Set for Variability as a Critical Predictor of Word Reading: Potential Implications for Early Identification and Treatment of Dyslexia.

Quasiregular orthographies such as English contain substantial ambiguities between orthography and phonology that force developing readers to acquire flexibility during decoding of unfamiliar words, a skill referred to as a "set for variability" (SfV). The ease with which a child can disambiguate the mismatch between the decoded form of a word and its actual lexical phonological form has been operationalized using the SfV mispronunciation task (e.g., the word wasp is pronounced to rhyme with clasp [i.e., /wæsp/] and the child must recognize the actual pronunciation of the word to be /wɒsp/). SfV has been shown to be a significant predictor of word reading variance. However, little is known about the relative strength of SfV as a predictor of word reading compared to other well-established predictors or the strength of this relationship in children with dyslexia. To address these questions, we administered the SfV task to a sample of grade 2-5 children (N=489) along with other reading related measures. SfV accounted for 15% unique variance in word reading above and beyond other predictors, whereas phonological awareness (PA) accounted for only 1%. Dominance analysis indicated SfV is the most powerful predictor, demonstrating complete statistical dominance over other variables including PA. Quantile regression revealed SfV is a stronger predictor at lower levels of reading skill, indicating it may be an important predictor in students with dyslexia. Results suggest that SfV is a powerful and potentially highly sensitive predictor of early reading difficulties and, therefore, may be important for early identification and treatment of dyslexia.

Attention Shifts to More Complex Structures With Experience.

Our environments are saturated with learnable information. What determines which of this information is prioritized for limited attentional resources? Although previous studies suggest that learners prefer medium-complexity information, here we argue that what counts as medium should change as someone learns an input's structure. Specifically, we examined the hypothesis that attention is directed toward more complicated structures as learners gain more experience with the environment. College students watched four simultaneous streams of information that varied in complexity. RTs to intermittent search trials (Experiment 1, N = 75) and eye tracking (Experiment 2, N = 45) indexed where participants attended during the experiment. Using two participant- and trial-specific measures of complexity, we demonstrated that participants attended to increasingly complex streams over time. Individual differences in structure learning also predicted attention allocation, with better learners attending to complex structures earlier in learning, suggesting that the ability to prioritize different information over time is related to learning success.

How you read affects what you gain: Individual differences in the functional organization of the reading system predict intervention gains in children with reading disabilities.

There is now considerable evidence regarding the types of interventions that are effective at remediating reading disabilities on average. It is generally unclear, however, what predicts the magnitude of individual-level change following a given intervention. We examine new predictors of intervention gains that are theoretically grounded in computational models of reading and focus on individual differences in the functional organization of the reading system. Specifically, we estimate the extent to which children with reading disabilities (n=118 3rd-4th graders) rely on two sources of information during an oral word reading task - print-speech correspondences and semantic imageability - before and after a phonologically-weighted intervention. We show that children who relied more on print-speech regularities and less on imageability pre-intervention had better intervention gains. In parallel, children who over the course of the intervention exhibited greater increases in their reliance on print-speech correspondences and greater decreases in their reliance on imageability had better intervention outcomes. Importantly, these two factors were differentially related to specific reading task outcomes, with greater reliance on print-speech correspondences associated with pseudoword naming, while (lesser) reliance on imageability related to word reading and comprehension. We discuss the implications of these findings for theoretical models of reading acquisition and educational practice.

Expanding horizons of cross-linguistic research on reading: The Multilingual Eye-movement Corpus (MECO).

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.

Theory-driven classification of reading difficulties from fMRI data using Bayesian latent-mixture models.

