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A wide variety of orthographic coding schemes and models of visual wordidentification have been developed to account for masked priming data thatprovide a measure of orthographic similarity between letter strings. Thesemodels tend to include hand-coded orthographic representations with single unitcoding for specific forms of knowledge (e.g., units coding for a letter in agiven position). Here we assess how well a range of these coding schemes andmodels account for the pattern of form priming effects taken from the FormPriming Project and compare these findings to results observed with 11 standarddeep neural network models (DNNs) developed in computer science. We find thatdeep convolutional networks (CNNs) perform as well or better than the codingschemes and word recognition models, whereas transformer networks did lesswell. The success of CNNs is remarkable as their architectures were notdeveloped to support word recognition (they were designed to perform well onobject recognition), they classify pixel images of words (rather thanartificial encodings of letter strings), and their training was highlysimplified (not respecting many key aspects of human experience). In additionto these form priming effects, we find that the DNNs can account for visualsimilarity effects on priming that are beyond all current psychological modelsof priming. The findings add to the recent work of (Hannagan et al., 2021) andsuggest that CNNs should be given more attention in psychology as models ofhuman visual word recognition.

Convolutional Neural Networks Trained to Identify Words Provide a Surprisingly Good Account of Visual Form Priming Effects