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相关概念视频

Observational Learning01:12

Observational Learning

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 because...

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相关实验视频

Updated: May 12, 2026

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

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一个强化学习和顺序采样模型,受凝视数据的限制.

William M Hayes1, Melanie J Touchard1

  • 1Psychology Department, Binghamton University State University of New York, Binghamton, New York, United States of America.

PLoS computational biology
|March 6, 2026
PubMed
概括
此摘要是机器生成的。

这项研究将强化学习与顺序采样模型相结合,通过整合眼睛凝视数据来增强预测. 这种新的方法揭示了视觉注意力和学习价值观如何共同塑造决策过程.

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Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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相关实验视频

Last Updated: May 12, 2026

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科学领域:

  • 认知科学 认知科学
  • 计算神经科学是一种神经科学.
  • 决策科学 决策科学 决策科学

背景情况:

  • 强化学习 (RL) 和顺序采样模型 (SSM) 用于分析选择反应时间 (RT) 数据.
  • 在重复的决策任务中,RL-SSM有效地捕捉了选择-RT模式.
  • 整合眼睛凝视数据与RL-SSM提供了一种新的方法来提高预测准确性.

研究的目的:

  • 开发和评估一个受约束的强化学习顺序采样模型 (RL-SSM),包括眼睛的目光数据.
  • 研究如何学习选项值和视觉注意力共同影响证据积累和选择.
  • 为了弥合RL和视觉注意力建模传统之间的差距.

主要方法:

  • 开发了一种新的计算模型,将RL-SSM与眼睛跟踪数据集成在一起.
  • 在两个眼睛追踪实验 (N=133) 的数据上评估模型性能.
  • 测试了具有不同价值观集成机制的模型变体.

主要成果:

  • 与标准RL-SSM相比,综合模型显著提高了预测能力.
  • 该模型成功地捕获了经验效应,包括对选择和响应时间的凝视偏差.
  • 在估值策略中展示了个体差异 (绝对与相对).

结论:

  • 用眼神视线数据限制RL-SSM可以提高对决策的理解.
  • 学习的价值观和视觉注意力有动态的相互作用,影响选择行为.
  • 统一模型为研究估值和选择注意力的相互作用提供了一个强大的工具.