Are people biased to individuals in their own age group

~ 3 pagesRead a classic or important paper having to do with memoryArticle is about how there is bias with recognizing people’s faces closer to your own age than older agesarticle attached below where one page of paper requires one page summary of experiment in article and the rest of paper includes responseSummarize the paper (1 page or less)Central question(s)Overview of methodKey resultsResponse – may include:Criticisms or limitations of the paperAdditional questions raised by the paper. How might those questions be addressed?Implications of the paper
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Evidence for a contact-based explanation of
the own-age bias in face recognition
Article in Psychonomic Bulletin & Review · April 2009
DOI: 10.3758/PBR.16.2.264 · Source: PubMed
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Virginia Harrison
Graham J Hole
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University of Sussex
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Psychonomic Bulletin & Review
2009, 16 (2), 264-269
doi:10.3758/PBR.16.2.264
Evidence for a contact-based explanation
of the own-age bias in face recognition
VIRGINIA HARRISON AND GRAHAM J. HOLE
University of Sussex, Brighton, England
Previous research has shown that we recognize faces similar in age to ourselves better than older or
younger faces (e.g., Wright & Stroud, 2002). This study investigated whether this own-age bias could be explained by the contact hypothesis used to account for the own-race bias (see Meissner & Brigham, 2001). If
the own-age bias stems from increased exposure to people of our own age, it should be reduced or absent in
those with higher exposure to other age groups. Participants were asked to remember facial photographs of
8- to 11- and 20- to 25-year-olds. Undergraduates were faster and more accurate at recognizing faces of their
own age. However, trainee teachers showed no such own-age bias; they recognized the children’s faces more
quickly than own-age faces and with comparable accuracy. These results support a contact-based explanation
of the own-age bias.
Previous research suggests that young adults are better at
recognizing faces and make more accurate eyewitness identifications than do older adults (e.g., Adams-Price, 1992;
Searcy, Bartlett, & Memon, 1999; Searcy, Bartlett, Memon,
& Swanson, 2001; Yarmey, 1993). Although cognitive performance deteriorates with age (see the review in Hedden
& Gabrieli, 2004), some of this apparent difference in facial
memory performance might stem from the stimuli that are
used: Typically, young adults (undergraduates) and older
adults are compared on their ability to recognize young
adult faces. Younger adults’ superiority might therefore
arise from an own-age face-processing bias.
Relatively little research has been performed on this
topic, but the few studies that exist do appear to provide
evidence for an own-age bias in face recognition. Wright
and Stroud (2002) showed young and middle-aged adults
videos of a “crime” in which the perpetrator was either
similar or dissimilar in age to themselves. All the participants were more likely to correctly identify the perpetrator from a lineup of people belonging to their own
age group, although further analysis revealed that this
own-age identification advantage was significant only for
the younger adults. Fulton and Bartlett (1991), using a
between-subjects recognition paradigm, found that young
adults were better at recognizing younger adult faces than
older ones, whereas older adults’ performance was similar
for faces of all age groups.
Although these studies showed no own-age bias for
older adult populations, others have done so. Using a paradigm to investigate unconscious transference, Perfect and
Harris (2003) found that older adults were significantly
more accurate at identifying perpetrators of a similar age
to themselves than those of a younger age. Older adults
were also more likely than younger adults to misidentify
a bystander as the perpetrator of a crime when the lineup
consisted of younger adults. This difference was eliminated when the lineup comprised older adults.
In addition to the forensic-style methodologies involving lineup procedures, the own-age bias has been investigated with more traditional recognition memory tasks. In
a number of studies, younger and older adults have been
shown photographs of younger and older faces, and a significant face age age group interaction indicative of
the own-age bias has been found. Of these studies, some
showed evidence of a full crossover effect (e.g., Anastasi & Rhodes, 2006; Bäckman, 1991; Perfect & Moon,
2005), some showed enhanced performance with own-age
faces only in younger adults (e.g., Bartlett & Leslie, 1986;
Lindholm, 2005; Mason, 1986; Wiese, Schweinberger, &
Hansen, 2008), and others reported it present only in older
adults (e.g., Lamont, Stewart-Williams, & Podd, 2005).
An own-age bias for children (Anastasi & Rhodes, 2005;
Lindholm, 2005) has also been reported. Thus, previous
research has demonstrated the existence of an own-age
bias, but its exact nature and the underlying mechanisms
that produce it remain unclear.
