Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Soc Neurosci. Author manuscript; available in PMC 2011 April 12.
Published in final edited form as:
Soc Neurosci. 2010; 5(4): 351–359.
Published online 2010 June 7. doi:  10.1080/17470911003597377
PMCID: PMC3075017

Neural responses to faces reflect social personality traits


Faces are a developmentally primary and critically important source of social information, and they are processed differently from most other visual percepts. Studies of brain electrophysiology reveal a face-sensitive component, the N170, which is typically enhanced to faces relative to other stimuli. Research in social disabilities suggests that an atypical N170 response in this population may stem from decreased developmental exposure to faces secondary to reduced social interest. Here we examined the relationship between neural responses to faces and social personality characteristics in a normative sample. Participants were pre-screened to identify individuals scoring high on extraversion or introversion. Both groups were presented upright and inverted face stimuli. An inversion effect, a marker of expertise for faces, was observed in people with high extraversion but not in those with high introversion. These findings suggest that, within typically developing populations, social attitudes are reflected in the neural correlates of face perception.

Keywords: Personality, extraversion, social motivation, N170, event-related potential (ERP / EEG), face perception

Faces are highly salient visual stimuli. Efficient analysis of faces likely serves evolutionary importance to perception, interaction, and communication in social situations (Ellis and Young 1989). Indeed multiple studies have shown preferential processing of faces throughout development, even in newborn infants (de Haan, Pascalis, & Johnson, 2002). Cognitive theorists have attributed this preference for faces as reflecting an innate functional specialization of the brain (Bruce and Young 1986); face-specific modules are active within the first months of life and become increasingly attuned to the processing of faces through maturational pruning and experience (Gauthier and Nelson 2001). Most adults are extraordinarily proficient in face perception (de Haan, Pascalis et al. 2002) and tend to process and recognize faces more quickly and accurately than other types of visual stimuli (Yin 1969; Ellis and Young 1989; Farah, Wilson et al. 1998).

Neuroimaging research indicates that face perception is subserved by specialized cortical regions. Spatial imaging studies demonstrate that the presentation of face stimuli selectively activates the fusiform gyrus located in the posterior occipital-temporal cortex (Kanwisher, McDermott et al. 1997; Kanwisher, Stanley et al. 1999). Temporal imaging studies using event-related potentials also reveal particular patterns of brain activity elicited by human faces. When viewing face stimuli, approximately 170 ms post-stimulus onset, a negative deflection of the visual event-related potential (ERP) waveform is observed, principally in the occipital-temporal areas of the right hemisphere (Bentin 1996; Rossion, Gauthier et al. 2000; Thierry 2007). This N170 component is enhanced in response to faces compared to non-face or object stimuli (Rossion, Gauthier et al. 2000) but appears insensitive to familiarity (Bentin and Deouell 2000) and emotional expression (Eimer, Holmes et al. 2003; Eimer and Holmes 2007). Consequently the N170 is believed to reflect the earliest stages of face perception, structural encoding (Eimer, 2000b), preceding higher-order processes, such as identity recognition. The N170 is elicited by impoverished stimuli, such as schematic faces (Sagiv and Bentin 2001), and even face-like objects (Churches, Baron-Cohen et al. 2009), but is also sensitive to the visual complexity of face stimuli, responding maximally to face stimuli most closely resembling natural faces (Sagiv and Bentin 2001; Churches, Baron-Cohen et al. 2009) The neural circuitry reflected in the N170 is honed through developmental experience, and the electrophysiological component follows a protracted developmental course, decreasing in latency until it approximates adult levels in adolescence (Taylor, McCarthy et al. 1999; Taylor, Batty et al. 2004).

