Applying the Attractor Field Model to Social Cognition:
Perceptual Discrimination is Facilitated but Memory is Impaired for Faces displaying
Evaluatively-Congruent Expressions
Olivier Corneille
Université catholique de Louvain
Kurt Hugenberg
Miami University
&
Timothy Potter
Université catholique de Louvain &
Fonds de la Recherche Scientifique
Manuscript in press, Journal of Personality and Social Psychology
Word count: 12928 (including footnotes)
Running title: Attractor Fields
Authors’ note: We thank Galen Bodenhausen, Amanda Diekman, Rob Goldstone, Allen
McConnell, Caroline Michel, Kirsten Ruys, and Jim Tanaka for their helpful comments on
this research. This work benefited from a grant ARC06/11-337 awarded to the first author
Correspondence concerning this article can be address to Olivier Corneille, Catholic
University of Louvain, Dpt. Psychology, 10, Place Cardinal Mercier, 1348 Louvain-la-Neuve,
Belgium. Email:olivier.corneille@psp.ucl.ac.be, or to Kurt Hugenberg, Dpt Psychology, 220
Psychology Building, Miami University, Oxford, Ohio 45056, Email:hugenbk@muohio.edu.
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1
Abstract
We introduce a new model of mental representation – an Attractor Field Model – to
social cognition. We propose that this model can account for a variety of seemingly unrelated
social cognition phenomena and can allow for novel predictions. Three studies are also
reported whose predictions were derived from the model, showing a perceptual advantage but
a memory disadvantage for faces displaying evaluatively-congruent expressions. In
Experiment 1, White participants in the U.S. completed a same/different perceptual
discrimination task involving morphed pairs of angry-to-happy Black and White faces. Pairs
of faces displaying evaluatively-incongruent expressions (i.e., happy Black; angry White)
were more likely to be labeled as similar to one another, and were less likely to be accurately
discriminated from one another, than faces displaying evaluatively-congruent expressions
(i.e., angry Black; happy White). Experiment 2 replicates this finding in a White Belgian
sample and using a larger set of realistic computer-generated faces. Experiment 2 also shows
that the objective discriminability of stimuli moderates the impact of attractor field effects on
perceptual discrimination accuracy. In Experiment 3, White Belgian participants completed a
recognition task for angry and happy, Black and White faces. Consistent with the Attractor
Field Model, and mirroring the perceptual effect, memory accuracy was this time better for
faces displaying evaluatively-incongruent expressions. We discuss the theoretical and
practical implications of this original set of findings.
Key words: Stereotyping, Prejudice, Emotions, Face perception, Face memory.
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Applying the Attractor Field Model to Social Cognition:
Perceptual Discrimination is Facilitated but Memory is Impaired for Faces displaying
Evaluatively-congruent Expressions
Despite a large social cognitive literature on the effects of stereotypes and prejudice on
person perception, there is a relative lack of research on the truly perceptual elements of
person perception. Indeed, a substantial amount of the literature on person perception
employs lexical stimuli to present information about stereotyped targets. However, few such
stimuli are present in many naturalistic instances of stereotyping to which our theories are
generalized (Quinn & Macrae, 2005). Indeed, social encounters outside of the laboratory
generally involve the processing of perceptual cues about individuals from various gender and
ethnic groups, as well as facial features, expressions, and non-verbal behaviors. Fortunately,
recent advances in technology and methodology have facilitated the generation and study of
well-calibrated visual materials. The most notable of these are faces, and their ongoing study
is clarifying how perceptual details of targets are to be encoded and recollected and how this
perceptual information can shape social inferences and decisions made about individuals.
A new model of mental representation applicable to a wide range of phenomena
recently emerged from these advancements: the Attractor Field Model. Below, we present
this model and explain how it can account for a variety of phenomena reported in the recent
cognitive and social cognitive literatures. Then, we consider how this model, combined with
more classic models of mental representation, can be used to derive novel predictions of
interest in social cognition, intergroup relations, and emotions. Finally, we report three
studies that provide original evidence for asymmetry effects in the perception and memory of
faces that differ in race and expression. Specifically, and consistent with the Attractor Field
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Model, we show that faces displaying evaluatively congruent expressions (i.e., same-race
faces displaying a positive expression; other-race faces displaying a negative expression) are
better discriminated perceptually, but are more poorly recognized, than are faces displaying
evaluatively-incongruent expressions (i.e., same-race faces displaying a negative expression;
other-race faces displaying a positive expression).
An Attractor Field Model of psychological representations
The Attractor Field Model (Tanaka, Giles, Kremen, & Simon, 1998) starts with the
assumption, common in most representational models, that features of objects we perceive in
our environment are normally distributed along a number of psychological dimensions.
Mental representations of objects are thought to be located in multi-dimensional
psychological spaces that involve a higher density of representations (i.e., objects) at their
center than at their periphery. For example, in the case of faces (see for instance Busey, 1998;
Lewis, 2004; Valentine, 1991), if we were to create a two-dimensional psychological space
with nose length and inter-eye distance, faces with moderate values on these features would
be relatively frequent, clustering together at the center of this two-dimensional psychological
face-space. Further away and less densely clustered would be faces that hold a more extreme
value on either of these dimensions (e.g., faces with a large inter-eye gap or a very long nose).
Even farther away from the face-space origin and located in even sparser face-space regions
should be faces that are extreme on both dimensions (e.g., faces with a tiny nose and a huge
inter-eye gap). A recent multi-dimensional study conducted by Potter, Corneille, Ruys and
Rhodes (in press) provides direct evidence for this conceptualization, with atypical (i.e.,
unattractive) faces located farther away from the face-space origin, and situated and in less
tightly clustered areas of the face-space than the more average (i.e., attractive) faces.
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Face-space, attractor fields and perception
What the Attractor Field Model adds to this classic conceptualization of mental
representation is that each object located in a psychological space carves out its own ‘attractor
field’ or ‘attractor basin’ (Tank & Hopfield, 1987) in that psychological space, with atypical
objects (lying at the periphery) carving out larger identification basins than typical objects
(lying at the center). As can be seen in Figure 1, because of the clustering of typical stimuli at
the center of the multi-dimensional space, the attractor basins of typical stimuli are relatively
modest in size. In contrast, because atypical stimuli exist in relatively sparsely populated
areas of the multi-dimensional space, the representations are not in close competition for
space, allowing for larger attractor basins.
These attractor basins are important because they determine which representation is
most strongly activated when presented with a stimulus. For example, when making a
similarity judgment as to whether a particular face is more similar to one representation or
another, the attractor basin in closest proximity to the stimulus’ values in the multidimensional space will ‘attract’ the activation. A consequence of this is that any exemplar
located at equal distance from one atypical and one typical exemplar will be judged more
similar to the atypical than to the typical exemplar. This atypicality bias will occur because
this middling-exemplar will be located nearer to the attractor field of the atypical than that of
the typical exemplar. Due to its greater size, the larger attractor field of that atypical
exemplar attracts activation from a wider swath of multi-dimensional space. These larger
attractor basins have powerful attraction effects on the perceptual system: their stronger
attraction pulls on slightly dissimilar stimuli, distorting them to appear more similar than they
actually are. Conversely, the smaller attractor basins of typical stimuli allow the perceptual
system to make finer grained distinctions than capable in the presence of stronger attractors.
Attractor Fields
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Thus, one clear hypothesis of the attractor field model is that fine-grained perceptual
distinctions will be made more readily and accurately among the densely-clustered typical
exemplars than among their more sparsely-clustered atypical brethren.
Thus, object identification and similarity judgments are not only a function of the
metric distances that exist between two objects in the psychological space. They also depend
on the density of the representations in the surrounding configuration (see also Krumhansl,
1978), with stronger assimilation effects occurring on the atypical side of perceptual continua.
As can be seen in Figure 1, the isolation of atypical stimuli allows for larger attractor basins,
causing stronger assimilation effects for these atypical stimuli. Busey (1998) observed this in
the context of a multidimensional scaling study. He showed that morphed faces that would be
predicted to lie near the center of the space have a tendency to shift away from the face-space
center. Presumably, this effect emerges because perceptions of differences are inflated in
denser regions of the face-space. No such shift was obtained for morphed faces whose
location was predicted to be farther from the center; that is, for faces located in sparser
regions where perceptions of similarities are exacerbated.
Providing direct support for the Attractor Field Model, Tanaka et al. (1998) morphed
two ‘parent’ faces together, one typical and one atypical, into a 50% / 50% blend.
Participants were then shown both of the parent faces, as well as their morphed offspring, and
were asked whether the morphed face appeared more like the typical or atypical parent. Even
though the 50/50 morph was mathematically equidistant from its two parent images,
participants judged the morph to be more similar to the atypical than typical parent. Tanaka
and Corneille (in press) recently extended these findings to the case of objects perception (i.e.,
cars and birds), which suggests the generality of this perceptual phenomena. Just as a
raindrop slides downhill toward the closest basin, so too do percepts slide toward the most
Attractor Fields
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powerful attractors, eliciting more perceived similarity to atypical than toward typical
exemplars, despite in this case, their similarity being mathematically equivalent.
Another consequence of this asymmetry in attractor basin size is that perceptual
discrimination accuracy should be weaker for stimuli lying on the atypical than on the typical
side of a perceptual continuum. Tanaka and Corneille (in press) obtained direct support for
this prediction. Across a series of studies, these authors employed a same/different perceptual
discrimination task, in which pairs of faces lying on a morphing continuum ranging from
typical stimuli (i.e., faces with features commonly observed among faces) to atypical stimuli
(i.e., faces with features less commonly observed among faces) were briefly presented on a
computer screen. Participants were simply required to decide whether the two stimuli were
identical, or were slightly different, from which an index of perceptual discrimination
accuracy can be derived. Despite the fact that the mathematical differences between the pairs
of faces were equivalent in the typical and atypical conditions, perceptual discrimination was
poorer for the faces located on the atypical side of the continuum. In other words, a stronger
perceptual assimilation effect was found for differentiating the atypical stimuli; as such,
perceivers were able to make finer grained distinctions between the typical than the atypical
stimuli.
