Brief Report: Specificity of Interpersonal Synchrony Deficits to Autism Spectrum Disorder and Its Potential for Digitally Assisted Diagnostics

Evidence of mutual non-verbal synchrony in learners with severe learning disability and autism, and their support workers: a motion energy analysis study

Introduction

Some research indicates that neurodivergent people are less likely than "neurotypical" people to adapt their movements to a partner's movements to facilitate interpersonal motor synchrony. Researchers therefore suggest synchrony deficits underlie the social differences associated with autism and other neurodivergences. Intensive Interaction (II) is a client-led approach, where Learning Support Workers (LSW) follow the lead of learners to create balanced and reciprocal interactions.

Methods

We aimed to examine the balance of synchrony in learners with autism and Severe Learning Disabilities and their LSWs in a special education college where learners had prior experience with II. Using Motion Energy Analysis, we assessed the degree to which each partner acted as a leader, and hence which partner acted as a follower, during moments of close synchrony.

Results

Overall, learners and LSWs showed higher than chance synchrony. There were no differences in the degree to which each partner led the moments of synchrony, or the amount pairs synchronized with zero-lag, where there was no delay between each partners' movements.

Discussion

The equal balance of leading and following in the learner and LSW pairs demonstrates that both partners consistently adapted their movements to their partner's movements to facilitate synchrony. The findings tentatively challenge the notion of a synchrony deficit in autism and suggest synchrony can be present in cross-neurotype pairs in comfortable and engaging conditions. We discuss the potential for client-led, movement-based approaches to support smooth interactions across neurotypes.

Social motor synchrony in autism spectrum conditions: A systematic review

LAY ABSTRACT

When two people interact, they often fall into sync with one another by moving their bodies at the same time. Some say autistic people are not as good as non-autistic people at moving at the same time as a partner. This has led some researchers to ask whether measuring synchrony might help diagnose autism. We reviewed the research so far to look at differences in Social Motor Synchrony (SMS) (the way we move together) between autistic people and people they interact with. The research suggests that interactions involving an autistic partner (either two autistic partners, or an autistic and non-autistic partner) show lower synchrony than a non-autistic pair. However, we recognised elements in the research so far that may have affected SMS in interactions involving an autistic person. One way SMS may have been affected in research so far might be the way interactions have been set up in the research studies. Few papers studied interactions between two autistic people or looked at synchrony in comfortable environments with autistic-preferred tasks. The studies also do not explain why synchrony might be different, or weaker, in pairs involving autistic partners. We use these limitations to suggest improvements for future research.

How and Why People Synchronize: An Integrated Perspective

Academic AbstractInterpersonal synchrony, the alignment of behavior and/or physiology during interactions, is a pervasive phenomenon observed in diverse social contexts. Here we synthesize across contexts and behaviors to classify the different forms and functions of synchrony. We provide a concise framework for classifying the manifold forms of synchrony along six dimensions: periodicity, discreteness, spatial similarity, directionality, leader-follower dynamics, and observability. We also distill the various proposed functions of interpersonal synchrony into four interconnected functions: reducing complexity and improving understanding, accomplishing joint tasks, strengthening social connection, and influencing partners' behavior. These functions derive from first principles, emerge from each other, and are accomplished by some forms of synchrony more than others. Effective synchrony flexibly adapts to social goals and more synchrony is not always better. Our synthesis offers a shared framework and language for the field, allowing for better cross-context and cross-behavior comparisons, generating new hypotheses, and highlighting future research directions.

Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews

Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.

