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The Science of Reading


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extent that the morphologically complex word is related in meaning to its stem. Componential representations would not be expected to develop for pseudo‐morphological words (e.g., corner) or for a morphologically complex word with a historical relationship to its stem (e.g., witness). To date, distributed‐connectionist models do not therefore provide a ready explanation for morpho‐orthographic segmentation (Rastle et al., 2004; Rastle & Davis, 2008).

      More recently, researchers have begun to use alternative computational approaches to understand morphological processing. Naïve discriminative learning models simulate the relationship between orthographic patterns and aspects of meaning (e.g., grammatical class) and propose that these relationships may explain some morphological effects (Baayen et al., 2011). Similarly, distributional semantic models suggest that the functions of morphemes and the constraints that govern their combination with stems may be captured through large‐scale analysis of text (Marelli & Baroni, 2015). Like distributed‐connectionist models, these approaches to morphological processing eschew the notion of explicit morpheme representations, and instead ascribe morphemic effects to overlap in orthographic and meaning representations. Thus, these models may also struggle to account for morpho‐orthographic segmentation effects. However, proponents of these models emphasize that there is considerable heterogeneity amongst morphologically structured words that are not clearly related to their stems. Some of the words used in the relevant experiments (e.g., Rastle et al., 2004) have a subtle relationship to their stems; for example, an initiative that is fruitless might be described as one that didn’t bear any fruit. Likewise, while the word cryptic is unrelated in meaning to crypt, the suffix ‐ic functions in the appropriate manner grammatically (i.e., forming adjectives from nouns). However, evidence that genuinely pseudo‐morphological words such as corner are segmented (e.g., Longtin et al., 2003) would seem to pose a challenge for these models.

      Evidence from skilled adult readers suggests that they have acquired morphological knowledge that is applied to any morphologically structured stimulus, irrespective of its lexical status. One question that arises immediately is why readers should acquire knowledge that gives rise to misleading segmentations for stimuli like corner ‐> {corn} + {‐er}. The answer goes back to the nature of the writing system, at least in the case of English spelling. Specifically, the analyses by Ulicheva et al. (2020) revealed that words like corner that appear erroneously to be morphologically complex are very rare in English spelling; typically, these words would be spelled in a way that does not make use of the suffix ‐er (e.g., martyr, sulphur, fibre). It is therefore unsurprising that readers should learn about such systematicity (i.e., ‐er as an affix), and capitalize on it to enable rapid, skilled reading (Rastle, 2019b).

      Recent research has conceptualized learning about affixes as a statistical learning problem (Lelonkiewicz, Ktori, & Crepaldi, 2020; Ulicheva et al., 2020). Lelonkiewicz et al. (2020) familiarized participants with a lexicon of novel words printed in a novel orthography. Each novel word contained an affix character at the beginning of the word or at the end. Following familiarization, participants were more likely to attribute a previously unseen word to the lexicon if it had an affix, and if the affix occurred in the correct position. These findings show that readers capture statistically salient chunks in language input and use this information in their analysis of new words. Ulicheva et al. (2020) selected nonwords with English suffixes that varied continuously in the consistency with which they reflect grammatical class. Performance in tests of reading, spelling, and meaning judgment showed a high degree of sensitivity to this meaningful information; and further, that this sensitivity was graded as a function of the consistency of the relationship between suffix spelling and grammatical category. Participants were more likely to judge a nonword like sedgeness as a noun than an adjective; and their eyes were more likely to regress back onto the nonword when it occurred in an adjective context than a noun context. Likewise, participants were more likely to spell a spoken word like /sEdZnƏs/ using ‐ness when it occurred in a noun context than a verb context (see also Treiman, Wolter, & Kessler, 2020 for similar findings). Together, these results suggest that morphological knowledge may increasingly mirror the writing system as readers gradually accumulate reading experience.

      Together, such recent findings suggest that we learn morphological statistical regularities through experience (Lelonkiewicz et al., 2020; Tamminen et al., 2015), and that the morphemic knowledge of skilled readers is graded in a manner that reflects the strength of these learned statistical regularities (Ulicheva et al., 2020). However, Treiman et al. (2020) raised an intriguing challenge. They tested adults’ sensitivity to the relationship between suffix spellings and grammatical category in a nonword spelling task, using spoken nonwords containing word‐final /Əs/ and /Ik/. They found that adults were more likely to use spellings ‐ous