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Representational Similarity Between Brain Activity Elicited by Concrete Nouns and Image Based Semantic Models

How concepts are represented and organised is a key question in cognitive science and is relevant to the design of artificial semantic systems. Neuroimaging techniques allow questions concerning neural representations to be directly addressed. A number of advances have recently been made that ingeniously use models from computational linguistics to predict fMRI activation (Mitchell et al., 2008, Murphy et al., 2009, 2011). In last years VL Object ρ=0.278261, p=0.028; Surround ρ=0.60, p=0.0002. Consistent with the previous comparison to behavioural measures Surround best explains the brain data. An implication of these results is that visual context has importance for neural encoding of objects and therefore that the (visual) distributional hypothesis is relevant also for fMRI activation. This is in line with the findings of Mitchell et al. [2008] which highlighted the significance of linguistic context. In future work we shall extend the coverage of artificial concept models and examine how text and image models can be used to segregate neural anatomic regions associated with linguistic and visual experience.