Learning to read places a strong challenge on the visual system. Years of
expertise lead to a remarkable capacity to separate highly similar letters and
encode their relative positions, thus distinguishing words such as FORM and
FROM, invariantly over a large range of sizes and absolute positions. How
neural circuits achieve invariant word recognition remains unknown. Here, we
address this issue by training deep neural network models to recognize written
words and then analyzing how reading-specialized units emerge and operate
across different layers of the network. With literacy, a small subset of units
becomes specialized for word recognition in the learned script, similar to the
"visual word form area" of the human brain. We show that these units are
sensitive to specific letter identities and their distance from the blank space
at the left or right of a word, thus acting as "space bigrams". These units
specifically encode ordinal positions and operate by pooling across low and
high-frequency detector units from early layers of the network. The proposed
neural code provides a mechanistic insight into how information on letter
identity and position is extracted and allow for invariant word recognition,
and leads to predictions for reading behavior, error patterns, and the
neurophysiology of reading.

通过训练深度神经网络模型来识别书面文字，并分析阅读专门化单元在网络的不同层次中是如何出现和运作的，我们揭示了这些单元敏感具体的字母身份和它们距离单词左侧或右侧的空格的距离，从而充当 “空格二元组”。这些单元特定地编码序数位置，通过从网络的早期层次中的低和高频率检测器单元汇聚来运作。所提出的神经编码提供了关于如何提取字母身份和位置信息并实现不变词语识别的机制性洞察，并导致了阅读行为、错误模式和阅读的神经生理学的预测。