The temporal dynamics of selective attention of the visual periphery as measured by classification images.
The temporal dynamics of selective attention of the visual periphery as measured by classification images.
复制标题
通过分类图像测量的视觉外围选择性注意的时间动态。
DOI：
10.1167/7.12.10
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发表时间：
2007
影响因子：
1.8
通讯作者：
Eckstein,MiguelP
中科院分区：
文献类型：
--
作者：
Shimozaki,StevenS;Chen,KellyY;Abbey,CraigK;Eckstein,MiguelP
这项研究利用分类图像估计了选择性注意的时间动态，这是一种通过跟踪响应如何与添加到刺激中的外部噪声相关来评估观察者信息使用的技术。三名观察者对可能出现在八个位置之一的高斯信号进行是/否判别（偏心率 - 4.6）。在刺激持续时间（300 毫秒）内，外围提示以 100% 的有效性指示了潜在的信号位置，并且刺激呈现在独立采样的高斯亮度图像噪声的帧（37.5 毫秒/帧）中。刺激的呈现可以有或没有后续的高对比度图像噪声掩蔽显示（100 毫秒），掩蔽的存在几乎没有影响。分类图像的结果表明，观察者能够从第一帧 (0–37.5 ms) 或第二帧 (37.5–75 ms) 开始，选择性地使用提示位置处的信息（相对于未提示位置）。这表明选择性注意效应早于之前行为和事件相关电位 (ERP) 研究中发现的结果，这些研究通常估计选择性注意效应的潜伏期为 75-100 毫秒。我们提出了一种使用已知的早期视觉时间脉冲响应的反卷积方法，该方法表明分类图像结果如何与之前的行为和 ERP 结果相关。将模型应用于分类图像表明，考虑已知的时间动态可以至少解释分类图像与先前研究之间结果之间的部分差异。
This study estimates the temporal dynamics of selective attention with classification images, a technique assessing observer information use by tracking how responses are correlated with external noise added to the stimulus. Three observers performed a yes/no discrimination of a Gaussian signal that could appear at one of eight locations (eccentricity—4.6). During the stimulus duration (300 ms), a peripheral cue indicated the potential signal location with 100% validity, and stimuli were presented in frames (37.5 ms/frame) of independently sampled Gaussian luminance image noise. Stimuli were presented either with or without a succeeding masking display (100 ms) of high-contrast image noise, with mask presence having little effect. The results from the classification images suggest that observers were able to use information at the cued location selectively (relative to the uncued locations), starting within the first (0–37.5 ms) or second (37.5–75 ms) frame. This suggests a selective attention effect earlier than those found in previous behavioral and event-related potential (ERP) studies, which generally have estimated the latency for selective attention effects to be 75–100 ms. We present a deconvolution method using the known temporal impulse response of early vision that indicates how the classification image results might relate to previous behavioral and ERP results. Applying the model to the classification images suggests that accounting for the known temporal dynamics could explain at least part of the difference in results between classification images and the previous studies.