1
Introduction
In typical adults, social stimuli and contexts are processed by specialized neural
systems including cortical and sub-cortical structures [2,41]. Further specialization
within this “social brain” network has been described. For instance, parts of the
fusiform cortex appear to be involved in detecting and identifying faces [31]. Sub-cortical
structures like the superior colliculus and the amygdala play a role in orienting
to faces and to relevant facial information (e.g. eyes) [3]. The orbitofrontal cortex
has been associated with encoding the reward value of social stimuli [5]. The developmental
basis of these patterns of cortical specialization remains the subject of debate [52,32].
One proposed model suggests that this cortical specialization for faces is partly
a result of early biases to orient toward and attend to faces. Specifically, [37]
proposed that a subcortical orienting system (which they termed “Conspec”) initially
biases the newborn to attend towards faces. This putative orienting system is driven
by low-spatial frequency patterns characteristic of faces, and is sufficient to bias
the input to developing cortical visual areas [38,39]. As a result of this biased
input, alongside other constraints, over development some visual cortical areas become
increasingly tuned to faces and related social stimuli. A manifestation of this functional
specialization is the emergence of cortical tissue selectively activated by faces
[40]. Based on this account we expect that infants’ face processing abilities will
be characterized by both very early biases and experience-dependent developmental
changes.
Partly motivated by such accounts of the emergence of the social brain in typical
development, several groups have proposed that a lack of attention to, or interest
in, social stimuli early in life may interfere with the emergence of developmental
milestones that are critical for social learning, such as shared attention [13,20,38,39,60].
These cascading influences could preclude the typical development of socio-communicative,
language and mentalizing skills, culminating in the behavioural presentation that
characterises autism. Compelling as they are, the key elements of such developmental
accounts have proved difficult to verify empirically.
Because a confirmed diagnosis of autism can only be made from around three years of
age, most findings regarding preference for, or orienting to, various social and non-social
stimuli have primarily been based on studies with older children and adults diagnosed
with autism and have given rise to mixed findings. While some have suggested that
face processing is the most informative model of the atypical development of the autistic
brain [60], others have questioned whether difficulties in this area are universal
[36]. Some of the inconsistencies have been attributed to possible changes over development
in face orienting biases and face processing abilities (reviewed in [21]).
While no study has directly tested face orienting, several have documented difficulties
in face processing in young children with autism, including recognition [10] and discrimination
[12], as well as understanding of emotion [27] and eye gaze processing [9,38,39].
A few studies also documented atypical neural responses to faces in young children
with autism [18,19,30]. These difficulties in childhood could be a result of reduced
face expertise, in turn driven by an early impairment in face orienting. However,
a few eye tracking studies draw a picture of emerging “disinterest” in faces during
childhood. Assessment of children with autism at the age of two compared to those
who are four-years-old suggests that relative to typically developing toddlers, toddlers
with autism looked increasingly away from faces with age and attended atypically to
key features of faces [10]. At four years of age, [1] also showed decreased attentional
engagement to faces as measured by pupillary responses in children with autism relative
to a control group. Atypical scanning and processing appear to be more pronounced
in children relative to adults with autism, where findings are more mixed, suggesting
the possibility that compensatory strategies may appear later in development (reviewed
in [21,59]).
Notwithstanding these findings, other developmental models of autism have suggested
that social orienting differences may not be the core deficits in autism, but instead
that they originate from early general difficulties in controlling visual attention
[6], which could, in turn, lead to problems in self-regulation as well as to a decrease
in social orienting [43]. Because such deficits in visual attention are neither universal
nor specific in autism, other researchers have proposed that an early specific deficit
in orienting to socially relevant stimuli may be a necessary condition but probably
not sufficient for autism to emerge. This deficit, however, would be compounded and
amplified by the presence of visual attention difficulties [21]. Differences in social
orienting would result in decreased input from socially relevant stimuli, while a
problem with flexibly switching attention between different stimuli would result in
‘locking’ onto certain irrelevant aspects of the input (e.g. moving objects or, within
the face, hairline instead of eyes). In support of this, one study which examined
attentional disengagement from faces relative to objects found that toddlers with
ASD disengaged visual attention from faces faster than developmentally delayed and
typically developing toddlers [11]. These findings suggest that visual attention difficulties
may also impair the acquisition of face processing skills.
