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首页> 外文期刊>Frontiers in Psychology >Distinct mechanisms subserve location- and object-based visual attention
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Distinct mechanisms subserve location- and object-based visual attention

机译:不同的机制可为基于位置和基于对象的视觉注意力提供服务

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Visual attention can be allocated to either a location or an object, named location- or object-based attention, respectively. Despite the burgeoning evidence in support of the existence of two kinds of attention, little is known about their underlying mechanisms in terms of whether they are achieved by enhancing signal strength or excluding external noises. We adopted the noise-masking paradigm in conjunction with the double-rectangle method to probe the mechanisms of location-based attention and object-based attention. Two rectangles were shown, and one end of one rectangle was cued, followed by the target appearing at (a) the cued location; (b) the uncued end of the cued rectangle; and (c) the equal-distant end of the uncued rectangle. Observers were required to detect the target that was superimposed at different levels of noise contrast. We explored how attention affects performance by assessing the threshold versus external noise contrast (TvC) functions and fitted them with a divisive inhibition model. Results show that location-based attention – lower threshold at cued location than at uncued location – was observed at all noise levels, a signature of signal enhancement. However, object-based attention – lower threshold at the uncued end of the cued than at the uncued rectangle – was found only in high-noise conditions, a signature of noise exclusion. Findings here shed a new insight into the current theories of object-based attention.
机译:视觉注意力可以分配给位置或对象,分别命名为基于位置或基于对象的注意力。尽管有越来越多的证据支持两种注意力的存在,但关于它们的潜在机制知之甚少,无论它们是通过增强信号强度还是排除外部噪声来实现的。我们结合双矩形方法采用了掩蔽噪声的范式来探究基于位置的注意力和基于对象的注意力的机制。显示了两个矩形,并提示了一个矩形的一端,随后目标出现在(a)提示的位置; (b)提示矩形的未提示端; (c)无提示矩形的等距末端。观察者需要检测在不同噪声对比水平下叠加的目标。我们通过评估阈值与外部噪声对比(TvC)函数,探讨了注意力如何影响性能,并为它们配备了分裂抑制模型。结果表明,在所有噪声级别上都观察到了基于位置的注意力-在提示位置处的阈值低于未提示位置的阈值,这是信号增强的标志。但是,只有在高噪声条件下才能发现基于对象的注意力-提示的未提示端的阈值低于未提示的矩形,这是排除噪音的标志。此处的发现为当前基于对象的注意力理论提供了新的见解。

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