Neuropsychologia 48 (2010) 51–59
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/neuropsychologia
The scope of social attention deficits in autism: Prioritized orienting
to people and animals in static natural scenes
Joshua J. Newa,b,∗, Robert T. Schultzc, Julie Wolfc, Jeffrey L. Niehausd, Ami Klinc,
Tamsin C. Germand, Brian J. Schollc
aYale University, Department of Psychology, United States
bBarnard College, Department of Psychology, United States
cYale University, School of Medicine, Child Study Center, United States
dUniversity of California, Santa Barbara, Department of Psychology, United States
a r t i c l ei n f o
Received 4 June 2009
Received in revised form 26 July 2009
Accepted 8 August 2009
Available online 15 August 2009
Autism spectrum disorder
a b s t r a c t
A central feature of autism spectrum disorder (ASD) is an impairment in ‘social attention’—the priori-
tized processing of socially relevant information, e.g. the eyes and face. Socially relevant stimuli are also
preferentially attended in a broader categorical sense, however: observers orient preferentially to people
and animals (compared to inanimate objects) in complex natural scenes. To measure the scope of social
attention deficits in autism, observers viewed alternating versions of a natural scene on each trial, and
object. Change detection performance was measured as an index of attentional prioritization. Individuals
with ASD showed the same prioritized social attention for animate categories as did control participants.
or inverted images. These results suggest that social attention – and its impairment in autism – may not
be a unitary phenomenon: impairments in visual processing of specific social cues may occur despite
intact categorical prioritization of social agents.
© 2009 Elsevier Ltd. All rights reserved.
1. Introduction: social attention
Humans are exceptionally social primates, and increasing evi-
dence suggests that human social cognition is not simply the
application of general cognitive abilities to social perception and
behavior, but may reflect the operation of distinct specialized pro-
cesses (e.g. Adolphs, 2006; Saxe, Moran, Scholz, & Gabrieli, 2006).
Evidence of dedicated mechanisms for social perception and atten-
has identified several brain areas that seem specialized for social
information processing, such as the superior temporal sulcus (e.g.
Allison, Puce, & McCarthy, 2000) and the medial prefrontal cortices
(Schultz et al., 2003). Clinical studies have identified specific neu-
ropsychological deficits for categories of social information such as
face recognition, as in prosopagnosia (e.g. Farah, Levinson, & Klein,
Corresponding author at: Department of Psychology, Barnard College, 3009
Broadway, New York, NY 10027, United States. Tel.: +1 212 854 3581;
fax: +1 212 854 3601.
E-mail addresses: firstname.lastname@example.org (J.J. New), email@example.com
URL: http://www.yale.edu/perception/ (J.J. New).
1995). Experimental psychology has revealed social phenomena
such as reflexive gaze following (e.g. Frischen, Bayliss, & Tipper,
2007). And developmental psychologists have demonstrated that
processes related to social evaluation operate even in infants as
young as 6 months (e.g. Hamlin, Wynn, & Bloom, 2007). The scope
ric shapes (e.g. Gao, Newman, & Scholl, 2009; Heider & Simmel,
1944) to the most nuanced inference of emotions as expressed in
these many types of social information have often been at least
implicitly regarded as products of an integrated social perception
system—the ‘social brain’ (e.g. Brothers, 1990). Indeed, such results
(and the importance of social information more generally) have
siderable part to their need to navigate a complex social world (e.g.
Byrne & Whiten, 1988; Hermann, Call, Hernández-Lloreda, Hare, &
Tomasello, 2007; Humphrey, 1976).
perception is a single and fully integrated network of faculties. It
may be, instead, that social perception results from a group of dis-
tinct processes that are only conceptually grouped together—and
0028-3932/$ – see front matter © 2009 Elsevier Ltd. All rights reserved.
J.J. New et al. / Neuropsychologia 48 (2010) 51–59
ticular, it is possible to constrain these possibilities by studying the
ways in which social perception is and is not selectively impaired
in individuals with autism spectrum disorder.
