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
Shengjie Xu1, Tom Verguts2, Senne Braem2
1Department of Experimental Psychology, Ghent University, Ghent, Belgium. sjx667@gmail.com.
Communications psychology
|January 21, 2026
概括
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.
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.
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