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相关概念视频

Deductive Reasoning01:16

Deductive Reasoning

55.3K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
378
Inductive Reasoning00:59

Inductive Reasoning

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Reasoning01:30

Reasoning

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
78
Cognitive Learning01:21

Cognitive Learning

243
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...
243
Observational Learning01:12

Observational Learning

179
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...
179

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相关实验视频

Updated: Jul 6, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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用监督对比学习进行图形推理,用于法律判决预测.

Jiawei Wang, Yuquan Le, Da Cao

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    概括
    此摘要是机器生成的。

    本研究介绍了GraSCL,这是一种新的图形推理和监督对比学习框架,用于法律判断预测. 它有效地建模了标签依赖性,提高了法律条款,指控和处罚的预测准确性.

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    科学领域:

    • 人工智能的人工智能
    • 自然语言处理自然语言处理.
    • 机器学习 机器学习

    背景情况:

    • 法律判断预测 (LJP) 涉及从案件事实中确定法律条款,指控和处罚.
    • 现有的LJP模型经常忽视不同预测任务中的标签依赖性.
    • 有效地利用任务和标签之间的关系信息对LJP至关重要.

    研究的目的:

    • 为LJP提出一个新的框架,GraSCL,它集成了图形推理和监督对比学习 (SCL).
    • 通过明确建模标签依赖来解决现有方法的局限性.
    • 提高法律判断预测的准确性和效率.

    主要方法:

    • 在图形推理框架内将LJP转化为节点分类问题.
    • 设计了一个图形推理网络,以捕捉依赖结构和关系学习.
    • 扩展监督对比学习 (SCL) 到节点级,以提供高效的培训.
    • 整合在线硬负面挖掘 (OHNM) 通过专注于具有挑战性的负样本来优化SCL.

    主要成果:

    • 拟议的GraSCL框架显示了LJP任务的显著改进.
    • 图形推理有效地建模了跨任务和跨标签的依赖关系.
    • 节点级的SCL和OHNM增强了模型性能,特别是在小批量.
    • 在基准数据集上的实验结果验证了与最先进的方法相比的有效性.

    结论:

    • 通过利用图形推理和对比学习,GraSCL提供了一种强大的法律判断预测方法.
    • 建模标签依赖性是提高LJP准确性的关键.
    • SCL和OHNM的整合为复杂的预测任务提供了高效和有效的培训策略.