We aim to apply an important new functional neuroimaging technique, known as multi-voxel pattern analysis, to the study of phonological processing. Multi-voxel pattern analysis focuses on distributed patterns of brain activation, as measured with functional magnetic resonance imaging (fMRI), applying pattern analytic techniques to determine the information these patterns may carry. Applications of the technique to visual processing have produced dramatic results, allowing surprisingly fine-grained discriminations among cortical states, and accessing the similarity structure of distributed neural representations. Our preliminary research indicates that multi-voxel pattern analysis may also represent a powerful tool for studying the neural basis of language, affording access to item-specific phonological representations and their similarity relations. The objective of the proposed work is to apply our established approach to several key issues concerning phonological processing. First, we aim to address the question of where phonemes are represented, seeking to confirm the role of several cortical regions in representing phonological information. Within these areas, we propose to investigate how phonemes are represented, examining the similarity structure of phonological representations and relating this to the acoustic and articulatory features of individual phonemes. This portion of the work will also investigate the neural correlates of categorical structure and voice-face integration in phoneme perception, and test for context-sensitivity in phoneme representation. A second major goal of the project is to use multi-voxel pattern analysis to investigate the neural representation of phonology in populations displaying deficits in phonological processing, and in particular among individuals with developmental dyslexia.
A final aim i s to track experience-induced changes in cortical representations of phonology, in both normal and dyslexic individuals. The proposed research is intended to shed light on basic aspects of normal and disordered language processing, as well as to pioneer the application of new methods for studying the neural basis of language. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS053366-02
Application #
7437303
Study Section
Cognitive Neuroscience Study Section (COG)
Program Officer
Babcock, Debra J
Project Start
2007-06-15
Project End
2012-05-31
Budget Start
2008-06-01
Budget End
2009-05-31
Support Year
2
Fiscal Year
2008
Total Cost
$336,566
Indirect Cost
Name
Princeton University
Department
Type
Organized Research Units
DUNS #
002484665
City
Princeton
State
NJ
Country
United States
Zip Code
08544
Schapiro, Anna C; Turk-Browne, Nicholas B; Norman, Kenneth A et al. (2016) Statistical learning of temporal community structure in the hippocampus. Hippocampus 26:3-8
Pereira, Francisco; Botvinick, Matthew; Detre, Greg (2013) Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments. Artif Intell 194:240-252
Allen, Kachina; Pereira, Francisco; Botvinick, Matthew et al. (2012) Distinguishing grammatical constructions with fMRI pattern analysis. Brain Lang 123:174-82
Pereira, Francisco; Botvinick, Matthew (2011) Information mapping with pattern classifiers: a comparative study. Neuroimage 56:476-96
Pereira, Francisco; Mitchell, Tom; Botvinick, Matthew (2009) Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45:S199-209