1.1. Impaired social perception and attention in autism spectrum
in a variety of neuropsychological contexts, such as prosopagnosia
(e.g. Farah et al., 1995) and amygdala damage (e.g. Adolphs &
Spezio, 2006). However, the most prevalent such impairments are
seen in individuals (especially children) with autism spectrum dis-
order (ASD) who have long been noted for their striking lack of
interest in – and their lack of responsiveness to – people, social
interactions, and communicative behaviors (Kanner, 1943). ASD
is in part defined by its pervasive disruptions of social abilities
(Klin, Jones, Schultz, Volkmar, & Cohen, 2002a) and so represents
a singularly informative disorder for uncovering the psychological
architecture underlying social perception and cognition.
attention – some of which are general in nature (e.g. Goldstein,
Johnson, & Minshew, 2001) – but many of which are specific to
the social domain. Children with ASD are markedly inattentive to
faces (Osterling & Dawson, 1994), show poorer facial identity dis-
crimination (e.g. Klin et al., 1999; Tantam, Monaghan, Nicholson,
& Stirling, 1989), fixate the eye region of the face less (e.g., Dalton
et al., 2005; Klin, Jones, Schultz, Volkmar, & Cohen, 2002b), and
make less frequent and abnormally timed eye contact (Dawson,
Osterling, Meltzoff, & Kuhl, 2000; Sigman, Mundy, Ungerer, &
Sherman, 1986). Even when they do attend to faces, they may
process them in the much the same manner as inanimate objects
(Schultz et al., 2000), or fail to reliably attend to facial expres-
sions per se (Adolphs, Sears, & Piven, 2001; Hobson, 1988; Klin,
Jones, Schultz, Volkmar, & Cohen, 2002b; Weeks & Hobson, 1987).
While their attention may be automatically cued by eye gaze in
Campbell, Milne, & Coleman, 2003; cf. Chawarska, Klin, & Volkmar,
2003) but not in others (Ristic et al., 2005), children with ASD cer-
tainly have significant impairments in interpreting the meaning
and social significance of the ‘language of the eyes’ (Baron-Cohen,
Joliffe, Mortimore, & Robertson, 1997; Baron-Cohen, Wheelwright,
taneously attribute the social meanings that typically developing
children automatically ascribe to displays of simple moving geo-
metric shapes (Abell, Happé, & Frith, 2000; Klin, 2000; Rutherford,
Pennington, & Rogers, 2006) and they fail to efficiently process
social information in point-light displays (Blake, Turner, Smoski,
Pozdol, & Stone, 2003; Klin, Jones, Schultz, & Volkmar, 2003; Klin,
Lin, Gorrindo, Ramsay, & Jones, 2009). All of these impairments,
however, can commonly occur in the context of normal intellec-
tual levels, indicating a pattern of impairments specific to social
abilities (e.g. Sturm, Fernell, & Gillberg, 2004).
1.2. Categorical animacy and the scope of social cues
Social cues, from the detection of animacy in simple shapes to
the tracking of eye gaze, may be processed via a widespread but
interacting network of neural regions—the ‘social brain’ (Brothers,
underlie separate and functionally specific processes of social cog-
nition (Adolphs, 2003), but even the most rudimentary perception
of animacy appears to activate the entire social network (Schultz
et al., 2003; Wheatley, Milleville, & Martin, 2007). This raises the
question of the scope of impairments in social attention and per-
ception in ASD. ASD could be characterized by multiple distinct
disruptions to individual cognitive processes (e.g. face perception,
gle impairment to an earlier (‘upstream’) form of social perception
(Pasley, Mayes, & Schultz, 2004) that then has cascading deleteri-
ous effects on the downstream processing of specific social cues.
These downstream effects could occur directly, via disruptions in
typical patterns of information flow, or could occur indirectly, via
disrupting the normal accumulation of socially relevant experi-
ences (Schultz, 2005). For example, one group of models proposes
that a lack of attention to social stimuli hinders the development
of social perceptual faculties, most notably face and speech per-
ception (Dawson et al., 2005; Grelotti, Gauthier, & Schultz, 2002).
However, it is as yet unclear whether such inattention to social
information results from a failure to find social information intrin-
sically rewarding or from some lower level perceptual processes
failing to direct attention to social information (Schultz, 2005).
Resolving these issues will clearly continue to require many
studies exploring various types of social perception. In the present
paper we seek to contribute to this project with a case study of
what is perhaps the most abstract form of social processing: the
categorical perception of animacy. Beyond specific cues such as eye
gaze (and perhaps certain motion patterns), we also categorize the
world into animate (and thus socially relevant) objects vs. inan-
imate objects, on the basis of their visual features. The category
of animate objects obviously includes people, but also animals.
