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1Volume 119| Number 3/4
March/April 2023
Research Article
https://doi.org/10.17159/sajs.2023/12126
© 2023. The Author(s). Published
under a Creative Commons
Attribution Licence.
Changes in the own group bias across immediate
and delayed recognition tasks
AUTHORS:
Colin Tredoux1,2
Ahmed M. Megreya3
Alicia Nortje1
Kate Kempen1
AFFILIATIONS:
1Department of Psychology,
University of Cape Town, Cape Town,
South Africa
2Cognition, Languages, Language,
Ergonomics Laboratory, University
of Toulouse – Jean Jaurès, Toulouse,
France
3Department of Psychological
Sciences, Qatar University, Doha, Qatar
CORRESPONDENCE TO:
Colin Tredoux
EMAIL:
colin.tredoux@uct.ac.za
DATES:
Received: 31 Aug. 2021
Revised: 25 Nov. 2022
Accepted: 25 Nov. 2022
Published: 29 Mar. 2023
HOW TO CITE:
Tredoux C, Megreya AM, Nortje
A, Kempen K. Changes in the own
group bias across immediate and
delayed recognition tasks. S Afr J Sci.
2023;119(3/4), Art. #12126. https://
doi.org/10.17159/sajs.2023/12126
ARTICLE INCLUDES:
☒ Peer review
☐ Supplementary material
DATA AVAILABILITY:
☐ Open data set
☐ All data included
☒ On request from author(s)
☐ Not available
☐ Not applicable
EDITOR:
Leslie Swartz
KEYWORDS:
own group bias, own race bias, face
recognition, encoding
FUNDING:
Programme for the Enhancement of
Research Capacity (PERC), University
of Cape Town; South African Centre
for Digital Language Resources
(SADILAR), Department of Science
and Technology of South Africa as
part of the South African Research
Infrastructure Roadmap (SARIR);
Oppenheimer Memorial Trust
Face recognition is biased in favour of in-group identity, particularly strongly for race or ethnicity but
to some extent also for sex and age. This ‘own group bias’ (OGB) can have profound implications in
practical settings, with incorrect identification of black suspects by white witnesses constituting 40%
of criminal exonerations investigated by the Innocence Project. Although authors have offered several
explanations for the OGB in face recognition, there is little consensus, apart from the acknowledgement
that the bias must reflect perceptual learning history. One matter that is not currently clear is whether the
bias occurs at encoding, or at retrieval from memory. We report an experiment designed to tease out bias
at encoding, versus bias at retrieval. Black and white South African participants encoded 16 target faces of
both the same and other race and gender, and attempted immediately afterward to match the target faces
to members of photograph arrays that either contained or did not contain the targets. After a further delay,
they attempted to identify the faces they had encoded from memory. Results showed a strong crossover
OGB in the delayed matching task, but an asymmetrical OGB at retrieval (only white participants showed
the OGB). Further investigation of recognition performance, considering only images correctly matched in
the delayed matching task, showed a narrowly non-significant OGB at retrieval, but the investigation was
likely not sufficiently powered to discover the effect, if it exists.
Significance:
• We demonstrate the presence of a crossover OGB in face recognition in a sample of black and white
South Africans in a delayed matching task (a measure of encoding).
• Our findings show that the OGB may change rapidly. In the present study, the OGB took a crossover
form at retrieval immediately after encoding, but was asymmetrical when assessed shortly afterwards.
• We used a novel approach for disentangling effects at encoding and at retrieval, but do not provide clear
evidence to distinguish whether the OGB is a failure of encoding or of memory retrieval.
Introduction
The own group bias (OGB)1 refers to a recognition advantage for faces of members of one’s own group. The most
common formulation posits an unequal advantage: groups that co-occupy a common environment will show an
advantage for members of their own group over members of another group. However, the recognition advantage
is typically asymmetrical in countries or regions where demographic or economic representation is unequal (e.g.
the United Kingdom2). Members of the dominant group will usually show an OGB, but members of the subordinate
groups often do not show it.2,3 The practical implications of the OGB are profound. The Innocence Project in the
USA4 has shown, for instance, that over 40% of exonerations of people falsely imprisoned based on eyewitness
identifications were convicted in part as a result of cross-race eyewitness identifications – a figure far out of
proportion to the demography of the USA.
Explanations of the OGB in the literature are plentiful, but it is probably fair to say that no single account has won
out empirically or theoretically. All theories appear to accept the notion that the OGB must be a consequence to
some degree of differential exposure to one’s own and other group faces.5 Our interest is in what might be called
the micro-chronology of the OGB. Whereas the OGB is often referred to in the literature as a memory bias – for
instance Hugenberg and colleagues declaring their intent to “…explain the proliferation of own group biases in face
memory”6(p.1392), and Yaros and co-authors7 arguing that the OGB may emerge due to “tuned” memory mechanisms
– researchers have pointed out for a number of years that the bias may stem from the differential ability to encode
faces of other groups, rather than a reduced ability to recognise or retrieve them from memory8. Recent claims
are that the OGB is likely due to the less efficient encoding of out-group faces in visual working memory9,10, or to
preferential distribution of attention during encoding11.
Megreya and co-authors12 have published research that questions the basis of the OGB in memory processes,
arguing instead that the OGB may be entirely due to encoding difficulties. Participants in Megreya et al.’s study had
difficulty in matching the face of a ‘target’ in a film still to digital photographs of the same person. This finding was
more pronounced when participants attempted to match faces from a group different from their own. This outcome
has important implications, especially for theoretical explanations of the OGB bias: many theoretical accounts have
focused on memory retrieval operations or are at least unclear on the relative role of encoding and retrieval in the
provenance of the OGB, and this should be addressed if the phenomenon to be explained is entirely about encoding
rather than retrieval. But this may be somewhat premature: at this stage in the development of thinking about the
OGB, it is not clear what the relative contribution of each process is. Indeed, several authors have offered evidence
that either questions whether the deficit is in any way encoding based, or that posits that it is in part retrieval based.