The face-specificity of the N170 and corresponding hemodynamic measures of activity in the fusiform gyrus is a matter of ongoing debate; behavioral evidence, as well as hemodynamic and electrophysiological imaging studies, suggest that similar processing strategies and brain regions are invoked during processing of non-face stimuli with which individuals have extensive experience, or perceptual expertise (Gauthier and Tarr 1997; Gauthier, Skudlarski et al. 2000). Additional developmental research is needed to determine whether these brain regions evolved for faces and are co-opted for other stimuli with which individuals develop perceptual expertise (i.e., a domain specific account) or whether these regions support a particular visual processing strategy irrespective of the visuoperceptual content (i.e., a domain-general account). For both faces and other expert stimuli, one manifestation of the development of expertise is the emergence of an inversion effect. The face inversion effect was first observed as a salient decrement in face recognition when faces are inverted without comparable deficit when objects are inverted (Yin 1969; Tanaka and Farah 1993). This phenomenon is presumed to reflect application of a configural processing strategy for faces in contrast to the featural strategy applied to objects; inversion disrupts the prototypic configuration, interfering application of this specialized processing strategy. This effect has also been demonstrated for non-face expert stimuli, such as dog experts viewing dogs (Diamond and Carey 1986). In this way, the inversion effect may be considered an empirical quantification of expertise for a visual stimulus. Experts can be expected to display relatively impaired recognition associated with inversion. In contrast, novices show no such impairment, presumably because these stimuli are perceived on their featural aspects alone.

Consistent with behavioral inversion effects, presentation of inverted faces also affects electrophysiological markers of expertise, eliciting a significantly larger amplitude and longer latency of the N170 component in contrast to upright face stimuli (Bentin 1996; Eimer 1998; Eimer 2000; Eimer 2000). These differences in N170 between upright and inverted faces are consistent with the notion that the N170 may be sensitive to the configural properties of faces, which are disrupted when the face is inverted. Interestingly, inverted schematic faces elicit a diminished N170 response compared to upright schematic faces (Sagiv and Bentin 2001), suggesting structural components, and potentially visual expertise, are important to inversion effects with face stimuli in studies employing the N170 as the dependent measure.

The protracted developmental course reflected in the maturation of electrophysiological markers of face perception, as well as the capacity for face-like neural processing in perceptual expertise, emphasize the role of experience in the neural specialization associated with face perception. To-date, the influence of variability in exposure or expertise with faces and face-related brain activity remains unexplored in typical development. This has, however, been a focus of study in social disability research, with the suggestion that reduced social drive may interfere with development of face expertise resulting in anomalous face perception. Autism spectrum disorder (ASD) is a developmental disorder characterized by deficits in social interaction, communication, and restricted patterns of behavior (American Psychiatric Association 1994). Though the universality of face processing difficulties in ASD is unclear (Behrmann, Thomas et al. 2006; Jemel, Mottron et al. 2006), many studies indicate impaired face recognition (Klin, Sparrow et al. 1999) and the absence of a behavioral inversion effect in this population (Langdell 1978). Recently, ERP studies have shown slowed processing of faces as reflected in the N170 and a failure to exhibit an N170 inversion effect in ASD participants compared to controls (McPartland, Dawson et al. 2004). These findings have been interpreted as secondary effects of a primary deficit in social motivation. From an early age, infants with ASD are not drawn to others and exposure to faces is reduced. This results in a failure to develop typical levels of expertise for faces and is thus reflected in poor behavioral performance and atypical neural responses to face stimuli (Dawson, Carver et al. 2002; Dawson, Webb et al. 2005).

ASD, of course, represents the extreme end of a theoretical continuum of social motivation and social ability. Research in personality traits indicates significant variance on such factors within the range of typical, or at least subclinical, social function. Indeed, central to personality theory has been the distinction between introversion and extraversion (Jung 1955) (Eysenck and Eysenck 1964). The Eysenck Personality Questionnaire-Revised (EPQ-R; (Eysenck and Eysenck 1994) was developed as a quantitative self-assessment of this personality attribute, among others. Those scoring high on introversion are characterized as socially inhibited, avoid social situations, and find time spent alone more rewarding than time spent with others; those scoring high on extraversion are instead socially uninhibited, actively seek social engagements, and find this more rewarding than time spent alone. Eysenck proposed a biological basis for this aspect of social drive; this biological model argues that people with lower arousability in the central nervous system require greater external stimulation for optimum arousal and vice-versa (Eysenck & Eysenck, 1964). (Gale, Joanne et al. 2001) obtained support for this hypothesis in an electroencephalographic (EEG) study that revealed greater magnitude of frontal, temporal, and occipital alpha activity in extraverts compared to introverts. Similarly, (Fink 2005) also showed that extraverts display a lower cortical activation than introverts during emotional face processing. Taken together, these findings indicate that introversion-extraversion scores provide a contrasting index of social motivation observable in both behavior and neurophysiology. Though at least one study has failed to demonstrate a relationship between this personality trait and face recognition (Thompson and Mueller 1984), correlations between introversion and extraversion and early stages of face perception, such as structural encoding, have not been explored.

