Neural network models have been proposed to explain the grapheme-phoneme mapping process in humans for many alphabet languages. These models not only successfully learned the correspondence of the letter strings and their pronunciation, but also captured human behavior in nonce word naming tasks. How would the neural models perform for a non-alphabet language (e.g., Chinese) unknown character task? How well would the model capture human behavior? In this study, we evaluate a set of transformer models and compare their performances with human behaviors on an unknown Chinese character naming task. We found that the models and humans behaved very similarly, that they had similar accuracy distribution for each character, and had a substantial overlap in answers. In addition, the models' answers are highly correlated with humans' answers. These results suggested that the transformer models can well capture human's character naming behavior.

翻译：神经网络模型已被提出用于解释许多字母语言中人类处理字形-音位映射的过程。这些模型不仅成功学会了字母字符串与其发音的对应关系，还在假词命名任务中捕捉到了人类行为。那么，对于非字母语言（如中文）的未知字符任务，神经网络模型的表现如何？模型能在多大程度上捕捉人类行为？在本研究中，我们评估了一组Transformer模型，并在未知汉字命名任务中将其表现与人类行为进行比较。我们发现，模型与人类的行为非常相似：它们对每个汉字的准确率分布相近，且在答案上具有显著的重叠。此外，模型的答案与人类的答案高度相关。这些结果表明，Transformer模型能够很好地捕捉人类的汉字命名行为。