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相关概念视频

Cognitive Learning01:21

Cognitive Learning

246
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
246
Purposive Learning01:22

Purposive Learning

121
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
121
Perception01:28

Perception

468
Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
468
Parallel Processing01:20

Parallel Processing

155
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...
155
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

823
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
823
Introduction to Learning01:18

Introduction to Learning

441
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
441

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相关实验视频

Updated: Jul 9, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

人类的感知学习:一个活跃的,自上而下的指导过程.

Heleen A Slagter1

  • 1Department of Cognitive Psychology, Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands h.a.slagter@vu.nlhttps://research.vu.nl/en/persons/heleen-slagter.

The Behavioral and brain sciences
|December 6, 2023
PubMed
概括

开发类似人类视觉的深度神经网络 (DNN) 需要超越白板训练. 相反,DNN应该包含自上而下的指导,反映人类感知学习以获得更准确的模型.

科学领域:

  • 认知科学 认知科学
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 目前用于人类视觉的深度神经网络 (DNN) 模型通常使用与视觉图像训练数据的"白板"方法.
  • 这种方法与人类感知和感知学习的既定理论形成鲜明对比.

研究的目的:

  • 为人类视觉建模提出DNN开发中的转变.
  • 在人工视觉系统中整合主动的,自上而下的感知原则.

主要方法:

  • 审查关于人类感知和感知学习的文献.
  • 基于主动感知原则,对训练DNNs的修订方法进行概念化.

主要成果:

  • 标准的DNN培训可能无法完全捕捉类似人类的视觉处理.
  • 一种主动的,自上而下的指导方法被认为是开发更类似于人类的DNN视觉模型的关键.

结论:

  • 开发精确模拟人类视觉的DNN需要从被动的,自下而上的处理中脱离.
  • 未来的DNN架构应采用活跃的,由人类感知学习提供信息的自上而下的引导机制.

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