The category of inanimate objects obviously includes manmade
artifacts (such as toasters and chairs), but also biological entities
such as plants. The categorization of entities into animate vs. inani-
1990; Keil, 1983; Mandler & McDonough, 1998), and is typically
associated with higher level cognition.
However, recent evidence suggests that this categorical distinc-
tion also influences the distribution of attention. Such effects are
tify the difference between two alternating versions of a natural
changes (‘change blindness’; Rensink, 2002; Rensink, O’Regan, &
Clark, 1997; Simons & Rensink, 2005). However, not all changes
are created equal. For example, observers tend to prioritize atten-
tion to certain specific social cues such as faces compared to other
categories (e.g. Ro, Russell, & Lavie, 2001). More generally, a recent
study (New, Cosmides, & Tooby, 2007) demonstrated that changes
(such as reflections or deletions) made to animate agents (people
or animals) were detected more readily and frequently than equiv-
alent changes in inanimate objects (artifacts or plants). This may
reflect an inherent prioritization for attending to animate agents,
our ancestral environments (New et al., 2007).
1.3. The current case study: categorical social cues in ASD
In the present experiment we employ the change detection
paradigm as a case study to help assess the scope of social atten-
tion impairments in ASD, and in particular to reveal the extent to
which individuals with ASD show prioritized attention for categor-
ical animacy. In each trial, observers – children and young adults
with ASD, control children, and control adults – viewed alternating
versions of a natural scene, and had to detect and then identify the
change between them (see Fig. 1). No information was given about
participants exercise a degree of volition and spontaneity which is
more reflective of real-world perception than many past types of
attentional measures (e.g. spatial cueing).
Recent studies have revealed that individuals with autism are
able to effectively complete change detection tasks, and stud-
ies of non-social cues using such tasks have revealed that they
selectively attend to the same general properties of object arrays
J.J. New et al. / Neuropsychologia 48 (2010) 51–59
Fig. 1. A depiction of the change detection method: participants must detect and
identify the difference between an original image and changed image.
and scenes as do typically developed observers (e.g. Burack et al.,
2009). For example, individuals with autism detect changes made
to regions that are judged to be central to a natural scene sooner
than changes made to marginal regions (Fletcher-Watson, Leekam,
Turner, & Moxon, 2006), and they find changes made to contextu-
ally inconsistent objects sooner than changes made to contextually
consistent objects (Fletcher-Watson et al., 2006; but see Loth,
Gómez, & Happé, 2008). Both of these results are also character-
istic of typically developed observers (Hollingworth & Henderson,
2000; Rensink et al., 1997). In addition, one prior study of a specif-
ically social cue observed that individuals with autism detected
changes to eye gaze direction more readily than changes to eye-
glasses (Fletcher-Watson, Leekam, Findlay, & Stanton, 2008).
Note that if individuals with ASD prioritize attention to categor-
ical animacy as well, this would not be primarily a null result (i.e. in
terms of differences with respect to typical observers), but would
rather be a surprising positive result (i.e. in terms of differences
with respect to animate vs. inanimate information). This would
constitute a latent ability that could be unexpected, given most
previous research demonstrating social perceptual impairments of
many kinds in ASD.
In the present study, the changing target in each scene was
disappeared and reappeared (see Figs. 1 and 2). Change detection
performance (in terms of both speed and accuracy) was measured
as an index of automatic attentional prioritization for each type of
object (Tse, 2004).
oping children also recruited at the Child Study Center (n=8); and (c) non-clinical
adults recruited at the University of California, Santa Barbara (n=27).
The typically developing children were screened for psychopathology using the
Child Symptom Inventory (Gadow & Sprafkin, 1994) as well as phone interviews
which asked about history of psychiatric illness. The children (7 males, 1 female)
had an average age of 9.8 years (SD=1.6) and a mean IQ in the Above Average Range
(Wechsler Abbreviated Scales of Intelligence, WASI; Wechsler, 1999). The TD children
were not matched to the clinical group with respect to demographic factors such
as age or full-scale IQ. Differences in such factors are typically controlled because
they might account for performance deficits in the clinical group relative to the
TD groups. To foreshadow this study’s results, however, there were no such differ-
ences in performance with respect to the hypotheses about semantic category, and
thus such matching is not critical. The non-clinical adults were UC Santa Barbara
undergraduates who participated for credit in an introductory psychology course.
interview (Autism Diagnostic Interview—Revised, ADI-R; Rutter, LeCouteur, & Lord,
2003) and direct observations of participants’ social and communicative behaviors
(Autism Diagnostic Observation Schedule, ADOS; Lord, Rutter, DiLavore, & Risi, 1999).
