Cognitive Neuroimaging Unit, CEA, INSERM U 992, Université Paris-Saclay, NeuroSpin center, Gif/Yvette, France.
Collège de France, Université Paris Sciences Lettres (PSL), Paris, France.
PLoS Comput Biol. 2024 Sep 6;20(9):e1012430. doi: 10.1371/journal.pcbi.1012430. eCollection 2024 Sep.
Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a large range of positions, sizes and fonts. How neural circuits achieve invariant word recognition remains unknown. Here, we address this issue by recycling deep neural network models initially trained for image recognition. We retrain them to recognize written words and then analyze how reading-specialized units emerge and operate across the successive layers. With literacy, a small subset of units becomes specialized for word recognition in the learned script, similar to the visual word form area (VWFA) in the human brain. We show that these units are sensitive to specific letter identities and their ordinal position from the left or the right of a word. The transition from retinotopic to ordinal position coding is achieved by a hierarchy of "space bigram" unit that detect the position of a letter relative to a blank space and that pool across low- and high-frequency-sensitive units from early layers of the network. The proposed scheme provides a plausible neural code for written words in the VWFA, and leads to predictions for reading behavior, error patterns, and the neurophysiology of reading.
学习阅读对视觉系统提出了巨大的挑战。多年的专业知识使我们能够出色地区分相似的字母并编码它们的相对位置，从而能够不变地在大范围的位置、大小和字体中区分出单词，例如 FORM 和 FROM。但是，神经回路如何实现不变的单词识别仍然未知。在这里，我们通过重新利用最初为图像识别而训练的深度神经网络模型来解决这个问题。我们重新训练它们以识别书面单词，然后分析阅读专业单元如何在连续的层中出现和运作。在读写能力方面，一小部分单元会专门识别所学文字中的单词，类似于人类大脑中的视觉单词形式区域（VWFA）。我们表明，这些单元对特定的字母身份及其在单词左侧或右侧的序号位置敏感。从视网膜坐标到序号位置的编码是通过“空间双元组”单元的层次结构实现的，这些单元检测字母相对于空白的位置，并在网络的早期层中汇总来自低和高频敏感单元的信息。所提出的方案为 VWFA 中的书面单词提供了一种合理的神经编码，并为阅读行为、错误模式和阅读的神经生理学提供了预测。
