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A perceptual paradigm and psychophysical evidence for hierarchy in scene gist processing

机译:场景要点处理中的层次结构的感知范式和心理物理学证据

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摘要

Abstract Abstract: Abstract?? What is the order of processing in scene gist recognition? Following the seminal studies by Rosch (1978) and Tversky and Hemmenway (1983) it has been assumed that basic-level categorization is privileged over the superordinate level because the former maximizes both within-category similarity and between-category variance. However, recent research has begun to challenge this view (Oliva & Torralba, 2001; Joubert, Rousselet, Fize, & Fabre-Thorpe, 2007; Loschky & Larson, 2010). Here we study these directions more fundamentally by investigating the perceptual relations between scene categories in a way that allows us to identify the order of processing of scene categories across taxonomic levels. We introduce the category discrimination paradigm where we briefly present two real scene stimuli simultaneously and ask human observers whether they belong to the same basic-level category or not (i.e., same/different task). As we show, analysis of the obtained data reveals a hierarchical perceptual structure between different scene categories and a corresponding hierarchical structure at the perceptual processing level. In particular, we show a new type of evidence to suggest that the decision whether the scene is manmade or natural is made first, and only then more complicated decisions are taken (such as whether a manmade scene is indoor or outdoor) among a smaller set of viable candidate categories. We argue that this hierarchical structure improves performance and efficiency in both biological and artificial gist recognition systems.
机译:摘要摘要:摘要?场景要点识别中的处理顺序是什么?根据Rosch(1978)和Tversky and Hemmenway(1983)的开创性研究,我们假定基本级别的分类优先于上级的级别,因为前者最大化了类内相似度和类间方差。但是,最近的研究已经开始挑战这一观点(Oliva和Torralba,2001; Joubert,Rousselet,Fize和Fabre-Thorpe,2007; Loschky和Larson,2010)。在这里,我们通过调查场景类别之间的知觉关系来更根本地研究这些方向,该方式使我们能够确定分类标准中场景类别的处理顺序。我们引入类别歧视范式,在该类别中我们同时简要介绍两个真实场景刺激,并询问人类观察者它们是否属于同一基本级别类别(即相同/不同的任务)。如我们所示,对获得的数据的分析揭示了不同场景类别之间的分层感知结构以及在感知处理级别上的相应分层结构。特别是,我们显示了一种新的证据,建议首先确定场景是人为还是自然场景,然后才在较小的场景中做出更复杂的决定(例如,人为场景是室内还是室外)。可行的候选人类别。我们认为这种层次结构可以提高生物学和人工要点识别系统的性能和效率。

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