  A wide variety of orthographic coding schemes and models of visual word
identification have been developed to account for masked priming data that
provide a measure of orthographic similarity between letter strings. These
models tend to include hand-coded orthographic representations with single unit
coding for specific forms of knowledge (e.g., units coding for a letter in a
given position). Here we assess how well a range of these coding schemes and
models account for the pattern of form priming effects taken from the Form
Priming Project and compare these findings to results observed with 11 standard
deep neural network models (DNNs) developed in computer science. We find that
deep convolutional networks (CNNs) perform as well or better than the coding
schemes and word recognition models, whereas transformer networks did less
well. The success of CNNs is remarkable as their architectures were not
developed to support word recognition (they were designed to perform well on
object recognition), they classify pixel images of words (rather than
artificial encodings of letter strings), and their training was highly
simplified (not respecting many key aspects of human experience). In addition
to these form priming effects, we find that the DNNs can account for visual
similarity effects on priming that are beyond all current psychological models
of priming. The findings add to the recent work of (Hannagan et al., 2021) and
suggest that CNNs should be given more attention in psychology as models of
human visual word recognition.

Computer Vision and Pattern Recognition