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基于客体的注意的回归:多区域客体的选择

The return of object-based attention: selection of multiple-region objects.

作者信息

Matsukura Michi, Vecera Shaun P

机构信息

Department of Psychology, University of Iowa, E11 Seashore Hall, Iowa City, IA 52242-1407, USA.

出版信息

Percept Psychophys. 2006 Oct;68(7):1163-75. doi: 10.3758/bf03193718.

Abstract

Objects can control the focus of attention, allowing features on the same object to be selected more easily than features on different objects. In the present experiments, we investigated the perceptual processes that contribute to such object-based attentional effects. Previous research has demonstrated that object-based effects occur for single-region objects but not for multiple-region objects under some conditions (Experiment 1, Watson & Kramer, 1999). Such results are surprising, because most objects in natural scenes are composed of multiple regions. Previous findings could therefore limit the usefulness of an object-based selection mechanism. We explored the generality of these single-region selection results by manipulating the extent to which different (i.e., multiple) regions of a single object perceptually grouped together. Object-based attentional effects were attenuated when multiple regions did not group into a single perceptual object (Experiment 1). However, when multiple regions grouped together based on (1) edge continuation (Experiments 2 and 3) or (2) part and occlusion cues (Experiment 4), we observed object-based effects. Our results suggest that object-based attention is a robust process that can select multiple-region objects, provided the regions of such objects cohere on the basis of perceptual grouping cues.

摘要

物体能够控制注意力的焦点,相较于不同物体上的特征,同一物体上的特征更容易被选中。在当前实验中,我们研究了促成这种基于物体的注意效应的感知过程。先前的研究表明,在某些条件下,基于物体的效应会出现在单区域物体上,但不会出现在多区域物体上(实验1,沃森和克莱默,1999年)。这样的结果令人惊讶,因为自然场景中的大多数物体都是由多个区域组成的。因此,先前的研究结果可能会限制基于物体的选择机制的实用性。我们通过操纵单个物体的不同(即多个)区域在感知上组合在一起的程度,来探究这些单区域选择结果的普遍性。当多个区域没有组合成一个单一的感知物体时,基于物体的注意效应会减弱(实验1)。然而,当多个区域基于(1)边缘连续性(实验2和3)或(2)部分和遮挡线索(实验4)组合在一起时,我们观察到了基于物体的效应。我们的结果表明,基于物体的注意是一个强大的过程,它可以选择多区域物体,前提是这些物体的区域基于感知分组线索连贯在一起。

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