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Social-Pragmatic Inferencing, Visual Social Attention and Physiological Reactivity to Complex Social Scenes in Autistic Young Adults

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This study examined social-pragmatic inferencing, visual social attention and physiological reactivity to complex social scenes. Participants were autistic young adults (n = 14) and a control group of young adults (n = 14) without intellectual disability. Results indicate between-group differences in social-pragmatic inferencing, moment-level social attention and heart rate variability (HRV) reactivity. A key finding suggests associations between increased moment-level social attention to facial emotion expressions, better social-pragmatic inferencing and greater HRV suppression in autistic young adults. Supporting previous research, better social-pragmatic inferencing was found associated with less autistic traits.
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Journal of Autism and Developmental Disorders (2022) 52:73–88
Social‑Pragmatic Inferencing, Visual Social Attention andPhysiological
Reactivity toComplex Social Scenes inAutistic Young Adults
KatjaDindar1 · SoileLoukusa1· TerhiM.Helminen2· LeenaMäkinen1· AnttiSiipo3· SeppoLaukka4·
AnttiRantanen4· Marja‑LeenaMattila5· TuulaHurtig5,6· HannaEbeling5
Accepted: 4 February 2021 / Published online: 27 February 2021
© The Author(s) 2021
This study examined social-pragmatic inferencing, visual social attention and physiological reactivity to complex social
scenes. Participants were autistic young adults (n = 14) and a control group of young adults (n = 14) without intellectual
disability. Results indicate between-group differences in social-pragmatic inferencing, moment-level social attention and
heart rate variability (HRV) reactivity. A key finding suggests associations between increased moment-level social attention
to facial emotion expressions, better social-pragmatic inferencing and greater HRV suppression in autistic young adults.
Supporting previous research, better social-pragmatic inferencing was found associated with less autistic traits.
Keywords Autism spectrum· Autistic traits· Heart rate variability· Physiological reactivity· Social-pragmatic ability·
Visual social attention
* Katja Dindar
Soile Loukusa
Terhi M. Helminen
Leena Mäkinen
Antti Siipo
Seppo Laukka
Antti Rantanen
Marja-Leena Mattila
Tuula Hurtig
Hanna Ebeling
1 Research Unit ofLogopedics, Faculty ofHumanities,
University ofOulu, PO Box1000, 90014Oulu, Finland
2 Psychology, Faculty ofSocial Sciences, Tampere University,
Tampere, Finland
3 Department ofEducational Sciences andTeacher Education,
Faculty ofEducation, University ofOulu, Oulu, Finland
4 Learning Research Laboratory, Faculty ofEducation,
University ofOulu, Oulu, Finland
5 PEDEGO Research Unit, Clinic ofChild Psychiatry, Faculty
ofMedicine, Oulu University Hospital, University ofOulu,
Oulu, Finland
6 Research Unit ofClinical Neuroscience, Psychiatry, Faculty
ofMedicine, University ofOulu, Oulu, Finland
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74 Journal of Autism and Developmental Disorders (2022) 52:73–88
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Autistic individuals1 commonly experience social-pragmatic
challenges such as difficulties in understanding and inter-
preting social interactions in context (e.g., Loukusa, in press;
Tager-Flusberg, Paul and Lord 2005). While prior studies
have made a significant contribution to the current under-
standing of social-pragmatic abilities in autistic individuals,
there is a limited number of studies combining behavioural
and psychophysiological data from the same individuals.
Combining such data could be highly useful in increasing
understanding on the interplay between key aspects of pro-
cessing complex social scenes, such as the ability to pro-
duce contextually relevant inferences, to focus visual social
attention in a contextually relevant manner and to regulate
physiological reactions. This study combines behavioural
and physiological data from young autistic adults and young
adults in a control group to investigate how pragmatically
complex social scenes are interpreted, visually attended to
and physiologically reacted to.
Social‑Pragmatic Inferencing
Social-pragmatic ability can be understood as contextually
relevant use and interpretation of language and communica-
tion (e.g., Loukusa, in press; Volden etal. 2009). The ability
to understand social interactions and to infer what others
mean has a significant role in everyday life. People may not
directly say what they mean and commonly use embodied
cues (e.g., facial emotion expressions) instead to commu-
nicate their actual intentions, requiring the ability to attend
to and interpret highly multimodal information in context
(Levinson 2006). Such processing of contextual information
to infer meaning is found challenging for autistic individu-
als, including children and adolescents (e.g., Angeleri etal.
2016; Loukusa etal. 2018; Mäkinen etal. 2014) and adults
(e.g., Loukusa, in press; Lönnqvist etal. 2017), and asso-
ciations are found between social-pragmatic challenges and
autism symptoms (e.g., Volden etal. 2009).
Linguistic or cognitive challenges alone are considered
insufficient to fully account for social-pragmatic difficul-
ties (e.g., Volden etal. 2009) that are viewed universal for
the autism spectrum (Tager-Flusberg etal. 2005). How-
ever, research suggests that autistic individuals experience
social-pragmatic challenges with varying degrees (e.g.,
Deliens etal. 2018; Heavey etal. 2000; Loukusa and Moil-
anen 2009), making it important to understand what kind of
challenges autistic individuals do have and how they might
be associated with other social challenges. A long line of
research has examined autistic individuals’ difficulties in
inferring and explaining others’ mental states (i.e. ‘Theory
of mind’) and has identified a tendency to interpret such
social information in isolation without taking full advantage
of the context (Heavey etal. 2000; Jolliffe and Baron-Cohen
1999, 2000).
Although social-pragmatic inferencing skills tend to
develop with age, challenges in these specific areas of social
pragmatic ability appear to persist into adulthood even in
autistic individuals who have linguistic and cognitive skills
within typical range (e.g., Lönnqvist etal. 2017). Impor-
tantly, autistic adults themselves also identify with what
could be viewed as social-pragmatic difficulties. For exam-
ple, challenges in interpreting neurotypical (NT) individuals’
expressions, reading ‘unspoken rules’ of social interaction
as well as feelings of anxiety during and exhaustion after
interacting with NT individuals are self-reported by autistic
adults (e.g., Crompton etal. 2020). Research indicates dif-
ferences between autistic and NT individuals particularly in
how situations requiring perspective shifting, interpretation
of multiple social cues and monitoring of complex social
interactions are interpreted and processed (e.g., Deliens
etal. 2018; Kotila etal. 2020). These findings suggest that
the ability to focus visual social attention in a contextu-
ally relevant manner is a crucial skill for social-pragmatic
The Interplay Between Social‑Pragmatic Inferencing
andVisual Social Attention
Using eye tracking methodology, studies have examined
differences between autistic individuals and NT individu-
als in how visual attention is allocated to social stimuli.
Previous research in general suggest that compared to NT
individuals, autistic individuals allocate less visual atten-
tion to social stimuli and particularly to faces, people and
their social actions, and in contrast, more visual attention
to non-social elements including objects (Chita-Tegmark
2016; Guillon etal. 2014; Tang etal. 2019). In addition,
measures of autism symptom severity and social competence
have been found associated with reduced attention to human
eyes and mouths and/or faces more broadly (Dijkhuis etal.
2019a; Klin etal. 2002; Norbury etal. 2009). There is how-
ever increasing evidence to suggest that differences in visual
social attention between autistic and NT individuals can be
subtle and may not occur in how visual attention is allocated
throughout social stimuli on an aggregated level (Dijkhuis
etal. 2019a) but rather on a contextually and temporally
sensitive moment-level (e.g., Falck-Ytter etal. 2013; Lön-
nqvist etal. 2017;Nakano etal. 2010; Nyström etal.2017).
Moment-level examinations have been conducted to
explore between-group differences in visual social attention
1 In this article, we use ‘identity-first’ terminology that is reportedly
preferred by many autistic adults (e.g., Bury etal. 2020; Kenny etal.
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75Journal of Autism and Developmental Disorders (2022) 52:73–88
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at a given moment in time. These studies provide evidence
that autistic adults (Lönnqvist etal. 2017; Nakano etal. 2010),
adolescents and children (Falck-Ytter etal. 2013; Skwerer
etal. 2019; Tenenbaum etal. 2021) differ from NT individu-
als in how they follow social interactions as they unfold. The
temporally focused ‘when question’ zooms the lens in on the
moments in time when it could be contextually relevant to
look at other people and their faces in particular (Dindar etal.