Papesh and Goldinger13 were able to show that disruptions during retention intervals modulated the OGB for out-
group faces, but not for in-group faces. Stelter et al.14 showed that eye movement activity differed across in-group
and out-group targets during a recognition, but not an encoding phase. They also showed that the OGB was a
function of poor performance in recognition of new faces specifically.
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Face Science: Immediate and delayed own group recognition bias
Page 2 of 7
Our aim was to explore the relative contributions of encoding and
retrieval in the manifestation of the OGB in face recognition. It is
important at the outset to acknowledge that it is difficult to disentangle
encoding from recognition, and in this respect the difficulty appears to
lie primarily in establishing that a stimulus has been encoded. We offer
one approach, which is to test recognition with a delayed matching
task, and then after a longer period with a recognition memory task.
In a delayed matching task, participants view a stimulus, and then
immediately attempt to match the stimulus in a test array once it has
been removed from view. If participants are accurate in the delayed
matching task, this suggests successful encoding. Note that Megreya
et al.12 did not use a delayed matching task, but an in-view matching
task. In other words, they attempted to match a target image to one
embedded in an array of images while both the target and the array
were in full view. However, we believe that in-view matching tasks do
not demonstrate encoding as clearly as delayed matching tasks because
matching targets in-view might be accomplished without encoding faces
at all. Participants may be able to use non-face properties of stimuli,
such as colour temperature, or other image artefacts to effect the match,
or might be able to do so with minimal encoding of the target image, e.g.
by relying on specific, transient and localised characteristics of faces,
such as skin blemishes, and hairstyle oddities. This distinction between
face identity encoding and face image encoding was powerfully made
over 40 years ago.15 For this reason we departed from the procedure
used by Megreya et al.12 There were some further depar tures, too, that
we thought important. In the first instance, we wished to see whether
the results they obtained replicate across different viewpoints and with
different stimulus materials. They used only frontal views of participants,
in both matching and target views, and they also appeared to have
made their matching arrays very difficult (the faces in the array were
the most similar in a moderately large set). Repeating their experiment
with different viewpoints of the faces (e.g. in profile vs. frontal views)
would test whether the memory trace of the face is well enough encoded
to match and recognise it over different transformations (i.e. different
face poses). Furthermore, repeating their experiment with line-up arrays
that are constructed around faces that are not specifically selected for
high similarity seems important to us, especially as one of the most
significant potential applications of knowledge about the OGB is in the
eyewitness domain, where line-ups are unlikely to be constructed in this
way (see Wells et al.16 for a discussion of why this is not a good idea).
However, the most impor tant goal in our experiment was to extend
Megreya et al.’s12 original experiment and to attempt to separate out
encoding and retrieval processes, and to estimate the relative importance
of each in the manifestation of OGB bias. Thus, in our experiment,
participants were tested for delayed face matching ability, and, after a
suitable delay, for their ability to recognise the face to which they were
exposed in the delayed matching task. We used a line-up task, designed
to permit the calculation of the signal detection theory (SDT) measures
d’ (discrimination) and c (criterion). Through this, and by calculating
successful recognition of faces that were incorrectly matched in the first
part of the procedure, we attempted to assess the relative contributions
of encoding and retrieval deficits in the OGB effect.
Method
Target stimuli
Stimuli were digital images taken from a large database of black
South African and white South African faces (containing multiple views
of over 1000 individuals) and curated by the first author. Sixteen target
faces were used in this experiment: four male and four female black
South African faces, and the same again for white South African faces.
The target faces were randomly sampled by the authors from the total
collection, and target ratings were obtained to ascertain whether there
were any idiosyncrasies present in the target images that may have
resulted in them being more memorable than the other target faces. A total
of 22 (15 female) (Mage = 20.52 years; SDage = 1.21 years) participants
rated the target faces on a number of dimensions that are known to
affect face recognition: typicality, distinctiveness, attractiveness,
perceived criminality, age, wealth, memorability, and familiarity. Each
face was rated on a scale from 0 (not at all) to 8 (extremely), whereas
age was estimated numerically. Two images of the same target were
presented side-by-side during the rating task: one photograph was of
the target in a neutral three-quarter side pose, the other image was of the
target in a frontal casual/smiling pose. Each image pair was presented
one at a time, along with a randomised order of rating dimensions.
The size of each face image was approximately 7.94 cm in width and
10.35 cm in height, with a resolution of 300 x 391 pixels (8-bit). The
image background and the target clothing were edited to be standard and
consistent across all the target images.
Line-up construction
Target present (TP) and target absent (TA) photographic line-ups were
constructed for the target images. TP line-ups contained the target and
five foils. In the TA line-ups, the target was replaced with a foil that was
randomly selected by the authors, resulting in a six-foil line-up (i.e.
there was no designated suspect). No foils were repeated or appeared
in any of the other line-ups. The line-up members were selected to be
subjectively moderately similar in appearance to the target (i.e. following
the principle set down in Wells et al.16 that line-up members should be
matched to the target on some but not all characteristics). Three of the
present authors independently selected ten possible foils for each target
face from a database of hundreds of black South African and white
South African faces. The most frequently chosen foils were used as the
final images in the line-ups.
Corresponding to the 16 target faces, each participant saw 16 line-ups.