As just discussed, the atypicality bias can manifest as a greater likelihood to label two
atypical faces as ‘similar’, (Tanaka et al, 1998; Tanaka et Corneille, in press) and/or in a
lesser likelihood of accurately discriminating between two atypical faces (Tanaka &
Corneille, in press). Of critical importance, recent work also suggests that the mere
psychological distinctiveness of stimuli may result in similar effects. In a recent research,
Corneille, Goldstone, Queller and Potter (2006) had participants learn which of a series of
faces lying on a face morphing continuum belonged to and did not belong to a distinctive
Attractor Fields
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social category: being a Club-member. Consistent with the predictions of the Attractor Field
Model, members of the psychologically distinctive category (club-members) were more likely
to be called ‘similar’ than were non-members, and in a visual search task were more easily
detected, another consequence of distinctiveness. In this case, the perceptual disadvantage for
discriminating between distinctive stimuli was observed for distinctiveness induced via a
purely psychological basis; the effect occurred even though the club and non-club faces were
equated in size, variability and frequency of exposure. In other words, making a category
psychologically distinctive (in the present case, by merely labeling it) can initiate a perceptual
assimilation effect for this category, relative to that occurring for a context category that does
not enjoy a similar psychological distinctiveness (see also Goldstone, Steyvers & Rogosky,
2003). We return to this point in the general discussion.
Face-space, attractor fields and memory
So far, we have seen that the Attractor Field Model predicts a less differentiated
perceptual treatment amongst atypical or psychologically distinctive stimuli than among
typical or psychologically indistinct stimuli. But, in the case of memory, the model predicts
an advantage for the atypical, psychologically distinctive, stimuli. Atypical stimuli, because
they hold more distinctive features and are located in sparser representational regions, are
better encoded, and are less likely to be confused with another previously-seen stimulus at
recognition.
Consider again the case of faces. As already noted, unattractive faces are located
farther away from the face-space origin and are situated in sparser face-space regions than are
attractive faces (Potter et al., in press). As a consequence, a memory advantage is to be found
for unattractive faces. Attractive faces tend to have features typical of the population in
Attractor Fields
8
general, and because they hold more average features, they are more difficult to encode (i.e.,
there is less unique information to be found in a typical than atypical face), induce perceptual
fluency biases (i.e., people will consider the higher perceptual fluency of typical faces as a
proxy for its familiarity), and are more likely to be confounded with another face that has
been previously encoded in memory (e.g., Bartlett, Hurry, & Thorley, 1984; Corneille, Potter
& Mwenge, 2007; Corneille, Monin & Pleyers, 2005; Light, Kayra-Stuart, & Hollander,
1979; Monin, 2003; Valentine, 1991, 2001; Vokey & Read, 1992).
To illustrate, Corneille et al. (2007) had participants verbally describe an attractive or
an unattractive face, with participants attempting to ensure other participants would be able to
identify the face from their description of it. The next participant (i.e., the communication
recipient) then had to identify the verbally-described face among four attractive and four
unattractive distracters. Two main findings emerged in this study: descriptions of unattractive
faces were found to contain a higher number of distinctive traits, as evidence of their
distinctiveness, and unattractive faces led to better recognition accuracy, again confirming the
predictions of the Attractor Field Model.
This ‘atypicality effect’ in memory is not unique to faces. For example, rare words are
better recognized than are common words (Gregg, 1976), and in real-world scenes, atypical
stimuli (e.g., a picnic basket in a graduate student office) are better remembered than are
typical stimuli (e.g., a telephone in a graduate student office; Pezdek, Whetstone, Reynolds,
Askari, & Dougherty, 1989). The better recognition of atypical than typical stimuli thus
generalizes across numerous classes of stimuli. This memory advantage for atypical stimuli
can be predicted by a variety of models. However, only the Attractor Field Model would seem
to allow for a joint and reversed pattern of prediction for the role of atypicality, and more
generally for the role of psychological distinctiveness, in perception (i.e., perceptual
Attractor Fields
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disadvantage) and memory (i.e., recognition advantage).
Attractor Fields in Social Cognition
Although the Attractor Field Model, combined with face-space models, has been used
to good effect in explaining how typicality and atypicality affect memory and visual
perception in cognition, we propose that this model may also be of use in explaining
memorial and perceptual phenomena that are relevant to social cognition. We will illustrate
this point with two examples in this section before proceeding to the studies.
Consider first the ingroup overexclusion effect (e.g., Leyens & Yzerbyt, 1992;
Yzerbyt, Leyens, & Bellour, 1995). When deciding whether a particular individual is an
ingroup member or an outgroup member, perceivers tend to err on the side of excluding
targets from the ingroup, a phenomenon especially likely for individuals high in ingroup
affiliation. For example, Castano, Yzerbyt, Bourguignon, and Seron (2002) morphed pairs of
faces pre-tested as ‘northern Italian’ and ‘southern Italian,’ creating for each morph pair, a
series of faces that ranged from primarily northern Italian to primarily southern Italian.
Participants from northern Italy were then recruited to categorize these blended faces as being
northern or southern Italian. In line with previous research, participants tended to classify
more of these morphed stimuli as outgroup members, with those who strongly identified with
their region of Italy showing this effect more strongly than low identifiers.
Although this ingroup overexclusion effect is commonly explained using motivational
terms, the basic phenomenon itself could also be explained via an Attractor Field Model. If
we assume that ingroup faces are considered more common in perceivers’ experience, and
thus more typical faces in perceivers’ representation, lying at more central regions in the
perceivers’ face space, the findings of Castano’s research map well onto Tanaka’s findings
Attractor Fields 10
with 50/50 morphed faces. Just as Tanaka found that a 50/50 blend looks more like an
atypical parent than a typical parent, so too might Castano’s ambiguous northern/southern
Italian faces appear more like the (atypical) outgroup than the (typical) ingroup. The
moderating impact of group identification may be due to differential exposures to ingroup and
outgroup faces among high and low-identifiers. As exposure to ingroup and outgroup faces
becomes more asymmetric, so should the asymmetry in the attractor fields of these categories
of faces, which should in turn magnify the overexclusion bias. This is not to say that
motivational factors played no role in these studies. However, attractor field effects, rooted in
basic perceptual processes, may be part of the story.
As a second illustration of how the attractor model can be applied to social cognitive
phenomena, consider the accentuation effects obtained in the recollection of faces of
ambiguous race (Corneille, Huart, Becquart & Bredart, 2004). If we assume that the center of
the face-space involves faces that have the most average features, then these faces should
display no strong gender-related or race-related features. That is, faces that hold features very
specific to their category should be somewhat more peripheral than inter-gender and interethnic blends in the face-space (i.e., 50/50 morphs lying on inter-gender or inter-ethnic
continua). In turn, if faces holding stronger gender or race features are located farther apart
from the center, then these relatively more distinctive faces should have larger attractor fields
and should assimilate identifications more than do inter-gender and inter-ethnic blends that
are lying closer to the face-space origin and have relatively smaller attractor basins.
Consistent with this view, Corneille et al (2004) exposed participants to morphed
Caucasian-Asian faces that were either moderately Caucasian or moderately Asian. For both
types of faces, recollection was accentuated towards face exemplars that held stronger
categorical features. For instance, participants were more likely to misidentify a moderately
Attractor Fields 11
Asian target face (i.e., a 30% Caucasian – 70% Asian morph) as being a 20% Caucasian –
80% Asian distracter than as being a 40% Caucasian – 60% Asian distracter. Hence, keeping
constant the objective differences between the target face and the distracters, identification
was biased towards distracter faces that held more distinctive categorical features (and so
were less representative of faces more generally). Of note, this accentuation effect in memory
was extended to the case of faces whose gender was made ambiguous (Huart, Corneille,
Becquart, & Bredart, 2005), with memory accentuation of gender-ambiguous faces towards
face distracters that held more pronounced (i.e., more disinctive) gender-related features.
This same phenomenon also generalizes to the recollection of auditory percepts. In a recent
set of studies, Stern, Mullenix, Corneille, and Huart (in press) obtained evidence for similar
memory biases in the recollection of voices of ambiguous pitch.
Deriving new predictions from the Attractor Field Model: The case of evaluativelyincongruent expressions
Not only can the Attractor Field Model explain seemingly disparate findings in the
social cognition literature, but this model also allows for novel predictions in social cognition.
In the present research, we sought to extend the Attractor Field Model into the inherently
social realm of social prejudice. An impressive amount of research suggests that people
generally tend to feel more positive about members of their groups than about members of
other groups. This in-group bias is not only a ubiquitous phenomenon, occurring even for the
most minimally defined of groups (Tajfel, Billig, Bundy, & Flament, 1971), but it can have
powerful sequelae in cognition and behavior. For example, not only do individuals distribute
more scarce resources to in-groups (Brewer, 1979; Tajfel et al., 1971), but in-group members
are associated with more positive features than are out-group members (Leyens & Yzerbyt,
1992). Given this association of positive features with the ingroup and of negative features
Attractor Fields 12
with the outgroup and, more generally, because of current or symbolic intergroup conflicts,
we hypothesize that faces may be considered more atypical when displaying expressions that
are evaluatively-incongruent (i.e., unfriendly expressions displayed by ingroup members;
friendly expressions displayed by outgroup members) than when displaying evaluativelycongruent expressions (i.e., friendly expressions displayed by ingroup members; unfriendly
expressions displayed by outgroup members).
