configural representations of faces in crowded scenes

Holistic crowding: Selective interference between
configural representations of faces in
crowded scenes
The Department of Psychology, University of California,

Davis, CA, USA
The Center for Mind and Brain, University of California,
University of California, Davis, CA, USA, &
The Center for Mind and Brain, University of California,
David Whitney Davis, CA, USA
It is difficult to recognize an object that falls in the peripheral visual field; it is even more difficult when there are other objects
surrounding it. This effect, known as crowding, could be due to interactions between the low-level parts or features of the
surrounding objects. Here, we investigated whether crowding can also occur selectively between higher level object
representations. Many studies have demonstrated that upright faces, unlike most other objects, are coded holistically.
Therefore, in addition to featural crowding within a face (M. Martelli, N. J. Majaj, & D. G. Pelli, 2005), we might expect an
additional crowding effect between upright faces due to interference between the higher level holistic representations of
these faces. In a series of experiments, we tested this by presenting an upright target face in a crowd of additional upright or
inverted faces. We found that recognition was more strongly impaired when the target face was surrounded by upright
compared to inverted flanker (distractor) faces; this pattern of results was absent when inverted faces and non-face objects
were used as targets. This selective crowding of upright faces by other upright faces only occurred when the target–flanker
separation was less than half the eccentricity of the target face, consistent with traditional crowding effects (H. Bouma,
1970; D. G. Pelli, M. Palomares, & N. J. Majaj, 2004). Likewise, the selective interference between upright faces did not
occur at the fovea and was not a function of the target–flanker similarity, suggesting that crowding-specific processes were
responsible. The results demonstrate that crowding can occur selectively between high-level representations of faces and
may therefore occur at multiple stages in the visual system.
Keywords: vision, perception, awareness, face recognition, ensemble, spatial, lateral, masking, object
Citation: Louie, E. G., Bressler, D. W., & Whitney, D. (2007). Holistic crowding: Selective interference between configural
representations of faces in crowded scenes. Journal of Vision, 7(2):24, 1–11, http://journalofvision.org/7/2/24/,
doi:10.1167/7.2.24.
Introduction
We have all experienced the difficulty of trying to
locate a face in a crowd. The fact that a face is much
easier to recognize when it is located in the central
visual field than when it is located in the periphery is not
entirely due to poor visual acuity in the periphery, but
also due to the presence of internal as well as
surrounding features that interfere with the identification
of the target face. This effect is called crowding (Bouma,
1970; Field, Hayes, & Hess, 1993; He, Cavanagh, &
Intriligator, 1996; Intriligator & Cavanagh, 2001; Latham
& Whitaker, 1996; Levi, Klein, & Aitsebaomo, 1985;
Martelli, Majaj, & Pelli, 2005; Pelli, Palomares, & Majaj,
2004; Strasburger, Harvey, & Rentschler, 1991; Toet &
Levi, 1992; Westheimer & Hauske, 1975). Unlike traditional
masking, in which a signal (e.g., the face) is rendered
invisible, crowding occurs when the signal is still
visible but its features blend with its neighbors (Martelli
et al., 2005; Pelli et al., 2004). Integrating surrounding
features with those of the signal results in the inability to
scrutinize or identify the target. According to most
models, crowding occurs because of interference or
pooling among low-level features, which likely happens
at a single, relatively early stage in visual processing
(Ariely, 2001; Chung, Levi, & Legge, 2001; He,
Cavanagh, & Intriligator, 1997; Levi et al., 1985; Parkes,
Lund, Angelucci, Solomon, & Morgan, 2001; Pelli et al.,
2004). To date, however, it has not been tested whether
crowding can occur selectively at higher levels in the
visual systemVonly crowding of low-level features has
been demonstrated. The possibility therefore remains that
crowding may operate at multiple levels in the visual
Journal of Vision (2007) 7(2):24, 1–11 http://journalofvision.org/7/2/24/ 1
doi: 10.1167/7.2.24 Received October 20, 2006; published November 26, 2007 ISSN 1534-7362 * ARVO
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system; for example, even between high-level representations of objects.
In this study, we tested whether crowding can occur
between high-level, holistic representations of objects, not
just between low-level features as past research has found.
To address this question, we used upright and inverted
faces as stimuli. It is well established that recognition of
an upright face is not necessarily based on the processing
of its individual features (featural processing). Rather, we
tend to identify upright faces holistically, or by analyzing
the configuration or relations between these features (Boutet
& Chaudhuri, 2001; Farah, Tanaka, & Drain, 1995; Farah,
Wilson, Drain, & Tanaka, 1998; Maurer, Grand, &
Mondloch, 2002; Tanaka & Farah, 1993; Thompson,
1980; Yin, 1969; Young, Hellawell, & Hay, 1987). Moreover, McKone and colleagues (McKone, Martini, &
Nakayama, 2001; Robbins & McKone, 2003, 2007) have
convincingly shown that holistic processing cannot be
learned for inverted faces or non-face objects, which
provides further evidence that the processing of upright
faces is distinct from that of inverted faces (though cf.
Carey, 1992; Diamond & Carey, 1986). Thus, the presence
of an inversion effect is a reliable indicator of holistic
processing of upright faces.
If crowding occurs selectively between the configural
representations of upright faces, and not just between the
features of the faces themselves (Martelli et al., 2005), we
would expect greater impairment of face recognition when
an upright target face is surrounded by similarly configured upright faces compared to when it is surrounded by
inverted faces. On the other hand, we would not expect
the same pattern of results to hold true for non-face target
objects (e.g., houses) or for inverted target faces. To test
these predictions, we measured face and house recognition
in a crowded display of other faces and houses.