Decades of research have led to several competing theories regarding the neural contributors to impaired reading. But how can we know which theory (or theories) identifies the types of markers that indeed differentiate between individuals with reading disabilities (RD) and their typically developing (TD) peers? To answer this question, we propose a new analytical tool for theory evaluation and comparison, grounded in the Bayesian latent-mixture modeling framework. We start by constructing a series of latent-mixture classification models, each reflecting one existing theoretical claim regarding the neurofunctional markers of RD (highlighting network-level differences in either mean activation, inter-subject heterogeneity, inter-region variability, or connectivity). Then, we run each model on fMRI data alone (i.e., while models are blind to participants' behavioral status), which enables us to interpret the fit between a model's classification of participants and their behavioral (known) RD/TD status as an estimate of its explanatory power. Results from n=127 adolescents and young adults (RD: n=59; TD: n=68) show that models based on network-level differences in mean activation and heterogeneity failed to differentiate between TD and RD individuals. In contrast, classifications based on variability and connectivity were significantly associated with participants' behavioral status. These findings suggest that differences in inter-region variability and connectivity may be better network-level markers of RD than mean activation or heterogeneity (at least in some populations and tasks). More broadly, the results demonstrate the promise of latent-mixture modeling as a theory-driven tool for evaluating different theoretical claims regarding neural contributors to language disorders and other cognitive traits.

Using information-theoretic measures to characterize the structure of the writing system: the case of orthographic-phonological regularities in English.

It is generally well accepted that proficient reading requires the assimilation of myriad statistical regularities present in the writing system, including in particular the correspondences between words' orthographic and phonological forms. There is considerably less agreement, however, as to how to quantify these regularities. Here we present a comprehensive approach for this quantification using tools from Information Theory. We start by providing a glossary of the relevant information-theoretic metrics, with simplified examples showing their potential in assessing orthographic-phonological regularities. We specifically highlight the flexibility of our approach in quantifying information under different contexts (i.e., context-independent and dependent readings) and in different types of mappings (e.g., orthography-to-phonology and phonology-to-orthography). Then, we use these information-theoretic measures to assess real-world orthographic-phonological regularities of 10,093 mono-syllabic English words and examine whether these measures predict inter-item variability in accuracy and response times using available large-scale datasets of naming and lexical decision tasks. Together, the analyses demonstrate how information-theoretical measures can be used to quantify orthographical-phonological correspondences, and show that they capture variance in reading performance that is not accounted for by existing measures. We discuss the similarities and differences between the current framework and previous approaches as well as future directions towards understanding how the statistical regularities embedded in a writing system impact reading and reading acquisition.

Measuring individual differences in statistical learning: Current pitfalls and possible solutions.

Most research in statistical learning (SL) has focused on the mean success rates of participants in detecting statistical contingencies at a group level. In recent years, however, researchers have shown increased interest in individual abilities in SL, either to predict other cognitive capacities or as a tool for understanding the mechanism underlying SL. Most if not all of this research enterprise has employed SL tasks that were originally designed for group-level studies. We argue that from an individual difference perspective, such tasks are psychometrically weak, and sometimes even flawed. In particular, the existing SL tasks have three major shortcomings: (1) the number of trials in the test phase is often too small (or, there is extensive repetition of the same targets throughout the test); (2) a large proportion of the sample performs at chance level, so that most of the data points reflect noise; and (3) the test items following familiarization are all of the same type and an identical level of difficulty. These factors lead to high measurement error, inevitably resulting in low reliability, and thereby doubtful validity. Here we present a novel method specifically designed for the measurement of individual differences in visual SL. The novel task we offer displays substantially superior psychometric properties. We report data regarding the reliability of the task and discuss the importance of the implementation of such tasks in future research.

Memory representations are flexibly adapted to orthographic systems: A comparison of English and Hebrew.

Across languages, speech unfolds in the same temporal order, constrained by the forward flow of time. But the way phonology is spatially mapped onto orthography is language-specific, ranging from left-to-right, right-to-left, and top-to-bottom, among others. While the direction of writing systems influences how known words are visually processed, it is unclear whether it influences learning and memory for novel orthographic regularities. The present study tested English and Hebrew speakers on an orthographic word-referent mapping task in their native orthographies (written left-to-right and right-to-left, respectively), where the onsets and offsets of words were equally informative cues to word identity. While all individuals learned orthographic word-referent mappings significantly above chance, the parts of the word that were most strongly represented varied. English monolinguals false alarmed most to competing foils that began with the same bigram as the target, representing word onsets most strongly. However, Hebrew bilinguals trained on their native orthography showed no difference between false alarm rates to onset and offset competitors, representing the beginning and ends of words equally strongly. Importantly, Hebrew bilinguals tested on English words displayed a more English-like false alarm pattern (although not a full switch) suggesting that memory biases adapt to the opposite directionality of encountered text while retaining traces of native language biases. These findings demonstrate that experience with different writing systems influences how individuals represent novel orthographic words, starting in the earliest stages of learning.

