Knowledge of letter shapes is central to reading. In experiments focusing primarily on a single letter shape-the "looptail" lowercase print -we found surprising gaps in skilled readers' knowledge. In Experiment 1 most participants failed to recall the existence of looptail g when asked if has two lowercase print forms, and almost none were able to write looptail g accurately. In Experiment 2 participants searched for s in text with multiple looptails. Asked immediately thereafter to write the g form they had seen, half the participants produced an "opentail" (the typical handwritten form), and only one wrote looptail accurately. In Experiment 3 participants performed poorly in discriminating looptail from distractors with important features mislocated or misoriented. These results have implications for understanding types of knowledge about letters, and how this knowledge is acquired. For example, our findings speak to hypotheses concerning the role of writing in learning letter shapes. More generally, our findings raise questions about the conditions under which massive exposure does, and does not, yield detailed, accurate, accessible knowledge. In this context we relate our findings to studies showing poor knowledge or memory for various types of stimuli despite extensive exposure. (PsycINFO Database Record

Orthographic processing skills (reading and spelling) are evolutionarily recent and mastered late in development, providing an opportunity to investigate how the properties of the neural networks supporting skills of this type compare to those supporting evolutionarily older, well-established “reference” networks. Although there has been extensive research using task-based fMRI to study the neural substrates of reading, there has been very little using resting-state fMRI to examine the properties of orthographic networks. In this investigation using resting-state fMRI, we compare the within-network and across-network coherence properties of reading and spelling networks directly to these properties of reference networks, and we also compare the network properties of the key node of the orthographic networks—the visual word form area—to those of the other nodes of the orthographic and reference networks. Consistent with previous results, we find that orthographic processing networks do not exhibit certain basic network coherence properties displayed by other networks. However, we identify novel distinctive properties of the orthographic processing networks and establish that the visual word form area has unusually high levels of connectivity with a broad range of brain areas. These characteristics form the basis of our proposal that orthographic networks represent a class of “high-level integrative networks” with distinctive properties that allow them to recruit and integrate multiple, lower level processes.

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