Work by a number of theorists supports this evaluative-congruence hypothesis; that is,
the idea that social stimuli are considered more typical when their social category matches in
valence their individual features. Ruys, Corneille & Dijksterhuis (in press) showed that social
categories are more easily accessed for target individuals who are displaying characteristics
that serve as an evaluative match in valence this social category. For instance, attractive
brides and unattractive prostitutes (i.e., evaluatively-congruent categories) are categorized
faster as such than are unattractive brides and attractive prostitutes (i.e., evaluativelyincongruent categories). Similarly, attractive fellow citizens and unattractive foreigners (i.e.,
evaluatively-congruent categories) are categorized faster as such than are unattractive fellow
citizens and attractive foreigners (i.e., evaluatively-incongruent categories). This speed of
categorization finding is consistent with the view that evaluatively-congruent stimuli are
considered more psychologically typical (and are hence categorized faster) than are
evaluatively-incongruent stimuli. Similarly, Niedenthal and Cantor (1986) showed that
participants are more likely to categorize a person into a social category that matches in
valence his or her individual features. For instance, people are more likely to apply the
category ‘recreational sportsman’ (a positive category) than the category ‘scheming
politician’ (a negative category) to an individual who displayed dilated pupils (a positive
feature) rather than constricted pupils (a negative feature). To the extent that people deem it
Attractor Fields 13
more likely that a target individual belongs to an evaluatively-congruent than to an
evaluatively-incongruent category, it is reasonable to consider here that the former type of
categories are seen more typical. Finally, and closer to the present research, Hugenberg
(2005; see also Hugenberg & Bodenhausen, 2003) found that White Americans recognize
happy expressions as happy more quickly on White than on Black faces. More importantly,
however, Whites recognized negatively-valenced expressions of anger and sadness more
quickly on Black than on White faces, providing additional evidence that evaluativelycongruent stimuli are considered more psychologically typical than evaluatively-incongruent
stimuli.
Overview of the present studies
Three experiments are reported here that propose an application of the Attractor Field
Model to social cognition. If evaluatively-incongruent expressions are indeed considered
more psychologically atypical than evaluatively-congruent expressions, then participants
should display a memory advantage for the former type of expressions but a perceptual
advantage for the latter type of expressions. The near-by attractor fields for evaluativelycongruent face stimuli should lead to minimal attraction effects, allowing for fine-grained
perceptual distinctions, leading to more differentiated perceptual performances. The close
proximity of these evaluatively-congruent face stimuli at the center of the psychological
space, however, should lead to great competition for activation at recognition, leading to
poorer memory performance for these stimuli. If this intuition is correct, happy White and
angry Black faces (i.e., evaluatively-congruent stimuli) should be characterized by smaller
attractor basins than angry White and happy Black faces (i.e., evaluatively-incongruent
stimuli), improving perceptual discrimination but impairing memory.
Attractor Fields 14
In Experiment 1, we relied on a sample of White participants in the U.S. In this
experiment, participants completed a same/different perceptual discrimination task and an
expression categorization task, both of which we describe in detail below. We expected
judgments on both tasks to reflect more perceptual differentiation for the processing of faces
displaying evaluatively-congruent than evaluatively-incongruent expressions. In Experiment
2, we relied on a sample of White Belgian participants and had them complete a similar
same/different perceptual discrimination task. Again, we expected judgments on the
perceptual discrimination task to reflect more perceptual differentiation for the processing of
evaluatively-congruent than evaluatively-incongruent expressions. Experiment 2 employed a
larger number of face continua, involving realistic computer-generated faces. This
experiment also involved a sample of participants where the aggressive Black stereotype may
be less salient than in the U.S. Finally, Experiment 3 consists of a recognition study, in which
we hypothesized that evaluatively-incongruent faces (i.e., happy Black; angry White) would
be better remembered than evaluatively-congruent faces (i.e., angry Black; happy White).
In sum, across these three studies, we examined perceptual and memory performance
for faces that differed in race and expression, and drawing on the Attractor Field Model, we
made the novel prediction of better perceptual performance but worse memory performance
for faces displaying evaluatively-congruent than evaluatively-incongruent expressions. We
essentially expected a Race by Expression interaction to emerge on our various dependent
measures, signaling a perceptual advantage for the typical expressions and a memory
advantage for the atypical expressions.
Experiment 1
Attractor Fields 15
In order to test these novel predictions that faces displaying evaluatively-incongruent
expressions would be seen as more similar to one another, we employed two separate tasks: a
Perceptual Discrimination Task and an Expression Categorization Task. For these two tasks,
we created two Black and two White angry-to-happy morph continua, such that each morph
continuum showed expressions ranging from completely angry to completely happy.
For the same/different perceptual discrimination task, participants were presented with
pairs of faces lying on these continua. The face pairs always consisted of either two identical
faces (e.g., two 80% angry morphs of the same individual) or of two faces that were separated
by 20% on the continua (e.g., an 80% angry and a 60% angry version of the same individual).
Participants were asked to deciding whether the two stimuli in any given trial were the same
stimulus (i.e., identical faces) or were different stimuli (i.e., slightly different faces). Thus,
this task is capable of simultaneously measuring an overall tendency to categorize faces as the
same, as well as the capacity to perceptually distinguish the fine-grained, subtle differences
among similar stimuli. Consistent with the Attractor Field Model, we hypothesized that pairs
of faces displaying evaluatively-incongruent expressions (i.e., happy Black; angry White)
would be more atypical, and would thus elicit stronger attractor effects than would faces
displaying evaluatively-congruent expressions (i.e., angry Black; happy White). If true, these
stronger attractor effects should be observed as both a stronger tendency to call evaluativelyincongruent expressions similar, and as a tendency to discriminate more poorly these faces
than their evaluatively-congruent brethren.
In the Expression categorization task, participants were presented with a series of
faces, one at a time, from across the perceptual continua, and were asked to categorize each of
them as happy or angry-looking. We examined the extent to which the content of participants’
categorization decisions varied as a function of Race and Emotion. If participants assimilate
Attractor Fields 16
perceptually facial expressions, they should apply relatively more similar categorization
decisions to faces that differ in their expression. For instance, considering extreme perceptual
assimilation for angry white faces, participants should report similar categorization decisions
irrespective of the actual morphing level of these faces (e.g., they should systematically
categorize 100% of the time an angry white face as being angry-looking, irrespective of
whether a white face is 100%, 90%, 80%, 70% or 60% angry-looking). In contrast, if
participants are sensitive to actual differences that exist between facial expressions, then their
categorical decisions should vary more widely as a function of the actual level of morphing of
the face (e.g., they should categorize 100% of the time a 100%, 90% or 80% angry black face
as being angry-looking, but categorize perhaps only 60% of the time a 60% or 70% angry
black face as being angry-looking). This assimilation effect should manifest as lower levels of
variability in categorization decisions across the various levels of morphing for faces
displaying evaluatively-incongruent than evaluatively-congruent expressions (see also below).
Method
Participants.
Forty-five White undergraduates at Miami University participated in this experiment
for partial course credit. One participant’s Discrimination Task data were lost due to
computer malfunction, thus 44 participants were included in the Discrimination Task
analyses.
Stimuli.
Four targets (2 Black male; 2 White male) were selected from the Nimstim face
database (Tottenham, Borscheid, Ellertsen, Marcus, & Nelson, 2002). Each target displayed
both an unambiguously angry and an unambiguously happy expression, for a total of 8 initial
Attractor Fields 17
stimuli (an angry and a happy version of each of the four target faces). Each initial stimulus
was rendered into grayscale, and resized to approximately 5 × 7 cm using Adobe Photoshop.
Each pair of happy and angry stimuli were then morphed using the Morpheus™ software to
create an expression continuum, running from angry to happy, with angry-happy blends being
generated in 10 equal steps. This generated 11 face images (2 initial ‘parent’ expressions; 9
angry-happy expression blends) for each of the four targets, with 10% increments between
each expression (see Figure 2 for an example).
Design and Procedure.
Participants arrived at the laboratory in groups of up to 4, and after giving informed
consent, were seated at computers (IBM-PC compatible) upon which all instructions, stimulus
presentation, and data collection was conducted. Participants completed a Perceptual
discrimination task and a Categorization task during the experiment. The order of these tasks
was counterbalanced on a between-subjects basis; this counterbalancing did not influence the
results, so this is not discussed further. Target Race, Target Expression, and Morph Level
were manipulated on a within-subjects basis in each of the two tasks.
Perceptual discrimination Task. Participants were asked to decide whether two faces
appearing simultaneously on the computer screen were identical (‘same’ response) or
different in any way (‘different’ response). Each trial began with a fixation point (+),
presented for 1000ms, at the center of the computer screen, followed by the simultaneous
presentation of two face stimuli, separated both horizontally and vertically by 5cm. The
vertical displacement prevented participants from directly comparing face features at a
particular height on the screen. The two faces remained on the screen for 250ms, after which
the screen was blanked. The next trial began (with the onset of the fixation point)
Attractor Fields 18
immediately upon a participant’s response. On each trial, the two faces to be compared were
always from the same individual, and were either identical (involved the same morph) or
differed by 20% in the morph continuum (e.g., a 20% morph presented simultaneously with a
40% morph). For each of the four identities (2 Black; 2 White), each of the 11 same-face
pairs (e.g., 40% with 40%) and each of the 9 different-face pairs (e.g., 20% with 40%) was
presented twice, totaling 160 trials.
Expression categorization Task. Participants were instructed they would see a series of
faces appear on the computer screen and to categorize them as either ‘angry’ or ‘happy’
expressions by keyboard button press. Each trial began with the presentation of a fixation
point (+) for 1000ms, followed by the presentation of one of the stimuli. Participants were
instructed to categorize the faces as quickly and accurately as possible. Each of the 44 faces
was presented three times, for a total of 132 trials.