What predicts successful literacy acquisition in a second language?

In the study reported here, we examined whether success (or failure) in assimilating the structure of a second language can be predicted by general statistical-learning abilities that are nonlinguistic in nature. We employed a visual-statistical-learning (VSL) task, monitoring our participants' implicit learning of the transitional probabilities of visual shapes. A pretest revealed that performance in the VSL task was not correlated with abilities related to a general g factor or working memory. We found that, on average, native speakers of English who more accurately picked up the implicit statistical structure embedded in the continuous stream of shapes better assimilated the Semitic structure of Hebrew words. Languages and their writing systems are characterized by idiosyncratic correlations of form and meaning, and our findings suggest that these correlations are picked up in the process of literacy acquisition, as they are picked up in any other type of learning, for the purpose of making sense of the environment.

Is there such a thing as a 'good statistical learner'?

A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from 'bad' to 'good' statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.

When the "Tabula" is Anything but "Rasa:" What Determines Performance in the Auditory Statistical Learning Task?

How does prior linguistic knowledge modulate learning in verbal auditory statistical learning (SL) tasks? Here, we address this question by assessing to what extent the frequency of syllabic co-occurrences in the learners' native language determines SL performance. We computed the frequency of co-occurrences of syllables in spoken Spanish through a transliterated corpus, and used this measure to construct two artificial familiarization streams. One stream was constructed by embedding pseudowords with high co-occurrence frequency in Spanish ("Spanish-like" condition), the other by embedding pseudowords with low co-occurrence frequency ("Spanish-unlike" condition). Native Spanish-speaking participants listened to one of the two streams, and were tested in an old/new identification task to examine their ability to discriminate the embedded pseudowords from foils. Our results show that performance in the verbal auditory SL (ASL) task was significantly influenced by the frequency of syllabic co-occurrences in Spanish: When the embedded pseudowords were more "Spanish-like," participants were better able to identify them as part of the stream. These findings demonstrate that learners' task performance in verbal ASL tasks changes as a function of the artificial language's similarity to their native language, and highlight how linguistic prior knowledge biases the learning of regularities.

Quantifying the regularities between orthography and semantics and their impact on group- and individual-level behavior.

Statistical views of reading highlight the link between proficient literacy and the assimilation of various regularities embedded in writing systems, including those in the mapping between print and meaning. Still, orthographic-semantic (O-S) regularities remain relatively understudied, with open questions regarding 3 issues: (a) how O-S regularities should be quantified, (b) how they impact the behavior of proficient readers, and (c) whether individual differences in sensitivity to these regularities predict reading skills. The goal of the current article is to address these questions. We start by reviewing previous studies estimating print-meaning regularities, where orthography-to-semantics consistency (OSC) is defined as the mean semantic similarity between a word and its orthographic neighbors. While we adopt this general strategy, we identify a potential confound in previous operational definitions. We therefore offer a modified measure, which we use to examine group-level OSC effects in available data sets of single word recognition and reading for comprehension. Our findings validate the existence of OSC effects but reveal variation across tasks, with OSC effects emerging more strongly in tasks involving a direct mapping of print to meaning. Next, we present a reanalysis of word naming data from 399 second through fifth graders, where we examine individual differences in reliance on O-S regularities and their relation to participants' reading skills. We show that early readers whose naming accuracy is more influenced by OSC (i.e., those who rely more on O-S) have better passage comprehension abilities. We conclude by discussing the role of O-S regularities in proficient reading and literacy acquisition. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The impact of computer-assisted technology on literacy acquisition during COVID-19-related school closures: Group-level effects and predictors of individual-level outcomes.

