What do we do with what we learn? Statistical learning of orthographic regularities impacts written word processing

Chetail, Fabienne

doi:10.1016/j.cognition.2017.02.015

Cited by 52 publications

(75 citation statements)

References 63 publications

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“…The results of the present study are broadly similar to those of studies that have used artificial materials to examine the learning of letter patterns (e.g., Chetail, 2017; Samara & Caravolas, 2014) in that participants in both types of studies learn about graphotactic patterns without explicit teaching. Studies using artificial materials have often focused on patterns involving position in a string, as when certain letters or digrams only occur at the beginnings of items and others occur only at the ends.…”

Section: Discussionsupporting

confidence: 81%

“…Studies using artificial materials have often focused on patterns involving position in a string, as when certain letters or digrams only occur at the beginnings of items and others occur only at the ends. Given that participants in previous studies learned these patterns (Chetail, 2017; Samara & Caravolas, 2014), why did the prephonological spellers studied here and by Treiman et al (2018) not show a sensitivity to position in their use of monograms and digrams? One reason may be that position effects in Portuguese and English are much more subtle than those in the experiments with artificial materials.…”

Section: Discussionmentioning

confidence: 86%

“…In the present study, we examined statistical learning as it takes place in the wild: learning about the letter patterns in the words of a real language. Rather than studying the course of learning in adults or primary-school children who are exposed to novel items in a laboratory (Chetail, 2017; Samara & Caravolas, 2014), we studied the outcome of learning in preschool children who are exposed to written language in their daily lives. Our analyses focused on preschoolers who, when trying to write words, did not yet use letters for their sound values.…”

Section: Discussionmentioning

confidence: 99%

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Statistical learning and spelling: Evidence from Brazilian prephonological spellers

et al. 2019

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We analyzed the spelling attempts of Brazilian children (age 3 years, 3 months to 6 years, 0 months) who were prephonological spellers, in that they wrote using letters that did not reflect the phonemes in the words they were asked to spell. We tested the hypothesis that children use their statistical-learning skills to learn about the appearance of writing and that older prephonological spellers, who have had on average more exposure to writing, produce more wordlike spellings than younger prephonological spellers. We found that older prephonological spellers produced longer spellings and were more likely to use letters and digrams in proportion to their frequency of occurrence in Portuguese. There were also some age-related differences in children's tendency to use letters from their own names when writing other words. The results extend previous findings with learners of English to children who are learning a more transparent orthography.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

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Statistical learning and spelling: Evidence from Brazilian prephonological spellers

et al. 2019

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“…In experiment 1 and 2, we provide new evidence that when readers are confronted with novel, unfamiliar words, they code for the statistics of co-occurrence between letters. These data sit well with recent work using a similar approach [32], and strengthens the idea that readers capitalise on letter co-occurrence statistics to learn to visually identify novel words. Completely new to this work is the finding that sensitivity to bigram frequency is independent of word length (see 3.4).…”

Section: Letter Co-occurrence and Models Of Visual Word Identificationsupporting

confidence: 85%

A general-purpose mechanism of visual feature association in visual word identification and beyond

Vidal

Viviani

Zoccolan

et al. 2020

Preprint

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Writing systems are a recent cultural invention, which makes it unlikely that specific cognitive mechanisms have developed through selective pressure for reading itself. Instead, reading might capitalize on evolutionary older mechanisms that originally supported other tasks. Accordingly, animals such as baboons can be trained to perform visual word recognition. This suggests that the visual mechanisms supporting reading might be phylogenetically old and domain-general. Here we propose that if the human reading system relies on domain-general visual mechanisms, effects that are typically found within the domain of reading should also be observable with non-orthographic visual stimuli. To test this hypothesis, we systematically tested different types of visual material with the same experimental design. Subjects were passively familiarized with a set of composite visual items, and then tested in an oddball paradigm for their ability to detect novel stimuli. Some of these novel stimuli shared their statistical structure with the familiar items, and were found to be hard to detect in two experiments using strings of letter-like symbols; this replicates the well-known, and supposedly reading-specific, bigram effect. Crucially, in two further experiments we show that the same effect emerges with made-up, 3D objects and sinusoidal gratings. The effect size was equivalent across experiments, despite the use of radically different stimuli. These data suggest that a fundamental mechanism behind visual word learning also supports the learning of other visual stimuli, implying that such mechanism is general-purpose. This mechanism would enable the statistical learning of regularities in the visual environment.

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“…More recent works, within the modern framework of SL, extended this view to virtually all aspects of language. Thus, for example, works on SL suggest that writing systems can be thought of as an array of correlations between letters (e.g., Chetail, ) and between letters to sounds (e.g., Treiman & Kessler, ); phonology and phonotactics can be described as sets of co‐occurrences between speech sounds (e.g., Onishi, Chambers, & Fisher, ), morphology can be thought of as co‐occurrences between morphemes (e.g., Pacton, Fayol, & Perruchet, ), word knowledge as co‐occurrences between words and their referents (e.g., Yu & Smith, ). Recent works also revisit the question regarding the statistical nature of syntax, discussing what aspects of syntax can be indeed reduced to sets of regularities spanning words and larger phrases (and see, Saffran & Wilson, ; Thompson & Newport, for a discussion of this more controversial issue).Assumption Humans can extract complex statistical regularities from the input using SL computations.…”

Section: Introductionmentioning

confidence: 99%

Statistical learning abilities and their relation to language

Siegelman

2019

Language and Linguist. Compass

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Numerous studies on statistical learning (SL) have demonstrated humans' sensitivity to complex statistical properties in their sensory environment. These observations have had a profound impact on the study of language, highlighting statistical aspects of the linguistic input that can be learned from experience, leading to the widespread claim that SL plays a key role in language acquisition and processing. But how can this theorized link be experimentally demonstrated? One increasingly popular avenue comes from studies of individual differences, which tie individual variability in SL to variance in linguistic behavior. This review presents the theoretical advances stemming from this line of research, as well as some of the challenges it currently faces. It contends that while previous studies had an important role in establishing the existence of some coarse‐grained link between SL and language, recent developments in SL research suggest that the exact nature of this relationship is more complex than originally conceived and is still far from being fully understood. I specifically discuss three outstanding challenges: (a) understanding individual differences in light of the componential nature of SL, (b) mapping the full array of SL processes given the complexity of real‐world statistics, and (c) estimating the strength of current empirical evidence while taking into account both positive and null findings. Confronting these issues, I argue, is a necessary step towards a full theory of the role of SL across language.

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What do we do with what we learn? Statistical learning of orthographic regularities impacts written word processing

Cited by 52 publications

References 63 publications

Statistical learning and spelling: Evidence from Brazilian prephonological spellers

Statistical learning and spelling: Evidence from Brazilian prephonological spellers

A general-purpose mechanism of visual feature association in visual word identification and beyond

Statistical learning abilities and their relation to language

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