Results
Of interest in both tasks was the extent to which faces displaying evaluativelyincongruent expressions would be perceived more similar to one another than would faces
displaying evaluatively-congruent expressions. Although different analytic techniques were
necessitated in the two different tasks, in all analyses the data are collapsed across the two
targets within each race. For the Perceptual discrimination task, we considered the percentage
of ‘same’ responses and the percentage of accurate perceptual discriminations responses as a
function of Race and Expression. For the Expression categorization task, we examined the
variability in categorization decisions across the levels of morphing as a function of Race and
Expression.
Perceptual discrimination Task.
Attractor Fields 19
Percentage of ‘same’ responses
We hypothesized that the atypicality effects caused by evaluatively-incongruent
expressions would elicit more ‘same’ responses for faces displaying evaluatively-incongruent
than evaluatively-congruent expressions. In order to test this interaction hypothesis, the
percentage of ‘same’ responses were first averaged into two levels: Angry morphs vs. Happy
morphs. For the Angry morphs, the percentage of ‘same’ responses on same and different
trials was averaged across stimuli that were primarily angry (i.e. trials involving the 100%
through 60% angry morphs). For the Happy morphs, the percentage of ‘same’ responses on
same and different trials was averaged across stimuli that were primarily happy (i.e. trials
involving the 40% through 0% angry morphs). This provided separate ‘same’ scores for
happy Black, angry Black, happy White, and angry White targets. The boundary (i.e.,
50%/50%) and cross-boundaries (i.e., 40%/60%) stimuli were left out of analyses, because
these stimuli by definition cannot speak to the issue of stereotype consistency or
inconsistency.
Thus, the ‘same’ scores consist of a measure of participants’ readiness to judge pairs
of faces as similar, regardless of their actual similarity. These scores were submitted to a 2
(Target Race: White vs. Black) by 2 (Expression: Angry vs. Happy morphs) repeatedmeasures ANOVA. This analysis yielded a main effect of Expression F(1, 43) = 42.65, p <
.001, qualified by the predicted Target Race by Expression interaction, F(1, 43) = 25.76, p <
.001, with more same scores obtained for the evaluatively-incongruent (M = 73.06; SD =
0.10) than for the evaluatively-congruent expressions (M = 69.26; SD = 0.10). The individual
means are respectively M = 62.96 (SD = 13.43) for the angry Black face, M = 77.41 (SD =
11.85) for the happy Black face, M = 68.71 (SD = 13.26) for the angry White faces, and M =
75.56 (SD = 10.94) for the happy White faces.
Attractor Fields 20
For the sake of comprehensiveness, Figure 3 (top panel) reports the same scores
obtained across each individual level of morphing from the most angry to most happy
expression for both Black and White faces. For example, the value for the 100% Black
averages the % ‘same’ responses to pairs involving two identical 100% angry Black faces
(same trials) and to pairs involving a 100% angry Black face and an 80% angry Black face
(different trials). The values for the cross-boundary stimuli –i.e., CB-40_60- are also reported
for illustrative purposes on this Figure. As can be seen, whereas ‘same’ responses are more
frequent for the White than for the Black faces under angry expressions, the effect reverses
for the happy expressions. A strong quadratic trend is also obtained, with a tendency to
respond with ‘same’ less frequently as one deals with stimuli lying closer to the category
boundaries, F(1, 43)=64.28, p < .001. This finding is consistent with the categorical
perception literature (Harnad, 1987) and has been recently demonstrated on ‘same’ scores for
perceptual continua of faces (Corneille et al., 2006).
Perceptual discrimination scores
Independent of participants’ readiness to call two faces ‘similar’, participants may also
vary in the accuracy with which they can perceptually discriminate between two faces. We
hypothesized that evaluatively-incongruent expressions would be perceived more similar to
one another, eliciting poorer perceptual discrimination. In order to test this interaction
hypothesis, the percentages of correct responses were first averaged into two levels: Angry
morphs vs. Happy morphs. For the Angry morphs, accuracy scores on same trials (i.e.,
responding ‘same ‘to a pair of identical faces) and on different trials (i.e., responding
‘different’ to a pair of different faces) were averaged across stimuli that were primarily angry
(i.e. trials involving the 100% through 60% angry morphs). For the Happy morphs, accuracy
scores for same and different trials were averaged across stimuli that were primarily happy
Attractor Fields 21
(i.e. trials involving the 40% through 0% angry morphs). This provided separate accuracy
scores for happy Black, angry Black, happy White, and angry White targets. Again, the
boundary (i.e., 50%/50%) and cross-boundaries (i.e., 40%/60%) stimuli were left out of
analyses, as these stimuli do not speak to the issue of evaluative consistency or inconsistency.
These perceptual discrimination scores consist of a measure of participants’ capacity
to discriminate perceptually between stimuli. It is the average frequency of correct answers
for ‘same’ and ‘different’ trials, for both primarily angry (100% through 60% angry) and
primarily happy (40% through 0% angry) trials. These scores were submitted to a 2 (Target
Race: White vs. Black) by 2 (Expression: Angry vs. Happy morphs) repeated-measures
ANOVA. This analysis yielded a main effect of Expression F(1, 43) = 31.16, p < .001,
qualified by the predicted Target Race by Expression interaction, F(1, 43) = 42.82, p < .001,
with more accurate scores obtained for the evaluatively-congruent (M = 65.32; SD = 0.05)
than for the evaluatively-incongruent (M = 61.52; SD = 0.05) expressions. The individual
means are respectively M = 67.72 (SD = 6.57) for the angry Black face, M = 60.01 (SD =
5.24) for the happy Black face, M = 63.03 (SD = 6.27) for the angry White faces, and M =
62.92 (SD = 6.03) for the happy White faces.
For the sake of comprehensiveness, Figure 3 (bottom panel) reports the perceptual
discrimination scores obtained across each individual level of morphing from the most angry
to most happy expression for both Black and White faces. For example, the value for the
100% angry Black face averages the % ‘same’ responses to pairs involving two identical
100% angry Black faces (same trials) and the % ‘different’ responses to pairs involving a
100% angry Black face and an 80% angry Black face (different trials). The values for the
cross-boundary stimuli –i.e., CB-40_60- are also reported for illustrative purposes on this
Figure. As can be seen, whereas perceptual discrimination scores are higher for the Black
Attractor Fields 22
than for the White faces displaying angry expressions, the situation reverses for the happy
expressions. A strong quadratic trend is also obtained, with a tendency to be more accurate in
discriminating perceptually between stimuli lying closer to the category boundaries, F(1, 43)
= 91.83, p < .001. This finding also represents a categorical perception effect and has been
reported on perceptual continua of faces in several studies (e.g., Levin & Beale, 2000).
Expression categorization Task.
Of interest in the expression categorization task was the extent to which evaluativelyincongruent expressions were assimilated more to one another than were evaluativelycongruent expressions. As already mentioned, perceptual assimilation should manifest as less
variable categorization responses across the various morphing levels. In the case of maximal
assimilation, participants should apply the exact same categorization decisions to faces that
vary in their actual level of morphing. In contrast, more sensitivity to variation betwen faces
should manifest as more variable categorization decisions across the different levels of
morphing.
To address this issue, we first developed for each participant a categorization score at
each level of morphing for the Black and White continua, separately. Because we used two
face continua within each race and that individual faces were presented three times at each
level of morphing, this resulted in six (i.e., 3 judgments X 2 individual faces) possible
categorization scores for each participant, for each race at each of the 11 levels of morphing.
These scores were either 0%, 16.6%, 33.3%, 50%, 66.7%, 83.3% or 100%. For instance, a
66.7% score occuring for a 70% angry Black face indicates that the 70% angry Black faces
were categorized four times out of six presentations as being angry-looking.
Attractor Fields 23
We then computed, for each participant, the standard deviation of his or her
categorization scores across the five levels of morphing for the primarily angry expressions
(i.e., 100% angry through 60% angry) and for the primarily happy expressions (i.e., 40%
angry through 0% angry), separately for the Black and White targets. A value of zero on this
score indicates a total absence of sensitivity to the level of morphing of the face, with the
exact same categorization scores observed across the various levels of morphing for a specific
race-expression combination (i.e., no variability in categorization decisions).
These standard deviation scores were submitted to a 2 (Target Race: White vs. Black)
by 2 (Target Expression: Angry vs. Happy morphs) repeated-measures ANOVA. This
analysis yielded both main effects of Race, F(1, 44) = 14.38, p < .001, and Expression, F(1,
44) = 12.47, p < .002, and the predicted Target Race by Expression interaction, F(1, 44) =
26.59, p < .001, with more variable categorization decisions obtained for the evaluativelycongruent (M = 21.38; SD = 8.9) than for the evaluatively-incongruent (M = 12.07; SD =
7.75) expressions. The individual means are respectively M = 27.26 (SD = 15.03) for the
angry Black face, M = 10.56 (SD = 10.16) for the happy Black face, M = 13.59 (SD = 10.03)
for the angry White faces, and M = 15.50 (SD = 10.32) for the happy White faces.
Again for the sake of comprehensiveness, Figure 4 depicts the categorization scores at
each level of morphing from the most angry to most happy Black and White faces. For
example, the value for the 30% White face represents the percentage of time that a 30% angry
White face was categorized, on average, as angry. As can be seen, the profile of
categorization scores across the various morphing levels is steeper (i.e., participants are more
sensitive to actual differences between faces) for the Black than for the White faces under the
angry expression, whereas the situation reverses for the happy expression. This greater
sensitivity to actual differences between faces (i.e., sensitivity to morphing level) in
Attractor Fields 24
categorizing faces displaying evaluatively-congruent expressions directly underlies the
aforementioned Race by Expression interaction obtained on the standard deviation scores.