Introduction: The COVID-19 pandemic led to school closure and loss of in-person instruction during the 2019-2020 academic year across the United States, which had a profound impact on the reading development of beginning readers. In this study we tested if a research-informed educational technology (EdTech) program-GraphoLearn-could help alleviate the COVID-19 slide. We also sought to understand the profiles of children who benefitted most from this EdTech program. Methods: We tested participants' (N = 172 K-2 children) early literacy skills using a standardized measure (STAR) before and after playing GraphoLearn, and used the pre to post difference as the dependent variable. We first compared children's STAR actual and expected growth. Then we conducted a multiple regression analysis with data about engagement with GraphoLearn included as predictors. Additional predictors were extracted from GraphoLearn performance at study onset to assess children's letter-sound knowledge, rime awareness, and word recognition. Results: The difference between actual average reading growth and expected growth in a regular school year was not statistically significant. This suggests that children in our sample seem to be gaining reading skills as expected in a regular school year. Our multiple linear regression model (which accounted for R2 = 48% of reading growth) showed that older children, with higher baseline GraphoLearn word recognition, who played more units in a fixed number of days, made significantly more early literacy progress. Discussion: While lacking a control group, our preliminary results suggest that an EdTech program such as GraphoLearn may be a useful reading instructional tool during school shutdowns. In addition, our results suggest that practice with GraphoLearn was more effective and efficient when foundational instruction was already in place.

Statistical Learning and Language Impairments: Toward More Precise Theoretical Accounts.

Statistical-learning (SL) theory offers an experience-based account of typical and atypical spoken and written language acquisition. Recent work has provided initial support for this view, tying individual differences in SL abilities to linguistic skills, including language impairments. In the current article, we provide a critical review of studies testing SL abilities in participants with and without developmental dyslexia and specific language impairment and discuss the directions that this field of research has taken so far. We identify substantial vagueness in the demarcation lines between different theoretical constructs (e.g., "statistical learning," "implicit learning," and "procedural learning") as well as in the mappings between experimental tasks and these theoretical constructs. Moreover, we argue that current studies are not designed to contrast different theoretical approaches but rather test singular confirmatory predictions without including control tasks showing normal performance. We end by providing concrete suggestions for how to advance research on SL deficits in language impairments.

Individual Differences in Learning Abilities Impact Structure Addition: Better Learners Create More Structured Languages.

Over the last decade, iterated learning studies have provided compelling evidence for the claim that linguistic structure can emerge from non-structured input, through the process of transmission. However, it is unclear whether individuals differ in their tendency to add structure, an issue with implications for understanding who are the agents of change. Here, we identify and test two contrasting predictions: The first sees learning as a pre-requisite for structure addition, and predicts a positive correlation between learning accuracy and structure addition, whereas the second maintains that it is those learners who struggle with learning and reproducing their input who add structure to it. This prediction is hard to test in standard iterated learning paradigms since each learner is exposed to a different input, and since structure and accuracy are computed using the same test items. Here, we test these contrasting predictions in two experiments using a one-generation artificial language learning paradigm designed to provide independent measures of learning accuracy and structure addition. Adults (N = 48 in each study) were exposed to a semi-regular language (with probabilistic structure) and had to learn it: Learning was assessed using seen items, whereas structure addition was calculated over unseen items. In both studies, we find a strong positive correlation between individuals' ability to learn the language and their tendency to add structure to it: Better learners also produced more structured languages. These findings suggest a strong link between learning and generalization. We discuss the implications of these findings for iterated language models and theories of language change more generally.

The multi-faceted nature of visual statistical learning: Individual differences in learning conditional and distributional regularities across time and space.

