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Neural network models have been proposed to explain the grapheme-phonememapping process in humans for many alphabet languages. These models not onlysuccessfully learned the correspondence of the letter strings and theirpronunciation, but also captured human behavior in nonce word naming tasks. Howwould the neural models perform for a non-alphabet language (e.g., Chinese)unknown character task? How well would the model capture human behavior? Inthis study, we first collect human speakers' answers on unknown characternaming tasks and then evaluate a set of transformer models by comparing theirperformances with human behaviors on an unknown Chinese character naming task.We found that the models and humans behaved very similarly, that they hadsimilar accuracy distribution for each character, and had a substantial overlapin answers. In addition, the models' answers are highly correlated with humans'answers. These results suggested that the transformer models can well capturehuman's character naming behavior.

Evaluating Transformer Models and Human Behaviors on Chinese Character Naming