The predictive impairment hypothesis in autism: An empirical assessment

  • Autism Research
  • Speakers
  • Pawan Sinha, Ph.D.

    Professor, Massachusetts Institute of Technology

    Dagmar Sternad, Ph.D.

    Professor, Northeastern University

Date & Time


Gerald D. Fischbach Auditorium
160 5th Avenue
New York, NY 10010 United States

Tea 4:15-5:00pm
Lecture: 5:00-6:15pm

Autism Research

Autism Research lectures bring together scientists and scholars to discuss diverse and important topics related to autism.

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On December 12, 2018, Pawan Sinha and Dagmar Sternad reviewed a recently proposed hypothesis about the nature of autism spectrum disorders (ASD) that posits that the common traits of the disorder are manifestations of an individual’s difficulty in making predictions about cause and effect.

Their talk was part of the Simons Foundation Autism Research lecture series.

About the lecture

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 comprises 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 reviewed some of the data accumulated so far.

Sinha considered both positive and negative findings and described 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 reviewed 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

Pawan Sinha is a professor of vision and computational neuroscience in the Department of Brain and Cognitive Sciences at Massachusetts Institute of Technology (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.

Dagmar 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.

Past Lectures

Rare variants and the genetics of autism

Evan E. Eichler, Ph.D.Professor, Department of Genome Sciences and Howard Hughes Medical Institute, University of Washington, Seattle

Evan Eichler discussed his research on the genetics of autism and related neurodevelopmental conditions.

Phenotyping sleep

Emmanuel Mignot, M.D., Ph.D.Craig Reynolds Professor of Sleep Medicine, Stanford University

Emmanuel Mignot discussed sleep biology as well as sleep disorders and their impact. He presented a link to what is known on the genetics of sleep and sleep disorders. He emphasized the need for large scale objective sleep recording studies with genomic and proteomic analysis to better understand the molecular pathways regulating sleep and circadian biology.

Progress in understanding the genetic basis of mental health

Benjamin Neale, Ph.D.Associate Professor, Analytic and Translational Genetics Unit, Massachusetts General Hospital
Associate Professor in Medicine, Harvard Medical School
Associated Researcher, Broad Institute

Benjamin Neale discussed progress in mapping genetic risk factors for autism, schizophrenia and bipolar disorder.

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