Methods
Four experienced psychophysical subjects with normal
or corrected-to-normal visual acuity participated in the
experiments. All experiments were approved by the human
subjects review board at UC Davis. Stimuli were presented
on a gamma-corrected, linearized, high-resolution CRT
monitor (Sony Multiscan G520, 1024 768 pixels, 120 Hz
refresh) using an Apple G4 Power Macintosh with OS9
running Vision Shell (www.visionshell.com). Participants
were seated in a dark soundproof room with a chin rest
placed 49 cm from the screen.
Stimuli
Stimuli were 30 houses and 30 male faces with neutral
expressions, drawn from Dr. Ken Nakayama’s Harvard
Face Database, with permission and consent. Using Adobe
Photoshop 8.0, all stimuli were grayscale filtered, noise
filtered (10%), and band-pass spatial frequency filtered
with cutoffs at 1 cycle/pixel and 0.2 cycles/pixel. All
stimuli were edited so that the main features fit inside an
oval window of 3.51 deg visual angle wide and 4.82 deg
high; the outlines of the stimuli (the edges of the faces and
houses) were not visible.
Experiment 1: Upright faces among upright
and inverted face flankers
Three conditions were presented. In the upright flankers
condition (Figure 1a), a central face, which could or could
not be the target face, was surrounded by six upright
flanker faces, creating an array of faces; an array was
presented on both sides of the fixation bull’s-eye (11.58 deg
center-to-center distance). In the inverted flankers condition, the array was composed of an upright central face
surrounded by six inverted flanker faces (Figure 1b). In
the third condition, the central face was presented without
the surrounding flankers (Figure 1c). The array of
crowding faces (the flankers) were presented on an
imaginary oval surrounding each central face (5.24 deg
vertical distance between the midpoint of the central face
and the oval, and 3.98 deg horizontal distance between the
midpoint of the central face and the oval). The spacing
between each flanker was fixed, but the absolute position
of the flankers on the oval was randomized on each trial.
The average feature-to-feature (e.g., nose-to-nose) distance between the central and surrounding faces was equal
for upright and inverted flankers. In addition to the noise
added by Photoshop, one of five levels of random dot
noise was added to the central face in both arrays on every
trial by increasing or decreasing the brightness of each
pixel by a random amount within T31 cd/m2, which
ensured that recognition performance did not reach ceiling
or floor (Figure 1d).
On each trial, the stimuli were presented for 400 ms. A
central target face was presented on 50% of the trials in
the array on either the left or right side of the screen.
While continuously fixating on the bull’s-eye, subjects
performed a 3 alternative forced-choice (3 AFC) task to
indicate whether the target face was on the left or right
side of the screen, or was not present. This is comparable
to a simultaneous detection and identification experiment
in which the stimulus was not present in one condition
(Green, Weber, & Duncan, 1977; Starr, Metz, Lusted, &
Goodenough, 1975). Prior to the main experiment, each
subject participated in a minimum of 2400 practice trials
to reach asymptotic recognition levels and become
familiarized with the target face (in 150 trial blocks; the
first four blocks included feedback for missed responses).
During these practice runs, an adaptive procedure was
used to manipulate the level of noise randomly superimposed on each central face such that in the lowest noise
Journal of Vision (2007) 7(2):24, 1–11 Louie, Bressler, & Whitney 2
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condition accuracy was above 90%, while in the highest
noise condition the accuracy was between 50% and 60%
(chance performance was 33%). In the main experiment,
subjects participated in 10 separate sessions, with 300
trials per session (3 flanker conditions 5 noise levels
20 trials per condition), for a total of 3000 trials per
subject. No feedback was provided. For each of the 10
sessions, d-prime was calculated for each condition as an
indicator of face recognition sensitivity, as in multiple
response classification methods (Haase & Fisk, 2001;
MacMillan & Creelman, 2004); statistics were calculated
on the mean d-primes.
Experiment 2: Upright houses among upright
and inverted house flankers
The methods in this experiment were identical to those
in Experiment 1, except the stimuli were images of houses
(Figure 2a). Each subject from Experiment 1 completed a
total of 2100 practice trials and 10 sessions (3000 trials) of
Experiment 2.
Experiment 3: Inverted faces among upright
and inverted face flankers
The methods in this experiment were identical to those
in Experiment 1, except the central faces were inverted
(Figure 3a). Three of the four subjects from Experiment 1
completed a total of 2400 practice trials and 10 sessions
(3000 trials) of Experiment 3.
Experiment 4: Upright face crowding as a
function of eccentricity
Several studies have demonstrated that crowding follows a unique half-eccentricity rule: Crowding occurs as
Figure 1. Three crowding conditions were presented in Experiment 1: upright flankers (a), inverted flankers (b), or no flankers (c); the
central face could or could not be the target face. Subjects indicated whether the target face (Figure 1a, left side) was on the left or right
side of the fixation point or not present. Across all conditions, recognition was impaired with increasing noise added to the stimuli (d).