As such, different hypotheses regarding the developmental origins and change in orienting
to faces in autism are difficult to test in childhood once symptoms have become clearly
expressed across multiple systems. Moreover, as described earlier, the human brain
undergoes substantial development during the first years of life, with clear emergence
and rapid change in social skill development in general and in face processing in
particular. Indirect support for early differences in face orienting in autism come
from retrospective studies looking back at the first two years of life using parental
report or home videos. These studies show less orienting towards social stimuli and
a reduced response to name calling from 9 months ([47,48]; Osterling et al., 2002;
[64]) or younger [46] in children later diagnosed with autism, compared to those later
diagnosed with developmental delay.
Against this background, a more recent approach has allowed for the prospective study
of infants who are at increased risk for developing autism (for reviews see [24,66]).
Later born siblings of children with autism are more likely to receive a diagnosis
themselves as toddlers, relative to infants with no family history of autism. Interest
in this group has been overwhelmingly driven by the search for ‘early markers’ as
well as intermediate phenotypes, defined as autism-related characteristics observed
in genetic relatives who do not have an autism diagnosis [24]. In other words, it
is hoped that studying infant siblings may reveal the primary deficits in autism before
symptoms are compounded by atypical interactions with the social and physical world,
and before compensatory strategies and systems cloud the basic processing difficulties.
Thus far, however, there has been little success in finding reliable markers for autism
within the first year of life. On the one hand, infants below 12 months of age who
are later diagnosed with autism show very few differences in the orienting to and
scanning of faces when they interact with their caregiver [68] or with an experimenter
[7,53]. By contrast, during the same period where infants at-risk show little behavioural
difference from controls with no family history of autism [8,67], other studies using
more direct measurements of brain activity have differentiated these groups in their
response sensitivity to faces [22,23,50]. These early findings have motivated the
view that understanding developmental changes in face orienting in infants at-risk
as a group, and prior to the age of reliable diagnosis, will provide clues into variability
in infants’ responses to genetic risk [24]. Moreover, because the majority of face
orienting studies with infants at-risk have relied on observing behaviour within the
context of complex interactions and in the absence of non-social stimuli, it remains
possible that a more structured observational setting may reveal more sensitive indicators
of social and communicative characteristics in toddlerhood.
In the current study we tested a group of infants at-risk for autism and a control
group of infants with no family history for autism on a ‘face pop-out’ task [28].
The infants were administered the task twice, first around 7 months and again around
14 months of age. In this task, infants are presented with arrays of a face along
with four non-social stimuli, including a ‘noise’ stimulus generated from the same
face within the array created to match its low-level visual properties [33]. Previous
findings using a similar task design showed a pronounced face preference in 6-months-olds
across a range of eye tracking measures and stimulus presentation contexts in typically
developing infants [28]. A first measure, the direction of the first look, singled
out faces over other non-face objects (the face ‘pop-out’ effect) and was not affected
by face inversion, with both upright and inverted faces attracting infant's first
looks above what was expected by chance. When looking time was analysed, faces again
received more fixations than other objects but infants also looked longer at upright
than inverted faces. It was concluded that orienting is driven by more general face
properties (e.g. the particular low spatial frequencies of the face), which may act
through both sub-cortical and cortical mechanisms [38,39]. Once on the face, and having
access to more visual detail, face-specific cortical mechanisms ensure that the more
prototypical upright orientation maintains infants engaged with the face. Although
initially designed to address questions about early social orienting, the complex
displays used in this paradigm also allow us to examine measures of visual attention
during early development.
