Decoding letter position in word reading

A fundamental computation underlying visual word recognition is the ability to transform a set of letters into a visual word form. Neuropsychological data suggest that letter position within a word may be independently affected by brain damage, resulting in a dissociable subtype of peripheral dyslexia. Here we used functional magnetic resonance imaging and supervised machine learning techniques to classify letter position based on activation patterns evoked during reading Hebrew words. Across the entire brain, activity patterns in the left intraparietal sulcus provided the best classification accuracy (80%) with respect to letter position. Importantly, the same set of voxels that showed highest classification performance of letter position using one letter-of-interest also showed highest classification performance using a different letter-of-interest. A functional connectivity analysis revealed that activity in these voxels co-varied with activity in the Visual Word Form Area, confirming cross-talk between these regions during covert reading. The results converge with reports of patients with acquired letter position dyslexia, who suffer from left occipito-parietal lesions. These findings provide direct and novel evidence for the role of left IPS within the reading network in processing relative letter positions.