Journal of Experimental Psychology: Human Perception and Performance 2014. Vol. 40. No. 3. 897-903

© 2014 American Psychological Association 0096-1523/14/$12.00 DOI: I0.1037/a0035939

OBSERVATION

Individual Differences in Adaptive Coding of Face Identity Are Linked to Individual Differences in Face Recognition Ability Gillian Rhodes, Linda Jeffery, and Libby Taylor

William G. Hayward

ARC Centre of Excellence in Cognition and its Disorders, School of Psychology, University of Western Australian

Department of Psychology, University of Hong Kong, Hong Kong & ARC Centre of Excellence in Cognition and its Disorders, School of psychology. University of Western Australia, Perth, WA, Australia

Louise Ewing ARC Centre of Excellence in Cognition and its Disorders, School of Psychology, University of Western Australian, Perth, WA, Australia Despite their similarity as visual pattems, we can discriminate and recognize many thousands of faces. This expertise has been linked to 2 coding mechanisms: holistic integration of information across the face and adaptive coding of face identity using norms tuned by experience. Recently, individual differences in face recognition ability have been discovered and linked to differences in holistic coding. Here we show that they are also linked to individual differences in adaptive coding of face identity, measured using face identity aftereffects. Identity aftereffects correlated significantly with several measures of face-selective recognition ability. They also correlated marginally with own-race face recognition ability, suggesting a role for adaptive coding in the well-known other-race effect. More generally, these results highlight the important functional role of adaptive face-coding mechanisms in face expertise, taking us beyond the traditional focus on hohstic coding mechanisms. Keywords: face recognition, face identity aftereffects, face adaptation, individual differences Supplemental materials: http://dx.doi.org/10.1037/a0035939.supp

Our ability to discriminate and recognize many faces, despite their similarity as visual pattems, has traditionally been linked to holistic coding mechanisms. These integrate information across

the face and represent spatial relations between component features, as well as the features themselves (Farah, Wilson, Drain, & Tanaka, 1998; Maurer, Grand, & Mondloch, 2002; McKone, Kanwisher, & Duchaine, 2007; McKone & Robbins, 2011; Rhodes, 2013; Rossion, 2008; Tanaka & Gordon, 2011). More recently, there has been growing interest in the role that adaptive face-coding mechanisms might play in face expertise (for reviews, see Rhodes, 2013; Rhodes & Leopold, 2011; Webster & MacLeod, 2011). The adaptive nature of face identity coding is highlighted by face identity aftereffects, where exposure to a face (e.g., anti-Dan) shifts the average (norm) toward that face, biasing perception selectively toward the opposite identity (e.g., Dan; Leopold, O'Toole, Vetter, & Blanz, 2001; Rhodes & Jeffery, 2006; Tsao, Freiwald, Tootell, & Livingstone, 2006; Figure 1). This selective bias toward the identity opposite (relative to the average) suggests that the average functions as a perceptual norm for coding identity. These aftereffects reflect adaptation of higher-level face-coding mechatiisms and cannot be fully explained by adaptation of low-level or midlevel (generic) shape-coding mechanisms (e.g., Rhodes, EvangeUsta, & Jeffery, 2009; Susilo, McKone, & Edwards, 2010). Two lines of evidence suggest that adaptive coding of identity may contribute to face expertise. First, face identity aftereffects are reduced in populations with face recognition difficulties (Fiorentini. Gray, Rhodes, Jeffery, & Pellicano, 2012; Palermo, Rivolta,

This article was published Online First March 31, 2014. Gillian Rhodes, Linda Jeffery, and Libby Taylor, ARC Centre of Excellence in Cognition and its Disorders, School of Psychology, University of Western Australia, Perth, WA, Australia; William G. Hayward, ARC Centre of Excellence in Cognition and its Disorders, School of Psychology, University of Western Australia; Department of Psychology, University of Hong Kong, Hong Kong; Louise Ewing, ARC Centre of Excellence in Cognition and its Disorders, School of Psychology, University of Western Australia. This research was supported by the Australian Research Council Centre of Excellence in Cognition and its Disorders (CEI 10001021), an ARC Professorial Fellowship to Rhodes (DP0877379) and an ARC Discovery Outstanding Researcher Award to Rhodes (DP 130102300). We thank Christian Meissner for supplying Caucasian faces used in the old/new recognition tests, Rachell Barker for assistance with stimulus preparation, Ainsley Read for assistance with testing, and Mayu Nishimura and Daphne Maurer for cocreating the Robbers Game used in the Identity Aftereffects task. Ethical approval was granted by the Human Research Ethics Committee of the University of Western Australia. Correspondence concerning this article should be addressed to Gillian Rhodes, School of Psychology, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia. E-mail: [email protected] 897

