通过调整输入到证据积累过程中,平衡间歇性感官目标的真假检测
Anna C Geuzebroek1, Hannah Craddock1,2, Redmond G O'Connell3
1School of Electrical and Electronic Engineering and UCD Centre for Biomedical Engineering, University College Dublin, Dublin, Ireland.
eLife
|August 30, 2023
概括
这项研究揭示了大脑如何调整决策限制,以检测持续运动中的目标. 与预期相反,神经信号表明,当目标强而不是弱时,决策边界会降低.
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.
背景情况:
- 证据积累模型解释了不确定性下的决策.
- 持续监控和间歇性目标检测的机制仍然不太了解.
- 据认为,决策边界根据任务需求进行调整.
研究的目的:
- 研究神经决策过程,用于在连续运动中检测目标.
- 检查决策边界如何适应不同的目标强度 (弱,强,混合).
- 开发和测试决策政策监管的神经信息模型.
主要方法:
- 行为实验测量命中和错误报警率.
- 电脑电图 (EEG) 用于记录神经活动,特别是β频段振荡.
- 计算建模,包括漏电蓄积器和不漏电蓄积器模型.
主要成果:
- 行为数据与一个模型保持一致,其中边界减少,弱目标的流行率更高.
- 贝塔频段EEG数据在强的目标环境中显示了下限,这与行为预测相矛盾.
- 一个可调节的感觉标准引用的非泄漏蓄积器模型最好地解释了行为和神经数据.
结论:
- 神经动力学,而不仅仅是行为模式,对于理解决策绑定调整至关重要.
- 决策政策监管可以涉及无泄漏积累和可调节的感觉标准.
- 神经信息计算建模为认知控制机制提供了新的见解.
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