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相关概念视频

Reinforcement01:23

Reinforcement

190
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
190
Reinforcement Schedules01:24

Reinforcement Schedules

138
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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Law of Effect01:06

Law of Effect

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B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
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Bootstrapping01:24

Bootstrapping

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
592
Primary and Secondary Reinforcers01:23

Primary and Secondary Reinforcers

218
In psychology, reinforcement is a key concept in behavior modification. B.F. Skinner demonstrated this with his experiments involving rats in what is known as a Skinner box. The rats learned to press a lever to receive food, a primary reinforcer that fulfilled their innate need for nourishment.
Effective reinforcers for humans vary depending on the individual and the context. Primary reinforcers, such as food, water, sleep, shelter, and pleasure, have inherent value and satisfy basic biological...
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Modeling in Therapy01:26

Modeling in Therapy

59
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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相关实验视频

Updated: Jun 14, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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我们有个性化吗? 通过使用重新采样的在线强化学习算法来评估个性化.

Susobhan Ghosh1, Raphael Kim2, Prasidh Chhabria3

  • 1Department of Computer Science, Harvard University.

Machine learning
|September 2, 2024
PubMed
概括
此摘要是机器生成的。

强化学习 (RL) 可以个性化数字健康治疗,但其有效性需要验证. 这项研究引入了一种方法来确认RL个性化是否真实,还是其固有的随机性的工件.

关键词:
强化学习是一种强化学习.探索性数据分析数据分析.移动健康的移动健康个性化个性化个性化在重新抽样时进行重新抽样.

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相关实验视频

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科学领域:

  • 数字健康干预措施 数字健康干预措施
  • 医疗保健中的机器学习
  • 行为科学 行为科学

背景情况:

  • 越来越多的人对使用强化学习 (RL) 进行个性化的数字健康治疗,以促进更健康的行为感兴趣.
  • 数字健康中的顺序决策依赖于治疗时间和类型的用户环境.
  • 在线RL提供了一种数据驱动的方法,以根据用户响应来个性化治疗.

研究的目的:

  • 评估数据证据,证实RL算法真正为用户个性化治疗.
  • 调查观察到的RL个性化是否是算法的随机性的一个工件.
  • 引入基于重新抽样的方法来评估RL个性化.

主要方法:

  • 为RL算法开发了个性化工作定义.
  • 引入了基于重新抽样的方法来区分真正的个性化和算法随机性.
  • 将该方法应用于HeartSteps体育活动临床试验中的数据.

主要成果:

  • 展示了一种严格评估在线RL算法的个性化能力的方法.
  • 案例研究说明了该方法如何增强对算法个性化的评估.
  • 个性化在所有用户和个人用户数据中都得到了评估.

结论:

  • 拟议的方法为数字健康中的RL个性化数据驱动的真相广告提供了一个框架.
  • 验证RL个性化对于优化数字健康干预措施的伦理和有效部署至关重要.
  • 这种方法有助于确保RL算法为行为变化提供有意义的,个性化的支持.