All participants met criteria for ASD in both instruments, and received a clinician-
was measured using the Wechsler Intelligence Scale for Children, third edition, WISC-
III (Wechsler, 1991). Social adaptive functioning was measured with the Vineland
sample of participants with ASD (30 males, 1 female) had an average age of 10.8
years (SD=3.4), an average Vineland socialization standard score of 60.1 (SD=12.8),
an ADOS socialization algorithm total of 7.9 (SD=3.4), an ADI-R social domain score
of 21.6 (SD=9.2), and an FSIQ score of 104.4 (SD=21.6).
2.2. Stimuli and apparatus
The stimuli were color photographs of natural scenes taken from commercially
available CD-ROM image galleries. A target object in each scene belonged to one of
the four semantic categories (people, animals, plants, and artifacts; see Fig. 2). Two
alternate versions of each scene were created using Adobe Photoshop software: one
in which the target was deleted and filled in with the surrounding background, and
one in which the target object was reflected from left to right. Fourteen scenes were
created for each semantic category (for a total of 56 image sets). Nine adult control
participants viewed the same scenes as the child and young adult participants; nine
after they had been filtered with a Gaussian blurring function (Adobe Photoshop,
6 pixel kernel blurring; see Fig. 3b). Such manipulations preserve many low-level
visual properties of the images while attenuating effects of the images’ semantic
content (Kelley, Chun, & Chua, 2003; New et al., 2007). The displays were presented
Tools, Inc.; www.pstnet.com/eprime).
2.3. Procedure and design
Non-clinical child participants and participants with ASD were tested individu-
via a computer mouse. Non-clinical adult control participants were tested in groups
of one to nine in a large room with semi-private workstation cubicles, and made
responses with a computer mouse and keyboard.
In each trial, a black fixation cross-appeared in the center of the display for
for 120ms. The alternate version of the scene was then displayed for 500ms, fol-
lowed again by a white masking screen for 120ms (see Fig. 1). This sequence was
repeated until the participant detected the changing object, as indicated by a mouse
click. If no response was made after 20s, the trial was terminated, and the response
back was then provided, by directly alternating the two images (again for 500ms
per image) without an intervening mask, rendering the change obvious (Rensink,
2002; Rensink et al., 1997).
Participants completed four practice trials followed by 56 experimental trials
presented in a different random order for each participant. The target objects in
each category were changed an equal number of times for both types of changes
(addition–deletion, left–right reflection).
long as the identification response then fell within 1cm of the tar-
get object’s nearest boundary (unless that location occurred within
another discrete object). The response latency for each seman-
tic category was calculated as the mean response time for all
‘hits’ involving that category. There was no effect of change type
(addition–deletion, left–right reflection) on either response time
or accuracy, nor did it interact with semantic category in any of the
three participant groups (all ps>.15). Performance was therefore
collapsed across this variable for the analyses reported below.
J.J. New et al. / Neuropsychologia 48 (2010) 51–59
Fig. 2. Examples of each of the four scene categories. The target objects are circled here, but of course this highlighting was not present in the actual displays.
3.1. Non-clinical adult control participants
Analyses with non-clinical adult control participants replicated
the semantic category effect first reported by New et al. (2007).
Omnibus repeated-measures MANOVAs revealed a main effect of
semantic category on both response time [F(3,6)=67.52, p<.01,
partial ?2=.97] and accuracy [F(3,6)=6.06, p=.03, partial ?2=.75].
