偏心率约束的卷积神经网络训练揭示了视野周围的自适应信息编码
Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field
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中文总结 AI 辅助
研究利用VEDB数据，通过对比学习训练ResNet - 18模型，探讨视野偏心率与信息编码关系，发现自我中心经验能自适应约束皮层信息处理，不同偏心率模型在任务表现及与神经数据匹配上有差异。
中文摘要 AI 辅助
在灵长类视觉系统中，中心偏好的皮层群体具有更高的空间分辨率，且与面部和单词选择区域重叠，而外周偏好群体的空间分辨率较低，与场景选择区域重叠。这种“偏心率偏差”可能反映了不同的任务相关性。为测试依赖偏心率的编码是否能从自然经验中产生，研究使用了来自视觉经验数据集（VEDB）的自我中心视频和眼动追踪数据。通过对比学习（SimCLR）在修改后的帧上训练ResNet - 18模型以分离不同偏心率。评估下游任务性能和模型与人类功能磁共振成像数据的对齐情况。结果表明自我中心经验可能自适应地约束皮层信息处理。
英文摘要
In the primate visual system, center-preferring cortical populations have higher spatial resolution and overlap face- and word-selective regions while periphery-preferring populations have lower spatial resolution and overlap scene-selective regions. This "eccentricity bias" may reflect differential task-relevance: central vision may better support fine-grained tasks like face recognition and reading, while peripheral vision may better support scene understanding. To test whether eccentricity-dependent coding can emerge from natural experience, we used egocentric video and eye-tracking data from the Visual Experience Dataset (VEDB). We trained ResNet-18 models using contrastive learning (SimCLR) on frames modified to isolate different eccentricities (gaze-contingent fovea-only crops, periphery-only crops, and periphery-only crops with a NeuroFovea transform applied). We evaluated downstream task performance and model alignment with human fMRI data (Natural Scenes Dataset; encoding models). In-domain VEDB frame classification showed systematic differences between fovea- and periphery-only models across categories, indicating differential informativeness across tasks. On downstream classification, VEDB-pretrained models generalized better to scene categorization (Places365) than face recognition (VGGFace2), with fovea-only models stronger on both. Across visual cortex, VEDB-pretrained models matched neural predictivity of models trained on mid-sized non-egocentric datasets (ImageNet-100), suggesting egocentric data supports emergence of cortically-aligned representations. In scene-selective cortex (PPA, RSC), periphery-only models held a small but consistent advantage in explained variance over fovea-only models, suggesting these regions are aligned with peripheral statistics. Together, these results suggest egocentric experience may adaptively constrain cortical information processing.
