The Predictive Impairment Hypothesis in Autism: An Empirical Assessment

In 2014, researchers proposed a new hypothesis about the nature of autism. This hypothesis posits that the common traits of autism spectrum disorders (ASD) are manifestations of an individual’s difficulty in making predictions about cause and effect. For an individual with compromised prediction skills, the world is seemingly a “magical” place where events occur unexpectedly and without reason. This unpredictable environment proves overwhelming and compromises the individual’s ability to interact with it.

The proposal, along with several related conceptualizations, has spurred several targeted empirical investigations of predictive processes in autism. In this lecture, Pawan Sinha and Dagmar Sternad will review some of the data accumulated so far.

Sinha will consider both positive and negative findings and describe efforts to test the proposal further. His lab has focused their studies on three domains: sensory habituation, motor control and high-level cognition. In each of these domains, the experiments probed whether the performance of individuals with autism is affected in a manner consistent with difficulty in prediction. The picture that has emerged has provided support for the hypothesis, although not unequivocally so.

Sternad will review her group’s experimental work examining the action of catching a ball in realistic and virtual environments. The scenario requires both the prediction of the ball’s path and the internal prediction needed to successfully complete the catching motion. A series of experiments that titrate the degree of prediction has yielded results consistent with the hypothesis: Kinematic data and muscle activation reveal selective impairments in ASD for actions where prediction is dominant. Control tasks without predictive elements, such as reaction time and postural balance, do not show differences.

About the Speakers

Sinha is a professor of vision and computational neuroscience in the Department of Brain and Cognitive Sciences at MIT. He received his undergraduate degree in computer science from the Indian Institute of Technology, New Delhi, and his master’s and doctoral degrees from the Department of Computer Science at MIT. He was at the University of California, Berkeley, for the first year of his graduate studies. Sinha’s laboratory uses a combination of experimental and computational modeling techniques to focus on understanding how the human brain learns to recognize objects through visual experience and how objects are encoded in memory. A key initiative of the lab is Project Prakash. This effort seeks to accomplish the twin goals of providing treatment to children with disabilities and also understanding mechanisms of learning and plasticity in the brain.

Sternad received her bachelor’s degree in movement science and linguistics from the Technical University and the Ludwig Maximilians University of Munich and her Ph.D. in experimental psychology from the University of Connecticut. From 1995 until 2008, she was an assistant, associate professor, and later a full professor, at Pennsylvania State University in Integrative Biosciences and Kinesiology. Since 2008, she holds an interdisciplinary appointment as full professor in the departments of Biology, Electrical and Computer Engineering, and Physics at Northeastern University in Boston. She is a member of the Center for Interdisciplinary Research on Complex Systems at Northeastern. Her research is documented in more than 150 peer-reviewed publications and book chapters, as well as several books.