A Computational Model of Dyslexics’ Perceptual Difficulties as Impaired Inference of Sound Statistics

Though often ignored, many studies have shown that implicit stimulus-specific expectations play an important role in perception. However, what information about the prior distribution of stimuli is integrated into these perceptual expectations and how this information is utilized in the process of perceptual decision making is not clear. Here we address this question for the case of a simple two-tone discrimination task. We find a large perceptual bias favoring the mean of previous stimuli, i.e. “contraction bias” -small magnitudes are overestimated and large magnitudes are underestimated. We propose a biologically plausible computational model that accounts for this phenomenon in the general population. We then apply this proposed model to a specific population - dyslexics - to characterize their poorer performance in this task computationally. Our findings show that dyslexics’ perceptual deficit can be accounted for by inadequate weighting of their implicit memory of past trials relative to their internal noise. Underweighting the stimulus statistics decreases dyslexics’ ability to compensate for noisy observations. This study provides the first description of a specific computational deficit associated with dyslexia.