Direct Viewing of Dyslexics' Compensatory Strategies in Speech in Noise Using Auditory Classification Images.

A vast majority of dyslexic children exhibit a phonological deficit, particularly noticeable in phonemic identification or discrimination tasks. The gap in performance between dyslexic and normotypical listeners appears to decrease into adulthood, suggesting that some individuals with dyslexia develop compensatory strategies. Some dyslexic adults however remain impaired in more challenging listening situations such as in the presence of background noise. This paper addresses the question of the compensatory strategies employed, using the recently developed Auditory Classification Image (ACI) methodology. The results of 18 dyslexics taking part in a phoneme categorization task in noise were compared with those of 18 normotypical age-matched controls. By fitting a penalized Generalized Linear Model on the data of each participant, we obtained his/her ACI, a map of the time-frequency regions he/she relied on to perform the task. Even though dyslexics performed significantly less well than controls, we were unable to detect a robust difference between the mean ACIs of the two groups. This is partly due to the considerable heterogeneity in listening strategies among a subgroup of 7 low-performing dyslexics, as confirmed by a complementary analysis. When excluding these participants to restrict our comparison to the 11 dyslexics performing as well as their average-reading peers, we found a significant difference in the F3 onset of the first syllable, and a tendency of difference on the F4 onset, suggesting that these listeners can compensate for their deficit by relying upon additional allophonic cues

Summary of the characteristics of the dyslexic and normal-reading groups.

<p>Summary of the characteristics of the dyslexic and normal-reading groups.</p

Comparison between the dyslexic and control subgroups matched in SNR (N = 11).

<p>Output of the cluster-based non-parametric test. Lines correspond to mean formant trajectories for /alda/ and /aʁda/ (red) and for /alga/ and /aʁga/ (blue).</p

Individual ACIs for all control (A.) and dyslexic (B.) participants.

<p>For each ACI the auto-prediction rate is given (in brackets), followed by the auto- and cross- prediction deviances. Positive weights (in red) are time-frequency bins where the presence of noise increases the probability that the listener gives the response “da” whereas negative weights (in blue) are time-frequency bins where the presence of noise increases the probability that the listener gives the response “ga”.</p

Diagram of the group-analysis of ACIs used in this study.

<p>A. mean ACI over all participants (left) and same ACI with all non-significant pixels (p>10⁻¹⁰) plotted in white (right panel) defining the regions used in the ROI analysis. B. mean ACIs for the control (left panel) and dyslexic (center panel) groups and output of the cluster-based non-parametric test (right panel). In the ACIs, lines correspond to mean formant trajectories for /alda/ and /aʁda/ (red) and for /alga/ and /aʁga/ (blue).</p

Summary of the characteristics of all sets of weights, sorted by bias and latency.

<p>Summary of the characteristics of all sets of weights, sorted by bias and latency.</p

Individual strategies and performances in the task.

<p>A. Auto- and cross-predictions for all participants. Left panel: cross-prediction deviance as a function of auto-prediction deviance. The dotted line indicates the cross-prediction = auto-prediction line. Right panel: difference between cross- and auto-prediction deviances for the two groups. B. SNR threshold and confidence intervals for each listener. From these two representations, 7 dyslexics clearly stand out as a low-performing subgroup.</p