To explain the own-age bias, it may be useful to consider the wealth of existing research on the own-race bias.1
It is well documented that people are more accurate at
recognizing faces of their own race than those of a different, less familiar race (see the review in Meissner &
Brigham, 2001).
The most popular explanation for this is the contact
hypothesis. This proposes that people become experts
at differentiating between faces of their own race due to
increased contact with them, as compared with faces of
V. Harrison, vmh20@sussex.ac.uk
© 2009 The Psychonomic Society, Inc.
264
OWN-AGE BIAS
other races (e.g., Brigham & Malpass, 1985; Chiroro &
Valentine, 1995). A number of studies have shown a significant positive relationship between memory for faces
of individuals from a certain race and the amount of contact the participant has had with that race (e.g., Slone,
Brigham, & Meissner, 2000; Wright, Boyd, & Tredoux,
2003). Meissner and Brigham’s (2001) meta-analysis also
showed a significant, although small (accounting for approximately 2% of the variability in the data), relationship
between other-race discrimination and self-report measures of interracial contact. However, it remains unclear
precisely what aspect of contact is important for the development of an own-race bias to occur.
One class of explanations proposes that increased
contact with a race (usually one’s own) somehow produces improved perceptual processing for that particular facial group. For example, Rhodes, Tan, Brake, and
Taylor (1989) suggested that exposure to own-race faces
enhances people’s ability to extract the configural information that is at the heart of expert face recognition
(Diamond & Carey, 1986). This explanation has been
supported by demonstrations that other-race faces are
processed less holistically (and hence, perhaps less efficiently) than own-race faces (e.g., Michel, Rossion, Han,
Chung, & Caldara, 2006; Rhodes et al., 1989; Tanaka,
Kiefer, & Bukach, 2004).
Another account based on perceptual processing is Valentine’s (1991) multidimensional face space model. This
suggests that faces are represented as points in a multidimensional space, whose dimensions consist of the facial
characteristics that will best serve to discriminate between
faces. It is thought that the dimensions develop in accordance with the individual’s experience of faces. The ownrace bias is explained by suggesting that a lack of exposure
to other-race faces means that the dimensions necessary
for individuating them are less well represented than those
needed to distinguish between own-race faces. Both of
these theories assume that perceptual face-processing
mechanisms become better tuned for the types of faces
with which we have a greater amount of experience.
An alternative, although not mutually exclusive, class
of explanations for the own-race bias focuses more on the
social psychology of person recognition. These theories
suggest that we automatically categorize faces according to whether or not they belong to our own in-group
(e.g., our own race). This has consequences for how we
subsequently process the face (e.g., Levin, 1996, 2000;
Sporer, 2001). For example, Sporer’s in-group/out-group
model (IOM) suggests that in-group faces are encoded in
an automatic, configural manner (typical of expert face
processing), whereas out-group faces automatically trigger a categorization of that person as belonging to an outgroup. This categorization leads to the faces being cognitively disregarded, resulting in a reduced, less efficient
processing strategy and associated recognition deficits.
Contact has a role to play in this process only insofar as
it might help define the inclusion criteria of in-group and
out-groups.2 This type of explanation could clearly be
extended to explain recognition deficits for out-groups
other than race, such as age and gender.
265
Yet another possibility is that contact per se does not
affect face processing directly; instead, contact may reflect or drive the degree of interest a person has in faces
of a particular kind and the resultant amount of attention
allocated to them. It is this interest/attention that may be
the important factor in determining how expert we are at
processing faces of a particular category (e.g., own-age
faces), and it may depend on the incentives for doing so
(e.g., social rewards or punishments). This theory is admittedly speculative; however, Wright et al. (2003) found that
although white and black university students had similar
opportunities for experience with the opposite race, only
the white students showed an own-race bias. Perhaps, due
to the asymmetrical power relationships within South African society, the black students had an incentive for trying
to recognize white faces, but the opposite was not true.
In the present study, we investigated the role of contact
in the context of the own-age bias. Two groups were compared in terms of their ability to recognize children’s faces
and faces of their own age: trainee teachers, who had high
occupational exposure to primary school children, together
with a strong interest in them; and similarly aged controls,
who had little exposure to children (or interest in them). By
analogy with the explanations proposed for the own-race
bias, we can make several competing predictions.
