Low Fidelity Imitation of Atypical Biological Kinematics in Autism Spectrum Disorders Is Modulated by Self-Generated Selective Attention

Observational learning of atypical biological kinematics in autism

Observing and voluntarily imitating the biological kinematics displayed by a model underpins the acquisition of new motor skills via sensorimotor processes linking perception with action. Differences in voluntary imitation in autism could be related to sensorimotor processing activity during action-observation of biological motion, as well as how sensorimotor integration processing occurs across imitation attempts. Using an observational practice protocol, which minimized the active contribution of the peripheral sensorimotor system, we examined the contribution of sensorimotor processing during action-observation. The data showed that autistic participants imitated both the temporal duration and atypical kinematic profile of the observed movement with a similar level of accuracy as neurotypical participants. These findings suggest the lower-level perception-action processes responsible for encoding biological kinematics during the action-observation phase of imitation are operational in autism. As there was no task-specific engagement of the peripheral sensorimotor system during observational practice, imitation difficulties in autism are most likely underpinned by sensorimotor integration issues related to the processing of efferent and (re)afferent sensorimotor information during trial-to-trial motor execution.

Motor differences in autism during a human-robot imitative gesturing task

BACKGROUND

Difficulty with imitative gesturing is frequently observed as a clinical feature of autism. Current practices for assessment of imitative gesturing ability-behavioral observation and parent report-do not allow precise measurement of specific components of imitative gesturing performance, instead relying on subjective judgments. Advances in technology allow researchers to objectively quantify the nature of these movement differences, and to use less socially stressful interaction partners (e.g., robots). In this study, we aimed to quantify differences in imitative gesturing between autistic and neurotypical development during human-robot interaction.

METHODS

Thirty-five autistic (n = 19) and neurotypical (n = 16) participants imitated social gestures of an interactive robot (e.g., wave). The movements of the participants and the robot were recorded using an infrared motion-capture system with reflective markers on corresponding head and body locations. We used dynamic time warping to quantify the degree to which the participant's and robot's movement were aligned across the movement cycle and work contribution to determine how each joint angle was producing the movements.

FINDINGS

Results revealed differences between autistic and neurotypical participants in imitative accuracy and work contribution, primarily in the movements requiring unilateral extension of the arm. Autistic individuals imitated the robot less accurately and used less work at the shoulder compared to neurotypical individuals.

INTERPRETATION

These findings indicate differences in autistic participants' ability to imitate an interactive robot. These findings build on our understanding of the underlying motor control and sensorimotor integration mechanisms that support imitative gesturing in autism which may aid in identifying appropriate intervention targets.

Influence of instructor's facial expressions in video lectures on motor learning in children with autism spectrum disorder

During the COVID-19 pandemic, video materials have played crucial roles in supporting learning among children with autism spectrum disorder (ASD). This study aimed to explore the effects of the instructor's facial expressions in video lectures on attention and motor learning in children with ASD versus typically developing (TD) children. A total of 60 children were randomly assigned to four groups: (1) ASD-happy, (2) ASD-neutral, (3) TD-happy, and (4) TD-neutral. Both happy groups paid more attention to the video lectures. The ASD groups achieved higher accuracy and fidelity of motor learning when the instructor smiled. Results revealed that greater attention to video lectures predicted better performance in children with ASD. This study has practical implications for the design of learning materials for children with ASD: An instructor should be encouraged to show a happy expression to promote learning.

A Point-Light Display Model for Teaching Motor Skills to Children With Autism Spectrum Disorder: An Eye-Tracking Study

People with autism spectrum disorder (ASD) have limitations in their attention and working memory that affect their motor learning. The aim of current study was to compare point-light display (PLD) to video observation as instructional models for teaching motor skills to children with ASD versus typically developing (TD) children. We randomly assigned 24 children with ASD aged 6-17-years-old and 24 age paired typically developing (TD) children to four groups: (a) ASD-Video, (b) ASD-PLD, (c) TD-Video, and (d) TD-PLD. After twenty training blocks (200 trials), all participants entered into late retention and transfer testing. We recorded all participants' visual gazes when observing each PLD and Video condition. Both PLD groups had better performance in the acquisition phase, and on retention and transfer tests. Also, gaze recordings revealed that children with ASD paid more attention to relevant demonstration points in the PLD than in the video condition. We discuss possible mechanisms and implications of these findings.