In the current study, we derived a number of task-related eye tracking measures, to
address three complementary questions regarding the origins of atypical development
of the social brain in autism. The first question was whether infants who later go
on to develop autism fail to selectively orient to faces. Consistent with previous
studies [28], we operationalized this measure using the ‘pop-out’ response, i.e.,
above-chance proportion of first looks directed towards faces at the onset of each
trial following a central fixation.
The second question was the extent to which the putative atypical development of general
attention systems in infants at-risk [22] exerts an influence on visual selection
in the current task. In other words, we tested whether automatic orienting to faces
which typically occurs at the onset of a trial is followed by the optimal distribution
of attention to other objects in the scene in the remainder of the trial. It is optimal
for infants to both pay attention to social information, but also allocate some time
to processing the other stimuli in the array, and an imbalance in any direction could
be problematic. We operationalized the general allocation of attention using two measures:
total looking time to the array and visual foraging, i.e., the number of Areas-of-Interest
(AOIs) sampled. Because the pop-out response occurs early on in each trial (presentation
of an array), and in view of previous findings highlighting time-dependent changes
in visual scanning of scenes, we analysed separately the onset of each trial (first
five seconds) and the remaining period [62,49].
The third question focused on the interaction between social orienting mechanisms
and attention systems reflecting the integration of bottom-up and top-down processing
mechanisms in the social brain. Such measures can only be ascertained indirectly.
In the current study we derived face time, defined as the proportion of total looking
time allocated to faces, and face foraging, defined as the likelihood that the face
will be sampled relative to all other sampled AOIs. For consistency, we also analysed
these measures separately for the trial onset and the remaining period.
2
Methods
2.1
Participants and clinical characterization
Recruitment, ethical approval (NHS NRES London REC 08/H0718/76) and informed consent,
as well as background data on participating families, were made available for the
current study through The British Autism Study of Infant Siblings (BASIS), a UK collaborative
network facilitating research with infants at-risk for autism (www.basisnetwork.org/).
Families enrol from various regions when their babies are younger than 5 months of
age and they are invited to attend multiple research visits until their children reach
three years of age or beyond. Each visit lasts a day or two and is adapted to meet
the families’ needs. Measures collected are anonymised and shared among scientists
to maximise collaborative value and to minimise burden on the families. A clinical
advisory team of senior consultants works closely together with the research team/s
and, if necessary, with the family's local health services, to ensure that any concerns
about the child arising during the study are adequately addressed.
One hundred and four infants from BASIS took part in the current study (54 at-risk
(21 male), and 50 low-risk (21 male). Along with several other measures, the infants
were seen for the eye tracking task at the Centre for Brain and Cognitive Development
when they were 6–10-months of age and again at 12–15 months. Subsequently, 52 (from
54) of those at-risk for ASD were seen for assessment around their second birthday
(mean = 23.9 months, sd = 1.2) and 53 around their third birthday (mean = 37.7 months,
sd = 3.0), by an independent team at the Centre for Research in Autism and Education,
Institute of Education.
2.2
Confirmation of risk status in the older sibling
At the time of enrolment, none of the infants had been diagnosed with any medical
or developmental condition. Infants at-risk all had an older sibling (hereafter, proband)
with a community clinical diagnosis of ASD (or in 4 cases, a half-sibling), and in
3 cases 2 probands with an ASD. 45 probands were male, 9 were female. Proband diagnosis
was confirmed by two expert clinicians (PB, TC) based on information using the Development
and Wellbeing Assessment (DAWBA) [29] and the parent-report Social Communication Questionnaire
(SCQ) [58]. Most probands met criteria for ASD on both the DAWBA and SCQ (n = 44).
While a small number scored below threshold on the SCQ (n = 4) no exclusions were
made, due to meeting threshold on the DAWBA and expert opinion. For 2 probands, data
were only available for either the DAWBA (n = 1) or the SCQ (n = 1). For 4 probands,
neither measure was available (aside from parent-confirmed local clinical ASD diagnosis
at intake). Parent-reported family medical histories were examined for significant
medical conditions in the proband or extended family members, with no exclusions made
on this basis.
