在自然场景中注意力部署:高阶场景统计而不是语义调节N2pc组件
Daniel Walper1,2, Alexandra Bendixen3,4, Sabine Grimm1,3,5
1Physics of Cognition Group, Chemnitz University of Technology, Chemnitz, Germany.
Journal of vision
|June 7, 2024
概括
在视觉搜索过程中,自然场景比噪音更容易分散注意力,原因是高阶统计数据,而不是语义或布局. 这会影响注意力选择和反应时间.
科学领域:
- 认知心理学 认知心理学
- 神经科学是一个神经科学.
- 计算机视觉 计算机视觉
背景情况:
- 在自然场景中进行视觉搜索是复杂的.
- 了解影响搜索难度的场景属性至关重要.
研究的目的:
- 调查低级统计,高级统计,语义或布局是否会影响自然场景中的视觉搜索难度.
- 为了确定哪些场景属性使自然场景成为有效的分心器.
主要方法:
- 进行了三项实验,参与者寻找Gabor补丁目标.
- 背景在保存属性方面有所不同:自然场景,1/f噪声 (低级统计),纹理 (低级和高级统计) 和倒置场景 (统计和语义).
- 与事件相关的潜力 (ERP),特别是N2pc组件,被测量为注意力选择的标志物.
主要成果:
- 与自然场景和纹理相比,对噪音背景的目标的反应时间更快,对倒置场景的反应时间更慢.
- 与其他背景相比,N2pc组件在噪声中显示了较短的延迟和更高的振幅,用于噪声中的目标.
- 与目标侧相反的背景影响了与目标侧类似的性能,噪音促进了更快的反应和更短的N2pc延迟.
结论:
- 在寻找简单目标时,自然场景比噪音更有效地分散注意力.
- 这种分心效应源于更高层次的场景统计数据,而不是语义内容或空间布局.
- 场景统计,特别是更高阶的场景统计,在调节视觉搜索效率和注意力过程中发挥着重要作用.
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