2017; Falck-Ytter etal. 2013; Hessels 2020; Hochhauser and
Grynszpan 2017). Looking at faces could be crucial for social-
pragmatic inferencing, for instance, when there is a discrep-
ancy between interlocutors’ facial emotion expressions and
what is being said. Missing out on such social cues could
hinder one from understanding others’ intentions and motiva-
tions (Levinson 2006), and lead to misunderstandings.
The relationship between visual social attention and the
assessment of social-pragmatic inferencing is not necessarily
straightforward, that is, people do not always report on what
they visually attend to (Freeth etal. 2011). It thus becomes
relevant to combine the analysis of visual social attention
with verbal reports rather than relying on one of these as a
measure of how stimuli are processed (Freeth etal. 2011;
Hochhauser and Grynszpan 2017). Yet, there is currently
little information available on the role that visual social
attention plays in social-pragmatic inferencing for autis-
tic individuals. Only a handful of studies have measured
visual social attention and related it to participants’ social-
pragmatic inferences or other verbal reports about social
stimuli (e.g., Freeth etal. 2011; Grynszpan and Nadel 2015;
Lönnqvist etal. 2017; see also Hanley etal. 2015; Sasson
etal. 2007). Existing evidence suggests that challenges in
focusing visual social attention in a contextually relevant
manner may be a part of the explanation of why inferring
contextual meaning can be difficult for autistic individuals.
For example, Grynszpan and Nadel (2015) found that when
presented with videos involving social interactions, the more
autistic adolescents and adults allocated visual attention to
the dynamically changing facial expressions of the people in
the videos, the more cognition verbs they produced in their
verbal reports of the videos. This association was not found
in NT adolescents and adults, which together with other pre-
vious evidence suggests that between-group differences in
visual social attention may play a key role in the commonly
observed differences between autistic and NT individuals in
social-pragmatic ability.
The Interplay Between Social‑Pragmatic
Inferencing, Visual Social Attention
andPhysiological Reactivity
Decades of research has shown that autistic individuals
do not only tend to look at social stimuli differently than
NT individuals, but their physiological reactions to such
stimuli also are different (see Lydon etal. 2016, for a
review). Functioning of the parasympathetic autonomic
nervous system, measured with heart rate variability
(HRV) during rest or as a response to a stressor (quantified
as a difference between baseline and task condition), have
been associated with multiple social, affective and cogni-
tive phenomena, including social engagement and men-
tal effort (Porges 2007; Thayer etal. 2012). HRV refers
to the variation in time between successive heartbeats.
Although both of the two branches of autonomic nervous
system, the sympathetic and the parasympathetic, control
the heart rate, certain measures of HRV, such as the root
mean successive squared difference (RMSSD), are known
to reflect parasympathetic influences (see e.g., Laborde
etal. 2017; Shaffer and Ginsberg 2017). Parasympathetic
control of the heart via the vagal nerve can indicate capac-
ity to engage with environmental demands, and therefore,
can be treated as a measure of mental effort allocation
to tasks demanding attention (Porges 2007; Porges etal.
2013). According to the Polyvagal Theory (Porges 2007),
withdrawal of vagal inhibition during mental effort tasks
could represent an adaptive response that prepares an
individual to react. Therefore, the examination of vagal
suppression (evident as HRV suppression) during tasks
that require inferring meaning in pragmatically complex
situations, could be useful in assessing the mental effort
an individual invests in the task.
Atypical physiological reactivity to social stimuli has
been found associated with autistic traits (e.g., Dijkhuis
etal. 2019a). Supportive of prior studies using other meas-
ures of autonomic nervous system activation (Lydon etal.
2016), Dijkhuis etal. (2019b) found lower HRV reactivity to
a social stress task in autistic adults compared to NT adults.
Interestingly, Toichi and Kamio (2003) reported an unex-
pected increase in HRV in response to non-social mental
effort allocation tasks in a subgroup of autistic adults (see
also Porges etal. 2013 for a similar finding in children).
Such an increase in HRV reportedly hinders the efficient
processing of stimuli during tasks, which is supported by the
finding that increased HRV during tasks is associated with
poorer task performance (in non-social auditory processing)
in children (Porges etal. 2013). Given that previous research
on HRV has primarily focused on children and adolescents
(e.g., Lory etal. 2020; Lydon etal. 2016), more research
involving autistic adults is needed to understand whether
atypical physiological reactivity continues into adulthood
and how it is associated with social-pragmatic ability.
Previous research provides evidence for associations
between physiological reactivity and social communicative
skills in autistic individuals (e.g., Lydon etal. 2016), yet a
limited number of studies have investigated HRV reactivity
to tasks specifically assessing social-pragmatic ability. We
are currently aware of only one study examining associations
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76 Journal of Autism and Developmental Disorders (2022) 52:73–88
1 3
between HRV reactivity and social-pragmatic ability in autistic
individuals. In their study, Klusek etal. (2013) found that in
children, HRV reactivity was negatively associated with social-
pragmatic ability, that is, the less HRV was suppressed from
baseline to a conversational task, the worse their pragmatic
performance was considered, calling for more research on
whether challenges in controlling the ‘vagal brake’ are associ-
ated with social-pragmatic difficulties. Similarly, prior research
suggests the need to examine the role of visual social atten-
tion in physiological reactivity. Findings regarding NT children
suggest that HRV during both baseline and social interaction
situations is associated with gazing behaviour toward a com-
municative partner (Heilman etal. 2007). Some results from
pupillary responses have also indicated possible associations
between visual social attention and autonomic responses:
Frost-Karlsson etal. (2019) found that autistic adolescents
and adults allocated visual attention to the social elements
of a scene later than NT adolescents and adults, and did not
show a greater pupillary response to stimuli involving humans
compared to non-human stimuli, unlike their NT counterparts
To summarise, although vast amount of research exists
on social-pragmatic inferencing, visual social attention and
physiological reactivity in autistic individuals, research has
predominantly focused on children, resulting in less infor-
mation on whether and how challenges possibly continue
into adulthood. Relatedly, findings concerning possible
interplay between these key aspects of processing complex
social scenes come from single, separate studies, creating a
valuable, yet limited evidence base which the current exami-
nation aims to contribute to.
The current study aimed to examine differences between
autistic young adults (the autistic group) and young adult
controls (the control group) in social-pragmatic inferencing,
visual social attention and physiological reactivity (HRV), and
the associations between these measures and autistic traits.
Based on previous research summarised above, we predicted
that P1) the autistic group would show more challenges in
social-pragmatic inferencing; P2) the autistic group would
allocate less visual social attention to key characters in the
scenes viewed during key social moments but not throughout
the scenes; and P3) the autistic group would show less HRV
reactivity, compared to the control group. Regarding the inter-
play between these measures, we further predicted that per-
haps more evidently in the autistic than in the control group
(see e.g., Grynszpan and Nadel 2015), P4a) better social-
pragmatic inferencing would be associated with increased
visual social attention to key characters in the scenes during
key social moments; P4b) better social-pragmatic inferenc-
ing would be associated with greater suppression in HRV in
response to social-pragmatic inferencing tasks; P4c) better
social-pragmatic inferencing would be associated with less
autistic traits; P4d) increased visual social attention to key
characters in the scenes during key social moments would
be associated with less autistic traits, and P4e) greater sup-
pression in HRV in response to social-pragmatic inferencing
tasks would be associated with less autistic traits. Finally, we
explored without a particular prediction whether visual social
attention to key characters in the scenes during key social
moments and physiological reactivity would be associated.
Initially, 34 autistic young adults and 37 young adult con-
trols participated in the study. Autistic individuals origi-
nally participated in an epidemiological study in the North-
ern Ostrobothia Hospital District area (Mattila etal. 2007,
2011) or clinic-based studies conducted at the Oulu Uni-
versity Hospital (Kuusikko etal. 2008, 2009; Weiss etal.
2009)in Finland. The 37 control individuals without an
autism spectrum disorder diagnosis were selected from (1)
the epidemiological study that was conducted in 2000–2003
(Mattila etal. 2007, 2011), (2) the audio-graphic study con-
ducted in 2003 (Jansson-Verkasalo etal. 2005), and (3) the
autism spectrum disorder and anxiety study conducted in
2006 (Kuusikko etal. 2008, 2009).
During the original recruiting processes, the ICD-10
criteria (World Health Organization 1993) were utilised in
detail to define the best clinical diagnosis of autism spectrum
disorder using the Autism Diagnostic Interview Revised
(ADI-R; Lord etal. 1995), the Autism Diagnostic Observa-
tion Schedule (ADOS; Lord etal. 2000), and other clinical
information. During the data collection for the current study
between 2013 and 2015, the Wechsler Adult Intelligence
Scale-IV (Wechsler 2012) was used to assess the partici-
pants’ general cognitive ability.
