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Research Article

Autism as a disorder of prediction

Pawan Sinha, Margaret M. Kjelgaard, Tapan K. Gandhi, Kleovoulos Tsourides, Annie L. Cardinaux, Dimitrios Pantazis, Sidney P. Diamond, and Richard M. Held
  1. aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
  2. bDepartment of Communication Sciences and Disorders, Massachusetts General Hospital Institute of Health Professions, Boston, MA 02129; and
  3. cDepartment of Biomedical Engineering, Defense Institute of Physiology and Allied Sciences, New Delhi, India DL 110054

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PNAS first published October 6, 2014; https://doi.org/10.1073/pnas.1416797111
Pawan Sinha
aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
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  • For correspondence: heldd@neco.edu psinha@mit.edu
Margaret M. Kjelgaard
aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
bDepartment of Communication Sciences and Disorders, Massachusetts General Hospital Institute of Health Professions, Boston, MA 02129; and
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Tapan K. Gandhi
aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
cDepartment of Biomedical Engineering, Defense Institute of Physiology and Allied Sciences, New Delhi, India DL 110054
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Kleovoulos Tsourides
aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
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Annie L. Cardinaux
aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
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Dimitrios Pantazis
aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
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Sidney P. Diamond
aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
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Richard M. Held
aDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;
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  • For correspondence: heldd@neco.edu psinha@mit.edu
  1. Contributed by Richard M. Held, September 5, 2014 (sent for review November 13, 2013; reviewed by Leonard Rappaport, Stephen M. Camarata, and Nouchine Hadjikhani)

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Significance

Autism is characterized by diverse behavioral traits. Guided by theoretical considerations and empirical data, this paper develops the hypothesis that many of autism's salient traits may be manifestations of an underlying impairment in predictive abilities. This impairment renders an otherwise orderly world to be experienced as a capriciously “magical” one. The hypothesis elucidates the information-processing roots of autism and, thereby, can aid the identification of neural structures likely to be differentially affected. Behavioral and neural measures of prediction might serve as early assays of predictive abilities in infants, and serve as useful tools in intervention design and in monitoring their effectiveness. The hypothesis also points to avenues for further research to determine molecular and circuit-level causal underpinnings of predictive impairments.

Abstract

A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly “magical” world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one’s ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy.

  • probabilistic processing
  • endophenotype
  • Markov models
  • theory
  • heterogeneity

Footnotes

  • ↵1To whom correspondence may be addressed. Email: heldd{at}neco.edu or psinha{at}mit.edu.
  • Author contributions: P.S., M.M.K., S.P.D., and R.M.H. designed research; P.S., M.M.K., T.K.G., K.T., A.L.C., D.P., S.P.D., and R.M.H. performed research; P.S., M.M.K., S.P.D., and R.M.H. wrote the paper; P.S., M.M.K., S.P.D., and R.M.H. formulated hypothesis; and T.K.G., K.T., A.L.C., and D.P. evaluated hypothesis.

  • Reviewers: L.R., Children’s Hospital Boston; S.M.C., Vanderbilt University School of Medicine; and N.H., Harvard Medical School.

  • The authors declare no conflict of interest.

Freely available online through the PNAS open access option.

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Autism as a disorder of prediction
Pawan Sinha, Margaret M. Kjelgaard, Tapan K. Gandhi, Kleovoulos Tsourides, Annie L. Cardinaux, Dimitrios Pantazis, Sidney P. Diamond, Richard M. Held
Proceedings of the National Academy of Sciences Oct 2014, 201416797; DOI: 10.1073/pnas.1416797111

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Autism as a disorder of prediction
Pawan Sinha, Margaret M. Kjelgaard, Tapan K. Gandhi, Kleovoulos Tsourides, Annie L. Cardinaux, Dimitrios Pantazis, Sidney P. Diamond, Richard M. Held
Proceedings of the National Academy of Sciences Oct 2014, 201416797; DOI: 10.1073/pnas.1416797111
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