Machine learning classification of autism spectrum disorder based on reciprocity in naturalistic social interactions

Autism spectrum disorder is characterized by impaired social communication and interaction. As a neurodevelopmental disorder typically diagnosed during childhood, diagnosis in adulthood is preceded by a resource-heavy clinical assessment period. The ongoing developments in digital phenotyping give rise to novel opportunities within the screening and diagnostic process. Our aim was to quantify multiple non-verbal social interaction characteristics in autism and build diagnostic classification models independent of clinical ratings. We analyzed videos of naturalistic social interactions in a sample including 28 autistic and 60 non-autistic adults paired in dyads and engaging in two conversational tasks. We used existing open-source computer vision algorithms for objective annotation to extract information based on the synchrony of movement and facial expression. These were subsequently used as features in a support vector machine learning model to predict whether an individual was part of an autistic or non-autistic interaction dyad. The two prediction models based on reciprocal adaptation in facial movements, as well as individual amounts of head and body motion and facial expressiveness showed the highest precision (balanced accuracies: 79.5% and 68.8%, respectively), followed by models based on reciprocal coordination of head (balanced accuracy: 62.1%) and body (balanced accuracy: 56.7%) motion, as well as intrapersonal coordination processes (balanced accuracy: 44.2%). Combinations of these models did not increase overall predictive performance. Our work highlights the distinctive nature of non-verbal behavior in autism and its utility for digital phenotyping-based classification. Future research needs to both explore the performance of different prediction algorithms to reveal underlying mechanisms and interactions, as well as investigate the prospective generalizability and robustness of these algorithms in routine clinical care.

Interacting with autistic virtual characters: intrapersonal synchrony of nonverbal behavior affects participants' perception

Temporal coordination of communicative behavior is not only located between but also within interaction partners (e.g., gaze and gestures). This intrapersonal synchrony (IaPS) is assumed to constitute interpersonal alignment. Studies show systematic variations in IaPS in individuals with autism, which may affect the degree of interpersonal temporal coordination. In the current study, we reversed the approach and mapped the measured nonverbal behavior of interactants with and without ASD from a previous study onto virtual characters to study the effects of the differential IaPS on observers (N = 68), both with and without ASD (crossed design). During a communication task with both characters, who indicated targets with gaze and delayed pointing gestures, we measured response times, gaze behavior, and post hoc impression formation. Results show that character behavior indicative of ASD resulted in overall enlarged decoding times in observers and this effect was even pronounced in observers with ASD. A classification of observer's gaze types indicated differentiated decoding strategies. Whereas non-autistic observers presented with a rather consistent eyes-focused strategy associated with efficient and fast responses, observers with ASD presented with highly variable decoding strategies. In contrast to communication efficiency, impression formation was not influenced by IaPS. The results underline the importance of timing differences in both production and perception processes during multimodal nonverbal communication in interactants with and without ASD. In essence, the current findings locate the manifestation of reduced reciprocity in autism not merely in the person, but in the interactional dynamics of dyads.

Automated extraction of speech and turn-taking parameters in autism allows for diagnostic classification using a multivariable prediction model

Autism spectrum disorder (ASD) is diagnosed on the basis of speech and communication differences, amongst other symptoms. Since conversations are essential for building connections with others, it is important to understand the exact nature of differences between autistic and non-autistic verbal behaviour and evaluate the potential of these differences for diagnostics. In this study, we recorded dyadic conversations and used automated extraction of speech and interactional turn-taking features of 54 non-autistic and 26 autistic participants. The extracted speech and turn-taking parameters showed high potential as a diagnostic marker. A linear support vector machine was able to predict the dyad type with 76.2% balanced accuracy (sensitivity: 73.8%, specificity: 78.6%), suggesting that digitally assisted diagnostics could significantly enhance the current clinical diagnostic process due to their objectivity and scalability. In group comparisons on the individual and dyadic level, we found that autistic interaction partners talked slower and in a more monotonous manner than non-autistic interaction partners and that mixed dyads consisting of an autistic and a non-autistic participant had increased periods of silence, and the intensity, i.e. loudness, of their speech was more synchronous.

