来自用户 象棋 的文献。
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1.
象棋
(2022-08-31 22:56):
#paper doi:10.1073/pnas.1719616115, PNAS, (2018), Mid-level visual features underlie the high-level categorical organization of the ventral stream. 这篇文章说明大脑的腹侧枕颞叶区域(VOTC)加工的是Mid-level(物体的大小属性、有无生命等特征)信息。研究者使用了一种纹理合成算法,这种算法生成的texforms保留了物体图片Mid-level的信息,但又不被识别出来是什么东西。然后分析texforms和原图在VOTC的激活,结果发现二者的激活非常相似,这说明VOTC区实际上加工的是物体Mid-level的信息。
IF:9.400Q1
Proceedings of the National Academy of Sciences of the United States of America,
2018-09-18.
DOI: 10.1073/pnas.1719616115
PMID: 30171168
Abstract:
Human object-selective cortex shows a large-scale organization characterized by the high-level properties of both animacy and object size. To what extent are these neural responses explained by primitive perceptual features …
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Human object-selective cortex shows a large-scale organization characterized by the high-level properties of both animacy and object size. To what extent are these neural responses explained by primitive perceptual features that distinguish animals from objects and big objects from small objects? To address this question, we used a texture synthesis algorithm to create a class of stimuli-texforms-which preserve some mid-level texture and form information from objects while rendering them unrecognizable. We found that unrecognizable texforms were sufficient to elicit the large-scale organizations of object-selective cortex along the entire ventral pathway. Further, the structure in the neural patterns elicited by texforms was well predicted by curvature features and by intermediate layers of a deep convolutional neural network, supporting the mid-level nature of the representations. These results provide clear evidence that a substantial portion of ventral stream organization can be accounted for by coarse texture and form information without requiring explicit recognition of intact objects.
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2.
象棋
(2022-07-31 23:20):
#paper doi:https://doi.org/10.1101/2021.03.16.435524, bioRxiv preprint, (2021), Decoding the Information Structure Underlying the Neural Representation of Concepts. 人类对于语义概念的表征有三种,taxonomic(动物,工具等,强调类别),sensory-motor(苹果是红色的圆圆的很甜,强调各种特征),distributed(消防员和水龙头,强调共同出现的频率)。作者利用各种语料库得到了三种表征方式的行为模型,然后将这些行为模型和脑信号模型做相关,发现大部分脑区的表征方式为sensory-motor。
bioRxiv,
2021.
DOI: 10.1101/2021.03.16.435524
Abstract:
AbstractThe nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation …
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AbstractThe nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts, and that other areas beyond the traditional “semantic hubs” contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.
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