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Schizophr Res. 2005 Oct 15;78(2-3):251-60.

Visual processing in schizophrenia: Structural equation modeling of visual masking performance.

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  • 1Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at UCLA, 760 Westwood Plaza (C8-747/NPI), Los Angeles, CA 90024-1759, USA. yurir@ucla.edu

Abstract

Schizophrenic patients consistently demonstrate performance deficits on visual masking procedures. In visual masking, the subject's ability to process a target stimulus is reduced by another stimulus (mask) presented either before (forward masking) or after (backward masking) the target. Masking procedures employed in schizophrenia research have used several experimental paradigms. Most early studies have used high-energy masks (i.e., the mask is stronger than the target) and spatially overlapping target and mask. More recently, studies have begun to employ relatively weak (i.e., low-energy) masks, as well as masks that surround, but do not spatially overlap, the target. Data for forward and backward masking components of four masking conditions (target location and identification with a high-energy mask, target identification with a low-energy mask, and target identification with equal energy paracontrast/metacontrast) were collected from 75 patients with schizophrenia. Based on theoretical distinctions among masking procedures, we compared four models of visual masking using structural equation modeling. Although high zero-order correlations were found among the masking parameters, a four-factor model, in which factors were separated on the type of response (target location and identification), the shape of the function (monotonic and non-monotonic), and the overlap of the stimuli (overlapping and non-overlapping), provided the best fit for the data. These findings suggest that the four masking procedures used in this study may tap unique aspects of visual processing and are not redundant. The results also support theories of the different mechanisms underlying performance on these measures.

PMID:
15975768
[PubMed - indexed for MEDLINE]
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