The role of interpersonal synchrony in forming impressions of autistic and non-autistic adults

When people meet, they almost instantaneously form an impression of each other. First impressions of character traits and rapport are less favourable when people with autism spectrum condition (ASC) are judged compared to non-autistic people. Little is known about the behavioural differences that drive these altered impressions. In the present study, we investigated the influence of interpersonal synchrony on impression formation of autistic and non-autistic people. Specifically, we used lagged cross-correlations to assess how much each interactant's motion energy, a measure which can be determined from video recordings, influenced the other interactant's motion energy. In short, silent clips of dyadic conversations, we asked non-autistic participants to rate their impression of one of the two interactants, which was solely based on the outlines of both interactants. We expected that the amount of leading of the target interactant, their diagnostic status as well as the interaction of these factors would influence impression formation. We found that while the amount of leading had a positive effect on the impressions of non-autistic interactants, this was not true for interactants with ASC. This suggests that interpersonal synchrony of motion energy is one driver of less favourable impressions of autistic compared to non-autistic people.

Differentiated, rather than shared, strategies for time-coordinated action in social and non-social domains in autistic individuals

Autism spectrum disorder (ASD) is a neurodevelopmental condition with a highly heterogeneous adult phenotype that includes social and non-social behavioral characteristics. The link between the characteristics assignable to the different domains remains unresolved. One possibility is that social and non-social behaviors in autism are modulated by a common underlying deficit. However, here we report evidence supporting an alternative concept that is individual-centered rather than deficit-centered. Individuals are assumed to have a distinctive style in the strategies they adopt to perform social and non-social tasks with these styles presumably being structured differently between autistic individuals and typically-developed (TD) individuals. We tested this hypothesis for the execution of time-coordinated (synchronized) actions. Participants performed (i) a social task that required synchronized gaze and pointing actions to interact with another person, and (ii) a non-social task that required finger-tapping actions synchronized to periodic stimuli at different time-scales and sensory modalities. In both tasks, synchronization behavior differed between ASD and TD groups. However, a principal component analysis of individual behaviors across tasks revealed associations between social and non-social features for the TD persons but such cross-domain associations were strikingly absent for autistic individuals. The highly differentiated strategies between domains in ASD are inconsistent with a general synchronization deficit and instead highlight the individualized developmental heterogeneity in the acquisition of domain-specific behaviors. We propose a cognitive model to help disentangle individual-centered from deficit-centered effects in other domains. Our findings reinforce the importance to identify individually differentiated phenotypes to personalize autism therapies.

Digitally assisted diagnostics of autism spectrum disorder

Digital technologies have the potential to support psychiatric diagnostics and, in particular, differential diagnostics of autism spectrum disorder in the near future, making clinical decisions more objective, reliable and evidence-based while reducing clinical resources. Multimodal automatized measurement of symptoms at cognitive, behavioral, and neuronal levels combined with artificial intelligence applications offer promising strides toward personalized prognostics and treatment strategies. In addition, these new technologies could enable systematic and continuous assessment of longitudinal symptom development, beyond the usual scope of clinical practice. Early recognition of exacerbation and simplified, as well as detailed, progression control would become possible. Ultimately, digitally assisted diagnostics will advance early recognition. Nonetheless, digital technologies cannot and should not substitute clinical decision making that takes the comprehensive complexity of individual longitudinal and cross-section presentation of autism spectrum disorder into account. Yet, they might aid the clinician by objectifying decision processes and provide a welcome relief to resources in the clinical setting.

Intrapersonal synchrony analysis reveals a weaker temporal coherence between gaze and gestures in adults with autism spectrum disorder

The temporal encoding of nonverbal signals within individuals, referred to as intrapersonal synchrony (IaPS), is an implicit process and essential feature of human communication. Based on existing evidence, IaPS is thought to be a marker of nonverbal behavior characteristics in autism spectrum disorders (ASD), but there is a lack of empirical evidence. The aim of this study was to quantify IaPS in adults during an experimentally controlled real-life interaction task. A sample of adults with a confirmed ASD diagnosis and a matched sample of typically-developed adults were tested (N = 48). Participants were required to indicate the appearance of a target invisible to their interaction partner nonverbally through gaze and pointing gestures. Special eye-tracking software allowed automated extraction of temporal delays between nonverbal signals and their intrapersonal variability with millisecond temporal resolution as indices for IaPS. Likelihood ratio tests of multilevel models showed enlarged delays between nonverbal signals in ASD. Larger delays were associated with greater intrapersonal variability in delays. The results provide a quantitative constraint on nonverbal temporality in typically-developed adults and suggest weaker temporal coherence between nonverbal signals in adults with ASD. The results provide a potential diagnostic marker and inspire predictive coding theories about the role of IaPS in interpersonal synchronization processes.

