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Description: Neuro-cognitive models of semantic memory have proposed that the ventral anterior temporal lobes (vATLs) encode a graded, distributed, and multidimensional semantic space—yet neuroimaging studies seeking brain regions that encode semantic structure rarely identify these areas. In computer simulations we show that this discrepancy may arise from limitations of a commonly-used multivariate analysis, and introduce a new approach, representational similarity learning (RSL), that resolves these. We then use RSL to decode semantic structure from ECoG data collected from the vATL cortical surface while participants named line drawings of common items. The results reveal a graded, multi-dimensional semantic space encoded in neural activity across the vATL, which evolves over time and simultaneously expresses both broad and finer-grained semantic structure amongst animate and inanimate concepts. The work thus resolves the apparent discrepancy within the semantic cognition literature and suggests a new approach to discovering representational structure in neural data more generally.