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人类视觉中的粗到精信息整合。

Coarse-to-fine information integration in human vision.

机构信息

Research Institute for Psychological Science, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.

Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands.

出版信息

Neuroimage. 2019 Feb 1;186:103-112. doi: 10.1016/j.neuroimage.2018.10.086. Epub 2018 Nov 4.

Abstract

Coarse-to-fine theories of vision propose that the coarse information carried by the low spatial frequencies (LSF) of visual input guides the integration of finer, high spatial frequency (HSF) detail. Whether and how LSF modulates HSF processing in naturalistic broad-band stimuli is still unclear. Here we used multivariate decoding of EEG signals to separate the respective contribution of LSF and HSF to the neural response evoked by broad-band images. Participants viewed images of human faces, monkey faces and phase-scrambled versions that were either broad-band or filtered to contain LSF or HSF. We trained classifiers on EEG scalp-patterns evoked by filtered scrambled stimuli and evaluated the derived models on broad-band scrambled and intact trials. We found reduced HSF contribution when LSF was informative towards image content, indicating that coarse information does guide the processing of fine detail, in line with coarse-to-fine theories. We discuss the potential cortical mechanisms underlying such coarse-to-fine feedback.

摘要

粗到精的视觉理论提出,视觉输入的低空间频率(LSF)携带的粗信息指导精细的高空间频率(HSF)细节的整合。LSF 是否以及如何在自然的宽带刺激中调节 HSF 处理仍然不清楚。在这里,我们使用 EEG 信号的多元解码来分离宽带图像诱发的神经反应中 LSF 和 HSF 的各自贡献。参与者观看了人脸、猴脸和相位随机化的图像,这些图像要么是宽带的,要么是经过滤波只包含 LSF 或 HSF 的。我们在经过滤波的随机化刺激诱发的 EEG 头皮模式上训练分类器,并在宽带随机化和完整试验上评估所得模型。当 LSF 对图像内容有信息时,我们发现 HSF 的贡献减少,这表明粗信息确实指导了精细细节的处理,符合粗到精的理论。我们讨论了这种粗到精反馈背后潜在的皮层机制。

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