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Interpretations of meaningful and ambiguous hand gestures in autistic and non-autistic adults: A norming study.

Brianna E CairneyStanley H WestEileen HaebigChristopher R CoxHeather D Lucas
Published in: Behavior research methods (2023)
Gestures are ubiquitous in human communication, and a growing but inconsistent body of research suggests that people with autism spectrum disorder (ASD) may process co-speech gestures differently from neurotypical individuals. To facilitate research on this topic, we created a database of 162 gesture videos that have been normed for comprehensibility by both autistic and non-autistic raters. These videos portray an actor performing silent gestures that range from highly meaningful (e.g., iconic gestures) to ambiguous or meaningless. Each video was rated for meaningfulness and given a one-word descriptor by 40 autistic and 40 non-autistic adults, and analyses were conducted to assess the level of within- and across-group agreement. Across gestures, the meaningfulness ratings provided by raters with and without ASD correlated at r > 0.90, indicating a very high level of agreement. Overall, autistic raters produced a more diverse set of verbal labels for each gesture than did non-autistic raters. However, measures of within-gesture semantic similarity among the responses provided by each group did not differ, suggesting that increased variability within the ASD group may have occurred at the lexical rather than semantic level. This study is the first to compare gesture naming between autistic and non-autistic individuals, and the resulting dataset is the first gesture stimulus set for which both groups were equally represented in the norming process. This database also has broad applicability to other areas of research related to gesture processing and comprehension. The video database and accompanying norming data are available on the Open Science Framework.
Keyphrases
  • autism spectrum disorder
  • public health
  • attention deficit hyperactivity disorder
  • intellectual disability
  • emergency department
  • deep learning