Sign-tracking and goal-tracking in humans: Utilising eye-tracking in clinical and non-clinical populations.

BACKGROUND: In Pavlovian conditioning, learned behaviour varies according to the perceived value of environmental cues. For goal-trackers (GT), the cue merely predicts a reward, whilst for sign-trackers (ST), the cue holds incentive value. The sign-tracking/goal-tracking model is well-validated in animals, but translational work is lacking. Despite the model's relevance to several conditions, including attention deficit hyperactivity disorder (ADHD), we are unaware of any studies that have examined the model in clinical populations. METHODS: The current study used an eye-tracking Pavlovian conditioning paradigm to identify ST and GT in non-clinical (N = 54) and ADHD (N = 57) participants. Eye movements were recorded whilst performing the task. Dwell time was measured for two areas of interest: sign (i.e., cue) and goal (i.e., reward), and an eye-gaze index (EGI) was computed based on the dwell time sign-to-goal ratio. Higher EGI values indicate sign-tracking behaviour. ST and GT were determined using median and tertiary split approaches in both samples. RESULTS: Despite greater propensity for sign-tracking in those with ADHD, there was no significant difference between groups. The oculomotor conditioned response was reward-specific (CS+) and present, at least partly, from the start of the task indicating dispositional and learned components. There were no differences in externalising behaviours between ST and GT for either sample. CONCLUSIONS: Sign-tracking is associated with CS+ trials only. There may be both dispositional and learned components to sign-tracking, potentially more common in those with ADHD. This holds translational potential for understanding individual differences in reward-learning.

Figure-Disembedding Is Inferior in Non-autistic Compared to Autistic Individuals but Can Be Improved by Training.

Background: Figure-disembedding is one of the most discussed visuo-cognitive functions, in which individuals with Autism Spectrum Disorder (ASD) have been reported to outperform non-autistic individuals. A local processing bias has been assumed to underlie such superior performance patterns. The aim of the current study is to investigate whether processing preferences can be modified by procedural priming. Method: The current study used a procedural priming task (Navon figures) to induce more local or global processing in 25 autistic and 21 typically developing (TD) control participants, using hierarchical figures preceding the figure-disembedding task. Results: Participants with ASD outperformed non-autistic individuals in the unprimed baseline task version. The performance was not modulated by priming in either direction (toward a local or global processing style) in both groups. However, the performance of TD control participants was improved by training to the same level as that observed in the ASD group. Conclusion: Figure-disembedding performance in ASD is superior to that in TD control participants and robust against procedural priming or training. In contrast, performance in the TD control group can be improved up to the level of the ASD group. Any studies reporting superiority in individuals with ASD in figure-disembedding should consider training effects when evaluating group differences.

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

Reliably diagnosing autism spectrum disorders (ASD) in adulthood poses a challenge to clinicians due to the absence of specific diagnostic markers. This study investigated the potential of interpersonal synchrony (IPS), which has been found to be reduced in ASD, to augment the diagnostic process. IPS was objectively assessed in videos of diagnostic interviews in a representative referral population from two specialized autism outpatient clinics. In contrast to the current screening tools that could not reliably differentiate, we found a significant reduction of IPS in interactions with individuals later diagnosed with ASD (n = 16) as opposed to those not receiving a diagnosis (n = 23). While these findings need to be validated in larger samples, they nevertheless underline the potential of digitally-enhanced diagnostic processes for ASD.

Inferring power and dominance from dyadic nonverbal interactions in autism spectrum disorder.