Inclusion in the present study required a participant to
have (1) no observed intellectual disabilities and (2) good-
quality recordings of all three levels of behavioural and
physiological data (social-pragmatic inferencing, visual
social attention and physiological reactivity). First, partici-
pants were excluded based on a General Ability Index score
(Wechsler 2012) less than 75 (n = 4). Since social-pragmatic
inferencing data were available for all the participants, they
were next excluded based on unsuccessfully recorded eye
tracking data (n = 39). Third, participants would have been
removed based on unsuccessfully recorded physiological
data, but all the remaining participants had successfully
recorded data (see Stimuli and Measures for more details).
Finally, these exclusion criteria resulted in 14 participants
in each group.
All participants were young adults(see Table1). There
were four females and ten males in each group. Groups did
not differ in terms of age (U = 96, p = 0.946, Mann–Whitney
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77Journal of Autism and Developmental Disorders (2022) 52:73–88
1 3
U test), Verbal Comprehension Index scores (t(26) = -1.218,
p = 0.234, independent samples t-test), Perceptual Reasoning
Index scores (U = 123, p = 0.265) or General Ability Index
scores (U = 123.5, p = 0.246). Participants’ autistic traits
were measured with the Finnish version of the Autism Quo-
tient (AQ) (Baron-Cohen etal. 2001; translated by Ulrika
Roine). The autistic group had statistically significantly
higher AQ scores than the control group (t(24) = -2.766,
p = 0.014).2
Data was collected for each participant at a single session
that took place at approximately the same time of the day
(morning) for all the participants. They were asked not to
consume any caffeine or alcohol during the morning of data
collection. Data collection took place in a quiet room with
one experimenter (out of three different female experiment-
ers) present throughout the entire experiment. Participants
were seated in front of a computer screen that was posi-
tioned above a remote eye-tracker. Participants were shown
six short video clips of naturalistic pragmatically complex
social scenes (referred to as ‘social-pragmatic videos’) as
part of the broader experimental study that lasted approxi-
mately 90min in total (Hurtig etal., in preparation). The
social-pragmatic video condition occurred approximately
20min after the beginning of the experiment, being pre-
ceded by another, albeit different social video task. Two of
these social-pragmatic videos, presented as third and fifth
in the series, were used in the present study based on their
high similarity. After watching each video, participants were
asked to respond to two-part inference questions about what
they thought that the interlocutors on the videos were think-
ing and to explain why they thought so. The questions were
asked and answered orally. Participants were shown a picture
captured from each video upon responding to the questions
to prevent any confusion about the interlocutors referred
to in the questions. Participants were informed in advance
that questions would be asked about the videos, framing the
watching as a task, rather than as free viewing of videos. The
content of the questions was not revealed to the participants
in advance.
Participants’ eye movements on the computer screen and
physiological data were recorded during the study. Physi-
ological measures included skin conductance (recorded
with a wristband, not reported in this study) and heart
rate. The broader experimental study involved a series of
other test tasks that are not reported in this study. Approxi-
mately 40min after the social-pragmatic video condition, a
break in the experimental protocol occurred, typically last-
ing approximately 2min. This transition period from one
experimental task to the next was used as a baseline for HRV
measurement (see more details below). An experimenter
was present both during the baseline and social-pragmatic
video condition, sitting behind a table and computer screen,
opposite a participant. Rather than at the beginning of the
recording, using this transition period as a baseline ensured
participants’ acclimatisation to the study environment, thus
reducing possible anxiety. These transition period baseline
situations were afterwards examined from video recordings
and considered similar between the participants. During the
transition period, the participants continued to sit in front
of the computer screen while an experimenter began to fill
in their personal information into the computer to set up
the next task. For setting up the next task, experimenters
typically asked about the participants’ age (occurred for 27
out of 28 participants) and began to tell generic information
about the next task (occurred for 28 out of 28 participants),
keeping the situation as natural and relaxed as possible.
2 The AQ scores were available from 13 autistic participants and 13
control participants. Loukusa etal. (in press) have shown that the cut-
off score and the mean AQ scores are considerably lower in a Finn-
ish sample than in an English sample (Baron-Cohen etal. 2001). The
reported mean AQ score for autistic adults (n = 52) is 22.5 (SD = 8.3)
and for control adults (n = 1686) 13.1 (SD = 6.4). Cut-off score of 18
for males and 16 for females have been suggested (Loukusa etal., in
press). The difference between the English and Finnish samples could
be considered to relate to cultural differences in interpreting commu-
nication, interaction and relatedly, autistic traits (see e.g., Gabbatore
etal. 2019).
Table 1 Mean and median
values, standard deviations
and interquartile ranges for
participants’ age (years),
Autism Quotient scores, Verbal
Comprehension Index scores,
Perceptual Reasoning Index
scores and General Ability
Index scores
Age years, AQ Autism Quotient (Finnish translation), VCI Verbal Comprehension Index (Wechsler Adult
Intelligence Scale-IV), PRI Perceptual Reasoning Index (Wechsler Adult Intelligence Scale-IV); GAI Gen-
eral Ability Index (Wechsler Adult Intelligence Scale-IV)
Autistic group Control group
Age 23.6 23.3 3.3 3.9 23.5 22.8 2.0 2.3
AQ 19.4 21.0 9.2 16.5 11.7 12.0 3.8 4.0
VCI 114.5 116.0 13.0 18.5 108.1 113.0 14.6 19.0
PRI 108.9 114.0 18.1 24.0 105.2 108.0 12.1 12.0
GAI 113.3 114.0 14.3 18.5 107.6 110.0 13.0 15.5
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78 Journal of Autism and Developmental Disorders (2022) 52:73–88
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From the broader experimental study, different parts of
the inference question data and eye tracking data involving
different stimuli have been previously reported in a study by
Lönnqvist etal. (2017) using different analytical approaches.
The sample of the Lönnqvist etal. study is not identical to
the current study.
Stimuli andMeasures
Social‑Pragmatic Videos
The two social-pragmatic videos used in this study involved
pragmatically complex social interactions in the partici-
pants’ native language, Finnish. The videos were 1min 8s
and 1min 13s in length. The videos involved naturalis-
tic interactions between interlocutors who were all young
women discussing everyday topics (weekend plans, plans to
buy a new coat). The first video involved four interlocutors,
and the second three interlocutors. The interactions involved
subtle social conflict as one or more interlocutors were
repeatedly interrupted or left without acknowledgement
when attempting to contribute to an on-going conversation
or to introduce a new conversational topic. This resulted
in these interlocutors’ submission and withdrawal from the
interaction. Identifying this social conflict required interpret-
ing the interlocutors’ intentions and thoughts from subtle
social cues such as repetitive turn interruptions and facial
emotion expressions. Complex multiparty interactions were
chosen as stimuli as there is evidence to suggest that the
social complexity of the stimuli presented appears to play
a key role in bringing out differences both in visual social
attention and social-pragmatic inferencing between autistic
and NT individuals (e.g., Chita-Tegmark 2016; Deliens etal.
2018; Guillon etal. 2014).
Based on previous research (e.g., Falck-Ytter etal. 2013;
Lönnqvist etal. 2017;Nakano etal. 2010), we assumed
that it would be crucial to focus on specific locations in the
social-pragmatic videos at specific moments in time. We first
made a basic distinction between the interlocutors’ appar-
ent roles in the social videos. Drawing on the interpersonal
theory (e.g., Horowitz etal. 2006), these interlocutor roles
were viewed along the submissiveness—dominance dimen-
sion, and the characters were categorised into ‘Dominant
Characters’ and ‘Submissive Characters’ based on whether
they were frequently interrupting and excluding others or
being interrupted and excluded by others, respectively. Next,
we zoomed our analysis in on key social moments that were
identified a priori on the social-pragmatic videos, building
on previous exploratory studies (e.g., Falck-Ytter etal. 2013;
Lönnqvist etal. 2017). Moments that were considered rel-
evant for inferring meaning were identified (see Table2).
These moments related to interactional trouble that was con-
veyed through repetitive turn interruptions and facial emo-
tion expressions (See Online Appendix A for more details),
and were annotated by the first author, who is experienced
in video-based analysis of social interactions.
