A comparison of statistical learning of naturalistic textures between DCNNs and the human visual hierarchy  

作　　者：LU XinCheng YUAN ZiQi ZHANG YiChi AI HaiLin CHENG SiYuan GE YiRan FANG Fang CHEN NiHong 

机构地区：[1]Department of Psychological and Cognitive Sciences,Tsinghua University,Beijing 100084,China [2]IDG/McGovern Institute for Brain Research,Tsinghua University,Beijing 100084,China [3]Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China [4]School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health,Peking University,Beijing 100871,China [5]Key Laboratory of Machine Perception(Ministry of Education),Peking University,Beijing 100871,China [6]Peking-Tsinghua Center for Life Sciences,Peking University,Beijing 100871,China [7]IDG/McGovern Institute for Brain Research,Peking University,Beijing 100871,China [8]State Key Laboratory of Brain and Cognitive Science,Institute of Psychology,Chinese Academy of Sciences,Beijing 100101,China

出　　处：《Science China(Technological Sciences)》2024年第8期2310-2318,共9页中国科学（技术科学英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos. 31971031, 31930053, and 32171039);the STI2030Major Projects (Grant Nos. 2021ZD0203600, 2022ZD0204802, and 2022ZD0204804)。

摘　　要：The visual system continuously adapts to the statistical properties of the environment. Existing evidence shows a close resemblance between deep convolutional neural networks(CNNs) and primate visual stream in neural selectivity to naturalistic textures above the primary visual processing stage. This study delves into the mechanisms of perceptual learning in CNNs,focusing on how they assimilate the high-order statistics of natural textures. Our results show that a CNN model achieves a similar performance improvement as humans, as manifested in the learning pattern across different types of high-order image statistics. While L2 was the first stage exhibiting texture selectivity, we found that stages beyond L2 were critically involved in learning. The significant contribution of L4 to learning was manifested both in the modulations of texture-selective responses and in the consequences of training with frozen connection weights. Our findings highlight learning-dependent plasticity in the mid-to-high-level areas of the visual hierarchy. This research introduces an AI-inspired approach for studying learning-induced cortical plasticity, utilizing DCNNs as an experimental framework to formulate testable predictions for empirical brain studies.

关 键 词：CNN perceptual learning naturalistic texture PSYCHOPHYSICS 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]