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Running head: NORM-BASED CODING OF FACE IDENTITY IN FOUR YEAR-OLDS 1

Four year-olds use norm-based coding for face identity Linda Jeffery, Ainsley Read, Gillian RhodesARC Centre of Excellence in Cognition and its Disorders School of Psychology, The University of Western Australia

Accepted for Cognition 29 January, 2013

Word Count: 2998 (including abstract)

Author Note

We thank the staff, students and parents who participated, Mayu Nishimura and Daphne Maurer for co-creating the “Robbers Task”, Elinor McKone, Elizabeth Pellicano, Kate Crookes and Elizabeth Taylor for helpful discussions. This research was supported by Australian Research


Council Discovery Grants DP0770923, DP0877379 and by ARC Centre of Excellence Grant CE110001021.

Correspondence concerning this article should be addressed to Linda Jeffery, School of Psychology M304, The University of Western Australia, 35 Stirling Highway, Crawley, W.A., 6009, AUSTRALIA. Email: [email protected]


Abstract

Norm-based coding, in which faces are coded as deviations from an average face, is an efficient way of coding visual patterns that share a common structure and must be distinguished by subtle

variations that define individuals. Adults and school-aged children use norm-based coding for face identity but it is not yet known if pre-school aged children also use norm-based coding. We reasoned that the transition to school could be critical in developing a norm-based system because school places new demands on children’s face identification skills and substantially increases experience with faces. Consistent with this view, face identification performance improves steeply between ages four and seven. We used face identity aftereffects to test whether norm-based coding emerges between these ages. We found that four year-old children, like adults, showed larger face identity aftereffects for adaptors far from the average than for adaptors closer to the average, consistent with use of norm-based coding. We conclude that experience prior to age four is sufficient to develop a norm-based face-space and that failure to use norm- based coding cannot explain four year-old children’s poor face identification skills.

Words 182Keywords: face perception, development, norm-based coding, aftereffects,

face-space


Four year-olds use norm-based coding for face identity

Faces convey a wealth of information that we use to guide our social interactions. As adults we swiftly extract information about identity, gender, ethnicity, age and emotional state from faces. Face identification, in particular, requires exquisite sensitivity to very subtle differences between highly similar visual patterns. Norm-based coding represents an efficient and elegant solution to this problem of representing visual patterns that share a common structure and must be distinguished by subtle variations that define individuals. A norm-based system represents what is distinctive

about each face by coding how faces deviate from a perceptual norm or prototype (see Figure 1). This system may be more efficient than one that codes a complete structural description, most elements of which are shared by all faces and therefore redundant. Moreover, face norms are updated by experience, fine-tuning our face perception coding mechanisms to our diet of faces (e.g., Rhodes, Jeffery, Watson, Clifford, & Nakayama, 2003). A variety of behavioural and neurophysiological evidence supports use of norm-based coding for facial identity in adults (see Rhodes & Leopold, 2011, for a review). The functional importance of adaptive norm-based coding is suggested by evidence that stronger adaptive norm- based coding of faces is associated with better face identification performance (Dennett, McKone, Edwards, & Susilo, 2012) and that groups with known face perception difficulties show reduced adaptive norm-based coding of facial identity (Congenital Prosopagnosia - Palermo, Rivolta, Wilson, & Jeffery, 2011; Autism Spectrum Disorders - Pellicano, Jeffery, Burr, & Rhodes, 2007).

Preschool aged children perform poorly on face identification tasks, relative to older children and adults (Bruce et al., 2000) but the source of their poor performance is controversial


(Crookes & McKone, 2009; McKone, Crookes, Jeffery, & Dilks, 2012). Early research suggested that a key mechanism of face perception, holistic coding (the integration of information across a face, including features and the spatial relations between them) was either absent or immature in young children (Carey, Diamond, & Woods, 1980; Diamond & Carey, 1977). However, more recent studies have established that holistic coding is present by age three (Macchi Cassia, Picozzi, Kuefner, Bricolo, & Turati, 2009) and is mature by five (see McKone et al., 2012; Pellicano & Rhodes, 2003). Therefore poor face identification performance in young

children cannot be attributed to immaturities in holistic coding. Here we ask whether failure to use norm-based coding, another key mechanism of face perception, could explain young children’s poor performance.


Figure 1. A simplified face-space showing two dimensions, two target identities (Ted and Rob) and the average face in the centre. In a norm-based model each identity is positioned on its own unique trajectory that passes through the average. For each face we can create an opposite “antiface” that lies on the same trajectory as the target but on the opposite side of the average (e.g., antiTed and antiRob). “Weaker” versions of each target are made by morphing each target with the average face by different amounts e.g. 60% Ted and 30% Ted as shown here. Adapting to antiTed facilitates

recognition of Ted, so that “weaker” versions of Ted are more accurately identified and the average face takes on the appearance of Ted.