Inference Questions
Responding to the inference questions required inferring
the stances of the Submissive Characters and Dominant
Characters from both spoken language and their disaffili-
ative facial emotion expressions. Two two-part inference
questions were asked for each social-pragmatic video,
one targeting a Submissive Character and one targeting a
Dominant Character. Participants’ responses to the infer-
ence questions were scored according to whether they
correctly inferred the key social aspect (subtle social
conflict), the extent to which they considered the per-
spectives of the characters in the videos, and the contex-
tual relevance of the explanations they provided for their
responses. These facets were aggregated as a sum score
Table 2 The identified key social moments, their descriptions, frequencies and durations in the social-pragmatic video 1 and video 2
Key social moment Key moment description Frequency in
video 1 and
video 2
Duration M (SD)
Submissive characters’ turn interruptions Getting interrupted by the Dominant Character(s)
or failing to join a discussion by not receiving any
3 + 5 2.7s (1.3)
Dominant characters’ turn interruptions Interrupting the Submissive Character(s). Getting
interrupted by the Submissive Character(s) due to
attempts to join a discussion
4 + 4 2.4s (1.4)
Submissive characters’ facial emotion expressions Facial expressions that conveyed disaffiliation, that is,
negative stance toward the Dominant Character(s) or
the situation more generally
2 + 6 3.5s (3.1)
Dominant characters’ facial emotion expressions Facial expressions that conveyed disaffiliation, that is,
negative stance toward the Submissive Character(s)
or the situation more generally
0 + 0
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79Journal of Autism and Developmental Disorders (2022) 52:73–88
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between 0 and 3 for each two-part interpretation question
(four in total). See Online Appendix B for more details.
Interrater reliability (IRR) analysis was conducted by
having a student code approximately 30% of the data that
was randomly selected for the IRR analysis. The student
had not seen the social-pragmatic videos and was naïve to
the participants’ group membership (i.e. autistic group vs.
control group). Coding by the student was compared with
the coding conducted by the first author who coded the
entire body of data. IRR analysis yielded Krippendorff’s
α of 0.793 and intra-class correlation coefficient (ICC(2),
two-way random, single measure, absolute agreement) of
0.780, indicating moderate to good reliability.
Eye Movement Data Collection andAnalysis
Participants’ eye movements were recorded using a Tobii
TX300 remote eye tracker with a sampling rate of 300Hz
and Tobii Pro Studio 3.3 software. A five-point calibration
procedure was conducted before each recording. Calibra-
tion was repeated until a satisfactory calibration result
was obtained. Participants sat approximately 60cm from
a computer screen that presented the calibration and social-
pragmatic video stimuli. Participants were asked to refrain
from excess movement during the experiment, but no chin or
head rests were used to ensure as comfortable participation
as possible. Data quality was inspected for each participant
separately. Participants’ data were excluded from the present
study if a participant’s data involved less than 50% valid
gaze samples in either of the social-pragmatic videos, if a
participant spent less than 80% of a video’s duration looking
at the screen during either of the social-pragmatic videos or
if spatial accuracy was assessed poor based on visual inspec-
tion of the raw data plots and scan path visualisations.
Gaze data were processed in Tobii Pro Studio (3.3.2)
using the Velocity-Threshold Identification (I-VT) fixation
classification algorithm. Parameter settings included the fol-
lowing. Gap fill-in using linear interpolation was enabled
(with a maximum gap length of 75ms). A strict average of
both eyes was used in calculations. No noise reduction was
used. A velocity calculator was set to 30ms. Adjacent fixa-
tions were merged (with maximum time and angle between
fixations set to 75ms and 0.5 degrees). Fixations shorter
than 60ms were discarded. We extracted total dwell time
measures (total visit duration in Tobii Pro Studio) for each
Area of Interest (AOI) and computed proportional total
dwell time by dividing each value by a participant’s total
dwell time to each social-pragmatic video overall, multiplied
by 100. This ‘proportional looking time’ value expresses the
proportional time a participant spent looking at a given AOI
in a time window of interest.
The AOIs were defined as rectangles (see Fig.1) and
included the facial areas of the interlocutors in the scenes
(grouped as Submissive Characters and Dominant Charac-
ters) and objects. We opted to use large Face AOIs so as to
capture participants’ total dwell time to each AOI irrespec-
tive of possible slight spatial offset in the gaze data. For two
participants (one in the control group, one in the autistic
group) with systematic offset in gaze data, AOIs were indi-
vidually adjusted in space to correct the offset. Addition-
ally, for all the participants, AOI positions were dynamically
adjusted on a frame-by-frame basis when the interlocutors
on the social-pragmatic videos moved.
We investigated participants’ visual social attention on
two different levels. An aggregated level analysis was con-
ducted focusing on overall distribution of visual attention
between Submissive Characters’ and Dominant Characters’
faces (Face AOIs) and objects in the scene (Object AOIs).
For a moment-level analysis, participants’ visual social
attention to each Face AOI was extracted for the specific
time windows of interest, i.e. key social moments (described
previously in Table2).
Heart Rate Variability Data Collection, Signal processing
Beat-to-beat RR interval data were recorded using the
Zephyr Bioharness 3 chest belt with a sampling rate of
250Hz. Data were pre-processed and analysed using Kubios
Standard version 3.3.1 (Tarvainen etal. 2014). Samples were
filtered using a detrending method based on the smooth-
ness priors approach with a 0.035Hz cut off frequency, as
Fig. 1 A sketch pen rendering of one of the social-pragmatic vid-
eos used in the study (anonymised). Rectangles represent the Areas
of Interest (AOIs) that were used to record visual attention alloca-
tion to Dominant Characters’ and Submissive Characters’ Face AOIs
throughout the video and during Turn Interruptions and Facial Emo-
tion Expressions (solid lines) or to Object AOIs (dashed lines) in the
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80 Journal of Autism and Developmental Disorders (2022) 52:73–88
1 3
suggested by Tarvainen etal. (2002). Data was examined
for artefacts through visual inspection and by investigating
physical movement data recorded by the chest belt. An auto-
matic threshold-based artefact correction algorithm based on
a cubic spline interpolation was used to replace the identified
artefacts (Tarvainen etal. 2014). To account for the indi-
vidual variation in signal quality, the correction threshold
values were adjusted individually for each participant, iden-
tifying all the inter-beat intervals smaller or larger than 0.25
(n = 24) or 0.15s (n = 2 autistic group and 2 control group
participants), compared to the local average. The groups
did not differ in terms of movement data recorded by the
device during the baseline (U = 122.5, p = 0.256) or social-
pragmatic video condition (U = 135.5, p = 0.084).
The time-domain calculation of the square root of the
mean squared differences between successive R-R intervals
(RMSSD) is considered to be less affected by respiratory
influences and is perceived as a good estimate of HRV for
very short-term recordings, compared to some other HRV
measures (e.g., Laborde etal. 2017; Shaffer and Ginsberg
2017). Therefore, we extracted RMSSD for statistical
Heart rate variability data was analysed and compared
between the baseline and social-pragmatic video conditions.
As RMSSD is sensitive to the duration of the recordings,
data duration from the baseline and social-pragmatic video
conditions were matched, including a maximum of one
minute of data from each condition. One minute of (log-
transformed) RMSSD data has been considered as a good
estimate of the more commonly used five-minute RMSSD
data (Esco and Flatt 2014). Only five participants (2 in the
control group, 3 in the autistic group) had data slightly less
than one minute in the baseline condition. For these partici-
pants, data from the social-pragmatic video condition was
reduced individually to match the length of the baseline
condition. There was no statistically significant between-
group difference in HRV data length (U = 93, p = 0.839). To
ensure similar content for all the participants, capturing the
HRV data from the baseline condition (i.e., the transition
period between tasks) began as the previous experimental
task came to its end. On the other hand, the HRV data from
the social-pragmatic video condition was captured from the
ending of the condition. For the latter, this meant cropping
the first 8 and 13s of video one and video two, respec-
tively. These first seconds of the videos did not include any
pragmatically complex interactions nor any of the key social
moments used in the eye-tracking data analysis. However,
for one control group participant with 43s of HRV data from
both conditions, one key social moment (out of 24) occurred
outside the 43s data window.
Heart rate variability data collected during the viewing of
the two social-pragmatic videos were averaged. HRV vari-
ables analysed in this study included baseline HRV, average
social-pragmatic video condition HRV, and HRV reactivity.
The average baseline and social-pragmatic video condition
HRV were used to calculate HRV reactivity by subtracting
the baseline condition HRV from the average social-prag-
matic video condition HRV.
