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Dynamic 3-D computer graphics for designing a diagnostic tool for patients with schizophrenia

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Abstract

We introduce a novel procedure that uses dynamic 3-D computer graphics as a diagnostic tool for assessing disease severity in schizophrenia patients, based on their reduced influence of top-down cognitive processes in interpreting bottom-up sensory input. Our procedure uses the hollow-mask illusion, in which the concave side of the mask is misperceived as convex, because familiarity with convex faces dominates sensory cues signaling a concave mask. It is known that schizophrenia patients resist this illusion and their resistance increases with illness severity. Our method uses virtual masks rendered with two competing textures: (a) realistic features that enhance the illusion; (b) random-dot visual noise that reduces the illusion. We control the relative weights of the two textures to obtain psychometric functions for controls and patients and assess illness severity. The primary novelty is the use of a rotating mask that is easy to implement on a wide variety of portable devices and avoids the use of elaborate stereoscopic devices that have been used in the past. Thus our method, which can also be used to assess the efficacy of treatments, provides clinicians the advantage to bring the test to the patient’s own environment, instead of having to bring patients to the clinic.

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Author information

Authors and Affiliations

  1. Laboratory of Vision Research, Center for Cognitive Science, Rutgers University, New Brunswick, USA

    Attila Farkas & Thomas V. Papathomas

  2. Department of Biomedical Engineering, Rutgers University, New Brunswick, USA

    Thomas V. Papathomas

  3. Division of Schizophrenia Research, Rutgers University Behavioral HealthCare, New Jersey, USA

    Steven M. Silverstein

  4. Robert Wood Johnson Medical School Department of Psychiatry, Rutgers Biomedical and Health Sciences, Newark, USA

    Steven M. Silverstein

  5. Center for Cognitive Science, Rutgers University, New Brunswick, USA

    Hristiyan Kourtev

  6. College of Engineering, Georgia Institute of Technology, Atlanta, USA

    John F. Papayanopoulos

Authors
  1. Attila Farkas
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  2. Thomas V. Papathomas
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  3. Steven M. Silverstein
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  4. Hristiyan Kourtev
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  5. John F. Papayanopoulos
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Correspondence to Attila Farkas.

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Farkas, A., Papathomas, T.V., Silverstein, S.M. et al. Dynamic 3-D computer graphics for designing a diagnostic tool for patients with schizophrenia. Vis Comput 32, 1499–1506 (2016). https://doi.org/10.1007/s00371-015-1152-5

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