, and

OFC. Finally, content-specific alterations in functional coupling were

observed within this network: Viewing emotional faces increased the

coupling between the FG and the amygdala, whereas viewing famous

faces increased the coupling between the FG and the OFC cortex (Figure

1). The FG is therefore a major entry node in the cortical network that

mediates face perception (Fairhall and Ishai 2007).

Based on the Fairhall and Ishai (2007) feed-forward model of the

core system, a recent study has shown that the effective connectivity

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between the IOG-FG and the IOG-STS gradually emerges during child-

hood. In children younger than 11 years old the connectivity was sig-

nificantly weaker than the connectivity in adults. Furthermore, in con-

trast with adults, children did not exhibit task-dependent effects on the

strength of the connectivity, suggesting that top-down modulations

develop and increase during childhood (Cohen Kadosh et al. 2011).

Previous DCM studies of face perception have also shown that effec-

tive connectivity between regions is task-specific. For example, viewing

faces was associated with an increase in bottom-up, forward connec-

tivity from extrastriate face-selective regions to the prefrontal cortex,

whereas the generation of mental images of faces was associated with

an increase in top-down, backward connectivity from prefrontal to

extrastriate regions (Mechelli et al. 2004). Similarly, perceptual deci-

sions about faces resulted in an increase in top-down connectivity from

the ventral medial frontal cortex to the fusiform gyrus (Summerfield

et al. 2006).

As we currently do not have sufficient temporal information about

the dynamics of face processing in the human brain, it is perhaps pre-

mature to propose a new functional model for face perception that inte-

grates all available data. When proposing their influential model for

the recognition of familiar faces, Bruce and Young stated, “In under-

standing face processing a crucial problem is to determine what uses

people need to make of the information they derive from faces” (Bruce

and Young 1986, 306). In line with this statement and with the above-

mentioned DCM studies (Mechelli et al. 2004; Summerfield et al. 2006;

Fairhall and Ishai 2007), I have recently suggested a working model for

face perception that accounts for existing findings and from which

new predictions are derived (Ishai 2008). The model depicted in Figure

1 postulates bidirectional connections between all visual, limbic, and

prefrontal face-selective regions (such large-scale integration could be

mediated by synchronization of activity, as suggested by Rodriguez et al.

1999). The model further assumes that the flow of information through

the face network is shaped by cognitive demands, namely that the effec-

tive connectivity between regions depends on the nature of faces and

the task at hand. For example, when we look for a friend in a crowded

Seeing Faces in the Brain

123

place, we have to match incoming visual input with faces stored in long-

term memory, whereas when performing laboratory experiments such

as gender discrimination, we have to focus on or attend to specific facial

features. Consequently, several new testable predictions are suggested:

focusing attention on gaze direction would likely increase the coupling

between the STS and the FG; viewing animated faces would increase

the effective connectivity between the STS and the IFG/OFC; viewing

disgusted faces would increase the coupling between the FG and the

insula. Consistent with a magnetoencephalography (MEG) study, which

showed that the prefrontal cortex generates predictions that influence

object processing in extrastriate regions (Bar et al. 2006), the model also

predicts that indeterminate facial input will increase the top-down

connectivity from the OFC to the FG. Future studies will determine the

extent to which various task demands are indeed associated with differ-

ential coupling among face-selective regions and the temporal dynam-

ics of these activation patterns.

prosopagnosia and activation in the face network

In 1947, the German neurologist Joachim Bodamer described three

patients who were unable to recognize faces but had no other recogni-

tion deficits. Bodamer assumed that this highly selective form of agno-

sia, which he termed “prosopagnosia,” implied that there was a discrete

area in the brain that specialized in face perception. Years later, it was

shown that the inability to recognize familiar faces (Whitely and War-

rington 1977; Damasio et al. 1990; Behrmann et al. 1992) is the result

of bilateral (Damasio et al. 1982) as well as right unilateral lesions in

the ventral occipitotemporal cortex (De Renzi 1986; Landis et al. 1986).

Some prosopagnosic patients, despite their profound inability to recog-

nize faces, exhibit normal patterns of activation in the FFA (e.g. Marotta

et al. 2001; Avidan et al. 2005), suggesting that activation in this region

is not sufficient for face recognition, which likely depends on integra-

tion across cortical regions. Consistently, a recent study has shown that

normal fMRI activation in the ventro-occipital regions of congenital

prosopagnosics is insufficient for intact face recognition, and that the

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functional impairment in congenital prosopagnosia is due to disrupted

information propagation between the core and the extended face-pro-

cessing network (Avidan and Behrmann 2009).

