Jove
Visualize
Contact Us
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 Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.9K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.9K
Decision Making01:20

Decision Making

1.3K
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Comparing risk factors in severe COVID-19 using machine learning and non-machine learning methods: analysis from 2 international randomized controlled trials.

JAMIA open·2026
Same author

A Unified Machine Learning Model for Relapse Prediction in Clinical Stage I Testicular Cancer.

Andrology·2026
Same author

Explainable machine learning to identify chronic lymphocytic leukemia and medication use based on gut microbiome data.

Microbiology spectrum·2025
Same author

Prediction of bloodstream infection using machine learning based primarily on biochemical data.

Scientific reports·2025
Same author

ExplaineR: an R package to explain machine learning models.

Bioinformatics advances·2024
Same author

Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients.

PLoS neglected tropical diseases·2023

Related Experiment Video

Updated: Apr 19, 2026

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram
06:12

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

11.2K

Qualitative modeling of the decision-making process using electrooculography.

Ramtin Zargari Marandi1, S H Sabzpoushan2

  • 1Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran, 16846-13114. r_zargari@elec.iust.ac.ir.

Behavior Research Methods
|December 18, 2014
PubMed
Summary

This study introduces electrooculography (EOG) to analyze decision-making. Eye-tracking via EOG revealed significant gaze patterns correlating with choices, leading to a new decision-making model.

Keywords:
Decision makingElectrooculographyEye movementsNeuromarketing

More Related Videos

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

14.7K
Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice
09:28

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice

Published on: June 23, 2023

4.1K

Related Experiment Videos

Last Updated: Apr 19, 2026

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram
06:12

Recording Horizontal Saccade Performances Accurately in Neurological Patients Using Electro-oculogram

Published on: March 13, 2018

11.2K
Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

14.7K
Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice
09:28

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice

Published on: June 23, 2023

4.1K

Area of Science:

  • Cognitive Science
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Decision-making processes are complex and not fully understood.
  • Objective physiological measures can offer insights into cognitive tasks.
  • Electrooculography (EOG) provides a non-invasive method to track eye movements.

Purpose of the Study:

  • To introduce a novel electrooculography (EOG) based method for studying decision-making.
  • To investigate the relationship between eye gaze patterns and item selection during a choice task.
  • To develop a qualitative model of the decision-making process based on empirical data.

Main Methods:

  • Subjects performed a timed choice task between two items.
  • Electrooculography (EOG) and voice signals were recorded.
  • Artificial neural networks calibrated EOG signals to gaze positions.
  • 16 parameters were extracted from response time and EOG data for analysis.

Main Results:

  • Subjects switched gaze between items approximately three times on average.
  • A significant correspondence was found between gaze direction at selection and the chosen item (t-test, p < .0001).
  • Extracted parameters provided functional insights into the decision-making process.

Conclusions:

  • The study successfully applied EOG to analyze decision-making behavior.
  • Eye gaze patterns during choice tasks contain significant information about the selection process.
  • A qualitative choice model for decision-making tasks is proposed based on the findings.