Discussion
Supporting and extending the Attractor Field Model, the results of Experiment 1
provide original evidence that evaluatively-incongruent expressions elicit stronger perceptual
assimilation effects than evaluatively-congruent expressions. In the Perceptual discrimination
Task, race-expression combinations in the evaluatively-incongruent side of the spectrum (i.e.,
angry Whites; happy Blacks) are more poorly differentiated perceptually than are
evaluatively-congruent combinations (i.e., happy Whites; angry Blacks), and this effect was
found on both the decisional component (% ‘same’ responses) and the perceptual
discrimination component (% correct responses) of the task. The Expression categorization
task yields the conceptually congruent finding that the variability of categorization responses
is smaller for the evaluatively-incongruent race-expression combinations. Taken together,
this data indicate that there are stronger assimilation effects in perceptual and categorical
judgments for evaluatively-incongruent stimuli than for evaluatively-congruent stimuli.
As predicted by the Attractor Field Model, evaluatively-incongruent race-expression
combinations have larger attractor basins by dint of their atypicality than do evaluativelycongruent race-expression combinations. These larger attractor basins have a powerful
attraction effect on the perceptual system. Namely, their stronger attraction pulls on slightly
dissimilar stimuli, distorting them to be judged more similar and also perceptually appear
more similar than they actually are. Conversely, the smaller attractor basins of evaluativelycongruent stimuli allow the perceptual system to make finer grained distinctions than capable
Attractor Fields 25
in the presence of stronger attractors. Thus, evaluatively-incongruent expressions are
perceptually assimilated more strongly together than evaluatively-congruent expressions.
Experiment 2
In Experiment 2, we sought to replicate Experiment 1 findings by relying on (1) a
different sample of participants, (2) an adapted same/different perceptual discrimination task,
and (3) a different and more complete set of realistic computer-generated faces.
Experiment 2 relied on a sample of White Belgian participants. Replicating the
findings of Experiment 1 in this sample would not only allow for a generalization of our
findings, but also for a replication of Experiment 1 findings in a sample where the aggressive
Blacks stereotype is probably less salient. In Belgium, it is rather the lighter-skinned North
Africans who are considered stereotypically aggressive or hostile; darker-skinned southern
Africans are stereotypically thought to be calm, casual, and even nonchalant. Yet, it is
reasonable to assume that both White Europeans and White Americans are at least implicitly
prejudiced against (southern African) Blacks, resulting in affective congruence for positive
Whites and negative Blacks associations and in affective incongruence for negative Whites
and positive Blacks associations. Obtaining evidence for attractor field effects in a sample of
Belgian participants would suggest that typicality effects for specific Race-Expression
combinations can be obtained even when participants have no or little explicit associations
between a racial category and the expression that members from this category are likely to
display. 1
In addition to allowing for a replication and generalization of the perceptual
discrimination task of Experiment 1, Experiment 2 also relied on a slightly adapted version of
the previous same/different perceptual discrimination task. In this revised method,
Attractor Fields 26
participants were presented with pairs of faces, and as in Experiment 1, in each trial, the pairs
were either the ‘same’ (i.e., identical) or were ‘different’ (i.e., two different morph levels of
the same individual). Critically, in the different trials, we manipulated the morphing distance
between the two faces. In different trials, one ‘parent’ morph face (i.e., 100% angry or 0%
angry) was always presented with a morph face that was 20, 30, 40 or 50% different. Thus,
the morphed faces (20% – 90%) were either presented with themselves (‘same’ trials) or with
one of their associated endpoint face on the continuum (i.e., 100% or 0% angry endpoints;
‘different’ trials).
In sum, the present experiment involved same/different judgments on pairs of faces
that varied in similarity (i.e., the two faces in the pair were the same or were different), race
(i.e., Black or White), expression (i.e., Angry or Happy) and morphing distance (i.e., the faces
were distant either by 20%, 30%, 40% or 50% from their associated endpoint face). This
allowed us to explore whether the findings obtained in Experiment 1 would be moderated by
the objective discriminability of the stimuli. Indeed, there is good reason to think that the
‘distance’ manipulation may moderate the effects observed in the first experiment.
Specifically, by moving two stimuli further away from one another on the morph continuum,
the stimuli become objectively more discriminable. Just as fraternal twins have fewer
objective phenotypic similarities than do identical twins, and are thus easier to tell apart, so
too should moving two stimuli further apart on the morph continuum create more easily
discernable stimuli. If morph distance serves as a moderator here, we predict that the
perceptual bias observed in Experiment 1 should only be observed for the difficult-todiscriminate trials (e.g., 20% or 30% differences) but not for the increasingly easy-todiscriminate trials (e.g., 40% or 50% differences). This finding would be consistent with past
research reporting larger category-driven perceptual biases in conditions of enhanced
Attractor Fields 27
uncertainty , a classic effect that was already anticipated by Allport (1954; for a discussion,
see Yzerbyt & Corneille, 2005). For instance, stronger categorization-driven accentuation
effects have been found for participants mapping perceptual estimates of length onto an
unfamiliar measurement unit (Corneille et al, 2002). Closer to the present research interest,
atypicality biases have been found to be larger during initial stages of categorization learning;
that is, when participants enjoy only a moderate expertise in their processing of the stimuli,
and thus still experience some difficulty in distinguishing between stimuli (Corneille et al.,
2006).
Finally, Experiment 2 sought to generalize Experiment 1 findings by relying on a new
and more complete set of faces, which in this experiment were computer-generated using a
state-of-the-art face modelling software program that allowed for a perfect standardization
and calibration of the face stimuli.
Experiment 2
Method
Participants and Design.
Forty seven undergraduates from the Catholic University of Louvain (43 females, 4
males, all Whites) took part in the experiment in exchange for partial course credit or
remuneration. The design was a 2 (Taget Race: White, Black) by 2 (Target Expression:
Happy, Angry) by 4 (Morphing Distance: face differed by 20, 30, 40, 50 % to its associated
source face), with all factors manipulated on a within-participants basis.
Materials.
We generated a set of target faces using state-of-the-art 3D Face modelling software
(FaceGen Modeller 3.1). This software allowed facial structures of Black and White targets
Attractor Fields 28
to be generated semi-randomly, with a high level of realism (see Figure 5 for an example).2
We created 5 face morphing continua per race, with parent faces consisting of a 100% angry
and a 100% happy version of the same face. Just as in Experiment 1, these parent faces were
used to construct the morph continua in increments of 10%, yielding 11 faces per continuum,
ranging from 100%angry to 0%angry (i.e., 100% happy).3
Procedure.
Participants were seated in front of an IBM-PC compatible computer, with all
instructions and stimuli presented onscreen. The experimenter announced that participants
were going to see pairs of images representing faces and decide if the images were the same
or different. The experimenter warned participants that they would have only 750 ms to view
the faces and that they could sometimes be only slightly different. Participants were asked to
maintain the distance to the screen proposed by the experimenter (about an arm’s length).
The experiment comprised 80 “different” trials, consisting in the random presentation of a
given morphed face with its associated 100 % angry or 100% happy parent face. An equal
number of “same” trials were included, consisting in the presentation of each one of the
morphs with itself, totalling 160 trials.
Each trial first began with a fixation cross appearing at the center of the screen for 500
ms. Then, two faces appeared simultaneously on the screen for 750 ms, one on the left-center
and the other on the right-center (with the parent-morph order for different trials
counterbalanced). Finally, a blank screen of 250 ms appeared followed by the response
screen asking participants to answer “i” for “identical” or “d” for “different”. The inter-trial
delay was 1 second.
Results
Attractor Fields 29
Percentage responses ‘same’
We computed the average percentage of ‘same’ responses across same and different
trials for the Angry and Happy, White and Black faces under the four levels of the Morphing
Distance. For instance, the ‘same’ score for a 30% distant angry White morph represents the
percentage of time that 70% angry White morphs (i.e., morphs that differed by 30% of
distance to their associated 100% angry White source face) were judged similar to themselves
(same trials) and were judged similar to the angry White source face (different trials) that was
used to create them.
These ‘same’ scores were then submitted to a Target Race by Target Expression by
Morphing Distance repeated-measures ANOVA. Unsurprisingly, this analysis yielded a main
effect of Morphing Distance, with lower same scores for faces that were more distant to the
source face, F(3, 138) = 155.48, p < .001, indicating that the morphing distance manipulation
was successful. A main effect of Target Expression was also obtained, with higher same
scores obtained for happy than angry faces, F(1, 46) = 4.14, p < .05. A strong main effect of
Target Race was also found, with higher same scores for Blacks than for White faces, F(1,
46) = 27.38, p < .001. Of importance, we also obtained the predicted Target Race by Target
expression interaction, F(1, 46) = 4.09, p < .05, with higher ‘same’ scores obtained for the
evaluatively-incongruent (M = 63.05; SD = 10.25) than for the evaluatively-congruent (M =
61.19; SD = 10.70) expressions. The individual means are respectively M = 62.55 (SD =
13.37) for the angry Black face, M = 67.61 (SD = 11.90) for the happy Black face, M = 58.51
(SD = 12.77) for the angry White faces, and M = 59.84 (SD = 11.47) for the happy White
faces.
Attractor Fields 30
For the sake of comprehensiveness, the percentage of ‘same’ responses obtained
across each individual level of distance from the most angry (left-end values) to most happy
(right-end values) expressions, separately for Black and White faces are reported on Figure 6
(top-panel). For example, the value for the 20%ToHappy Black face averages the % ‘same’
responses to pairs involving two identical 20% angry Black faces (same trials) and to pairs
involving a 20% angry Black face and the 0% angry Black face (different trials). The
aforementioned main effect of Race, with ‘same’ responses more frequent for Black than
White faces, appears clearly on this Figure. More important to our concern, this higher
likelihood to categorize Black than White faces as the ‘same’ appears to be stronger for Black
faces displaying happy (evaluatively-incongruent) than angry (evaluatively-congruent)
expressions.
