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Orienting to face expression during encoding improves men's recognition of own gender faces
Acta Psychologica 161 (2015) 18–24
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Orienting to face expression during encoding improves men's recognition of own gender faces Erika K. Fulton, Megan Bulluck, Christopher Hertzog ⁎ School of Psychology, Georgia Institute of Technology, 654 Cherry St., Atlanta, GA 30332-0170, United States
a r t i c l e
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Article history: Received 10 September 2014 Received in revised form 3 August 2015 Accepted 11 August 2015 Available online xxxx Keywords: Gender differences Episodic memory Face recognition Face processing Emotion expression
a b s t r a c t It is unclear why women have superior episodic memory of faces, but the benefit may be partially the result of women engaging in superior processing of facial expressions. Therefore, we hypothesized that orienting instructions to attend to facial expression at encoding would significantly improve men's memory of faces and possibly reduce gender differences. We directed 203 college students (122 women) to study 120 faces under instructions to orient to either the person's gender or their emotional expression. They later took a recognition test of these faces by either judging whether they had previously studied the same person or that person with the exact same expression; the latter test evaluated recollection of specific facial details. Orienting to facial expressions during encoding significantly improved men's recognition of own-gender faces and eliminated the advantage that women had for male faces under gender orienting instructions. Although gender differences in spontaneous strategy use when orienting to faces cannot fully account for gender differences in face recognition, orienting men to facial expression during encoding is one way to significantly improve their episodic memory for male faces. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Successful human social interaction depends on accurate face recognition. For instance, faces serve as retrieval cues for qualities of an individual that are relevant to social exchange (Nachson, 1995; Riggio, 1992). However, there are individual differences in face recognition speed and accuracy (Guillem & Mograss, 2005; Hall, Hutton, & Morgan, 2010; Herlitz & Rehnman, 2008; Herlitz & Yonker, 2002; Hofmann, Suvak, & Litz, 2006; Lewin & Herlitz, 2002; Lewin, Wolgers, & Herlitz, 2001; McBain, Norton, & Chen, 2009; Rehnman & Herlitz, 2007; Vuilleumier, George, Lister, Armony, & Driver, 2005), some of which have relatively serious consequences. Impaired episodic memory of faces is seen in disorders such as schizophrenia (Calkins, Gur, Ragland, & Gur, 2005; Silver et al., 2006), autism (Weigelt, Koldewyn, & Kanwisher, 2012), and prosopagnosia (Kress & Daum, 2003), and is part of a general episodic memory disorder in Alzheimer's disease (Hawley & Cherry, 2004; Plaza, López-Crespo, Antúnez, Fuentes, & Estévez, 2012). Understanding the factors that lead to superior face recognition could support the development of training and treatments to improve face recognition in these and other populations. It could even ⁎ Corresponding author at: School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332-0170, United States. E-mail addresses: [email protected] (E.K. Fulton), [email protected] (M. Bulluck), [email protected] (C. Hertzog).
http://dx.doi.org/10.1016/j.actpsy.2015.08.005 0001-6918/© 2015 Elsevier B.V. All rights reserved.
inform the development of software that could emulate human facial recognition, which has multiple applications (e.g., Hu, Klare, Bonnen, & Jain, 2013; Konen, 1996). One way to understand the factors that lead to superior facial recognition is to examine gender differences therein, which are commonly found (Bengner et al., 2006; Guillem & Mograss, 2005; Herlitz & Rehnman, 2008; Lewin & Herlitz, 2002; Megreya, Bindemann, & Havard, 2011; Rehnman & Herlitz, 2007; Yonker, Eriksson, Nilsson, & Herlitz, 2003). A recent meta-analysis by Herlitz and Lovén (2013) reported that women are better at recognizing faces (Hedges' g = .36), with the advantage seen primarily for female faces. Several explanations have been offered for women's advantage, such as their superior face perception (Megreya et al., 2011), greater self-reported social engagement (Sommer, Hildebrandt, Kunina-Habenicht, Schacht, & Wilhelm, 2013), increased encoding specificity of faces (Guillem & Mograss, 2005; Lovén, Herlitz, & Rehnman, 2011), and superior recognition or detection of facial expression (Hall et al., 2010). Women's face recognition may also benefit from better use of increased encoding time (McKelvie, 1981), higher circulating estradiol (Yonker et al., 2003) and own-gender faces (Herlitz & Lovén, 2013; Lewin & Herlitz, 2002; Lovén, Svärd, Ebner, Herlitz, & Fischer, 2014; Lovén et al., 2011; McKelvie, 1981; Megreya et al., 2011; Wolff, Kemter, Schweinberger, & Wiese, 2014; Wright & Sladden, 2003). Although a complicated interplay of biological and social factors likely accounts for gender differences in face recognition, much of the existing research suggests that women excel at face
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recognition because they process faces differently (e.g., Everhart, Shucard, Quatrin, & Shucard, 2001; Lovén et al., 2011; Lovén et al., 2014; Megreya et al., 2011). Herlitz and Lovén (2013) suggest that women's advantage in face recognition may arise because they allocate attention during encoding differently from men. The authors showed that women's advantage is primarily for female faces when a mix of female and male faces is shown, but that women also outperform men when only male faces are shown (g = .22). Women, they suggested, may focus more attentional resources on remembering female faces when presented with a mix of male and female faces, but when only male faces are to be remembered they can outperform men because all attentional resources can be devoted to male faces. If this account is correct, then we can ask to what, specifically, women allocate more attentional resources that allows them to better recognize faces. One possibility is that women allocate more attentional resources to the emotional expressions of faces. Women excel at the recognition of emotional expression in faces (Hall et al., 2010), performing more accurately (Hampson, van Anders, & Mullin, 2006; Sasson et al., 2010; Thayer & Johnsen, 2000) and efficiently (Hampson et al., 2006; Vassallo, Cooper, & Douglas, 2009) than men, especially with subtle variations in facial expressions (Hoffmann, Kessler, Eppel, Rukavina, & Traue, 2010; Montagne, Kessels, Frigerio, de Haan, & Perrett, 2005). Furthermore, processing emotional expression involves particular attention to eyes (Beaudry, Roy-Charland, Perron, Cormier, & Tapp, 2014; Gupta & Srinivasan, 2009; Hall et al., 2010), and women are more likely to focus on these features (Everhart et al., 2001; Hall et al., 2010), paying more attention to eyes than males as early as infancy (Ashear & Snortum, 1971; R. Exline, Gray, & Schuette, 1965; R. V. Exline, 1963; Hall et al., 2010; Hittelman & Dickes, 1979; Leeb & Rejskind, 2004; Levine & Sutton-Smith, 1973; Sæther, Van Belle, Laeng, Brennen, & Øvervoll, 2009). There are gender differences in face recognition even for neutral faces, so female superiority in this domain is not dependent upon the presence of emotional expressions in faces (see Herlitz & Lovén, 2013; e.g., McBain et al., 2009). The present study tested the