Infants in the low-risk group were recruited from a volunteer database at the Birkbeck
Centre for Brain and Cognitive Development. Inclusion criteria included full-term
birth (with one exception), normal birth weight, and lack of any ASD within first-degree
family members (as confirmed through parent interview regarding family medical history).
All low-risk infants had at least one older-sibling (in 3 cases, only half-sibling/s).
28 of the older siblings were male, 22 were female. Screening for possible ASD in
these older siblings was undertaken using the SCQ, with no child scoring above instrument
cut-off for ASD (≥15) (one score was missing).
2.3
Background characterisation measures
Two measures of general developmental level were obtained for the infants and toddlers
at each visit. The Mullen Scales of Early Learning (MSEL) ([51]) is a direct assessment
of verbal and non-verbal abilities appropriate for children from birth to 6 years.
Scores across four domains – Visual Reception, Fine Motor, Receptive Language, and
Expressive Language – are combined to yield an overall Early Learning Composite (ELC;
mean = 100, sd = 15). Gross motor skills are also assessed but do not contribute to
the ELC. An estimate of non-verbal developmental ability (NVT-score) was computed
by averaging the T scores (mean = 50, sd = 10) for Visual Reception and Fine Motor
subscales. The Vineland Adaptive Behaviour Scales (VABS) [61] is a parent-report measure
of everyday skills in the domains of communication, daily living skills, social interaction,
and motor skills. These combine to yield an adaptive behaviour composite (ABC; mean = 100,
sd = 15).
These developmental assessments were undertaken at each of the visits, when infants
were 6–10 months, 12–15 months, and again around the second and third birthday, each
time by independent research teams. While the MSEL is always administered directly
with the child, the VABS has alternative administration formats. The Parent/Caregiver
Rating Form (i.e., questionnaire booklet) was used at the 6–10-month and 12–15-month
visits, and the Survey Interview Form was used at the 24-month and 36-month visits.
Scores from these measures are presented in Table 1.
2.4
Outcome characterization of the at-risk and low-risk groups
Alongside the standard measures of cognitive (MSEL) and adaptive (VABS) development
taken at each visit, at 24 months (at-risk group only; 50 Module 1, 2 Module 2) and
36 months (both groups; Table 1, 3 Module 1, 98 Module 2) a semi-structured play assessment,
the Autism Diagnostic Observation Schedule (ADOS) [44] was used to assess autism-related
social and communication behavioural characteristics. This was augmented at 36 months
(at-risk group only) with the parent-report Autism Diagnostic Interview – Revised
(ADI-R) [45].
Characterisation of outcomes in the at-risk cohort at 36-months was done by ascertaining
three sub-groups (Table 1): Those who were typically-developing, those classified
as having ASD, and those exhibiting some form of developmental concerns. For the at-risk
group consensus ICD-10 [65], ASD (including childhood autism; atypical autism, other
pervasive developmental disorder (PDD)) was diagnosed using all available information
from all visits by experienced researchers (TC, KH, SC, GP), hereafter At-risk-ASD.
From the initial group of 53 toddlers assessed at 36-months, 17 (11 boys, 6 girls)
met criteria for an ASD diagnosis (32.1%). Given the young age of the children, and
in line with the proposed changes to DSM-5 [4], no attempt was made to assign specific
sub-categories of PDD/ASD diagnosis. Another subgroup of toddlers from the at-risk
group who were classified as not having ASD were considered to still have other developmental
concerns. These were 12 toddlers (22.6%; 3 boys, 9 girls) who either scored above
the ADOS or ADI [56] cut-off for ASD or scored <1.5SD on the Mullen ELC or RL and
EL subscales but did not meet ICD-10 criteria for an ASD (9 scored > ADOS cut-off,
1 > ADOS cut-off and <1.5SD Mullen ELC cut-off, 1 > ADI cut-off, and 1 < 1.5SD Mullen
ELC cut-off).