In the present study, we investigated the relationship between introversion and extraversion and neural correlates of face perception in typically developing individuals. Following hypotheses put forth to describe atypical social development (Dawson, Webb, McPartland, 2005), we predicted that normal variation in social motivation would modulate face perception.

Participants scoring high in extraversion or introversion viewed a series of upright and inverted natural and cartoon faces whilst we simultaneously recorded EEG. We predicted that individuals who scored high on extraversion would demonstrate higher-levels of perceptual expertise, reflected in enhanced ERP indices of expertise. Specifically, we predicted that extraverts would exhibit an N170 inversion effect exceeding that manifest by introverts. For novel, digitized cartoon faces, with which both groups were similarly naïve, we predicted that, as per Sagiv and Bentin (2001), both groups would display an N170 but neither group would display an inversion effect.



Ninety-six college undergraduates (62 female, 34 male) completed the Eysenck Personality Questionnaire Revised (EPQ-R; described more below). The mean EPQ-R score was 6.90 (S.D. = 3.83). Based on published norms (Eysenck, Eysenck et al. 1985), 28 participants scoring in the extreme high (i.e., extrovert) and low (i.e., introvert) spectrum by 1 S.D. of the mean score (6.90 +/− 3.83 were invited to participate in the EEG portion of the study. Participants completed a neuropsychological and neurological screening form and were excluded for current or historical trauma or disease that could influence neural response. EEG data from three participants were removed from the analysis owing to more than 25% of EEG channels being marked as bad channels. One further participant was excluded owing to unreported psychopathology evident during the EEG session. Therefore, the final sample included 14 participants in the extraversion group (7 females, 7 males; mean age 23 years) and 10 in the introversion group (7 females, 3 males; mean age 22 years). All participants (N=24; 22 right handed) had normal or corrected-to-normal visual acuity and were naïve to the purpose of the study. All procedures were approved by the Human Investigation Committee at Yale School of Medicine, and this was in accordance with the Declaration of Helsinki (1975/1983).

Apparatus and Stimuli

Stimuli were presented on a Pentium-IV computer controlling a 51 cm color monitor (75-Hz, 1024×768 resolution) running E-Prime 2.0 software (Schneider, Eschman et al. 2002). Displays were viewed at a viewing distance of 75 cm in a sound attenuated room, with low ambient illumination. EEG was recorded using NetStation 4.3. A 128 lead Geodesic sensor net (Electrical Geodesics Incorporated; (Tucker 1993) was placed on the participant’s head and fitted according to the manufacturer’s specifications. The electrodes were evenly spaced and symmetrically covered the scalp from nasion to inion and from left to right ear. Impedances were kept below 40 kilo-ohms.

All stimuli were presented on a uniform black background. Face stimuli consisted of 60 color digital images (presented at 6.26° by 10.32°; drawn from (Tottenham 2009) and (Minear and Park 2004) of frontal face views, all neutral in expression, which were cropped within an oval frame to remove non-face features. These faces were presented both upright and inverted, producing a total of 120 natural face stimuli. Using the Cartoon Wizard program (Chen, Xu et al. 2001), digitized cartoon faces were generated for each face stimulus (example stimuli are presented in Figure 1). Twenty-five of these faces were shaded red and randomly interspersed to serve as target stimuli. Participants pressed a button upon detection of a target, ensuring that they were attending to the faces throughout the EEG session.

Figure 1
Examples of upright and inverted natural and cartoon ace stimuli. Note that faces were presented in full color and only exemplars of female faces are provided here.


