Traditional distributional semantic models extract word meaning representations from cooccurrence patterns of words in text corpora. Recently, the distributional approach has been extended to models that record the cooccurrence of words with visual features in image collections. These image-based models should be complementary to text-based ones, providing a more cognitively plausible view of meaning grounded in visual perception. In this study, we test whether image-based models capture the semantic patterns that emerge from fMRI recordings of the neural signal. Our results indicate that, indeed, there is a significant correlation between image-based and brain-based semantic similarities, and that image-based models complement text-based ones, so that the best correlations are achieved when the two modalities are combined. Despite some unsatisfactory, but explained outcomes (in particular, failure to detect differential association of models with brain areas), the results show, on the one hand, that imagebased distributional semantic models can be a precious new tool to explore semantic representation in the brain, and, on the other, that neural data can be used as the ultimate test set to validate artificial semantic models in terms of their cognitive plausibility.
However, the experiments in REF failed to detect differential interactions of semantic models with brain areas.
Of Words, Eyes and Brains: Correlating Image-Based Distributional Semantic Models with Neural Representations of Concepts
EMNLP
Computer Science