Intensional superpositions: exploring comprehension and incomprehension in a neurophysical framework

In this thesis, we present a new logical model based on strong empirical evidence. This model is designed to formalize the comprehension and incomprehension phenomena in natural language-mediated interactions among humans. We called this model Intensional Disjunctive Superposition Evaluation (IDSE). The model builds on formal semantics lambda calculus computation of natural language, where words and sentences are intensionalized as meanings superpositions, using this quantum borrowed concept instead of the Lewisian and Kripkean approach. Superpositions are characterized as possibly infinite always-true disjunctions of intensions, guaranteed by quantum theory experimental evidence applied to brain physiology and cognitive behaviour. Lambda terms in superposition are processed by a distributive evaluation operator linked to either a speaker or a listener. This operator outputs an interpretation of the disjunction, restricted by the superposition's design. Distributivity is an important feature that consents us to preserve the Fregean principle of compositionality in our calculation. Finally, we input evaluated lambda terms into an equality check function. If the inputs are equal, the function outputs the comprehension of the lambda term. Otherwise, it outputs incomprehension. The model assumes that quantum mechanics laws govern the electrochemical signals in an individual's nervous system. Therefore, we materialistically define linguistic elements as the cited signals, locating the model in a quantum biological framework which tries to connect theoretical linguistics consolidated instruments to the leading research in physics and neuroscience. In particular, we cover various hyperscanning methods, like fMRIs, EEGs and microendoscopic calcium imaging. Moreover, the architecture can be easily detached from its evidential grounding to explore the system's adaptability in abstract theories.