Children With Autism Exhibit More Individualized Responses to Live Animation Biofeedback Than Do Typically Developing Children

Children with autism have displayed imbalances in responding to feedback and feedforward learning information and they have shown difficulty imitating movements. Previous research has focused on motor learning and coordination problems for these children, but little is known about their motoric responses to visual live animation feedback. Thus, we compared motor output responses to live animation biofeedback training in both 15 children with autism and 15 age- and sex-matched typically developing children (age range: 8-17 years). We collected kinematic data via Inertial Measurement Unit devices while participants performed a series of body weight squats at a pre-test, during live animation biofeedback training, and at post-test. Dependent t-tests (α = 0.05), were used to test for statistical significance between pre- and post-test values within groups, and repeated measures analyses of variance (α = 0.05) were used to test for differences among the training blocks, within each group. The Model Statistic technique (α = 0.05) was used to test for pre- and post-test differences on a single-subject level for every participant. Grouped data revealed little to no significant findings in the children with autism, as these participants showed highly individualized responses. However, typically developing children, when grouped, exhibited significant differences in their left hip position (p = 0.03) and ascent velocity (p = 0.004). Single-subject analyses showed more individualistic live animation responses of children with autism than typically developing children on every variable of interest except descent velocity. Thus, to teach children with autism new movements in optimal fashion, it is particularly important to understand their individualistic motor learning characteristics.

The multiple process model of goal-directed aiming/reaching: insights on limb control from various special populations

Several years ago, our research group forwarded a model of goal-directed reaching and aiming that describes the processes involved in the optimization of speed, accuracy, and energy expenditure Elliott et al. (Psychol Bull 136:1023-1044, 2010). One of the main features of the model is the distinction between early impulse control, which is based on a comparison of expected to perceived sensory consequences, and late limb-target control that involves a spatial comparison of limb and target position. Our model also emphasizes the importance of strategic behaviors that limit the opportunity for worst-case or inefficient outcomes. In the 2010 paper, we included a section on how our model can be used to understand atypical aiming/reaching movements in a number of special populations. In light of a recent empirical and theoretical update of our model Elliott et al. (Neurosci Biobehav Rev 72:95-110, 2017), here we consider contemporary motor control work involving typical aging, Down syndrome, autism spectrum disorder, and tetraplegia with tendon-transfer surgery. We outline how atypical limb control can be viewed within the context of the multiple-process model of goal-directed reaching and aiming, and discuss the underlying perceptual-motor impairment that results in the adaptive solution developed by the specific group.

Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis

Autism is a developmental condition currently identified by experts using observation, interview, and questionnaire techniques and primarily assessing social and communication deficits. Motor function and movement imitation are also altered in autism and can be measured more objectively. In this study, motion and eye tracking data from a movement imitation task were combined with supervised machine learning methods to classify 22 autistic and 22 non-autistic adults. The focus was on a reliable machine learning application. We have used nested validation to develop models and further tested the models with an independent data sample. Feature selection was aimed at selection stability to assure result interpretability. Our models predicted diagnosis with 73% accuracy from kinematic features, 70% accuracy from eye movement features and 78% accuracy from combined features. We further explored features which were most important for predictions to better understand movement imitation differences in autism. Consistent with the behavioural results, most discriminative features were from the experimental condition in which non-autistic individuals tended to successfully imitate unusual movement kinematics while autistic individuals tended to fail. Machine learning results show promise that future work could aid in the diagnosis process by providing quantitative tests to supplement current qualitative ones.

Facilitating sensorimotor integration via blocked practice underpins imitation learning of atypical biological kinematics in autism spectrum disorder

The reduced efficacy of voluntary imitation in autism is suggested to be underpinned by differences in sensorimotor processing. We examined whether the imitation of novel atypical biological kinematics by autistic adults is enhanced by imitating a model in a predictable blocked practice trial order. This practice structure is expected to facilitate trial-to-trial sensorimotor processing, integration and encoding of biological kinematics. The results showed that neurotypical participants were generally more effective at imitating the biological kinematics across all experimental phases. Importantly, and compared to a pre-test where imitation was performed in a randomised (unpredictable) trial order, the autistic participants learned to imitate the atypical kinematics more effectively following an acquisition phase of repeatedly imitating the same model during blocked practice. Data from the post-test showed that autistic participants remained effective at imitating the atypical biological kinematics when the models were subsequently presented in a randomised trial order. These findings show that the reduced efficacy of voluntary imitation in autism can be enhanced during learning by facilitating trial-to-trial processing and integration of sensorimotor information using blocked practice.

Instructions to attend to an observed action increase imitation in autistic adults

This study investigated whether reduced visual attention to an observed action might account for altered imitation in autistic adults. A total of 22 autistic and 22 non-autistic adults observed and then imitated videos of a hand producing sequences of movements that differed in vertical elevation while their hand and eye movements were recorded. Participants first performed a block of imitation trials with general instructions to imitate the action. They then performed a second block with explicit instructions to attend closely to the characteristics of the movement. Imitation was quantified according to how much participants modulated their movement between the different heights of the observed movements. In the general instruction condition, the autistic group modulated their movements significantly less compared to the non-autistic group. However, following instructions to attend to the movement, the autistic group showed equivalent imitation modulation to the non-autistic group. Eye movement recording showed that the autistic group spent significantly less time looking at the hand movement for both instruction conditions. These findings show that visual attention contributes to altered voluntary imitation in autistic individuals and have implications for therapies involving imitation as well as for autistic people's ability to understand the actions of others.

Applying machine learning to identify autistic adults using imitation: An exploratory study

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.