USING ENRICHED SEMANTIC REPRESENTATIONS IN PREDICTIONS OF HUMAN BRAIN ACTIVITY

There have been many different theoretical proposals for ways of representing word meaning in a distributed fashion. We ourselves have put forward a framework for expressing aspects of lexical semantics in terms of patterns of word co-occurrences measured in large linguistic corpora. Recent advances in the modelling of fMRI measures of brain activity have started to examine patterns of activation across the cortex rather than averaging activity across a sub-volume. Mitchell et al. [11] have shown that simple linear models can successfully predict fMRI data from patterns of word co-occurrence for a task where participants mentally generate properties for presented word-picture pairs. Using their MRI data, we replicate their models and extend them to use our independently optimised co-occurrence patterns to demonstrate that enriched representations of word/concept meaning produce significantly better predictions of brain activity. We also explore several aspects of the parameter space underlying the supervised learning techniques used in these models.