Jove
Visualize
Contact Us

Related Concept Videos

Physiology of Emotion01:20

Physiology of Emotion

2.9K
The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...
2.9K
Physiological Theories: James-Lange Theory of Emotion01:16

Physiological Theories: James-Lange Theory of Emotion

1.7K
The James-Lange theory of emotion, proposed by William James and Carl Lange in the late 19th century, asserts that emotions are the result of physiological reactions to external stimuli. Contrary to the traditional view, which suggests that emotions directly arise from the perception of stimuli, this theory proposes that emotions occur as a consequence of the body's responses to such stimuli. According to this framework, an emotional experience is a cognitive interpretation of physiological...
1.7K
Physiological Theories: Cannon-Bard Theory of Emotion01:22

Physiological Theories: Cannon-Bard Theory of Emotion

1.4K
The Cannon-Bard theory of emotion, proposed by Walter Cannon and Philip Bard, challenges the notion that emotions are solely the result of physiological responses. Instead, this theory suggests that emotional experiences and physiological arousal occur simultaneously but operate through independent mechanisms. This dual response is initiated by the brain, specifically by the thalamus, which plays a critical role in processing sensory information.
Upon perceiving a stimulus, such as a dangerous...
1.4K
Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

1.3K
Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
1.3K
Labeling Emotion01:20

Labeling Emotion

528
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
528
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

455
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...
455

You might also read

Related Articles

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

Sort by
Same author

Sepsis Alerts in the Pre-hospital Setting: An Observational Retrospective Study of Emergency Medical Services' Response in Portugal (2020-2023).

Cureus·2025
Same author

Extraction of compression indices from maternal-fetal heart rate simultaneous signals.

PloS one·2025
Same author

JARVIS3: an efficient encoder for genomic data.

Bioinformatics (Oxford, England)·2024
Same author

AltaiR: a C toolkit for alignment-free and temporal analysis of multi-FASTA data.

GigaScience·2024
Same author

The effect of anxiety and its interplay with social cues when perceiving aggressive behaviours.

Quarterly journal of experimental psychology (2006)·2024
Same author

Improved Perception of Aggression Under (un)Related Threat of Shock.

Cognitive science·2024
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: Dec 17, 2025

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

11.3K

Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification.

Gisela Pinto1, João M Carvalho1,2, Filipa Barros3,4,5

  • 1Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3810-193 Aveiro, Portugal.

Sensors (Basel, Switzerland)
|June 25, 2020
PubMed
Summary

This study found that combining multiple physiological signals, including electrocardiogram (ECG), electromyogram, and electrodermal activity, offers the best performance for accurate emotion classification. The electrocardiogram signal alone is also highly effective for identifying emotions.

Keywords:
affective computingfeature extractionmultimodalneural networkrandom forest

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.0K
Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

9.4K

Related Experiment Videos

Last Updated: Dec 17, 2025

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

11.3K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.0K
Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

9.4K

Area of Science:

  • Psychophysiology
  • Affective Computing
  • Biomedical Signal Processing

Background:

  • Emotional responses involve distinct physiological changes crucial for adaptive behaviors and survival.
  • Existing emotion classification systems often rely on limited or isolated physiological signals, hindering comprehensive understanding.
  • Developing objective emotion detection systems requires evaluating the informativeness of individual and combined physiological signals.

Purpose of the Study:

  • To develop a physiological model for emotion classification using multiple biosignals.
  • To compare the effectiveness of unimodal and multimodal approaches for emotion identification.
  • To determine the contribution of individual physiological signals to emotion classification accuracy.

Main Methods:

  • Collected electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) data from 55 healthy subjects.
  • Employed signal preprocessing, feature extraction, and classification using random forest and neural networks.
  • Utilized both unimodal (single signal) and multimodal (combined signals) analysis strategies.

Main Results:

  • The electrocardiogram (ECG) signal demonstrated the highest effectiveness as a unimodal indicator for emotion classification.
  • The multimodal approach, integrating all three signals (ECG, EMG, EDA), achieved the best overall emotion identification performance.
  • Each physiological signal contributed unique and crucial information to the multimodal classification system.

Conclusions:

  • A comprehensive physiological model of emotions can be built by integrating multiple biosignals.
  • The combination of ECG, EMG, and EDA provides a robust and informative approach to emotion classification.
  • This research has significant implications for developing advanced emotion evaluation, monitoring, and regulation tools in research and clinical settings.