The line-ups appeared in one of two orders: eight of the line-ups were TP
line-ups, and the other eight were TA line-ups. Two line-ups were created
for each target face: a frontal pose line-up, and three-quarter pose line-
up. The frontal neutral pose and the three-quarter pose were used to
control for picture recognition and to ensure that identification was made
on memory for the target and not the target photograph. The line-up
photographs were in colour and standardised. The backgrounds were
edited to remain consistent across all the images. All clothing, jewellery,
and distinctive markings were digitally removed from the images.
The size of each face image was approximately 6 cm x 7.8 cm, with
a resolution of 227 x 296 pixels (8-bit). The image background and the
target clothing were edited to be consistent across the target images.
Participants
A total of 64 (53 female) participants from the University of Cape Town
participated in the study in exchange for course credit; 32 (50%) of the
participants identified themselves as white South Africans, and 32 (50%)
of the participants identified themselves as black South Africans. All
participants repor ted normal or corrected to normal vision.
Procedure
Encoding and delayed matching phase
The study was conducted at the University of Cape Town, in a quiet
computer laboratory. The experiment was presented on computer using
E-Prime 2.0, at a resolution of 1024 x 768 pixels.
After providing demographic information, par ticipants were informed
that they would be presented with a series of target faces, one at a time,
for five seconds. One of the target faces appeared on the screen, in a
frontal casual/smiling pose. The size of the target image was 12 cm x
15.7 cm, at a resolution of 456 x 594 pixels.
After five seconds, the face disappeared, and a six-member simultaneous
TP or TA line-up was immediately displayed, in a delayed matching
task. Participants either saw a three-quarter line-up or a frontal line-up.
Participants were informed that the target they had just seen may or may
not be present in the line-up. They were cautioned that the clothing and
background of the target image may be different from the image they
had studied. If participants recognised the target, they had to indicate
the corresponding number above the line-up member on the keypad;
if they thought the target was not present, they had to indicate ‘0’ on
the keypad. This delayed matching procedure was repeated for the
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remaining 15 target faces; the order of the target faces and matching
line-up pairs was randomised across participants.
When the line-up delayed matching phase was finished, participants
completed a filler task for 10 minutes (a Sudoku game).
Recognition phase
Following the distractor task, participants were told that they would view
a series of six-member line-ups and that their task was the same as
before – to select the target face that they had studied earlier. They were
informed that the line-ups may be different from the line-ups they had
seen in the delayed matching session. Participants were again cautioned
that the target face may or may not be present in the line-up. It was
emphasised that the line-ups would appear in a random order and would
not necessarily correspond with the order in which the participants had
studied the target faces.
Participants attempted to identify the targets from the same line-ups
used in the delayed matching procedure. For example, if they saw a
TA three-quarter pose line-up at delayed matching, they saw the
same line-up at recognition. The position of the line-up members was
rearranged between delayed matching and recognition to guard against
commitment effects – if participants recognised the line-up from the
delayed matching session, they may have been tempted to select the
same line-up position as before.
If participants recognised the target, they indicated the corresponding
number above the line-up member on the keypad; if they thought the
target was not present, they selected the appropriate option on the
keypad. This recognition procedure was repeated for the remaining 15
line-ups; the order of arrays was randomised across participants.
When all recognition trials were completed, participants who responded
‘0’ (not present) to a TP line-up – i.e. who had incorrectly rejected the
line-up – were given another chance to respond to the line-up. They were
shown the same line-ups to which they had responded ‘not present’ and
were then required to make a selection (i.e. a forced choice; however,
data from the forced choices were not included in the present analyses).
The order of these line-ups was randomised.
Once participants had completed their forced-choice line-up tasks, they
were thanked, debriefed, and dismissed from the study. Please note that
further details regarding preparation of stimuli, as well as descriptive
statistics of task performance, appear in Figure 1.
Results
Hit and false alarm scores were computed for each participant and
were used to derive discrimination (d’) and criterion (c) signal detection
measures. Because we did not use designated suspects in our TA line-
ups, false alarms were computed as the total number of identifications
of foils in TA line-ups. (Although eyewitness researchers often divide the
total number of foil identifications to estimate the false alarm rate, this
constrains the false alarm rate to a maximum of 1/number_of_line-up
members. This may be appropriate for a perfectly fair line-up, but very
few line-ups are perfectly fair.17 We have not divided by the number of
foils. The consequence is that false alarms are more probable than hits,
and this can be expected to decrease d’, but as long as one recognises
that it is comparisons across d’ and c that are important, not absolute
levels, it should make little difference. Judging absolute levels might lead
one to believe that participants in this study had poor discrimination in
some conditions (indeed, d’<0 in some conditions), but that is not a real
reflection of ability).
Our analyses are generally based on these SDT measures, rather than
on raw hit, false Alarm, or other untransformed accuracy measures.
Figure 2 shows raw measures for the information of interested readers.
We conducted a four-way linear mixed model analysis of the experimental
data, that is stimulus group (black vs. white) X line-up face view (frontal
vs. three-quarter) X task condition (delayed matching vs. recognition)
X participant group (black vs. white), using the package LME418, within
R19. ‘Stimulus group’ and ‘task condition’ were within-participant
factors. The analysis of variance revealed several significant effects, as
shown in Table 1.
At the level of main effects, we observed differences in d’ across
‘line-up face view’, and for ‘task condition’ (p<0.05). We found two-
way interactions for both ‘task condition and stimulus group’, and for
‘stimulus group and participant group’. Finally, we found three-way
interactions for ‘participant group, stimulus group, and task condition’,
as well as for ‘stimulus group, task condition and line-up face view’.
