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相关概念视频

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

578
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
578
Observational Learning01:12

Observational Learning

188
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...
188
Associative Learning01:27

Associative Learning

412
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...
412
Introduction to Learning01:18

Introduction to Learning

446
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...
446
Purposive Learning01:22

Purposive Learning

123
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...
123
Cognitive Learning01:21

Cognitive Learning

249
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...
249

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Brenden M Lake1, Marco Baroni2,3

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此摘要是机器生成的。

神经网络可以通过优化其构成技巧来实现类似人类语言和思维的系统性. 构成性的元学习 (MLC) 方法使网络能够灵活地泛化,解决人工智能的长期挑战.

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科学领域:

  • 认知科学
  • 人工智能
  • 计算语言学

背景情况:

  • 人类的认知依赖于系统的构成,使已知元素的新组合成为可能.
  • 福多尔和皮利希恩的挑战是,人工神经网络缺乏这种系统性,限制了它们作为思维模型的可行性.
  • 尽管取得了进展,但在神经网络中实现系统的概括仍然是一个持续的挑战.

研究的目的:

  • 证明神经网络可以实现类似人类的系统性.
  • 引入和评估组合性 (MLC) 的元学习方法.
  • 将MLC的概括能力与其他模型和人类性能进行比较.

主要方法:

  • 开发了组合性的元学习 (MLC) 方法,指导神经网络训练与多种组合任务.
  • 使用指令学习模式进行人类行为实验.
  • 评估了七种不同的模型,包括MLC,概率符号模型和标准神经网络,以系统的概括基准.

主要成果:

  • MLC成功地实现了系统性和灵活性,超过了严格的符号模型和非系统的神经网络.
  • 在对比中,MLC表现出类似人类的概括能力.
  • 在多个基准中,MLC显著提高了机器学习系统的构成技能.

结论:

  • 通过对神经网络进行优化,
  • 这种方法为开发更有能力,更像人类的人工智能提供了可行的方法.
  • 这项研究弥合了人造神经网络与人类思维和语言的系统性之间的差距.