Word processing during reading sentences in patients with schizophrenia: evidences from the eyetracking technique

Fernández, Gerardo; Sapognikoff, Marcelo; Guinjoan, Salvador M.; Orozco, David; Agamennoni, Osvaldo

doi:10.1016/j.comppsych.2016.04.018

Cited by 14 publications

(19 citation statements)

References 39 publications

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“…Clinical studies show that schizophrenia produces more unconscious and unnecessary head movements in cognitive tasks (Yoo et al, 2005 ). More head movements in reading tasks are associated with repeated reading (Fernández et al, 2016 ) and negative symptoms (Dickinson and Coursey, 2002 ; Chakraborty et al, 2017 ) in patients with schizophrenia. To quantify the subjects' head movement during the text reading task, the head-movement-related features are extracted from the video modality.…”

Section: Methodsmentioning

confidence: 99%

Automatic Schizophrenia Detection Using Multimodality Media via a Text Reading Task

Zhang

Yang

et al. 2022

Front. Neurosci.

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Schizophrenia is a crippling chronic mental disease that affects people worldwide. In this work, an automatic schizophrenia detection algorithm is proposed based on the reading deficit of schizophrenic patients. From speech and video modalities, the automatic schizophrenia detection algorithm illustrates abnormal speech, head movement, and reading fluency during the reading task. In the speech modality, an acoustic model of speech emotional flatness in schizophrenia is established to reflect the emotional expression flatness of schizophrenic speech from the perspective of speech production and perception. In the video modality, the head-movement-related features are proposed to illustrate the spontaneous head movement caused by repeated reading and unconscious movement, and the reading-fluency-related features are proposed to convey the damaged degree of schizophrenic patients' reading fluency. The experimental data of this work are 160 segments of speech and video data recorded by 40 participants (20 schizophrenic patients and 20 normal controls). Combined with support vector machines and random forest, the accuracy of the proposed acoustic model, the head-movement-related features, and the reading-fluency-related features range from 94.38 to 96.50%, 73.38 to 83.38%, and 79.50 to 83.63%, respectively. The average accuracy of the proposed automatic schizophrenia detection algorithm reaches 97.50%. The experimental results indicate the effectiveness of the proposed automatic detection algorithm as an auxiliary diagnostic method for schizophrenia.

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Section: Methodsmentioning

confidence: 99%

Automatic Schizophrenia Detection Using Multimodality Media via a Text Reading Task

Zhang

Yang

et al. 2022

Front. Neurosci.

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“…The development of mobile eye trackers in the last years (Babcock & Pelz, 2004;Li et al, 2006;Pfeiffer & Renner, 2014) has enabled researcher to examine eye movements during reading in a natural context (Rayner, 1998). Mobile eye tracking of reading may enhance clinical diagnosis, for example, by differentiating progressive supranuclear palsy from Parkinson's disease (Marx et al, 2012) or for mental or linguistic disorders (Fernández et al, 2016;Vidal et al, 2012). The measurement of eye movements in such tasks has led to the further understanding of learning (Rosch & Vogel-Walcutt, 2013) e.g.…”

Section: Mobile Eye Tracking and Readingmentioning

confidence: 99%

“…They are for instance used to investigate eye movements during reading, scene perception and visual search task, see Rayner (2009) for review. Eye movement abnormalities like corrective saccades in a smooth pursuit task were shown to supplement clinical diagnosis of schizophrenia (Benson et al, 2012;Sereno & Holzman, 1995;Shulgovskiy et al, 2015) and can be linked to cognitive deficits in word processing of schizophrenia patients (Fernández et al, 2016). Furthermore, saccadic eye movements can be used as objective indicators in screening mental health (Vidal et al, 2012) for instance of dyslexia (Biscaldi et al, 1998;Eden et al, 1994;Nilsson Benfatto et al, 2016) or autism (Kemner et al, 1998;Rosenhall et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Sampling rate influences saccade detection in mobile eye tracking of a reading task

Leube¹,

Rifai²,

Wahl³

2017

JEMR

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The purpose of this study was to compare saccade detection characteristics in two mobile eye trackers with different sampling rates in a natural task. Gaze data of 11 participants were recorded in one 60 Hz and one 120 Hz mobile eye tracker and compared directly to the saccades detected by a 1000 HZ stationary tracker while a reading task was performed. Saccades and fixations were detected using a velocity based algorithm and their properties analyzed. Results showed that there was no significant difference in the number of detected fixations but mean fixation durations differed between the 60 Hz mobile and the stationary eye tracker. The 120 Hz mobile eye tracker showed a significant increase in the detection rate of saccades and an improved estimation of the mean saccade duration, compared to the 60 Hz eye tracker. To conclude, for the detection and analysis of fast eye movements, such as saccades, it is better to use a 120 Hz mobile eye tracker.

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“…Each eye movement ends up at a fixation point, which allows the brain to process incoming information and program the following saccade. Different neuropsychiatric pathologies produce abnormalities in eye movements and disturbances in reading each have a particular pattern that can be recorded and measured [6][7][8][9][10][11]. Eye movements can be classified into three groups:…”

Section: Introductionmentioning

confidence: 99%

Eye movement behavior identification for Alzheimer's disease diagnosis

Biondi¹,

Fernández²,

Castro³

et al. 2018

J. Integr. Neurosci.

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We develop a deep-learning approach to differentiate between the eye movement behavior of people with neurodegenerative diseases during reading compared to healthy control subjects. The subjects with and without Alzheimer's disease read welldefined and previously validated sentences including high-and low-predictable sentences, and proverbs. From these eyetracking data trial-wise information is derived consisting of descriptors that capture the reading behavior of the subjects. With this information a set of denoising sparse-autoencoders are trained and a deep neural network is built using the trained autoencoders and a softmax classifier that identifies subjects with Alzheimer's disease with 89.78% accuracy. The results are very encouraging and show that such models promise to be helpful for understanding the dynamics of eye movement behavior and its relation with underlying neuropsychological processes.

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Word processing during reading sentences in patients with schizophrenia: evidences from the eyetracking technique

Cited by 14 publications

References 39 publications

Automatic Schizophrenia Detection Using Multimodality Media via a Text Reading Task

Automatic Schizophrenia Detection Using Multimodality Media via a Text Reading Task

Sampling rate influences saccade detection in mobile eye tracking of a reading task

Eye movement behavior identification for Alzheimer's disease diagnosis

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