The most important findings from the analysis for our concerns here are
the classic two-way interaction of ‘stimulus group and participant group’
(the OGB effect), and the three-way interaction of ‘stimulus group,
Face Science: Immediate and delayed own group recognition bias
Page 3 of 7
Repeat 16 x (8 Black and 8 White)
Repeat 32x (16 Black and 16 White)
Exposure
5s
→
Delay
±
.05s
→
Delayed matching test
(stimulus not in view)
→
Distractor
task
300s
→
Recognition test
(not timed)
→
Debriefing
Frontal view, TP or TA
Frontal view, TP or TA
OR
OR
¾ view, TP or TA
¾ view, TP or TA
Figure 1: Experimental procedure, showing timeline and other details. For display purposes, faces are synthetic, i.e. not of real people. Assignment to target
present (TP) or target absent (TA), and to frontal or three-quarter view, was random. Faces at exposure were casual or smiling, and neutral at
delayed matching and/or recognition.
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participant group, and task condition’. The latter effect is par ticularly
important to our enquiry – it tells us that the OGB effect in the present
sample differs between the two types of task condition (delayed
matching and recognition).
Table 1: Mixed linear model ANOVA on discrimination (d’), and criterion
(c), by participant group, stimulus group, task condition
(delayed match vs recognition), and view (frontal vs three-
quarter). Only fixed effects are shown; for random effects ICC
= 0.49 for d’, and 0.48 for c. β coefficients are standardised.
All df1 = 1, and df2 = 60.
Discrimination (d’) Criterion (c)
Effect F p-value | β | F p-value | β |
Participant group 0.03 0.869 0.87 3.40 0.070 0.53
Stimulus group 0.01 0.905 0.55 0.50 0.484 0.54
Task condition 34.37 0.001 0.93 12.58 0.001 0.00
View 11.50 0.001 0.89 2.73 0.104 0.10
Participant : Stimulus 26.29 0.001 1.08 5.70 0.020 1.25
Participant : Task 0.20 0.653 0.29 0.01 0.912 0.81
Stimulus : Task 5.05 0.028 0.30 0.00 0.958 0.72
Participant : View 2.38 0.128 0.49 0.37 0.546 0.81
Stimulus : View 0.24 0.627 0.53 0.61 0.439 0.40
Task : View 9.42 0.003 0.28 0.03 0.871 0.51
Participant :
Stimulus : Task 4.10 0.047 0.41 3.91 0.053 1.19
Participant :
Stimulus : View 0.12 0.727 0.36 0.89 0.350 0.87
Participant : Task :
View 0.89 0.349 0.08 0.51 0.476 0.86
Stimulus : Task : View 2.74 0.103 0.76 0.45 0.506 0.74
Participant : Stimulus :
Task : View 0.53 0.467 0.47 2.09 0.154 1.01
delayed match recognition
Hits False_alarms
BlackSA WhiteSA BlackSA WhiteSA
1.5
2.0
2.5
3.0
3.5
1.5
2.0
2.5
3.0
3.5
Stimulus Group
Total (out of 4)
Black − South African White − South African
Figure 2: Hits and false alarms as a function of stimulus group, race of
respondent, and task type. Error bars are standard errors of
the mean.
A classic bias in face recognition ability is for own group members
to recognise faces belonging to their own group with greater facility
than members of other groups. Megreya et al.12 have argued though
that this facility is also present when participants are asked to match
faces to arrays: own-group faces are matched more accurately than
out-group faces. This raises the question of whether there is a retrieval
component at all in the own-group bias in face recognition – the effect
could be entirely due to poorer encoding of out-group faces. In the
present experiment, we observed the classic crossover interaction
of stimulus and participant group, on d’, as shown in Figure 3. White
participants were better at delayed matching and recognising white
faces (mean d’=0.52, SD=0.95) than they were at delayed matching
and recognising black faces (mean d’=0.02, SD=0.89), and black
participants were better at delayed matching and recognising black
faces (mean d’=0.56, SD=1.22) than they were at delayed matching
and recognising white faces (mean d’=0.04, SD=1.12). A set of linear
contrasts showed that white South Africans performed better, overall,
with white South African faces than with black South African faces
(t(60)=3.54, SE=0.14, p<0.001, Cohen’s d=0.52. Cohen’s d was
estimated by dividing the mean difference by the square root of the sum
of the variances of the random, and residual effects20), and that black
South Africans performed better with black South African faces than with
white South African faces (t(60)=3.71, SE=0.14, p<0.001, Cohen’s
d=0.54). As we were interested in testing whether viewing faces in
frontal vs three-quarter view would impact the OGB, it is wor th noting
that the ‘view’ factor was not implicated in any interaction involving either
participant group or stimulus group, but it was involved in a significant
interaction with task condition, showing that participants found it difficult
to match or recognise faces across frontal and three-quarter views, but
this effect was greater for the delayed matching condition than for the
recognition condition (∆d’=0.89 vs 0.34).
dprime criterion
BlackSA WhiteSA BlackSA WhiteSA
−0.25
0.00
0.25
0.50
0.75
Stimulus Group
Black − South African White − South African
Figure 3: Own group bias in discrimination (d’) and criterion (c),
collapsed over delayed matching and recognition tasks. Error
bars are standard errors of the mean. Both interactions shown
here are statistically significant.
Most pertinent to our concerns was the three-way interaction of
participant group, stimulus group, and task condition – or in other
words, the two-way interaction of participant group and stimulus group
(the OGB effect) considered as a function of which task was being
completed. As Table 1 shows, this effect was significant, and Figure
4 shows the effect graphically. Follow-up contrasts showed that, in
the delayed matching task, both groups of participants were better at
matching their own-group faces: black South Africans were better at
matching black South African faces than white South African faces
(Mblack-black=0.98 vs Mblack-white=0.1, SDblack-black=1.02 vs SDblack-white=1.35,
t(118)=4.78, p<0.001, Cohen’s d=0.92), and white South Africans were
better at matching white South African faces than black South African faces
(Mwhite-white=0.79 vs Mwhite-black=0.31, SDblack-black=0.93 vs SDblack-white=0.88,
t(118)=2.57. p<0.012, Cohen’s d=0.50).
