Lexical neighborhood effects in pseudoword spelling

The general aim of this study is to contribute to a better understanding of the cognitive processes that underpin skilled adult spelling. More specifically, it investigates the influence of lexical neighbors on pseudo-word spelling with the goal of providing a more detailed account of the interaction between lexical and sublexical sources of knowledge in spelling. In prior research examining this topic, adult participants typically heard lists composed of both words and pseudo-words and had to make a lexical decision to each stimulus before writing the pseudo-words. However, these priming paradigms are susceptible to strategic influence and may therefore not give a clear picture of the processes normally engaged in spelling unfamiliar words. In our two Experiments involving 71 French-speaking literate adults, only pseudo-words we represented which participants were simply requested to write to dictation using the first spelling that came to mind. Unbeknownst to participants, pseudo-words varied according to whether they did or did not have a phonological word neighbor. Results revealed that low-probability yphoneme/grapheme mapping(e.g.,/o/-> aud in French)were used significantly more often in spelling pseudo-words with a close phonological lexical neighbour with that spelling(e.g.,/krepo/derived from “crapaud,”/krapo/)than in spelling pseudo-words with no close neighbors(e.g.,/frøpo/).In addition, the strength of this lexical influence increased with the lexical frequency of the word neighbors as well as with their degree of phonetic overlap with the pseudoword targets. These results indicate that information from lexical and sublexical processes is integrated in the course of spelling, and a specific theoretical account as to how such integration may occur is introduced

Varieties of developmental dyslexia in Greek children

The current study aimed to investigate in a group of nine Greek children with dyslexia (mean age 9.9 years) whether the surface and phonological dyslexia subtypes could be identified. A simple regression was conducted using printed word naming latencies and nonword reading accuracy for 33 typically developing readers. Ninety per cent confidence intervals were established and dyslexic children with datapoints lying outside the confidence intervals were identified. Using this regression-based method three children with the characteristic of phonological dyslexia (poor nonword reading), two with surface dyslexia (slow word naming latencies) and four with a mixed profile (poor nonword reading accuracy and slow word naming latencies) were identified. The children were also assessed in spelling to dictation, phonological ability, rapid naming, visual memory and multi-character processing (letter report). Results revealed that the phonological dyslexia subtype children had difficulties in tasks of phonological ability, and the surface subtype children had difficulties in tasks of multi-character simultaneous processing ability. Dyslexic children with a mixed profile showed deficits in both phonological abilities and multi-character processing. In addition, one child with a mixed profile showed a rapid naming deficit and another showed a difficulty in visual memory for abstract designs. Overall the results confirm that the surface and phonological subtypes of developmental dyslexia can be found in Greek-speaking children. They also indicate that different subtypes are associated with different underlying disorders

Sensory theories of developmental dyslexia: three challenges for research.

Recent years have seen the publication of a range of new theories suggesting that the basis of dyslexia might be sensory dysfunction. In this Opinion article, the evidence for and against several prominent sensory theories of dyslexia is closely scrutinized. Contrary to the causal claims being made, my analysis suggests that many proposed sensory deficits might result from the effects of reduced reading experience on the dyslexic brain. I therefore suggest that longitudinal studies of sensory processing, beginning in infancy, are required to successfully identify the neural basis of developmental dyslexia. Such studies could have a powerful impact on remediation.This is the accepted manuscript. The final version is available from NPG at http://www.nature.com/nrn/journal/v16/n1/abs/nrn3836.html

Brain classification reveals the right cerebellum as the best biomarker of dyslexia

Background Developmental dyslexia is a specific cognitive disorder in reading acquisition that has genetic and neurological origins. Despite histological evidence for brain differences in dyslexia, we recently demonstrated that in large cohort of subjects, no differences between control and dyslexic readers can be found at the macroscopic level (MRI voxel), because of large variances in brain local volumes. In the present study, we aimed at finding brain areas that most discriminate dyslexic from control normal readers despite the large variance across subjects. After segmenting brain grey matter, normalizing brain size and shape and modulating the voxels' content, normal readers' brains were used to build a 'typical' brain via bootstrapped confidence intervals. Each dyslexic reader's brain was then classified independently at each voxel as being within or outside the normal range. We used this simple strategy to build a brain map showing regional percentages of differences between groups. The significance of this map was then assessed using a randomization technique. Results The right cerebellar declive and the right lentiform nucleus were the two areas that significantly differed the most between groups with 100% of the dyslexic subjects (N = 38) falling outside of the control group (N = 39) 95% confidence interval boundaries. The clinical relevance of this result was assessed by inquiring cognitive brain-based differences among dyslexic brain subgroups in comparison to normal readers' performances. The strongest difference between dyslexic subgroups was observed between subjects with lower cerebellar declive (LCD) grey matter volumes than controls and subjects with higher cerebellar declive (HCD) grey matter volumes than controls. Dyslexic subjects with LCD volumes performed worse than subjects with HCD volumes in phonologically and lexicon related tasks. Furthermore, cerebellar and lentiform grey matter volumes interacted in dyslexic subjects, so that lower and higher lentiform grey matter volumes compared to controls differently modulated the phonological and lexical performances. Best performances (observed in controls) corresponded to an optimal value of grey matter and they dropped for higher or lower volumes. Conclusion These results provide evidence for the existence of various subtypes of dyslexia characterized by different brain phenotypes. In addition, behavioural analyses suggest that these brain phenotypes relate to different deficits of automatization of language-based processes such as grapheme/phoneme correspondence and/or rapid access to lexicon entries. article available here: http://www.biomedcentral.com/1471-2202/10/6