It is worth noting that the recurrence rate reported in the current study (32.1%)
is higher than that reported in the large consortium paper recently published by Ozonoff
and colleagues (18.7%). This is likely to reflect the modest size at-risk sample in
the current study (N = 53). Whilst recurrence rates approaching 30% have been found
in other moderate size samples (e.g., [42,55]) these rates are sample specific and
will likely not be generalizable as findings from larger samples where autism recurrence
rates converge on between 10% and 20% [16,54]. However, similar procedures combining
all information from standard diagnostic measures and clinical observation and arriving
at a ‘clinical best estimate’ ICD-10 diagnosis was used in the present study by an
experience group to that used in other familial at-risk studies.
2.5
Face pop-out task at 6–10 months and 12–15 months: stimuli, procedure, and data processing
During their first and second visits infants were administered a face preference task
very similar to that reported by [28]. Looking behaviour was recorded with a Tobii
eye tracker. The Tobii system has an infrared light source and a camera mounted below
a 17 in. flat-screen monitor to record corneal reflection data. The Tobii system measures
the gaze direction of each eye separately and from these measurements evaluates where
on the screen the individual is looking. During the eye tracker tasks the child is
seated on his/her caregivers lap, at 50–55 centimeters from the Tobii screen. The
height and distance of the screen are adjusted for each child to obtain good tracking
of the eyes. First a five-point calibration sequence is run, with recording only started
when at least four points are marked as properly calibrated for each eye. Gaze data
were recorded at 50 Hz.
In the present task, 14 different arrays, each with five stimuli, were presented (see
Fig. 1 for an example). Each array contained a colour image of one of fourteen different
faces with direct gaze used as the target. Different exemplars from each of the following
categories: mobile phones, birds, and cars were also included in the array. Another
stimulus was a visual ‘noise’ image, generated from the same face presented within
the array, by randomizing the phase spectra of the faces whilst keeping the amplitude
and colour spectra constant [33]. The slides were counterbalanced for gender, ethnicity,
and vertical and horizontal location of the face within the array. To verify that
faces were similar to other categories in terms of visual saliency, saliency ranks
were calculated for each area of interest on all 14 slides using the Saliency Toolbox
2.2 [63].2 Categories had very similar average saliency ranks. When placed at a distance
of 55 cm from the child the five individual images on the slide had an eccentricity
of 9.3° and covered an approximate area of 5.2° × 7.3°.
Before each slide a small animation was presented in the center of the screen to ensure
that the children's gaze was directed to the centre. Each slide presentation lasted
15 s. To assist in maintaining the children's attention, the visual presentation was
accompanied by music. If the child stopped looking at the slide one of the experimenters
prompted the infant to look at the screen again, without naming or referring to any
of the stimuli. When the infant looked away for more than 5 s, the experimenter terminated
presentation of the given slide. Rectangular AOIs were defined around each object
image and the center of the screen using Tobii Studio software. Gaze data were extracted
for each AOI: centre, face, noise, car, bird, phone, and total (the entire slide).
2.5.1
Eye tracking measures
2.5.1.1
Face pop-out
Face pop-out response was tested using the proportion of valid trials where the infant
was fixated at the centre at onset of the trial and then fixated one of the five AOIs
corresponding to the face, against a chance level of .2. For this analysis, a trial
was considered valid if the infant fixated the centre at onset of the trial and then
moved their gaze to one of the five AOIs corresponding to any of the stimulus categories
in the first three seconds of trial onset. Data were excluded for any infants with
less than three valid trials. The measures are reported in Table 2.
2.5.1.2
Total looking time
Total looking time was calculated as the total time spent looking at the array. Moreover,
face looking time was calculated as the proportion of time spent on the face AOI relative
to all target AOIs in the array. As discussed earlier, to highlight any temporal differences
in these measures over the course of the trial, data were extracted for the first
5 s (first segment) of the trial time and for the remaining 10 s (final segment).
Trials were considered valid if the infant spent longer than one second fixating the
slide in total. Data were excluded for any infants with fewer than three valid trials.