The case study of PS, a patient with bilateral and asymmetrical

lesions in right the inferior occipital gyrus (IOG) and left fusiform gyrus

(FG), demonstrates that she is prosopagnosic despite her intact left IOG

and right FG (Rossion et al. 2003; Sorger et al. 2007). PS is therefore a

living proof that bilateral and distributed activation is necessary for

face recognition. Adaptation experiments in this patient have shown

that although her neural response to repeated objects in extrastriate

object-selective regions was reduced, repeated and unrepeated faces

evoked similar activation in the FG (Schiltz et al. 2006). It therefore

seems that while activation in the FFA per se is not sufficient, adapta-

tion in this region may be necessary for face recognition. The FG does

not seem to work in isolation; it is a vital node in a cortical network that

stretches from the occipital to the prefrontal cortex, which mediates

both the recognition of faces that is based on knowledge and the famil-

iarity of faces that is based on feeling and association.

beauty and the brain

Facial beauty is considered a marker for reproductive fitness (Thorn-

hill and Gangestad, 1999). Attributes such as symmetry (Langlois and

Roggman, 1990) and sexually dimorphic features (Perrett et al. 1998)

contribute to the assessment of facial attractiveness. The perception of

beauty has not only biological but also social and economical impact:

good-looking people get promotion and earn more than average-look-

ing people, regardless of occupation, a phenomenon called “the plain-

ness penalty” (Hamermesh and Biddle 1994; Mueller and Mazur 1996;

Senior et al. 2007). With the advent of brain imaging techniques, iden-

tifying the neural correlates of “beauty” is a timely empirical endeavor.

The everyday appraisal of facial beauty seems to be automatic and fast:

event-related brain potentials reveal that attractive faces, as compared

with unattractive ones, elicit an early posterior negativity (around 250

ms) and a late parietal positivity (400–600 ms), suggesting a differential

Seeing Faces in the Brain

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response within half a second after an encounter with a beautiful face

(Werheid et al. 2007). Functional MRI studies have reported that facial

beauty evokes activation in the extrastriate core system (Chatterjee et

al. 2009), amygdala (Winston et al. 2007), and the reward circuitry (Aha-

ron et al. 2001; O’Doherty et al. 2003), where direct eye gaze (Kampe et al.

2001) and happy expressions (O’Doherty et al. 2003) increase the appeal

of attractive faces. It has been suggested that the rewarding, adaptive

value of an attractive face can be dissociated from its aesthetic value.

An attractive opposite-sex face may signal that a potential sexual part-

ner has a healthy genotype, whereas an attractive, same-sex face cannot

have such reproductive benefits (Senior 2003).

Kranz and Ishai (2006) used fMRI to test whether subjects would

respond more to sexually preferable faces and predicted such modu-

lation in the reward circuitry. Forty hetero- and homosexual men and

women viewed photographs of male and female faces or assessed facial

attractiveness. Behaviorally, regardless of their gender and sexual ori-

entation, all subjects rated the attractiveness of both male and female

faces. Within multiple, bilateral face-selective regions in the visual cor-

tex, limbic system, and prefrontal cortex, similar patterns of activa-

tion were found in all subjects in response to both male and female

faces. A significant interaction between stimulus gender and the sex-

ual preference of the subject was found in the mediodorsal nucleus of

the thalamus and medial OFC, where heterosexual men and homosex-

ual women responded more to female faces, and heterosexual women

and homosexual men responded more to male faces (Kranz and Ishai

2006). Furthermore, a three-way interaction between stimulus gender,

beauty, and sexual preference was found in the OFC, where attractive

male faces elicited stronger activation than attractive female faces in

heterosexual women and homosexual men, and attractive female faces

evoked stronger activation than attractive male faces in heterosexual

men and homosexual women (Ishai 2007). Taken collectively, these find-

ings suggest that the OFC represents the value of salient sexually rele-

vant faces, irrespective of their reproductive fitness. Our data therefore

do not support the proposed neural dissociation between attractive

faces of the opposite-sex that reflect evolutionary benefits and attrac-

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126

tive faces of the same-sex that reflect aesthetic appraisal of beauty (Sen-

ior 2003). Rather, our findings demonstrate that the OFC represents the

reward value of faces of potential sexual partners, including same-sex

mates, irrespective of reproduction.

Recent findings suggest that the OFC has a role not only in evaluating

attractive faces, but also in consumer behavior and memory of beauty.

Classical conditioning with attractive faces shows that arbitrary stim-

uli can acquire conditioned value when paired with an attractive face,

as reflected by prediction error-related activity in the ventral striatum

(Bray and O’Doherty 2007). Thus, with repeated exposure to simple asso-

ciations between products and rewarding attractive faces, advertisers

can influence consumer behavior. Furthermore, stronger functional

connectivity between the OFC and the hippocampus was observed dur-

ing encoding of attractive rather than neutral faces, suggesting that sub-

sequent memory of attractive faces depends on the interaction between

the reward circuitry and medial temporal structures associated with

memory formation (Tsukiura Cabeza 2011). Attractive faces therefore

are special “human stimuli” that communicate socio-economical fea-

tures and facilitate memory.

the social brain: self and others

Gallup, who first demonstrated that chimpanzees, and later bonobos

and orangutans, can recognize themselves in the mirror (Gallup 1970;

Suarez and Gallup 1981), postulated that mirror self-recognition leads

to the ability to be introspective, an ability required to infer the men-

tal states of others, known as “theory of mind” (Premack and Woodruff

1978). Human and non-human species that exhibit self-face recognition

have highly developed frontal lobes and live in social groups, where

they constantly need to process information relating to conspecifics.

