Feature-based attention enhances performance by increasing response gain.
Feature-based attention enhances performance by increasing response gain.
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DOI：
10.1016/j.visres.2012.04.016
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发表时间：
2012-12-01
期刊：
影响因子：
1.8
通讯作者：
Carrasco, Marisa
中科院分区：
文献类型：
--
作者：
Herrmann, Katrin;Heeger, David J.;Carrasco, Marisa
关键词：
内隐空间注意可以通过对比度增益的变化或反应增益的变化来增加对比敏感度，这取决于注意力场的大小和刺激的大小，正如注意力的归一化模型所预测的那样。对于基于特征的注意，与空间注意不同，该模型仅预测响应增益的变化，而不管注意力场的特征范围是小还是大。为了验证这一预测，我们测量了基于特征的注意力的对比度依赖性。观察员进行了定向歧视任务的空间阵列光栅补丁。光栅的空间位置是随机变化的，因此观察者无法注意到特定的位置。以75%有效和25%无效的预提示操纵基于视觉的注意力，并通过引入关于即将到来的光栅方向的不确定性操纵注意力场的特征范围。性能的准确性是更好的有效比无效的预线索，响应增益的变化一致，当注意力领域的特征范围是小的（低不确定性）或当它是大的（高不确定性）相对于特征范围的刺激。基于特征的注意力的这些结果与空间注意力的类似实验结果明显不同，但两者都支持注意力归一化模型的关键预测。
Covert spatial attention can increase contrast sensitivity either by changes in contrast gain or by changes in response gain, depending on the size of the attention field and the size of the stimulus, as predicted by the normalization model of attention. For feature-based attention, unlike spatial attention, the model predicts only changes in response gain, regardless of whether the featural extent of the attention field is small or large. To test this prediction, we measured the contrast dependence of feature-based attention. Observers performed an orientation-discrimination task on a spatial array of grating patches. The spatial locations of the gratings were varied randomly so that observers could not attend to specific locations. Feature-based attention was manipulated with a 75% valid and 25% invalid pre-cue, and the featural extent of the attention field was manipulated by introducing uncertainty about the upcoming grating orientation. Performance accuracy was better for valid than for invalid pre-cues, consistent with a change in response gain, when the featural extent of the attention field was small (low uncertainty) or when it was large (high uncertainty) relative to the featural extent of the stimulus. These results for feature-based attention clearly differ from results of analogous experiments with spatial attention, yet both support key predictions of the normalization model of attention.
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