The Eysenck Personality Questionnaire Revised Short Scale (EPQ-R; Eysenck, 1994) contains 48 items designed to assess four personality dimensions: Extroversion, neuroticism, psychoticism, and tendency to lie. Only the extraversion subscale was employed in the current study. This subscale contains 12 items, each requiring a “yes” or “no” response. Both the score reliability (r = 0.82; (Caruso, Witkiewitz et al. 2001) and test-retest reliability (r = 0.92; (Eysenck and Tambs 1990) for extraversion scores are high. This questionnaire was selected based on this combination of brevity and excellent psychometric properties.

Design and Procedure

At the beginning of each trial, a central fixation cross was presented for time period varying randomly between 500 and 1000 ms. This was followed by the onset of a randomly selected face stimulus for 500 ms and then a 700 ms blank screen. There were 265 trials in total: 60 presenting upright natural faces, 60 presenting upright cartoon faces, 60 presenting inverted natural faces, 60 presenting inverted cartoon faces, and 25 target faces (excluded from data analysis). The experiment took approximately 10 minutes to complete. EEG was recorded continuously throughout each trial.

Data Analysis

Data were averaged for each participant by orientation (upright, inverted), face type (natural, cartoon), and hemisphere (left, right) across trials. Averaged data were digitally filtered with a 30 Hz low-pass filter to reduce environmental noise artifacts and transformed to correct for baseline shifts. The window for segmentation of the ERP was set from 100 ms before and 600 ms after stimulus onset. NetStation artifact detection settings were set to 150 µv for bad channels, 150 µv for eye blinks, and 100 µv for eye movements. Channels with artifacts on more than 25% of trials were marked as bad channels and replaced through spline interpolation. Segments that contained eye blinks, eye movement, and those with more than 10 bad channels were also marked as bad and excluded. All participants detected at least 75% of the targets, with both groups obtaining comparable average accuracy of 93%, t (1,22) = .31, p= .76. Electrodes of interest were selected based on maximal observed amplitude of the N170 to natural faces and to conform to those used in previous research (McPartland et al., 2004) and the scalp regions characteristically eliciting the N170 (Bentin et al, 1996; Rossion et al, 2000). Data were averaged across six electrodes over the left lateral posterior scalp (58, 59, 64, 65, 69, & 70) and six electrodes over the right lateral posterior scalp (90, 91, 92, 95, 96, & 97).

The time window for analysis of the N170 was chosen by visual inspection of the grand averaged data. The resultant time window, extending from 99 ms - 231 ms post-stimulus onset, was then examined for each subject’s individual average to confirm that the component of interest was captured at each electrode for each subject. Peak amplitude and latency to peak measures were averaged across each electrode group within the specified time window and were extracted for each participant.

The N170 amplitudes and latencies to peak were separately analyzed using 3 × 2 repeated measures analyses of variance (ANOVA). Both analyses had three within-subject factors, each with two levels: Face type (natural, cartoon), hemisphere (left, right) and orientation (upright, inverted). Personality group (extraversion, introversion) was the between subjects factor. Effect size is presented as partial eta-squared (η2partial), where .01 represents a small effect size, .06 represents a medium effect size, and .14 represents a large effect size (Kittler, Menard et al. 2007).


N170 Amplitude

N170 amplitudes are presented in Figure 2 as a function of hemisphere, face type, orientation, and personality group. A main effect of hemisphere indicated that N170 was right lateralized (main effect of hemisphere; F (1,22) = 4.34, p = 0.05, MSE = 4.43, η2partial = .17). The difference between cartoon and natural faces was not significant (main effect of face type; F (1, 22) = 2.76, p = .11, MSE = 0.48, η2partial = .11). Individuals in the extraversion group tended to display larger N170 amplitudes, but this effect did not attain statistical significance (main effect of personality group; F (1,22) = 3.84, p= .06, MSE = 35.00, η2partial = .15). Consistent with predictions, we found that N170 amplitude was modulated by face orientation. As reflected in a main effect of orientation, N170 amplitudes were larger in response to inverted versus upright faces (F (1,22) = 6.81, p = .02, MSE = 0.86, η2partial = .24), although the inversion effects were not modulated by face type (F (1,22) = 0.99, p = .33, MSE = 0.45, η2partial = .04). However, this inversion effect significantly interacted with personality type, as shown in a significant personality group by orientation interaction (F (1, 22) = 5.11, p = .03, MSE = 0.86, η2partial = .19). Across face type and hemisphere, individuals in the extraversion group but not the introversion group exhibited discrepant N170 amplitude associated with inversion.

