CCAS Researchers Uncover Autism Insights

New research spearheaded by George Washington University Associate Professor of Cognitive Neuroscience Gabriela Rosenblau and a team of scholars is yielding insights into how autistic and non-autistic people learn about one another's preferences.

In a study in the journal "Nature Mental Health," the researchers suggest that both groups rely on similar learning strategies; however, key differences may help understand how autistic and non-autistic peers understand one another.

The research-led by Rosenblau, director of the Cognitive Neuroscience Doctoral Program at the Columbian College of Arts and Sciences, and Ph.D. student Shannon Cahalan-examines whether autistic and non-autistic adolescents apply different types of social knowledge and learning strategies when inferring the preferences of others.

The team recruited large samples of autistic and non-autistic individuals who participated in an online study. In the first experiment, they captured participants' personal preferences for certain food and activities. Next, they compared how non-autistic adults and autistic adolescents inferred the food and activity preferences of both autistic and non-autistic adolescents.

The study, titled "Modeling how autistic and non-autistic groups learn about their own and each other's preferences," found that:

• Autistic participants exhibited greater variability in their personal preferences for foods and activities than their non-autistic peers, underscoring the heterogeneity of this population and the limitations of inferring individual preferences from group-level patterns.
  
• Both autistic adolescents and non-autistic adults were less accurate at predicting the preferences of autistic adolescents.
  
• Both autistic and non-autistic participants relied on similar learning strategies to make their predictions and used additional feedback to update their predictions.
  
• As autistic severity increased, however, learning rates and the ability to update preference predictions based on feedback were lower.

"These results suggest that misunderstandings between autistic and non-autistic people may not stem from fundamentally different learning mechanisms," Rosenblau said. "Instead, they may arise because autistic individuals' preferences are more varied, making them harder to predict using typical social assumptions."

The research, which was supported by the National Institutes of Mental Health, offers new evidence for the "double empathy problem," a theory that suggests that communication barriers between autistic and non-autistic people arise from differences in how each group interprets the social world, rather than from a lack of empathy in autistic individuals.

According to Rosenblau, the work highlights the value of combining large datasets with computational modeling to better understand social learning in autism. The framework may also help researchers identify meaningful differences within the autism spectrum that could inform future interventions.