The paper presents quantum model of subjective text perception based on binary cognitive distinctions corresponding to words of natural language. The result of perception is quantum cognitive state represented by vector in the qubit Hilbert space. Complex-valued structure of the quantum state space extends the standard vector-based approach to semantics, allowing to account for subjective dimension of human perception in which the result is constrained, but not fully predetermined by input information. In the case of two distinctions, the perception model generates a two-qubit state, entanglement of which quantifies semantic connection between the corresponding words. This two-distinction perception case is realized in the algorithm for detection and measurement of semantic connectivity between pairs of words. The algorithm is experimentally tested with positive results. The developed approach to cognitive modeling unifies neurophysiological, linguistic, and psychological descriptions in a mathematical and conceptual structure of quantum theory, extending horizons of machine intelligence.
The paper presents quantum model of subjective text perception based on binary cognitive distinctions corresponding to words of natural language. The result of perception is quantum cognitive state represented by vector in the qubit Hilbert space. Complex-valued structure of the quantum state space extends the standard vector-based approach to semantics, allowing to account for subjective dimension of human perception in which the result is constrained, but not fully predetermined by input information. In the case of two distinctions, the perception model generates a two-qubit state, entanglement of which quantifies semantic connection between the corresponding words. This two-distinction perception case is realized in the algorithm for detection and measurement of semantic connectivity between pairs of words. The algorithm is experimentally tested with positive results. The developed approach to cognitive modeling unifies neurophysiological, linguistic, and psychological descriptions in a mathematical and conceptual structure of quantum theory, extending horizons of machine intelligence.
The representation and processing of concepts is one of the hardest challenges in cognitive science. While computer scientists and engineers have focused on developing advances for particular tasks, philosophers and cognitive scientists have focused on elucidating the structural nature of meaning. A remarkable bridge between these two limited-success approaches can be found in behavioral research, since, in a variety of tasks, humans process information at a conceptual level in a way that is incompatible with classical probability and fuzzy set theory. Recently, attempts have been made to use mathematical schemes founded on quantum structures as alternative approaches. For this reason, the application of quantum structures to this type of phenomena has received increasing attention. The quantum approach allows to faithfully model a number of non-classical deviations observed in experimental data. Moreover, it shows that genuine quantum theoretical notions, such as contextuality, superposition, emergence, and entanglement, are powerful epistemic tools to understand and represent cognitive phenomena. In this thesis, we identify the limitations of classical theories to handle some important cognitive tasks, and introduce the fundamentals of the quantum cognitive approach to concepts. Next, we perform a mathematical analysis of current concept combination models and develop an extension that allows for concrete representations of multiple exemplars simultaneously. Our analysis indicates that a superposition of logical reasoning and a specific form of non-logical reasoning, where non-logical reasoning is dominant, allows to faithfully represent the experimental data. Therefore, the non-logical reasoning introduced by this model represents an important but unexplored form of reasoning in humans. In addition, we develop novel experimental methodologies to identify quantum conceptual structures for concept combinations in the context of natural language processing and psychological experiments. Namely, we present a methodology to build entangled concepts represented as sets of words with respect to a corpus of text, and present a computational and psychological methodology to discern if a collection of concepts behaves statistically as a collection of quantum or classical particles. Using both methodologies we have identified a significant presence of quantum conceptual structure in the context of natural language processing and psychological experiments.
The mathematical formalism of quantum theory has been successfully used in human cognition to model decision processes and to deliver representations of human knowledge. As such, quantum cognition inspired tools have improved technologies for Natural Language Processing and Information Retrieval. In this paper, we overview the quantum cognition approach developed in our Brussels team during the last two decades, specifically our identification of quantum structures in human concepts and language, and the modeling of data from psychological and corpus-text-based experiments. We discuss our quantum-theoretic framework for concepts and their conjunctions/disjunctions in a Fock-Hilbert space structure, adequately modeling a large amount of data collected on concept combinations. Inspired by this modeling, we put forward elements for a quantum contextual and meaning-based approach to information technologies in which 'entities of meaning' are inversely reconstructed from texts, which are considered as traces of these entities' states.