Encoding order and developmental dyslexia:a family of skills predicting different orthographic components

We investigated order encoding in developmental dyslexia using a task that presented nonalphanumeric visual characters either simultaneously or sequentially—to tap spatial and temporal order encoding, respectively—and asked participants to reproduce their order. Dyslexic participants performed poorly in the sequential condition, but normally in the simultaneous condition, except for positions most susceptible to interference. These results are novel in demonstrating a selective difficulty with temporal order encoding in a dyslexic group. We also tested the associations between our order reconstruction tasks and: (a) lexical learning and phonological tasks; and (b) different reading and spelling tasks. Correlations were extensive when the whole group of participants was considered together. When dyslexics and controls were considered separately, different patterns of association emerged between orthographic tasks on the one side and tasks tapping order encoding, phonological processing, and written learning on the other. These results indicate that different skills support different aspects of orthographic processing and are impaired to different degrees in individuals with dyslexia. Therefore, developmental dyslexia is not caused by a single impairment, but by a family of deficits loosely related to difficulties with order. Understanding the contribution of these different deficits will be crucial to deepen our understanding of this disorder

Are children with developmental dyslexia all the same? A cluster analysis with more than 300 cases

Reading is vital to every aspect of modern life, exacerbated by reliance of the internet, email, and social media on the written medium. Developmental dyslexia (DD) characterizes a disorder in which the core deficit involves reading. Traditionally, DD is thought to be associated with a phonological impairment. However, recent evidence has begun to suggest that the reading impairment in some individuals is provoked by a visual processing deficit. In this paper, we present WISC‐IV data from more than 300 Italian children with a diagnosis of DD to investigate the manifestation of phonological and visual subtypes. Our results indicate the existence of two clusters of children with DD. In one cluster, the deficit was more pronounced in the phonological component, while both clusters were impaired in visual processing. These data indicate that DD may be an umbrella term that encompasses different profiles. From a theoretical perspective, our results demonstrate that dyslexia cannot be explained in terms of an isolated phonological deficit alone; visual impairment plays a crucial role. Moreover, general rather than specific accounts of DD are discussed

Bayesian comparators: a probabilistic modeling tool for similarity evaluation between predicted and perceived patterns

International audienceA central component of the predictive coding theoretical framework concerns the comparison between predictions and sensory decoding. In the probabilistic setting, this takes the form of assessing the similarity or distance between probability distributions. However, such similarity or distance measures are not associated with explicit probabilistic models, making their assumptions implicit. In this paper, we explore an original variation on probabilistic coherence variables; we define a probabilistic component, that we call a "Bayesian comparator", that mathematically yields a particular similarity measure. A geometrical analogy suggests two variants of this measure. We apply these similarity measures to simulate the comparison of known, predicted patterns to patterns from sensory decoding, first in a simple, illustrative model, and second, in a previous model of visual word recognition. Experimental results suggest that the variant that is scaled by the norms of both predicted and perceived probability distributions yields better robustness and more desirable dynamics

Bayesian comparators: a probabilistic modeling tool for similarity evaluation between predicted and perceived patterns

International audienceA central component of the predictive coding theoretical framework concerns the comparison between predictions and sensory decoding. In the probabilistic setting, this takes the form of assessing the similarity or distance between probability distributions. However, such similarity or distance measures are not associated with explicit probabilistic models, making their assumptions implicit. In this paper, we explore an original variation on probabilistic coherence variables; we define a probabilistic component, that we call a "Bayesian comparator", that mathematically yields a particular similarity measure. A geometrical analogy suggests two variants of this measure. We apply these similarity measures to simulate the comparison of known, predicted patterns to patterns from sensory decoding, first in a simple, illustrative model, and second, in a previous model of visual word recognition. Experimental results suggest that the variant that is scaled by the norms of both predicted and perceived probability distributions yields better robustness and more desirable dynamics