title: Decoding the representation of numerical values from brain activation patterns.
abstract: Human neuroimaging studies have increasingly converged on the possibility that the neural representation of specific numbers may be decodable from brain activity, particularly in parietal cortex. Multivariate machine learning techniques have recently demonstrated that the neural representation of individual concrete nouns can be decoded from fMRI patterns, and that some patterns are general over people. Here we use these techniques to investigate whether the neural codes for quantities of objects can be accurately decoded. The pictorial mode (nonsymbolic) depicted a set of objects pictorially (e.g., a picture of three tomatoes), whereas the digit-object mode depicted quantities as combination of a digit (e.g., 3) with a picture of a single object. The study demonstrated that quantities of objects were decodable from neural activation patterns, in parietal regions. These brain activation patterns corresponding to a given quantity were common across objects and across participants in the pictorial mode. Other important findings included better identification of individual numbers in the pictorial mode, partial commonality of neural patterns across the two modes, and hemispheric asymmetry with pictorially-depicted numbers represented bilaterally and numbers in the digit-object mode represented primarily in the left parietal regions. The findings demonstrate the ability to identify individual quantities of objects based on neural patterns, indicating the presence of stable neural representations of numbers. Additionally, they indicate a predominance of neural representation of pictorially depicted numbers over the digit-object mode.
authors.affiliation: Center for Cognitive Brain Imaging, Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania.
authors.forename: Saudamini Roy
authors.initials: SR
authors.lastname: Damarla
authors.forename: Marcel Adam
authors.initials: MA
authors.lastname: Just
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D001931:Brain Mapping; D002560:Cerebrovascular Circulation; D003210:Concept Formation / Q000502:physiology*; D004292:Dominance, Cerebral / Q000502:physiology; D017352:Echo-Planar Imaging; D005260:Female; D006801:Humans; D008297:Male; D008433:Mathematics; D009434:Neural Pathways / Q000502:physiology; D010296:Parietal Lobe / Q000098:blood supply / Q000502:physiology*; D010363:Pattern Recognition, Automated; D010775:Photic Stimulation; D012660:Semantics; D013561:Symbolism; D055815:Young Adult