Statistical Analyses
Data transformations were tested for non-normally distrib-
uted data, but no transformation enabled transformation of
all the variables into normal distribution. Depending on the
normality of the data distributions, between-group differ-
ences were investigated using an independent samples t-test
or a Mann–Whitney U test. When necessary, Bonferroni
adjusted p-values were used to account for multiple com-
parisons. Non-normally distributed HRV data were log-
transformed (natural logs), as recommended by Laborde
etal. (2017). A mixed repeated measures ANOVA was con-
ducted to examine the main effects of group (autistic and
control group), HRV measurement condition (baseline and
social-pragmatic video condition), and interaction effects
between group and condition. Effect sizes were estimated
using Cohen’s d for independent samples t-tests, partial
eta squared for mixed repeated measures ANOVA, and
r = Z/√N for nonparametric tests. For Cohen’s d, an effect
size above 0.8 could be considered as a large, above 0.5 as
a medium and above 0.2 as a small effect. For partial eta
squared, an effect size of 0.14 could be considered as a large,
above 0.06 medium and above 0.01 as a small effect. For r,
an effect size above 0.5 could be considered as a large, above
0.3 as a medium and above 0.1 as a small effect (Cohen
1988). However, considering the sample size of the current
study, the effect sizes should be interpreted with caution.
Depending on the normality of the data distributions, associ-
ations between variables were investigated using parametric
Pearson correlation coefficient or nonparametric Spearman
rank-correlation coefficient. For HRV, correlations based
on log-transformed data are reported to de-emphasise the
possible effect of outliers. Results were similar when using
log-transformed and untransformed data. A correlation of
0.5 was considered large, 0.3 medium and 0.1 small (Cohen
1988). All statistical tests were two-tailed. Statistical analy-
ses were conducted using IBM SPSS Statistics 25.
Social‑Pragmatic Inferencing
A Mann–Whitney U test yielded a statistically significant
between-group difference in the inference question scores
with a medium effect (U = 144,5 p = 0.031, r = 0.412).
The control group had a higher score (M = 9.14, Mdn = 9,
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81Journal of Autism and Developmental Disorders (2022) 52:73–88
1 3
SD = 2.32) than the autistic group (M = 6.50, Mdn = 6,
SD = 3.82). Figure 2 depicts the variability within each
group, showing that four participants in the autistic group
(one female) scored more than two standard deviations
below the control group mean. The figure also shows that
four autistic participants (one female) had clearly better per-
formance in the inference questions when compared to the
rest of the autistic group.
Aggregated Level Analysis ofVisual Social Attention
Between-group differences in aggregated visual social atten-
tion (proportional looking time) to Submissive Characters’
and Dominant Characters’ Face and Object AOIs were inves-
tigated. There were no statistically significant between-group
differences observed. Effects varied from small to nonsig-
nificant (see Table3).
Moment‑Level Analysis ofVisual Social Attention
Between-group differences in visual social attention
(proportional looking time) to Submissive Characters’
and Dominant Characters’ Face AOIs during key social
moments were investigated. Using the Bonferroni-cor-
rected alpha level 0.017, the analysis showed that the pro-
portional looking time to Submissive Characters’ Turn
Interruptions was statistically significantly higher in the
control group compared to the autistic group, with a large
effect size (see Table4). Other investigated between-group
differences were statistically nonsignificant with nonsig-
nificant effects.
Fig. 2 Participants’ total scores (0–12). Each dot represents an indi-
vidual participant. Black dots represent the males, black rectangles
females. The solid lines represent group medians. The dashed black
lines represent group means. The dashed grey line represents 2 SDs
below the control group mean
Table 3 Means, medians, standard deviations and comparisons between the autistic and control group in proportional looking time (%) to Sub-
missive Characters’ and Dominant Characters’ Face and Object AOIs
Autistic group Control group
AOI (proportional looking time) M (%) Mdn SD M Mdn SD Test statistic pEffect size
Submissive characters (Face AOI) 26.95 27.14 8.93 30.90 30.80 8.88 t(26) = 1.175 0.251 d = 0.444
Dominant characters (Face AOI) 44.48 44.97 14.10 46.91 44.06 11.99 t(26) = 0.490 0.628 d = 0.186
Objects 3.21 1.27 4.71 1.18 1.06 1.10 U = 116.5 0.401 r = 0.161
Table 4 Means, standard deviations and comparisons between the autistic and control group in proportional looking time (%) to Submissive
Characters’ Turn Interruptions, Dominant Characters’ Turn Interruptions and Submissive Characters’ Facial Emotion Expressions
Autistic group Control group
Face AOIs (proportional looking time) M (%) SD M SD Test statistic pEffect size d
Turn interruptions: submissive characters 4.65 1.60 6.37 1.45 t(26) = 2.991 0.006 1.127
Turn interruptions: dominant characters 3.87 1.12 3.78 1.20 t(26) = − 0.215 0.832 − 0.078
Facial emotion expressions: submissive characters 4.05 2.53 4.02 2.27 t(26) = − 0.029 0.977 − 0.012
Table 5 Heart rate variability (ms, RMSSD, original untransformed
data) during the baseline and social-pragmatic video conditions
Baseline (RMSSD, ms) Social-pragmatic video
(RMSSD, ms)
Autistic group Control group Autistic group Control group
M 35.99 39.70 40.41 34.62
Mdn 29.70 36.86 40.41 29.23
SD 15.12 14.17 24.84 15.49
IQR 18.46 18.34 23.98 23.06
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82 Journal of Autism and Developmental Disorders (2022) 52:73–88
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Physiological Reactivity
No statistically significant difference in baseline HRV
(between-tasks transition period) appeared between the
groups (t(26) = 0.825, p = 0.417); however, baseline HRV
was lower in the autistic group than in the control group
(see Table5). A mixed repeated measures ANOVA using
log-transformed HRV data showed a statistically signifi-
cant interaction effect between group and condition [base-
line, social-pragmatic video; F(1, 26) = 4.315, p = 0.048,
ηp2 = 0.142] with a large effect size. The main effect for
condition [F(1, 26) = 1.631, p = 0.213, ηp2 = 0.059] or
the main effect for group [F(1, 26) = 0.006, p = 0.939,
ηp2 = 0.000] were not statistically significant, with a small
and a nonsignificant effect size, respectively, see Table5).
The significant interaction effect suggests that HRV
reactivity in autistic young adults was different from con-
trol young adults. Using a Bonferroni-corrected alpha level
0.025, the post-hoc t-tests showed that there was a statis-
tically significant suppression in HRV in social-pragmatic
video condition when compared to baseline condition in the
control group (t(13) = 2.767, p = 0.016) but not in the autistic
group (t(13) = − 0.503, p = 0.623).
Figure3 shows individual level HRV during the baseline
and social-pragmatic video conditions. Visual inspection of
the figure shows that first, 10 out of 14 participants in the
control group experienced HRV suppression in response to
the social-pragmatic video condition (grey lines in Fig.3),
compared to five out of 14 in the autistic group. Second,
for four participants in the autistic group, HRV activation
in response to the social-pragmatic video condition (black
solid lines in Fig.3) appears considerably high. These par-
ticipants’ HRV activation was two standard deviations or
more above the control group mean HRV reactivity.
Fig. 3 Heart rate variability reactivity between the baseline and
social-pragmatic video conditions represented using individual level
log transformed HRV data. The grey lines represent the participants
who experienced HRV suppression. The black dotted lines represent
the participants who experienced HRV activation. The black solid
lines represent the participants in the autistic group with HRV acti-
vation 2 standard deviations or more above the control group mean
HRV reactivity. Dot symbols represent males, rectangle symbols
Table 6 Associations between social-pragmatic inferencing, visual moment-level social attention, physiological reactivity and autistic traits in
the autistic (n = 14) and control group (n = 14)
AQ Autism Quotient, HRV heart rate variability
*p < 0.05
a Spearman rank correlation coefficients for all variables (skewed data)
b Pearson correlation coefficients for all variables, except Spearman rank correlation coefficients for Inference question score (skewed data)
c AQ scores were available from 13 autistic participants and 13 control participants
Inference question scoreaHRV reactivitybAQ scoreb c
Autistic group Control group Autistic group Control group Autistic group Control group
Social-pragmatic inferencing
Inference question score 1.000 1.000 −0.342 0.304 −0.556* 0.169
Moment-level visual social attention
Turn interruptions: submissive charac-
ters’ face AOI −0.459 0.388 −0.145 0.089 −0.124 −0.164
Turn interruptions: dominant characters’
face AOI 0.277 −0.216 −0.568* −0.315 −0.144 0.162
Facial emotion expressions: submissive
character face AOI
0.660* −0.009 −0.616* 0.144 −0.215 −0.084
Physiological reactivity
HRV reactivity −0.342 0.304 1.000 1.000 0.090 −0.244
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83Journal of Autism and Developmental Disorders (2022) 52:73–88
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Associations Between Social‑Pragmatic Inferencing,
Visual Social Attention, Physiological Reactivity
andAutistic Traits
Associations between the inference question scores, visual
social attention, physiological reactivity and autistic traits
were examined in each group separately following the pre-
dictions set. Overall, significant, large correlations were only
found in the autistic group (see Table6). Inference ques-
tion score was positively correlated with percent looking
time at Submissive Characters’ Facial Emotion Expressions
(p = 0.010). No other significant correlations between infer-
ence question score and visual moment-level social atten-
tion allocation were observed. However, a nonsignificant,
medium negative correlation was observed in the autistic
group between inference question score and percent look-
ing time at Submissive Characters’ Turn Interruptions. In
the control group, similar size nonsignificant, yet positive
correlation was observed.