Purpose The paper aims to provide a semiotic interpretation of the role played by entanglement in quantum-based models aimed to information retrieval and suggests possible improvements. Actual models are capable of retrieving documents relevant to a query composed of a keyword and its acceptation expressed by a given context. The paper also considers some analogies between this technique and quantum-based approaches in other disciplines to discuss the consequence of this quantum turn, as epistemology and philosophy of language are concerned. Design/methodology/approach We use quantum geometry to design a formal model for textual semiotics. In particular, the authors refer to Greimas’s work on semantics and information theory, to Eco’s writings on semantic memory and to Lotman’s work on a cybernetic notion of culture. Findings Quantum approaches imply a particular point of view on meaning. Meaning is not a real, positive quality of a given word. It is a net of relations constructed in the text, whose value is progressively determined during the reading process. Furthermore, reading is not a neutral operation: to read is to determine meaning. If it is said that, from a general semiotic point of view, meaning is stored in quantum semantic memories and is read/written by semantic machines, then the operation of “reading/writing” is analogous to the operation of measuring in quantum theory: in other terms, meaning is a value, and this implies an instance (not necessarily human) according to which values are valuable. Research limitations/implications The authors are not proposing a complete quantum semantics. At the present, quantum information retrieval can detect the presence of semantic relations. The authors suggest a way to characterize them, leaving open the problem on how to formalize the document as a vector in four-state semantic space. Practical implications A quantum turn shows deep semiotic implications on the approach to language, which shows an immanent semantic organization not reducible to syntax and morphology. This organization is probabilistic and indeterministic and explains to what extent text fixes the meaning of its lexical units. Social implications In the authors’ perspective, signification is not the exclusivity of a human subject. Criticizing Turing test, the great semiotic and cybernetic scholar Jurij Lotman wrote that if we identify “intelligent” and “human”, we raise the failings of an actual form of intelligence to the rank of an essential characteristic. On this line, meaning is considered as a feature of social, artificial and biological systems. Originality/value The adoption of quantum formalism seems in line with cybernetic framework, involving a probabilistic, non-cartesian point of view on meaning aimed to critically discuss the human–machine relation. Furthermore, Quantum theory (QT) implies a phenomenological point of view on the conditions of possibility of meaning.
Abstract Language is not fixed. It is entangled, probabilistic, and observer-dependent. This paper introduces a new paradigm in linguistic theory named "Quantum Linguistics," inspired by the principles of Quantum Physics. Quantum physics and Linguistics have long been regarded as unrelated disciplines,describing nature at the inanimate microlevel on the one hand and language and its scientific study on the other hand. Over the past decades the linguistics has succeeded in providing ever more and refined explanations of generative grammar. Since quantum physics and linguistics deal with the symbolic, rule-governed structure of human language and the non-classical, probabilistic behaviour of subatomic particles, respectively, they have historically been seen as two separate disciplines. This framework allows modeling of polysemy, ambiguity, free word order, and argument structure in a unified, mathematically rigorous manner.To operationalize the model, we implement a Quantum Syntax-Semantics Integration (QSSI) pipeline, which encodes sentences into vector states, applies quantum operations for compositional syntax and semantics, and produces contextually resolved meanings through measurement. Two variants of the pipeline are considered: a classical simulation for experimental validation and a quantum-simulated version for future hardware implementation. The linguistic theory with quantum principles such as superposition, entanglement, and measurement, our model provides a novel probabilistic and context-dependent approach to understand language, explore new insights into human cognitive-linguistic processes, semantic ambiguity observation, and advanced natural language processing applications.
A lexeme can be represented as a vector in a Hilbert space. Each element of the vector represents the weight of a semantic relation with a second vector: the context of a second lexeme. Two query operators are defined in a way that they attribute the value +1 to the component of the state that corresponds to the word meaning we are interested in, and -1 in the orthogonal direction. Entangled states have been used to detect semantic relationships in Information Retrieval (IR). In particular, since the first operator corresponds to negation and the conditional gate corresponds to an implication in classical logic, they can represent two basic semantic relationships : hyponymy (e.g. genus-species) and antonymy (e.g. masculine-feminine). In fact, according to Semantics, if A and B are antonymic terms, then A is an hyponym of (i.e. implies) not-B as in the Gremias semiotic square. We underline how the relations of a given semantic universe, and the corresponding result of our operators, depends only on the considered text. Among others, this analogy suggests the application of quantum-based models to language and IR.
The article presents a review of foreign scientific literature on the quantum-like approach to understanding the modeling of cognition and decision-making processes. It is shown that the current application of quantum physics approaches to consciousness, psychological approaches such as perception, cognition, emotions, consciousness and learning, involves the search for a single objective mathematical model of what underlies the experience of the brain. The use of quantum physics algorithms can help in understanding the mechanisms of brain malfunctions and memory impairments that are found in pathologies such as dementia caused by Alzheimer's disease, schizophrenia, etc. Quantum physics approaches are applicable to issues of modeling cognition and decision-making processes, as well as in cognitive research, information processing by the human brain, artificial intelligence development, psychology, social and political sciences. Application of the abstract mathematical principles of quantum theory can help in understanding human consciousness and behavior. Quantum probability theory is applied to modeling human behavior due to the prevalence of uncertainty in human interaction with the world. Quantum cognitive models are especially useful in research in such areas as errors in decision making, order effects of questions, conceptual combinations, accumulation of evidence, perception, etc. Of particular interest to researchers are issues of quantum-like modeling of cognition and decision-making processes, the apparatus of quantum information and probability theory, useful for such modeling, as well as the use of such apparatus in cognitive research, the development of artificial intelligence, psychology, social and political sciences.