Journal of Applied Psychology
2003, Vol. 88, No. 4, 760 –763
Copyright 2003 by the American Psychological Association, Inc.
0021-9010/03/$12.00 DOI: 10.1037/0021-9010.88.4.760
Children’s Face Recognition Memory: More Evidence for the
Cross-Race Effect
Kathy Pezdek, Iris Blandon-Gitlin, and Catherine Moore
Claremont Graduate University
It is well established that own-race faces are recognized more accurately than cross-race faces. However,
there are mixed results regarding the developmental consistency of the cross-race effect. White and Black
kindergarten children, 3rd graders, and young adults viewed a Black and a White target individual. One
day later, recognition memory for each target was tested with a 6-person lineup. The interaction of race
of participant by race of target face on Ag scores was significant, demonstrating an overall cross-race
effect. The 2nd-order interaction with age did not approach significance; for each age group, own-race
identification was more accurate than cross-race identification. The age consistency of the cross-race
effect in light of the significant main effect of age suggests quantitative but not qualitative differences in
face memory processing at various ages. For children, as well as adults, own-race faces are recognized
more accurately than cross-race faces.
An important goal for the field of applied psychology is to resolve
inconsistencies in the research literature upon which expert testimony is frequently based. In the first study on this topic, Feinman
and Entwisle (1976) tested White and Black children in grades
one, two, three, and six on their ability to recognize photographs of
White and Black children. Face recognition memory improved
significantly with age, and although the race of participant by race
of target face interaction was highly significant, this first-order
interaction did not interact with age. The cross-race effect was
evident at each age level; whereas White children were more
accurate recognizing White faces than Black faces, Black children
were more accurate recognizing Black faces than White faces.
However, Chance, Turner, and Goldstein (1982) and Goldstein
and Chance (1980) tested White children (grades one to eight) and
adults on their ability to recognize photographs of White and
Japanese adults. In both studies it was reported that d⬘ scores
significantly increased with age, and that, whereas for children, d⬘
scores were similar for White and Japanese faces, for adults, White
faces were recognized more accurately than Japanese faces. With
these inconsistencies in the literature, it is difficult to know what
advice expert witnesses should offer jurors regarding whether the
cross-race effect operates for children as well as adults.
In cognitive development research, it is important to determine
which developmental effects result from quantitative differences
(older participants and younger participants use the same cognitive
processes to perform a task, but the older participants simply
perform better) versus qualitative differences (older participants
and younger participants utilize different cognitive processes to
perform a task). In an analysis of variance (ANOVA) model, main
effects of age suggest quantitative differences, and interactions
with age suggest qualitative differences. In numerous studies, it
has been reported that face recognition memory increases with age,
thus suggesting quantitative differences in face memory across
ages. In meta-analyses of face recognition memory studies, Pozzulo and Lindsay (1998) and Shapiro and Penrod (1986) reported
that age yielded a large effect size for measures of both hits and
false alarms. However, there do not appear to be major qualitative
A major factor that affects the accuracy of eyewitness memory
is whether the witness and the perpetrator are of the same race or
of different races. Own-race faces are recognized more accurately
than cross-race faces. This cross-race effect (also termed own-race
bias) has been demonstrated in field settings (Brigham, Maass,
Snyder, & Spaulding, 1982; Platz & Hosch, 1988) as well as in
numerous laboratory studies (see Meissner & Brigham, 2001, for
a review of this research). The effect is a robust one that is evident
in measures of both hit rates and false alarm rates (Meissner &
Brigham, 2001) as well as reaction time (Valentine, 1991) and in
forensically relevant tasks such as photo lineup construction by
police officers (Brigham & Ready, 1985) and the Photofit system
for face reconstruction (Ellis, Davies, & McMurran, 1979). The
cross-race effect in eyewitness identification has also been endorsed by attorneys (Brigham, 1981; Brigham & WolfsKeil,
1983), which accounts for the fact that expert testimony on this
topic is so common. In this study, we examined the developmental
consistency of the cross-race effect. In light of the fact that in real
eyewitness identification situations the witnesses are often children, and in many cases involving children, the only evidence is a
child’s eyewitness account, it is important to study factors that
specifically relate to children’s identification accuracy.
