Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

526
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...
526
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

4.6K
The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
4.6K
Association Areas of the Cortex01:21

Association Areas of the Cortex

8.7K
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,...
8.7K
Emotional Expression01:26

Emotional Expression

892
Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
Universal Facial Expressions
Psychologist Paul Ekman identified seven basic...
892
Encoding01:19

Encoding

713
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
713
Labeling Emotion01:20

Labeling Emotion

582
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...
582

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Neural encoding of biomechanically (im)possible human movements in occipitotemporal cortex.

PLoS computational biology·2025
Same author

Encoding of movement style: From kinematics to neurons: Comment on "Kinematic coding': Measuring information in naturalistic behavior" by C. Becchio, J. Pullar, E. Scaliti, & S. Panzeri.

Physics of life reviews·2025
Same author

Lessons learned from a multimodal sensor-based eHealth approach for treating pediatric obsessive-compulsive disorder.

Frontiers in digital health·2024
Same author

Neural Encoding of Bodies for Primate Social Perception.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2024
Same author

Digital Gait Measures Capture 1-Year Progression in Early-Stage Spinocerebellar Ataxia Type 2.

Movement disorders : official journal of the Movement Disorder Society·2024
Same author

Perceptual encoding of emotions in interactive bodily expressions.

iScience·2024
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K

基于多域标准引用编码的面部表情识别.

Michael Stettler1, Alexander Lappe1, Martin A Giese1

  • 1Hertie Instutute for Clinical Brain Research & Centre for Integrative Neuroscience, University Clinic Tübingen, IMPRS-IS, Germany.

Neural networks : the official journal of the International Neural Network Society
|December 23, 2025
PubMed
概括
此摘要是机器生成的。

神经科学的洞察力改善了机器学习的面部表情识别. 一种新的标准引用编码方法使模型能够用最小的数据对新头形状进行概括,从而增强计算机视觉能力.

关键词:
深度神经网络是一种深度神经网络.面部表情识别 面部表情识别标准引用编码是指标准引用的编码.转移学习转移学习

更多相关视频

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.8K
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

相关实验视频

Last Updated: Jan 8, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K
Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.8K
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

科学领域:

  • 计算机视觉 计算机视觉
  • 神经科学是一个神经科学.
  • 机器学习 机器学习

背景情况:

  • 人类面部表情识别在各种头部形状中是强大的.
  • 当前的机器学习模型在没有广泛的训练数据的情况下,在面部表情识别的域外转移方面扎.

研究的目的:

  • 将神经科学原理集成到计算机视觉模型中,以改进面部表情识别.
  • 开发一种方法,以高数据效率将学习的面部表情转移到新型头部形状.

主要方法:

  • 提出了一个基于标准引用编码的生物启发机制.
  • 将输入表示为与特定域的参考向量的偏差.
  • 假设偏离参考的偏差在各域中被保留,以实现概括.

主要成果:

  • 拟议的模型一般化为新的头部形状,只使用单个额外的训练图像.
  • 在具有高度变化的头形状的数据集上展示了概括能力和数据效率.
  • 在计算机视觉模型中展示了标准引用编码的可扩展性和有效性.

结论:

  • 受神经科学启发的规范引用编码显著增强了计算机视觉模型在面部表情识别方面的概括能力.
  • 这种方法提供了一个数据效率高的解决方案,用于将学习表达式转移到新领域.
  • 为面部图案识别和其他相关领域的更广泛应用铺平了道路.