No significant associations were observed between the
inference question score and HRV reactivity. However, there
were nonsignificant, medium correlations between the infer-
ence question score and HRV reactivity in both groups: this
association was negative in the autistic and positive in the
control group.
The investigation of associations between the other
measures and AQ scores showed a significant large nega-
tive correlation between the inference question score and
the AQ score in the autistic group (p = 0.048). The cor-
relations between the AQ score and visual moment-level
social attention were small to non-existent and statistically
nonsignificant in both groups. There was a nonsignificant
small negative correlation between the AQ score and the
HRV reactivity in the control group and no correlation in
the autistic group.
The exploration of the associations between HRV reac-
tivity and visual moment-level social attention showed that
HRV reactivity was negatively correlated with both percent
looking time at Submissive Characters’ Facial Emotion
Expressions (p = 0.019) and percent looking time at Domi-
nant Characters’ Turn Interruptions (p = 0.034) in the autis-
tic group. There was also a nonsignificant, medium negative
correlation in the control group between HRV reactivity and
Dominant Characters’ Turn Interruptions. Other nonsignifi-
cant correlations were small to non-existent.
First, this study examined differences between autistic and
control young adults in social-pragmatic inferencing, vis-
ual social attention and physiological reactivity, and sec-
ond, investigated how social-pragmatic inferencing, visual
social attention, physiological reactivity and autistic traits
were associated. Our findings, as predicted, confirm previ-
ous findings reporting that, at a group level, autistic young
adults have social-pragmatic challenges in inferring others’
thoughts (see, e.g., Deliens etal. 2018; Heavey etal. 2000;
Jolliffe and Baron-Cohen 1999, 2000; Loukusa, in press;
Lönnqvist etal. 2017). Such challenges in context-sensitive
inferencing of meaning and in interpreting others’ intentions
can have a major impact on everyday interactions for these
individuals. Our findings also show notable variation among
the autistic group, suggesting that the identified challenges
are distinctly evident in a subgroup of autistic young adults.
Our study also expectedly found that higher autistic traits
were associated with poorer performance in social-pragmatic
inferencing (lending support for prior studies, e.g., Volden
etal. 2009). However, interestingly, another subgroup of
autistic individuals showed social-pragmatic inferencing
skills comparable to those of the highest performing control
participants, demonstrating the heterogeneity in the autism
spectrum. It should be however noted that structured test
situations can only ever measure some specific aspects of
social-pragmatic inferencing, and therefore, do not directly
tell how these individuals navigate social-pragmatic situa-
tions in their daily lives (see e.g., Loukusa etal. 2018).
In line with our predictions, the findings further show
that differences between autistic and control young adults in
visual social attention are related to how key social moments
in interaction are attended to and thus, are evident on a
moment-level rather than on an aggregated level (Falck-Ytter
etal. 2013; Freeth etal. 2011; Lönnqvist etal. 2017;Nakano
etal. 2010; Nyström etal. 2017). However, rather than con-
cerning all the key social moments, we found between-group
difference concerning only one of the investigated moment-
level variables (i.e., percent looking time at Submissive
Characters’ Turn Interruptions). One explanation for this
could be that the control young adults were better at using
social cues to predict how the interactions might unfold and
thus, in the context of our stimuli, were quicker at attend-
ing to the Submissive Characters’ Turn Interruptions. Since
these moments involved getting interrupted and/or being left
without acknowledgement by the Dominant Characters who
namely dominated the interactions, they could be considered
as more difficult to predict than Dominant Characters’ Turn
Interruptions and Submissive Characters’ Facial Emotion
Expressions (latter of which were reactive in nature). This
interpretation is supported by prior research suggesting that
autistic and NT individuals differ in how they use social
information to predict others’ actions (von der Lühe etal.
Our study finds that the differences in visual social atten-
tion between the autistic and control group are very subtle
but social-pragmatically relevant given our finding showing
that attention to interlocutors’ facial emotion expressions
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84 Journal of Autism and Developmental Disorders (2022) 52:73–88
1 3
(i.e. percent looking time at Submissive Characters’ Facial
Emotion Expressions) was positively associated with
social-pragmatic inferencing in the autistic group. That is,
it seems that looking longer at the faces during these key
social moments was relevant for inferring social-pragmatic
meaning for the autistic group, while this was not the case
for the control group. This might reflect different kind of
processing styles. Our results could be interpreted in the
light of a more local processing style in autistic individuals
that relies on focusing on specific local details whereas NT
individuals might process social scenes more globally and
could be quicker in taking advantage of a variety of social
cues and their combinations (see also e.g., Grynzspan and
Nadel 2015; Jolliffe and Baron-Cohen 2000; Lönnqvist etal.
2017;van der Hallen etal. 2015). Such differences in pro-
cessing styles could result in autistic individuals focusing
on some local details while missing out on others (such as
facial emotion expressions that could give insights about an
interlocutor’s thoughts), and perhaps explain some of the
misunderstandings autistic individuals experience in social
situations that unfold at fast pace. Investigating moment-
level visual social attention therefore appears critical since
not only between-group differences do exist but in addition,
these differences can have practical significance and as pre-
dicted, associations between social-pragmatic inferencing
and visual social attentionappear more pronounced in the
autistic group (supporting prior studies by Grynszpan and
Nadel 2015; Hanley etal. 2015; Lönnqvist etal. 2017;Sas-
son etal. 2007). However, we did not find the predicted
association between autistic traits and visual social attention.
The present study also contributes to the currently rela-
tively scarce literature on physiological reactivity in autis-
tic adults as measured via HRV reactivity, specifically as
regard to social-pragmatic inferencing. We predicted that
the autistic group would show less physiological reactivity
in response to the social-pragmatic videos than the control
group, which our findings provided support for. This indi-
cates that at the group level, autistic individuals do not show
typical HRV suppression, lending support for previous stud-
ies with similar results (e.g., Dijkhuis etal. 2019b; Toichi
and Kamio 2003). Interestingly, a small subgroup of autistic
young adults showed a clear increase in HRV during the task
condition (instead of suppression or no reactivity). Previ-
ously, Toichi and Kamio (2003) found a similar pattern in
autistic adults, and Porges etal. (2013) in children. Lack of
HRV suppression, and especially the increase in HRV, could
hinder the efficient processing of stimuli and have a negative
impact on performance (Porges etal. 2013).
We further predicted that greater physiological reactivity
would be associated with better social-pragmatic inferencing
and with less autistic traits. Correlational analyses showed
moderate associations between HRV reactivity and social-
pragmatic inferencing in both groups (notably of different
directions) but these were statistically nonsignificant. We
did not find support for the predicted association between
HRV reactivity and autistic traits. In the autistic group,
however, anecdotal evidence suggests that an association
between HRV reactivity and social-pragmatic inferencing
could be present in the small subgroup of individuals who
experienced distinct parasympathetic activation in response
to the social-pragmatic videos: In responding to the infer-
ence questions, all four participants showing a clear increase
in HRV scored below the autistic group mean (scores rang-
ing between 0 and 6). On the other hand, two out of the
four participants in the autistic group who performed well
in responding to the inference questions, showed HRV sup-
pression in response to the social-pragmatic video condition,
lending support for prior research on physiological reactiv-
ity and task performance (Klusek etal. 2013; Porges etal.