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RHODES, JEFFERY, TAYLOR, HAYWARD, AND EWING

Figure 1. A hypothetical two-dimensional face-space with two tatget identities, reduced identity strength versions of those faces, and an average face. Each target identity (e.g., Dan) has a matching antiface (e.g., antiDan) with opposite attributes (e.g., Dan has thin lips, anti-Dan has thick lips). In the identity aftereffect, exposure to a face (e.g., anti-Dan) shifts the average (norm) toward that face, biasing perception selectively toward the opposite identity (e.g., Dan) and making it easier to identify low identitystrength versions of Dan.

Wilson, & Jeffery, 2011; Pellicano, Jeffery, Burr, & Rhodes, 2007). Second, in typical populations, discrimination can be better around an average than a nonaverage face (Armann, Jeffery, Calder, Bülthoff, & Rhodes, 2011; Wilson, Loffler, & Wilkinson, 2002), suggesting that adaptive calibration of one's norm to match the population average is useful (Rhodes, Watson, Jeffery, & Clifford, 2010). However, functional benefits are not always found, despite extensive testing (e.g., Nishimura, Doyle, Humphreys, & Behrmann, 2010; Rhodes, Maloney, Turner, & Ewing, 2007), and it is an open question whether adaptive coding contributes to face recognition ability. Here we seek new evidence for a functional role of adaptive coding in face expertise using an individual differences approach. Despite a long-standing view that we are all face experts, it is now clear that there are strong and stable individual differences in face recognition ability (Wilhelm et al, 2010; Wilmer et al., 2010). These have been linked to variation in holistic coding, supporting a functional role holistic coding in face expertise (DeGutis, Wilmer, Mercado, & Cohan, 2013b; Richler, Cheung, & Gauthier, 2011; Wang, Li, Fang, Tian, & Liu, 2012; but see Konar, Bennet, & Sekuler, 2010). Here, we asked whether they are also linked to individual differences in adaptive coding of identity, consistent with a functional role for adaptive coding mechanisms. Individual differences in face-selective recognition ability have recently been linked to figurai eye-height aftereffects (Dennett, McKone, Edwards, & Susilo, 2012). However, although eye-height may be relevant to identity, these are distortion aftereffects, which transfer

across identity and require judgments about normality, rather than identity. It remains to be seen, therefore, whether adaptive coding of identity itself is linked to face recognition ability. Our first aim was to determine whether there are stable individual differences in face identity aftereffects that are related to face recognition ability. We measured adaptive coding of identity directly, using face identity aftereffects. We measured face recognition ability using the well-known Cambridge Face Memory Test (CFMT; Duchaine & Nakayama, 2006) and an old-new recognition memory test developed for the present study. We also measured nonface memory, using the Cambridge Car Memory Test (CCMT; Dennett et al, 2011), so that we could derive indices of face-selective recognition ability (DeGutis et al., 2013b). Our second aim was to determine whether adaptive coding of identity might be linked more specifically to own-race face expertise; that is, to better recognition of own-race than other-race faces (Meissner & Brigham, 2001). Individual differences in own-race expertise have been linked to holistic coding differences (in Caucasian individuals; DeGutis, Mercado, Wilmer, & Rosenblatt, 2013a), but it is not known whether they are linked to adaptive coding differences. Previous studies have shown that Caucasian individuals maintain distinct norms for faces of different races (Caucasian and Asian) and have similar-sized face aftereffects for own- and other-race faces (Armann et al., 2011; Jaquet, Rhodes, & Hayward, 2008). Therefore, it is unlikely that greater adaptation to own- than other-race faces contributes to superior expertise for own-race faces. However, in a predominantly own-race environment, the coding mechanisms of people who adapt more (to all faces) might become more selectively tuned (calibrated) to ownrace faces. If this calibration helps us recognize faces, as proposed, then face identity aftereffects, which index strength of adaptation, should be linked to own-race expertise. To test whether adaptive coding of identity is linked specifically to own-race face expertise, we measured old-new recognition of otherrace (Chinese) and own-race (Caucasian) faces in our Caucasian participants, and used residuals from a regression in which other-race recognition scores predicted own-race recognition scores to isolate own-race-selective expertise (following DeGutis et al., 2013a). A positive correlation between these residuals and face identity aftereffects would link adaptability to own-race expertise.