Despite the eccentric location of the target face (c), all subjects were able to recognize and discriminate it, consistent with previous studies
(McKone, 2004). Panel e shows mean discrimination for four individual subjects (symbols represent separate subjects). Averaged across
six subjects (the four original subjects plus two new subjects from Experiment 4), there was a significant reduction in d-prime in the upright
versus inverted flanker conditions (t(5) = 3.6, p G 0.01, r = 0.95; Wilcoxon test, Z = j1.99, P = 0.023). This demonstrates that recognition
was significantly and selectively impaired in the upright flanker condition (f). Error bars, within subjects TSEM.
Journal of Vision (2007) 7(2):24, 1–11 Louie, Bressler, & Whitney 3
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long as the target–flanker separation is less than half the
eccentricity of the target (Bouma, 1970; Martelli et al.,
2005; Pelli et al., 2004). To ensure that the effect we
observed is due to crowding and not masking, we repeated
Experiment 1 at two additional eccentricities. One subject
from Experiment 1 and two naı ¨ve subjects each participated. In one condition, targets were presented at 6.13 deg
eccentricity, and subjects participated in 10 sessions (3000
trials); all other experiment details were identical to
Experiment 1. In the second condition, targets were
presented foveally (0 deg eccentricity). All stimulus
details were identical to Experiment 1, but the task was
a temporal version of the spatial simultaneous detection
and identification task in Experiment 1: An array was
flashed for 400 ms, followed by a 400-ms ISI, and a
second array was flashed for 400 ms (Green et al., 1977;
Starr et al., 1975). Subjects performed a 3 AFC task in
which they indicated whether the target face appeared in
the first interval, the second interval, or did not appear at
all.
Experiment 5: Famous face recognition at
eccentric locations
To determine whether observers can recognize an
isolated face at an eccentric location, we tested the ability
of seven naı ¨ve subjects to recognize upright and inverted
famous faces in isolation at the three eccentricities
presented in Experiments 1 and 4 (0 deg, 6.13 deg, and
11.58 deg from fixation). Images of 50 celebrities (25
male and 25 female) were collected using the Google
search engine and were edited using Adobe Photoshop
8.0. Faces were gray-scaled and sized so that the main
features fit within a 3.50 deg 4.81 deg oval (same as
Experiment 1). Not all of the faces were familiar to each
of the subjects; to establish individual baseline recognition
of the 50 celebrities, subjects fixated on the upright faces
and identified (named) the famous person in a pretest.
Each subject recognized a subset of the 50 faces; average
recognition was 34.6/50 (69.2%). Following this pretest,
all 50 famous faces (familiar ones and non-familiar ones)
Figure 2. The conditions and tasks in Experiment 2 remained the same as those in Experiment 1, except houses were used as stimuli (a);
the target house is located on the left in the top panel. Without crowding, each of the four subjects was easily able to identify the target
house (b; each symbol type represents a separate subject). Panel c shows the average recognition across the four subjects. Although
there was a difference between the no flanker and inverted flanker conditions (c), indicating that crowding was effective, there was no
significant or selective difference between the upright and inverted flanker conditions (F(1, 3) = 0.075, p 9 0.05, eta2 = 0.02). Error bars,
within subjects TSEM.
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were then presented in random order at 6.13 deg or
11.58 deg eccentricity for 400 ms, while subjects fixated on
the central point and named the famous person. Of the subset
of familiar faces (determined in the pretest), the proportion
of correctly identified faces was calculated at each eccentricity (i.e., performance was normalized, for each subject, to
the set of familiar faces). Because the average number of
familiar faces was 34.6, chance performance in the
recognition (naming) task was 1/34.6, on average (this is a
conservative estimate; chance performance was probably
lower than this, considering that subjects did not memorize
the set of faces in the pretest).
Results
Target face recognition selectively impaired
by upright flankers
In the first experiment, we presented an upright target
face either in isolation, or in a crowd of upright or
inverted flanker faces (Figures 1a–1c; see Methods).
There was a significant difference in recognition between
the no flanker and flanker conditions (F(2, 6) = 14.21,
p = 0.005, eta2 = 0.83), indicating that crowding was effective
at impairing recognition (Figures 1d–1f). Importantly,
recognition was impaired most when the target face was
surrounded by upright compared to inverted flankers
(Figure 1f; F(1, 3) = 22.3, p G 0.05, eta2 = 0.88; Wilcoxon
test, Z = j1.84, p = 0.03). Not surprisingly, sensitivity
decreased significantly with increasing noise added to the
images (Figure 1d; F(4, 12) = 17.92, p G 0.05, eta2 =
0.86). The results show that target face identification was
significantly and selectively impaired in the upright
flankers condition.
Target house recognition not selectively
impaired by upright flankers
In the second experiment, we tested whether the same
crowding effect would occur with objects that are not
holistically processed, using images of houses as stimuli
(Figure 2a). Unlike the results predicted for Experiment 1,
Figure 3. The methods used in Experiment 3 remained the same as those in Experiment 1, except the central target face was inverted (a).
Discrimination was impaired with upright and inverted flankers for each of the three subjects, indicating that crowding was effective (b;
each symbol represents a separate subject). Panel c shows the average recognition across subjects. Recognition of the target face was
better in the inverted flankers condition compared to the upright flankers condition (F(1, 2) = 11.1, p = 0.08, eta2 = 0.84; Wilcoxon test,
Z = 1.61, p = 0.055), contrary to what one would expect if the similarity in the orientation of the flankers and target were responsible for the
selective crowding effect found in Experiment 1. Error bars, within subjects TSEM.
