Individual typological differences in a neurally distributed semantic processing system: Revisiting the Science article by Mitchell et al. on computational neurolinguistics

Abstract

Background: Revisiting the 2008 Science article by Mitchell et al. on computational neurolinguistics, individual typological differences were found as striking characteristics in the patterns of informative voxels crucial for the distributed semantic processing system. Methods: The results of different feature selection methods (ANOVA and Stability) were compared based on the open datasets of each subject for evaluating how these features were decisive in predicting human brain activity associated with language meaning. Results: In general, the two selection results were similar and the voxel-wise ranks were correlated but they became extremely dispersive for a subgroup of subjects exhibiting mediocre precision when examined without regularization. Quite interestingly, looking at the anatomical location of these voxels, it appears that the modality-specific areas were likely to be monitored by the Stability score (indexing “identity”), and that the ANOVA (emphasizing “difference”) tended to detect supramodal semantic areas. Conclusions: This minor finding indicates that in some cases, seemingly poor data may deeply and systematically conceal information that is significant and worthwhile. It may have potential for shedding new light on in the controversy pertaining to cognitive semantics, which is divided into modality-biased (embodied) and amodal symbol theories.