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

Attila Farkas, Thomas V. Papathomas, Steven M. Silverstein, Hristiyan Kourtev, John F. Papayanopoulos

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1499-1506
Number of pages8
JournalVisual Computer
Issue number11
StatePublished - Nov 1 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


  • 3-D computer graphics
  • Clinical diagnosis
  • Perception
  • Schizophrenia
  • Visual illusion


Dive into the research topics of 'Dynamic 3-D computer graphics for designing a diagnostic tool for patients with schizophrenia'. Together they form a unique fingerprint.

Cite this