Three studies have examined developmental differences in the
cross-race effect, and the results reported have been inconsistent.
Kathy Pezdek, Iris Blandon-Gitlin, and Catherine Moore, Department of
Psychology, Claremont Graduate University.
This article is based on work supported by the National Science Foundation conducted by Kathy Pezdek under Grant SES-0111240. Any opinions, findings, and conclusions or recommendations expressed in this
article are those of the authors and do not necessarily reflect the views of
the National Science Foundation. This research was conducted as Catherine Moore’s master of arts thesis.
Correspondence concerning this article should be addressed to Kathy
Pezdek, Department of Psychology, Claremont Graduate University,
123 East Eighth Street, Claremont, California 91711. E-mail:
kathy.pezdek@cgu.edu
760
RESEARCH REPORTS
differences across age in the processes underlying face recognition
memory (Baenninger, 1994; Chung, 1997; Pedelty, Levine, &
Shevell, 1985).
On the basis of these findings, we hypothesized in this study that
face recognition memory will improve with age, and that the
cross-race effect will be evident for children as well as adults. In
this study, White and Black kindergarten children, third-graders,
and young adults were compared on their recognition memory for
a White man and a Black man. The three age groups in this study
were selected from similar heterogeneous neighborhoods. Consequently, there is no reason to think that the relative amount of
exposure to own-race faces as compared with cross-race faces
should differ across groups. It is predicted that the first-order
interaction of race of participant by race of target face will not
interact with age, suggesting a consistency in the cross-race effect
with age. This result would suggest that it is the relative amount of
exposure to own-race faces as compared with cross-race faces that
affects the size of the cross-race effect.
Method
Participants and Design
A total of 186 children and young adults participated, including 62
kindergarten children (M age ⫽ 5.63 years; 32 male, 30 female), 62
third-graders (M age ⫽ 8.63 years; 29 male, 33 female), and 62 young
adults (M age ⫽ 24.61 years; 31 male, 31 female). Half of the participants
in each age group were White; half were Black. There was no statistical
difference in the mean age for the White versus the Black participants in
each age group. The children were recruited from public schools in the Los
Angeles metropolitan area. The young adults were volunteers from psychology classes at two community colleges in the same area. Each participant viewed a White man and a Black man and was tested on their
memory for each. The design was a 3 (age) ⫻ 2 (race of participant) ⫻ 2
(race of target face) mixed factorial design, with race of the target face
being the only within-subjects factor.
Procedure and Materials
The experiment consisted of a presentation phase followed by a recognition phase. In the presentation phase, participants viewed a 2.5 min video
in which two men, one White and one Black, were working at a counter,
facing the camera, making cinnamon rolls. The video had the appearance
of a cooking show. There was a natural conversation between the two men
concerning their baking task. Prior to viewing the video participants were
told that they would view a brief video in which two men were cooking,
and that their task was to pay close attention to the video as they would be
asked to identify the men later. This instruction was included so that any
age differences that resulted could be attributed to memory processing
differences rather than to age differences in attention allocated to the target
faces. The children were shown the presentation tape in groups of four or
five but were tested individually. The adults participated with their classmates as part of a class exercise.
Participants returned the following day for the test phase. They were
presented a video of a six-person lineup for each of the two target men.
Each lineup included a target man and five foil individuals who matched
the appearance of the target. The order of presenting the two test lineups
was counterbalanced such that half of the participants viewed the White
lineup first; half viewed the Black lineup first. In each test lineup, the six
men first walked into the room and stood facing the camera. Then one at
a time, each man stepped forward and faced the camera for a 15-s headshot.
As each man stepped forward, participants were asked to respond on a
scale from 1 (absolutely sure this was not the target man) to 5 (absolutely
761
sure this was the target man). This scale provides recognition memory
judgments on a continuous scale rather than a discrete scale, thereby
allowing for a signal detection analysis of the responses. Although this type
of response does not simulate police identification procedures, it provides
a more sensitive memory measure that reduces the effect of participants’
response criteria on identification accuracy. The experimenter recorded the
children’s responses. The adults marked their responses on a sheet
provided.
