认知过程模型揭示了青少年大脑的有意义的大脑行为关联认知发展研究停止信号任务
Anthony Barrows1, Alexander Weigard2, Matthew McCabe1
1University of Vermont.
Journal of cognitive neuroscience
|February 18, 2026
概括
计算认知模型比传统的测量方法更好地将大脑活动与行为联系起来. 正式模型参数解释了更多的大脑激活差异,揭示了对认知过程的见解,如证据积累和注意力缺失.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 了解大脑活动和行为的个体差异在神经成像中至关重要.
- 计算模型提供了对认知任务执行的机械洞察力.
研究的目的:
- 将一个正式的计算模型应用于青少年大脑认知发展研究的停止信号任务 (SST).
- 使用机器学习量化计算模型参数与大脑激活之间的关系.
- 通过表型测量来评估计算模型的构造有效性.
主要方法:
- 利用来自SST的9至11岁儿童行为和神经成像数据的大型数据集 (n=6469).
- 采用基于机器学习的预测建模方法,将模型参数与功能磁共振成像 (fMRI) 数据联系起来.
- 检查了模型参数与抑制,冲动性和认知的表型措施之间的关联.
主要成果:
- 正式的SST模型参数解释了fMRI激活 (R2高达26.86%) 与实证性能测量 (R2高达20.89%) 相比的显著更大的差异.
- 神经成像数据与"开始"选择的证据积累和停止时注意力缺失 ("触发失败") 密切相关.
- 突出网络活动与证据积累相关,而视觉皮层活动与注意力缺陷相关.
结论:
- 计算认知建模提供了一种更敏感的方法来将大脑功能与行为联系起来.
- 该研究通过与行为现象型的关联来验证计算模型的构造有效性.
- 这些发现强调了计算模型在发现发育过程中细微的脑行为关系方面的实用性.
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