Learning Low-Dimensional Semantics for Music and Language via Multi-Subject fMRI.

Embodied Cognition (EC) states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according to its subjective experience, making this biological semantic machinery noisy with respect to semantics inherent to media, such as music and language. We propose to represent media semantics using low-dimensional vector embeddings by jointly modeling the functional Magnetic Resonance Imaging (fMRI) activity of several brains via Generalized Canonical Correlation Analysis (GCCA).

Evaluating pictogram prediction in a location-aware augmentative and alternative communication system.

This study compared the performance of two statistical location-aware pictogram prediction mechanisms, with an all-purpose (All) pictogram prediction mechanism, having no location knowledge. The All approach had a unique language model under all locations. One of the location-aware alternatives, the location-specific (Spec) approach, made use of specific language models for pictogram prediction in each location of interest.