Figure 2Figure 2
Mean N170 amplitude (microvolts) for upright and inverted natural and cartoon faces presented as a function of personality type in the left hemisphere (Panel A) and the right hemisphere (Panel B). Error bars represent +/− 1 S.E.

As displayed in Figure 3, N170 amplitude to inverted faces was enhanced in individuals in the extraversion group but not in individuals in the introversion group. In the extraversion group, post-hoc analyses using paired-sample t-tests revealed a significant difference in N170 amplitude between upright and inverted faces (across natural and cartoon faces) in the right hemisphere (t (13) = 4.43, p = .001) and a marginal difference in the left hemisphere (t (13) = 1.91, p = .08). In the introversion group, there was no inversion effect in either the right hemisphere (t (9) =1.21, p = .26) or the left hemisphere (t (9) = −.65, p = .53). No other significant or marginally significant effects were detected for N170 amplitude.

Figure 3
Mean N170 amplitude (microvolts) for upright and inverted faces (across hemisphere and face type) displayed for introversion and extraversion groups.

N170 Latency

N170 latency tended to be faster in the right hemisphere, though this effect did not attain statistical significance, main effect of hemisphere, F (1,22) = 3.79, p = .06, MSE = 325.31, η2partial = .15. Analysis of N170 latency effects also revealed an interaction between face type and hemisphere (F (1,22) = 5.35, p = .03, MSE = 60.50, η2partial = .20), with N170 latency being longer in response to cartoon faces than natural faces in the left hemisphere only. No other significant or marginally significant effects were detected for N170 latency.

General Discussion

Here we report for the first time the influence of personality traits on face perception as reflected by EEG. The N170 was employed as a neurophysiological marker of face processing in participants scoring high on introversion or extraversion. Though individuals in the extraversion group were comparable to those with introversion in terms of general response to faces, only those in the extraversion group showed the expected inversion effect in response to upside down faces, demonstrating an enhanced N170 relative to upright faces, more prominent in the right hemisphere. In direct contrast with this, those in the introversion group showed no differential sensitivity to inverted faces in either hemisphere. In both personality groups, this inversion effect was not modulated by the natural or cartoon nature of the stimuli presented.

We interpret these findings as consistent with the influence of social motivation on the neural response to faces, that is, a developmental account emphasizing the role of amassed experience over time. The diminished face inversion effect observed in individuals with high introversion scores parallels similar findings in individuals with more pronounced social difficulties (McPartland et al., 2004; (Grelotti, Gauthier et al. 2002). However, unlike individuals with clinical levels of social impairment, those scoring high in introversion in this study did not show an attenuation of the latency of the N170 to upright faces, indicating that the overall efficiency of face processing did not differ as a function of personality type. We interpret this as reflective of differences only at the highest levels of expertise, as manifested in sensitivity to disruption of configural information via inversion. Indeed, the developmental maturation of the N170 follows a protracted course characterized by decreasing latency, only approximating adult parameters around age 14 (Taylor, Batty et al. 2004). With respect to both behavior and neural response, the inversion effect is one of the latest developing and most mature manifestations of the development of perceptual expertise for faces. Thus, our current findings suggest differential brain response associated with introversion only at the most subtle levels, an intuitive effect among a high-functioning, typical population. In this instance, the N170 inversion effect appears to be an extremely sensitive measure of inter-individual differences in social perception. Our developmental account is necessarily speculative, as we have assessed the relationship between social personality traits and neural response to faces at a single point in adulthood. We cannot, therefore, determine whether these effects are stable across development or even within an individual adult over time.

An alternative interpretation of our findings is that the observed variation in N170 response reflects current social attitude towards other people rather than a developmental effect. Personality traits of introversion and extraversion are characteristic of differences in social interest, with extroverts seeking out socialization and introverts being less motivated in this regard. It is therefore possible that differences in neural responses reflect variation in salience, reward value, or indirect effects of a person-oriented attentional stance. We find this interpretation of our data less persuasive, as differences emerged only at the most sensitive measure of expertise, the inversion effect. It is unclear why modulation of neural response by social attitudes would manifest only at this level, as opposed to more extensive effects (e.g., on N170 amplitude or latency to upright face stimuli).

