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相关概念视频

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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通过基于激活和基于体重的适应,认知灵活性与稳定性相比.

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  • 1Department of Experimental Psychology, Ghent University, Ghent, Belgium. sjx667@gmail.com.

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此摘要是机器生成的。

这项研究引入了一个神经网络模型,解释了认知控制如何以不同的速度适应. 它将基于激活的快速适应与基于体重的慢速适应区分开来,反映了人类的灵活性-稳定性转变.

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科学领域:

  • 认知神经科学 认知神经科学
  • 计算神经科学是一种神经科学.
  • 人工智能的人工智能

背景情况:

  • 人类的认知控制涉及平衡灵活性和稳定性的任务切换.
  • 控制适应发生在多个时间尺度上,这表明不同的潜在机制.

研究的目的:

  • 开发一个循环神经网络模型,模拟认知灵活性和稳定性动态.
  • 研究快速 (基于激活) 和缓慢 (基于重量) 控制适应背后的计算机制.

主要方法:

  • 开发了一个循环神经网络模型来模拟认知控制的行为指数.
  • 进行了四项模拟研究来分析模型动态.
  • 使用现有的人类数据集 (N=102) 验证的模型预测.

主要成果:

  • 基于激活的适应促进了认知灵活性的快速转变.
  • 基于权重的适应,取决于控制设置,表现出较慢的学习动态.
  • 该模型学会了将上下文与控制设置联系起来,模仿人类上下文依赖的灵活性-稳定性转移.

结论:

  • 集成快速和慢速的控制适应到一个统一的计算框架.
  • 对人类适应性行为和调节机制的高级理解.
  • 突出了基于激活和基于体重的适应在认知控制中的不同作用.