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相关概念视频

Purposive Learning01:22

Purposive Learning

93
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...
93
Cognitive Learning01:21

Cognitive Learning

108
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...
108
Introduction to Learning01:18

Introduction to Learning

309
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...
309
Observational Learning01:12

Observational Learning

106
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...
106
Evolutionary Psychology01:20

Evolutionary Psychology

201
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
201
Piaget's Theory of Cognitive Development from Childhood into Adulthood01:25

Piaget's Theory of Cognitive Development from Childhood into Adulthood

51
Jean Piaget's theory of cognitive development emphasizes the role of thinking in a child's learning process, suggesting that children are naturally curious about their environment. His approach to development is discontinuous, proposing that cognitive abilities progress through distinct stages, each with unique characteristics. Central to Piaget's theory is schemata—mental structures that allow individuals to understand and interpret the world.
Schemata: Building Blocks of...
51

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相关实验视频

Updated: May 15, 2025

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

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在持续学习中进行探索和利用.

Kiseong Hong1, Hyundong Jin2, Sungho Suh3

  • 1Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea.

Neural networks : the official journal of the International Neural Network Society
|April 8, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种适应性探索-利用方法,用于持续学习 (CL). 它优化了对新任务的参数分配,显著优于现有的CL方法.

关键词:
持续的学习 持续的学习参数隔离的参数是资源分配资源的分配.

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相关实验视频

Last Updated: May 15, 2025

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Published on: June 1, 2015

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 持续学习 (CL) 旨在通过参数隔离来防止灾难性遗忘.
  • 现有的方法经常使用固定的参数比率,忽视特定任务的需求,导致性能低于最佳.

研究的目的:

  • 开发一种新的探索-利用方法,用于持续学习中的适应性参数分配.
  • 让持续学习者能够动态地平衡获取新信息 (探索) 和保留旧知识 (利用).

主要方法:

  • 引入了一个适应性的探索-利用战略,用于持续学习中的资源分配.
  • 开发了一个分配学习器,以管理所有层次的勘探和开采之间的相互作用.
  • 在各种CL场景中对图像分类基准的方法进行了评估,包括域移动任务增量学习.

主要成果:

  • 拟议的勘探开发方法适应地为每个新任务分配参数.
  • 与现有的持续学习方法相比,表现出优异的表现.
  • 在各种场景中实现了5.3%的平均绩效改进.

结论:

  • 使用探索-利用的自适应参数分配对持续学习是有效的.
  • 拟议的方法解决了固定参数比率方法的局限性.
  • 这项工作通过优化各种任务的资源管理来推进持续学习.