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首页> 外文期刊>Journal of vision >Parallel Processing in Difficult Visual Search in both Noisy and Noiseless Displays
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Parallel Processing in Difficult Visual Search in both Noisy and Noiseless Displays

机译：嘈杂和无噪显示中的困难视觉搜索中的并行处理

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Visual search accuracy in time-limited displays often shows a set size effect, decreasing as the number of distractors increases. This is especially so for difficult visual searches, e.g., searching for an O among C's. Original models proposed a serial search processing architecture for difficult search (Treisman & Gelade, 1980, and many others), but a growing body of evidence shows an unlimited-capacity parallel probabilistic model provides a better account of the time course of visual search in the absence of external visual noise (for time-limited displays) (Dosher, Han, & Lu, 2004; Dosher, Han, & Lu, 2010; McElree & Carrasco, 1999). Because spatial attention has been shown primarily to exclude external noise (Dosher & Lu, 2001), the current experiment tested whether a parallel probabilistic model (PPM) of search dynamics also accounts for visual search in external (masking) noise, or whether external noise induces different processing demands with capacity limits. We performed a visual search task for an O among C's in a cued-response speed-accuracy experiment, manipulating set size (2, 4, and 8), delay to the response cue (0.05 s through 1.8 s) and the presence or absence of external noise; accuracy, d', was measured as a function of processing time. Stimulus contrasts (100% and 30% contrast) were set to approximately equate overall asymptotic discrimination in the presence and absence of external noise. In the PPM, the probability of hits and false alarms at different processing times (and therefore d') depends on the independent finishing times of processing for all items simultaneously, while requiring different decision criteria for each set size. For all observers and conditions, the unlimited-capacity PPM provided an excellent account of both time course and asymptotic accuracy. External noise does not alter the dynamics of information accumulation in visual search.
机译：限时显示器中的视觉搜索精度通常会显示出一定的大小效果，随着干扰物数量的增加而降低。对于困难的视觉搜索，例如在C中搜索O的情况尤其如此。原始模型提出了用于困难搜索的串行搜索处理架构（Treisman＆Gelade，1980等），但是越来越多的证据表明，无限容量的并行概率模型可以更好地说明视觉搜索的时间过程。没有外部视觉噪声（用于限时显示）（Dosher，Han，＆Lu，2004; Dosher，Han，＆Lu，2010; McElree＆Carrasco，1999）。因为已经显示出空间注意力主要被排除在外（Dosher＆Lu，2001），所以本实验测试了搜索动力学的并行概率模型（PPM）是否也考虑了外在（掩盖）噪声中的视觉搜索，还是外在噪声限制容量会导致不同的处理需求。在提示响应速度精度实验中，我们对C中的O执行了视觉搜索任务，操纵了集合大小（2、4和8），响应提示延迟（0.05 s至1.8 s）以及是否存在外部噪音;测量精度d'作为处理时间的函数。在存在和不存在外部噪声的情况下，将刺激对比（100％和30％对比）设置为大致等同于总体渐近分辨力。在PPM中，在不同处理时间（因此为d'）的命中率和误报率取决于同时处理所有项目的独立完成时间，而每个设置大小都需要不同的决策标准。对于所有观察者和条件，无限容量的PPM很好地说明了时程和渐近精度。外部噪声不会改变视觉搜索中信息积累的动态。

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