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相关概念视频

Operant Conditioning Intervention01:24

Operant Conditioning Intervention

18
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
18
Reinforcement Schedules01:24

Reinforcement Schedules

107
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,...
107
Randomized Experiments01:13

Randomized Experiments

6.6K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.6K
Behavior Modification01:21

Behavior Modification

89
Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
89

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相关实验视频

Updated: May 7, 2025

A Protocol for Measuring Cue Reactivity in a Rat Model of Cocaine Use Disorder
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ReBandit:基于随机效应的在线RL算法,用于减少大麻使用

Susobhan Ghosh1, Yongyi Guo2, Pei-Yao Hung3

  • 1Department of Computer Science, Harvard University.

IJCAI : proceedings of the conference
|December 30, 2024
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概括
此摘要是机器生成的。

一个新的算法,reBandit,个性化移动健康干预措施,以减少新兴成年人大麻使用. 它在适应多样化的人口方面表现有前途,解决了一个关键的公共卫生挑战.

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科学领域:

  • 数字健康数字健康
  • 机器学习 机器学习
  • 公共卫生 公共卫生

背景情况:

  • 大麻使用和大麻使用障碍 (CUD) 是全球越来越多的公共卫生问题.
  • 存在很大的治疗差距,特别是在新兴成年人 (EA;年龄18-25岁) 中.
  • 解决CUD问题与联合国2030年可持续发展目标相一致.

研究的目的:

  • 开发和评估一个在线强化学习 (RL) 算法,reBandit,用于个性化的移动健康干预.
  • 通过定制的数字健康策略,减少新兴成年人中的大麻使用.
  • 评估reBandit在现实世界,杂的移动健康环境中的有效性.

主要方法:

  • 开发了reBandit,一个在线RL算法,结合了随机效应和有信息的贝叶斯先验.
  • 利用经验贝叶斯和优化自主超参数更新.
  • 使用先前的研究数据创建了一个模拟测试台,将reBandit与基线算法进行比较.

主要成果:

  • reBandit表现出与现有的移动健康算法相提并论或优于它们的性能.
  • 该算法的性能优势随着人口异质性增加而增加.
  • 在模拟中,reBandit在适应不同参与者群体方面表现得很好.

结论:

  • reBandit是一个有效的工具,可以为CUD提供个性化的移动健康干预.
  • 该算法的自适应性使得它适合异质人群.
  • 这种方法提供了一个有希望的策略,以解决CUD新兴成年人的治疗差距.