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Consistency reflects the mapping between spelling and sound. That is, a word is feedforward consistent if its pronunciation matches that of similarly spelled words, and feedback consistent if its spelling matches that of similar pronounced words. For a quasi-regular language such as English, the study of consistency effects on lexical processing has been limited by the lack of readily accessible norms. In order to improve current methodological resources, feedforward (spelling-to-sound) and feedback (sound-to-spelling) consistency measures for 37,677 English words were computed. The consistency measures developed here are operationalized at the composite level for multisyllabic words, and at different sub-syllabic segments (onset, nucleus, coda, oncleus, and rime) for both monosyllabic and multisyllabic words. These measures constitute the largest database of English consistency norms to be developed, and will be a valuable resource for researchers to explore the effects of consistency on lexical processes, such as word recognition and spelling. The norms are available as supplementary material with this paper. Keywords Consistency . Feedforward . Feedback . Word norms . Word recognition The relationship between spelling and sound is a key factor in determining skilled reading, and has been the focus of a number of influential models of word recognition (e.g., Coltheart,

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While a number of tools have been developed for researchers to compute the lexical characteristics of words, extant resources are limited in their useability and functionality. Specifically, some tools require users to have some prior knowledge of some aspects of the applications, and not all tools allow users to specify their own corpora. Additionally, current tools are also limited in terms of the range of metrics that they can compute. To address these methodological gaps, this article introduces LexiCAL, a fast, simple, and intuitive calculator for lexical variables. Specifically, LexiCAL is a standalone executable that provides options for users to calculate a range of theoretically influential surface, orthographic, phonological, and phonographic metrics for any alphabetic language, using any user-specified input, corpus file, and phonetic system. LexiCAL also comes with a set of well-documented Python scripts for each metric, that can be reproduced and/or modified for other research purposes.

Current theories of morphological processing include form-then-meaning accounts, form-with-meaning accounts, and connectionist theories. Form-then meaning accounts argue that the morphological decomposition of complex words is based purely on orthographic structure, while form-with meaning accounts argue that decomposition is influenced by the semantic properties of the stem. Connectionist theories, on the other hand, argue that morphemes are encoded as statistical patterns of occurrences between form and meaning. The weight of evidence from the literature thus far suggests that morphological decomposition is best explained by form-then-meaning accounts. That said, conflicting empirical findings exist, and more importantly, semantic transparency effects in morphological processing have been examined almost exclusively with the lexical decision task, in which participants discriminate between words and nonwords. Consequently, the extent to which observed results reflect the specific demands of the lexical decision task remains unclear. The present study extends previous work by testing if the processing dynamics of early morphological processing are moderated by task requirements. Using the masked morphological priming paradigm, this hypothesis was tested by examining semantic transparency effects for a common set of words across semantic categorization and lexical decision. In both tasks, priming was stronger for transparent (e.g., painter-PAINT) than opaque (e.g., corner-CORN) prime-target pairs; these results speak against form-then-meaning accounts. These findings further inform theories of morphological processing and underscore the importance of examining the interplay between task-general and task-specific mechanisms.