scholarly journals Nonlinear Dynamics Identification of the Oculo-Motor System based on Eye Tracking Data

2021 ◽  
Vol 15 ◽  
pp. 569-577
Author(s):  
Vitaliy Pavlenko ◽  
Tetiana Shamanina ◽  
Vladislav Chori
Keyword(s):  
Eye Tracking ◽  
Motor System ◽  
Real Data ◽  
Software Tools ◽  
Volterra Model ◽  
Innovative Technology ◽  
Tracking Data ◽  
Nonlinear Dynamic Model ◽  
Nonlinear Properties ◽  
Dynamics Identification

Instrumental computing and software tools have been developed for constructing a nonlinear dynamic model of the human oculo-motor system (OMS) based on the data of input–output experiments using test visual stimuli and innovative technology eye tracking. For identification the Volterra model is used in the form of multidimensional transient functions of the 1st, 2nd and 3rd orders, taking into account the inertial and nonlinear properties of the OMS. Software tools for processing eye tracking data developed in the Matlab environment are tested on real data from an experimental study of OMS.

scholarly journals Identification of the Oculo-Motor System in the Form Volterra Model Based on Eye-Tracking Data

2021 ◽  
Vol 248 ◽  
pp. 01009
Author(s):  
Vitaliy D. Pavlenko ◽  
Tetiana V. Shamanina ◽  
Vladislav V. Chori
Keyword(s):  
Eye Tracking ◽  
Motor System ◽  
Volterra Model ◽  
Innovative Technology ◽  
Tracking Data ◽  
Input Output ◽  
Nonlinear Dynamic Model ◽  
Nonlinear Properties ◽  
Transition Functions ◽  
Identification Tool

Instrumental computing and software tools have been developed for constructing a nonlinear dynamic model of the human oculo-motor system (OMS) based on the data of input-output experiments using test visual stimulus and innovative technology. Volterra model in the form of multidimensional transition functions of the 1st, 2nd and 3rd orders, taking into account the inertial and nonlinear properties of the OMS was used as the identification tool. Eye-tracking data developed in the Matlab environment are tested on real datasets from an experimental study of OMS.

Building a classifier in the personality recognition system based on eye tracking data

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Vladislav Chori ◽  
Tetyana Shamanina ◽  
Vitaliy Pavlenko
Keyword(s):  
Eye Tracking ◽  
Motor System ◽  
Recognition System ◽  
Volterra Model ◽  
Biometric Identification ◽  
Eye Tracker ◽  
Transition Functions ◽  
Computational Errors ◽  
Personality Recognition ◽  
Tracking Technology

Identification systems that use biometric characteristics to solve the problem of access to information systems are becoming more common. The article proposes a new method of biometric identification of computer systems users, based on the determination of the integral Volterra model of the human oculo-motor system (OMS) according to experimental research "input-output" using innovative eye tracking technology. With the help of the Tobii Pro TX300 eye tracker, the data of OMC responses to test visual stimuli were obtained, displayed as bright dots on the computer screen at different distances from the start position in the "horizontal" direction. Based on the data obtained, the transition functions of the first, second and third orders of the OMS for two people were determined. To construct a personality classifier, the informativeness of the proposed heuristic features, determined on the basis of the transition functions in terms of the probability of correct recognition (PCR), is investigated. Pairs of features are established that are resistant to computational errors and have a high PCR value - in the range 0.92 - 0.97. Fig.: 8. Table: 5. Bibliography: 30 items. Key words: biometric identification, personality recognition, Volterra model, oculo-motor system, eye tracking technology, informativeness of features, classification.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

Attentional Bias to Untrustworthy Faces: Evidence From Eye Tracking Data

2015 ◽  
Vol 23 (9) ◽  
pp. 1508
Author(s):  
Qiandong WANG ◽  
Qinggong LI ◽  
Kaikai CHEN ◽  
Genyue FU
Keyword(s):  
Eye Tracking ◽  
Attentional Bias ◽  
Tracking Data

scholarly journals Mining reading patterns from eye-tracking data: method and demonstration

2019 ◽  
Vol 19 (2) ◽  
pp. 345-369 ◽  
Author(s):  
Constantina Ioannou ◽  
Indira Nurdiani ◽  
Andrea Burattin ◽  
Barbara Weber
Keyword(s):  
Eye Tracking ◽  
Tracking Data ◽  
Reading Patterns

scholarly journals Classifying Eye-Tracking Data Using Saliency Maps

2021 ◽  
Author(s):  
Shafin Rahman ◽  
Sejuti Rahman ◽  
Omar Shahid ◽  
Md. Tahmeed Abdullah ◽  
Jubair Ahmed Sourov
Keyword(s):  
Eye Tracking ◽  
Tracking Data ◽  
Saliency Maps
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