Face Science: Immediate and delayed own group recognition bias
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Figure 4: Own group bias in discrimination (d’) and criterion (c), across
delayed matching and recognition tasks. Error bars are
standard errors of the mean. At matching, both black and white
participants show the OGB in d’, but only the white par ticipants
show the OGB at recognition. The interactions between
stimulus group and participant group are not significant.
In the recognition task, however, white South Africans showed the OGB
(Mwhite-white=0.25 vs Mwhite-black= -0.27, SDwhite-white=0.91 vs SDwhite-black=0.82,
t(118)=2.8, p<0.007, Cohen’s d=0.54), whereas black South Africans
did not (Mblack-black=0.14 vs Mblack-white= -0.01, SDblack-black=1.26 vs SDblack-
white=0.87, t(118)=0.82. p<0.416, Cohen’s d=0.16).
In addition to d’, we computed criterion for each participant in the experiment.
The analysis of criterion as the dependent variable for the full four-way
design produced a significant two-way interaction between participant
group and stimulus group (F(1, 60)=5.70, p<0.02). This interaction
shows that white and black participants were both more conservative
when considering faces of their own group than when considering faces
of the other group (Mwhite-white= -0.07 vs Mwhite-black= -0.21, SDwhite-white=0.47 vs
SDwhite-black=0.44, and Mblack-black= -0.24 vs Mblack-white=-0.32, SDblack-black=0.45
vs SDblack-white=0.56). The three-way interaction between participant group,
stimulus group, and task condition was narrowly not significant (F(1,
60)=3.91, p<0.054). We show the marginally non-significant three-way
interaction in Figure 4, where it seems that (1) there is a typical OGB for
criterion present, and that (2) the OGB may be apparent in the delayed
matching task, but not in the recognition task.
Thus far the results have shown a clear crossover OGB in the delayed
matching task, as well as an asymmetrical OGB in the recognition task.
It is not clear that the OGB in the recognition task can be said to be
independent of that in the delayed matching task – that is, it seems
evident that faces that are not matched or recognised accurately in the
delayed matching task will not be recognised accurately in the later
task either. Failure in the matching task may also be an indication of
encoding failure. In order to test the point that Megreya et al. make about
the possible entire dependency of the OGB on encoding processes,
we reduced our data set to only those faces that had been accurately
matched in the delayed matching phase and re-analysed the recognition
data for just those faces. The idea here was to choose faces that we
were confident had been encoded successfully.
One implication of selecting only faces that were correctly matched in the
delayed matching task, is that the OGB shown in the delayed matching
task was effectively controlled for – as all faces were correctly matched,
there could be no bias. This reduction left us with 78% of our original
participants (selecting those who made at least one correct decision at
delayed matching), but as some participants per formed better than others
in the delayed matching task, this resulted in an uneven distribution of
stimuli across conditions. Thus, to take the extreme conditions, whereas
86% of the original stimuli were taken into account for black participants
viewing white South African faces in TP conditions, 46% of stimuli were
taken into account for white participants viewing black South African
faces in TA conditions. This ‘attrition’ undoubtedly affected the potential
power of analyses of recognition of stimuli that had been successfully
matched in the delayed matching task.
A linear mixed model testing discrimination (d’) for faces correctly
matched in the delayed matching task showed a non-significant
effect for the key two-way interaction of interest (between participant
group and stimulus group), that was significant in the earlier model
(F(1, 49.35)=2.80, p<0.104). Although the interaction effect was
not significant, it seemed appropriate to us to follow up with focused
contrasts directly exploring a potential OGB, and these showed that, in the
recognition task, testing recognition of only faces successfully matched
in the delayed matching task, neither white nor black South Africans
were better at their own-group faces, although this was narrowly not the
case for white South Africans (for black South Africans: Mblack-black=0.38
vs Mblack-white=0.29, SDblack-black=0.59 vs SDblack-white=0.46, t(64.8)=0.18,
p<0.86, Cohen’s d=0.18, and for white South Africans: Mwhite-white=0.33
vs Mwhite-black=0.09, SDwhite-white=0.43 vs SDwhite-black=0.47, t(43.5)=1.94.
p<0.059, Cohen’s d=0.48). While the asymmetrical OGB seen in the
recognition test using all stimuli was not technically significant in the
recognition test using only faces successfully encoded (matched in
the delayed matching task), it was very close to being so. Of course,
this analysis is based of necessity on a smaller sample, and has less
statistical power, so it is not strong evidence that controlling for the OGB
in the matched delay task (our operationalisation of encoding) eliminates
the OGB at recognition. We also conducted a mixed linear model analysis
on criterion scores, but did not find any significant results, or any
suggestion of an effect (all p>0.48).
Discussion
The OGB in face recognition is a well-established phenomenon, with
serious consequences in applied contexts, especially when recognition
is treated as person identification, as one finds in law enforcement
procedures. Despite often being the subject of empirical and theoretical
investigation, not much is known about the cognitive processes
underlying the phenomenon. One important question concerns whether
the OGB is an encoding or a recognition phenomenon, and we have
brought results from an empirical study to bear on this question, or at
least on the micro-chronology of the OGB.