Norm-based coding of facial identity has been demonstrated in adults and 7-9 year-old children using face adaptation techniques (e.g., Jeffery et al., 2011; Leopold, O'Toole, Vetter, & Blanz, 2001; Rhodes & Jeffery, 2006; Robbins, McKone, & Edwards, 2007; Webster & MacLin, 1999). Adaptation (exposure) to faces biases the appearance of subsequently viewed faces so that they look wider after seeing narrow faces, more male after seeing female faces, and so on (e.g.,


Webster, Kaping, Mizokami, & Duhamel, 2004). In a norm-based account these “aftereffects” are argued to reflect opponent coding, with pairs of neural channels tuned to above- and below- average values on each dimension in face space and the norm signaled by balanced activity in both pools. The further the adaptor is from the norm the larger the effect of adaptation on an average test face because more extreme adaptors produce greater activation, and greater subsequent suppression, in the preferred channel (Rhodes et al., 2005; Robbins et al., 2007). Alternative models of face coding that do not posit a norm, e.g., exemplar coding, neurally instantiated by multichannel coding, predict a different pattern. Multichannel models predict that small increases in the distance of the adaptor from the average face will increase aftereffects but thereafter increasing adaptor distance will reduce aftereffects because these extreme adaptors will have less effect on channels that code the average face (Clifford, Wenderoth, & Spehar, 2000; Dickinson, Almeida, Bell, & Badcock, 2010). The pattern of aftereffects predicted by norm-based coding has been found for adults (Robbins et al., 2007) and 7 year-old children (Jeffery et al., 2011).

Here we tested whether norm-based coding for facial identity emerges earlier in development, by measuring four-year-olds’ face identity aftereffects for near and far adaptors. Four year-olds are the youngest age group who do not yet to attend school but can complete adult-like adaptation tasks. The transition from preschool to primary (grade) school typically results in a substantial and sudden increase the number of individuals that children need to distinguish and remember. This sudden increase in experience may drive critical changes in face-space organisation, consistent with the assumption that face-space is built via experience with faces (Johnston & Ellis, 1995; Valentine, 1991) and proposals that changing demands on face perception skills during development prompt reorganisation of the face-perception system


(Scherf & Scott, 2012). Certainly performance appears to improve more steeply between these ages than during later childhood (Bruce et al., 2000; Ellis, 1992; Kinnunen, Korkman, Laasonen, & Lahti-Nuuttila, 2012), consistent with a qualitative shift in processing between four and seven years of age. While four year-old children possess at least a rudimentary face-space that produces distinctiveness effects (e.g., McKone & Boyer, 2006) and produces atypicality biases (Tanaka, Meixner, & Kantner, 2011) it is has not been established that their face-space is norm- based. It remains possible that young children represent faces in an exemplar manner (neurally instantiated by multichannel coding) until sufficient demands are placed on their face- recognition system to prompt reorganisation of face-space to be norm-based. Increased exposure to faces at school may also be crucial in developing a representation of the average face that is sufficiently good to function as a norm, or in developing sufficiently good representations of the dimensions on which to code individual faces as deviating from this norm.

We sought evidence of norm-based coding of face identity in four year-olds using the face identity aftereffect in which adaptation to an individual face (e.g. Ted) biases perception of an identity neutral average face so that it resembles an individual with characteristics opposite the adapting face (e.g., antiTed, see Figure 1). Identity aftereffects directly tap face identification processes (Rhodes, Evangelista, & Jeffery, 2009) and have not previously been demonstrated in young children. We asked whether children’s identity aftereffects would be larger for more extreme versus less extreme adaptors, consistent with norm-based coding.

Participants

Method


Sixteen children (M = 4:5 years, range 4:0 – 5:1, 4 female) were recruited from a preschool in Perth, Western Australia. Data from three additional children were removed from all analyses. Two failed to learn the target faces and one was an outlier (see Results). Seventeen undergraduates from the University of Western Australia participated for course credit (M = 18 years, range 17 – 22 years, 16 female). Participants were primarily Caucasian/European (16 adults, 11 children). Written consent was obtained from adult participants and the children’s parents.Stimuli