1. Improved perceptual processing explanations (e.g.,
Rhodes et al., 1989; Valentine, 1991) might predict that
teachers and controls will perform similarly with both
children’s faces and adult faces. During their own development, both groups should presumably have had sufficient experience to become face experts with children’s
and adults’ faces alike. Note that this prediction would be
true only if one assumes that exposure to a certain class
of face has enduring effects. If recent exposure to faces
carries more weight than past experience, perceptual expertise explanations would predict that the controls should
perform as well with adult faces as the trainee teachers did
but worse with children’s faces than the teachers did. We
shall return to this point in the Discussion section.
2. Social categorization explanations (e.g., Levin, 1996,
2000; Sporer, 2001) predict that teachers and controls
will be similar in performance; however, in this case, both
groups should show better recognition for own-age faces,
because the two groups of participants are the same age
and children should constitute an out-group in both cases.
3. A third explanation is in terms of motivation to attend to faces (e.g., Wright et al., 2003). This would predict
that teachers and controls will be similar in performance
with adult faces, but not with children’s faces: Because
of trainee teachers’ increased interest in, and attention to,
children, they should be better than controls at recognizing children’s faces.
METHOD
Design
A mixed design was used, with one between-subjects variable
(group; two levels: trainee teachers and controls) and one repeated
measures variable (age of person in photograph; two levels: child
[8–11 years old] and own age [19–30 years old]). Measures of latency and accuracy (d ?) were calculated.
266
HARRISON AND HOLE
Participants
There were 66 participants in total: 33 in the trainee teacher
group (mean age, 24.21 years; SD, 2.46; range, 21–30 years) and
33 controls (mean age, 22.94 years; SD, 2.94; range, 19–30 years).
To ensure that contact was successfully operationalized into highand low-contact groups, the amount of occupational contact of the
participants with 8- to 11-year-old children since leaving school was
recorded. Controls had no contact of this type. Trainee teachers had
a mean contact score of 16.50 months (SD 17.05).
All the participants were University of Sussex students: undergraduates, postgraduates, or trainee teachers (students in a postgraduate Certificate of Education course).
Materials
Digital photographs were taken of 64 Caucasian males. Half were
8–11 years old, and half were 19–30 years old. Two photographs
were taken of each individual, one smiling and the other neutral. All
the photographs were close-up, frontal face images without glasses,
jewelry, facial hair, or other identifying features. Using Adobe
Photoshop, each photograph was converted to grayscale and resized
to 300 350 pixels. The picture’s background and any information
outside of the external face outline was removed (see Figure 1).
To ensure that the faces belonging to both age groups were similarly
distinctive, 18 volunteers (age, 18–30 years old) rated each face on a
5-point scale (1 extremely distinctive, 5 not at all distinctive).
There was no significant difference in the distinctiveness ratings of the
two groups of faces [paired samples t(17) 0.23, p .82, d .05].
Procedure
For the initial learning phase, the participants were shown 32 photographs (16 from each age group) in a random order at a 3-sec rate,
using SuperLab. A fixation cross was displayed in the center of the
screen for 500 msec before each face appeared. The participants
were instructed to remember the faces as best they could, since they
would later be asked to identify them. Following the learning phase,
the participants completed a 3-min filler task (the F–A–S verbal fluency task, in which participants are given 1 min per letter to name as
many words as possible beginning with F, A, or S).
This was followed by the recognition test that consisted of 64
photographs, 32 of which had previously been seen in their alternate pose during the learning phase, and 32 of which were new. The
photographs were counterbalanced for pose and old/new status and
appeared in a different random order for each participant. The participants used the computer keyboard to indicate whether or not they
recognized each face. Each face was preceded by a central fixation
cross for 500 msec. Faces appeared individually, at a rate determined
by the participant’s speed of response. Each face remained on the
screen until either a response was made or 2,500 msec had elapsed.
RESULTS
Since there was no effect of pose type on either accuracy or reaction time (RT), data were collapsed across this
variable for the purpose of analysis.
Accuracy
Estimates of d? were used for analysis, rather than the
percentage of correct responses: d? is a better index of recognition discriminability, since it takes into account false
alarms (false recognition of distractor faces). Table 1 shows
hit (correct identification of target faces) and false alarm
rates. In calculating d?, a flattening constant was used (as
in Wright & Sladden, 2003) so that z scores could be calculated when the hit or false alarm rate was either 0 or 1.
