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

Vision01:24

Vision

53.4K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
53.4K
Visual System01:26

Visual System

585
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
585
Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

3.9K
The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex....
3.9K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

661
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
661
Parallel Processing01:20

Parallel Processing

152
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
152
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.4K
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,...
5.4K

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

Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training.

Nature communications·2026
Same author

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same author

A collaborative guide to Rapid Invisible Frequency Tagging (RIFT): Methods, insights, and recommendations.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Using Steady-State Visual Evoked Potentials to Characterize Wide-Ranging Retinopathy Linked to <i>CRB1</i>: Implications for Clinical Trials.

Computational and structural biotechnology journal·2026
Same author

Autistic individuals benefit from gestures during degraded speech comprehension.

Autism : the international journal of research and practice·2024
Same author

Explaining the Sentence Superiority Effect and N400s Elicited by Words and Short Sentences with OB1-Reader.

Journal of cognition·2024

相关实验视频

Updated: Jul 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

557

人类视觉皮层和深层卷积神经网络深入关注对象背景.

Jessica Loke1, Noor Seijdel1, Lukas Snoek1

  • 1University of Amsterdam.

Journal of cognitive neuroscience
|January 2, 2024
PubMed
概括
此摘要是机器生成的。

深层卷积神经网络 (DCNNs) 和人类大脑在早期视觉阶段优先考虑对象背景处理而不是对象类别. 图形-地面分离是DCNN和视觉皮层对象识别的关键.

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

8.3K

相关实验视频

Last Updated: Jul 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

557
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

8.3K

科学领域:

  • 神经科学是一个神经科学.
  • 计算机视觉 计算机视觉
  • 认知科学 认知科学

背景情况:

  • 深层卷积神经网络 (DCNNs) 在对象分类中部分预测大脑活动.
  • 驱动DCNN预测能力的因素仍然不清楚.
  • 图形与地面的分离对于人类对象识别至关重要.

研究的目的:

  • 在对象分类中调查影响DCNN预测能力的因素.
  • 确定图形-地面分离是否解释了DCNN预测大脑活动的能力.
  • 将DCNN和人类EEG对物体背景的反应与类别进行比较.

主要方法:

  • 将四个DCNN架构与62名人类参与者的EEG数据进行比较.
  • 使用与相同的物体在各种背景的刺激来隔离背景影响.
  • 分析了早期EEG活动 (<100毫秒) 和早期DCNN层.

主要成果:

  • 早期的EEG活动和DCNN层的过程对象背景,而不是类别.
  • 脑电图活动的DCNN预测是由背景处理驱动的.
  • 与未经训练的网络相比,训练有素的DCNN显示出不同的激活,突出显示了图形-地面隔离的作用.

结论:

  • 人类视觉皮层和DCNN都优先考虑对象分类的图形-地面分离.
  • 对象背景处理是生物和人工视觉系统共享的基本机制.
  • 图形-地面分离可能是对象特征识别的先决条件.