The human brain is developed and shaped during interaction with

other people, and social interactions stimulate an ability to imagine the

mind of others in order to predict their behavior. Social communication

in all its forms (cooperation, competition, negotiation, etc.) is driven by

the individual’s personality, stereotypes, and attachment style and is

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127

modulated by emotions such as pride, envy, and regret. Faces, which

enable not only verbal but also non-verbal communication, are the ulti-

mate social stimuli and can be used to investigate the neural correlates

of social interactions.

In the past decade, a plethora of studies have localized the neural sub-

strates that mediate the perception of “self” and “others.” What hap-

pens in our brain when we view ourselves? A meta-analysis of nine func-

tional brain imaging studies of self-face recognition revealed that the

left FG, right precuneus, and bilateral IFG are consistently activated,

suggesting that a distributed neural system mediates the processing of

sensory and self-referential information (Platek et al. 2008). If self-aware-

ness and introspection lead to the development of theory of mind, what

happens in our brain when we view others? Perhaps, not surprisingly,

recent studies suggest that we are automatically biased toward famil-

iar faces, same-race faces, and in-group faces. When we view unfamiliar

faces, as compared with personally familiar and famous faces, stronger

activation is observed in the amygdala, which maintains a vigilant atti-

tude toward strangers (Gobbini and Haxby 2007). When we view same-

and other-race faces, a same-race advantage is manifested by differen-

tial neural activation: Faces of the subject’s own race evoke stronger

responses in the FG and are associated with superior recognition mem-

ory, i.e., white faces evoke more activation than black faces in Euro-

pean-Americans, whereas black faces evoke stronger activation than

white faces in African Americans (Golby et al. 2001). When white sub-

jects viewed briefly presented white and black faces, greater response

was observed in the amygdala for black than white faces. Longer expo-

sure to the faces reduced the enhanced response to black faces in the

amygdala and elicited greater activation for black than white faces in

regions of the frontal cortex associated with control and regulation

(Cunningham et al. 2004), suggesting that the automatic concern and

heightened caution evoked by out-group faces can be top-down modu-

lated. Another striking example was reported when participants were

assigned to an arbitrary mixed-race-team, assuming their team would

later compete against another team: greater activity was found in the

amygdala, FG, OFC, and dorsal striatum when participants viewed novel

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128

in-group faces than when they viewed novel out-group faces. Moreover,

activity in the OFC mediated the in-group bias in self-reported liking

for the faces. As the in-group bias was independent of the participants’

own race, it is likely that behavioral and neural responses to one’s social

group may occur automatically (Van Bavel et al. 2008).

Mutual trust forms the basis for engagement in cooperation, which

is integral to daily life and a prerequisite for cultural and social evolu-

tion. Assessing an individual’s trustworthiness might be related to a

broader categorization into “good guy/bad guy” (Todorov 2008), guiding

approach versus avoidance behavior (Cosmides and Tooby 2000). Corre-

lations between facial trustworthiness and various other facial judg-

ments, e.g. how caring, happy, or dominant a person is (Todorov et al.

2008), indicate that trustworthiness judgments may summarize

numerous derived trait inferences. A recent meta-analysis of neuroim-

aging studies of trustworthiness judgment found activation in the pos-

terior STS and the amygdala. Moreover, attractiveness judgments over-

lapped with trustworthiness judgments and co-activated the amygdala,

suggesting that socially and evolutionarily important judgments based

on facial aspects may be mediated by a common neural substrate (Bzdok

et al. 2010). Interestingly, a recent study of first impressions reported

that greater activation in the amygdala was correlated with post-scan

higher ratings of faces of Chief Executive Officers’ (CEOs) leadership abil-

ity, a subjective judgment, and greater profits made by the CEOs’ compa-

nies, which is an objective measure (Rule et al. 2011). These new findings

extend the traditional role of the amygdala beyond threat detection,

suggesting that this region also processes positive emotional and social

stimuli that influence mate choice and social exchange.

With the advent of functional brain imaging techniques and the devel-

opment of cutting-edge analytic tools, a shift from “where-” to “how-”

type studies is inevitable. I believe that the descriptive brain imaging

studies that localize activation in the brain in response to a certain face

or during a specific task will be gradually replaced by more mechanis-

tic studies that are embedded in theoretical and computational neuro-

science. These future studies will define the algorithmic nature of the

transformations between seeing a face and the neural response to its

Seeing Faces in the Brain

129

various (visual, emotional, social, and economical) features. One can

only hope that by using face perception as a model for information pro-

cessing we will be able to understand how the human brain works.

acknowledgment

The Swiss National Science Foundation grant 320030-129793 and Swiss

National Center for Competence in Research: Neural Plasticity and

Repair are kindly acknowledged for their support.

Alumit Ishai

130

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