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相关概念视频

Cognitive Learning01:21

Cognitive Learning

672
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...
672
Associative Learning01:27

Associative Learning

608
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...
608
Observational Learning01:12

Observational Learning

323
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...
323
Purposive Learning01:22

Purposive Learning

212
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...
212
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

89
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
89
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

977
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
977

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相关实验视频

Updated: Sep 20, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

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一个针对非强化学习的个体差异的计算模型.

Tom Salomon1, Alon Itzkovitch1, Nathaniel D Daw2

  • 1School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Sciences, Tel Aviv University.

Journal of experimental psychology. General
|May 22, 2025
PubMed
概括
此摘要是机器生成的。

一个新的计算模型在Cue-Approach Training (CAT) 期间量化了内部学习信号. 该模型成功预测并影响了偏好变化,突出了决策中的内在学习过程.

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
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科学领域:

  • 认知心理学 认知心理学
  • 计算神经科学是一种神经科学.
  • 行为经济学是一种行为经济学.

背景情况:

  • 暗示方法培训 (Cue-Approach Training,CAT) 增强了没有外部奖励的偏好,暗示了内部学习机制.
  • 了解这些内在的学习信号对于解释决策和动机至关重要.

研究的目的:

  • 开发一个贝叶斯计算模型来量化CAT期间的预测响应模式.
  • 识别反映项目级内部学习信号的计算标记.
  • 测试这个标记在驾驶偏好变化中的因果作用.

主要方法:

  • 开发了一种新的贝叶斯计算模型来分析CAT的响应模式.
  • 将模型与28个之前的CAT实验的元分析数据相匹配.
  • 进行了两项新的实验,操纵训练程序以影响模型预测的学习标记.

主要成果:

  • 计算模型成功预测了非强化偏好变化中的个体差异.
  • 正如预测的那样,操纵训练程序引发了差异偏好变化.
  • 这些结果支持已识别的计算标记物的因果作用.

结论:

  • 开发的计算框架为调查内在学习过程提供了一个强大的工具.
  • 这种模型可以预测偏好变化,进步我们对内在动机和决策的理解.
  • 这些发现为认知科学和行为经济学的研究开辟了新的途径.