Supervised Learning Occurs in Visual Perceptual Learning of Complex Natural Images
Supervised Learning Occurs in Visual Perceptual Learning of Complex Natural Images
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DOI：
10.1016/j.cub.2020.05.050
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发表时间：
2020-08-03
期刊：
影响因子：
9.2
通讯作者：
Watanabe, Takeo
中科院分区：
文献类型：
--
作者：
Frank, Sebastian M.;Qi, Andrea;Watanabe, Takeo
关于视觉感知学习（VPL）中是否涉及监督或无监督学习机制一直存在争议[1-14]。然而，这些争论都是基于简单反馈的影响，仅涉及低水平视觉特征（如方向）的检测或辨别任务中的反应准确性[15-22]。在这里，我们研究是否响应反馈的内容起着至关重要的作用，复杂的自然图像的获取和长期保留的VPL。我们训练了三组人类受试者（总共n = 72），以更好地检测乳房X线照片中的“分组微钙化”或“结构扭曲”病变（以下称为钙化和扭曲），无论是没有逐个试验反馈，部分逐个试验反馈（仅响应正确性）还是详细的逐个试验反馈（响应正确性和目标位置）。畸变病变比钙化病变包含更复杂的视觉结构[23-26]。我们发现，部分反馈是必要的钙化的VPL，而详细的反馈是必要的失真的VPL。此外，培训期间的详细反馈对于变形和钙化病变的VPL保留6个月是必要的。这些结果表明，尽管监督学习在复杂自然图像的VPL中有很大的参与，但VPL的监督程度在不同类型的复杂自然图像中有所不同。VPL的这种差异要求，以提高乳腺X线照片中病变的可检测性是潜在的放射科医生的专业培训的信息。
There have been long-standing debates regarding whether supervised or unsupervised learning mechanisms are involved in visual perceptual learning (VPL) [1-14]. However, these debates have been based on the effects of simple feedback only about response accuracy in detection or discrimination tasks of low-level visual features such as orientation [15-22]. Here, we examined whether the content of response feedback plays a critical role for the acquisition and long-term retention of VPL of complex natural images. We trained three groups of human subjects (n = 72 in total) to better detect "grouped microcalcifications'' or "architectural distortion'' lesions (referred to as calcification and distortion in the following) in mammograms either with no trial-by-trial feedback, partial trial-by-trial feedback (response correctness only), or detailed trial-by-trial feedback (response correctness and target location). Distortion lesions consist of more complex visual structures than calcification lesions [23-26]. We found that partial feedback is necessary for VPL of calcifications, whereas detailed feedback is required for VPL of distortions. Furthermore, detailed feedback during training is necessary for VPL of distortion and calcification lesions to be retained for 6 months. These results show that although supervised learning is heavily involved in VPL of complex natural images, the extent of supervision for VPL varies across different types of complex natural images. Such differential requirements for VPL to improve the detectability of lesions in mammograms are potentially informative for the professional training of radiologists.