Saarela Toni P, Landy Michael S
Department of Psychology, University of Pennsylvania, 3401 Walnut Street, Philadelphia, PA 19104, USA; Department of Psychology, New York University, 6 Washington Place, Room 550, New York, NY 10003, USA; Center for Neural Science, New York University, 4 Washington Place, Room 809, New York, NY 10003, USA.
Department of Psychology, New York University, 6 Washington Place, Room 550, New York, NY 10003, USA; Center for Neural Science, New York University, 4 Washington Place, Room 809, New York, NY 10003, USA.
Curr Biol. 2015 Mar 30;25(7):920-7. doi: 10.1016/j.cub.2015.01.068. Epub 2015 Mar 19.
Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection.
寻找和识别物体是视觉的一项基本任务。物体可以通过多种“线索”（颜色、亮度、纹理等）来定义，并且人类能够整合感官线索以提高检测和识别能力[1 - 3]。皮质机制融合来自多种线索的信息[4]，并且形状选择性神经机制可以通过对给定形状做出反应而不依赖于定义它的视觉线索来表现出线索不变性[5 - 8]。相比之下，选择性注意通过分离视觉信息的一个子集来提高识别能力[9]。人类可以在感知维度（颜色或方向）内选择单个特征（红色或垂直），对具有被关注特征的项目给出更快、更准确的反应[10, 11]。如心理物理学和建模[11, 12]、成像[13 - 16]以及单细胞和神经群体记录[17, 18]研究所示，注意会提高神经反应并锐化对被关注特征的神经调谐。除了单个特征，注意还可以选择整个物体[19 - 21]。物体是被建议的注意“单元”之一，因为对物体单个特征的注意会导致其所有特征的选择[19 - 21]。在这里，我们在物体识别中比较整合与注意选择。我们首先发现，人类能够近乎最优地整合来自几个感知维度（颜色、纹理）的信息以提高识别能力。然而，即使其他维度提供与任务无关的、可能相互冲突的信息，他们也无法分离出单个维度。对于物体识别而言，似乎存在来自视觉体验多个维度的信息的强制整合。然而，这种整合带来的优势是以注意选择为代价的。
