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相关概念视频

Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Cognitive Learning01:21

Cognitive Learning

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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...
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Causality in Epidemiology01:21

Causality in Epidemiology

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Cognitivism01:17

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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...
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Cognitive Theories: Lazarus Mediational Theory of Emotion01:17

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Richard Lazarus' cognitive mediational theory highlights the pivotal role of cognitive appraisal in shaping emotional responses. According to this theory, the evaluation of a stimulus — based on personal values, goals, beliefs, and expectations — mediates the emotional response. This appraisal process is immediate and often occurs unconsciously, influencing the intensity and nature of the resulting emotion.
Cognitive Appraisal and Emotional Response
Lazarus proposed that...
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Beck's Cognitive Therapy01:25

Beck's Cognitive Therapy

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Cognitive therapy is a psychological approach designed to address distortions in thinking, which can lead to negative emotions and unrealistic beliefs. These cognitive distortions often influence how individuals interpret and respond to situations, exacerbating emotional distress. Below are some prevalent cognitive distortions, their characteristics, and examples of how they manifest in thought processes.
Arbitrary Inference
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Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
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为因果认知解脱纠的表征.

Filippo Torresan1, Manuel Baltieri2

  • 1University of Sussex, Falmer, Brighton, BN1 9RH, United Kingdom.

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PubMed
概括
此摘要是机器生成的。

这项研究将因果认知和机器学习联系在一起,以解释代理人如何学习因果关系. 它提出了一个统一的计算框架,用于理解动物和人工智能的因果学习.

关键词:
动物认知动物的认知能力.因果认知是一种因果认知.因果强化学习的学习解开纠的表示形式.解脱纠 纠 解开纠

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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
  • 机器学习 机器学习

背景情况:

  • 复杂的适应性代理解决需要对代理-环境系统的因果信息的问题.
  • 因果认知研究描述了学习和推理,但缺乏获得因果理解的计算账户,而没有先前的知识.
  • 机器学习,特别是解,为学习因果关系的代理提供计算模型.

研究的目的:

  • 开发一个统一的因果认知计算框架.
  • 连接动物认知和机器学习的研究.
  • 为开发新的因果强化学习算法提供见解.

主要方法:

  • 将因果认知的心理/行为研究与机器学习方法联系起来.
  • 使用基于干预的正式因果关系模型 (例如,因果贝叶斯网络).
  • 研究解作为一种在人工智能中构建因果表征的过程.

主要成果:

  • 这项研究提出了一个关于因果认知的新的计算视角.
  • 它将自然和人工系统中的因果关系的理解联系在一起.
  • 它为能够进行因果学习的先进AI算法奠定了基础.

结论:

  • 介绍了因果认知的统一框架,结合了动物研究和人工智能.
  • 这个框架提供了一个计算镜头来理解代理人如何从头开始学习因果关系.
  • 这项研究有助于开发更复杂的人工智能系统,能够进行因果推理.