As suggested by Fig. 4c, this was driven by the considerably greater
speed and frequency of detecting changes to targets in both ani-
mate categories (people and animals) compared to targets in
were verified by the relevant pairwise statistical comparisons, as
reported in Table 1.
egorical differences themselves rather than solely by lower level
visual differences, such effects were not reliable when inverted
images were used (tested in a subgroup with the same size, and
F(3,6)=1.62, p=.28, partial ?2=.45; accuracy: F(3,6)=3.08, p=.11,
partial ?2=.61)—a pattern that was also true in the initial report
of New et al. (2007). In the subgroup of the same size tested
with blurred images, the significant effect of category on accu-
inanimate objects (RT: F(3,6)=2.32, p=.18, partial ?2=.54; accu-
racy: F(3,6)=13.94, p<.01, partial ?2=.87). These means, from best
to worst, were artifacts (85%), animals (83%), plants (81%), and
people (71%). This suggests that the lower level visual factors in
these scenes actually competed with the categorical salience, such
that our observed means with upright nonblurred images may be
Fig. 3. Examples of the image manipulations used for the separate groups of adult control participants: (a) scene inversion, and (b) blurring.
J.J. New et al. / Neuropsychologia 48 (2010) 51–59
Fig. 4. Average change detection latency and accuracy for each of the four semantic categories (people, animals, artifacts, and plants) for each of the three participant groups:
(a) typically developing child controls, (b) participants with ASD, and (c) adult controls.
in lower level factors such as luminance, size, and eccentricity, but
the results of our control analyses effectively rule out the possi-
bility that such factors were responsible for the primary effects,
since all of these factors were maintained in the control condi-
tions too, but nonetheless yielded no reliable differences in the
predicted direction in performance between animate and inani-
mate detection. Because the inversion and blurring manipulations
were intended as a control for the image sets themselves (rather
than for any feature of subjects’ performance), we can be confident
that such factors are not responsible for any animate/inanimate
differences obtained with this image set, in any population (Fig. 4).
3.2. Typically developing child control participants
In the typically developing children, there was a marginally
significant effect of semantic category on both response time
[F(3,5)=6.06, p=.06, partial ?2=.74]. As Fig. 4a illustrates (and
as the pairwise comparisons in Table 1 confirm), these effects
were driven by the greater detection performance for changes to
animate objects compared to those made to inanimate objects.
Moreover, the difference in response times for changes to animate
vs. inanimate targets was comparable to that in the adult sample
(a roughly 1500ms effect). (The respective accuracy effect was
much larger in the child sample, of course, since they had more
3.3. Clinical participants
In the participants with ASD there was again a highly significant
effect of semantic category on both response time [F(3,28)=23.6,
p<.01, partial ?2=.72] and percent correct [F(3,28)=19.55, p<.01,
partial ?2=.68]. As Fig. 4b illustrates (and as the pairwise compar-
isons in Table 1 confirm), this was again strongly driven by the
considerably greater speed and frequency of detection for chang-
Pairwise comparisons of response times and accuracy between each semantic category in each participant group.
Typically developing children (df=7)Group with ASD (df=30)Typically developing adults (df=8)
AnimalsPlantsArtifacts AnimalsPlantsArtifacts Animals PlantsArtifacts
J.J. New et al. / Neuropsychologia 48 (2010) 51–59
Fig. 5. Change detection latency as a function of ASD participant age for (a) people
relative to artifacts, and (b) animals relative to artifacts.
ing animate objects relative to inanimate objects. Moreover, the
difference in response times for changes to animate vs. inanimate
targets was comparable to those in both the adult and child control
samples (a roughly 1500ms effect).
3.4. Group analyses
To test for development trends, all of the participants were ana-
lyzed in a 3×4 mixed-model MANOVA which included all four
semantic categories (as the within-subjects factor) and all three
participant groups (as the between-subjects factor). As entailed
by the preceding analyses, there were highly significant omnibus
p<.01, partial ?2=.68] and percent correct [F(3,43)=16.69, p<.01,
partial ?2=.54], in which RT and accuracy performance for detect-
There were also significant effects of participant group on both RT
are not relevant for the category-specific questions under investi-
gation here (and the present within-subjects design does control
for such individual differences). Participants with ASD were slower
to detect changes than both non-clinical child controls [4679ms
vs. 3622ms, t(37)=6.46, p<.01, r=.73] and non-clinical adults
[4679ms vs. 3280ms, t(38)=3.93, p<.01, r=.54]. Non-clinical chil-
dren and adults, however, did not differ in their response speed
[3622ms vs. 3280ms, t(15)=1.06, p=.31, r=.26]. Participants with
ASD were comparable to non-clinical child controls [85% vs. 92%,
t(37)=1.33, p=.19, r=.21], but lower in accuracy than non-clinical
adult controls [85% vs. 97%, t(37)=2.56, p=.02, r=.39]. Non-
clinical adult controls were marginally significantly more accurate
than non-clinical child controls [92% vs. 97%, t(37)=2.10, p=.05,
Critically, however, there was no interaction between the par-
ticipant groups and the particular semantic categories in either
response time [F(6,88)=.88, p=.51, partial ?2=.06] or percent
Spearman correlations between each attentional index and clinical score. See text
for details. p-Values reflect one-tailed tests. Tests reaching statistical significance
(p<.05) are in bold.