Additional research is required to elucidate the specificity of the observed effects and differentiate between the two interpretations of our findings (social motivation, stimulus salience) presented here. Our interpretation is consistent with prior research demonstrating the face inversion effect to be associated with exposure to faces (Gajewski, Schlegel & Stoerig, 2008) and a presumption that extroverts will have more exposure to faces; however, the present study has no direct measure of face exposure. Future work could, by assessing self-report of social contact, directly examine this relationship. Conversely, the role of exposure versus social drive could be assessed by examining brain responses in non-extraverts with high levels of face exposure (e.g., an introverted airport security screener). An additional objective of future research will be to directly measure proficiency in face perception (as a reflection of expertise) through behavioral tests assessing accuracy and response time for face identification and categorization tasks.

A third possible interpretation of our results is that the between-group differences observed reflect basic biological differences in the brains of introverts versus extroverts. This interpretation would be consistent with accounts of basic biological differences between individuals varying in sociability, such as low-level discrepancies in central nervous system arousability (Eysenck & Eysenck, 1964). Again, we find this interpretation less viable given the absence of gross between-group differences in neurophysiological response. We observed differences between introverts and extroverts in particular responses associated with viewing experience, rather than at all levels (e.g., N170 latency to upright faces). We cannot, however, rule out that inversion effects may be more evident in particular personality-derived groups, irrespective of the relationship between social behavior and social stimuli. Future research could examine this hypothesis directly by contrasting inversion effects for face and non-face expert stimuli in groups of introverts and extraverts.

Contrary to our predictions, the inversion effect was not modulated by differences between natural and cartoon face stimuli. Moreover, there was no difference in the N170 elicited between the two face types. In contrast, previous research (Bentin, Sagiv et al. 2002) found differential N170 sensitivity to inverted schematic faces relative to inverted natural faces. We speculate that, because the cartoon faces employed in the current study were considerably more realistic than schematic faces used in prior research, participants responded to them as natural faces. In future work, we will examine whether differential response to cartoon versus natural faces may be a sensitive measure of failure to generalize social information in individuals with clinical levels of social dysfunction; for example, in those with ASD.

There are several limitations of the current study that will be addressed in ongoing and planned research. Social personality traits here were only manipulated by extreme scores of introversion-extraversion, but similar to autistic symptomatology, these personality traits are likely to exist on a continuum. Therefore, we also plan to correlate neural responses to faces with personality measures across a complete range of scores on this measure of extraversion, examining the relationship between face perception and personality in a dimensional, rather than categorical, fashion. It is possible that unreported psychopathology may have influenced our results; in upcoming studies, we will assess current and lifetime psychopathology to examine this potential contribution to face perception. Finally, because eye-movements were not recorded during the EEG, it is possible that between-group differences in viewing patterns to faces (e.g., increased attention to the eyes versus the mouth) impacted our results. The target detection task ensured maintained visual attention to the face stimuli, and the visual fixation crosshair increased the likelihood of consistent attention between groups. Nevertheless, a focus of our ongoing work is to integrate simultaneous EEG and eye-tracking data so that differences in visual attention can be accounted for in future work. Our limited sample size was adequate to detect between-group differences in inversion effects, but limited statistical power may have prevented detection of other between-group differences (e.g., N170 amplitude to upright faces); it will therefore be essential to replicate these findings in larger samples.

In conclusion, we found the face elicited N170 response to be influenced by differences in social personality traits. These results highlight the importance of understanding individual differences in social motivation in both typical and atypical populations. Furthermore, the extent to which faces are highly salient visual stimuli may be uniquely determined by personality and individual differences in social motivation.


This work was submitted in partial fulfillment of the requirement of the degree of M.Sc. Psychodynamic Developmental Neuroscience by C.C. This work was supported by the Anna Freud Centre (UK) and the Gustavus and Louise Pfeiffer Research Foundation (USA) and by CTSA Grant Number UL1 RR024139 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH roadmap for Medical Research (USA). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. We are grateful to Dr. Victoria Morrow for theoretical insight and Christopher Bailey for technical assistance.