Research studies to date have revealed conflicting results with respect to the processing of nonverbal cues from social interactions in autism spectrum disorder (ASD). Therefore, the aim of the present study was to investigate the contribution of two important factors for the perception of dyadic social interactions, namely (a) the movement contingency and (b) the spatial context. To this end, 26 adult participants with ASD and 26 age-, sex-, and IQ-matched typically developed control participants observed animations presenting nonverbal interactions between two human virtual characters enacting power relationships. We manipulated (a) movement contingency by exchanging one of the two original agents with an agent from another dyad and (b) spatial context by changing agents' spatial orientation to a back-to-back position. Participants were asked to rate dominance and submissiveness of these agents. Results showed that the movement contingency manipulation affected accuracy and consistency of power perception and that the spatial context manipulation slowed down reaction times comparably in both groups. With regard to group differences, individuals with ASD were found to judge power relationships slower compared to control participants, potentially suggesting a more explicit processing style in ASD. Furthermore, the spatial context manipulation slowed down the reaction times more in the contingent compared to the non-contingent conditions only in the ASD group. These findings contribute to the ongoing debate whether individuals with ASD have difficulties in understanding nonverbal cues in a dyadic context by suggesting that they do so in more subtle ways than previously investigated. Autism Res 2019, 12: 505-516 © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: This study shows that the ability and speed of judging who is dominant in a social interaction depends on two factors: (a) whether their movements are matched and (b) whether they are facing each other or not. This is similarly the case for participants with and without autism. Interestingly, however, individuals with autism seem to judge generally slower, suggesting a more explicit processing style. The two factors seem to interact, suggesting that nonverbal processing difficulties are subtler than previously thought.

Machine Learning to Study Social Interaction Difficulties in ASD.

Autism Spectrum Disorder (ASD) is a spectrum of neurodevelopmental conditions characterized by difficulties in social communication and social interaction as well as repetitive behaviors and restricted interests. Prevalence rates have been rising, and existing diagnostic methods are both extremely time and labor consuming. There is an urgent need for more economic and objective automatized diagnostic tools that are independent of language and experience of the diagnostician and that can help deal with the complexity of the autistic phenotype. Technological advancements in machine learning are offering a potential solution, and several studies have employed computational approaches to classify ASD based on phenomenological, behavioral or neuroimaging data. Despite of being at the core of ASD diagnosis and having the potential to be used as a behavioral marker for machine learning algorithms, only recently have movement parameters been used as features in machine learning classification approaches. In a proof-of-principle analysis of data from a social interaction study we trained a classification algorithm on intrapersonal synchrony as an automatically and objectively measured phenotypic feature from 29 autistic and 29 typically developed individuals to differentiate those individuals with ASD from those without ASD. Parameters included nonverbal motion energy values from 116 videos of social interactions. As opposed to previous studies to date, our classification approach has been applied to non-verbal behavior objectively captured during naturalistic and complex interactions with a real human interaction partner assuring high external validity. A machine learning approach lends itself particularly for capturing heterogeneous and complex behavior in real social interactions and will be essential in developing automatized and objective classification methods in ASD.

The use of virtual characters to assess and train non-verbal communication in high-functioning autism.

High-functioning autism (HFA) is a neurodevelopmental disorder, which is characterized by life-long socio-communicative impairments on the one hand and preserved verbal and general learning and memory abilities on the other. One of the areas where particular difficulties are observable is the understanding of non-verbal communication cues. Thus, investigating the underlying psychological processes and neural mechanisms of non-verbal communication in HFA allows a better understanding of this disorder, and potentially enables the development of more efficient forms of psychotherapy and trainings. However, the research on non-verbal information processing in HFA faces several methodological challenges. The use of virtual characters (VCs) helps to overcome such challenges by enabling an ecologically valid experience of social presence, and by providing an experimental platform that can be systematically and fully controlled. To make this field of research accessible to a broader audience, we elaborate in the first part of the review the validity of using VCs in non-verbal behavior research on HFA, and we review current relevant paradigms and findings from social-cognitive neuroscience. In the second part, we argue for the use of VCs as either agents or avatars in the context of "transformed social interactions." This allows for the implementation of real-time social interaction in virtual experimental settings, which represents a more sensitive measure of socio-communicative impairments in HFA. Finally, we argue that VCs and environments are a valuable assistive, educational and therapeutic tool for HFA.