hypothesis that gender differences in attention to facial expression explain at least some of the gender difference in face recognition. Specifically, we hypothesized that women's superior face recognition memory is due to a higher likelihood of spontaneously using the strategy of attending to emotional expression. Women mimic more facial expressions (Dimberg & Lundquist, 1990), show more emotional contagions (Doherty, Orimoto, Singelis, Hatfield, & Hebb, 1995), show greater affective priming to happy faces (Donges, Kersting, & Suslow, 2012), and are faster in labeling a happy expression (Hampson et al., 2006; Vassallo et al., 2009). The specific expression displayed (e.g. happy or neutral) may have a small (but significant) effect on recognition accuracy (Patel, Girard, & Green, 2012; Wang, 2013) but greater attention to any facial expression may support subsequent face recognition accuracy. To test our hypothesis, we instructed men and women to encode faces in one of two ways under incidental learning conditions (participants were unaware of the memory test that followed). Participants were briefly shown a face and asked to either report the gender of the face (male or female) or its emotional valence (happy or neutral). The underlying premise was that if women are more likely to use the strategy of attending to emotional expression when viewing faces, then guiding men to orient to facial expressions would improve their encoding of facial features, thereby differentially benefiting males' face recognition and reducing the size of the gender difference. Orienting to a face's gender can result in more global or holistic facial processing (Tanaka & Farah, 1993), which should not affect gender differences in facial recognition. Orienting to a face's emotional expression, however, requires more local feature processing (Martin, Slessor, Allen, Phillips, & Darling, 2012), which women may do more effectively than men. Gender differences might not be reduced by our manipulation if both women and men experience additional benefit from explicit instruction
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to orient to expression during encoding, but expression orienting instructions were at least expected to significantly improve men's facial recognition accuracy. We also expected to replicate women's own-gender bias in face recognition, originally shown by McKelvie (1981) and recently reviewed by Herlitz and Lovén (2013), but were curious whether our manipulation would affect men's relative recognition rates for male and female faces. Social-cognitive accounts explain the own-gender effect in terms of increased individuation when encoding faces from one's ingroup (Hugenberg, Young, Bernstein, & Sacco, 2010). Therefore, an open question was whether orienting men to expression might not only improve their face recognition overall but provide a larger boost in accuracy for male faces. One study previously showed that men recognize male faces better than female faces (Wright & Sladden, 2003), so if expression orienting boosts face recognition it may do so for own-gender faces more than other-gender faces. We also hypothesized that women outperform men in face recognition because they have access at test to more encoded detail of the faces. In order to evaluate this possibility we used a test of recognition memory that focuses on participants' discrimination of generic personrecognition (I've seen that person before) from recognition of the person with the same prior facial expression (I've seen that facial expression on that person before). Adapting work by Koutstaal (2003, 2006), our recognition test varied in whether participants were to judge if the face was simply of the same person they had seen at study (Same Person) or of the same person with the exact same expression they had seen at study (Exact Face; see the Method section for details). When the discrimination required determining whether the same person with the exact same expression was seen at study, we assumed that greater recollection of specific facial feature information was required. Thus, we hypothesized that if women have higher recollection of specific facial features at test, gender differences would be largest when memory for the exact facial expression was required after gender orienting instructions at encoding (which can be done on the basis of more global features). We expected gender differences to be reduced if instructions during encoding required men to evaluate the emotion expressed by the face. 2. Method 2.1. Design and participants The study was a 2 (participant gender: male, female) × 2 (encoding task: gender orienting, expression orienting) × 2 (recognition task: same person, exact face) between-subjects design. The sample consisted of 203 students (122 women), ages 18–25, who volunteered to participate for extra credit in a psychology class. Students under 18 years of age or who reported having received an Asperger's Syndrome diagnosis were excluded from the study. 2.2. Materials and procedure All stimuli were presented on Samsung LCD 15-inch displays controlled by Dell 4660 computers with 1024 × 768 screen resolution. Instructions were presented in black, 18 pt., centered, Courier New font on a white background. Face stimuli (240) were drawn from the Center for Vital Longevity Face Database (Minear & Park, 2004) and presented in color at 100% of screen size. The faces in this database are diverse in ethnicity and gender. There were 240 pictures of faces used in the study. Half of the selected faces were male and half showed either a happy or neutral expression, distributed equally across gender. The database contained an insufficient number of available faces with negative emotional expressions to afford contrasting happy versus negative expressions. Half of the faces (120) were presented to each participant at both encoding and test. At test, each person also saw 60 completely
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new faces and 60 faces of the same person they saw at encoding with a different expression. At encoding, participants were assigned to one of two betweensubjects conditions. They were either instructed to report the gender of the face by pressing the ‘f’ or ‘m’ key on the computer keyboard or to report the emotional expression of the face by pressing the ‘p’ or ‘n’ key. Three practice trials were provided to verify comprehension of instructions. Participants were not told there would be a test on the faces. Faces were presented one at a time for five seconds each (separated by a one second fixation cross) at both encoding and test. At test, they were either instructed to judge whether they had seen the same person at encoding, regardless of match in expression (Same Person) or whether they had seen the same person with the same expression they saw at encoding (Exact Face). In both conditions they were asked to press “y” for yes or “n” for no, in response to the instructions of their particular recognition condition. Examples of correct and incorrect responses for each condition were given to participants and are shown in Figs. 1 and 2. To create a delay between the encoding phase and test phase, a 36-item Raven's Progressive Matrices (Raven, Court, & Raven, 1977) test was administered after the encoding phase, with 10 min allowed for completion. There were no gender differences in Raven's scores, t (201) = 1.07, p = .29. Furthermore, Raven's scores were unrelated to face recognition accuracy, r = .05, p = .49, consistent with the lack of correlation between intelligence and face recognition ability previously reported (Herlitz & Yonker, 2002).