American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorder. Forth edition 1994.
Behrmann M, Thomas C, et al. Seeing it differently: visual processing in autism. Trends Cogn Sci. 2006;10(6):258–264. [PubMed]
Bentin S, Allison T, Puce A, Perez E, McCarthy G. Electrophysiological studies of face perception in humans. J Cogn Neurosci. 1996;8:551–565. [PMC free article] [PubMed]
Bentin S, Deouell LY. Structural encoding and identification in face processing: ERP evidence for separate mechanisms. Cognitive Neuropsychology. 2000;17:35–54. [PubMed]
Bentin S, Sagiv N, et al. Priming visual face-processing mechanisms: electrophysiological evidence. Psychol Sci. 2002;13(2):190–193. [PubMed]
Bentin SAT, Puce A, Perez E, McCarthy G. Electrophysiological studies of face perception in humans. J Cogn Neurosci. 1996;8:551–565. [PMC free article] [PubMed]
Bruce V, Young A. Understanding face recognition. Br J Psychol. 1986;77(Pt 3):305–327. [PubMed]
Caruso JC, Witkiewitz K, et al. Reliability of scores from the Eysenck Personality Questionnaire: A reliability generalization study. Educatioinal and Psychological Measurement. 2001;61:675–689.
Chen C, Xu YQ, et al. Example-based facial sketch generation with non-parametric sampling. Proceedings of the International Conference on Computer Vision. 2001
Churches O, Baron-Cohen S, et al. Seeing face-like objects: an event-related potential study. NeuroReport. 2009;20(14):1290–1294. [PubMed]
Dawson G, Carver L, et al. Neural correlates of face and object recognition in young children with autism spectrum disorder, developmental delay, and typical development. Child Dev. 2002;73(3):700–717. [PMC free article] [PubMed]
Dawson G, Webb SJ, et al. Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies. Dev Neuropsychol. 2005;27(3):403–424. [PubMed]
de Haan M, Pascalis O, et al. Specialization of neural mechanisms underlying face recognition in human infants. J Cogn Neurosci. 2002;14(2):199–209. [PubMed]
Diamond R, Carey S. Why faces are and are not special: an effect of expertise. J Exp Psychol Gen. 1986;115(2):107–117. [PubMed]
15. Eimer M. Does the face-specific N170 component reflect the activity of a specialized eye processor? Neuroreport. 1998;9(13):2945–2948. [PubMed]
Eimer M. Effects of face inversion on the structural encoding and recognition of faces. Evidence from event-related brain potentials. Brain Res Cogn Brain Res. 2000;10(1–2):145–158. [PubMed]
Eimer M. The face-specific N170 component reflects late stages in the structural encoding of faces. Neuroreport. 2000;11(10):2319–2324. [PubMed]
Eimer M, Holmes A. Event-related brain potential correlates of emotional face processing. Neuropsychologia. 2007;45(1):15–31. [PMC free article] [PubMed]
Eimer M, Holmes A, et al. The role of spatial attention in the processing of facial expression: an ERP study of rapid brain responses to six basic emotions. Cogn Affect Behav Neurosci. 2003;3(2):97–110. [PubMed]
Ellis HD, Young A. Are face special? Handbook of Research on Face processing. 1989:1–26.
Eysenck HJ, Eysenck SBG. The manual of the Eysenck Personality Inventory. London: University of Lodon Press; 1964.
Eysenck HJ, Eysenck SBG. Manual of the Eysenck Personality Questionnaire: Comprising the EPQ-Revised (EPQ-R) and EPQ-R Short Scale. San Diego, CA: EdITS; 1994.
Eysenck SBG, Eysenck HJ, et al. A revised version of the Psychoticism scale. Personality and Individual Differences. 1985;6:21–29.
Eysenck SBG, Tambs K. Cross-cultural comparison of personality: Norway and England. Scandinavian Journal of Psychology. 1990;31:191–197.
Farah MJ, Wilson KD, et al. What is "special" about face perception? Psychol Rev. 1998;105(3):482–498. [PubMed]
Fink A. Event-related desynchronization in the EEG during emotional and cognitive information processing: differential effects of extraversion. Biol Psychol. 2005;70(3):152–160. [PubMed]