The stimuli were taken from Jeffery et al., (2011). Grayscale photographs of two male identities (Ted and Rob) comprised the targets. Reduced strength versions were made by morphing each face toward an average face (constructed from 20 adult male faces) using Gryphon Morph 2.5 (Maxwell, 1994), in varying increments to produce 40%, 60% and 80% versions of each target. The 40% and 60% stimuli were used only in training and the 80% and average (0%) stimuli were used as test faces (see Figure 2). Test

and training faces subtended a visual angle of 6.0° x 5.6° when viewed from approximately 45cm. For each target face, two adapting stimuli (antifaces) were made by caricaturing the average face away from each target (after Leopold et al., 2001) by 40% (near adaptors) and 80% (far adaptors) using Gryphon Morph (see Figure 2). These levels were chosen because both are sufficiently extreme that a multichannel model would be unlikely to predict an increase in adaptation across this range. The textures of the average were applied to all stimuli. Adapting stimuli were larger than test stimuli (7.5° x 7.4°) to rule out purely low-level (retinotopic) adaptation as the source of any aftereffects (Zhao & Chubb, 2001).Procedure


Children and adults were tested individually in quiet rooms at their preschool and the university, respectively. The task was presented on a Mac Powerbook Pro (15-inch LCD screen with anti-glare covering), using Cedrus Superlab 4.06 software (Abboud, Schultz, & Zeitlin, 2008). The task was presented as a “Robbers Game” for both children and adults. Participants began with training in recognizing the targets (Ted and Rob) and familiarization with lower strength versions of the targets, who were introduced as the brothers of each target. Participants were told to respond with the team leader’s name (target) whenever they saw him or a member of his team (his brothers). Children responded verbally and adults used labeled keys (“x” key/Ted, “,” key/Rob). Auditory feedback (beeps) was provided for correct and incorrect responses throughout training.


Figure 2. The adapting stimuli for (A) near adaptors and (B) far adaptors. (C) Shows the test (black rectangles) and training stimuli, with identity strength and target identity indicated below.

Participants then completed the adaptation task. The experimenter explained that a robber’s face (adapting antiface) would appear on the screen, “while he was stealing things”, and then it would disappear and be followed by a very brief presentation of the face of the team member (test stimulus) who caught the robber. Presenting the adapting face as a threatening individual is likely to boost attention to the faces in both children and adults (e.g., Chiappe et al., 2004; Kinzler & Shutts, 2008). The participant was asked to identify the catcher’s team. Two practice trials followed. The task comprised 80 trials. The average face was the test face on 48 trials, which comprised 12 trials with each adapting face (antiTed/antiRob) at each adaptor distance (near/far). The remaining 32 trials featured 80% targets as test faces, with four trials for


each target (Ted/Rob) with each adapting face, at each adaptor strength. The 80% test faces were included to check that participants could accurately identify “strong” versions of the faces. Adapting antifaces were shown for 5000ms followed by a 150ms inter-stimulus interval. The test stimulus was then displayed for 400ms followed by a blank gray screen that remained until a response was registered. The experimenter initiated the next trial for children and adults did so themselves by pressing the spacebar. The experimenter sat beside the children and ensured that they looked at the adapting faces for the entire time they were shown.

The trials were presented in a pseudo-random order and divided into eight blocks of ten trials and completed over two sessions, conducted on different days. Each session contained four blocks and was preceded by the training and practice described above. To maintain attention, each block featured an “escape” trial, in which the adaptor was followed by images of the robber’s victim and the “loot” that the robber has escaped with, rather than a test face. Children received stickers after each block and a confectionary item at the end of the second session. Each session took approximately 15 minutes.

Results

To measure adaptation we calculated the proportion of times the average (0%) face was identified as the face opposite the adaptor (i.e., the proportion of “Ted” responses after adaptation to antiTed and the proportion of “Rob” responses after adaptation to antiRob), in both adaptation conditions (near/far), for each participant. An aftereffect is indicated by greater than chance (0.5) performance. One female child’s scores were more than three standard deviations below the mean, for both near and far conditions, and her data were removed.

The mean proportions are shown in Figure 3. Performance was significantly above chance for each age group, in each condition, indicating that adaptation biased perception toward


the opposite identity as predicted (Children: near t(15) = 8.44, p < .001, d = 2.25; far t(15) = 6.74, p < .001, d = 1.70; Adults: near t(16) = 3.79, p = .002, d = 0.87; far t(16) = 7.10, p < .001, d = 1.82).