3. Results 3.1. Manipulation check Encoding response accuracy was extremely high under both orienting instructions, indicating that participants were following instructions and were highly capable of both gender and expression discrimination. Mean gender discrimination accuracy across the sample was functionally at ceiling, M = .99. Mean expression discrimination
accuracy was .93, with women performing better than men, F(1,100) = 4.70, p = .03 (Mwomen = .94, SE = .01, Mmen = .92, SE = .01, d = .28), consistent with previous studies.
3.2. Effects of participant gender, orienting task, and recognition task on recognition accuracy The general linear model (GLM) procedure from SPSS 21.0 (IBM, 2010) was used for all analyses with an adjusted hit rate (hits minus false alarms) as the dependent variable. Adjusted hit rate is standard in studies of recognition memory and was necessary to correct for bias and the unequal number of “old” trials at test between the Same Person condition (180 old faces) and Exact Face condition (120 old faces). Of the 240 faces presented at test, 120 were the exact same as those studied by both groups and 60 were completely new. However, there were an additional 60 photos of people previously seen with different expressions which should have been detected as “old” by those in the Same Person condition but not as “old” by those in the Exact Face condition. Given the literature on the own-gender bias (for a meta-analytic review, see Herlitz & Lovén, 2013) and general superior recognition accuracy of female faces, we conducted a repeated-measures GLM with face gender at test as a within-subjects variable. As expected, women accurately recognized more faces than men, F(1,195) = 13.57, p b .001, d = 0.59 (Mfemales = .39, SE = .01, Mmales = .33, SE = .01) and female faces were better recognized than male faces overall, F(1,195) = 43.79, p b .001, d = 0.36 (Mfemales = .39, SE = .01, Mmales = .34, SE = .01), as is often found (Ellis, Shepherd, & Bruce, 1973; Godard & Fiori, 2010; Ino, Nakai, Azuma, Kimura, & Fukuyama, 2010; Rehnman & Herlitz, 2007). However, there was a significant interaction of participant gender and test face gender such that women recognized female faces much better than male faces (d = 0.72), but men recognized female and male faces with similar accuracy (d = 0.12), F(1,195) = 23.56, p b .001, difference in d = 0.60. Furthermore, consistent with a recent meta-analysis by Herlitz and Lovén (2013), women were better than men at recognizing female faces (t(201) = 5.45, p b .001) but
Fig. 1. Instructional example given for Same Person recognition task condition. Participants are ‘correct’ if they respond “y” to the left or middle face because for the Same Person condition they were asked, “Have you previously studied this same person?”, and it does not matter if the expression differs from the one seen at study.
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Fig. 2. Instructional example given for Exact Face recognition task condition. Participants are only ‘correct’ if they respond “y” to the left face because for the Exact Face condition they were asked “Have you previously studied this exact face?”, and the person and expression must match the face they studied.
not better at recognizing male faces (t(201) = 1.09, p = .276). In summary, women's face recognition was superior to men's and this was driven primarily by their performance on female faces. Contrary to our hypothesis, orienting individuals to emotional expressions of faces at encoding did not reduce the overall gender difference in recognition accuracy, F(1,195) = .087, p = .768. Instead, orienting to expression tended to benefit both men and women, with a trend for a significant main effect, F(1,195) = 3.43, p = .065, d = 0.30 (Mexp = .38, SE = .01, Mgender = .35, SE = .01). However, there was a 3-way interaction of participant gender, test face gender, and orienting instruction. Under gender orienting instructions, men and women both recognized significantly more female faces than male faces. In contrast, expression orienting instructions boosted women's
accuracy on female faces more than male faces, but it boosted men's accuracy on male faces more than female faces (men's recognition of female faces was actually slightly lower under expression orienting than gender orienting), F(1,195) = 6.29, p b .05, difference in d = 0.41. Importantly, pairwise comparisons revealed that expression orienting only significantly improved recognition, relative to gender orienting, of male faces by men t(79) = −2.51, p = .014, d = 0.56. In contrast, expression orienting did not significantly improve recognition accuracy for men tested on female faces t(79) = − 0.18, p = .855, d = 0.04, women tested on male faces, t(120) = − .738, p = .462, d = 0.13, or women tested on female faces t(120) = −1.37, p = .17, d = 0.25 . In summary, the primary effect of expression orienting at encoding was to improve men's recognition of own-gender faces (see Fig. 3).
Fig. 3. Mean face recognition accuracy (adjusted hit rate) as a function of participant gender, test face gender, and orienting instructions at encoding. Error bars represent standard errors.
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Our prediction about the relative size of the gender difference between test conditions was not borne out — gender differences in face recognition were not larger for the Exact Face condition than the Same Person condition. Women significantly outperformed men in both the Same Person condition (t(99) = − 2.23, p = .03, Mwomen = .40 , SE = .01, Mmen = .35 , SE = .02) and the Exact Face condition (t(100) = − 2.98, p = .004, Mwomen = .39, SE = .01, Mmen = .32, SE = .02) and there was no interaction of participant gender with recognition task F(1,195) = 0.19, p = .66, d = 0.13. The only significant difference between the same person and exact face conditions was for men under gender orienting instructions. Given that there was no overall significant difference in recognition accuracy between the Same Person and Exact Face conditions, F(1,195) = 1.42, p = .24, d = 0.10 (MSP = .37, SE = .01, MEF = .36, SE = .01), the two recognition tasks may have not differed enough in difficulty to allow for unequally sized gender differences between recognition test conditions. 4. Discussion Our hypothesis that expression-orienting instructions would reduce overall gender differences in face recognition was not supported. However, expression orienting did significantly increase men's recognition of male faces, without significantly improving women's face recognition. Our hypothesis that women outperform men in face recognition because they have access at test to more encoded detail of the faces was also not supported. Although women outperformed men under both recognition task instructions, gender differences were not larger in the Exact Face condition. Expression orienting may not have reduced overall gender differences because women are generally superior at face processing (Fine, Semrud-Clikeman, & Zhu, 2009; Goodman, Phelan, & Johnson, 2012; Herlitz & Lovén, 2013; Ino et al., 2010) and specifically at emotion processing in facial expressions (Franklin & Adams, 2010; Hall et al., 2010; Hampson et al., 2006; Sasson et al., 2010). Women in our sample were better at discriminating between neutral and happy emotion during study, further instantiating their superior emotion processing abilities and indicating one possible cause of their face recognition advantage. Emotional expression can capture attention (Wronka & Walentowska, 2011) and minimize attentional blink (Mather, 2007), which may allow women to encode more information, especially emotion-relevant information. Expression orienting also may not have reduced gender differences in face recognition because it gave some boost to women's face recognition accuracy as well, although this was not statistically significant. However, the small boost that women experienced may have been large enough to prevent the boost experienced by men from closing the gender gap. Expression orienting may have only improved men's recognition of male faces for sociocultural reasons. Men may be able