scholarly journals Chronological states of viewer’s intentions using hidden Markov models and features of eye movement

2014 ◽  
Vol 1 (1) ◽  
pp. e5
Author(s):  
Minoru Nakayama ◽  
Naoya Takahashi
Keyword(s):  
Hidden Markov Models ◽  
Eye Movement ◽  
Markov Models ◽  
Hidden Markov

scholarly journals Caucasian and Asian eye movement patterns in face recognition: A computational exploration using hidden Markov models

2014 ◽  
Vol 14 (10) ◽  
pp. 1212-1212 ◽  
Author(s):  
T. Chuk ◽  
A. X. Luo ◽  
K. Crookes ◽  
W. G. Hayward ◽  
A. B. Chan ◽  
...  
Keyword(s):  
Face Recognition ◽  
Hidden Markov Models ◽  
Eye Movement ◽  
Markov Models ◽  
Hidden Markov ◽  
Movement Patterns ◽  
Computational Exploration

scholarly journals Eye movement analysis with hidden Markov models (EMHMM) with co-clustering

2021 ◽  
Author(s):  
Janet H. Hsiao ◽  
Hui Lan ◽  
Yueyuan Zheng ◽  
Antoni B. Chan
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Hidden Markov Models ◽  
Eye Movement ◽  
Markov Models ◽  
Hidden Markov ◽  
Movement Patterns ◽  
Movement Analysis ◽  
Background Information ◽  
Eye Movement Analysis

AbstractThe eye movement analysis with hidden Markov models (EMHMM) method provides quantitative measures of individual differences in eye-movement pattern. However, it is limited to tasks where stimuli have the same feature layout (e.g., faces). Here we proposed to combine EMHMM with the data mining technique co-clustering to discover participant groups with consistent eye-movement patterns across stimuli for tasks involving stimuli with different feature layouts. Through applying this method to eye movements in scene perception, we discovered explorative (switching between the foreground and background information or different regions of interest) and focused (mainly looking at the foreground with less switching) eye-movement patterns among Asian participants. Higher similarity to the explorative pattern predicted better foreground object recognition performance, whereas higher similarity to the focused pattern was associated with better feature integration in the flanker task. These results have important implications for using eye tracking as a window into individual differences in cognitive abilities and styles. Thus, EMHMM with co-clustering provides quantitative assessments on eye-movement patterns across stimuli and tasks. It can be applied to many other real-life visual tasks, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.

scholarly journals When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7569
Author(s):  
Hsing-Hao Lee ◽  
Zih-Ling Chen ◽  
Su-Ling Yeh ◽  
Janet Hui-Wen Hsiao ◽  
An-Yeu (Andy) Wu
Keyword(s):  
Hidden Markov Models ◽  
Eye Movement ◽  
Markov Models ◽  
Hidden Markov ◽  
Movement Patterns ◽  
Movement Analysis ◽  
Mind Wandering ◽  
Focused Attention ◽  
Eye Tracker ◽  
Eye Movement Analysis

Mind-wandering has been shown to largely influence our learning efficiency, especially in the digital and distracting era nowadays. Detecting mind-wandering thus becomes imperative in educational scenarios. Here, we used a wearable eye-tracker to record eye movements during the sustained attention to response task. Eye movement analysis with hidden Markov models (EMHMM), which takes both spatial and temporal eye-movement information into account, was used to examine if participants’ eye movement patterns can differentiate between the states of focused attention and mind-wandering. Two representative eye movement patterns were discovered through clustering using EMHMM: centralized and distributed patterns. Results showed that participants with the centralized pattern had better performance on detecting targets and rated themselves as more focused than those with the distributed pattern. This study indicates that distinct eye movement patterns are associated with different attentional states (focused attention vs. mind-wandering) and demonstrates a novel approach in using EMHMM to study attention. Moreover, this study provides a potential approach to capture the mind-wandering state in the classroom without interrupting the ongoing learning behavior.

scholarly journals Eye movement analysis with switching hidden Markov models

2019 ◽  
Vol 52 (3) ◽  
pp. 1026-1043 ◽  
Author(s):  
Tim Chuk ◽  
Antoni B. Chan ◽  
Shinsuke Shimojo ◽  
Janet H. Hsiao
Keyword(s):  
Hidden Markov Models ◽  
Eye Movement ◽  
Markov Models ◽  
Hidden Markov ◽  
Movement Analysis ◽  
Eye Movement Analysis

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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