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Purpose: This study investigated how correlations between rapid automatized naming (RAN) and reading depend on characteristics of the stimuli. RAN tasks using stimuli with high phonological demands were predicted to be the strongest correlates of decoding efficiency, while high semantic demands were predicted to lead to stronger correlations with comprehension. Method: At two time points, 132 Grade 2 children completed four different RAN versions, two using letter stimuli (low semantic load) and two using object stimuli (high semantic load). Both types of stimuli were used in either a repeated version, where a set of four items were repeated multiple times (low semantic load), or in a unique version, where each item appeared only once (high semantic load). Decoding efficiency and reading comprehension were assessed in Grade 5. Results: Analyses showed that confirmatory factor models with separate factors for each version provided better fit than grouping factors according to time point. Repetition (lowering semantic load) increased the longitudinal association between RAN objects and decoding efficiency. There was a tendency for conditions with higher semantic load to correlate more strongly with reading comprehension after control for decoding efficiency, but the differences were not significant. Conclusion: The results indicate that increasing semantic load weakens the relationship with decoding efficiency.

This study investigated (1) sequential (between-word) processing efficiency at various points during development, (2) its importance within a range of measures typically used to assess reading fluency, and 3) how subskills typically associated with word-level and text-level processes may interact and uniquely contribute to various reading fluency outcomes. The sample included 139 intermediate-level (Grade 3, n = 78) and more advanced (Grade 5, n = 61) readers of Dutch. Word-level subskills covered within-word processes and sequential between-word processing efficiency. Subskills associated with text-level processing were vocabulary and syntactic skills. Fluency measures included simple and complex lists of words and nonwords, and a text. Data were analyzed through hierarchical regressions and commonality analyses. The findings extend the importance of sequential (between-word) processing efficiency for fluent reading of simple word lists and texts, after controlling for within-word processes, to complex word lists and texts as well as lists of nonwords. The findings hold for both intermediate-level and more advanced readers and, as expected, the contribution of between-word processing increased with reading-skill level. Effects of vocabulary were generally absent, aside from a small effect on text reading fluency in Grade 3. Effects of syntactic skills were not found, not even in more advanced readers. The results support the idea that once efficient individual word recognition is in place, further fluency development is driven by more efficient between-word processing. They also confirm that vocabulary may be less prominent in processing mechanisms underlying fluent word identification in transparent languages, across reading levels. Keywords: Fluency, language skills, serial naming, text reading, word reading, word recognition.

In this study, we investigated how word- and text-level processes contribute to different types of reading fluency measures. We aimed to increase our understanding of the underlying processes necessary for fluent reading. The sample included 73 Dutch Grade 3 children, who were assessed on serial word reading rate (familiar words), word-list reading fluency (increasingly difficult words), and sentence reading fluency. Word-level processes were individual word recognition speed (discrete word reading) and sequential processing efficiency (serial digit naming). Text-level processes were receptive vocabulary and syntactic skills. The results showed that word- and text-level processes combined accounted for a comparable amount of variance in all fluency outcomes. Both word-level processes were moderate predictors of all fluency outcomes. However, vocabulary only moderately predicted sentence reading fluency, and syntactic skills merely contributed to sentence reading fluency indirectly through vocabulary. The findings indicate that sequential processing efficiency has a crucial role in reading fluency across various measures besides individual word recognition speed. Additionally, text-level processes come into play when complexity and context availability of fluency measures increases, but the exact timing requires further study. Findings are discussed in terms of future directions and their possible value for diagnostic assessment and intervention of reading difficulties.