Crucially, both children and adults showed larger aftereffects for far than near adaptors, as predicted by norm-based coding. This pattern was confirmed by ANOVA1. We found a main effect of Adaptor Distance, F(1, 31) = 30.74, p < .001, p

2 = 0.50 (near M = 0.58, SD = 0.07; far M = 0.68, SD = 0.11). This effect was not moderated by age, with neither the interaction, F(1, 31) = 1.51, p = .229, p

2 = 0.05, nor the main effect of Age, F(1, 31) = 0.01, p = .938, p

2 < 0.01, being significant. Planned t-tests confirmed that aftereffects were larger for far than near adaptors for each age group (children t(15) = 3.14, p = .007, d = 1.05; adults t(16) = 4.69, p < .001, d = 1.35). Further, the majority of participants in each age group showed larger aftereffects for far than near adaptors (11/16 children and 14/17 adults). These proportions did not differ significantly for children versus adults (Fisher Exact Test p = .438).

1 Levene’s test indicated unequal variances for the near condition. Transformation of the data could not equalize the variances. However, we note that an independent t-test, corrected for unequal variances, showed no significant difference in the size of children’s and adults’ aftereffects, t(25.31) = 0.904, p = .374.

Running head: NORM-BASED CODING OF FACE IDENTITY

IN FOUR YEAR-OLDS 14

Figure 3. The mean proportion of responses opposite the adaptor for children and adults for near and far adaptors. The dotted horizontal line shows chance performance, the level expected if adaptation failed to bias perception to the opposite identity. Errors bars show one standard error either side of the mean.

Finally, we note that all participants performed well at identifying the high strength test stimuli (80%), confirming that they had learned the targets and maintained good performance throughout the task (Children M = 0.84, Range = 0.69 – 0.97; Adults M = 0.98, Range = 0.84 – 1.00). Adults were significantly more accurate than children, as confirmed by Mann-Whitney U- tests2

(Near; U = 10.00, p < .001, r =.84, Far; U = 48.5, p = .001, r =.58, Overall; U = 13.5, p < .001, r =.79).

2 Accuracy data were highly skewed for both children and adults so a non-parametric test was used.


Discussion

We have shown for the first time that four year-old children use norm-based (two-pool) coding of identity in face-space. Face identity aftereffects were larger for adaptors that are far from the average than for adaptors close to the average, as predicted by a norm-based model. Furthermore, children did not differ from adults in their sensitivity to the distance of the adaptor from the average. Therefore, we conclude that experience prior to age four is sufficient to develop a norm-based face-space and that failure to use norm-based coding cannot explain four year-old children’s poor face identification skills.

Previous research indicated that young children possess at least a rudimentary face-space that produces distinctiveness effects (4 year-olds, McKone & Boyer, 2006) and face identity aftereffects (5 year-olds, Jeffery et al., 2011). However, an exemplar-based face-space can also account for these effects (Robbins et al., 2007; Valentine, 1991). One prior study provided suggestive evidence of norm-based coding in five year-olds (Jeffery et al., 2010). However this study could not establish norm-based coding of face identity because the effects could equally have arisen from generic shape coding mechanisms (see Jeffery et al., 2011). The present study rules out an exemplar organization of face-space in young children and demonstrates that norm- based coding arises from face identity coding mechanisms.

How early in development might norm-based coding of face identity emerge? Our results show that experience with faces gained in the first four years of life is sufficient to develop a norm-based face-space. However, it is possible that much less experience is required. The foundations of norm-based coding may be present very early. Infants respond to an average face


as if it is familiar within a few months of birth, suggestive of a prototype effect (de Haan, Johnson, Maurer, & Perrett, 2001). Five-to-eight month old infants also discriminate faces differing in averageness/distinctiveness (Rhodes, Geddes, Jeffery, Dziurawiec, & Clark, 2002). These findings are consistent with norm-based coding in infancy, although they are certainly not conclusive because exemplar models can account for both distinctiveness and prototype effects (e.g., Nosofsky, 1988).

In conclusion we have established that children’s face-space is adult-like, in that it appears to be norm-based by age four. Therefore two key mechanisms of face perception, norm- based coding and holistic coding both emerge prior to age four, consistent with the view that face perception mechanisms are qualitatively mature relatively early in development (Crookes & McKone, 2009; McKone et al., 2012). Experience gained with faces prior to starting school is sufficient to develop a norm-based face-space. Therefore changes in the fundamental organization of face-space are unlikely to be the source of improvements in face recognition beyond the preschool years. Our results leave open the possibility that there are more subtle changes in face-space, such as refinement of the number and nature of the dimensions of face- space, that could underlie the improvement in face identification performance with age.Capacity to store more individuals and/or speed of identification may also increase during childhood, consistent with age-related increases in the size of face-selective cortical regions (e.g., Golarai et al., 2007; though see McKone et al., 2012). Finally, general cognitive or perceptual changes that are not specific to face perception, such as Vernier acuity or working memory (Crookes & McKone, 2009) could account for some or all of the improvement in face identification performance during childhood.


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