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we did not expect to find a significant difference between
the two flanker conditions (upright vs. inverted) because
houses are processed in a part-based manner, regardless of
orientation. Figures 2b–2c show that the presence of
flankers effectively disrupted recognition of target houses
(crowding condition effect, F(2, 6) = 9.86, p = 0.013,
eta2 = 0.77). However, there was not a significant difference
in recognition of the target house when it was surrounded by
either upright or inverted flanker houses (F(1, 3) = 0.075,
p 9 0.05, eta2 = 0.02). Thus, there is crowding of house
targets by house flankers, but this crowding is not gated by
orientation.
Impaired target face recognition not due to
flanker similarity
Could the selective impairment in Experiment 1 be due
to the similarity of the upright flanker faces interfering
with recognition of the upright target face? To address this
question, we used inverted faces as the central target
stimuli in Experiment 3. If similarity were responsible for
the impaired performance in Experiment 1, we would
expect observers’ performance to be worse in the inverted
flankers condition and better in the upright flankers
condition. However, this was not the case. Figures 3b
and 3c show that recognition of inverted targets was worse
in the upright flankers condition compared to the inverted
flankers condition (F(1, 2) = 11.1, p = 0.08, eta2 = 0.84;
Wilcoxon test, Z = 1.61, p = 0.055). Note that the trend of
this effect was opposite from that predicted by the
similarity argument, and each subject showed the same
pattern of results (Figure 3b), demonstrating that a floor
effect cannot be responsible for the results. More
importantly, when compared to the first experiment, there
was a significant interaction between the flanker–target
similarity and the recognition of the target (Figure 4a; F(1,
2) = 34.7, p G 0.05, eta2 = 0.95). The interaction was also
significant between the first and second experiments
(Figure 4b; F(1, 3) = 9.9, p G 0.05, eta2 = 0.76). Finally,
we analyzed the correct trials (hits)Vindependent of the
constant proportion of false alarms (Figure 4c)Vand
found the same pattern of results and the same significant
interactions (Figure 4d), showing that the impaired
recognition in the first experiment was unique to upright
target faces with upright flankers.
To more closely examine whether the similarity in the
orientation of the flanker and target stimuli can explain the
results in Experiment 1, each subject’s data in the two
flanker conditions (upright vs. inverted) in all three
experiments were directly compared. According to the
similarity argument, recognition should be most impaired
when the target and flankers have the same orientation. To
test this prediction, we subtracted the discrimination
values in the similar flanker condition from those in the
dissimilar flanker condition for each experiment, within
each subject (Figure 5a). When the data are normalized in
this way, positive scores along the ordinate (Figure 5)
would indicate impaired recognition when the flankers
had an orientation similar to that of the target. If
orientation similarity were responsible for the crowding
effect in Experiment 1, then we should observe positive
scores in Figure 5 across all three experiments (dashed
line). Experiments 2 and 3, however, did not show
impaired discrimination for similarly oriented flankers,
Figure 4. Comparison of the two crowding conditions in each of
the first three experiments. Along the abscissa is the similarity in
the orientation of the flankers relative to the target. Panel a
illustrates that there was a significant interaction between
recognition performance and the target–flanker similarity across
the first (circles) and third (squares) experiments (F(1, 2) = 34.7,
p G 0.05, eta2 = 0.95). That is, in the first experiment, recognition
was most impaired when an upright target face was surrounded
by similarly oriented flankers (upright faces). In the third experiment, on the other hand, inverted target face recognition was
most impaired when surrounded by dissimilarly oriented flankers
(upright faces), indicated by the dashed line. Comparing the first
(circles) and second (triangles) experiments (b) also revealed a
significant interaction (F(1, 3) = 9.9, p G 0.05, eta2 = 0.76). Error
bars in the bottom right corner of the graphs are representative
within-subjects SEM. The proportion of false alarms (trials in
which a target was incorrectly reported as present) across the two
flanker conditions was very small (c), indicating that subjects
employed strict criterions; the fact that the false alarm rate was
constant across the different conditions validates direct dV
comparisons (MacMillan & Creelman, 2004). A selective analysis
of the correct responses (hits) revealed the same pattern of
results (d), and the same significant interactions between the first
and third experiments (F(1, 2) = 114.7, p G 0.05, eta2 = 0.98) and
the first and second experiments (F(1, 3) = 18.8, p G 0.05, eta2 =
0.86). Within-subject error bars (TSEM) are smaller than some
symbols.
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and this deviation from the similarity prediction was
significant (F(2, 8) = 8.6, p = 0.01, eta2 = 0.68; a nonparametric Kruskal–Wallis test on the individual subject
data confirmed this significant interaction, #2(2) = 9.8,
p G 0.05). Planned post hoc comparisons revealed that there
was a significant difference in the normalized discrimination score for the first versus the second experiment
(t(6) = 2.8, two-tailed p G 0.05, r = 0.75), and for the first
versus the third experiment (t(5) = 5.5, two-tailed p G 0.05,
r = 0.93). There was not a significant difference between
the second and third experiments (t(5) = 1.26, two-tailed
p = 0.26). The results of this analysis provide strong
evidence that similarity was not responsible for the
selective crowding found in Experiment 1.
