在动态的现实世界场景中,物体指导人类的目光行为
Nicolas Roth1,2, Martin Rolfs1,3,4, Olaf Hellwich1,5
1Cluster of Excellence Science of Intelligence, Technische Universität Berlin, Germany.
PLoS computational biology
|October 26, 2023
概括
人类在动态场景中的目光行为更好地通过基于对象的注意力而不是仅仅是空间突出性来解释. 结合对象级关注单元和基于突出性的优先级的模型最能复制人类扫描路径数据.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 计算机视觉 计算机视觉
背景情况:
- 在复杂的自然场景中,人类的目光行为很难通过实验来研究.
- 从历史上看,人们认为注意力是由空间突出驱动的,但基于对象的注意力越来越受支持.
- 了解注意力指导对于模拟现实的人类视觉感知至关重要.
研究的目的:
- 开发和评估用于模拟动态场景中人类目光行为的计算框架.
- 研究基于对象的注意力与基于空间的注意力在指导眼睛运动中的相对重要性.
- 使用现实的扫描路径模拟来比较不同的注意力模型.
主要方法:
- 开发了一个模块化计算框架,模拟了冲刺时间和平稳的追逐.
- 实施了五种不同的模型:纯粹的空间 (低/高突出) 和基于对象的 (有/没有突出),以及混合模型.
- 利用进化算法来优化模型参数与人类扫描路径数据分布.
主要成果:
- 一个基于对象的注意力模型,具有以突出为导向的优先级和回归抑制,显示出与人类扫描路径统计数据的最高相似性.
- 这个模型准确地重现了空间和时间固定行为,包括对象交互.
- 只有空间模型在捕捉人类凝视模式方面表现不那么有效.
结论:
- 对象级注意力单位对于在动态环境中指导人类注视行为至关重要.
- 计算模型从整合基于对象的注意力和选择机制中获得了显著的好处.
- 这些发现表明,在视觉注意力研究中,从纯粹基于位置的转向以对象为中心的处理.
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