Decoding the Semantic Content of Natural Movies from Human Brain Activity.

ABSTRACT: One crucial test for any quantitative model of the brain is to show that the model can be used to accurately decode information from evoked brain activity. Several recent neuroimaging studies have decoded the structure or semantic content of static visual images from human brain activity. Here we present a decoding algorithm that makes it possible to decode detailed information about the object and action categories present in natural movies from human brain activity signals measured by functional MRI. Decoding is accomplished using a hierarchical logistic regression (HLR) model that is based on labels that were manually assigned from the WordNet semantic taxonomy. This model makes it possible to simultaneously decode information about both specific and general categories, while respecting

SUBMITTER: Huth AG 

PROVIDER: S-EPMC5057448 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature