Predicting individual differences in speech perception using pattern-based fMRI analysis of phonemic representations
Rajeev Raizada, Feng-Ming Tsao, Hui-Mei Liu and Patricia Kuhl.
http://faculty.washington.edu/raizada
The brain's ability to discriminate stimuli depends on how fine-grained its stimulus representations are. This representational granularity can vary across individuals, as a function of factors such as sensory environment and learning history. A key goal of cognitive neuroscience has been to relate the properties of such representations in individuals' brains to their levels of behavioural performance. However, because the neural representations of different but related stimuli are typically colocalised within the same brain area, their distinctness from each other has been difficult for fMRI to measure. This problem has been overcome in low-level sensory cortices, where the representational grain can be calculated from well-defined spatiotopic maps, or from direct mappings between stimulus-energy and levels of neural activation. However, for all but the simplest stimuli, no such mappings are available. For example, different phonemes such as /ra/ and /la/ activate the same areas of cortex, but there is no known "phonotopic map" that might allow the distinctness of the evoked neural representations to be measured. I will describe how, by analysing the multi-voxel spatial fMRI patterns elicited by these stimuli in English and Japanese speakers, the statistical separability of such neural representations can be directly quantified. Moreover, in right primary auditory cortex, the separability of these fMRI patterns strongly predicted the degree to which subjects could behaviourally discriminate the stimuli that gave rise to them. This opens up a new method, which may have broad applicability, for relating neural representations in the human brain to levels of behavioural performance, and also reveals a hitherto unknown role played by right auditory cortex in processing speech.