The measures are reported in Table 3.
2.5.1.3
Visual foraging
Visual foraging was measured using the average number of AOIs sampled within the array
over the course of each trial (ranging from 1 to 5). Face foraging was calculated
as the ratio of face visiting (0 or 1) to the total AOIs visited. Similar to looking
time measures, data was extracted for the onset and later segments of each trial.
The same criteria for trial validity for looking time measures were applied to visual
foraging. The measures are reported in Table 3.
3
Analytic approach
In addition to simple t-tests, the repeated measures data was analysed using a generalized
estimating equations approach to fit the nature of proportion data. Infant first look
behaviour within each of the two sets of trials (7-month and 14-month) was analysed
as binomial proportions and logistic link function with robust standard errors to
account for overdispersion and correlation between 7- and 14-month assessments. For
the remaining measures, formed as averages over each segment of trials, of times,
counts and their ratios, a Gaussian error, identity link, and an unstructured correlation
matrix were used. Group differences were assessed from Wald tests with a parameter
covariance matrix (and thus test statistics) calculated accounting for the number
of parameters estimated. This approach is equivalent to multiple analysis of variance.
Due to the variation of age within each group at the 7- and 14-months assessments
stage, in all analyses the infant's age at was included as a covariate.
4
Results
4.1
Face pop-out
The proportion of valid trials in which the infant looked towards the face AOI before
any other AOI was calculated for each group at each age (Table 2). One sample t-tests
showed that the proportion of trials with first looks towards the face was significantly
above chance level (.2) at 7-months for all groups defined based on risk status (control
vs. at-risk) or on outcomes (at-risk: ASD, typical, other; all p < 0.001). Similar
analyses were conducted for the second visit and the results did not differ. This
demonstrates that the face pop-out effect was observed in all groups, including those
with a clinical classification of ASD by the age of three years. Repeated measures
analysis indicated no significant group differences in the rate of increase over the
two assessments among the risk groups (group × time (χ
2(1) = 0.23, p = .633) nor significant mean differences among the groups (group χ
2(1) = 2.83, p = .092).
4.2
Total looking time and visual foraging measures
4.2.1
Risk group effects
The average amount of looking anywhere in the array is shown in Table 3. A model was
constructed with the following terms: between subjects groups (control, at-risk),
and within-participants time (7-months vs. 14-months) and trial segment (first 5 s
vs. last 10 s). In addition, age in months at the two points of measurement was used
as a covariate. For the average time per trial examining AOIs the repeated measures
analyses indicated no significant interactions involving group: group × time × segment
(χ
2(1) = 0⋅48, p = .490), group × segment (χ
2(1) = 0⋅38, p = .535), group × time (χ
2(1) = 1⋅79, p = .181).
For visual foraging, i.e., AOI count, the 3-way interaction of group × age × segment
was not significant (χ
2(1) = 0.06, p = .803) nor were the 2-way group × segment (χ
2(1) = 0.06, p = .81) interactions but the group × time was marginal (χ
2(1) = 3.28, p = .070) as was the group main effect (χ
2(1) = 3.82, p = .051), with infants at-risk tending to sample fewer AOIs relative
to the control group at the older age.
4.2.2
Diagnostic group effects
The same analysis was repeated based on diagnostic classification of infants at 36-months
(control, at-risk ASD, at-risk typical, at-risk other). No significant interactions
involving diagnosis were observed: diagnosis × time × segment (χ
2(3) = 3.10, p = .38), diagnosis × segment (c
2(3) = 1.05, p = .79) diagnosis × time (χ
2(3) = 6.2, p = .10). For visual foraging, i.e., AOI count, the 3-way interaction
of diagnosis × age × segment was not significant (χ
2(3) = 3.0, p = .39) nor were the 2-way diagnosis × time (χ
2(3) = 4.18, p = .24) and diagnosis × segment (χ
2(3) = 0.53, p = .91) interactions. There was no main effect of diagnosis (χ
2(3) = 4.87, p = .18). These findings suggest that the risk group effect is not explained
by diagnostic outcomes.
