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In summary, in concordance with the prediction, the results show correlations between speech perception and cognitive tests, particularly in the cases of sentence perception (ASL) and DTTV S. Although speech perception was also correlated with hearing sensitivity, the fundamental pattern of correlation between cognition and speech did not change  much when hearing loss was partialled out. This suggests a genuine role of cognition for speech perception performance. It is also interesting to note that the significant difference between correlation coefficients is often between ASL and DTTV S for a particular cognitive variable. For instance in Supplementary Table S2, a significant correlation exists between ASL and both Matrix Reasoning and TEA6. The same is not true between DTTV S and Matrix Reasoning and TEA6. In addition to being significant, the correlation coefficient between these cognitive variables and ASL was also significantly larger than that between the same cognitive variables and DTTV S. Similarly, for TEA7, the correlation was significant with DTTV S but not ASL, and the difference in correlation coefficient was in itself significant. Hence, while both DTTV S and ASL correlate with cognitive measures, the correlation profile for these two speech perception tests differs, suggesting their cognitive requirements are different. Prediction 1.2. The Contribution of Cognition will Increase  as the Complexity of the Speech Perception Task Increases Principal components analysis (PCA) The  principal component solutions based on the shared variance between all seven cognitive tests are shown in Table ?Table22 Extracting a single principal component explained 40% of shared variance [KMO: 0.71, Bartlett: ��2 (21) = 74.8, p [http://en.wikipedia.org/wiki/MCF2L MCF2L] representing a broad cognitive factor that includes non-verbal intelligence, WM, and attention. Only VLM Speed representing processing speed was not well represented by this latent factor. Table 2 Factor loadings for all cognitive tests for the two principal component analysis. Alternatively, aiming for the solution with the greatest amount of explained variance by extracting all factors with eigenvalue > 1 resulted in two factors and a total explained variance of 63% [KMO: 0.71, Bartlett: ��2 (21) = 74.8, p