Neurobiological Mechanisms for Semantic Feature Extraction and Conceptual Flexibility

Signs and symbols relate to concepts and can be used to speak about objects, actions, and their features. Theories of semantic grounding address the question how the latter two, concepts and real‐world entities, come into play and interlink in symbol learning. Here, a neurobiological model is used to spell out concrete mechanisms of symbol grounding, which implicate the “association” of information about sign and referents and, at the same time, the extraction of semantic features and the formation of representations best described as conjoined and disjoined feature sets that may or may not have a real‐life equivalent. The mechanistic semantic circuits carrying these feature sets are not static conceptual entries, but exhibit rich activation dynamics related to memory, prediction, and contextual modulation. Four key issues in specifying these activation dynamics will be highlighted: (a) the inner structure of semantic circuits, (b) mechanisms of semantic priming, (c) task specificity in semantic activation, and (d) context‐dependent semantic circuit activation in the processing of referential, existential, and universal statements. These linguistic‐semantic examples show that specific mechanisms are required to account for context‐dependent semantic function or conceptual “flexibility.” Static context‐independent concepts as such are insufficient to account for these different semantic functions. Whereas amodal models of concepts did so far not spell out concrete mechanisms for context‐dependent semantic function, neuronal assembly mechanisms offer a workable perspective. Neurons repeatedly exposed to similar perceptual experiences fire together and wire together to form ‘meaning kernels’ of concepts. Pulvermueller argues that concepts may be devoid of meaning kernels, because the perceptual experiences that construct concepts are subject to great variation and share few common features. concept are therefore grounded in the brain through features that belong to ‘meaning halos’, rather than to ‘meaning kernels’.