Percentage correct responses
As in Experiment 1, we computed the average percentage of correct responses across
same and different trials for the angry and happy, White and Black faces under the four levels
of the Morphing Distance. For instance, the accuracy score for a 40% distant happy Black
morph represents the percentage of time that the 40% angry Black morphs were correctly
judged similar to themselves (same trials) and were correctly judged different to the 0% angry
Black face (different trials) that was used to create them. These accuracy scores were then
submitted to a Target Race by Target Expression by Morphing Distance repeated-measures
ANOVA. As could be expected, this analysis yielded a main effect of Morphing Distance,
with higher accuracy scores for faces that were more distant to the source face, F(3, 138) =
85.36, p < .001, again indicating the success of the morphing distance manipulation. A main
effect of Target Race was also found, with higher accuracy scores for White than for Blacks
faces, F(1, 46) = 9.98, p < .003.
Attractor Fields 31
More importantly, we found a moderation of the predicted Target Race by Target
Expression interaction by the Morphing distance, F(3, 138) = 2.85, p < .04. Complementary
analyses revealed that the predicted Target Race by Target Expression interaction was
obtained for trials involving the close morphs (i.e., 20 and 30% morphs), F(1, 46) = 5.75, p <
.021, with higher accuracy scores found in this case for the evaluatively-congruent (M =
67.02 ; SD = 9.63) than for the evaluatively-incongruent (M = 63.88 ; SD = 8.84) faces. The
individual means are respectively M = 64.04 (SD = 10.46) for the angry Black face, M =
64.14 (SD = 13.44) for the happy Black face, M = 63.61 (SD = 10.15) for the angry White
faces, and M = 70 (SD = 13.51) for the happy White faces.
In contrast, no such Target Race by Target Expression interaction was obtained for the
trials involving the more distant, hence more easily discriminable, morphs (i.e., 40% and
50 % morphs), F(1, 46) = 2.43, p > .12. If anything, in the latter case the accuracy scores
were found to be descriptively higher for the evaluatively-incongruent (M = 83.08; SD =
10.96) than for the evaluatively-congruent (M = 82.13; SD = 11.69) faces.
Again for the sake of comprehensiveness, the full pattern of means for the accuracy
scores obtained on the Angry and Happy, Black and White faces at each level of morphing
distance is reported on Figure 6 (bottom panel). For example, the 40%ToHappy value for the
Black faces averages the % ‘same’ responses to pairs involving two identical 40% angry
Black faces (same trials) and the % ‘different’ responses to pairs involving a 40% angry
Black face and the 0% angry Black face (different trials). It is apparent on this Figure that the
White faces were generally better discriminated perceptually than the Black faces, except for
the close (i.e., 20 and 30 %) morphs displaying an angry expression. In the latter case, the
higher typicality of the angry Black relative to the angry White faces compensated for the
perceptual advantage of the White faces generally found at other levels of morphing.
Attractor Fields 32
Discussion
Experiment 2 is informative in many respects. First, it provides evidence for the
robustness of the effect. Indeed, the Race by Expression interaction obtained in Experiment 1
was replicated here (1) on both perceptual scores, and (2) in the context of a study that relied
(a) on a different version of the same/different perceptual discrimination task, (b) on different
face materials and (c) on a sample of participants where the aggressive black stereotype is
arguably less salient than in the U.S. Experiment 2 also provides evidence for a main effect
of race on the two perceptual scores that was not observed in Experiment 1: Black faces were
more likely to be called similar and were less accurately discriminated than the White faces.
This finding is certainly consistent with the literature on the cross-race deficit which finds that
cross-race faces are more difficult for perceivers to discriminate as compared to same-race
faces (e.g., Brigham, 2006; Meisser, Brigham & Butz, 2005; Meissner & Brigham, 2001).
We note that the strong main effect of Race obtained here on both the ‘same’ scores
and on the accuracy scores prevented the emergence of a cross-over interaction obtained in
Experiment 1. Yet, despite this more complicated pattern of findings, our predicted Race by
Expression interaction was significantly replicated on both the ‘same’ scores, and on the
accuracy scores for the least discriminable faces (i.e., the ‘close’ morph comparisons). The
present experiment thus not only replicates Experiment 1 but also extends it by revealing a
boundary condition for the emergence of attractor field effects in perceptual discrimination
accuracy: when stimuli are objectively more discriminable (i.e., morphs distant by 30% and
40% rather than by 10% and 20% to another face in a pair), the perceptual bias elicited by the
asymmetry in the attractor fields of the evaluatively-congruent versus evaluativelyincongruent expressions tends to be attenuated.
Attractor Fields 33
Perception, however, is only half of the story. The Attractor Field Model makes
separate and seemingly divergent predictions for memory. That is, while evaluativeinconsistency hinders perceptual discrimination, it will actually facilitate recognition. Just as
atypical faces, cars, events, and behaviors are more memorable than are their hum-drum
relatives, so too should faces displaying atypical expressions be more memorable than their
typical brethren. To test this novel prediction regarding memory, we conducted a third
experiment in which recognition accuracy for happy and angry, Black and White faces was
tested. Experiment 3 again relies on a sample of Belgian participants and on computergenerated faces similar to those used in Experiment 2.
Experiment 3
Method
Participants and Design.
Thirty-three undergraduates from the Catholic University of Louvain (31 females, 2
males) took part in the experiment in exchange for partial course credit or remuneration; four
participants were African European, the rest were White. Data from the African European
participants were removed from analyses, as we suspected these participants would be highly
familiar with Black faces and may not share the same prejudice towards African faces as the
Caucasian participants. Target Race (Black, White), Target Expression (happy, angry), and
Face Status (target, distracter), were all manipulated on a within-subjects basis.
Materials.
We first created 80 faces; 40 Black faces, and 40 White faces, again using FaceGen
Modeller 3.1. The morphing feature of FaceGen was then used to manipulate each of the
80 faces to display both a happy and an angry expression, for a total of 160 stimulus faces.
Attractor Fields 34
The happy and angry facial expressions were always identical across stimuli (see Figure 7 for
examples of stimuli). 3
These 160 stimulus faces were split into two lists of 80 faces (40 happy, 40 angry; 40
Black, 40 White) such that each face identity was present in each list, but with the expression
of face (happy vs. angry) varying across list. We further counterbalanced which stimuli were
targets and distracters across conditions. Hence, across the four counterbalancing conditions,
smiling and angry versions of each face were equally likely to be seen by a participant as
either distracter or as target.
Procedure.
Participants arrived at the laboratory in groups of up to 10, and were seated at
computers (IBM-PC compatible) upon which all instructions, stimulus presentation, and data
collection was conducted. Participants were first exposed to 40 faces (10 angry Black, 10
happy Black, 10 angry White, and 10 happy White) in a shallow processing mode.
Participants then completed a brief distractor task. Finally, participants took part in a surprise
recognition task, in which they distinguished which of 80 faces were seen during the initial
exposure phase (i.e., old faces), and which faces had not been seen before (i.e., new faces).
The 80 faces observed at the recognition phase included the 40 previously seen faces, and 40
new faces (i.e., an additional 10 angry Black, 10 happy Black, 10 angry White, and 10 happy
White faces). Once the study was completed, participants were thanked and debriefed.
Face Exposure. Participants were told they would have to detect the location of a grey
circle appearing randomly on the left or the right of the computer screen. On each trial the
following sequence occurred: (1) a fixation point appeared for 1 second at the center of the
screen, (2) a face appeared for 2 seconds in the center of the screen, (3) the face disappeared
Attractor Fields 35
and was directly followed by the presentation of a grey circle appearing randomly on the left
or the right side of the face. On each trial, participants had to respond to the location of the
probe as quickly and accurately as possible, using the “a” for left and “p” for right. After
participants responded, the probe disappeared, and the next trial began. We anticipated that
this procedure would ensure a relatively shallow processing of the faces, thereby enhancing
encoding errors, allowing for the predicted effects of typicality to be observed (see also
Valentine, 1991, 2001; Lewis, 2004)
Distraction task. After the face exposure stage, participants were instructed that they
were to count the number of ‘a’s present in a short paragraph. Once participants were ready,
a text of 158 words on non-Euclidean geometry was presented on the screen for 60 seconds,
after which participants reported the number of ‘a’s they had counted.
Surprise Recognition Task. Immediately after the distraction task, participants were
informed that they would now complete a surprise recognition task for the faces seen in the
exposure phase. This consisted of the presentation of the 40 target faces seen in during the
exposure phase, and 40 distracter faces. Each face was preceded by a fixation cross,
appearing at the center of the screen for 1500ms. After the fixation cross, a face appeared at
the center of the screen until participants responded, after which the next trial began. Faces
were presented one at a time, at the center of the screen, in a separate random order for each
participant. Participants reported whether each face was previously seen (i.e., an ‘old’ face)
or unseen (i.e., a ‘new’ face) using the ‘o’ and ‘n’ keys, respectively.