to take advantage of more lifetime perceptual exposure (Diamond & Carey, 1986) to other men to individuate and recognize faces within their in-group, but only when task instructions induce this type of processing. Although men did not show an own-gender effect, which accompanies women's superior face recognition (Herlitz & Lovén, 2013; McKelvie, 1981), expression-orienting instructions significantly improved men's recognition of male faces. Importantly, under gender-orienting instructions, the condition most similar to previous studies, both men and women were better at recognizing female faces and women were better than men with male faces. Although women still recognized more faces overall under expression-orienting instructions, they were not better than men at recognizing male faces. Thus, expression orienting had a powerful effect on men's face recognition, allowing them to achieve parity with women on male face recognition and to no longer show inferior recognition of male faces relative to female ones. Although expression orienting did not significantly improve women's face recognition relative to gender orienting instructions, its effect on own-gender face recognition should not be ignored — only in the expression-orienting
condition did women show an own-gender effect. Women may also be able to take better advantage of life perceptual exposure to owngender faces when induced to orient to the expressions of faces. Expression orienting may benefit men's face recognition by inducing them to spend more time processing parts of the face that women process without strategic guidance. Three lines of evidence support this inference. First, men have been found to process emotion more unconsciously and earlier than women, resulting in an analysis of faces that is too coarse-grained (Knyazev, Slobodskoj-Plusnin, & Bocharov, 2010). Second, fMRI evidence indicates men show more right face fusiform area (FFA) activation, which has been linked to whole face processing, but women show more left FFA activation, which has been linked to facial feature processing (Njemanze, 2007; Rossion et al., 2000). Specifically, women spend more time focusing on the mouth and eyes (Hall et al., 2010), which is where participants devote more attention when orienting to expression (Gupta & Srinivasan, 2009), and which aids in face identification (Schyns, Bonnar, & Gosselin, 2002). Third, Schyns, Petro, and Smith (2009) provided EEG evidence that face processing occurs in three stages, beginning with a focus on the eyes, followed by a zooming out to globally process the face, and ending with local processing of diagnostic features. More local processing, which women have been shown to do more of, can result from earlier fixations on the eyes and mouth (Miellet, Caldara, & Schyns, 2011). Therefore, expression orienting may improve men's recognition because it induces them to engage in more local processing of faces and perhaps affecting how time is spent in the three face processing stages. It may also induce more detailed encoding of own-gender faces. As evidence, men in our study were particularly more likely to respond “old” under expression-orienting than gender-orienting instructions with associated increases in accuracy, an effect similar to that reported by Wright and Sladden (2003). Assuming that one reason women excel at face recognition is that they encode more perceptual details, we expected a larger gender difference in the Exact Face condition than the Same Person condition. This hypothesis, of course, assumes that women use the encoded perceptual detail to make subtle discriminations when the task demands necessitate it. Although the interactive effect of participant gender and test instruction was not reliable, the sample means were in the expected direction (See Fig. 4) — the difference between women and men in the Exact Face condition was 6.7% whereas the mean difference in the Same Person condition was only 5.2%, d = 0.13. We may have simply failed in this experiment by chance to detect a small population effect. It is also possible that the difficulty of the Exact Face discrimination may overshadow any benefit that women naturally have above men in using perceptual detail to recognize faces. 4.1. Limitations and future directions The nature of our stimuli and the fixed presentation rate may have constrained our ability to reduce gender differences with orienting instructions. We studied only neutral and happy faces. Future inclusion of faces with negative expressions (insufficient numbers of negativeexpression faces were available from the Center for Vital Longevity Face Database for our purposes) could affect gender differences. Also, we allotted 5 s to study each face. Although this is a relatively generous amount of time for face processing, men may be better able to take fuller advantage of expression orienting if study time is self-paced, which could result in a significant reduction in gender differences on faces generally, and particularly in the Exact Face test condition. We would not claim that these results indicate that different manipulations of encoding processes could not alter gender differences in face recognition. Future studies could consider other types of orienting instructions and/or discriminations that might affect elaborative face encoding, such as explicit face-based trait impression formation or paired comparisons of faces with respect to specific features. In principle, these approaches could identify gender differences in encoding
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Fig. 4. Mean face recognition accuracy (adjusted hit rate) as a function of participant gender, orienting instructions at encoding, and test instructions. Error bars represent standard errors.
processes that explain gender differences in face recognition. The use of eye-tracking could also elucidate gender differences in how attention is allocated to facial features and how time may be differentially allocated to global and local processing. Men and women may differ not only in their natural processing of faces but in how they respond to instructions to orient in less automatic ways. Even to the extent that additional encoding manipulations do not affect gender differences in face memory, one cannot easily rule out the argument that women's advantage in face memory may be due to differential encoding processes when attending to relevant features (e.g., knowledge-based elaboration, such as noticing similarity of the presented face to faces of friends). However, a failure to detect gender-related interactions when manipulations clearly influence subsequent memory performance, as in this study, increases the likelihood that gender differences arise from factors besides encoding strategies per se, such as endogenous hormonal influences on memory binding (Yonker et al., 2003) or effects arising during confrontation with recognition stimuli. 4.2. Conclusion We showed that orienting men to emotional expression can significantly improve their recognition of male faces. Our results suggest that expression orienting may induce a quality of processing in men that is more likely to be engaged in women and that gender differences in uninstructed orienting to emotional expression during encoding may account for some of the gender difference in overall face recognition memory. Although orienting men to facial expression information was not sufficient to eliminate gender differences in facial recognition memory, we have identified a novel way to improve face recognition that may benefit other populations in ways we have shown it does for men. There are likely multiple causes of the gender difference in face recognition which can be explored with further study. Acknowledgments These data were generated as part of a senior thesis project by Megan Bulluck, under the supervision of Christopher Hertzog. We thank Anna Babaie, Laura Ma, Nabila Nazarali, Shivani Shah, Radhika Solanki and Rebekah Stewart for help with data collection, and Audrey Duarte for her suggestions as a second reader on the senior thesis. For more information on our research program, see http://psychology. gatech.edu/CHertzog/.
