Evidence for a deep, distributed and dynamic code for animacy in human ventral anterior temporal cortex

How does the human brain encode semantic information about objects? This paper reconciles two seemingly contradictory views. The first proposes that local neural populations independently encode semantic features; the second, that semantic representations arise as a dynamic distributed code that changes radically with stimulus processing. Combining simulations with a well-known neural network model of semantic memory, multivariate pattern classification, and human electrocorticography, we find that both views are partially correct: information about the animacy of a depicted stimulus is distributed across ventral temporal cortex in a dynamic code possessing feature-like elements posteriorly but with elements that change rapidly and nonlinearly in anterior regions. This pattern is consistent with the view that anterior temporal lobes serve as a deep cross-modal 'hub' in an interactive semantic network, and more generally suggests that tertiary association cortices may adopt dynamic distributed codes difficult to detect with common brain imaging methods.

Automated summary

The study reconciles two seemingly contradictory views on how the human brain encodes semantic information about objects. It finds that information about the animacy of a stimulus is distributed across ventral temporal cortex in a dynamic code with feature-like elements posteriorly but rapidly changing and nonlinear elements in anterior regions. This suggests that anterior temporal lobes may serve as a deep cross-modal 'hub' in an interactive semantic network, and that tertiary association cortices may adopt dynamic distributed codes difficult to detect with common brain imaging methods.