Results and Discussion
We computed a recognition accuracy score for each face category (i.e., angry Black,
happy Black, angry White, and happy White) by dividing, for each participant, the total
Attractor Fields 36
number of correct answers (i.e., responding ‘seen’ to a previously seen face and responding
‘unseen’ to a previously unseen face) by 20 (as there was a total of 20 recognition trials in
each category of face). For each face category, a score of 50% thus represents chance level in
recognition accuracy. We submitted these scores to a 2 (Target Race: Black vs. White) by 2
(Target Expression: Angry vs. Happy) repeated-measures ANOVA. This analysis yielded the
predicted interaction between Target Race and Target Expression, F(1, 28) = 4.95, p < .034,
with faces displaying evaluatively-congruent expressions (i.e., angry Black; happy White)
associated with lower recognition accuracy scores than faces displaying evaluativelyincongruent expressions (i.e., happy Black; angry White; see Figure 8). No other effects were
significant. Notably, recognition accuracy differed significantly from chance (50%) for faces
displaying an evaluatively-incongruent expression (happy Black, t(28) = 2.48, p < .02 and
angry White, t(28) = 2.81, p < .01), but did not differ from chance for faces displaying
evaluatively-congruent expressions, ps > .54.
In line with predictions, faces displaying evaluatively-congruent expressions were
better recognized than faces displaying evaluatively-incongruent expressions. Drawing upon
the Attractor Field Model, we proposed that faces displaying evaluatively-incongruent
expressions would seem atypical, and would thus be encoded in sparser and more peripheral
regions of the psychological space. In turn, we anticipated that this would result in a
recognition advantage for these faces, especially given the shallow face processing mode
considered here (see also Valentine, 1991; Lewis, 2004). We now turn to the general
discussion of the theoretical and practical findings obtained in the present research.
General Discussion
Attractor Fields 37
Across a series of experiments relying (1) on three different tasks (i.e., Perceptual
discrimination task, Expression categorization task, Recognition task), (2) on four different
dependent variables (i.e., percentage of ‘same’ responses, perceptual discrimination accuracy,
variability in categorization responses, and recognition accuracy), (3) on three different sets of
faces, and (4) on two different population samples (i.e., U.S and Belgian participants), we
showed and replicated novel interaction patterns consistent with, and providing strong support
for, an Attractor Field Model of mental representation in social cognition. Specifically, these
data clearly indicate that worse perceptual discrimination but better recognition for faces
displaying evaluatively-incongruent than evaluatively-congruent expressions.
The Attractor Field Model predicts that, for perceptual stimuli, evaluatively-congruent
stimuli will be discriminated better than evaluatively-incongruent stimuli. In Experiment 1,
using both a perceptual discrimination and categorization task, we found support for this
hypothesis. Perceivers made finer graded distinctions between evaluatively-congruent
expressions, leading to more discriminant perceptual judgments and more variable
categorization decisions for these stimuli. This result is due to the larger attractor basins of
evaluatively-inconsistent stimuli leading to a stronger attractor effect, “blurring” out fine
perceptual distinctions. Experiment 2 replicated this finding on a different sample of
participants, and by relying on a different set of faces as well as on an adapted version of the
same/different perceptual discrimination task. In Experiment 2, we also found the impact of
the atypicality bias on perceptual discrimination accuracy to be moderated by the objective
distance existing between stimuli, providing evidence for a boundary condition of this effect.
The Attractor Field Model, however, also predicts that while typical stimuli can
facilitate perceptual discrimination, they can impair memory. In support of this prediction,
Experiment 3 found that recognition was better for evaluatively-incongruent than for
Attractor Fields 38
evaluatively-congruent faces. Evaluatively-congruent stimuli, being typical, are likely to be
packed densely together near the origin of the face space, leading to poorer recognition
performance. Evaluatively-incongruent stimuli, being atypical, are not represented in close
proximity to one another, and do not compete as strongly for activation. Thus, they can be
recognized with greater accuracy.
Admittedly, simple effects (i.e., comparison of Black/White effects within a given
Expression level, or comparisons of Angry/Happy effects within a given Race level) were not
systematically observed in these studies. For instance, the strong Race effect obtained in
Experiment 2 on the ‘same’ scores prevented the emergence of higher ‘same’ scores in the
processing of angry White relative to angry Black stimuli. Independent of such main effects,
however, our predicted interaction pattern was obtained on all four dependent variables in all
three studies, with evaluatively-incongruent stimuli more likely, at least on average, to benefit
from recognition advantages but to suffer from perceptual discrimination disadvantages.
The Perceptual Elements of Social Cognition
Despite a decade-long focus on lexically-mediated social cognition (see also Quinn &
Macrae, 2005), a great deal of seminal social cognitive research was inherently perceptual in
nature, investigating how social contexts, social environments, and social cognitions influence
seemingly ‘basic’ perceptual processes. For example, beginning even in Sherif’s earliest
studies (1935), participants’ estimates about the illusory motion of a light were shaped
through the creation of norms. In the classic work of Bruner and Goodman (1947), the
perceptual qualities of well-known stimuli were biased by the perceiver’s motives; indeed, the
entire “New Look” movement exemplified in Bruner’s (1957) article On Perceptual
Readiness is a clear case of how motivation, expectations, and social cognition more
Attractor Fields 39
generally, can play out in the perceptual field. In a similar vein, Tajfel and Wilkes (1963)
defining work on how categorization biases the perception of line lengths, and the subsequent
extensions to theories on categorization in inter-group relations clearly have their foundations
in how cognition influences perception (see Corneille & Judd, 1999; Corneille, Klein,
Lambert, & Judd, 2002, for a replication and extension).
Having noted this recent divergence from the perceptual origins of social cognition,
there has been a resurgent interest in the perceptual qualities of important social stimuli. A
spate of recent research studies have shown that the perceptual qualities of stimuli are biased
by perceivers’ expectations or categorizations, providing a revitalization of this “New Look”
perspective. To provide a few examples, Maclin and Malpass (2003) found that racially
ambiguous faces, when believed to be Black (based upon their hairstyle), were perceived to
have more afrocentric features in their faces, as compared to faces believed to be Hispanic
(again, based on hairstyle). In this same vein, Levin and Banaji (2006) found that the
perceived skin tone of racially ambiguous faces is lighter when the face is labeled as White,
rather than Black. Similarly, Hugenberg and Bodenhausen (2004) showed that facial
expressions of racially ambiguous targets can change how perceivers categorize faces
according to race. Recent evidence from Michel, Corneille and Rossion (in press) also
indicates that these effects are not merely evident in judgments, but also in the nature of the
perceptual processes employed. For example, racially ambiguous faces are processed more
holistically when believed by participants to be of their same-race, as compared to identical
faces believed to be cross-race faces.
Recent social cognition research also suggests that social categorization alone cannot
describe the complexity of social judgment and interaction. For example, the research on
afrocentrism (e.g., Blair, Chapleau, & Judd, 2004; Blair, Judd, & Fallman, 2004) and
Attractor Fields 40
colorism (e.g., Maddox & Gray, 2002; Maddox, 2004) suggests that not all members of social
categories are treated equally. Instead, they may be treated differently depending on their
perceptual qualities. In the case of Black targets, negative stereotypes may be activated and
applied more strongly to targets with darker skin tones, or more ‘Afrocentric’ facial features,
than targets with lighter skin or more ‘Eurocentric’ features. Thus, individuals more
perceptually typical of their category are more likely to be the subject of relevant stereotypes.
Recent evidence also importantly suggests that the perceptual features of targets
themselves may be bound up with evaluation outside of category activation (Livingston &
Brewer, 2002). Other perceptual features of targets, such as facial maturity, can have
analogous effects. For example, even slight variations in facial structures, such as increasing
or decreasing eye size, can make faces appear more mature or babyish. Such subtle
perceptual variations can have powerful implications for who is trusted, who is helped, who
appears competent (see Zebrowitz, 1997, for a review), and even how facial expressions are
interpreted (Sacco & Hugenberg, 2007). Related research by Eberhardt and colleagues (e.g.,
Eberhardt, Goff, Purdie, & Davies, 2004) has found that the perceptual system itself can be
tuned by stereotypic expectancies. For example, seeing Black faces (as compared to White
faces) seems to facilitate the perception of crime-related stimuli (e.g., handguns). The
perceptual quality of visual stimuli can lead to major consequences, such as influencing
criminal sentencing decisions (Blair, Judd, & Chapleau, 2004) and even life-or-death
decisions (Eberhardt, Davies, Purdie-Vaughns, & Johnson, 2006).
Because so much of our social behavior, categorization, and interaction are based on
or influenced by these perceptual qualities, we believe that a clear focus on how the
perceptual system operates, and how it is biased, can offer a richer understanding of the social
experience. We believe the Attractor Field Model is in line with this burgeoning interest in
Attractor Fields 41
how social cognition and behavior can be subject to critical perceptual qualities of stimuli.
We propose that extending the Attractor Field Model, originally designed to deal with face
and object perception and recognition, to social cognition can offer a significant theoretical
advance to the field.
Percept, Concept, and Familiarity in the Attractor Field Model
The current research follows the example Corneille et al. (2006) in suggesting that
atypicality effects may not be specific to objectively atypical (i.e., objectively unfamiliar) but
also generalize to conceptually atypical (i.e., psychologically distinctive) percepts. In other
words, our findings suggest that concepts (i.e., psychological representations) can affect
percepts (Goldstone, 1994, 1998; see also Bruner, 1957). As previously discussed, Corneille
et al. (2006) found that learning stimuli in reference to membership in a distinctive category
(i.e., club membership) was sufficient to lead to atypicality-like phenomena in perception and
attention. That is, even though the club members were not objectively more distinctive or
atypical, they were conceptually so and this impacted on how these stimuli were perceptually
processed and were attended to. Similarly, the present research was based on the idea that
faces displaying evaluatively-incongruent expressions would be considered more
psychologically distinct, more atypical, resulting in perceptual disadvantages but in memory
advantages.