Figure 2 shows the mean d ? scores for both experimental groups for the stimulus conditions (children and
Figure 1. Examples of the facial stimuli used.
own-age photos). Both groups performed at above-chance
levels (d ? 0) throughout.
A two-way mixed ANOVA (two levels of group two
levels of face age) revealed that although there was no
significant main effect of group [F(1,64) 1.24, p .27,
h2p .02] or face age [F(1,64) 0.57, p .45, h2p .01],
there was a significant interaction between these two variables [F(1,64) 7.70, p .01, h2p .11], indicative of
an own-age bias.
Follow-up paired t tests3 demonstrated a significant effect of face age for the controls [t(32) 2.04, p .05,
d .50], showing an own-age bias in terms of accuracy.
In contrast, the trainee teachers showed no such effect
[trainee teachers, t(32) 2.00, p .05, d .27], with a
nonsignificant trend toward more accurately recognizing
children’s faces than faces of their own age group. Further
independent t tests revealed that trainee teachers were significantly more accurate than controls for children’s faces
[t(64) 2.59, p .01, d .64], whereas the two groups
performed similarly with faces of their own age [t(64)
0.62, p .54, d .15].
Latency
To minimize the variability often found in the RT data,
each individual’s performance was examined for every
trial. Any RTs longer than the individual’s mean 2.5
standard deviations were replaced by that participant’s
mean RT (a method discussed in Ratcliff, 1993). In this
way, 2.2% of the values for the trainee teachers and 2.4%
for the control group were replaced. Corrected mean RTs
are shown in Figure 2.
Table 1
Mean Proportion of Hits and False Alarms
and d? Accuracy Scores
Participant
Group
Controls
Trainee teachers
Photograph
Age
Own age
Children
Own age
Children
Hit
Rate
M SD
.85 .10
.78 .11
.82 .12
.85 .09
False
Alarm
Rate
M SD
.16 .10
.16 .11
.14 .07
.13 .08
Accuracy
(d ?)
M
SD
2.27 0.64
1.95 0.64
2.17 0.73
2.35 0.61
OWN-AGE BIAS
1,100
3
2.5
Control
Trainee teacher
1,050
Reaction Time
Accuracy (d )
267
2
1.5
1
1,000
950
900
0.5
850
0
Own Age
Children
Own Age
Children
Figure 2. Mean accuracy scores and reaction times for both groups for the different facial stimuli (bars show
the mean one standard error).
Mean RTs for correct responses were entered into a
mixed 2 2 ANOVA with group and face age as variables of interest. This revealed no significant main effect of group [F(1,64) 1.61, p .21, h2p .02] or face
age [F(1,64) 0.05, p .83, h2p .01], but there was
a significant interaction between these two variables
[F(1,64) 39.44, p .001, h2p .38], indicative of an
own-age bias.
Paired t tests revealed a significant effect of face age for
both experimental groups [trainee teachers, t(32) 4.29,
p .001, d .50; controls, t(32) 4.64, p .001,
d .28]: Controls reacted more quickly for own-age
faces, whereas trainee teachers responded more quickly
to children’s faces. Curiously, further independent t tests
revealed that both groups performed at similar speeds
for children’s faces [t(64) 0.19, p .85, d .05],
but controls were faster at responding to own-age faces
[t(64) 2.71, p .01, d .67]. We have no explanation
for why the controls were faster than the trainee teachers
with own-age faces. However, in terms of the difference
in speed of responding to own- and other-age faces, the results for the control group are consistent with an own-age
bias, whereas those for the trainee teachers are not.
DISCUSSION
Previous research has suggested that we are better at
recognizing faces of our own age group than those of a
different age (e.g., Wright & Stroud, 2002); however, the
reasons for this remain unclear. Inspired by explanations
of the own-race bias, the present study investigated the
role of contact in the own-age bias in face recognition.
Controls exhibited an own-age bias, in both accuracy and
speed. In contrast, trainee teachers (who had high exposure to primary school children) showed no own-age bias;
in fact, they were faster at recognizing children’s faces
than own-age faces and showed a trend toward being more
accurate with children’s faces.
Clearly, contact has a role to play in the own-age bias,
but how exactly does it exert its effects? In the light of
these results, let us reconsider the possib …
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