Together with the fact that the autistic group showed dif-
ficulties with the inference questions, yet no HRV suppres-
sion was observed, our result may indicate that the autistic
group engaged less with the inferential process overall, per-
haps reflecting motivational issues with the task. Alterna-
tively, instead of spotting the subtle social conflict in the
social-pragmatic scenes, they may have treated the watched
interactions untroubled, setting a different frame for the
amount of mental effort the task would require. Considered
the other way around, a capability of self-regulation in this
kind of attention-demanding task may contribute to better
performance in the control group, as compared to the autis-
tic group. For the exploration of these hypotheses, a more
detailed qualitative analysis of the responses to the inference
questions would provide crucial insights on both similari-
ties and differences in how the scenes were processed. In
addition, more research is needed to clarify the amount of
mental effort that social-pragmatic inferencing in different
contexts requires from autistic and NT individuals. Toichi
and Kamio (2003) have pointed out another possible expla-
nation for the increase in HRV during task condition, as
compared to baseline: It may be that the individuals who
showed increased HRV instead of HRV suppression, were
not relaxed in the chosen baseline condition, thus, the base-
line did not work for them as a condition requiring less men-
tal effort when compared to the task condition. In our study,
the participants with the clearest increase in HRV also had
a relatively high HRV at baseline compared to other autistic
participants, which does not provide support for the hypoth-
esis on extensive anxiety during baseline. Importantly, there
were no significant between-group differences in HRV at
baseline, which indicates that our baseline condition was
comparable for both groups. However, the possible differ-
ences in how the participants experienced the baseline situa-
tion should be kept in mind when making conclusions based
on the findings.
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85Journal of Autism and Developmental Disorders (2022) 52:73–88
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We also explored associations between physiological
reactivity and moment-level visual social attention which
have received limited attention in previous research. Our
findings indicate that the longer the autistic group looked
at specific key social moments (Dominant Characters’ Turn
Interruptions and Submissive Characters’ Facial Emotion
Expressions specifically), the more their HRV was supressed
in response to the social-pragmatic inferencing tasks. This
suggests that perceptual processes could play a role in how
some autistic individuals physiologically react to complex
social scenes as they may miss out on crucial social cues that
could elicit a physiological reaction. One explanation for the
significance of these particular moments could be that these
moments could be viewed as emotionally charged: attend-
ing to the Dominant Characters’ Turn Interruptions would
show to a participant that the Dominant Characters were
deliberately, not by accident, interrupting the Submissive
Characters whereas attending to the Submissive Characters’
Facial Emotion Expressions would reveal to a participant the
Submissive Characters’ negative stance toward the Domi-
nant Characters. Relatedly, Lory etal. (2020) have recently
observed an association between overall HRV (indicating
dysregulation of the autonomic nervous system) and parent-
reported atypical social attention in children. As HRV reac-
tivity is also considered to be associated with self-regulation
(e.g., Porges etal. 2013), an alternative explanation could
be that autistic individuals with better state regulation (i.e.,
a better so-called vagal brake, evident in HRV suppression
from baseline to social-pragmatic video condition) could
be better overall and/or quicker at orienting to social stimuli
(albeit not necessarily better at social-pragmatic inferenc-
ing). Together, these findings encourage future research to
investigate these associations in greater detail, particularly
by looking at both direct and indirect effects.
Some limitations of the current study merit note. First,
due to the limited amount of high-quality eye tracking data
available from the study participants, our sample size was
relatively small. It is probable that our experimental pro-
tocol that prioritised comfort and thus, did not require the
participants to use a chin or head rest, resulted in the con-
siderable amount of unsuccessfully recorded eye tracking
data for the stimuli investigated here. It should be noted that
the small sample size has had an impact on the statistical
power of the analyses and therefore, our findings could be
considered as preliminary and should be confirmed with
larger data sets. Second, in assessing the generalizability
of our findings, it should be kept in mind that the partici-
pants in our study did not have any observed intellectual
disabilities and do not represent the entire heterogeneous
autism spectrum. Additionally, since many participants were
excluded based on inadequate data quality, it is possible that
the findings particularly hold for autistic young adults with
such cognitive and behavioural characteristics that allow the
reliable recording of their eye movements in an unstrained
set-up (e.g., the ability to sit rather still throughout a rela-
tively long experiment), which is a common limitation for
eye tracking studies with similar set-ups. Third, the par-
ticipants were diagnosed with autism spectrum disorder in
their childhood and since this study was part of a follow-up
phase involving the same individuals, diagnoses were not
re-assessed at adulthood. Albeit not a diagnostic tool, the
between-group difference in the AQ scores provided evi-
dence for the significantly higher number of autistic traits
in the autistic group. Fourth, the transition period used as a
baseline in this study differs from baseline situations used
in some other studies. Previous studies have used variable
situations as baseline, for example, from quietly looking at
a wall (Toichi and Kamio 2003) to watching a neutral, non-
social video (Dijkhuis etal. 2019a, b), yet there is no clear
consensus of what an optimal baseline situation would be
(Laborde etal. 2017). In the present study, we chose to use a
between-tasks transition period as a baseline, to have as nat-
ural a baseline situation as possible. In this situation, some
structure was provided by the experimenter and some social
elements were involved (e.g., there were minimal interac-
tions with an experimenter) in order to help participants to
be as relaxed as possible. Fifth, the stimuli used in the study
involved dynamic, complex social situations, yet a passive
third-person perspective typical of most structured test situ-
ations does not allow for the social participation inherent
in real-life interactions. Examination of attention in real-
life social interactions may therefore shed light on different
aspects of visual social attention, in particular, how gaze is
used in interaction (see, e.g., Dindar etal. 2017; Gobel etal.
2015; Hessels 2020), and may bring out perhaps different
information on both competencies and challenges than found
in the current study. In the future, such moment-level analy-
ses of visual social attention in real-life interactions would
be fruitful in increasing understanding of the role gaze plays
in navigating pragmatically complex real-life interactions.
Given the between-group differences in social-pragmatic
inferencing, visual moment-level social attention and physi-
ological reactivity, and the observed associations between
these, our study lends support for theoretical accounts that
consider perceptual processes and their integration having
a central role in autism spectrum (Frith and Happé 1994;
Murray etal. 2005). It is possible that the challenges in self-
regulation and in controlling the ‘vagal brake’ initially hin-
der the autistic individuals from efficiently processing social
situations, having a potentially profound effect on how they
navigate the social world (e.g., Porges etal. 2013). If this
is the case, what follows then is, first, the need to under-
stand in practice how to improve autistic individuals’ self-
regulation to allow for more capacity to engage with the
social world. Second, if visual moment-level social atten-
tion plays a role in social-pragmatic inferencing (and in the
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
86 Journal of Autism and Developmental Disorders (2022) 52:73–88
1 3
social domain more broadly) in autism spectrum, it could
be useful to develop autistic individuals’ understanding of
both where and when to look in their social interactions with
neurotypical interlocutors so as not to miss out on key social
cues. Third, since social interaction is inescapably a ‘two-
way street’ (see e.g., Milton 2012), it would be valuable to
assist neurotypical interlocutors to interact in a manner that
is less likely to result in misunderstandings, such as carefully
considering what kind of embodied social cues are used to
communicate meaning and particularly, when in interaction
these are used.
Supplementary Information The online version of this article https :// 3-021-04915 -y contains supplementary material,
which is available to authorized users.
Acknowledgments Funding for this research was awarded from the
Academy of Finland (276578, 333672), Eudaimonia Institute of the
University of Oulu, Finland, the Alma and K. A. Snellman Founda-
tion, Finland, and the Finnish Brain Foundation, Finland (earlier the
Rinnekoti Research Foundation, Finland, and the Child Psychiatric
Research Foundation, Finland). We wish to thank all the participants
for taking part in the study. We feel reverently grateful to Professor
emerita Irma Moilanen for acting as the principal innovator of the Oulu
Autism Research Group and to Sirkka-Liisa Linna, Ph.D., Marko Kie-
linen, Ph.D., and Katja Jussila, Ph.D., who gave their expertise to the
diagnostic processes. We also wish to thank Linda Lönnqvist and Laura
Mämmelä for participating in the data collection process and Sampsa
Toivonen for scoring the participants’ responses to the inference ques-
tions for the interrater reliability analyses. We thank the rest of the Oulu
Autism Research Group, including Aija Kotila and Veera Pirinen, for
collaboration.We are also grateful for the anonymous reviewers for
their valuable comments on earlier versions of this article.
