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注意力与非视网膜拓扑特征整合

Attention and non-retinotopic feature integration.

作者信息

Otto Thomas U, Öğmen Haluk, Herzog Michael H

机构信息

Laboratory of Psychophysics, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

出版信息

J Vis. 2010 Oct 1;10(12):8. doi: 10.1167/10.12.8.

Abstract

Features of moving objects are non-retinotopically integrated along their motion trajectories as demonstrated by a variety of recent studies. The mechanisms of non-retinotopic feature integration are largely unknown. Here, we investigated the role of attention in non-retinotopic feature integration by using the sequential metacontrast paradigm. A central line was offset either to the left or right. A sequence of flanking lines followed eliciting the percept of two diverging motion streams. Although the central line was invisible, its offset was perceived within the streams. Observers attended to one stream. If an offset was introduced to one of the flanking lines in the attended stream, this offset integrated with the central line offset. No integration occurred when the offset was in the non-attended stream. Here, we manipulated the allocation of attention by using an auditory cueing paradigm. First, we show that mandatory non-retinotopic integration occurred even when the cue came long after the motion sequence. Second, we used more than two streams of which two could merge. Offsets in different streams were integrated when the streams merged. However, offsets of one stream were not integrated when this stream had to be ignored. We propose a hierarchical two stage model, in which motion grouping determines mandatory feature integration while attention selects motion streams for optional feature integration.

摘要

正如最近的各种研究所表明的,运动物体的特征会沿着其运动轨迹进行非视网膜拓扑整合。非视网膜拓扑特征整合的机制在很大程度上尚不清楚。在这里,我们使用序列元对比范式研究了注意力在非视网膜拓扑特征整合中的作用。一条中心线向左或向右偏移。随后出现一系列侧翼线,引发了两条发散运动流的感知。尽管中心线不可见,但其偏移在运动流中被感知到。观察者关注其中一条运动流。如果在被关注的运动流中的一条侧翼线上引入偏移,这个偏移会与中心线的偏移整合。当偏移出现在未被关注的运动流中时,则不会发生整合。在这里,我们使用听觉提示范式来操纵注意力的分配。首先,我们表明即使提示在运动序列之后很久才出现,强制性的非视网膜拓扑整合也会发生。其次,我们使用了两条以上的运动流,其中两条可以合并。当运动流合并时,不同运动流中的偏移会进行整合。然而,当必须忽略某条运动流时,该运动流中的偏移不会进行整合。我们提出了一个分层两阶段模型,其中运动分组决定强制性特征整合,而注意力选择运动流进行选择性特征整合。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ca6/3248829/7cc6e0ff195b/nihms329371f1.jpg

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