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

Theory of Romantic Attachment in Adulthood03:34

Theory of Romantic Attachment in Adulthood

44.4K
Attachment is a long-standing connection or bond with others. While Attachment Theory was conceived in developmental psychology to describe infant-caregiver bonding, it's been extended into adulthood to include romantic relationships. 
44.4K
Attachment01:20

Attachment

130
Attachment is vital for infant development, as warm social interactions support growth and well-being. In a classic 1958 study by Harry Harlow, the significance of warmth and comfort in forming attachments was examined. Harlow separated newborn monkeys from their mothers and provided two artificial "mothers": one made of cold wire and the other covered in soft cloth. Despite the wire mother offering food, the infant monkeys preferred the comfort of the cloth mother, demonstrating that...
130

You might also read

Related Articles

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

Sort by
Same author

Frontal EEG Asymmetry and Attachment Style During Sequential Decision-Making in the Secretary Problem.

Behavioral sciences (Basel, Switzerland)·2026
Same author

Event-Based Camera Modeling for Atmospheric Turbulence Prediction.

Sensors (Basel, Switzerland)·2025
Same author

Predicting attachment style from EEG data on the Flanker task.

Frontiers in human neuroscience·2025
Same author

Attachment Style, Task Difficulty, and Feedback Type: Effects on Cognitive Load.

Behavioral sciences (Basel, Switzerland)·2025
Same author

Editorial: Neuroplasticity and imaging methods in rehabilitation.

Frontiers in human neuroscience·2025
Same author

Real-World Spatial Synchronization of Event-CMOS Cameras through Deep Learning: A Novel CNN-DGCNN Approach.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Sep 9, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K

Improving attachment style clustering with ROCKET and CatBoost: Insights from EEG analysis.

Dor Mizrahi1, Ilan Laufer1, Inon Zuckerman1

  • 1Department of Industrial Engineering and Management, Ariel University, Ariel, Israel.

Plos One
|September 2, 2025
PubMed
Summary

Predicting psychological attachment styles using electroencephalography (EEG) and machine learning (ML) is now more feasible. This study shows ML models can classify attachment styles from neural data, revealing attachment as a spectrum.

More Related Videos

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
08:08

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

1.0K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.0K

Related Experiment Videos

Last Updated: Sep 9, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K
Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
08:08

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

1.0K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.0K

Area of Science:

  • Neuroscience
  • Psychology
  • Machine Learning

Background:

  • Attachment styles are crucial in psychology and neuroscience.
  • Predicting attachment styles using objective neural data is challenging.
  • Existing methods lack objective neural markers for nuanced attachment classification.

Purpose of the Study:

  • To explore the use of machine learning (ML) models and electroencephalography (EEG) analysis for improved attachment style classification.
  • To investigate the relationship between EEG features and different attachment styles (secure, avoidant, anxious, fearful-avoidant).
  • To assess the potential of ML-driven EEG analysis for psychological assessment.

Main Methods:

  • EEG data were collected from 27 university students.
  • Attachment styles were assessed using the ECR-R questionnaire.
  • EEG features were extracted using the ROCKET algorithm, followed by Principal Component Analysis (PCA) and CatBoost for prediction, with a two-stage data pruning approach.

Main Results:

  • A strong relationship was found between the number of EEG epochs and predictive accuracy.
  • Secure and Fearful-Avoidant attachment styles were predicted most reliably.
  • Anxious and Avoidant styles showed greater variability, indicating complex neural signatures.

Conclusions:

  • Findings support attachment as a spectrum influenced by experiences, emotional regulation, and social context, rather than fixed categories.
  • ML-driven EEG analysis shows potential for predicting attachment styles, offering new avenues for psychological assessment.
  • The study highlights attachment as a dynamic process, informing clinical interventions and research on neural markers.