Antisaccade performance in schizophrenia: a neural model of decision making in the superior colliculus

Cutsuridis, Vassilis and Kumari, Veena and Ettinger, Ulrich (2014) Antisaccade performance in schizophrenia: a neural model of decision making in the superior colliculus. Frontiers in Neuroscience . ISSN 1662-4548

Full content URL: https://doi.org/10.3389/fnins.2014.00013

Documents
CutVenEtt2014FrontNeurosci.pdf
[img]
[Download]
[img]
Preview
PDF
CutVenEtt2014FrontNeurosci.pdf - Whole Document
Available under License Creative Commons Attribution 4.0 International.

1MB
Item Type:Article
Item Status:Live Archive

Abstract

Antisaccade performance deficits in schizophrenia are generally interpreted as an impaired top–down inhibitory signal failing to suppress the erroneous response. We recorded the antisaccade performance (error rates and latencies) of healthy and schizophrenia subjects performing the mirror antisaccade task. A neural rise-to-threshold model of antisaccade performance was developed to uncover the biophysical mechanisms giving rise to the observed deficits in schizophrenia. Schizophrenia patients displayed greater variability in the antisaccade and corrected antisaccade latency distributions, increased error rates and decreased corrected errors, relative to healthy participants. Our model showed that (1) increased variability is due to a more noisy accumulation of information by schizophrenia patients, but their confidence level required before making a decision is unaffected, and (2) competition between the correct and erroneous decision processes, and not a third top-down inhibitory signal suppressing the erroneous response, accounts for the antisaccade performance of healthy and schizophrenia subjects. Local competition further ensured that a correct antisaccade is never followed by an error prosaccade.

Keywords:antisaccade performance, rise-to-threshold model, neural model, superior colliculus, eye movements, schizophrenia, JCOpen
Subjects:B Subjects allied to Medicine > B140 Neuroscience
G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
ID Code:27734
Deposited On:03 Jul 2017 11:06

Repository Staff Only: item control page