correct [F(6,88)=1.14, p=.35, partial ?2=.07]. Thus, although
preferential attention to people may grow slightly with age in
participants with ASD (as reported below), the overall animate
attentional bias (New et al., 2007) was evident and of roughly the
same magnitude in each of our participants groups. (And in fact,
the overall animate vs. inanimate effect was slightly but nonsignif-
icantly larger in the ASD group, indicating that this null interaction
effect was not simply a result of insufficient statistical power.)
3.5. Correlational analyses
Three indices of animate attentional biases were calculated, as
the quotients of people over artifacts, animals over artifacts, and
all animate objects over all inanimate objects. For example, an RT
index of .50 for people over artifacts indicates that it took twice as
long to detect changes to artifacts compared to changes to people.
Greater animate attentional biases are thus indicated by smaller
values of these indices for response times, but larger values for
accuracy. Spearman correlations were used to evaluate whether
scores (Vineland, ADI, ADOS), or FSIQ. As Table 2 illustrates, there
were no significant correlations between any of the three atten-
tional bias indices and either clinical scores or FSIQ. There was,
however, a significant negative correlation for RT: increasing age
was associated with a faster detection of changes to people rela-
tive to artifacts (see Fig. 5a). In contrast, there was no relationship
between age and performance when considering animals relative
to artifacts (see Fig. 5b). The only significant correlation involv-
ing a clinical score – the ADI – suggests that increasingly higher
scores (signalling a greater diagnosed severity of ASD) correspond
to slower detection of changes to animals relative to artifacts—but
since there was no hint of such an effect for changes made to peo-
ple relative to artifacts, this clearly does not reflect any general
weakening of the “animate advantage” with severity of ASD. There
were no significant correlations between any of the factors when
considering percent correct.
J.J. New et al. / Neuropsychologia 48 (2010) 51–59
The central result of this study was that children with ASD,
despite their many social impairments, nevertheless exhibited
robust social attentional biases for categorical animacy—detecting
changes faster and more reliably to people and animals, compared
to artifacts and plants. Moreover, these effects were of roughly
the same magnitude as those exhibited in both children who were
older on average than the ASD participants.
4.1. Spared prioritization of categorical social information in ASD
The results of the single ‘case study’ experiment reported here
go against the grain of many previous findings of impaired social
information processing in ASD—e.g. in the perceptual processing
of faces (e.g. Schultz et al., 2000), eye gaze (e.g. Ristic et al., 2005),
biological motion (e.g. Blake et al., 2003; Klin et al., 2009), and the
the same time, the results reported here are in no way inconsistent
with these previous reports, since they involve a different – and
more abstract – type of social information, namely the categorical
representation of perceived objects as animate or inanimate on the
basis of a wide variety of visual surface features.
The case study of spared social attention reported here is a use-
ful complement to the more commonly observed impairments,
as it helps to constrain the scope of impaired social information
processing in ASD. In particular, the results reported here, when
considered in the context of the broader literature on impaired
social information processing, raise the interesting possibility that
tary phenomenon: impairments in processing specific social cues
(such as eyes and faces) may occur despite intact categorical pri-
oritization of animacy. This makes it all the more important to
continue investigating a diverse array of specific social cues in ASD,
since they may not all stand or fall together.
4.2. The architecture of social attention
What kind of cognitive architecture could explain this overall
pattern of results—both the spared performance observed in our
experiment, and the broader array of impairments? ‘Categorical
social processing, compared to typically developing children and
adults. Just because you orient preferentially to certain types of
information, in other words, does not mean that you know what to
do with that prioritized information (cf. Schultz et al., 2000).