Results
The data were coded in terms of the signal detection measure,
Ag. This measure is the nonparametric analog of d⬘and is used to
test recognition accuracy in a “yes–no” forced choice test. Ag
measures obtained from confidence ratings are similar to d⬘ measures computed from two alternative forced choice tests, and Ag is
relatively unaffected by violations of the assumptions underlying
d⬘ (Banks, 1970). Ag is the area under the receiver operator
characteristic (the ROC curve). We followed the procedures specified in MacMillan and Creelman (1991) to compute Ag measures
from responses to one target face and five foil faces. The Ag values
ranged from .00 to 1.0 (perfect performance) with .50 being the
chance rate. Essentially, higher Ag values represent better ability to
distinguish the target face from the foils.
A 3 (age) ⫻ 2 (race of participant) ⫻ 2 (race of target face)
ANOVA was performed on the Ag data. The results of this analysis
with effect sizes indicated are provided in Table 1. The interaction
of race of participant by race of target face was significant, F(1,
180) ⫽ 14.98, p ⬍ .001, demonstrating an overall cross-race
effect. White participants were more accurate in recognizing the
White target face (M ⫽ .74, SD ⫽ .29) than the Black target face
(M ⫽ .68, SD ⫽ .30), and Black participants were more accurate
in recognizing the Black target face (M ⫽ .81, SD ⫽ .24) than the
White target face (M ⫽ .66, SD ⫽ .28). More important, this effect
did not vary across age groups; the Age ⫻ Race of Participant ⫻
Race of Target Face interaction did not approach significance,
F ⬍ 1.00. As can be seen in Figure 1, for each age group, own-race
identification was more accurate than cross-race identification.
The only other significant effect in this analysis was the main
effect of age, F(2, 180) ⫽ 13.65, p ⬍ .001. As expected, recognition accuracy was highest for the adults (M ⫽ .80, SD ⫽ .28),
next highest for the third graders (M ⫽ .77, SD ⫽ .25), and lowest
for the kindergarten children (M ⫽ .62, SD ⫽ .29).
Table 1
Results of the Analysis of Variance With Effect Sizes Indicated
Source
Between subjects
Participant race
Age
Participant Race ⫻ Age
Within subjects
Target race
Target Race ⫻ Participant Race
Target Race ⫻ Age
Target Race ⫻ Participant Race ⫻ Age
* p ⬍ .001.
df
MS
F
n2
1
2
2
0.05
1.08
0.22
0.65
13.65*
1.36
.00
.13
.02
1
1
2
2
0.18
0.99
0.06
0.02
2.73
14.98*
0.90
0.33
.02
.08
.01
.00
RESEARCH REPORTS
762
Figure 1. Mean Ag values as a function of the age and race of the
participants and the race of the target face. White bars indicate the White
target face; black bars indicate the Black target face.
Discussion
These findings are consistent with those of Feinman and
Entwisle (1976) and support the notion that the cross-race effect is
similar across age from 5 years of age through adulthood. Not only
were own-race faces recognized more accurately than cross-race
faces at each age level, but also the size of the cross-race effect did
not vary with age. In addition, the main effect of age reported in
this study supports the findings reported elsewhere that face recognition memory improves with age. In meta-analyses of face
recognition memory studies, Pozzulo and Lindsay (1998) and
Shapiro and Penrod (1986) reported that age yielded a large effect
size for measures of both hits and false alarms.
