通过多维缩放"插件"扩大引导搜索的计算能力
Collin Scarince1, Michael C Hout2,3
1Department of Psychology and Sociology, Texas A&M University-Corpus Christi, USA.
概括
这项研究引入了一种新的计算方法,将多维缩放 (MDS) 与引导搜索 (GS) 模型相结合. 这种方法增强了对复杂物体的视觉搜索研究,表明搜索在不相似的分心因素下更快.
科学领域:
- 认知心理学 认知心理学
- 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:
- 指导搜索 (GS) 模型使用简单的刺激 (线条,颜色) 进行视觉搜索.
- 对GS来说,对复杂的现实世界物体的视觉质量进行量化是具有挑战性的.
- 现有的GS模型与自然主义视觉刺激的复杂性作斗争.
研究的目的:
- 提出和测试一个修改的GS模型,包括多维缩放 (MDS).
- 为了使复杂刺激的视觉搜索能够进行计算分析.
- 研究刺激的相似性如何影响搜索效率.
主要方法:
- 应用多维缩放 (MDS) 来分析刺激相似性.
- 将MDS衍生的相似性数据集成到修改后的引导搜索 (GS) 模型中.
- 进行了两项实验,参与者在各种分心因素中寻找新奇的物体.
主要成果:
- 当分散注意力的因素与目标不同时,搜索效率会增加.
- 修改后的GS模型准确地预测了目标-当前搜索响应趋势.
- MDS有效量化了用于计算建模的刺激相似性.
结论:
- 多维缩放 (MDS) 是在视觉搜索中建模刺激相似性的可行方法.
- 拟议的GS"插件"将计算GS应用扩展到复杂的刺激.
- 这种方法为进一步研究没有目标的搜索模式提供了潜力.
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