Reverse-Engineering the Cortical Architecture for Controlled Semantic Cognition

We present a reverse engineering approach to deconstruct cognition into neurocomputational mechanisms and their underlying cortical architecture, using controlled semantic cognition as a test case. By systematically varying the structure of a computational model and assessing the functional consequences, we identified architectural properties necessary for generating the core functions of the semantic system. Semantic cognition presents a challenging test case as the brain must achieve two seemingly contradictory functions: abstracting context-invariant conceptual representations across time and modalities, whilst producing specific context-sensitive behaviours appropriate for the immediate task. These functions were best achieved in models possessing a single, deep multimodal hub with sparse connections from modality-specific inputs, and control systems acting on peripheral rather than deep network layers. These architectural features correspond well with those suggested by neural data, strongly supporting the efficacy of the reverse engineering approach, and further generating novel hypotheses about the neuroanatomy of controlled semantic cognition.