Is there evidence for a noisy computation deficit in developmental dyslexia? - Tan, Yufei; Chanoine, Valérie; Cavalli, Eddy; Anton, Jean-Luc; Ziegler, Johannes C. (Frontiers in Human Neuroscience, 2022)
The noisy computation hypothesis of developmental dyslexia (DD) is particularly appealing because it can explain deficits across a variety of domains, such as temporal, auditory, phonological, visual and attentional processes. A key prediction is that noisy computations lead to more variable and less stable word representations. A way to test this hypothesis is through repetition of words, that is, when there is noise in the system, the neural signature of repeated stimuli should be more variable. The hypothesis was tested in an fMRI experiment with dyslexic and normal readers by repeating words twelve times. Variability measures were computed both at the behavioral and neural levels. At the behavioral level, we compared the standard deviation of reaction time distributions of repeated words. At the neural level, in addition to standard univariate analyses and measures of intra-item variability, we also used multivariate pattern analyses (representational similarity and classification) to find out whether there was evidence for noisier representations in dyslexics than controls. Results showed that there were no significant differences between the two groups in any of the analyses despite robust results within each group (i.e., high representational similarity between repeated words, good classification of words versus nonwords). In summary, there was no evidence in favor of the idea that dyslexic readers would have noisier neural representations than normal readers.