Emerging research has demonstrated that statistical learning is a modality-specific ability governed by domain-general principles. Yet limited research has investigated different forms of statistical learning within modality. This paper explores whether there is one unified statistical learning mechanism within the visual modality, or separate task-specific abilities. To do so, we examined individual differences in spatial and nonspatial conditional and distributional statistical learning. Participants completed four visual statistical learning tasks: conditional spatial, conditional nonspatial, distributional spatial, and distributional nonspatial. Performance on all four tasks significantly correlated with each other, and performance on all tasks accounted for a large portion of the variance across tasks (57%). Interestingly, a portion of the variance of task performance (between 11% and 18%) was also accounted for by performance on each of the individual tasks. Our results suggest that visual statistical learning is the result of the interplay between a unified mechanism for extracting conditional and distributional statistical regularities across time and space, and an individual's ability to extract specific types of regularities.

Individual differences in learning the regularities between orthography, phonology and semantics predict early reading skills.

Statistical views of literacy development maintain that proficient reading requires the assimilation of myriad statistical regularities present in the writing system. Indeed, previous studies have tied statistical learning (SL) abilities to reading skills, establishing the existence of a link between the two. However, some issues are currently left unanswered, including questions regarding the underlying bases for these associations as well as the types of statistical regularities actually assimilated by developing readers. Here we present an alternative approach to study the role of SL in literacy development, focusing on individual differences among beginning readers. Instead of using an artificial task to estimate SL abilities, our approach identifies individual differences in children's reliance on statistical regularities as reflected by actual reading behavior. We specifically focus on individuals' reliance on regularities in the mapping between print and speech versus associations between print and meaning in a word naming task. We present data from 399 children, showing that those whose oral naming performance is impacted more by print-speech regularities and less by associations between print and meaning have better reading skills. These findings suggest that a key route by which SL mechanisms impact developing reading abilities is via their role in the assimilation of sub-lexical regularities between printed and spoken language -and more generally, in detecting regularities that are more reliable than others. We discuss the implications of our findings to both SL and reading theories.

What Determines Visual Statistical Learning Performance? Insights From Information Theory.

In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co-occurrences between them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct-the rate of information in the sensory input-explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large-scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning.

What exactly is learned in visual statistical learning? Insights from Bayesian modeling.

It is well documented that humans can extract patterns from continuous input through Statistical Learning (SL) mechanisms. The exact computations underlying this ability, however, remain unclear. One outstanding controversy is whether learners extract global clusters from the continuous input, or whether they are tuned to local co-occurrences of pairs of elements. Here we adopt a novel framework to address this issue, applying a generative latent-mixture Bayesian model to data tracking SL as it unfolds online using a self-paced learning paradigm. This framework not only speaks to whether SL proceeds through computations of global patterns versus local co-occurrences, but also reveals the extent to which specific individuals employ these computations. Our results provide evidence for inter-individual mixture, with different reliance on the two types of computations across individuals. We discuss the implications of these findings for understanding the nature of SL and individual-differences in this ability.

Is the Hebb repetition task a reliable measure of individual differences in sequence learning?

The Hebb repetition task, an operationalization of long-term sequence learning through repetition, is the focus of renewed interest, as it is taken to provide a laboratory analogue for naturalistic vocabulary acquisition. Indeed, recent studies have consistently related performance in the Hebb repetition task with a range of linguistic (dis)abilities. However, despite the growing interest in the Hebb repetition effect as a theoretical construct, no previous research has ever tested whether the task used to assess Hebb learning offers a stable and reliable measure of individual performance in sequence learning. Since reliability is a necessary condition to predictive validity, in the present work, we tested whether individual ability in visual verbal Hebb repetition learning displays basic test-retest reliability. In a first experiment, Hebrew-English bilinguals performed two verbal Hebb tasks, one with English and one with Hebrew consonant letters. They were retested on the same Hebb tasks after a period of about 6 months. Overall, serial recall performance proved to be a stable and reliable capacity of an individual. By contrast, the test-retest reliability of individual learning performance in our Hebb task was close to zero. A second experiment with French speakers replicated these results and demonstrated that the concurrent learning of two repeated Hebb sequences within the same task minimally improves the reliability scores. Taken together, our results raise concerns regarding the usefulness of at least some current Hebb learning tasks in predicting linguistic (dis)abilities. The theoretical implications are discussed.