Author Contributions Participant recruitment and diagnostic assess-
ment were conducted by: TH, HE and MLM. Data collection protocol
for social-pragmatic inference questions, eye tracking and physiological
data was designed and/or performed by: SL, LM, AS, SL, AR, TH and
HE. Research questions and data analyses for this article were designed
by KD, SL and TMH. Formal analysis was performed by: KD. The first
draft of the manuscript was written by: KD. All authors commented
on previous versions of the manuscript. All authors read and approved
the final manuscript.
Funding Open access funding provided by University of Oulu includ-
ing Oulu University Hospital.
Compliance with Ethical Standards
Conflict of interest The authors declare that they have no conflict of
Ethical Approval The study was approved by the Regional Ethics
Committee of the Northern Ostrobothnia Hospital District and was
conducted in accordance with the 1964 Declaration of Helsinki. Par-
ticipants gave their informed consent to participate in the study.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
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Eye-tracking is often used to study attention in children with autism spectrum disorder (ASD). Previous research has identified multiple atypical patterns of attention in children with ASD based on areas-of-interest analysis. Fewer studies have investigated gaze path, a measure which is dependent on the dynamic content of the stimulus presented. Here, rather than looking at proportions of looking time to areas of interest, we calculated mean fixations frame-by-frame in a group of typically developing children (36 to 72 months) and determined the distance from those typical fixations for 155 children with ASD (27–95 months). Findings revealed that distance from the typical scan path among the children with ASD was associated with lower communication abilities and greater ASD symptomatology.
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Social and pragmatic difficulties in autism spectrum disorder (ASD) are widely recognized, although their underlying neural level processing is not well understood. The aim of this study was to examine the activity of the brain network components linked to social and pragmatic understanding in order to reveal whether complex socio-pragmatic events evoke differences in brain activity between the ASD and control groups. Nineteen young adults (mean age 23.6 years) with ASD and 19 controls (mean age 22.7 years) were recruited for the study. The stimulus data consisted of video clips showing complex social events that demanded processing of pragmatic communication. In the analysis, the functional magnetic resonance imaging signal responses of the selected brain network components linked to social and pragmatic information processing were compared. Although the processing of the young adults with ASD was similar to that of the control group during the majority of the social scenes, differences between the groups were found in the activity of the social brain network components when the participants were observing situations with concurrent verbal and non-verbal communication events. The results suggest that the ASD group had challenges in processing concurrent multimodal cues in complex pragmatic communication situations.
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Social challenges represent a significantly under-researched area when it comes to the poor employment outcomes in autism. In this exploratory study employees on the autism spectrum (N = 29) and supervisors (N = 15), representing seven continents, provided 128 written examples of workplace-based social challenges, their interpretation, consequences and resolution. Content analysis revealed that types of social challenges were individually oriented or associated with the work-environment. Social challenges were frequently attributed to internal or personal factors with direct consequences for the employee. Resolutions were more frequently targeted toward the individual than the workplace, and hindered employees’ experience of work. This international study represents a first look at the types of social challenges that impact equitable work participation of autistic people.
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Gaze—where one looks, how long, and when—plays an essential part in human social behavior. While many aspects of social gaze have been reviewed, there is no comprehensive review or theoretical framework that describes how gaze to faces supports face-to-face interaction. In this review, I address the following questions: (1) When does gaze need to be allocated to a particular region of a face in order to provide the relevant information for successful interaction; (2) How do humans look at other people, and faces in particular, regardless of whether gaze needs to be directed at a particular region to acquire the relevant visual information; (3) How does gaze support the regulation of interaction? The work reviewed spans psychophysical research, observational research, and eye-tracking research in both lab-based and interactive contexts. Based on the literature overview, I sketch a framework for future research based on dynamic systems theory. The framework holds that gaze should be investigated in relation to sub-states of the interaction, encompassing sub-states of the interactors, the content of the interaction as well as the interactive context. The relevant sub-states for understanding gaze in interaction vary over different timescales from microgenesis to ontogenesis and phylogenesis. The framework has important implications for vision science, psychopathology, developmental science, and social robotics.
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Dysregulation of the autonomic nervous system (ANS), which can be indexed by heart rate variability (HRV), has been posited to contribute to core features of autism spectrum disorder (ASD). However, the relationship between ASD and HRV remains uncertain. We assessed tonic and phasic HRV of 21 children with ASD and 21 age- and IQ-matched typically developing (TD) children and examined (1) group differences in HRV and (2) associations between HRV and ASD symptomatology. Children with ASD showed significantly lower tonic HRV, but similar phasic HRV compared to TD children. Additionally, reduced tonic HRV was associated with atypical attentional responsivity in ASD. Our findings suggest ANS dysregulation is present in ASD and may contribute to atypical attentional responses to sensory stimulation.
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Lay abstract: Although autistic people may struggle to interact with others, many autistic people have said they find interacting with other autistic people more comfortable. To find out whether this was a common experience, we did hour-long interviews with 12 autistic adults. We asked them questions about how it feels when spending time with their friends and family, and whether it felt different depending on whether the friends and family were autistic or neurotypical. We analysed the interviews and found three common themes in what our participants said. First, they found spending with other autistic people easier and more comfortable than spending time with neurotypical people, and felt they were better understood by other autistic people. Second, autistic people often felt they were in a social minority, and in order to spend time with neurotypical friends and family, they had to conform with what the neurotypical people wanted and were used to. Third, autistic people felt like they belonged with other autistic people and that they could be themselves around them. These findings show that having time with autistic friends and family can be very beneficial for autistic people and played an important role in a happy social life.
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There has been a recent shift from person-first to identity-first language to describe autism. In this study, Australian adults who reported having a diagnosis of autism (N = 198) rated and ranked autism-terms for preference and offensiveness, and explained their choice in free-text. ‘Autistic’, ‘Person on the Autism Spectrum’, and ‘Autistic Person’ were rated most preferred and least offensive overall. Ranked-means showed ‘person on the autism spectrum’ was the most preferred term overall. Six qualitative themes reflected (1) autism as core to, or (2) part of one’s identity, (3) ‘spectrum’ reflecting diversity, (4) the rejection of stigmatising and (5) medicalised language, and (6) pragmatics. These findings highlight the importance of inclusive dialogue regarding individual language preference.
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Typically, developing humans innately place subjective value on social information and orient attention to it. This can be shown through tracking of gaze patterns and pupil size, the latter of which taps into an individual’s cognitive engagement and affective arousal. People with Autism Spectrum Disorder (ASD) present with atypical social, communicative and behavioral patterns, but underlying substrates of these behavioral differences remain unclear. Moreover, due to high comorbidity with other neurodevelopmental disorders, it is often difficult to distinguish which differences are distinctive to ASD. In this study, a group of 35 adolescents and young adults with neurodevelopmental disorders were tested to investigate the processing of social and non-social scenes in individuals who meet the diagnostic criteria for autism and those who do not. Eye tracking and pupillometry measures were collected while participants observed images of tightly controlled natural scenes with or without a human being. Contrary to individuals without autism diagnosis, participants with autism did not show greater pupillary response to images with a human. Participants with autism were slower to fixate on social elements in the social scenes, and this latency metric correlated with clinical measures of poor social functioning. The results confirm the clinical relevance of eye-tracking and pupillometric indices in the field of ASD. We discuss the clinical implications of the results and propose that analysis of changes in visual attention and physiological level to social stimuli might be an integral part of a neurodevelopmental assessment.
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Children with autism spectrum disorder (ASD) are generally characterized by marked impairments in processing of social emotional information, but less is known about emotion processing in adults with the disorder. This study aimed to address this by collecting data on social attention (eye tracking), emotional arousal (skin conductance level, SCL), and emotional awareness (self-report) in a paradigm with social emotional video clips. Fifty-two young, intelligent adults with ASD (IQrange = 88-130, Agerange = 18-24) and 31 typically developing (TD) ASD (IQrange = 94-139, Agerange = 19-28) gender matched controls participated and reported on severity of autism symptoms [Social Responsiveness Scale for Adults (SRS-A)]. Results showed no group difference in social attention, while autism symptom severity was related to decreased attention to faces across participants (r = -.32). Average SCL was lower in the ASD group, but no group difference in arousal reactivity (change from baseline to emotional phases) was detected. Lower SCL during video clips was related to autism symptom severity across participants (r = -.29). ASD individuals reported lower emotional awareness. We conclude that, even though no deviations in social attention or emotional reactivity were found in ASD, an overall lower level of social attention and arousal may help explain difficulties in social functioning in ASD.