Quantum Cognition based on an Ambiguous Representation Derived from a Rough Set Approximation

TL;DR

This paper links quantum cognition to ambiguous representations derived from rough set approximations, revealing that the logical structure of such cognitive processes forms an orthomodular lattice, akin to quantum logic.

Contribution

It introduces a novel model connecting ambiguous representations with quantum logic in decision making, based on rough set approximations and inverse Bayes inference.

Findings

Cognitive decision processes can be modeled with ambiguous representations.

The logical structure of these processes is an orthomodular lattice.

This approach bridges quantum cognition and rough set theory.

Abstract

Over the last years, in a series papers by Arrechi and others, a model for the cognitive processes involved in decision making has been proposed and investigated. The key element of this model is the expression of apprehension and judgement, basic cognitive process of decision making, as an inverse Bayes inference classifying the information content of neuron spike trains. For successive plural stimuli, it has been shown that this inference, equipped with basic non-algorithmic jumps, is affected by quantum-like characteristics. We show here that such a decision making process is related consistently with ambiguous representation by an observer within a universe of discourse. In our work ambiguous representation of an object or a stimuli is defined by a pair of maps from objects of a set to their representations, where these two maps are interrelated in a particular structure. The a priori and a posteriori hypotheses in Bayes inference are replaced by the upper and lower approximation, correspondingly, for the initial data sets each derived with respect to a map. We show further that due to the particular structural relation between the two maps, the logical structure of such combined approximations can only be expressed as an orthomodular lattice and therefore can be represented by a quantum rather than a Boolean logic. To our knowledge, this is the first investigation aiming to reveal the concrete logic structure of inverse Bayes inference in cognitive processes.