人类注意力的新算法
Ilker Yildirim1, Mario Belledonne1
1Department of Psychology, Yale University, New Haven, CT, USA ilker.yildirim@yale.edu mario.belledonne@yale.edu.
The Behavioral and brain sciences
|November 26, 2025
概括
目标通过分配计算资源来指导感知. 自适应计算根据其决策影响优先考虑感知任务,为注意力控制提供了新的解释.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 计算视觉 计算机视觉 计算机视觉
背景情况:
- 目标显著影响个人如何处理感官信息.
- 现有的模型通常通过特定任务的机制或感知计算规范来解释这一点.
- 对目标导向注意力的统一算法解释仍然是一个活跃的研究领域.
研究的目的:
- 提出和研究自适应计算作为一个统一的算法框架的注意力.
- 解释感知处理是如何被当前目标调节的.
- 展示如何根据决策相关性分配注意力资源.
主要方法:
- 开发了一个新的自适应计算算法模型.
- 在计算资源有限的情况下,模拟感知任务.
- 分析了资源分配如何影响任务绩效和决策结果.
主要成果:
- 适应计算有效地根据它们对决策的有用性来分配感知资源.
- 该模型解释了注意力如何优先考虑与实现当前目标相关的信息.
- 资源分配随着感知计算对决策的影响的变化而变化.
结论:
- 适应计算为目标导向的注意力提供了一个节的解释.
- 这一框架表明注意力是一个积极的配给过程,而不仅仅是选择.
- 未来的研究应该探索适应计算在人类感知和决策中的经验验证.
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