Group Differences in Facial Emotion Expression in Autism: Evidence for the Utility of Machine Classification

Toward feeling, understanding, and caring: The development of empathy in young autistic children

LAY ABSTRACT

Empathy is a highly valued human capacity. Yet, autistic people are often portrayed as lacking in empathy. Recent research, which views empathy as a complex construct emerging from multiple interrelated emotional and cognitive processes, argues that, although many autistic people do have difficulty understanding others' emotions, and this may hinder them from responding to others in a prosocial manner, they are not indifferent to other people's feelings. Hoping to contribute to a better understanding of the unique challenges that autistic children face in their empathy development, we followed the development of four empathy abilities: emotion contagion, attention to others, emotion acknowledgment, and prosocial actions, in 1- to 6-year-old autistic children, in comparison with non-autistic children. Once a year, for 4 consecutive years, children's empathy abilities were evaluated by experimenters who acted out emotional episodes to provoke empathy in children, and by parents who filled out empathy questionnaires. We found that autistic children experienced indeed more difficulty attending to others, acknowledging others' emotions, and initiating prosocial actions toward others. However, according to parents, they did not differ from their non-autistic peers in feeling along with others' negative emotions. This indicates that it might not be the case that autistic children did not want to act empathetically toward others. Rather, they might not know how to do so. Notably, despite these difficulties, when looking at children's developmental trajectories, autistic children showed similar improvements over time as non-autistic children. This provides evidence that autistic children have the potential to learn and to improve their empathy skills.

Emotional Expression in Children With ASD: A Pre-Study on a Two-Group Pre-Post-Test Design Comparing Robot-Based and Computer-Based Training

Several studies have found a delay in the development of facial emotion recognition and expression in children with an autism spectrum condition (ASC). Several interventions have been designed to help children to fill this gap. Most of them adopt technological devices (i.e., robots, computers, and avatars) as social mediators and reported evidence of improvement. Few interventions have aimed at promoting emotion recognition and expression abilities and, among these, most have focused on emotion recognition. Moreover, a crucial point is the generalization of the ability acquired during treatment to naturalistic interactions. This study aimed to evaluate the effectiveness of two technological-based interventions focused on the expression of basic emotions comparing a robot-based type of training with a "hybrid" computer-based one. Furthermore, we explored the engagement of the hybrid technological device introduced in the study as an intermediate step to facilitate the generalization of the acquired competencies in naturalistic settings. A two-group pre-post-test design was applied to a sample of 12 children (M = 9.33; ds = 2.19) with autism. The children were included in one of the two groups: group 1 received a robot-based type of training (n = 6); and group 2 received a computer-based type of training (n = 6). Pre- and post-intervention evaluations (i.e., time) of facial expression and production of four basic emotions (happiness, sadness, fear, and anger) were performed. Non-parametric ANOVAs found significant time effects between pre- and post-interventions on the ability to recognize sadness [t ₍₁₎ = 7.35, p = 0.006; pre: M (ds) = 4.58 (0.51); post: M (ds) = 5], and to express happiness [t ₍₁₎ = 5.72, p = 0.016; pre: M (ds) = 3.25 (1.81); post: M (ds) = 4.25 (1.76)], and sadness [t ₍₁₎ = 10.89, p < 0; pre: M (ds) = 1.5 (1.32); post: M (ds) = 3.42 (1.78)]. The group^*time interactions were significant for fear [t ₍₁₎ = 1.019, p = 0.03] and anger expression [t ₍₁₎ = 1.039, p = 0.03]. However, Mann-Whitney comparisons did not show significant differences between robot-based and computer-based training. Finally, no difference was found in the levels of engagement comparing the two groups in terms of the number of voice prompts given during interventions. Albeit the results are preliminary and should be interpreted with caution, this study suggests that two types of technology-based training, one mediated via a humanoid robot and the other via a pre-settled video of a peer, perform similarly in promoting facial recognition and expression of basic emotions in children with an ASC. The findings represent the first step to generalize the abilities acquired in a laboratory-trained situation to naturalistic interactions.

Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism

Commonly used screening tools for autism spectrum disorder (ASD) generally rely on subjective caregiver questionnaires. While behavioral observation is more objective, it is also expensive, time-consuming, and requires significant expertise to perform. As such, there remains a critical need to develop feasible, scalable, and reliable tools that can characterize ASD risk behaviors. This study assessed the utility of a tablet-based behavioral assessment for eliciting and detecting one type of risk behavior, namely, patterns of facial expression, in 104 toddlers (ASD N = 22) and evaluated whether such patterns differentiated toddlers with and without ASD. The assessment consisted of the child sitting on his/her caregiver's lap and watching brief movies shown on a smart tablet while the embedded camera recorded the child's facial expressions. Computer vision analysis (CVA) automatically detected and tracked facial landmarks, which were used to estimate head position and facial expressions (Positive, Neutral, All Other). Using CVA, specific points throughout the movies were identified that reliably differentiate between children with and without ASD based on their patterns of facial movement and expressions (area under the curves for individual movies ranging from 0.62 to 0.73). During these instances, children with ASD more frequently displayed Neutral expressions compared to children without ASD, who had more All Other expressions. The frequency of All Other expressions was driven by non-ASD children more often displaying raised eyebrows and an open mouth, characteristic of engagement/interest. Preliminary results suggest computational coding of facial movements and expressions via a tablet-based assessment can detect differences in affective expression, one of the early, core features of ASD. LAY SUMMARY: This study tested the use of a tablet in the behavioral assessment of young children with autism. Children watched a series of developmentally appropriate movies and their facial expressions were recorded using the camera embedded in the tablet. Results suggest that computational assessments of facial expressions may be useful in early detection of symptoms of autism.

New Technologies as Promising Tools for Assessing Facial Emotion Expressions Impairments in ASD: A Systematic Review

The ability to recognize and express emotions from facial expressions are essential for successful social interactions. Facial Emotion Recognition (FER) and Facial Emotion Expressions (FEEs), both of which seem to be impaired in Autism Spectrum Disorders (ASD) and contribute to socio-communicative difficulties, participate in the diagnostic criteria for ASD. Only a few studies have focused on FEEs processing and the rare behavioral studies of FEEs in ASD have yielded mixed results. Here, we review studies comparing the production of FEEs between participants with ASD and non-ASD control subjects, with a particular focus on the use of automatic facial expression analysis software. A systematic literature search in accordance with the PRISMA statement identified 20 reports published up to August 2020 concerning the use of new technologies to evaluate both spontaneous and voluntary FEEs in participants with ASD. Overall, the results highlight the importance of considering socio-demographic factors and psychiatric co-morbidities which may explain the previous inconsistent findings, particularly regarding quantitative data on spontaneous facial expressions. There is also reported evidence for an inadequacy of FEEs in individuals with ASD in relation to expected emotion, with a lower quality and coordination of facial muscular movements. Spatial and kinematic approaches to characterizing the synchrony, symmetry and complexity of facial muscle movements thus offer clues to identifying and exploring promising new diagnostic targets. These findings have allowed hypothesizing that there may be mismatches between mental representations and the production of FEEs themselves in ASD. Such considerations are in line with the Facial Feedback Hypothesis deficit in ASD as part of the Broken Mirror Theory, with the results suggesting impairments of neural sensory-motor systems involved in processing emotional information and ensuring embodied representations of emotions, which are the basis of human empathy. In conclusion, new technologies are promising tools for evaluating the production of FEEs in individuals with ASD, and controlled studies involving larger samples of patients and where possible confounding factors are considered, should be conducted in order to better understand and counter the difficulties in global emotional processing in ASD.