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Orienting to face expression during encoding improves men's recognition of own gender faces Erika K. Fulton, Megan Bulluck, Christopher Hertzog ⁎ School of Psychology, Georgia Institute of Technology, 654 Cherry St., Atlanta, GA 30332-0170, United States
a r t i c l e
i n f o
Article history: Received 10 September 2014 Received in revised form 3 August 2015 Accepted 11 August 2015 Available online xxxx Keywords: Gender differences Episodic memory Face recognition Face processing Emotion expression
a b s t r a c t It is unclear why women have superior episodic memory of faces, but the benefit may be partially the result of women engaging in superior processing of facial expressions. Therefore, we hypothesized that orienting instructions to attend to facial expression at encoding would significantly improve men's memory of faces and possibly reduce gender differences. We directed 203 college students (122 women) to study 120 faces under instructions to orient to either the person's gender or their emotional expression. They later took a recognition test of these faces by either judging whether they had previously studied the same person or that person with the exact same expression; the latter test evaluated recollection of specific facial details. Orienting to facial expressions during encoding significantly improved men's recognition of own-gender faces and eliminated the advantage that women had for male faces under gender orienting instructions. Although gender differences in spontaneous strategy use when orienting to faces cannot fully account for gender differences in face recognition, orienting men to facial expression during encoding is one way to significantly improve their episodic memory for male faces. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Successful human social interaction depends on accurate face recognition. For instance, faces serve as retrieval cues for qualities of an individual that are relevant to social exchange (Nachson, 1995; Riggio, 1992). However, there are individual differences in face recognition speed and accuracy (Guillem & Mograss, 2005; Hall, Hutton, & Morgan, 2010; Herlitz & Rehnman, 2008; Herlitz & Yonker, 2002; Hofmann, Suvak, & Litz, 2006; Lewin & Herlitz, 2002; Lewin, Wolgers, & Herlitz, 2001; McBain, Norton, & Chen, 2009; Rehnman & Herlitz, 2007; Vuilleumier, George, Lister, Armony, & Driver, 2005), some of which have relatively serious consequences. Impaired episodic memory of faces is seen in disorders such as schizophrenia (Calkins, Gur, Ragland, & Gur, 2005; Silver et al., 2006), autism (Weigelt, Koldewyn, & Kanwisher, 2012), and prosopagnosia (Kress & Daum, 2003), and is part of a general episodic memory disorder in Alzheimer's disease (Hawley & Cherry, 2004; Plaza, López-Crespo, Antúnez, Fuentes, & Estévez, 2012). Understanding the factors that lead to superior face recognition could support the development of training and treatments to improve face recognition in these and other populations. It could even ⁎ Corresponding author at: School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332-0170, United States. E-mail addresses: [email protected] (E.K. Fulton), [email protected] (M. Bulluck), [email protected] (C. Hertzog).
http://dx.doi.org/10.1016/j.actpsy.2015.08.005 0001-6918/© 2015 Elsevier B.V. All rights reserved.
inform the development of software that could emulate human facial recognition, which has multiple applications (e.g., Hu, Klare, Bonnen, & Jain, 2013; Konen, 1996). One way to understand the factors that lead to superior facial recognition is to examine gender differences therein, which are commonly found (Bengner et al., 2006; Guillem & Mograss, 2005; Herlitz & Rehnman, 2008; Lewin & Herlitz, 2002; Megreya, Bindemann, & Havard, 2011; Rehnman & Herlitz, 2007; Yonker, Eriksson, Nilsson, & Herlitz, 2003). A recent meta-analysis by Herlitz and Lovén (2013) reported that women are better at recognizing faces (Hedges' g = .36), with the advantage seen primarily for female faces. Several explanations have been offered for women's advantage, such as their superior face perception (Megreya et al., 2011), greater self-reported social engagement (Sommer, Hildebrandt, Kunina-Habenicht, Schacht, & Wilhelm, 2013), increased encoding specificity of faces (Guillem & Mograss, 2005; Lovén, Herlitz, & Rehnman, 2011), and superior recognition or detection of facial expression (Hall et al., 2010). Women's face recognition may also benefit from better use of increased encoding time (McKelvie, 1981), higher circulating estradiol (Yonker et al., 2003) and own-gender faces (Herlitz & Lovén, 2013; Lewin & Herlitz, 2002; Lovén, Svärd, Ebner, Herlitz, & Fischer, 2014; Lovén et al., 2011; McKelvie, 1981; Megreya et al., 2011; Wolff, Kemter, Schweinberger, & Wiese, 2014; Wright & Sladden, 2003). Although a complicated interplay of biological and social factors likely accounts for gender differences in face recognition, much of the existing research suggests that women excel at face
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recognition because they process faces differently (e.g., Everhart, Shucard, Quatrin, & Shucard, 2001; Lovén et al., 2011; Lovén et al., 2014; Megreya et al., 2011). Herlitz and Lovén (2013) suggest that women's advantage in face recognition may arise because they allocate attention during encoding differently from men. The authors showed that women's advantage is primarily for female faces when a mix of female and male faces is shown, but that women also outperform men when only male faces are shown (g = .22). Women, they suggested, may focus more attentional resources on remembering female faces when presented with a mix of male and female faces, but when only male faces are to be remembered they can outperform men because all attentional resources can be devoted to male faces. If this account is correct, then we can ask to what, specifically, women allocate more attentional resources that allows them to better recognize faces. One possibility is that women allocate more attentional resources to the emotional expressions of faces. Women excel at the recognition of emotional expression in faces (Hall et al., 2010), performing more accurately (Hampson, van Anders, & Mullin, 2006; Sasson et al., 2010; Thayer & Johnsen, 2000) and efficiently (Hampson et al., 2006; Vassallo, Cooper, & Douglas, 2009) than men, especially with subtle variations in facial expressions (Hoffmann, Kessler, Eppel, Rukavina, & Traue, 2010; Montagne, Kessels, Frigerio, de Haan, & Perrett, 2005). Furthermore, processing emotional expression involves particular attention to eyes (Beaudry, Roy-Charland, Perron, Cormier, & Tapp, 2014; Gupta & Srinivasan, 2009; Hall et al., 2010), and women are more likely to focus on these features (Everhart et al., 2001; Hall et al., 2010), paying more attention to eyes than males as early as infancy (Ashear & Snortum, 1971; R. Exline, Gray, & Schuette, 1965; R. V. Exline, 1963; Hall et al., 2010; Hittelman & Dickes, 1979; Leeb & Rejskind, 2004; Levine & Sutton-Smith, 1973; Sæther, Van Belle, Laeng, Brennen, & Øvervoll, 2009). There are gender differences in face recognition even for neutral faces, so female superiority in this domain is not dependent upon the presence of emotional expressions in faces (see Herlitz & Lovén, 2013; e.g., McBain et al., 2009). The present study tested the hypothesis that gender differences in attention to facial expression explain at least some of the gender difference