Animate objects are strongly and reliably prioritized for
attentional selection compared to inanimate objects in typically
developing observers, even when little or no social information is
involved. This prioritization is perhaps due to the differential bio-
logical salience of animate and inanimate objects in our species’
mals very often presented exigent dangers or opportunities, plants
and artifacts very often did not. Thus, preferentially attending to
animate objects might not always be beneficial, but it could be
invaluable for those cases when organisms encountered a poten-
tial predator or mate. (In contrast, learning to voluntarily attend to
such information – especially in the context of predation – could
be expensive, and a miss outweighs the cost of a false alarm.)
This type of attentional prioritization for animate information
may thus arise from a perceptual mechanism that is phylogeneti-
cally separate from – and perhaps prior to – the processes involved
in more socially specific perceptual abilities. The results of the
present case study – prioritized categorical animacy during change
detection in the face of other social impairments – may reflect how
animate objects are the subject of both ‘animate attention’ in a
general sense, and of social information processing more specifi-
cally. This raises the interesting possibility that animacy and social
tive mechanisms—with general processing of ‘categorical animacy’
spared even while other specific forms of social processing are
4.3. Future directions
It will be important for future research to evaluate the degree
to which such sparing generalizes to other types of methods and
displays, and to real-world social contexts. The spared attentional
prioritization for animate objects observed here may in part result
from the static nature of the stimuli. Individuals with ASD do pref-
erentially fixate social information (i.e. eye regions) when viewing
static scenes (Fletcher-Watson et al., 2008; Speer, Cook, McMahon,
& Clark, 2007) but not when viewing comparable dynamic scenes
(Speer et al., 2007). Indeed, other types of perceptual faculties such
as face perception may appear normal under drastically simplified
laboratory conditions (e.g. van der Geest, Kemner, Verbaten, & van
Engeland, 2002), and yet be considerably disordered under more
naturalistic viewing conditions (e.g. Klin et al., 2002b). Increasing
evidence, therefore, indicates that dynamic stimuli are necessary
for accurately evaluating how individuals with ASD react to social
information under more naturalistic conditions (Klin et al., 2003;
in future work to explore prioritized attention to categorical ani-
macy in both dynamic displays and in more naturalistic contexts.
Although there appeared to be no overarching developmental
trajectory for the animate attentional bias in the non-clinical pop-
ulation, the small but significant increase of the effect with age for
people in the group with ASD may also warrant further examina-
tion. Such developmental effects may reflect some compensatory
processing strategies arising over time that may be sensitive to
interventional approaches. We may have relatively reflexive biases
to attend to animate information, but we could also learn to do so
more overtly over time, just as high-functioning individuals with
ASD learn to overtly compensate for other types of impaired mech-
anisms of social cognition (e.g. Golan & Baron-Cohen, 2006).
Autism spectrum disorder has been widely recognized as a dis-
order that impacts many different processes, from relatively early
types of perceptual processing (as in face recognition) to relatively
recognized by cognitive psychologists have not all been equally
studied in the context of ASD. In particular, while ASD researchers
have paid considerable attention to both perception and cognition,
they have not paid equal attention to ... attention. When attention
as a ‘suspect’ of sorts: perhaps various social impairments are due
2000). In contrast, the present case study illustrates how a focus on
attention can reveal a type of spared categorical social processing,
despite both disordered perception and cognition.
an open question whether individuals with ASD will similarly
attend preferentially to categorical animacy in either real-world
situations or even in other experimental paradigms. However, the
case study presented here does strongly suggest an underlying
latent ability in individuals with ASD, irrespective of the specific
conditions necessary to evoke it. This latent ability may seldom be
J.J. New et al. / Neuropsychologia 48 (2010) 51–59
expressed since it may be hindered by a host of other perceptual,
cognitive, or task-related factors, but the current study reveals at
this ability is expressed. We hope that this demonstration spurs
additional questions and research on where and how such latent
abilities may be preserved, but as yet uncovered.
JJN is now at the Department of Psychology, Barnard Col-
lege, New York City, NY. RTS is now at the Center for Autism
Research, Department of Pediatrics, Children’s Hospital of Penn-
sylvania, Philadelphia, PA. We are indebted to Carla Brown, Dan
Grupe, and Lauren Herlihy for assistance with data collection; to
Max Krasnow for assistance with software development; and to
the members of the Scholl and Chun laboratories at Yale Univer-
sity for helpful conversation and/or comments on earlier drafts. JJN
was supported by a National Research Service Award from NIMH.
TG was supported by a grant from the M.I.N.D. Institute, UC Davis.
BJS was supported by NSF grant #BCS-0132444.
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