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The pervasive use of distributional semantic models or word embeddings in avariety of research fields is due to their remarkable ability to represent themeanings of words for both practical application and cognitive modeling.However, little has been known about what kind of information is encoded intext-based word vectors. This lack of understanding is particularly problematicwhen word vectors are regarded as a model of semantic representation forabstract concepts. This paper attempts to reveal the internal information ofdistributional word vectors by the analysis using Binder et al.'s (2016)brain-based vectors, explicitly structured conceptual representations based onneurobiologically motivated attributes. In the analysis, the mapping fromtext-based vectors to brain-based vectors is trained and prediction performanceis evaluated by comparing the estimated and original brain-based vectors. Theanalysis demonstrates that social and cognitive information is better encodedin text-based word vectors, but emotional information is not. This result isdiscussed in terms of embodied theories for abstract concepts.

A Neurobiologically Motivated Analysis of Distributional Semantic Models