Black and white South African participants in our study were asked to
match black or white target faces to corresponding images in arrays of
same group faces immediately after viewing them. They were then asked,
after an intervening period in which they completed a distractor task, to
recognise the target faces from memory, in the same arrays. Our results
show a strong OGB in discrimination accuracy for both black and white
participants at the delayed matching phase of the study, but only white
participants showed the bias at the recognition memor y phase of the study.
We found similar results for the measure of response criterion that we
computed, although the result for the important interaction of par ticipant
group, stimulus group, and task condition was narrowly not significant. Our
finding of an asymmetry in the delayed recognition task for discrimination,
rather than in the delayed matching task, is partly novel, and par tly in line
with extant research. Most studies that have been conducted on the OGB
(for race) in South Africa have reported an asymmetric OGB at recognition.
Usually, white participants show a strong OGB, whereas black participants
rarely show an OGB at all (but see Wittwer et al.’s Study 621 for an example
of a crossover), and sometimes show an inverse OGB, recognising white
faces with more accuracy than they do black faces22). Some authors,
including ourselves, have ascribed this to the way in which the social and
political context have structured intergroup contact in South Africa (and
other countries with similar intergroup histories), and therefore perceptual
contact. It is not easy, though, to explain the finding of a crossover OGB
among black participants at encoding (the delayed matching task) that fails
to materialise in the recognition test. One possibility is that performance
on the recognition task bottomed out, approaching basement level, but
this does not seem to have occurred differentially for black vs white
participants, and is not a convincing explanation. It is possible that it could
reflect a real difference in memory consolidation of out-group faces by
black participants. What does seem clear to us is that the argument that
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the typical asymmetric OGB between majority and minority groups is due
to socio-political factors – e.g. that social and political inequality might
impact perceptual learning of out-group faces – is made more difficult to
sustain by our results. If we had considered only data from the recognition
task we might well have reported another instance of an asymmetric
OGB, but in looking at the encoding/delayed matching task, we noted a
crossover OGB. A source of evidence that is sometimes marshalled in
an attempt to understand the OGB as it manifests in particular contexts
is the perceptual contact history of participants. Methods of assessing
past perceptual history generally rely on self-repor ting, and in the meta-
analysis reported in Meissner and Brigham1 were only poorly correlated
with the OGB (r=0.13). We did not collect self-reported data on perceptual
contact history, but do not believe it would have shed any light on the
nature of the OGB in our study, given its poor predictive record reported
in Meissner and Brigham1. We thus do not have a good sense of why we
observed an OGB at encoding but not at recognition for black participants.
An anonymous reviewer has suggested that it could be a task-dependent
difference, possibly interacting with social-motivational factors, which
we think is possible. On the more general point of why there is often an
asymmetric pattern for the OGB, with disadvantaged groups showing a
weaker or non-existent OGB when compared to advantaged groups,
we think that Malpass’23 notion of a ‘social interaction utility’ is useful:
members of the disadvantaged group have a positive utility associated
with interaction and person recognition (often being economically
dependent on the advantaged group, it is important to interact with, and
recognise members of that group), whereas members of the advantaged
group have a negative utility (there is no clear advantage to interaction and
recognition).
An important question in research on the OGB concerns the degree to
which the OGB is a function of encoding, or of recognition. The use of
delayed matching and recognition tasks allowed this to be addressed in
our study. We conducted a second analysis on images correctly matched
(therefore showing no OGB), and although we did not find an OGB for
these images at recognition using a statistical significance test as the
criterion, the size of the effect we observed in this task (d=0.48) was
very similar to that observed in the recognition task (d=0.52). Our results
likely have equivocal bearing in the end on whether the OGB is an encoding
phenomenon, rather than a recognition memory phenomenon. What is
most interesting about our findings, though, and which has nothing to do
with the final task we used, is that we observed a strong crossover OGB
at delayed matching, which is not at all usual in South African studies,
and after a brief delay of 5 minutes, the OGB manifested in a recognition
memory task as an asymmetrical effect, which is typically the form other
studies in South Africa have previously reported.
The claim that the OGB is an encoding phenomenon makes sense
from a perceptual learning viewpoint, especially in line with Valentine’s
multidimensional face space model24, and his explanation of the OGB in
terms of the model25: the dimensions available to represent out-group
faces are fewer, and less well developed than for in-group faces, and
encoding of out-group faces will be less well differentiated from other
exemplars of that group. Of course, our method for separating encoding
and recognition processes is admittedly ad hoc: we tested recognition
of faces that had been correctly matched, thus attempting to control
encoding processes. We did not directly show that own-group faces were
better encoded than out-group faces, although in demonstrating an OGB
at encoding we think that differences were implicitly demonstrated. Face
encodings are complex patterns of electrical activation across multiple
brain regions, and not directly accessible, although there are some clues to
the neural underpinnings of the OGB and its connections to socio-affective
processes.26 Some researchers have shown differential event-related
potentials to own- and other-group faces27, with a potentially special role
for the P200 visual component of the event-related potentials28. These
investigations suggest that we are making progress toward a better
understanding of the brain mechanisms underlying the OGB.