From a theoretical point of view, the age consistency of the
cross-race effect, in light of the significant main effect of age,
suggests quantitative but not qualitative changes with age in the
cognitive processes underlying face recognition memory. In other
words, the cognitive processes underlying face recognition memory are similar for participants at each age level; the older participants are simply performing these cognitive processes better. To
illustrate this distinction, one model is considered for how faces
might be represented in memory. This is the multidimensional
space framework proposed by Valentine (1991) and Valentine and
Endo (1992). According to this model, faces are represented in a
hypothetical space that retains each face on the basis of various
dimensions representing features or sets of features. These dimensions, including both configural and featural aspects of faces,
developed from an individual’s prior experience with faces. In
accordance, the representational space for own-race faces includes
a better differentiation of the variant and the invariant dimensions
of faces than does the representational space for cross-race faces.
As a consequence, own-race faces are spread more evenly throughout the multidimensional “face space” and are more distinctively
represented and more accurately recognized. On the other hand,
less distinction is made between the variant and the invariant
dimensions of cross-race faces, and as a consequence, cross-race
faces are more tightly clustered in the face space and thus less
distinctively represented and less accurately recognized.
The results of this study suggest that to the extent that this
multidimensional space framework can account for the cross-race
effect with adults—and it does appear to fit the adult data quite
well (Chiroro & Valentine, 1995)—it is likely to describe the
cognitive processes underlying the cross-race effect for children as
well. The significant main effect of age, however, suggests that the
developmental difference is a quantitative one. For example, the
representational face space for children is likely to retain less and
less appropriate featural and configural information than does the
representational face space for adults. Accordingly, face memory
is generally less accurate for young children than for older children
and adults, but the size of the cross-race effect is similar among
age groups. The significant main affect of age in light of the
nonsignificant interaction of race of participant by race of target
face by age suggests that it is the relative amount of exposure to
own-race faces as compared with cross-race faces that affects the
size of the cross-race effect. Because the three age groups in this
study were selected from similar heterogeneous neighborhoods,
there is no reason to think that the relative amount of exposure to
own-race faces as compared with cross-race faces differed across
groups.
It can also be predicted from this model that for both adults and
children, relatively more contact with cross-race faces would reduce the cross-race effect by increasing the recognition accuracy
of cross-race faces relative to own-race faces. This, in fact, has
been reported in a number of studies involving adults (Brigham et
al., 1982; Carroo, 1987; Chiroro & Valentine, 1995; Platz &
Hosch, 1988) as well as children (Feinman & Entwisle, 1976).
From an applied point of view, these results suggest that the
advantage of own-race identification over cross-race identification
would be expected to be less for both adults and children who have
had higher levels of cross-race contact.
How can we account for the differences obtained between the
results of the present study and those of Chance, Turner and
Goldstein (1982) and Goldstein and Chance (1980), who reported
a cross-race effect for adults but not for children? First, it should
be noted that in both of these previous studies, only White participants were tested, and they were compared with their ability to
recognize White and Japanese target individuals. The cross-race
effect is more apparent if both the race of the target faces and the
race of the participants are varied. This design is necessary to
reject the interpretation that perhaps the specific own-race faces
included in the study were more discriminable and thus more
memorable than the specific cross-race faces selected. Second, an
unusual pattern resulted in the Goldstein and Chance (1980) study,
raising concerns about the validity of these findings. Whereas
adults recognized the White faces better than the Japanese faces,
White children at all grade levels from first to sixth recognized the
Japanese faces better than the White faces. In addition, Chance,
Turner, and Goldstein (1982) reported that d⬘ scores for first and
second graders to both White and Japanese faces were less than
chance (i.e., d⬘ ⬍ 1.00). This suggests that perhaps the cross-race
effect was masked by a floor effect with the children’s data in this
study.
One might argue that the reliability of the study was limited by
the fact that each child recognized only one White face and one
Black face. It is true that in typical face recognition memory
research, participants are presented with dozens of faces to remember and are then tested on their ability to distinguish old from new
RESEARCH REPORTS
test faces. However, this is quite a different situation from the real
world situation of viewing one suspect and having to pick him out
of a lineup later. We designed this study to test recognition
memory for only two faces, the two men interacting in the video,
to more closely approximate the situation of real eyewitness identification. The fact that there was no main effect of race of face
suggests that the memorability of the White and Black face did not
differ. Even more important is the finding that, although White
participants identified the White face better than the Black face,
Black participants identified the Black face better than the White
face.