in face recognition. Specifically, we hypothesized that women's superior face recognition memory is due to a higher likelihood of spontaneously using the strategy of attending to emotional expression. Women mimic more facial expressions (Dimberg & Lundquist, 1990), show more emotional contagions (Doherty, Orimoto, Singelis, Hatfield, & Hebb, 1995), show greater affective priming to happy faces (Donges, Kersting, & Suslow, 2012), and are faster in labeling a happy expression (Hampson et al., 2006; Vassallo et al., 2009). The specific expression displayed (e.g. happy or neutral) may have a small (but significant) effect on recognition accuracy (Patel, Girard, & Green, 2012; Wang, 2013) but greater attention to any facial expression may support subsequent face recognition accuracy. To test our hypothesis, we instructed men and women to encode faces in one of two ways under incidental learning conditions (participants were unaware of the memory test that followed). Participants were briefly shown a face and asked to either report the gender of the face (male or female) or its emotional valence (happy or neutral). The underlying premise was that if women are more likely to use the strategy of attending to emotional expression when viewing faces, then guiding men to orient to facial expressions would improve their encoding of facial features, thereby differentially benefiting males' face recognition and reducing the size of the gender difference. Orienting to a face's gender can result in more global or holistic facial processing (Tanaka & Farah, 1993), which should not affect gender differences in facial recognition. Orienting to a face's emotional expression, however, requires more local feature processing (Martin, Slessor, Allen, Phillips, & Darling, 2012), which women may do more effectively than men. Gender differences might not be reduced by our manipulation if both women and men experience additional benefit from explicit instruction
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to orient to expression during encoding, but expression orienting instructions were at least expected to significantly improve men's facial recognition accuracy. We also expected to replicate women's own-gender bias in face recognition, originally shown by McKelvie (1981) and recently reviewed by Herlitz and Lovén (2013), but were curious whether our manipulation would affect men's relative recognition rates for male and female faces. Social-cognitive accounts explain the own-gender effect in terms of increased individuation when encoding faces from one's ingroup (Hugenberg, Young, Bernstein, & Sacco, 2010). Therefore, an open question was whether orienting men to expression might not only improve their face recognition overall but provide a larger boost in accuracy for male faces. One study previously showed that men recognize male faces better than female faces (Wright & Sladden, 2003), so if expression orienting boosts face recognition it may do so for own-gender faces more than other-gender faces. We also hypothesized that women outperform men in face recognition because they have access at test to more encoded detail of the faces. In order to evaluate this possibility we used a test of recognition memory that focuses on participants' discrimination of generic personrecognition (I've seen that person before) from recognition of the person with the same prior facial expression (I've seen that facial expression on that person before). Adapting work by Koutstaal (2003, 2006), our recognition test varied in whether participants were to judge if the face was simply of the same person they had seen at study (Same Person) or of the same person with the exact same expression they had seen at study (Exact Face; see the Method section for details). When the discrimination required determining whether the same person with the exact same expression was seen at study, we assumed that greater recollection of specific facial feature information was required. Thus, we hypothesized that if women have higher recollection of specific facial features at test, gender differences would be largest when memory for the exact facial expression was required after gender orienting instructions at encoding (which can be done on the basis of more global features). We expected gender differences to be reduced if instructions during encoding required men to evaluate the emotion expressed by the face. 2. Method 2.1. Design and participants The study was a 2 (participant gender: male, female) × 2 (encoding task: gender orienting, expression orienting) × 2 (recognition task: same person, exact face) between-subjects design. The sample consisted of 203 students (122 women), ages 18–25, who volunteered to participate for extra credit in a psychology class. Students under 18 years of age or who reported having received an Asperger's Syndrome diagnosis were excluded from the study. 2.2. Materials and procedure All stimuli were presented on Samsung LCD 15-inch displays controlled by Dell 4660 computers with 1024 × 768 screen resolution. Instructions were presented in black, 18 pt., centered, Courier New font on a white background. Face stimuli (240) were drawn from the Center for Vital Longevity Face Database (Minear & Park, 2004) and presented in color at 100% of screen size. The faces in this database are diverse in ethnicity and gender. There were 240 pictures of faces used in the study. Half of the selected faces were male and half showed either a happy or neutral expression, distributed equally across gender. The database contained an insufficient number of available faces with negative emotional expressions to afford contrasting happy versus negative expressions. Half of the faces (120) were presented to each participant at both encoding and test. At test, each person also saw 60 completely
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new faces and 60 faces of the same person they saw at encoding with a different expression. At encoding, participants were assigned to one of two betweensubjects conditions. They were either instructed to report the gender of the face by pressing the ‘f’ or ‘m’ key on the computer keyboard or to report the emotional expression of the face by pressing the ‘p’ or ‘n’ key. Three practice trials were provided to verify comprehension of instructions. Participants were not told there would be a test on the faces. Faces were presented one at a time for five seconds each (separated by a one second fixation cross) at both encoding and test. At test, they were either instructed to judge whether they had seen the same person at encoding, regardless of match in expression (Same Person) or whether they had seen the same person with the same expression they saw at encoding (Exact Face). In both conditions they were asked to press “y” for yes or “n” for no, in response to the instructions of their particular recognition condition. Examples of correct and incorrect responses for each condition were given to participants and are shown in Figs. 1 and 2. To create a delay between the encoding phase and test phase, a 36-item Raven's Progressive Matrices (Raven, Court, & Raven, 1977) test was administered after the encoding phase, with 10 min allowed for completion. There were no gender differences in Raven's scores, t (201) = 1.07, p = .29. Furthermore, Raven's scores were unrelated to face recognition accuracy, r = .05, p = .49, consistent with the lack of correlation between intelligence and face recognition ability previously reported (Herlitz & Yonker, 2002).
