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。

卷积神经网络中单词识别的神经编码破解

Cracking the neural code for word recognition in convolutional neural  networks

Human fixation patterns have been shown to correlate strongly with
Transformer-based attention. Those correlation analyses are usually carried out
without taking into account individual differences between participants and are
mostly done on monolingual datasets making it difficult to generalise findings.
In this paper, we analyse eye-tracking data from speakers of 13 different
languages reading both in their native language (L1) and in English as language
learners (L2). We find considerable differences between languages but also that
individual reading behaviour such as skipping rate, total reading time and
vocabulary knowledge (LexTALE) influence the alignment between humans and
models to an extent that should be considered in future studies.

通过分析母语和英语学习者阅读不同语言时的眼动数据，发现个体阅读行为会影响人类和模型之间的关联性，这需要在未来的研究中被考虑进去。