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In a quasiregular orthography like English, children inevitably encounter irregular words during reading. Previous research suggests successful reading of an irregular word depends at least partially on a child's ability to address the mismatch between decoded form and stored word pronunciation, referred to as a child's set for variability, and the word's relative transparency, measured here using a spelling to pronunciation transparency rating. Item-level analyses were used to explore the relationship between general child performance on the set for variability mispronunciation task, word specific set for variability (predicting reading of that word), spelling to pronunciation transparency rating, and irregular word reading. Significant predictors included general word reading, general set for variability performance, and item-specific set for variability performance; word frequency and spelling to pronunciation transparency rating; and an interaction between word reading and the transparency rating. Results underscore the importance of considering both general and item-specific factors affecting irregular word reading. [ABSTRACT FROM AUTHOR]

As children learn to read, they become sensitive to context-dependent vowel pronunciations in words, considered a form of statistical learning. The work of Treiman and colleagues demonstrated that readers' vowel pronunciations depend on the consonantal context in which the vowel occurs and reading experience. Using explanatory item-response models we examined child- and nonword-factors associated with children's assignment of more versus less frequent grapheme-phoneme correspondences (GPC) to vowel pronunciations as a function of rime coda in monosyllabic nonwords. Students (N = 96) in grades 2-5 read nonwords in which more versus less frequent vowel GPCs were wholly supported or partially favored by the rime unit. Use of less frequent vowel GPCs was predicted by set for variability, word reading, and rime support for the context-dependent vowel pronunciation. We interpret the results within a developmental word reading model in which initially incomplete and oversimplified GPC representations become more context dependent with reading experience. [ABSTRACT FROM AUTHOR]

To investigate the neural basis of a common statistical learning mechanism involved in motor sequence learning and decoding, we recorded brain activation from participants during a serial reaction time (SRT) task and a word reading task using functional magnetic resonance imaging. In the SRT task, a manual response was made depending on the location of a visual cue, and the order of the locations was either fixed or random. In the word reading task, visual words were passively presented. In the inferior frontal gyrus pars triangularis (IFGpTr) and the insula, differences in activation between the ordered and random condition in the SRT task and activation to printed words in the word reading task were correlated with the participants' decoding ability. We speculate that extraction of statistically predictable patterns in the IFGpTr and insula contributes to both motor sequence learning and orthographic learning, and therefore predicts individual differences in decoding skill. [ABSTRACT FROM AUTHOR]

The strategy underlying most computational models of word reading is to specify the organization of the reading system—its architecture and the processes and representations it employs—and to demonstrate that this organization would give rise to the behavior observed in word reading tasks. This approach fails to adequately address the variation in reading behavior observed across and within linguistic communities. Only computational models that incorporate learning can fully account for variation in organization. However, even extant learning models (e.g., the triangle model) must be extended if they are to fully account for variation in organization. The challenges associated with extending theories in this way are discussed. [ABSTRACT FROM AUTHOR]

Investigations of the impact of morphemic boundaries on transposed-letter (TL) priming effects have yielded conflicting results. Five masked priming lexical decision experiments were conducted to examine the interaction of letter transpositions and morphemic boundaries with English suffixed derivations. Experiments 1-3 found that responses to monomorphemic target words (e.g., SPEAK) were facilitated to the same extent by morphologically related primes containing letter transpositions that did (SPEAEKR) or did not (SPEKAER) cross a morphemic boundary. This pattern was also observed in Experiments 4 and 5, in which the targets (e.g., SPEAKER) were the base forms of the TL primes. Thus, in these experiments the influence of the morphological structure of a TL prime did not depend on whether the letter transposition crossed a morphological boundary. [ABSTRACT FROM AUTHOR]

Previous studies haves shown that under masked priming conditions, CORNER primes CORN as strongly as TEACHER primes TEACH and more strongly than BROTHEL primes BROTH. This result has been taken as evidence of a purely structural level of representation at which words are decomposed into morphological constituents in a manner that is independent of semantics. The research reported here investigated the influence of semantic transparency on long-term morphological priming. Two experiments demonstrated that while lexical decisions were facilitated by semantically transparent primes like TEACHER, semantically opaque words like CORNER had no effect. Although differences in the nonword foils used in each experiment gave rise to somewhat different patterns of results, this difference in the effects of transparent and opaque primes was found in both experiments. The implications of this finding for accounts of morphological effects on visual word identification are discussed. [ABSTRACT FROM AUTHOR]

This article provides an overview of a dynamical systems approach to visual word recognition. In this approach, the dynamics of word recognition are characterized in terms of a connectionist network model. According to this model, seeing a word results in changes in the pattern of activation over the nodes in the lexical network such that, over time, the network moves into an attractor state representing the orthographic, phonological, and semantic properties of that word. At a slower timescale, a learning process modifies the strengths of the connections among the nodes in a way that attunes the network to the statistical regularities in its environment. This view of word identification accommodates a wide body of empirical results, a representative sampling of which is discussed here. Finally, the article closes with a discussion of some of the theoretical issues that should be addressed as the dynamical approach continues to develop. [ABSTRACT FROM AUTHOR]