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相关概念视频

Purposive Learning01:22

Purposive Learning

119
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...
119
Cognitive Learning01:21

Cognitive Learning

239
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...
239
Upsampling01:22

Upsampling

232
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
232
Observational Learning01:12

Observational Learning

170
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...
170
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

237
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
237
Sampling Theorem01:15

Sampling Theorem

335
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
335

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Updated: Jun 29, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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通过故意低样本进行因果学习.

Kseniya Solovyeva1, David Danks2, Mohammadsajad Abavisani3

  • 1TReNDS center, Georgia State University, Atlanta.

Proceedings of machine learning research
|March 28, 2024
PubMed
概括
此摘要是机器生成的。

更慢的测量系统可以透露更多关于因果机制的信息. 我们的研究引入了一种用于因果结构推断的新算法,使用来自多个测量时间尺度的数据,挑战了更快总是更好的假设.

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科学领域:

  • 因果推理的原因推理.
  • 动态系统分析 动态系统分析
  • 时间序列分析时间序列分析.

背景情况:

  • 科学家经常面临社会,物理和生物系统的测量频率的限制.
  • 一个普遍的假设是,为了获得更丰富的因果结构数据,需要更高的测量频率.

研究的目的:

  • 挑战这样的假设,即更高的测量频率总是因果推理的最佳.
  • 为了证明测量系统的速度较慢可以产生关于因果结构的额外信息.
  • 通过多个时间尺度的数据推断因果结构的新算法.

主要方法:

  • 开发一种利用多个测量时间尺度的图形表示的算法.
  • 通过结合来自较慢测量频率的数据来推断潜在的因果结构.
  • 模拟研究,以评估从故意低采样中获得的收益的概率和规模.

主要成果:

  • 证明,在某些情况下,更慢的测量系统可以提供更多关于因果结构的信息数据.
  • 包括较慢的时间尺度结构可以减少可能的因果模型的模糊性 (等效类大小).
  • 模拟数据量化了以下样本改善因果推断的条件和程度.

结论:

  • 假设更快的测量对于理解因果机制总是优越的假设是有缺陷的.
  • 整合来自较慢时间尺度的数据提供了一种可行的策略,用于增强因果结构推理.
  • 开发的算法提供了一种利用多时间尺度数据的实用方法,以提高对复杂系统的科学理解.