Autism traits outweigh alexithymia traits in the explanation of mentalising performance in adults with autism but not in adults with rejected autism diagnosis

Background

Pronounced alexithymia traits have been found in autism spectrum disorder (ASD) and recent research has been carving out the impact alexithymia traits might have on mentalising deficits associated with ASD.

Method

In this cross-sectional study, a large representative referral population for diagnostic examination for possible ASD (n = 400) was screened for clinical alexithymia with a German version of the Reading the Mind in the Eyes test (RME). In contrast to previous attempts to carve out the impact of alexithymia traits on mentalising deficits though, we employed dominance analysis to account for the correlation between predictors. The relative relationship between alexithymia traits and autism traits with RME performance was investigated in the group of individuals with confirmed ASD diagnosis (N = 281) and compared to the clinical referral sample in which ASD was ruled out (N = 119).

Results

Dominance analysis revealed autism traits to be the strongest predictor for reduced mentalising skills in the ASD sample, whereas alexithymia contributed significantly less. In the sample of individuals with ruled out diagnosis, autism traits were the strongest predictor, but alexithymia traits were in sum equally associated to mentalising, with the External-Oriented Thinking subscale as an important predictor of this association.

Limitations

It needs to be considered that the cross-sectional study design does not allow for causal inference. Furthermore, mentalising is a highly facetted capacity and measurements need to reduce this complexity into simple quantities which limits the generalizability of results.

Discussion

While alexithymia traits should be considered for their mental health importance, they do not dominate the explanation of reduced mentalising skills in individuals with ASD, but they might do to a larger degree in individuals with ruled out ASD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-022-00510-9.

The Components of Interpersonal Synchrony in the Typical Population and in Autism: A Conceptual Analysis

Interpersonal synchrony – the tendency for social partners to temporally co-ordinate their behaviour when interacting – is a ubiquitous feature of social interactions. Synchronous interactions play a key role in development, and promote social bonding and a range of pro-social behavioural outcomes across the lifespan. The process of achieving and maintaining interpersonal synchrony is highly complex, with inputs required from across perceptual, temporal, motor, and socio-cognitive domains. In this conceptual analysis, we synthesise evidence from across these domains to establish the key components underpinning successful non-verbal interpersonal synchrony, how such processes interact, and factors that may moderate their operation. We also consider emerging evidence that interpersonal synchrony is reduced in autistic populations. We use our account of the components contributing to interpersonal synchrony in the typical population to identify potential points of divergence in interpersonal synchrony in autism. The relationship between interpersonal synchrony and broader aspects of social communication in autism are also considered, together with implications for future research.

A Virtual Reality Based System for the Screening and Classification of Autism

Autism - also known as Autism Spectrum Disorders or Autism Spectrum Conditions - is a neurodevelopmental condition characterized by repetitive behaviours and differences in communication and social interaction. As a consequence, many autistic individuals may struggle in everyday life, which sometimes manifests in depression, unemployment, or addiction. One crucial problem in patient support and treatment is the long waiting time to diagnosis, which was approximated to thirteen months on average. Yet, the earlier an intervention can take place the better the patient can be supported, which was identified as a crucial factor. We propose a system to support the screening of Autism Spectrum Disorders based on a virtual reality social interaction, namely a shopping experience, with an embodied agent. During this everyday interaction, behavioral responses are tracked and recorded. We analyze this behavior with machine learning approaches to classify participants from an autistic participant sample in comparison to a typically developed individuals control sample with high accuracy, demonstrating the feasibility of the approach. We believe that such tools can strongly impact the way mental disorders are assessed and may help to further find objective criteria and categorization.