Method Participants A total of 240 Caucasian adults (63 males; M = 19.3 years, SD = A.\ years, range = 17- 46) participated for course credit.

Tasks Face identity aftereffect. This task measures adaptive coding of identity and was adapted from previous studies (Jeffery et al., 2011; Rhodes et al., 2011; details in Supplemental Materials). Briefiy, on each trial participants view an adapting face, followed by a (low-identity-strength) target face, which they must identify (Figure 1). On match trials, the adapting antiface lies opposite the target identity (e.g., adapt anti-Dan, Test Dan), facilitating its identification. On mismatch trials (e.g., adapt anti-Jim, Test Dan), the adapting face is not opposite the target, impairing identification

INDIVIDUAL DIFFERENCES IN FACE ADAPTATION

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Table 1 Descriptive Statistics and Reliability for All Tasks

Face Identity AEs CFMT CFMT residuals Own-race d' Other-race d' Own-race residuals CCMT

Reliability

N

Minimum

Maximum

Mean

SD

Skew

Kurtosis

.54 .89 .88

129 240 112 149 149 149 112

-.09 34 -2.90 -.08 -.08 -2.35 30

.69 72 1.81 2.34 2.00 2.60 72

.26 57.23 0.00 1.16 .85 0.00 51.61

.17 8.10 1.00 .46 .43 1.00 8.41

.10 -.47 -.35 .21 .26 .23 .02

-.45 -.34 -.32 -.21 -.25 -.17 -.48

.66 .36 .43 .83

Note. AEs = After Effects; CFMT = Cambridge Face Memory Test; CCMT = Cambridge Car Memory Test. Reliabilities for AEs, own-race d\ and other-race d' are Spearman-Brown corrected split-half reliabilities (means from 50 random splits). Reliability for the face identity AE task was .60 when calculated using the two halves in which it was administered. CFMT reliability (from Bowles et al., 2009) and CCMT reliability (from Dennett et al., 2011) are Cronbach's alphas. CFMTjesiduals are residuals from a regression using CCMT scores to predict CFMT scores. Own-race_residuals are residuals from a regression using other-race d' scores to predict own-race d' scores.

(because perception is biased toward the nontarget identity that lies opposite the adaptor, e.g., Jim). The aftereffect is measured as accuracy on match trials minus accuracy on mismatch trials. Adapt and test faces were different sizes to minimize the contribution of low-level, retinotopic adaptation. Cambridge Face Memory Test (CFMT). The CFMT is a well-validated and widely used test of face recognition ability (Duchaine & Nakayama, 2006). Briefly, it tests memory for six male Caucasian faces, under three conditions: test faces (3AFC) that match the images studied, test faces that are different images from the study faces and test faces that are different images with visual noise added. We used the total score (maximum = 72) as the dependent measure. Old-new recognition memory. We created an old-new recognition test to further measure memory for own-race (Caucasian) faces, and to measure memory for other-race (Asian) faces (details in Supplemental Materials). Briefly, participants saw study faces (male) in front view and had to identify these from a series of test faces shown in 3/4 view. They saw six study-test blocks, each with 10 study faces (3,000 ms each), followed by 20 test faces (5,000 ms each). Half the blocks contained own-race faces and half contained other-race faces, with blocks alternating by race. The dependent measure, d', was calculated separately for own-race and other-race faces. Cambridge Car Memory Test (CCMT). The CCMT is analogous to the CFMT, but uses cars instead of faces (Dennett et al., 2011).

Reliability was reasonable for face identity aftereffects, indicating that there are stable individual differences in the adaptive coding of identity. Reliability was poorer for old-new recognition of

-.2

.0

.2

.4

.6

.8

.6

.8

Face Identity Aftereffect

Procedure Participants were tested individually in two sessions, lasting up to 40 min, 1 week apart. Participants completed two or more of the following tasks, in the order indicated; Face Identity Aftereffect (FIAE; N = 129), Old-New Recognition {N = 149), CFMT (N = 240), CCMT (A' = 65). The FIAE task was split in half and completed over two sessions. These tasks were part of a larger battery that included tasks unrelated to the present study. All tasks were presented using SuperLab 4.0.6.