Another possibility, however, is that the stronger attractor effects for evaluativelyincongruent stimuli observed in the current research resulted from participants’ greater
familiarity with these stimuli. If participants have relatively more experience processing
angry Black than angry White faces, and happy White than happy Black faces, then perhaps
greater expertise underlies the capacity to distinguish perceptually between evaluatively-
Attractor Fields 42
congruent race-expression combinations. Given the infrequency with which anger is
displayed in naturalistic settings, it seems unlikely that perceivers have actually experienced
more angry than happy Blacks. It is true, however, that media portrayals of Blacks in the U.S.
are, on average, more negative than are portrayals of Whites (e.g., Dixon & Linz, 2000). So it
may be that many White participants have seen more anger from Black than White targets,
affording relatively more expertise in processing affectively-congruent expressions. Note that
this conjecture is somewhat less likely to apply to the Belgian sample considered in
Experiments 2 and 3.
To investigate whether the current data could plausibly be attributed to an expertise
explanation (i.e., more frequent exposure to angry Black and happy White faces), we
conducted a follow-up study in which the procedure of Experiment 1 was replicated, with the
addition of a between-subjects manipulation of face orientation: upright vs. inverted.
Although perceptual expertise typically affords greater facility with discriminating between
faces, perceptual expertise mechanisms in face perception have been shown to be modulated
by the orientation of the face. Specifically, the inversion of faces (180° rotation) seems to
interfere with expertise effects in face perception. For example, Rhodes, Brake, Taylor, and
Tan (1989) demonstrated that for ‘own race’ faces, with which perceivers have a great deal of
expertise, inversion reduces recognition. The effects of inversion for ‘other race’ (i.e., low
expertise) faces, however, were less potent.
We reasoned that, if relatively greater expertise with angry Black and happy White
faces is driving the interaction of race and expression observed in the previous study,
inverting the stimuli should substantially weaken the effects. If instead the greater perceived
similarity for evaluatively-incongruent stimuli expressions is due to increased psychological
distinctiveness of unexpected information, inversion should not moderate the asymmetry
Attractor Fields 43
effects. Notably, although there was a strong main effect for face inversion (p < .001),
suggesting that the inversion manipulation was sufficient to influence processing, inversion
was not found to weaken the stronger assimilation effects for evaluatively-incongruent
stimuli, perceptual sensitivity (p > .23) or categorization variability (p > .5). In the absence of
positive evidence for the role of expertise, at the present time it seems sensible to attribute the
current data to intergroup prejudice rather than to more exposure to specific Race-Expression
combination.
How do facial expressions moderate the Cross-Race effect?
Although the current research was not specifically concerned with the Cross-Race
Effect (which would have required to compare recognition accuracy in a sample of Caucasian
and African European participants), it may be worth briefly elaborating on how the memory
findings from Experiment 3 pertain to this well-established memory bias. Explained briefly,
the Cross-Race Effect (CRE) is the tendency for people to have better recognition memory for
members of their own racial in-group than for other racial groups (e.g., Brigham, 2006;
Hugenberg, Miller, & Claypool, in press; Meissner & Brigham, 2001). Whereas a race by
expression interaction was obtained on recognition performance in Experiment 3, no main
effect of race emerged. Thus, the classic cross-race bias was not found. Importantly, tests of
the CRE can be difficult to interpret without members of two separate racial groups; in this
case, there were not sufficient participants of African descent to constitute a strong test of the
CRE. Despite this, the lack of a race effect is worthy of note.
One possibility is that the cross-race effect is to be obtained on faces displaying
neutral expressions but not on faces that display emotional expressions. The higher expertise
at encoding same-race than other-race faces (e.g., Valentine, 1991) or the tendency to process
Attractor Fields 44
more holistically same-race than other-race faces (e.g., Michel, Rossion, Han, Chung &
Caldara, in press) may thus be restricted to faces of neutral expression. Consistent with this
view, Ackerman et al. (2006) recently obtained evidence for a cross-race effect using Black
and White faces of neutral but not of angry expression. For the angry faces, a recognition
advantage was found in recollecting the Black faces, presumably because these faces elicited
a feeling of threat among participants, which in turn induced a deeper face encoding. Thus,
the present research, along with the recent one by Ackerman et al. (2006) suggests that
emotional expression may be a powerful moderator of the cross-race effect.
Contrary to Ackerman et al. (2006), however, the present research did not reveal a
recognition advantage for the angry Black faces. To the contrary, we predicted and obtained
a recognition advantage for faces displaying evaluatively-incongruent expressions (happy
Black and angry White) relative to faces displaying evaluatively-congruent expressions
(angry Black and happy White). A major difference between the study by Ackerman et al.
(2006) and Experiment 2 reported here, however, is that these authors relied on an intentional
learning paradigm whereas we relied on an incidental learning paradigm. As already noted,
the incidental and shallow encoding of the faces that was considered here was aimed at
enhancing encoding errors and at minimizing the impact of motivational processes in face
encoding, thereby providing an ideal ground for testing our typicality hypotheses. It remains
to be examined whether a longer exposure to or more intentional perceiver motives to process
the faces would qualify our atypicality effects.
Conclusions
In line with the increasing interest in how the perceptual system is influenced by and
influences social cognition, our goal has been to introduce a new model of mental
Attractor Fields 45
representation to social cognition, the Attractor Field Model. From this model, we provide
evidence for the novel hypotheses that atypicality effects can lead to a perceptual advantage
but a memory disadvantage for faces displaying evaluatively-congruent expressions.
Although more research is certainly needed to fully understand the intersection of concepts
and percepts, the current research suggests that this theoretical model may afford a novel
integrative and stimulating perspective on how psychological expectancies influence the
perceptual system.
Attractor Fields 46
Footnotes
1
Such apparent dissociation between face-space representations and explicit representations
held about category exemplars is not uncommon in the face-space literature. For instance,
people are generally surprised to hear that attractive faces are actually more average whereas
plenty evidence exists that supports this claim. To some extent, it may echo the classic
dissociation that is made between explicit and implicit judgments in the social cognition
literature (Devine, 1989)
2
The FaceGen program relies on an algorithm that ensures randomization in the generation of
faces. The racial distributions are multivariate normal distributions in face space, based on a
total of about 300 samples whose detailed breakdown can be obtained upon request to the first
author. Face space is defined by principal components analysis of the registered geometries
and shape-neutral textures. In order to generate faces in a specific race, the program has a
feature called “rand lock” that enables the user to constrain the random generation of faces by
keeping key dimensions constant. In essence, the program relied on a large population of
Black and White faces, abstracted dimensions on which those Black and White faces tend to
vary, as well as their average values, and generated morphologically plausible Black and
White faces from those dimensions.
3
The complete stimulus set can be accessed at the following internet link:
http://www.psor.ucl.ac.be/personal/corneille/
Attractor Fields 47
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Attractor Fields 54
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Attractor Fields 55
Figures
Figure 1. Example diagram of an Attractor Field Model.
Figure 2. Examples of Black and White morph continua, ranging from 100% angry morphs
(left) to 100% happy morphs (right) used in Experiment 1.
Figure 3. Mean Percentage of response Same (top panel) and Perceptual discrimination
accuracy (bottom panel) as a Function of Race and Morphing Level for the Perceptual
Discrimination task in Experiment 1.
Figure 4. Mean Percentage of Categorization as Angry as a Function of Race and Morphing
Level for the Expression Categorization task in Experiment 1.
Figure 5. Examples of the Black and White morph continua, ranging from 100% angry (left)
to 100% happy (right) used in Experiment 2.
Figure 6. Mean Percentage of response Same (top panel) and Perceptual discrimination
accuracy (bottom panel) as a Function of Race and Comparison Type in Experiment 2.
Figure 7. Examples of the Angry and Happy Black and White faces generated for the
Recognition task in Experiment 3.
Figure 8. Mean Recognition Accuracy as Function of Race and Expression in Experiment 3
Attractor Fields 56
Figure 1.
Dimension 1
Dimension 2
Attractor Fields
50/50 Morph
Typical Stimulus
Morph Continuum
Center of the psychological space
Atypical Stimulus
Attractor Fields 57
Figure 2.
Attractor Fields 58
Figure 3.
Black Faces
% Same Responses
80
White Faces
70
60
0%
10
%
20
%
30
%
70
%
CB
-4
0_
60
80
%
90
%
10
0%
50
% Anger in Morphs
80
White Faces
70
60
% Anger in Morphs
0%
10
%
20
%
30
%
60
40
_
CB
-
70
%
80
%
90
%
50
10
0%
% Perceptual Discrimination
Black Faces
Attractor Fields 59
Figure 4.
100
80
Blac k Fac es
70
White Fac es
60
50
40
30
20
10
% Anger in Morphs
0%
10
%
20
%
30
%
60
CB %
50
/5
0
40
%
70
%
80
%
90
%
0
10
0%
% Categorization as Angry
90
Attractor Fields 60
Figure 5.
Angry Morphs
Happy Morphs
Comparison Type
H
ap
p
To
20
%
H
ap
p
H
ap
p
To
30
%
y
y
y
y
50
To
60
H
ap
p
To
20
%
H
ap
p
H
ap
p
To
30
%
y
y
y
y
ry
An
g
H
ap
p
To
40
%
To
50
%
ry
An
g
To
50
%
ry
An
g
To
40
%
ry
An
g
To
30
%
To
20
%
% Same Responses
80
40
%
ry
An
g
ry
% Perceptual Discrimination
90
H
ap
p
To
50
%
50
%
To
An
g
40
%
To
ry
ry
An
g
30
%
To
An
g
20
%
To
Attractor Fields - 61
Figure 6.
100
Black
White
70
60
50
40
30
Comparison Type
100
90
80
70
Black
White
40
30
Attractor Fields - 62
Figure 7.
Attractor Fields - 63
Figure 8.
60
Black Face
Recogniiton Accuracy
58
White Face
56
54
52
50
Angry
Happy
Expression of the Face