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Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Vision01:24

Vision

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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.
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Visual System01:26

Visual System

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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...
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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.
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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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相关实验视频

Updated: Sep 16, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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在计算机视觉中对可解释的人工智能 (XAI) 的全面审查

Zhihan Cheng1,2, Yue Wu1,2, Yule Li3,4

  • 1Department of Mathematics, College of Science, Mathematics and Technology, Wenzhou-Kean University, Wenzhou 325060, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括
此摘要是机器生成的。

在计算机视觉中的可解释的人工智能 (XAI) 方法进行了比较. 基于变压器的方法在医学成像中表现有前途,尽管需要仔细解释.

关键词:
这是Grad-CAM.起 起 的意思计算机视觉 (CV) 计算机视觉可解释的人工智能 (XAI)混合解释性框架的混合解释性框架图像理解 (IU) 是指图像的理解.基于变压器的XAI

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 可解释的人工智能 (XAI)

背景情况:

  • 可解释的人工智能 (XAI) 对于理解复杂的计算机视觉模型至关重要.
  • 现有的XAI方法缺乏标准化的比较,阻碍了有效的应用.

研究的目的:

  • 进行计算机视觉中突出的XAI方法的比较分析.
  • 在关键指标上评估XAI技术并提出分类系统.

主要方法:

  • 将XAI方法分为基于归属的,基于激活的,基于扰动的和基于变压器的方法.
  • 使用诸如忠实度,本地化准确度,效率等指标进行评估,并与医学注释重叠.
  • 为 XAI 技术进行分类,制定一个层次分类法.

主要成果:

  • 基于扰乱的方法 (例如,RISE) 显示出高可靠性,但在计算上是密集的.
  • 基于变压器的方法在医学成像任务 (IoU分数) 中实现了高性能.
  • 没有任何一种方法在所有评估的指标中脱而出,突出了权衡.

结论:

  • 环境意识评估和混合XAI方法是必要的,以平衡可解释性和效率.
  • 标准化的基准和特定领域的调整对于推进XAI至关重要.
  • 解决伦理和实际挑战对于可靠的XAI部署至关重要.