Apart from the obvious limitation of not being able to assess encoding
proficiency directly, there are other limits to what we are able to conclude
from our study. An important methodological limitation may be our
method of checking that a stimulus had been encoded, and the knock-on
effect of this for the accuracy of our recognition test. As a reminder, we
assessed encoding with a delayed matching task: participants attempted
a match immediately after viewing a target stimulus. After a fur ther delay
of 5 minutes, we assessed recognition memory by asking participants
to choose the target image from a line-up, which either contained or
did not contain the target. Our recognition test could thus be said to
have exposed participants to the target stimulus twice, and this taints
the comparison between encoding and recognition in which we are
interested. In other words, although the delayed matching task tests
memory for a stimulus seen once, the recognition task tests memory for
a stimulus seen once on its own, and once within an array. It is important
to note that participants were not told the position of the target in the
array in the delayed matching task, and that the photographs of the target
changed from the original presentation (frontal casual/smiling view) to
that in the line-up arrays used in the delayed matching and recognition
tasks (three-quarter profile, or neutral/passpor t style view). It is still
possible that there was some strengthening of the memory assessed
in the recognition memory task, but our results show that memory
performance decreased considerably between the delayed matching
task and the recognition memory task (see Figures 3 and 4), so it does
not seem to have counteracted that decrease, if at all. It would also have
been unlikely to affect our white and black participants differentially, and
because our key effect of interest was the nature of the OGB at the two
time points, it does not appear to us to be confounded with the two- or
three-way interactions in which we were interested.
Whereas we studied matching and recognition of two-dimensional
images of faces, face recognition in real contexts is of three-dimensional
surfaces that change locally, and globally, over time, in interaction
with perceivers. We also used a short delay period between encoding
(matching), and recognition, unlike many face recognition tasks ‘in the
wild’. However, it is likely that the OGB would be higher in more naturalistic
contexts, because there are known additional biases across groups for
emotion29 and age30, among other biases, and we do not see a reason
to believe that this would differentially affect encoding and recognition
deficits already present. There is an extensive and convincing argument
in support of the view that one expects real witnesses to per form far
worse than participants in laboratories.31
In conclusion, although we have not reported clear evidence in favour of
the view that the OGB in face recognition is likely a consequence of poor
encoding of other group faces, we have identified an interesting, rapid
change in the manifestation of the OGB. Whilst it took a strong crossover
form at encoding (delayed matching), it reverted to an asymmetric
form after a brief delay. There are some applied implications of this
result, although the evidence is at this stage too slight to base strong
recommendations on. If the OGB is a failure of encoding then there
may be little justification for devising methods that focus on improving
recall or recognition of out-group faces, and it may be better to develop
training programmes that focus on encoding processes. There is some
evidence that in-group members focus on different face regions when
encoding out-group faces than out-group members do32, although it is
not yet clear that reshaping cross-group encoding will work33. On the
other hand, if the OGB is a failure of retrieval, some methods such as
the Cognitive Interview34 and the Person Description Interview35 may be
useful interventions when recovering information from memory about
face appearance and identity – although it should be borne in mind that
these methods are typically good for improving recall memory, but not
recognition memory36. It could also be that the OGB is a function of
both encoding and retrieval processes, and we are presently considering
ways of adapting our procedure to accommodate this possibility.
Ethical considerations
Ethical clearance to conduct the study was granted by the Department
of Psychology Ethics Committee of the University of Cape Town. All
participation was voluntary and informed consent was obtained before
study commencement.
Competing interests
We have no competing interests to declare.
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Authors’ contributions
C.T.: Conceptualisation, method, data analysis, data curation,
writing, project leadership, funding acquisition, student supervision.
A.M.M.: Conceptualisation, method, writing, project leadership. A.N.:
Conceptualisation, method, data collection, data analysis, writing – revision.
K.K.: Conceptualisation, method, data collection, writing – revision.
References
1. Meissner CA, Brigham JC. Thirty years of investigating the own-race bias in
memory for faces. Psychol Public Policy Law. 2001;7(1):3–35. https://doi.
org/10.1037/1076-8971.7.1.3
2. Walker PM, Hewstone M. A perceptual discrimination investigation of the own-
race effect and intergroup experience. Appl Cogn Psychol. 2006;20(4):461–
475. https://doi.org/10.1002/acp.1191
3. Malpass RS, Kravitz J. Recognition for faces of own and other race. J Pers
Soc Psychol. 1969;13(4):330–334. https://doi.org/10.1037/h0028434
4. Innocence Project. How eyewitness misidentification can send innocent people
to prison [webpage on the Internet]. c2020 [cited 2023 Feb 03]. Available from:
https://innocenceproject.org/how-eyewitness-misidentification-can-send-
innocent-people-to-prison/
5. Rhodes G. Face recognition. In: Reisberg D, editor. The Oxford handbook of
cognitive psychology. New York: Oxford University Press; 2013. p. 46–68.
6. Hugenberg K, Wilson JP, See PE, Young SG. Towards a synthetic model of
own group biases in face memory. Vis Cogn. 2013;21(9–10):1392–1417.
https://doi.org/10.1080/13506285.2013.821429
7. Yaros JL, Salama DA, Delisle D, Larson MS, Miranda BA, Yassa MA. A memory
computational basis for the other-race effect. Sci Rep. 2019;9(1):19399.
https://doi.org/10.1038/s41598-019-55350-0
8. Bornstein BH, Laub CE, Meissner CA, Susa KJ. The cross-race effect:
Resistant to instructions. J Criminol. 2013;2013:745836. https://doi.
org/10.1155/2013/745836
9. Zhou X, Mondloch CJ, Emrich SM. Encoding differences affect the number
and precision of own-race versus other-race faces stored in visual working
memory. Atten Percept Psychophys. 2018;80(3):702–712. https://doi.
org/10.3758/s13414-017-1467-6
10. Stelter M, Degner J. Investigating the other-race effect in working memory. Br
J Psychol. 2018;109(4):777–798. https://doi.org/10.1111/bjop.12304
11. Brown TI, Uncapher MR, Chow TE, Eberhardt JL, Wagner AD. Cognitive
control, attention, and the other race effect in memory. PLoS ONE. 2017;12(3),
e0173579. https://doi.org/10.1371/journal.pone.0173579
12. Megreya AM, White D, Burton AM. The other-race effect does not rely
on memory: Evidence from a matching task. Q J Exp Psychol (Hove).