Selective interference between upright faces
is crowding, not masking
The fourth experiment tested the eccentricity dependence
of the crowding effect found in Experiment 1. Figure 6
shows that there were main effects of eccentricity (F(2, 4) =
8.9, p = 0.034, eta2 = 0.82) and crowding (F(2, 4) = 22.1,
p = 0.007, eta2 = 0.92). The eccentricity effect indicates
that faces are harder (though not impossible) to recognize
at eccentric locations, consistent with previous findings
(Goren & Wilson, 2006; McKone, 2004). More importantly, the crowding effect increased significantly with
increasing stimulus eccentricity (crowding eccentricity
interaction; F(4, 8) = 9.9, p = 0.003). At the fovea, there
was no significant crowding (F(2, 4) = 3.2, p 9 0.05). The
eccentricity dependence of the crowding and the fact that
it did not occur at the fovea suggest that masking is not
responsible for the results (Pelli et al., 2004). To ensure
that the lack of a crowding effect at the fovea was not due
to ceiling performance, two subjects from Experiment 5
completed 6 additional runs with more noise added to the
central faces (Figure 6, insets). The open and solid gray
symbols in Figure 6 show that increasing the noise
reduced overall accuracy, but there was still no difference
in performance across the three flanker conditions (F(2, 2)
= 0.77, p = 0.56). This indicates that the lack of a
crowding effect observed at the fovea was not due to
subjects’ ceiling performance.
Consistent with the first experiment, recognition of targets
presented at 11.58 deg eccentricity was impaired by the
presence of flankers (Figure 6). Paired t-tests revealed a
significant difference between the upright and inverted
flanker conditions (t(5) = 4.6, p = 0.006), and the upright
and no flanker conditions (t(5) = 4.5, p = 0.007). The
eccentricity dependence of these results is consistent with
previous definitions of crowding (Bouma, 1970; Pelli et al.,
2004) and demonstrates that the impaired face recognition
due to upright flankers observed in Experiment 1 is a result
of crowding, and not masking or some other effect.
Famous face recognition at eccentric
locations
The purpose of Experiment 5 was to confirm that
subjects can recognize familiar faces presented at eccentric locations, and to rule out the possibility that subjects
might rely on simple feature detection when faces are
Figure 5. Summary of the flanker conditions in all three experiments for each subject showing that orientation similarity was not
responsible for the results of Experiment 1. According to the
similarity prediction, when the target and flankers have similar
orientations, recognition will be impaired. For each of the three
experiments (abscissa), the ordinate shows the normalized
recognition (the difference in d-prime for dissimilar minus similar
flankers). In each of the three experiments, the orientation of the
flankers could be similar or dissimilar to the central target; in the
first two experiments, upright flankers were similar to the upright
targets, and in the third experiment, inverted flankers were similar
to the inverted targets. Positive values on the ordinate indicate
that similar flankers reduced recognition (e.g., in the first experiment, the upright flankers were more effective at impairing
discrimination). The dashed line indicates the prediction of
similarityVthe expected results if the similar orientation of the
targets and flankers was responsible for the crowding effect. The
data do not obey the similarity prediction. Panel B shows that
there is a significant difference between the three experiments
(F(2, 8) = 8.6, p = 0.01, eta2 = 0.68). Planned post hoc comparisons
revealed that the first experiment was significantly different than
both the second (t(6) = 2.8, two-tailed p G 0.05, r = 0.75) and third
(t(5) = 5.5, two-tailed p G 0.05, r = 0.93) experiments. There was
not a significant difference between the second and third control
experiments (t(5) = 1.26, two-tailed p = 0.26). The results show
that the similarity between the orientation of targets and flankers is
not responsible for the impaired recognition in the first experiment.
Error bars, between subjects TSEM.
Journal of Vision (2007) 7(2):24, 1–11 Louie, Bressler, & Whitney 7
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presented in the periphery. Consistent with previous
studies (Goren & Wilson, 2006; McKone, 2004), subjects
were significantly above chance at recognizing famous
faces at all tested eccentricities (least significant condition
was inverted faces at 11.58 deg, t(6) = 5.6, p = 0.001).
Figure 7 shows that there were significant effects of
eccentricity (F(1, 6) = 10.6, p = 0.018) and inversion (F(1,
6) = 10.9, p = 0.017) on face recognition. The significant
effect of eccentricity supports previous findings that face
recognition becomes difficult in the periphery (Goren &
Wilson, 2006; McKone, 2004), and that this is likely due
to self-crowding of the features within the face (Martelli
et al., 2005). Nevertheless, peripheral face recognition is
far from impossible (Figure 7). Further, the fact that there
was a significant inversion effect (difference between
upright and inverted face recognition) at 11.58 deg
eccentricity (t(6) = 8.4, p = 0.001) indicates that subjects
were not just performing a simple detection task of a
single feature but were using configural or holistic
information to identify the faces (Farah et al., 1995;
Freire, Lee, & Symons, 2000; Leder & Bruce, 2000).
Discussion
The experiments reported here suggest that, in addition
to low-level crowding, there is selective crowding
Figure 6. Holistic crowding as a function of stimulus eccentricity.
