Predicting Identity-Preserving Object Transformations in Human Posterior Parietal Cortex and Convolutional Neural Networks.
Predicting Identity-Preserving Object Transformations in Human Posterior Parietal Cortex and Convolutional Neural Networks.
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DOI：
10.1162/jocn_a_01916
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发表时间：
2022-11-01
影响因子：
3.2
通讯作者：
Xu, Yaoda
中科院分区：
文献类型：
--
作者：
Mocz, Viola;Vaziri-Pashkam, Maryam;Chun, Marvin;Xu, Yaoda
先前的研究表明，在人类枕颞皮层（OTC）内，我们可以使用一般的线性映射函数来将视觉对象响应与非同一性特征变化联系起来，包括欧几里得特征（例如，位置和大小）和非欧几里德特征（例如，图像统计和空间频率）。虽然学习的映射能够预测未包括在训练中的对象的响应，但这些预测对于包括在训练中的类别比那些未包括在训练中的类别更好。这些研究结果表明，在整个人类OTC的对象身份和非身份特征的近正交表示。在这里，我们扩展了这些研究结果，以检查人类后顶叶皮层（PPC）中的欧几里得和非欧几里得特征变化的映射，包括下顶叶沟和上级顶叶沟中功能定义的区域。我们还检查了五个卷积神经网络（CNN）中的响应，这些卷积神经网络经过对象分类预训练，因为CNN被认为是灵长类动物腹侧视觉系统的当前最佳模型。我们分别比较了PPC和CNN与OTC的结果。我们发现，线性映射函数可以成功地将人类PPC和CNN中不同状态的非同一性变换中的对象响应联系起来，用于欧几里得和非欧几里得特征。总的来说，我们发现对象身份和非身份特征在PPC和CNN中以接近正交的方式表示，而不是完全正交的方式，就像它们在OTC中一样。同时，OTC、PPC和CNN之间存在一定差异。这些结果证明了如何在OTC，PPC和CNN中表示跨身份保持图像变换的视觉对象信息的相似性和差异。
Previous research shows that, within human occipito-temporal cortex (OTC), we can use a general linear mapping function to link visual object responses across nonidentity feature changes, including Euclidean features (e.g., position and size) and non-Euclidean features (e.g., image statistics and spatial frequency). Although the learned mapping is capable of predicting responses of objects not included in training, these predictions are better for categories included than those not included in training. These findings demonstrate a near-orthogonal representation of object identity and nonidentity features throughout human OTC. Here, we extended these findings to examine the mapping across both Euclidean and non-Euclidean feature changes in human posterior parietal cortex (PPC), including functionally defined regions in inferior and superior intraparietal sulcus. We additionally examined responses in five convolutional neural networks (CNNs) pretrained with object classification, as CNNs are considered as the current best model of the primate ventral visual system. We separately compared results from PPC and CNNs with those of OTC. We found that a linear mapping function could successfully link object responses in different states of nonidentity transformations in human PPC and CNNs for both Euclidean and non-Euclidean features. Overall, we found that object identity and nonidentity features are represented in a near-orthogonal, rather than completeorthogonal, manner in PPC and CNNs, just like they do in OTC. Meanwhile, some differences existed among OTC, PPC, and CNNs. These results demonstrate the similarities and differences in how visual object information across an identity-preserving image transformation may be represented in OTC, PPC, and CNNs.
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影响因子：
25
作者：
Kar K;Kubilius J;Schmidt K;Issa EB;DiCarlo JJ
通讯作者：
DiCarlo JJ
影响因子：
5.7
作者：
Bressler, David W.;Silver, Michael A.
通讯作者：
Silver, Michael A.
影响因子：
2.5
作者：
Bettencourt, Katherine C.;Xu, Yaoda
通讯作者：
Xu, Yaoda
DOI：
10.1093/cercor/bhw419
发表时间：
2017-01-01
期刊：
Cerebral cortex (New York, N.Y. : 1991)
影响因子：
--
作者：
Bracci S;Daniels N;Op de Beeck H
通讯作者：
Op de Beeck H
影响因子：
5.7
作者：
Eickenberg, Michael;Gramfort, Alexandre;Thirion, Bertrand
通讯作者：
Thirion, Bertrand