4.3
Face time and face foraging measures
4.3.1
Risk group effects
Analyses were conducted using a similar model to that described above with the factors:
risk group, time and trial segment. For face time proportion, the 3-way interaction
of risk group × age × segment was not significant (χ
2(1) = 1.18, p = .277) nor were the 2-way group × time (χ
2(1) = 2.06, p = .151) and group × segment (χ
2(1) = 0.97, p = .325) interactions. There was a significant main effect of group
(χ
2(1) = 4.85, p = .028) indicating that the at-risk group spent proportionally more
time on the face relative to other AOIs. The 3-way interaction of group × time × segment
was marginally significant (χ
2(1) = 3.14, p = .077) and the 2-way interaction of group × segment (χ
2(1) = 0.86, p = .353) was non-significant for group × time (χ
2(1) = .67, p = .155). There was a significant main effect of group (χ
2(1) = 8.41, p = .004). Taken together, these findings suggest that overall, infants
at-risk are more likely to sample the face compared to other AOIs, and a trend toward
this pattern being more pronounced at 14-months relative to 7-months.
4.3.2
Diagnostic group effects
For face time proportion, estimated means for each diagnostic group are shown in Fig.
2. The 3-way interaction of diagnosis × age × segment was not significant (χ
2(3) = 1.42, p = .70) nor were the 2-way diagnosis × time (χ
2(3) = 2.81, p = .42) and diagnosis × segment (χ
2(3) = 0.80, p = .85) interactions. There was no significant main effect of diagnosis
(χ
2(3) = 4.71, p = .19). For the face foraging proportion, the estimated means for each
group are shown in Fig. 3. The 3-way interaction of group × time × segment was not
significant (χ
2(3) = 3.72, p = .29) nor were the 2-way interactions for group × time interaction
(χ
2(3) = 2.02, p = .57) and group × segment (χ
2(3) = 4.53, p = .21). These findings suggest that the risk group effects are not
related to diagnostic outcomes.
5
Discussion
In the current study, we addressed three complementary questions regarding the early
development of the social brain in infants at-risk for autism. First, we tested a
popular idea in developmental psychopathology positing that autism results from the
lack of a bias to orient towards social information. Our findings suggest that, similar
to other groups, infants who later develop ASD exhibit a clear face pop-out response,
i.e., attentional capture by faces. Second, to examine the possible impact of atypical
general mechanisms of attention, looking time and foraging measures provided complementary
information regarding the distribution of infants’ looking behaviour beyond face pop
out. Our findings indicate that, as a group, infants at-risk had a tendency to sample
fewer stimuli in the array relative to the control group, with this being most clearly
evident at the beginning of the second year and regardless of their clinical outcomes
assessed at 3-years. Finally, we examined the interaction between social orienting
and general attention mechanisms in the developing brain. Contrary to some current
hypotheses, those infants who later developed autism were equally captured by faces
relative to other groups at 7-months. In fact, infants with familial liability for
autism as a group tended to be more captured by faces in this early developmental
period. While these findings were marginal and require replication with other groups
they appear to contradict social orienting and social reward models of autism, which
argue for lesser engagement with people and faces early in life [20,13].
These findings are consistent with a growing body of evidence from less structured
observational settings that orienting towards people does not distinguish, at least
at six months of age, those infants who go on to a later diagnosis [53,68,69]. In
the current study, we assessed the integrity of early developing neural systems biasing
infants to orient and sustain attention to socially relevant stimuli. While the static
arrays used in the current study may differ from the infants’ naturalistic environment,
the task allowed us to isolate and directly test different factors contributing to
previous findings based on observing infants in dynamic and naturalistic settings
where they interact with their caregivers [68] or an experimenter [53,69].