On the basis of the findings of this study, expert witnesses on
eyewitness identification should be confident testifying that for
children as well as adults, own-race faces are recognized more
accurately than cross-race faces. Although face identification accuracy does improve with age, this effect appears to result from
quantitative rather than qualitative changes with age in the cognitive processes underlying face recognition memory.
References
Baenninger, M. A. (1994). The development of face recognition: Featural
or configurational procession? Journal of Experimental Child Psychology, 57, 337–396.
Banks, W. P. (1970). Signal detection theory and human memory. Psychological Bulletin, 74, 81–99.
Brigham, J. C. (1981). The accuracy of eyewitness evidence: How do
attorneys see it? Florida Bar Journal, 55, 714 –721.
Brigham, J. C., Maass, A., Snyder, L. D., & Spaulding, K. (1982). Accuracy of eyewitness identifications in a field setting. Journal of Personality and Social Psychology, 42, 673– 681.
Brigham, J. C., & Ready, D. J. (1985). Own-race bias in lineup construction. Law & Human Behavior, 9, 415– 424.
Brigham, J. C., & WolfsKeil, M. P. (1983). Opinions of attorneys and law
enforcement personnel on the accuracy of eyewitness identifications.
Law & Human Behavior, 7, 337–349.
Carroo, A. W. (1987). Recognition of faces as a function of race, attitudes,
and reported cross-racial friendships. Perceptual & Motor Skills, 64,
319 –325.
Chance, J. E., Turner, A. L., & Goldstein, A. G. (1982). Development of
763
differential recognition for own- and other-race faces. Journal of Psychology, 112, 29 –37.
Chiroro, P., & Valentine, T. (1995). An investigation of the contact
hypothesis of the own-race bias in face recognition. Quarterly Journal of
Experimental Psychology: Human Experimental Psychology, 48(A),
879 – 894.
Chung, M. S. (1997). Recognition of upright and inverted faces by children
and adults: A test of Carey’s encoding shift hypothesis. Korean Journal
of Psychology: Developmental, 10, 139 –151.
Ellis, H. D., Davies, G. M., & McMurran, M. M. (1979). Recall of White
and Black faces by White and Black witnesses using the Photofit system.
Human Factors, 21, 55–59.
Feinman, S., & Entwisle, D. R. (1976). Children’s ability to recognize
other children’s faces. Child Development, 47, 506 –510.
Goldstein, A. G., & Chance, J. E. (1980). Memory for faces and schema
theory. Journal of Psychology, 105, 47–59.
MacMillan, N. A., & Creelman, C. D. (1991). Detection theory: A user’s
guide. Cambridge, England: Cambridge University Press.
Meissner, C. A., & Brigham, J. C. (2001). Thirty years of investigating the
own-race bias in memory for faces. Psychology, Public Policy, and
Law, 7, 3–35.
Pedelty, L., Levine, S. C., & Shevell, S. K. (1985). Developmental changes
in face processing: Results from multidimensional scaling. Journal of
Experimental Child Psychology, 39, 421– 436.
Platz, S. J., & Hosch, H. M. (1988). Cross-racial/ethnic eyewitness identification: A field study. Journal of Applied Social Psychology, 18,
972–984.
Pozzulo, J. D., & Lindsay, R. C. L. (1998). Identification accuracy of
children versus adults: A meta-analysis. Law and Human Behavior, 22,
549 –570.
Shapiro, P. N., & Penrod, S. (1986). Meta-analysis of facial identification
studies. Psychological Bulletin, 100, 139 –156.
Valentine, T. (1991). A unified account of the effects of distinctiveness,
inversion, and race on face recognition. Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 43(A), 161–204.
Valentine, T., & Endo, M. (1992). Towards an exemplar model of face
processing: The effects of race and distinctiveness. Quarterly Journal of
Experimental Psychology: Human Experimental Psychology, 44(A),
671–703.
Received May 28, 2002
Revision received December 4, 2002
Accepted December 19, 2002 䡲