Dataset Information

Holistic processing of faces and words predicts reading accuracy and speed in dyslexic readers.

ABSTRACT: We compared the performance of dyslexic and typical readers on two perceptual tasks, the Vanderbilt Holistic Face Processing Task and the Holistic Word Processing Task. Both yield a metric of holistic processing that captures the extent to which participants automatically attend to information that is spatially nearby but irrelevant to the task at hand. Our results show, for the first time, that holistic processing of faces is comparable in dyslexic and typical readers but that dyslexic readers show greater holistic processing of words. Remarkably, we show that these metrics predict the performance of dyslexic readers on a standardized reading task, with more holistic processing in both tasks associated with higher accuracy and speed. In contrast, a more holistic style on the words task predicts less accurate reading of both words and pseudowords for typical readers. We discuss how these findings may guide our conceptualization of the visual deficit in dyslexia.

SUBMITTER: Brady N

PROVIDER: S-EPMC8673614 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Holistic processing of faces and words predicts reading accuracy and speed in dyslexic readers.

Brady Nuala N Darmody Kate K Newell Fiona N FN Cooney Sarah M SM

PloS one 20211215 12

We compared the performance of dyslexic and typical readers on two perceptual tasks, the Vanderbilt Holistic Face Processing Task and the Holistic Word Processing Task. Both yield a metric of holistic processing that captures the extent to which participants automatically attend to information that is spatially nearby but irrelevant to the task at hand. Our results show, for the first time, that holistic processing of faces is comparable in dyslexic and typical readers but that dyslexic readers ...[more]

PMID: 34910756

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Project description:This study was aimed at predicting individual differences in text reading fluency. The basic proposal included two factors, i.e., the ability to decode letter strings (measured by discrete pseudo-word reading) and integration of the various sub-components involved in reading (measured by Rapid Automatized Naming, RAN). Subsequently, a third factor was added to the model, i.e., naming of discrete digits. In order to use homogeneous measures, all contributing variables considered the entire processing of the item, including pronunciation time. The model, which was based on commonality analysis, was applied to data from a group of 43 typically developing readers (11- to 13-year-olds) and a group of 25 chronologically matched dyslexic children. In typically developing readers, both orthographic decoding and integration of reading sub-components contributed significantly to the overall prediction of text reading fluency. The model prediction was higher (from ca. 37 to 52% of the explained variance) when we included the naming of discrete digits variable, which had a suppressive effect on pseudo-word reading. In the dyslexic readers, the variance explained by the two-factor model was high (69%) and did not change when the third factor was added. The lack of a suppression effect was likely due to the prominent individual differences in poor orthographic decoding of the dyslexic children. Analyses on data from both groups of children were replicated by using patches of colors as stimuli (both in the RAN task and in the discrete naming task) obtaining similar results. We conclude that it is possible to predict much of the variance in text-reading fluency using basic processes, such as orthographic decoding and integration of reading sub-components, even without taking into consideration higher-order linguistic factors such as lexical, semantic and contextual abilities. The approach validity of using proximal vs. distal causes to predict reading fluency is discussed.

Dataset Information

Holistic processing of faces and words predicts reading accuracy and speed in dyslexic readers.

Publications

Holistic processing of faces and words predicts reading accuracy and speed in dyslexic readers.

Similar Datasets

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