3. Results 3.1. Manipulation check Encoding response accuracy was extremely high under both orienting instructions, indicating that participants were following instructions and were highly capable of both gender and expression discrimination. Mean gender discrimination accuracy across the sample was functionally at ceiling, M = .99. Mean expression discrimination
accuracy was .93, with women performing better than men, F(1,100) = 4.70, p = .03 (Mwomen = .94, SE = .01, Mmen = .92, SE = .01, d = .28), consistent with previous studies.
3.2. Effects of participant gender, orienting task, and recognition task on recognition accuracy The general linear model (GLM) procedure from SPSS 21.0 (IBM, 2010) was used for all analyses with an adjusted hit rate (hits minus false alarms) as the dependent variable. Adjusted hit rate is standard in studies of recognition memory and was necessary to correct for bias and the unequal number of “old” trials at test between the Same Person condition (180 old faces) and Exact Face condition (120 old faces). Of the 240 faces presented at test, 120 were the exact same as those studied by both groups and 60 were completely new. However, there were an additional 60 photos of people previously seen with different expressions which should have been detected as “old” by those in the Same Person condition but not as “old” by those in the Exact Face condition. Given the literature on the own-gender bias (for a meta-analytic review, see Herlitz & Lovén, 2013) and general superior recognition accuracy of female faces, we conducted a repeated-measures GLM with face gender at test as a within-subjects variable. As expected, women accurately recognized more faces than men, F(1,195) = 13.57, p b .001, d = 0.59 (Mfemales = .39, SE = .01, Mmales = .33, SE = .01) and female faces were better recognized than male faces overall, F(1,195) = 43.79, p b .001, d = 0.36 (Mfemales = .39, SE = .01, Mmales = .34, SE = .01), as is often found (Ellis, Shepherd, & Bruce, 1973; Godard & Fiori, 2010; Ino, Nakai, Azuma, Kimura, & Fukuyama, 2010; Rehnman & Herlitz, 2007). However, there was a significant interaction of participant gender and test face gender such that women recognized female faces much better than male faces (d = 0.72), but men recognized female and male faces with similar accuracy (d = 0.12), F(1,195) = 23.56, p b .001, difference in d = 0.60. Furthermore, consistent with a recent meta-analysis by Herlitz and Lovén (2013), women were better than men at recognizing female faces (t(201) = 5.45, p b .001) but
Fig. 1. Instructional example given for Same Person recognition task condition. Participants are ‘correct’ if they respond “y” to the left or middle face because for the Same Person condition they were asked, “Have you previously studied this same person?”, and it does not matter if the expression differs from the one seen at study.
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Fig. 2. Instructional example given for Exact Face recognition task condition. Participants are only ‘correct’ if they respond “y” to the left face because for the Exact Face condition they were asked “Have you previously studied this exact face?”, and the person and expression must match the face they studied.
not better at recognizing male faces (t(201) = 1.09, p = .276). In summary, women's face recognition was superior to men's and this was driven primarily by their performance on female faces. Contrary to our hypothesis, orienting individuals to emotional expressions of faces at encoding did not reduce the overall gender difference in recognition accuracy, F(1,195) = .087, p = .768. Instead, orienting to expression tended to benefit both men and women, with a trend for a significant main effect, F(1,195) = 3.43, p = .065, d = 0.30 (Mexp = .38, SE = .01, Mgender = .35, SE = .01). However, there was a 3-way interaction of participant gender, test face gender, and orienting instruction. Under gender orienting instructions, men and women both recognized significantly more female faces than male faces. In contrast, expression orienting instructions boosted women's
accuracy on female faces more than male faces, but it boosted men's accuracy on male faces more than female faces (men's recognition of female faces was actually slightly lower under expression orienting than gender orienting), F(1,195) = 6.29, p b .05, difference in d = 0.41. Importantly, pairwise comparisons revealed that expression orienting only significantly improved recognition, relative to gender orienting, of male faces by men t(79) = −2.51, p = .014, d = 0.56. In contrast, expression orienting did not significantly improve recognition accuracy for men tested on female faces t(79) = − 0.18, p = .855, d = 0.04, women tested on male faces, t(120) = − .738, p = .462, d = 0.13, or women tested on female faces t(120) = −1.37, p = .17, d = 0.25 . In summary, the primary effect of expression orienting at encoding was to improve men's recognition of own-gender faces (see Fig. 3).
Fig. 3. Mean face recognition accuracy (adjusted hit rate) as a function of participant gender, test face gender, and orienting instructions at encoding. Error bars represent standard errors.
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Our prediction about the relative size of the gender difference between test conditions was not borne out — gender differences in face recognition were not larger for the Exact Face condition than the Same Person condition. Women significantly outperformed men in both the Same Person condition (t(99) = − 2.23, p = .03, Mwomen = .40 , SE = .01, Mmen = .35 , SE = .02) and the Exact Face condition (t(100) = − 2.98, p = .004, Mwomen = .39, SE = .01, Mmen = .32, SE = .02) and there was no interaction of participant gender with recognition task F(1,195) = 0.19, p = .66, d = 0.13. The only significant difference between the same person and exact face conditions was for men under gender orienting instructions. Given that there was no overall significant difference in recognition accuracy between the Same Person and Exact Face conditions, F(1,195) = 1.42, p = .24, d = 0.10 (MSP = .37, SE = .01, MEF = .36, SE = .01), the two recognition tasks may have not differed enough in difficulty to allow for unequally sized gender differences between recognition test conditions. 4. Discussion Our hypothesis that expression-orienting instructions would reduce overall gender differences in face recognition was not supported. However, expression orienting did significantly increase men's recognition of male faces, without significantly improving women's face recognition. Our hypothesis that women outperform men in face recognition because they have access at test to more encoded detail of the faces was also not supported. Although women outperformed men under both recognition task instructions, gender differences were not larger in the Exact Face condition. Expression orienting may not have reduced overall gender differences because women are generally superior at face processing (Fine, Semrud-Clikeman, & Zhu, 2009; Goodman, Phelan, & Johnson, 2012; Herlitz & Lovén, 2013; Ino et al., 2010) and specifically at emotion processing in facial expressions (Franklin & Adams, 2010; Hall et al., 2010; Hampson et al., 2006; Sasson et al., 2010). Women in our sample were better at discriminating between neutral and happy emotion during study, further instantiating their superior emotion processing abilities and indicating one possible cause of their face recognition advantage. Emotional expression can capture attention (Wronka & Walentowska, 2011) and minimize attentional blink (Mather, 2007), which may allow women to encode more information, especially emotion-relevant information. Expression orienting also may not have reduced gender differences in face recognition because it gave some boost to women's face recognition accuracy as well, although this was not statistically significant. However, the small boost that women experienced may have been large enough to prevent the boost experienced by men from closing the gender gap. Expression orienting may have only improved men's recognition of male faces for sociocultural reasons. Men may be able to take advantage of more lifetime perceptual exposure (Diamond & Carey, 1986) to other men to individuate and recognize faces within their in-group, but only when task instructions induce this type of processing. Although men did not show an own-gender effect, which accompanies women's superior face recognition (Herlitz & Lovén, 2013; McKelvie, 1981), expression-orienting instructions significantly improved men's recognition of male faces. Importantly, under gender-orienting instructions, the condition most similar to previous studies, both men and women were better at recognizing female faces and women were better than men with male faces. Although women still recognized more faces overall under expression-orienting instructions, they were not better than men at recognizing male faces. Thus, expression orienting had a powerful effect on men's face recognition, allowing them to achieve parity with women on male face recognition and to no longer show inferior recognition of male faces relative to female ones. Although expression orienting did not significantly improve women's face recognition relative to gender orienting instructions, its effect on own-gender face recognition should not be ignored — only in the expression-orienting
