1. Volume 19, Issue 6, Pages 498-502 (March 2009)
Sound Categories Are Represented as Distributed Patterns in the Human Auditory Cortex 
Noël Staeren, Hanna Renvall, Federico De Martino, Rainer Goebel, Elia Formisano 
Current Biology 
Volume 19, Issue 6, Pages (March 2009)
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2. Figure 1 Spectrograms of Exemplary Stimuli
The four stimulus categories at High (920 Hz; top) and Medium (480 Hz; bottom) fundamental-frequency levels. The time-varying fundamental frequency of the cat sound (purple rectangle) was imposed onto the other stimuli. The harmonic structure of the sounds was modified accordingly.
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3. Figure 2 Multivariate Pattern Recognition—Learning of Sound “Category”
Group-averaged classification accuracies (left) and group discriminative maps (right) for between-category comparisons. For all binary discriminations, the black dots indicate the classification accuracy of test trials for each individual category and the colored dots indicate the classification accuracy averaged over the two categories. Error bars indicate the standard errors. For all classifications, the recursive algorithm was able to learn the functional relation between the sounds and the corresponding evoked spatial patterns and to classify the unlabeled sound-evoked patterns significantly above chance level (0.5). Discriminative patterns are visualized on the inflated representation of the auditory cortex resulting from the realignment of the cortices of the eight participants. A location was color-coded if it was present on the individual maps of at least five of the eight subjects.
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4. Figure 3
Multivariate Pattern Recognition—Learning of Sound “Fundamental Frequency”
Group-averaged classification accuracies (left) and group discriminative maps (right) for between-frequency comparisons. For all binary discriminations, the black dots indicate the classification accuracy of test trials for each individual frequency and the colored dots indicate the classification accuracy averaged over the two frequencies. Error bars indicate the standard errors. For all classifications, the recursive algorithm was able to learn the functional relation between the sounds and corresponding evoked spatial patterns and to classify the unlabeled sound-evoked patterns significantly above chance level (0.5). Discriminative patterns are visualized on the inflated representation of the auditory cortex resulting from the realignment of the cortices of the eight participants. A location was color-coded if it was present on the individual maps of at least five of the eight subjects.
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5. Figure 4 Comparison of Discriminative Maps
The cortex-based aligned group discriminative maps for category (blue) and fundamental frequency (red) discrimination. Category and fundamental frequency discriminative maps were obtained by the logic union of the discriminative maps corresponding to the three binary classifications (Figures 2 and 3, respectively). A vertex was color-coded if it was present on the individual maps of at least five of the eight subjects. This corresponds to a false discovery rate-corrected threshold of q = 7.9 × 10−3 for the category map and q = 2.6 × 10−3 for the fundamental frequency map (see Supplemental Experimental Procedures). Note that the discrimination map for fundamental frequency was more clustered than that for category.
Current Biology  , DOI: ( /j.cub )
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