Adolescents with high-functioning autism (HFA) have significant deficits in non-verbal social communication, which often lead to depression and social withdrawal. These deficits are particularly apparent in their ability to process and produce qualitatively appropriate facial expressions. It has been suggested that a lack of synchrony between verbal and non-verbal speech acts may significantly contribute to this social awkwardness. And yet, very little attention has been paid to the interaction between receptive and expressive non-verbal communication and their integration with verbal language. In the receptive domain, evidence from eyetracking studies increasingly shows that participants with HFA have subtle, qualitative differences in their scan paths for communicative facial expressions. Production of non-verbal communication in this population is still very poorly understood, but preliminary evidence points to expressions that are categorically accurate, but qualitatively awkward and less synchronous with verbal language than those of their typically developing (TD) peers. In a highly innovative approach the proposed project will quantify the qualitative differences of facial expression processing and production by adolescents with HFA. We will use eyetracking and motion-capture methodologies to record objective measures of facial expression production and processing and relate them to the subjective appearance of awkwardness recorded by TD peers. We will specifically aim to determine the contribution of an underlying verbal/non-verbal synchrony deficit in adolescents with HFA to this social communication awkwardness. Eyetracking and motion-capture data will be correlated with behavioral measures and diagnostic severity for each participant. Motion-capture data will also be correlated with judgments of awkwardness by na?ve TD observers and will analyze qualitative components for expressive and receptive social communication skills and their synchrony with verbal language. The importance of this work lies in establishing the relationship between specific, quantifiable features of non-verbal communication and the social communication success of each participant with HFA, as measured by symptom severity and by qualitative perceptions of TD coders. We hypothesize that adolescents with HFA have reduced verbal/non- verbal synchrony in receptive and expressive modalities, and that individuals with higher ASD symptom severity will show increased asynchrony in receptive and expressive tasks. In addition to documenting fine- grained deficits, we will also ascertain whether individuals with HFA are able to improve their productions in response to temporal-spatial cueing. This component of our proposed project will ultimately lead to improved social communication intervention by creating more clearly defined intervention targets, such as verbal/nonverbal synchrony.

Public Health Relevance

Individuals with HFA constitute the largest growing segment of the ASD population. Although they have the cognitive and linguistic abilities to become contributing members of society, their lack of social skills and inability to conduct effective interpersonal communication leave them bullied in school and underemployed as adults. The proposed studies are directly responsive to that urgent clinical need by investigating plausible intervention targets for verbal/non-verbal asynchrony, which we believe to be foundational to communicative awkwardness and social rejection in this population.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01DC012774-02
Application #
8697034
Study Section
Child Psychopathology and Developmental Disabilities Study Section (CPDD)
Program Officer
Cooper, Judith
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Emerson College
Department
Other Clinical Sciences
Type
Schools of Arts and Sciences
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02116
Bone, Daniel; Goodwin, Matthew S; Black, Matthew P et al. (2015) Applying machine learning to facilitate autism diagnostics: pitfalls and promises. J Autism Dev Disord 45:1121-36