condition did women show an own-gender effect. Women may also be able to take better advantage of life perceptual exposure to owngender faces when induced to orient to the expressions of faces. Expression orienting may benefit men's face recognition by inducing them to spend more time processing parts of the face that women process without strategic guidance. Three lines of evidence support this inference. First, men have been found to process emotion more unconsciously and earlier than women, resulting in an analysis of faces that is too coarse-grained (Knyazev, Slobodskoj-Plusnin, & Bocharov, 2010). Second, fMRI evidence indicates men show more right face fusiform area (FFA) activation, which has been linked to whole face processing, but women show more left FFA activation, which has been linked to facial feature processing (Njemanze, 2007; Rossion et al., 2000). Specifically, women spend more time focusing on the mouth and eyes (Hall et al., 2010), which is where participants devote more attention when orienting to expression (Gupta & Srinivasan, 2009), and which aids in face identification (Schyns, Bonnar, & Gosselin, 2002). Third, Schyns, Petro, and Smith (2009) provided EEG evidence that face processing occurs in three stages, beginning with a focus on the eyes, followed by a zooming out to globally process the face, and ending with local processing of diagnostic features. More local processing, which women have been shown to do more of, can result from earlier fixations on the eyes and mouth (Miellet, Caldara, & Schyns, 2011). Therefore, expression orienting may improve men's recognition because it induces them to engage in more local processing of faces and perhaps affecting how time is spent in the three face processing stages. It may also induce more detailed encoding of own-gender faces. As evidence, men in our study were particularly more likely to respond “old” under expression-orienting than gender-orienting instructions with associated increases in accuracy, an effect similar to that reported by Wright and Sladden (2003). Assuming that one reason women excel at face recognition is that they encode more perceptual details, we expected a larger gender difference in the Exact Face condition than the Same Person condition. This hypothesis, of course, assumes that women use the encoded perceptual detail to make subtle discriminations when the task demands necessitate it. Although the interactive effect of participant gender and test instruction was not reliable, the sample means were in the expected direction (See Fig. 4) — the difference between women and men in the Exact Face condition was 6.7% whereas the mean difference in the Same Person condition was only 5.2%, d = 0.13. We may have simply failed in this experiment by chance to detect a small population effect. It is also possible that the difficulty of the Exact Face discrimination may overshadow any benefit that women naturally have above men in using perceptual detail to recognize faces. 4.1. Limitations and future directions The nature of our stimuli and the fixed presentation rate may have constrained our ability to reduce gender differences with orienting instructions. We studied only neutral and happy faces. Future inclusion of faces with negative expressions (insufficient numbers of negativeexpression faces were available from the Center for Vital Longevity Face Database for our purposes) could affect gender differences. Also, we allotted 5 s to study each face. Although this is a relatively generous amount of time for face processing, men may be better able to take fuller advantage of expression orienting if study time is self-paced, which could result in a significant reduction in gender differences on faces generally, and particularly in the Exact Face test condition. We would not claim that these results indicate that different manipulations of encoding processes could not alter gender differences in face recognition. Future studies could consider other types of orienting instructions and/or discriminations that might affect elaborative face encoding, such as explicit face-based trait impression formation or paired comparisons of faces with respect to specific features. In principle, these approaches could identify gender differences in encoding
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Fig. 4. Mean face recognition accuracy (adjusted hit rate) as a function of participant gender, orienting instructions at encoding, and test instructions. Error bars represent standard errors.
processes that explain gender differences in face recognition. The use of eye-tracking could also elucidate gender differences in how attention is allocated to facial features and how time may be differentially allocated to global and local processing. Men and women may differ not only in their natural processing of faces but in how they respond to instructions to orient in less automatic ways. Even to the extent that additional encoding manipulations do not affect gender differences in face memory, one cannot easily rule out the argument that women's advantage in face memory may be due to differential encoding processes when attending to relevant features (e.g., knowledge-based elaboration, such as noticing similarity of the presented face to faces of friends). However, a failure to detect gender-related interactions when manipulations clearly influence subsequent memory performance, as in this study, increases the likelihood that gender differences arise from factors besides encoding strategies per se, such as endogenous hormonal influences on memory binding (Yonker et al., 2003) or effects arising during confrontation with recognition stimuli. 4.2. Conclusion We showed that orienting men to emotional expression can significantly improve their recognition of male faces. Our results suggest that expression orienting may induce a quality of processing in men that is more likely to be engaged in women and that gender differences in uninstructed orienting to emotional expression during encoding may account for some of the gender difference in overall face recognition memory. Although orienting men to facial expression information was not sufficient to eliminate gender differences in facial recognition memory, we have identified a novel way to improve face recognition that may benefit other populations in ways we have shown it does for men. There are likely multiple causes of the gender difference in face recognition which can be explored with further study. Acknowledgments These data were generated as part of a senior thesis project by Megan Bulluck, under the supervision of Christopher Hertzog. We thank Anna Babaie, Laura Ma, Nabila Nazarali, Shivani Shah, Radhika Solanki and Rebekah Stewart for help with data collection, and Audrey Duarte for her suggestions as a second reader on the senior thesis. For more information on our research program, see http://psychology. gatech.edu/CHertzog/.
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