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相关概念视频

Associative Learning01:27

Associative Learning

428
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...
428
Modeling in Therapy01:26

Modeling in Therapy

114
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...
114
Modeling and Similitude01:12

Modeling and Similitude

284
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
284
Cognitive Learning01:21

Cognitive Learning

278
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...
278
Purposive Learning01:22

Purposive Learning

135
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...
135
Observational Learning01:12

Observational Learning

202
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...
202

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相关实验视频

Updated: Jul 15, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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通过积极学习进行预测性规模桥梁模拟.

Satish Karra1,2, Mohamed Mehana3, Nicholas Lubbers4

  • 1Energy and Natural Resources Security Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

Scientific reports
|September 27, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种主动学习方法,通过将细度分子动力学与粗度水力学相结合来改进计算模拟. 这种方法可以提高复杂系统的物理保真性,例如纳米孔介质和聚变能源研究.

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科学领域:

  • 计算科学 计算科学
  • 多尺度建模多尺度建模
  • 积极学习是积极学习.

背景情况:

  • 越来越多的计算能力需要先进的方法,超出了粗暴武力模拟.
  • 精确建模像纳米孔介质运输和惯性封闭融合等现象,需要结合分子层面的相互作用.

研究的目的:

  • 开发一种新的主动学习方法来优化微量级模拟,以告知粗量级水力学.
  • 通过桥梁尺度来增强计算模型中的物理忠实性.

主要方法:

  • 利用主动学习指导执行局部微量分子动力学计算.
  • 预测连续粗度轨迹,以投机地启动新的细度模拟.
  • 动态更新基于细度结果的大规模模型.
  • 在神经网络模型中量化不确定性.

主要成果:

  • 开发了一种集成多尺度模拟的新功能.
  • 该方法有效地使用细度数据来增强粗度水力动力学预测.
  • 在预测模型中已经解决了不确定性量化问题.

结论:

  • 开发的积极学习框架为提高计算模拟的准确性和效率提供了一个强大的策略.
  • 这种方法可以在需要多尺度分析的领域进行更精确的预测,例如能源提取和核聚变研究.