Abstract
Traditional cognitive science rests on a foundation of classical logic and probability theory. This foundation has been seriously challenged by several findings in experimental psychology on human decision making. Meanwhile, the formalism of quantum theory has provided an efficient resource for modeling these classically problematical situations. In this paper, we start from our successful quantum-theoretic approach to the modeling of concept combinations to formulate a unifying explanatory hypothesis. In it, human reasoning is the superposition of two processes -- a conceptual reasoning, whose nature is emergence of new conceptuality, and a logical reasoning, founded on an algebraic calculus of the logical type. In most cognitive processes however, the former reasoning prevails over the latter. In this perspective, the observed deviations from classical logical reasoning should not be interpreted as biases but, rather, as natural expressions of emergence in its deepest form.
Highlights
Concepts are envisaged as the structural units of human thought
It has been shown that experimental estimates of typicalities [10] and of memberships [11] cannot be modeled respectively within fuzzy logics and classical probability
One believed that the observed probabilities could be cast into the Kolmogorovian structures of classical probability theory, revealing an underlying classical logical behaviour, possibly extended to include fuzzy set logic and its basic connectives
Summary
Concepts are envisaged as the structural units of human thought. Understanding the nature of these units can lead to a first principles basis for a. Concepts have been conceived to represent classes of similar items, it is generally accepted that they do not have a fixed representational structure [3] This view is based on three conceptual phenomena. We have developed a modeling approach for concept combination that uses the mathematical formalism of quantum theory [11, 16,17,18,19,20,21] This approach enables faithful representation of a large amount of data collected on conjunctions and disjunctions of two concepts
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