Gale A, Joanne E, et al. Extraversion-introversion, neuroticism-stability, and EEG indicators of positive and negative empathic mood. Personality and Individual Differences. 2001;30:449–461.
Gauthier I, Nelson CA. The development of face expertise. Curr Opin Neurobiol. 2001;11(2):219–224. [PubMed]
Gauthier I, Skudlarski P, et al. Expertise for cars and birds recruits brain areas involved in face recognition. Nat Neurosci. 2000;3(2):191–197. [PubMed]
Gauthier I, Tarr MJ. Becoming a "Greeble" expert: exploring mechanisms for face recognition. Vision Res. 1997;37(12):1673–1682. [PubMed]
Grelotti DJ, Gauthier I, et al. Social interest and the development of cortical face specialization: what autism teaches us about face processing. Dev Psychobiol. 2002;40(3):213–225. [PubMed]
Jemel B, Mottron L, et al. Impaired face processing in autism: fact or artifact? J Autism Dev Disord. 2006;36(1):91–106. [PubMed]
Jung CG. The interpretation of Nature and Psyche. New York: Patheon Books; 1955.
Kanwisher N, McDermott J, et al. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neurosci. 1997;17(11):4302–4311. [PubMed]
Kanwisher N, Stanley D, et al. The fusiform face area is selective for faces not animals. Neuroreport. 1999;10(1):183–187. [PubMed]
Kittler JE, Menard W, et al. Weight concerns in individuals with body dysmorphic disorder. Eating Behaviors. 2007;8(1):115–120. [PMC free article] [PubMed]
Klin A, Sparrow S, et al. A normed study of face recognition in autism and related disorders. Journal of Autism & Developmental Disorders. 1999;29(6):499–508. [PubMed]
Langdell T. Recognition of faces: an approach to the study of autism. J Child Psychol Psychiatry. 1978;19(3):255–268. [PubMed]
McPartland J, Dawson G, et al. Event-related brain potentials reveal anomalies in temporal processing of faces in autism spectrum disorder. J Child Psychol Psychiatry. 2004;45(7):1235–1245. [PubMed]
Minear M, Park DC. A lifespan database of adult facial stimuli. Behavior Research Methods, Instruments, & Computers. 2004;36:630–633. [PubMed]
Rossion B, Gauthier I, et al. The N170 occipito-temporal component is delayed and enhanced to inverted faces but not to inverted objects: an electrophysiological account of face-specific processes in the human brain. Neuroreport. 2000;11(1):69–74. [PubMed]
Sagiv N, Bentin S. Structural encoding of human and schematic faces: holistic and part-based processes. J Cogn Neurosci. 2001;13(7):937–951. [PubMed]
Schneider W, Eschman A, et al. E-prime user's guide. Pittsburg: Psychology Software Tools Inc; 2002.
Tanaka JW, Farah MJ. Parts and wholes in face recognition. Q J Exp Psychol A. 1993;46(2):225–245. [PubMed]
Taylor MJ, Batty M, et al. The faces of development: a review of early face processing over childhood. J Cogn Neurosci. 2004;16(8):1426–1442. [PubMed]
Taylor MJ, McCarthy G, et al. ERP evidence of developmental changes in processing of faces. Clin Neurophysiol. 1999;110(5):910–915. [PubMed]
Thierry G, Martin CD, Downing P, Pegna AJ. Controlling for interstimulus perceptual variance abolishes N170 face-selectivity. Nature Neuroscience. 2007;10(4):505–511. [PubMed]
Thompson WB, Mueller JH. Face memory and hemispheric preference: emotionality and extraversion. Brain Cogn. 1984;3(3):239–248. [PubMed]
Tottenham N, Tanaka J, Leon AC, McCarry T, Nurse M, Hare TA, Marcus DJ, Westerlund A, Casey BJ, Nelson CA. The NimStim set of facial expressions: judgments from untrained research participants. Psychiatry Research. 2009 [PMC free article] [PubMed]
Tucker DM. Spatial sampling of head electrical fields: the geodesic sensor net. Electroencephalogr Clin Neurophysiol. 1993;87(3):154–163. [PubMed]
Yin R. Looking at upside down faces. j Exp Psychol. 1969;81:141–145.