Using Eye Tracking as a Functional Biomarker for Schizophrenia: A Scoping Review

Abstract

Abnormalities in eye movements have been identified as a reliable functional biomarker for schizophrenia, but have not been widely applied in clinical practice. The aim of this study is to review the current literature in order to identify the new unexplored areas of eye tracking methodology. A literature search was followed PRISMA-ScR guidelines and was conducted by using the eLibrary, PubMed, CNKI, and Google Scholar databases in December 2025. The results are summarized according to the main approaches. In the Free viewing paradigm: patients have a reduced number of fixations, longer fixation durations, and a narrower scan path, often focusing on non-important details. In the Smooth pursuit eye movement paradigm: patients demonstrate significant impairment characterized by increased velocity errors and frequent "catch-up" saccades. In saccadic tasks: deficits are observed both in prosaccade (increased latency, reduced accuracy) and antisaccade (significantly increased error rates) tasks. Neuroimaging studies have identified a correlation with dysfunctions in the prefrontal cortex, frontal eye fields, parietal lobes, and cerebellum. These disturbances have been linked to impairments in attention, working memory, and cognitive processing speed. The application of advanced statistical analyses and artificial neural networks has shown a high degree of accuracy in distinguishing patients with schizophrenia from healthy individuals, with reported accuracy rates reaching up to 90%. Therefore, eye tracking is a valid method for identifying oculomotor biomarkers of schizophrenia. However, the dynamics of changes in eye movement in schizophrenia and under the influence of various therapeutic agents are poorly understood.

Received: 3 November 2025 | Revised: 6 January 2026 | Accepted: 26 January 2026

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the results of our review are openly available in ELibrary at https://elibrary.ru/, PubMed at https://pubmed.ncbi.nlm.nih.gov/, Google Scholar at https://scholar.google.com/, and CNKI at https://cnki.net/index/.

Author Contribution Statement

Ilya Fedotov: Conceptualization, Methodology, Software, Writing – original draft, Writing – review & editing, Visualization, Supervision. Anna Faustova: Validation, Writing – original draft, Writing – review & editing, Project administration. Darya Kryazhkova: Resources, Data curation, Writing – original draft.