.0

.2

.4

Face Identity Aftereffect

Results Table 1 shows task reliability. Reliability is well established for the CFMT (Bowles et al, 2009) and CCMT (Dennett et al., 2011).

Figure 2. Scatterplots with best-fitting regression lines illustrating the relationship between face identity aftereffects and two measures of face recognition ability; Cambridge Face Memory Test (CFMT) and own-race d'.

RHODES, JEFFERY, TAYLOR, HAYWARD, AND EWING

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Table 2 Pearson Correlations Between Face Identity Aflereffects (AEs), Face Memory Variables, and Non-Face (Car) Memory Variables CFMT Face identity ABs r p N CFMT r P N Own-race d' r P N Other-race d'

Own-race d'

.173* .049

129

Other-race d'

.248* .022 85

CCMT

Car PC

.320** .009

-.085 .499 65

-.106 .897 65

.707** .000

.159 .094 112

-.285* .022 112

.159 .145 85

.265" .000

189

Face PC

65 .339" .000

189

65 .438** .000

189

.656** .000 65

r P N Face PC r P N CCMT

.789** .000 65

.134 289 65

.477" .000 65

-.042 742 65 .028 .824

65

-.121 .339

65 .000 1.000 65

r P N

.876** .000 65

Note. Ns are lower than in Table 1 because not all participants did all tasks. Face PC (principal component) and Car PC are principal components from a principal component analysis conducted on face recognition scores (CFMT, own-race d', other-race d') and Car Recognition Scores (CCMT). > < .05. "p < .01.

other-race faces. There were no multivariate outliers, according to Mahalanobis distances. Univariate SPSS outliers were replaced by scores 2 SDs above/below the mean, as appropriate (CFMT, N = 1; own-race d', N = 1, other-race d', N = 1). All variables were normally distributed, except for the CFMT, but skew and kurtosis were within acceptable limits for parametric analysis (Table 1; Stuart & Kendall, 1958). Descriptive statistics for the final distributions are shown in Table 1.

Face Identity Aftereffects Correlate With FaceSelective Recognition Ability We found small-to-moderate, significant positive correlations of face identity aftereffects with the CFMT and own-race d' scores (Figure 2; Table 2). Although these correlations weren't large, they were quite substantial given the upper bounds imposed by reliability (square root of product of the two reliabilities; CFMT upper bound r = .69; own-race d' upper bound r = .60). In contrast, identity aftereffects correlated negatively (and nonsignificantly) with nonface (CCMT) recognition (Table 2).' These results link adaptive coding of identity selectively with face recognition rather than visual recognition generally. To more directly test whether adaptive coding of identity is linked to face-selective recognition ability, we used residuals from a regression that predicted CFMT scores from CCMT scores as an explicit measure of face-selective recognition ability. These residuals correlated moderately and significantly with face identity aftereffects, r = .288, p = .02, N = 65 (Figure 3).

We also derived a second measure of face-selective recognition ability from a Principal Component Analysis (PCA) conducted on the three face recognition scores (CFMT, own-race d', other-race d') and car recognition (CCMT) scores. As expected, the PCA yielded two factors, with face scores loading on a "face PC (principal component)" (explaining 40.2% of variance), and car scores loading on a "car PC" (explaining 27.3% of variance). Face identity aftereffects correlated positively and significantly with the face PC, but negatively (and nonsignificantly) with the car PC (Table 2), further indicating that adaptive coding is linked selectively to face recognition ability, and not to visual memory generally.

Face Identity Aftereffects Correlate With Own-Race Expertise As expected, recognition (d') was better for own-race than other-race faces, i(148) = 7.97, p < .0001 (Cohen's d = .71), confirming that our test was sensitive to own-race expertise (Table 1). We used residuals from a regression that predicted own-race recognition from other-race recognition as our measure of ownrace expertise. Face identity aftereffects showed a small-tomoderate correlation with own-race expertise, r = .194, p = .076, Af = 85 (but none with other-race expertise, i.e., residuals from ' The low correlation with CCMT scores cannot be attributed to poor reliability, because this test has excellent reliability.

