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相关概念视频

Cognitive Learning01:21

Cognitive Learning

229
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...
229
Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

451
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...
451
Purposive Learning01:22

Purposive Learning

104
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...
104
Observational Learning01:12

Observational Learning

149
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
149
Cognitivism01:17

Cognitivism

1.4K
Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
Previously dominated by behaviorism, which prioritized observable behaviors and largely ignored mental processes, psychology transformed in the 1950s. Cognitive psychologists argue that understanding how we think and process...
1.4K
Metacognition01:26

Metacognition

142
Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
142

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相关实验视频

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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概率编程与元学习作为认知模型.

Desmond C Ong1, Tan Zhi-Xuan2, Joshua B Tenenbaum2

  • 1Department of Psychology, University of Texas at Austin, Austin, TX, USA desmond.ong@utexas.edu https://cascoglab.psy.utexas.edu/desmond/.

The Behavioral and brain sciences
|September 23, 2024
PubMed
概括

概率编程为理解人类认知提供了一个统一的框架. 整合连接主义和贝叶斯方法可以增强元学习方法.

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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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科学领域:

  • 认知科学 认知科学
  • 人工智能的人工智能
  • 计算神经科学是一种神经科学.

背景情况:

  • 人类的认知整合了概率,象征和数据驱动的元素.
  • 概率编程 (PP) 已经成为一个强大的形式主义来建模这些方面.
  • 现有的认知建模方法往往侧重于特定的方面,缺乏统一的框架.

研究的目的:

  • 总结最近在认知科学中的概率编程方面的进展.
  • 将概率编程与元学习进行比较,强调关键差异.
  • 通过结合各种理论观点,提出对元学习的改进建议.

主要方法:

  • 关于认知建模中的概率编程的最新文献的综述.
  • 对概率编程和元学习框架的比较分析.
  • 在元学习中,连接主义和贝叶斯主义方法的概念整合.

主要成果:

  • 概率编程为认知的概率,象征和数据驱动方面提供了一个统一的形式主义.
  • 关于灵活性,统计假设和关于认知单元 (cognitons) 的推断,PP和元学习之间存在显著差异.
  • 通过结合连接主义和贝叶斯观点,可以潜在地改进元学习.

结论:

  • 概率编程代表了迈向统一认知理论的重要一步.
  • 通过采用更广泛的计算和统计方法,可以实现更强大的元学习框架.
  • 未来的研究应该探索在认知科学中整合各种建模范式的协同潜力.