2011;64(8):1473–1483. https://doi.org/10.1080/17470218.2011.575228
13. Papesh MH, Goldinger SD. Deficits in other-race face recognition: No
evidence for encoding-based effects. Can J Exp Psychol. 2009;63(4):253–
262. https://doi.org/10.1037/a0015802
14. Stelter M, Rommel M, Degner J. (Eye-) tracking the other-race effect:
Comparison of eye movements during encoding and recognition of ingroup
faces with proximal and distant outgroup faces. Soc Cogn. 2021;39(3):366–
395. https://doi.org/10.1521/soco.2021.39.3.366
15. Bruce V. Changing faces: Visual and non-visual coding processes
in face recognition. Br J Psychol. 1982;73(1):105–116. https://doi.
org/10.1111/j.2044-8295.1982.tb01795.x
16. Wells GL, Rydell SM, Seelau EP. The selection of distractors for eyewitness
lineups. J Appl Psychol. 1993; 78(5):835–844. https://doi.org/10.1037/0021-
9010.78.5.835
17. Fitzgerald R, Tredoux CG, Juncu S. Estimation of eyewitness identification error
rates in fair and biased lineups. Paper presented at: American Psychology and
Law Society Annual Meeting; 2022 Mar 16–20; Denver, CO, USA.
18. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models
using lme4. J Stat Softw. 2015;67(1):1–48. https://doi.org/10.18637/jss.
v067.i01
19. R Core Team. R: A language and environment for statistical computing
[software on the Internet]. Vienna: R project; 2018. Available from: https://
www.R-project.org /
20. Westfall J, Kenny DA, Judd CM. Statistical power and optimal design in
experiments in which samples of participants respond to samples of stimuli.
J Exp Psychol Gen. 2014;143(5):2020–2045. https://doi.org/10.1037/
xge0000014
21. Wittwer T. The own group bias in face processing: The effect of training on
recognition performance [disser tation]. Cape Town: University of Cape Town;
2020.
22. Wright DB, Boyd CE, Tredoux CG. Inter-racial contact and the own-race
bias for face recognition in South Africa and England. Appl Cogn Psychol.
2003;17(3):365–373. https://doi.org/10.1002/acp.898
23. Malpass RS. An excursion into utilitarian analysis. Behav Sci Res.
1990;24(1–4):1–15. https://doi.org/10.1177/106939719002400101
24. Valentine T. Face-space models of face recognition. In: Wenger MJ, Townsend
JT, editors. Computational, geometric, and process perspectives on facial
cognition: Contexts and challenges. New York: Lawrence Erlbaum Associates
Publishers; 2001. p. 83–113.
25. Valentine T. A unified account of the effects of distinctiveness, inversion, and
race in face recognition. Q J Exp Psychol A. 1991;43(2):161–204. https://doi.
org/10.1080/14640749108400966
26. Natu V, O’Toole AJ. Neural perspectives on the other-race effect. Vis Cogn.
2013;21(9–10):1081–1095. https://doi.org/10.1080/13506285.2013.811455
27. Herzmann G, Willenbockel V, Tanaka JW, Curran T. The neural correlates
of memory encoding and recognition for own-race and other-race faces.
Neuropsychologia. 2011;49(11):3103–3115. https://doi.org/10.1016/j.
neuropsychologia.2011.07.019
28. Stahl J, Wiese H, Schweinberger SR. Learning task affects ERP-correlates
of the own-race bias, but not recognition memory performance.
Neuropsychologia. 2010;48(7):2027–2040. https://doi.org/10.1016/j.
neuropsychologia.2010.03.024
29. Halberstadt AG, Cooke AN, Garner PW, Hughes SA, Oertwig D, Neupert SD.
Racialized emotion recognition accuracy and anger bias of children’s faces.
Emotion. 2022;22(3):403–417. https://doi.org/10.1037/emo0000756
30. Dehon H, Brédart S. An ‘other-race’ effect in age estimation from faces.
Perception. 2001;30(9):1107–1113. https://doi.org/10.1068/p3122
31. Davis D, Loftus EF. Eyewitness science in the 21st century. In Wixted J, editor.
Stevens’ handbook of experimental psychology and cognitive neuroscience.
4th ed. Hoboken, NJ: John Wiley & Sons; 2018. p. 1–38. https://doi.
org/10.1002/9781119170174.epcn116
32. Hills PJ, Lewis MB. Reducing the own-race bias in face recognition by
shifting attention. Q J Exp Psychol (Hove). 2006;59(6):996–1002. https://
doi.org/10.1080/17470210600654750
33. Wittwer T, Tredoux CG, Py J, Paubel PV. Training participants to focus on
critical facial features does not decrease own-group bias. Front Psychol.
2019;10:2081. https://doi.org/10.3389/fpsyg.2019.02081
34. Fisher RP, Geiselman RE. Memory enhancing techniques for investigative
interviewing: The cognitive interview. Springfield, IL: Charles C Thomas
Publisher; 1992.
35. Demarchi S, Py J. A method to enhance person description: A field study. In: Bull
R, Valentine T, Williamson T, editors. Handbook of psychology of investigative
interviewing: Current developments and future directions. Chichester: Wiley-
Blackwell; 2009. p. 241–256. https://doi.org/10.1002/9780470747599.ch14
36. Memon A, Meissner CA, Fraser J. The cognitive interview: A meta-analytic
review and study space analysis of the past 25 years. Psychol Public Policy
Law. 2010;16(4):340–372. https://doi.org/10.1037/a0020518
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