Bay Maxwell, Wyble Brad
Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
Atten Percept Psychophys. 2014 Jul;76(5):1287-97. doi: 10.3758/s13414-014-0645-z.
Recent findings have suggested that transient attention can be triggered at two locations simultaneously. However, it is unclear whether doing so reduces the effect of attention at each attended location. In two experiments, we explored the consequences of dividing attention. In the first experiment, we compared the effects of one or two cues against an uncued baseline to determine the consequences of dividing attention in a paradigm with four rapid serial visual presentation (RSVP) streams. The results indicated that two simultaneous cues increase the accuracy of reporting two targets by almost the same amount as a single cue increases the report of a single target. These results suggest that when attention is divided between multiple locations, the attentional benefit at each location is not reduced in proportion to the total number of cues. A consequent prediction of this finding is that the identification of two RSVP targets should be better when they are presented simultaneously rather than sequentially. In a second experiment, we verified this prediction by finding evidence of lag-0 sparing: Two targets presented simultaneously in different locations were reported more easily than two targets separated by 100 ms. These findings argue against a biased-competition theory of attention. We suggest that visual attention, as triggered by a cue or target, is better described by a convergent gradient-field attention model.
最近的研究结果表明,瞬时注意力可以同时在两个位置被触发。然而,尚不清楚这样做是否会降低在每个被关注位置的注意力效果。在两项实验中,我们探究了分散注意力的后果。在第一个实验中,我们将一个或两个提示的效果与无提示基线进行比较,以确定在具有四个快速序列视觉呈现(RSVP)流的范式中分散注意力的后果。结果表明,两个同时出现的提示提高报告两个目标的准确性,其提高幅度几乎与单个提示提高报告单个目标的幅度相同。这些结果表明,当注意力分散在多个位置时,每个位置的注意力益处并不会与提示的总数成比例地减少。这一发现的一个后续预测是,当两个RSVP目标同时呈现而不是相继呈现时,对它们的识别应该更好。在第二个实验中,我们通过发现零延迟节省的证据验证了这一预测:在不同位置同时呈现的两个目标比相隔100毫秒呈现的两个目标更容易被报告。这些发现与注意力的偏向竞争理论相悖。我们认为,由提示或目标触发的视觉注意力,用收敛梯度场注意力模型来描述会更好。