Because autism is a complex condition encompassing symptoms in both social and non-social
skills, others have suggested that in early infancy, multiple brain systems are likely
to be affected, including those related to flexibly and efficiently allocating attention
towards different stimuli in the environment [6]. Our findings support the growing
consensus in research on infants at-risk that autism begins with subtle manifestations
early in life which then transform over time into emergent symptoms in a minority
of children. We found little evidence that autism-related symptoms are a consequence
of early impairment of mechanisms that mediate attentional capture by socially-relevant
information. However, mechanisms mediating efficient foraging of environmental stimuli
in the presence of socially-relevant stimuli tended to distinguish infants at-risk
as a group. Considering the two foraging measures together suggests that infants at-risk
tend to only look at the face and a few other AOIs (while other infants distribute
their looking more equally between AOIs), a finding possibly consistent with an emerging
overly focal attention style.
Distribution of looking time and visual foraging are likely to reflect the combined
operations of multiple sub-cortical and cortical systems giving rise to differences
in response to social stimuli early on. This pattern suggests that interactions among
multiple brain systems over the first two years give rise to variable pathways in
infants at-risk. While it may appear paradoxical that those infants at-risk spent
marginally more time on faces relative to other stimuli, the combination of typical
face orienting mechanisms with differences in cortical mechanisms mediating efficient
selection may manifest in a subset of infants at-risk looking longer towards faces.
The underlying mechanisms and the functional consequence of this increased proportional
looking towards faces remain to be explored. Longer dwell time on faces or other visual
stimuli have been previously associated with processing difficulties. For example,
infants, who have a pattern of prolonged fixation time, rely more on local elements
when processing visual stimuli [14,25,26]. Individual differences in looking time,
i.e., ‘long vs. short lookers’ also predict later cognitive outcomes in typical infants
[15] and ‘longer looking’ has been documented in other atypical populations such as
preterm infants [57] or prenatal cocaine-exposed infants [17]. Specific effects of
these attentional constraints on face processing have also been reported. ‘Long-lookers’
were better at detecting feature changes [14], potentially giving rise to differences
in local vs. configural processing strategies [34]. Future studies should explore
other measures of visual behaviour (e.g. fixation duration, scanning paths) to test
the hypothesis of a visual processing deficit in infants who subsequently develop
autism. Use of brain imaging methods such as EEG or fMRI will also support clarifying
the underlying brain processes mediating the eye tracking findings observed in the
current study.
The extent to which the behavioural patterns observed in the current study reflect
processing differences that have functional consequences as development proceeds needs
further investigation. In particular, the face pop-out measure in the current task
is not sensitive to face properties which are believed to be important signals for
social communication, like the up-right (vs. inverted) position or direct (vs. indirect)
gaze. For example, typically developing six-month-old infants oriented equally frequently
towards inverted faces and faces with averted gaze [28]. This orienting mechanism,
while necessary, is not sufficient to appropriately modulate infants’ participation
in social interactions.
Despite these limitations, our findings carry significant implications for ‘classical’
developmental accounts using autism as a model for understanding the emergence of
the social brain. While it remains possible that autism results from early differences
in response to socially-relevant information, our findings with a group of infants
at-risk for autism decrease the likelihood that these early differences are primarily
related to sub-cortical systems mediating an early bias to orient towards faces. This
is in line with recent studies highlighting differences between adults with amygdala
lesions and with ASD. ASD participants oriented to faces and eyes more often than
amygdala patients but they did not modulate their orienting depending on task demands,
an ability which probably depends on cortical functions (Birmingham, Cerf & Adolphs,
2011). Cortical mechanisms mediating efficient selection and distribution of attention
appear to modulate infants’ early response to faces, reflecting interactions among
multiple developing systems. A reasonable alternative to the classical accounts would
be that autism provides a useful framework for understanding how differences in face
processing mechanisms may emerge as a consequence of early atypical interactions among
different systems. Future translational research concerned with developing early markers
of autism can also benefit from the current findings through shifting the focus towards
models of cumulative risk reflected in variable trajectories, a subset of which result
in a diagnosis in toddlerhood or beyond [24].