Salience-based selection: attentional capture by distractors less salient than the target.

Salience-based selection: attentional capture by distractors less salient than the target.
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基于显着性的选择:分散术者的注意力捕获比目标少。

DOI:
10.1371/journal.pone.0052595
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发表时间:
2013
期刊:
影响因子:
3.7
通讯作者:
Müller HJ
Müller HJ
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Zehetleitner M;Koch AI;Goschy H;Müller HJ

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目前对注意力捕获的研究预测,最显着的刺激总是被首先选择。然而,现有的显着性和视觉搜索模型假设在地图计算或选择过程中的噪声。因此,他们预测第一个选择是随机的,取决于显著性,这意味着注意力甚至可以首先被第二个最显著的(而不是最显著的)刺激捕获。然而,捕获不太突出的干扰还没有被报道,基于显着性的选择帐户声称,干扰更显着,以捕捉注意。我们测试了这一预测使用的视觉搜索干扰范式的实证和建模方法。对于实证部分,我们操纵的目标和干扰参数的显着性和测量的反应时干扰时,干扰相比,不存在。反应时干扰与相对于目标的干扰物显著性密切相关。此外,即使干扰不突出的目标捕捉注意力,如反应时间干扰和oculerative捕获。在建模部分,我们采用显著性的行为测量方法模拟了分心物范式中的第一选择,并考虑了包括噪声在内的选择的时间过程。我们能够复制我们在经验部分获得的结果模式。我们的结论是,每个显着值遵循一个特定的选择时间分布和注意捕获发生时,目标和分心物的选择时间分布重叠。因此,选择本质上是随机的,注意力捕获以一定的概率发生,具体取决于相对显着性。
Current accounts of attentional capture predict the most salient stimulus to be invariably selected first. However, existing salience and visual search models assume noise in the map computation or selection process. Consequently, they predict the first selection to be stochastically dependent on salience, implying that attention could even be captured first by the second most salient (instead of the most salient) stimulus in the field. Yet, capture by less salient distractors has not been reported and salience-based selection accounts claim that the distractor has to be more salient in order to capture attention. We tested this prediction using an empirical and modeling approach of the visual search distractor paradigm. For the empirical part, we manipulated salience of target and distractor parametrically and measured reaction time interference when a distractor was present compared to absent. Reaction time interference was strongly correlated with distractor salience relative to the target. Moreover, even distractors less salient than the target captured attention, as measured by reaction time interference and oculomotor capture. In the modeling part, we simulated first selection in the distractor paradigm using behavioral measures of salience and considering the time course of selection including noise. We were able to replicate the result pattern we obtained in the empirical part. We conclude that each salience value follows a specific selection time distribution and attentional capture occurs when the selection time distributions of target and distractor overlap. Hence, selection is stochastic in nature and attentional capture occurs with a certain probability depending on relative salience.
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