Scientific studies of language behavior need to grapple with a large diversity of languages in the world and, for reading, a further variability in writing systems. Yet, the ability to form meaningful theories of reading is contingent on the availability of cross-linguistic behavioral data. This paper offers new insights into aspects of reading behavior that are shared and those that vary systematically across languages through an investigation of eye-tracking data from 13 languages recorded during text reading. We begin with reporting a bibliometric analysis of eye-tracking studies showing that the current empirical base is insufficient for cross-linguistic comparisons. We respond to this empirical lacuna by presenting the Multilingual Eye-Movement Corpus (MECO), the product of an international multi-lab collaboration. We examine which behavioral indices differentiate between reading in written languages, and which measures are stable across languages. One of the findings is that readers of different languages vary considerably in their skipping rate (i.e., the likelihood of not fixating on a word even once) and that this variability is explained by cross-linguistic differences in word length distributions. In contrast, if readers do not skip a word, they tend to spend a similar average time viewing it. We outline the implications of these findings for theories of reading. We also describe prospective uses of the publicly available MECO data, and its further development plans.

Orthographic learning is the topic of many recent studies about reading, but much is still unknown about conditions that affect orthographic learning and their influence on reading fluency development over time. This study investigated lexicality effects on orthographic learning in beginning and relatively advanced readers of Dutch. Eye movements of 131 children in Grades 2 and 5 were monitored during an orthographic learning task. Children read sentences containing pseudowords or low-frequency real words that varied in number of exposures. We examined both offline learning outcomes (i.e., orthographic choice and spelling dictation) of target items and online gaze durations on target words. The results showed general effects of exposure, lexicality, and reading-skill level. Also, a two-way interaction was found between the number of exposures and lexicality when detailed orthographic representations were required, consistent with a larger overall effect of exposure on learning the spellings of pseudowords. Moreover, lexicality and reading-skill level were found to affect the learning rate across exposures based on a decrease in gaze durations, indicating a larger learning effect for pseudowords in Grade 5 children. Yet, further interactions between exposure and reading-skill level were not present, indicating largely similar learning curves for beginning and advanced readers. We concluded that the reading system of more advanced readers may cope somewhat better with words varying in lexicality, but is not more efficient than that of beginning readers in building up orthographic knowledge of specific words across repeated exposures.

Published online: 27 Jul 2017, Supplemental data for this article can be accessed at https://doi.org/10.1080/23273798.2017.1355059, We evaluated the dual route cascaded (DRC) model of visual word recognition using Greek behavioural data on word and nonword naming and lexical decision, focusing on the effects of syllable and bigram frequency. DRC was modified to process polysyllabic Greek words and nonwords. The Greek DRC and native speakers of Greek were presented with the same sets of word and nonword stimuli, spanning a wide range on several psycholinguistic variables, and the sensitivity of the model to lexical and sublexical variables was compared to the effects of these factors on the behavioural data. DRC pronounced correctly all the stimuli and successfully simulated the effects of frequency in words, and of length and bigram frequency in nonwords. However, unlike native speakers of Greek, DRC failed to demonstrate sensitivity to word length and syllabic frequency. We discuss the significance of these findings in constraining models of visual word recognition.

This study addresses the lexical representation of stress in a series of five intra-modal and cross-modal priming experiments in the Greek language using lexical decision tasks with auditory and visual targets. Three-syllable primes and targets were matched in first syllable segments, length, and other variables, and differed segmentally in the second and third syllable. Primes matched or mismatched targets in stress, which was placed on the penultimate or antepenultimate syllable. There was no evidence for stress priming in either accuracy or latency of responses to either words or pseudowords in any of these experiments, either intra-modally or cross-modally. In contrast, a control fragment priming experiment using only the first two syllables of the primes produced a significant effect of stress congruence for words but not for pseudowords. The results are interpreted in the context of previous findings in the literature as arising from lexical activation rather than from matching stress patterns. Overall, findings are consistent with lexical representations including stress information that is inseparable from segmental specification, rather than with abstract representations of metrical templates.

We develop an unsupervised dual-network connectionist model of category learning in which rules gradually emerge from a standard Kohonen network. The architecture is based on the interaction of a statistical-learning (Kohonen) network and a competitive-learning rule network. The rules that emerge in the rule network are weightings of individual features according to their importance for categorisation. Once the combined system has learned a particular rule, it de-emphasizes those features that are not sufficient for categorisation, thus allowing correct classification of novel, but atypical, stimuli, for which a standard Kohonen network fails. We explain the principles and architectural details of the model and show how it works correctly for stimuli that are misclassified by a standard Kohonen network.