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Developmental studies examining relations between word reading (WR) and decoding in typical and dyslexic populations routinely cut the reading distribution to form distinct groups. However, dichotomizing continuous variables to study development is problematic for multiple reasons. Instead, we modeled and visualized the parallel growth of WR and nonword reading (NWR) factor scores longitudinally in a Grade 1–4 developmental sample (N = 588). The results indicate that while WR and NWR growth factors are highly related (r =.71), the relation between WR and NWR trajectories change as a function of initial WR. Results are interpreted within computational models of dyslexia in which children with dyslexia overfit orthography → phonology relations at the level of the word, limiting the development of sublexical representations needed to read nonwords. [ABSTRACT FROM AUTHOR]

Word learning is critical for the development of reading and language comprehension skills. Although previous studies have indicated that word learning is compromised in children with reading disability (RD) or developmental language disorder (DLD), it is less clear how word learning difficulties manifest in children with comorbid RD and DLD. Furthermore, it is unclear whether word learning deficits in RD or DLD include difficulties with offline consolidation of newly learned words. In the current study, we employed an artificial lexicon learning paradigm with an overnight design to investigate how typically developing (TD) children (N = 25), children with only RD (N = 93), and children with both RD and DLD (N = 34) learned and remembered a set of phonologically similar pseudowords. Results showed that compared to TD children, children with RD exhibited: (i) slower growth in discrimination accuracy for cohort item pairs sharing an onset (e.g. pibu‐pibo), but not for rhyming item pairs (e.g. pibu‐dibu); and (ii) lower discrimination accuracy for both cohort and rhyme item pairs on Day 2, even when accounting for differences in Day 1 learning. Moreover, children with comorbid RD and DLD showed learning and retention deficits that extended to unrelated item pairs that were phonologically dissimilar (e.g. pibu‐tupa), suggestive of broader impairments compared to children with only RD. These findings provide insights into the specific learning deficits underlying RD and DLD and motivate future research concerning how children use phonological similarity to guide the organization of new word knowledge. [ABSTRACT FROM AUTHOR]

Visual word recognition is facilitated by the presence of orthographic neighbors that mismatch the target word by a single letter substitution. However, researchers typically do not consider where neighbors mismatch the target. In light of evidence that some letter positions are more informative than others, we investigate whether the influence of orthographic neighbors differs across letter positions. To do so, we quantify the number of enemies at each letter position (how many neighbors mismatch the target word at that position). Analyses of reaction time data from a visual word naming task indicate that the influence of enemies differs across letter positions, with the negative impacts of enemies being most pronounced at letter positions where readers have low prior uncertainty about which letters they will encounter (i.e., positions with low entropy). To understand the computational mechanisms that give rise to such positional entropy effects, we introduce a new computational model, VOISeR (Visual Orthographic Input Serial Reader), which receives orthographic inputs in parallel and produces an over-time sequence of phonemes as output. VOISeR produces a similar pattern of results as in the human data, suggesting that positional entropy effects may emerge even when letters are not sampled serially. Finally, we demonstrate that these effects also emerge in human subjects' data from a lexical decision task, illustrating the generalizability of positional entropy effects across visual word recognition paradigms. Taken together, such work suggests that research into orthographic neighbor effects in visual word recognition should also consider differences between letter positions. [ABSTRACT FROM AUTHOR]

Despite the lack of invariance problem (the many‐to‐many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side‐stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real‐world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two‐layer network that borrows one element from ASR, long short‐term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human‐like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR. [ABSTRACT FROM AUTHOR]

The overarching goal of the new Florida State University/Haskins Laboratory/University of Connecticut Learning Disability (LD) Hub project is to align computational and behavioral theories of individual word reading development more closely with the challenges of learning to read a quasi‐regular orthography (i.e., English) for both typically developing (TD) children and, more specifically, children with dyslexia. Our LD Hub adopts an integrated approach to better understand the neurocognitive bases of individual differences in word reading development by specifically examining the experiential (exogenous) and child‐specific (endogenous) factors that determine acquisition of orthographic–phonological knowledge at different subword granularities using behavioral and computational modeling. Findings are intended to enrich understanding of the processes that influence individual differences in word reading development in TD and dyslexic children and significantly inform issues of practice (e.g., curriculum, instruction, diagnosis, and intervention). Here, we briefly provide the rationale for the Hub and present findings from the initial behavioral and computational modeling studies. [ABSTRACT FROM AUTHOR]

We investigated whether preschool children with specific language impairment ( SLI) exhibit the shape bias in word learning: the bias to generalize based on shape rather than size, color, or texture in an object naming context ('This is a wek; find another wek') but not in a non-naming similarity classification context ('See this? Which one goes with this one?'). Fifty-four preschool children (16 with SLI, 16 children with typical language [ TL] in an equated control group, and 22 additional children with TL included in individual differences analyses but not group comparisons) completed a battery of linguistic and cognitive assessments and two experiments. In Experiment 1, children made generalization choices in object naming and similarity classification contexts on separate days, from options similar to a target object in shape, color, or texture. On average, TL children exhibited the shape bias in an object naming context, but children with SLI did not. In Experiment 2, we tested whether the failure to exhibit the shape bias might be linked to ability to detect systematicities in the visual domain. Experiment 2 supported this hypothesis, in that children with SLI failed to learn simple paired visual associations that were readily learned by children with TL. Analyses of individual differences in the two studies revealed that visual paired-associate learning predicted degree of shape bias in children with SLI and TL better than any other measure of nonverbal intelligence or standard assessments of language ability. We discuss theoretical and clinical implications. [ABSTRACT FROM AUTHOR]