Jove
Visualize
Contact Us

Related Concept Videos

Association Areas of the Cortex01:21

Association Areas of the Cortex

10.1K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
10.1K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

943
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
943

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparability between AI and human cognition and its role in psychological research and AI ethics.

British journal of psychology (London, England : 1953)·2026
Same author

Reading an Artist's Intention from the Composition (RAIC): eye movements and aesthetic experience in Japanese woodblock prints.

Frontiers in psychology·2025
Same author

Using eye movements, electrodermal activities, and heart rates to predict different types of cognitive load during reading with background music.

Scientific reports·2025
Same author

Regional crowd flow estimation from aerial view.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Understanding the role of eye movement pattern and consistency during face recognition through EEG decoding.

NPJ science of learning·2025
Same author

How is emotional evidence from multiple sources used in perceptual decision making?

Psychophysiology·2024
Same journal

AI-generated faces are becoming more trustworthy.

Journal of vision·2026
Same journal

Attenuated boundary extension in observer perspective memory compared to field perspective memory.

Journal of vision·2026
Same journal

Comparing masking and habituation roles in saccadic omission of stimuli optimized for intra-saccadic vision.

Journal of vision·2026
Same journal

Analysis of human visual experience data.

Journal of vision·2026
Same journal

Pyramid-based Bayesian modeling for high-resolution behavioral analysis.

Journal of vision·2026
Same journal

Sensation without perception: The white whale effect and perceptual blindness in autonomous vehicles.

Journal of vision·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Apr 23, 2026

Eye Movement Monitoring of Memory
08:06

Eye Movement Monitoring of Memory

Published on: August 15, 2010

14.4K

Understanding eye movements in face recognition using hidden Markov models.

Tim Chuk1, Antoni B Chan2, Janet H Hsiao1

  • 1Department of Psychology, University of Hong Kong, Hong Kong.

Journal of Vision
|September 18, 2014
PubMed
Summary
This summary is machine-generated.

Hidden Markov models (HMMs) reveal distinct holistic and analytic eye movement patterns in face recognition among Asian participants. These patterns, differing in fixation transitions, impact response times but not accuracy.

Keywords:
eye movementface recognitionhidden Markov models

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.6K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.0K

Related Experiment Videos

Last Updated: Apr 23, 2026

Eye Movement Monitoring of Memory
08:06

Eye Movement Monitoring of Memory

Published on: August 15, 2010

14.4K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.6K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.0K

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Human-Computer Interaction

Background:

  • Face recognition involves complex visual processing.
  • Eye movement patterns provide insights into cognitive strategies.
  • Individual differences in perception are well-documented.

Purpose of the Study:

  • To analyze eye movement data in face recognition using Hidden Markov Models (HMMs).
  • To identify and categorize distinct eye movement patterns (holistic vs. analytic).
  • To investigate the relationship between these patterns, response times, accuracy, and recognition outcomes.

Main Methods:

  • Applied Hidden Markov Models (HMMs) to time-series eye movement data from a face recognition task.
  • Modeled individual scan paths, including regions of interest and transition probabilities.
  • Clustered HMMs to categorize participants into holistic or analytic eye movement patterns.

Main Results:

  • Identified distinct holistic and analytic eye movement patterns in Asian participants.
  • Found significant individual differences in scan path strategies within the same cultural group.
  • Analytic patterns correlated with longer response times but not lower recognition accuracy.
  • Distinguished between correct and incorrect face recognition based on fixation transition patterns, not just locations.

Conclusions:

  • Hidden Markov Models effectively characterize individual eye movement strategies in face recognition.
  • Eye movement patterns in face recognition can be categorized as holistic or analytic, reflecting underlying cognitive processes.
  • Scan path dynamics, particularly fixation transitions, are crucial for understanding face recognition performance and errors.