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Linking patterns of infant eye movements to a neural network model of the ventral stream using representational similarity analysis
Developmental Science ( IF 3.1 ) Pub Date : 2021-07-09 , DOI: 10.1111/desc.13155
John E Kiat 1 , Steven J Luck 1 , Aaron G Beckner 1 , Taylor R Hayes 1 , Katherine I Pomaranski 1 , John M Henderson 1 , Lisa M Oakes 1
Affiliation  

Little is known about the development of higher-level areas of visual cortex during infancy, and even less is known about how the development of visually guided behavior is related to the different levels of the cortical processing hierarchy. As a first step toward filling these gaps, we used representational similarity analysis (RSA) to assess links between gaze patterns and a neural network model that captures key properties of the ventral visual processing stream. We recorded the eye movements of 4- to 12-month-old infants (N = 54) as they viewed photographs of scenes. For each infant, we calculated the similarity of the gaze patterns for each pair of photographs. We also analyzed the images using a convolutional neural network model in which the successive layers correspond approximately to the sequence of areas along the ventral stream. For each layer of the network, we calculated the similarity of the activation patterns for each pair of photographs, which was then compared with the infant gaze data. We found that the network layers corresponding to lower-level areas of visual cortex accounted for gaze patterns better in younger infants than in older infants, whereas the network layers corresponding to higher-level areas of visual cortex accounted for gaze patterns better in older infants than in younger infants. Thus, between 4 and 12 months, gaze becomes increasingly controlled by more abstract, higher-level representations. These results also demonstrate the feasibility of using RSA to link infant gaze behavior to neural network models. A video abstract of this article can be viewed at https://youtu.be/K5mF2Rw98Is

中文翻译:

使用代表性相似性分析将婴儿眼球运动模式与腹侧流的神经网络模型联系起来

人们对婴儿期视觉皮层高级区域的发育知之甚少,而对视觉引导行为的发展与皮质处理层次结构的不同层次之间的关系则知之甚少。作为填补这些空白的第一步,我们使用代表性相似性分析(RSA) 来评估凝视模式与捕获腹侧视觉处理流关键属性的神经网络模型之间的联系。我们记录了 4 到 12 个月大婴儿的眼球运动(N = 54) 当他们观看场景照片时。对于每个婴儿,我们计算了每对照片的注视模式的相似性。我们还使用卷积神经网络模型分析了图像,其中连续层大致对应于沿腹侧流的区域序列。对于网络的每一层,我们计算了每对照片的激活模式的相似性,然后将其与婴儿注视数据进行比较。我们发现,与视觉皮层低层区域对应的网络层对年幼婴儿的注视模式比大婴儿更好,而与视觉皮层高级区域对应的网络层对大龄婴儿的注视模式的解释比大婴儿更好在年幼的婴儿中。因此,在 4 到 12 个月之间,凝视越来越受到更抽象、更高层次的表征的控制。这些结果还证明了使用 RSA 将婴儿注视行为与神经网络模型联系起来的可行性。可以在 https://youtu.be/K5mF2Rw98Is 查看本文的视频摘要
更新日期:2021-07-09
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