INDIVIDUAL DIFFERENCES IN FACE ADAPTATION

o

ÙL - 1 -2-

.0

.2

.4

.6

.8

Face Identity Aftereffect Figure 3. Scatterplot with best-fitting regression line illustrating the relationship between face identity aftereffects and face-selective recognition ability (residuals from regression predicting Cambridge Face Memory Test [CFMT] scores from Cambridge Car Memory Test [CCMT] scores).

regression predicting other-race from own-race recognition, r = .068, p = .534, N = 85). Although only marginally significant, the effect size indicates a modest link between adaptive coding of identity and own-race expertise.

Discussion Our results provide direct evidence that individual differences in face recognition ability are linked to differences in adaptive coding of identity. Face identity aftereffects correlated positively with several measures of face recognition, including measures of faceselective recognition, but negatively (nonsignificantly) with nonface (car) recognition ability. This link between adaptive coding of identity and face-selective recognition ability supports a functional role for adaptive coding in face expertise. Our results are consistent with evidence that face adaptation is reduced in some clinical populations with face-processing difficulties (Ewing, Pellicano, & Rhodes, 2013; Palermo et al., 2011; Pellicano et al., 2007; Pellicano, Rhodes, & Calder, 2013). It is important that they show that this link between face adaptation and recognition performance extends across the full range of neurotypical performance. One previous study has linked face aftereffects to face recognition ability in the neurotypical population (Dennett et al., 2012). However, that study measured how adaptation to a single face dimension, eye-height, affected perceptions of normality. Here we measured identity aftereffects, which directly assess how adaptation on all identity-related dimensions affects the perception of identity. We also found a small-moderate, marginally significant, correlation between face identity aftereffects (for own-race faces) and own-race face expertise in Caucasian participants. This result is expected if face adaptation calibrates coding mechanisms to the diet of faces, because people who adapt more strongly should have mechanisms that are tuned more selectively to own-race faces than their peers (when own-race faces predominate). However, caution

901

is needed. The correlation was only marginally significant, possibly because our measure of other-race recognition had relatively low reliability, and only Caucasian participants were tested. Therefore, it will be important to confirm this link between adaptability of face-coding mechanisms and own-race-selective expertise in future studies. A plausible reason for the observed link between adaptive coding of identity and recognition ability is that over time adaptation calibrates coding mechanisms to the population of faces that we experience and this calibration facilitates recognition (for reviews, see Clifford & Rhodes, 2005; Rhodes & Leopold, 2011; Webster & MacLeod, 2011). Alternatively, Dennett et al. (2012) have proposed that the link between adaptation and face recognition could stem from the slope of the tuning functions of the neural populations that code face dimensions. They argue that steeper tuning curves (over a fixed range) produce both larger aftereffects (because adaptors will produce more activation and hence more adaptation) and better recognition (because subtler discriminations can be made). A similar account might apply to all identity-related dimensions. This account avoids any chicken-and-egg problem of whether more adaptation causes better recognition or vice versa, because both reflect the shape of the underlying neural tuning functions. Of course, the question then becomes, what long-term factors determine the slope of the tuning functions? A good candidate is adaptation (Georgeson, 2004). Individual differences in face recognition ability have a genetic basis (McKone & Palermo, 2010; Wilmer et al., 2010; Zhu et al., 2010; but see DeGutis, Wilmer, Mercado, & Cohan, 2013b for a critique of subtraction-based measures used by Zhu et al.). It is possible, therefore, that the individual differences in adaptive coding of identity that are linked to those differences also have a genetic basis. Certainly, individual differences in holistic coding that have been linked to recognition ability appear to have a genetic basis (McKone & Palermo, 2010). Future studies using twins could determine whether the variation in adaptive facecoding mechanisms observed here also has a genetic basis. Our expertise in recognizing faces has been linked to two coding mechanisms: holistic integration of information across the face and adaptive norm-based coding of identity using norms tuned by experience. The effects obtained here between adaptive coding and face recognition ability were similar in size (small-moderate) to those reported previously between holistic coding and face recognition ability (DeGutis et al., 2013a; Richler et al., 2011; Wang et al., 2012). Therefore, holistic and adaptive coding of identity may make similar contributions to our face expertise. It will be interesting in future studies to directly compare the relative contributions of these two coding mechanisms, and try to determine how much of the individual variation in face expertise they can explain together.

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Received August 15, 2013 Revision received December 23, 2013 Accepted January 2, 2014 •

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Individual differences in adaptive coding of face identity are linked to individual differences in face recognition ability.

Despite their similarity as visual patterns, we can discriminate and recognize many thousands of faces. This expertise has been linked to 2 coding mec...
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