Abstract
We investigated whether semantic information about object categories can be obtained from human fMRI BOLD responses with Formal Concept Analysis (FCA), an order-theoretic approach for the analysis of semantic information, such as specialization hierarchies and parts-based codes. Unlike other analysis methods (eg hierarchical clustering), FCA does not impose inappropriate structure on the data. FCA is a mathematical formulation of the explicit coding hypothesis (Foldiak, 2009 Current Biology19R904-R906). A human subject was scanned viewing 72 gray-scale pictures of animate and inanimate objects in a target detection task. To apply FCA, we employ a hierarchical Bayesian classifier, which maps fMRI responses onto binary attributes, and these onto object labels. The connectivity matrix between attributes and labels is the formal context for FCA. FCA revealed a clear dissociation between animate and inanimate objects in a high-level visual area (inferior temporal cortex, IT), with the inanimate category including plants. The inanimate category was subdivided into plants and non-plants when we increased the number of attributes extracted from the fMRI responses. FCA also displayed organizational differences between the IT and the primary visual cortex, V1. We show that familiarity and similarity ratings are strongly correlated with the attributes computed from the fMRI signal.