The holistic crowding effect-selective crowding of upright target
faces by upright flankers (Figure 1) was modulated by the
eccentricity of the stimuli. All three subjects performed above
chance at all eccentricities (chance level was 33% correct). As the
stimuli were presented more foveally (abscissa), the crowding
effect decreased (F(4, 8) = 9.9, p = 0.003, eta2 = 0.83). Pairwise
comparisons revealed that, at 11.58 deg eccentricity, upright
flankers impaired recognition significantly more than inverted
flankers (t(5) = 4.6, p = 0.006) and significantly more than
no flankers (t(5) = 4.5, p = 0.007). At 0 deg and 6.13 deg
eccentricity, there was not a significant difference between the
upright and inverted flanker conditions (the more significant effect
was at 6.13 deg; t(2) = 1.9, p = 0.10 one-tailed). To ensure that the
lack of a crowding effect at 0 deg eccentricity was not due to
subjects’ ceiling performance, we tested two subjects in 6 additional runs with more noise added to the central faces (open and
solid gray symbols show performance at two noise levels, see
inset face images). With increasing noise, overall accuracy was
reduced, but there was no differential crowding effect (F(2, 2) =
0.77, p = 0.56). Asterisks indicate significant pairwise comparisons
(p G 0.05). In all conditions, the center-to-center separation
between the target and flankers was 3.94 deg. The dashed
vertical line indicates the eccentricity that was twice this separation
and is the approximate point at which crowding occurs in experiments using other kinds of features and letters (Bouma, 1970; Pelli
et al., 2004). The data closely follow this half-eccentricity rule,
supporting the conclusion that the selective impairment of upright
face recognition by upright face flankers at eccentric locations is a
genuine crowding effect, and not due to pattern masking or
salience of upright faces. Error bars, between subjects TSEM.
Figure 7. Eccentric recognition of famous faces without crowding.
Famous faces were presented in isolation at one of three
eccentricities (foveal, 6.13 deg, or 11.58 deg, identical to the
eccentricities of the faces in the previous experiments). Subjects
first identified (named) 50 famous faces while fixating the face.
For each subject, the subset of recognized faces (mean = 69%
correct, 34.6 faces) were then presented in a random order at one
of two eccentricities (abscissa). Subjects were required to identify
(name) the famous face presented in their periphery. The ordinate
shows the proportion of correctly identified faces (normalized to
the total number of faces that each subject recognized; hence, the
data point at 100% correct at 0 deg eccentricity). Average chance
level performance was 2.9% (1/34.6), represented by the dashed
line. At all eccentricities, subjects were significantly above chance
performance. There were significant effects of eccentricity and
inversion (F(1, 4) = 13.1, p G 0.05, and F(1, 4) = 9.4, p G 0.05,
respectively). Inverting the faces impaired recognition at each
eccentricity (least significant pairwise comparison was in the
6.13 deg eccentricity condition, t(6) = 3.1, p G 0.05). The results
demonstrate that subjects are able to identify familiar faces at
eccentric locations, and this recognition is not restricted to
feature-based processes. Error bars, within subjects TSEM.
Journal of Vision (2007) 7(2):24, 1–11 Louie, Bressler, & Whitney 8
Downloaded from jov.arvojournals.org on 03/23/2020
between high-level representations of faces. The results of
Experiment 1 demonstrated that when an upright target
face was surrounded by upright flanker faces, recognition
was significantly worse than when the target was
surrounded by inverted flankers or none at all. However,
this occurred only when the central stimuli were upright
faces and cannot be attributed to similarity. Further, the
crowding effect displayed eccentricity dependence and
did not occur at the fovea, ruling out alternative
explanations.
In addition to the crowding between upright representations of faces, Experiments 2 and 3 revealed crowding at a
lower level of analysis. In Experiment 2 (house recognition), subjects’ average d-prime scores were nearly
identical for upright and inverted house flankers. This is
not surprising because house images are processed in a
part-based manner; because this feature-based processing
strategy is employed for both upright and inverted houses,
we would not expect a difference in performance. Experiment 3 showed that inverted target faces were more
impaired by upright than inverted flanker faces, though
this effect was not significant. Still, this is an interesting
result and could be due to the fact that the upright face
flankers carry feature and configural information, both of
which might crowd the inverted target. The inverted
flankers, on the other hand, can only exert feature-level
crowding. The fact that the inverted target face is
processed by features does not preclude the possibility
that nearby configural information (at a super-ordinate
level) trumps or crowds the feature level information of
the inverted target.
How do we know that the selective interference
between upright faces is crowding and not salience,
masking, or some other effect? We addressed this in
Experiment 4 by measuring crowding as a function of
eccentricity. If the salience or distractibility of upright
face flankers was responsible for the results in Figure 1,
then we should have observed an interference effect at all
eccentricitiesVeven at the fovea (Palermo & Rhodes,
2002). However, we found that the selective interference
between upright faces only occurred when the target–
flanker separation was less than half the eccentricity of the
target. Moreover, there was no interference found at the
fovea, which rules out upright face salience and masking
as explanations. Together, these results demonstrate a
precise spatial dependence of the effect we observed in
Experiment 1, and they are consistent with the operational
definition of crowding proposed by Pelli et al. (2004).
The results here suggest that there is crowding between
configural (upright) representations of faces. This is just
one type of crowding at one particular level; however, it
does not discount past research that has characterized
other levels of crowding. Figures 6 and 7 clearly illustrate
that crowding also occurs within a single face (Martelli
et al., 2005): Recognition of an isolated face declined
significantly with increasing eccentricity. This supports
Martelli and colleagues’ (2005) conclusion that crowding
among the features within a face, or self-crowding, is one
of the critical limits to face recognition in the periphery.
Indeed, self-crowding may be the single most influential
limitation on peripheral face recognition (Figures 6 and
7). This is not the only type of crowding, however, and it
cannot explain our results. For example, the feature-tofeature separations (e.g., bars, edges, facial features) were
constant in all conditions, and yet we still observed
selective crowding between upright faces. Therefore, in
addition to feature-level crowding within the face, our
results demonstrate that crowding occurs selectively
between the holistic or configural representations of
upright faces as well.
The results here are somewhat surprising, given what is
known about the neural mechanisms of face recognition.
Our results show spatially precise interactions between
holistic representations of faces, which would seem to
require a region that topographically codes configural
information about faces. The configural or holistic
information about faces is believed to be analyzed in the
fusiform face area (Schiltz & Rossion, 2006; Yovel &
Kanwisher, 2005). The FFA, however, has been reported
as either non-retinotopic or coarsely retinotopic (Levy,
Hasson, Avidan, Hendler, & Malach, 2001; Malach, Levy,
& Hasson, 2002). The lack of fine retinotopy in the
FFA, however, does not mean that it codes images with
position invariance. Even with very large receptive fields,
if there is sufficient overlap across the population of
neurons, the FFA could effectively carry a coarse-code for
face position on a very precise scale (Eurich & Schwegler,
1997). The fact that FFA topography results are currently
mixed is less revealing about FFA architecture than it is
about the limitations of current fMRI analytic techniques;
with advances in fMRI, a more precise picture of FFA
topography will soon emerge. Even if the FFA is not
ultimately responsible, our results indicate that some other
region or network must carry both configural information
about faces and precise spatial information.
Whether due to mandatory grouping, averaging, interference, lateral masking, or attention, current models of
crowding (Ariely, 2001; Blake, Tadin, Sobel, Raissian, &
Chong, 2006; Chung et al., 2001; He et al., 1997;
Intriligator & Cavanagh, 2001; Levi et al., 1985; Parkes
et al., 2001; Pelli et al., 2004) must be updated to account
for the fact that high-level representations of objects can
selectively crowd each other. Likewise, models that posit
a single mechanism for crowding or suggest that it
operates at a single level in the visual system must be
revised in light of the likelihood that crowding occurs at
multiple stages of processing.
Acknowledgments
Thanks to Ken Nakayama and Frank Tong for providing
stimuli, to Thuc-Nhi Nguyen, Tom Harp, Jennifer Temple,
Journal of Vision (2007) 7(2):24, 1–11 Louie, Bressler, & Whitney 9
Downloaded from jov.arvojournals.org on 03/23/2020
Lica Iwaki, Nicole Spotswood, Jason Fischer, Santani
Teng, and Nicole Wurnitsch for help collecting data, and
to Denis Pelli, Marialuisa Martelli, and an anonymous
reviewer for helpful comments on an earlier draft. This
work was supported by NIH.
Commercial relationships: none.
Corresponding author: David Whitney.
Email: dwhitney@ucdavis.edu.
Address: Center for Mind and Brain, and Department of
Psychology, UC Davis, One Shields Avenue, Davis, CA
95618, USA.
References
Ariely, D. (2001). Seeing sets: Representation by statistical
properties. Psychological Science, 12, 157–162.
[PubMed]
Blake, R., Tadin, D., Sobel, K. V., Raissian, T. A., &
Chong, S. C. (2006). Strength of early visual
adaptation depends on visual awareness. Proceedings
of the National Academy of Sciences of the United
States of America, 103, 4783–4788. [PubMed]
[Article]
Bouma, H. (1970). Interaction effects in parafoveal letter
recognition. Nature, 226, 177–178. [PubMed]
Boutet, I., & Chaudhuri, A. (2001). Multistability of
overlapped face stimuli is dependent upon orientation. Perception, 30, 743–753. [PubMed]
Carey, S. (1992). Becoming a face expert. Philosophical
Transactions of the Royal Society of London B:
Biological Sciences, 335, 95–103. [PubMed] [Article]
Chung, S. T., Levi, D. M., & Legge, G. E. (2001). Spatialfrequency and contrast properties of crowding. Vision
Research, 41, 1833–1850. [PubMed]
Diamond, R., & Carey, S. (1986). Why faces are and are not
special: An effect of expertise. Journal of Experimental
Psychology: General, 115, 107–117. [PubMed]
Eurich, C., & Schwegler, H. (1997). Coarse coding:
Calculation of the resolution achieved by a population
of large receptive field neurons. Biological Cybernetics, 76, 357–363. [PubMed]
Farah, M. J., Tanaka, J. W., & Drain, H. M. (1995). What
causes the face inversion effect? Journal of Experimental Psychology: Human Perception and Performance,
21, 628–634. [PubMed]
Farah, M. J., Wilson, K. D., Drain, M., & Tanaka, J. N.
(1998). What is “special” about face perception?
Psychological Review, 105, 482–498. [PubMed]
Field, D. J., Hayes, A., & Hess, R. F. (1993). Contour
integration by the human visual system: Evidence
for a local “association field.” Vision Research, 33,
173–193. [PubMed]
Freire, A., Lee, K., & Symons, L. (2000). The faceinversion effect as a deficit in the encoding of
configural information: Direct evidence. Perception,
29, 159–170. [PubMed]
Goren, D., & Wilson, H. R. (2006). Quantifying facial
expression recognition across viewing conditions.
Vision Research, 46, 1253–1262. [PubMed]
Green, D. M., Weber, D. L., & Duncan, J. E. (1977).
Detection and recognition of pure tones in noise.
Journal of the Acoustical Society of America, 62,
948–954. [PubMed]
Haase, S. J., & Fisk, G. (2001). Confidence in word
detection predicts word identification: Implications
for an unconscious perception paradigm. American
Journal of Psychology, 114, 439–468. [PubMed]
He, S., Cavanagh, P., & Intriligator, J. (1996). Attentional
resolution and the locus of visual awareness. Nature,
383, 334–337. [PubMed]
He, S., Cavanagh, P., & Intriligator, J. (1997). Attentional
resolution. Trends in Cognitive Sciences, 1, 115–121.
