title: Modality-independent representations of small quantities based on brain activation patterns.
abstract: Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these techniques to investigate whether neural representations of quantities depicted in one modality (say, visual) can be decoded from brain activation patterns evoked by quantities depicted in the other modality (say, auditory). The main finding demonstrated, for the first time, that quantities of dots were decodable by a classifier that was trained on the neural patterns evoked by quantities of auditory tones, and vice-versa. The representations that were common across modalities were mainly right-lateralized in frontal and parietal regions. A second finding was that the neural patterns in parietal cortex that represent quantities were common across participants. These findings demonstrate a common neuronal foundation for the representation of quantities across sensory modalities and participants and provide insight into the role of parietal cortex in the representation of quantity information.
authors.affiliation: Department of Psychology, Center for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, Pennsylvania.
authors.forename: Saudamini Roy
authors.initials: SR
authors.lastname: Damarla
authors.affiliation: Department of Psychology, Center for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, Pennsylvania.
authors.forename: Vladimir L
authors.initials: VL
authors.lastname: Cherkassky
authors.affiliation: Department of Psychology, Center for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, Pennsylvania.
authors.forename: Marcel Adam
authors.initials: MA
authors.lastname: Just
mesh_terms: D000161:Acoustic Stimulation / Q000379:methods*; D000328:Adult; D005260:Female; D005625:Frontal Lobe / Q000502:physiology*; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010296:Parietal Lobe / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods*; D011897:Random Allocation; D055815:Young Adult
keywords: cross-modality; fMRI; multivoxel pattern analysis; number representation