Correlated attributes: Toward a labeling algorithm of complementary categorial features - Uriagereka, Juan (Frontiers in Language Sciences, 2023)
The present piece revisits classical syntactic features from an algebraic perspective, recalling the argument that the ±N vs. ±V distinction is profitably understood as conceptually orthogonal, which can be accurately represented in the algebraic format of ±1 vs. ±i, within a vectorial space. Coupled with natural assumptions about shared information (“semiotic”) systems, such a space allows us to deduce fundamental properties of syntax that do not follow from the presumed computation itself, such as core selectional restrictions for the major lexical categories or their being presupposed in a system of grammatical categories. The new contribution of this piece is suggesting how such a fundamental distinction can be coupled with neurophysiological realities, some of which (those represented as mathematically real) can be pinpointed into ultimately punctual representations, while others (those represented as mathematically complex) are, instead, fundamentally distributed. Sharing standard properties of quantum computation systems, at least in abstract formulation, the postulated matrix mechanics provides a novel perspective on how to possibly analyze neurophysiological signals.