Intriligator, J., & Cavanagh, P. (2001). The spatial
resolution of visual attention. Cognitive Psychology,
43, 171–216. [PubMed]
Latham, K., & Whitaker, D. (1996). Relative roles of
resolution and spatial interference in foveal and
peripheral vision. Ophthalmic & Physiological
Optics, 16, 49–57. [PubMed]
Leder, H., & Bruce, V. (2000). When inverted faces are
recognized: The role of configural information in face
recognition. Quarterly Journal of Experimental Psychology A: Human Experimental Psychology, 53,
513–536. [PubMed]
Levi, D. M., Klein, S. A., & Aitsebaomo, A. P. (1985).
Vernier acuity, crowding and cortical magnification.
Vision Research, 25, 963–977. [PubMed]
Levy, I., Hasson, U., Avidan, G., Hendler, T., & Malach, R.
(2001). Center-periphery organization of human object
areas. Nature Neuroscience, 4, 533–539. [PubMed]
[Article]
MacMillan, N. A., & Creelman, C. D. (2004). Detection
theory: A user’s guide (2nd ed.). Hillsdale, NJ:
Lawrence Erlbaum Associates.
Malach, R., Levy, I., & Hasson, U. (2002). The topography of high-order human object areas. Trends in
Cognitive Sciences, 6, 176–184. [PubMed]
Martelli, M., Majaj, N. J., & Pelli, D. G. (2005). Are faces
processed like words? A diagnostic test for recognition by parts. Journal of Vision, 5(1):6, 58–70,
http://journalofvision.org/5/1/6/, doi:10.1167/5.1.6.
[PubMed] [Article]
Journal of Vision (2007) 7(2):24, 1–11 Louie, Bressler, & Whitney 10
Downloaded from jov.arvojournals.org on 03/23/2020
Maurer, D., Grand, R. L., & Mondloch, C. J. (2002). The
many faces of configural processing. Trends in
Cognitive Sciences, 6, 255–260. [PubMed]
McKone, E. (2004). Isolating the special component of face
recognition: Peripheral identification and a Mooney
face. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 30, 181–197. [PubMed]
McKone, E., Martini, P., & Nakayama, K. (2001).
Categorical perception of face identity in noise
isolates configural processing. Journal of Experimental Psychology: Human Perception and Performance,
27, 573–599. [PubMed]
Palermo, R., & Rhodes, G. (2002). The influence of
divided attention on holistic face perception. Cognition, 82, 225–257. [PubMed]
Parkes, L., Lund, J., Angelucci, A., Solomon, J. A., &
Morgan, M. (2001). Compulsory averaging of
crowded orientation signals in human vision. Nature
Neuroscience, 4, 739–744. [PubMed] [Article]
Pelli, D. G., Palomares, M., & Majaj, N. J. (2004). Crowding
is unlike ordinary masking: Distinguishing feature
integration from detection. Journal of Vision, 4(12):12,
1136–1169, http://journalofvision.org/4/12/12/,
doi:10.1167/4.12.12. [PubMed] [Article]
Robbins, R., & McKone, E. (2003). Can holistic processing
be learned for inverted faces? Cognition, 88, 79–107.
[PubMed]
Robbins, R., & McKone, E. (2007). No face-like processing for objects-of-expertise in three behavioural tasks.
Cognition, 103, 34–79. [PubMed]
Schiltz, C., & Rossion, B. (2006). Faces are represented
holistically in the human occipito-temporal cortex.
Neuroimage, 32, 1385–1394. [PubMed]
Starr, S. J., Metz, C. E., Lusted, L. B., & Goodenough, D. J.
(1975). Visual detection and localization of radiographic images. Radiology, 116, 533–538. [PubMed]
Strasburger, H., Harvey, L. O., Jr., & Rentschler, I.
(1991). Contrast thresholds for identification of
numeric characters in direct and eccentric view.
Perception & Psychophysics, 49, 495–508. [PubMed]
Tanaka, J. W., & Farah, M. J. (1993). Parts and wholes in
face recognition. Quarterly Journal of Experimental
Psychology: A Human Experimental Psychology, 46,
225–245. [PubMed]
Thompson, P. (1980). Margaret Thatcher: A new illusion.
Perception, 9, 483–484. [PubMed]
Toet, A., & Levi, D. M. (1992). The two-dimensional
shape of spatial interaction zones in the parafovea.
Vision Research, 32, 1349–1357. [PubMed]
Westheimer, G., & Hauske, G. (1975). Temporal and
spatial interference with Vernier acuity. Vision
Research, 15, 1137–1141. [PubMed]
Yin, R. K. (1969). Looking at upside-down faces. Journal
of Experimental Psychology: Human Perception and
Performance, 81, 141–145.
Young, A. W., Hellawell, D., & Hay, D. C. (1987).
Configurational information in face perception. Perception, 16, 747–759. [PubMed]
Yovel, G., & Kanwisher, N. (2005). The neural basis of
the behavioral face-inversion effect. Current Biology,
15, 2256–2262. [PubMed] [Article]
Journal of Vision (2007) 7(2):24, 1–11 Louie, Bressler, & Whitney 11
Downloaded from jov.arvojournals.org on 03/23/2020

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