Quantum cognition

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Quantum cognition is an emerging field which applies the mathematical formalism of quantum theory to model cognitive phenomena such as information processing by the human brain, decision making, human memory, concepts and conceptual reasoning, human judgment, and perception.[1][2] [3][4] The field clearly distinguishes itself from the quantum mind as it is not reliant on the hypothesis that there is something micro-physical quantum mechanical about the brain. Quantum cognition is based on the quantum-like paradigm[5][6] or generalized quantum paradigm [7] or quantum structure paradigm [8] that information processing by complex systems such as the brain, taking into account contextual dependence of information and probabilistic reasoning, can be mathematically described in the framework of quantum information and quantum probability theory.

Quantum cognition uses the mathematical formalism of quantum theory to inspire and formalize models of cognition that aim to be an advance over models based on traditional classical probability theory. The field focuses on modeling phenomena in cognitive science that have resisted traditional techniques or where traditional models seem to have reached a barrier (e.g., human memory [9] ), and modeling preferences in decision theory that seem paradoxical from a traditional rational point of view (e.g., preference reversals [10]). Since the use of a quantum-theoretic framework is for modeling purposes, the identification of quantum structures in cognitive phenomena does not presuppose the existence of microscopic quantum processes in the human brain.[11]

Main subjects of research[edit]

Quantum-like models of information processing ("quantum-like brain")[edit]

The brain is definitely a macroscopic physical system operating on the scales (of time, space, temperature) which differ crucially from the corresponding quantum scales. (The macroscopic quantum physical phenomena such as e.g. the Bose-Einstein condensate are also characterized by the special conditions which are definitely not fulfilled in the brain.) In particular, the brain is simply too hot to be able perform the real quantum information processing, i.e., to use the quantum carriers of information such as photons, ions, electrons. As is commonly accepted in brain science, the basic unit of information processing is a neuron. It is clear that a neuron cannot be in the superposition of two states: firing and non-firing. Hence, it cannot produce superposition playing the basic role in the quantum information processing. Superpositions of mental states are created by complex neural networks of neurons (and these are classical neural networks). Quantum cognition community states that the activity of such neural networks can produce effects which are formally described as interference (of probabilities) and entanglement. In principle, the community does not try to create the concrete models of quantum (-like) representation of information in the brain.[12]

The quantum cognition project is based on the observation that various cognitive phenomena are more adequately described by quantum information theory and quantum probability than by the corresponding classical theories, see examples below. Thus the quantum formalism is considered as an operational formalism describing nonclassical processing of probabilistic data. Recent derivations of the complete quantum formalism from simple operational principles for representation of information supports the foundations of quantum cognition. The subjective probability viewpoint on quantum probability which was developed by C. Fuchs and collaborators [13] also supports the quantum cognition approach, especially using of quantum probabilities to describe the process of decision making.

Although at the moment we cannot present the concrete neurophysiological mechanisms of creation of the quantum-like representation of information in the brain, we can present general informational considerations supporting the idea that information processing in the brain matches with quantum information and probability. Here, contextuality is the key word, see the monograph of Khrennikov [1] for detailed representation of this viewpoint. Quantum mechanics is fundamentally contextual.[14] Quantum systems do not have objective properties which can be defined independently of measurement context. (As was pointed by N. Bohr, the whole experimental arrangement must be taken into account.) Contextuality implies existence of incompatible mental variables, violation of the classical law of total probability and (constructive and destructive) interference effects. Thus the quantum cognition approach can be considered as an attempt to formalize contextuality of mental processes by using the mathematical apparatus of quantum mechanics.

Decision making[edit]

Suppose a person is given an opportunity to play two rounds of the following gamble: a coin toss will determine whether the subject wins $200 or loses $100. Suppose the subject has decided to play the first round, and does so. Some subjects are then given the result (win or lose) of the first round, while other subjects are not yet given any information about the results. The experimenter then asks whether the subject wishes to play the second round. Performing this experiment with real subjects gives the following results:

1) When subjects believe they won the first round, the majority of subjects choose to play again on the second round.

2) When subjects believe they lost the first round, the majority of subjects choose to play again on the second round.

Given these two separate choices, according to the sure thing principle of rational decision theory, they should also play the second round even if they don’t know or think about the outcome of the first round.[15] But, experimentally, when subjects are not told the results of the first round, the majority of them decline to play a second round.[16] This finding violates the law of total probability, yet it can be explained as a quantum interference effect in a manner similar to the explanation for the results from double-slit experiment in quantum physics.[2][17]

The above deviations from classical rational expectations in agents’ decisions under uncertainty produce well known paradoxes in behavioral economics, that is, the Allais, Ellsberg and Machina paradoxes.[18][19][20] These deviations can be explained if one assumes that the overall conceptual landscape influences the subject’s choice in a neither predictable nor controllable way. A decision process is thus an intrinsically contextual process, hence it cannot be modeled in a single Kolmogorovian probability space, which justifies the employment of quantum probability models in decision theory. More explicitly, the paradoxical situations above can be represented in a unified Hilbert space formalism where human behavior under uncertainty is explained in terms of genuine quantum aspects, namely, superposition, interference, contextuality and incompatibility.[21][22][23]

Human probability judgments[edit]

Quantum probability provides a new way to explain human probability judgment errors including the conjunction and disjunction errors.[24] A conjunction error occurs when a person judges the probability of a likely event L and an unlikely event U to be greater than the unlikely event U; a disjunction error occurs when a person judges the probability of a likely event L to be greater than the probability of the likely event L or an unlikely event U. Quantum probability theory is a generalization of Bayesian probability theory because it is based on a set of von Neumann axioms that relax some of the classic Kolmogorov axioms. The quantum model introduces a new fundamental concept to cognition—the compatibility versus incompatibility of questions and the effect this can have on the sequential order of judgments. Quantum probability provides a simple account of conjunction and disjunction errors as well as many other findings such as order effects on probability judgments.[25][26][27]

The liar paradox - The contextual influence of a human subject on the truth behavior of a cognitive entity is explicitly exhibited by the so-called liar paradox, that is, the truth value of a sentence like "this sentence is false". One can show that the true-false state of this paradox is represented in a complex Hilbert space, while the typical oscillations between true and false are dynamically described by the Schrödinger equation.[28][29]

Knowledge representation[edit]

Concepts are basic cognitive phenomena, which provide the content for inference, explanation, and language understanding. Cognitive psychology has researched different approaches for understanding concepts including exemplars, prototypes, and neural networks, and different fundamental problems have been identified, such as the experimentally tested non classical behavior for the conjunction and disjunction of concepts, more specifically the Pet-Fish problem or guppy effect,[30] and the overextension and underextension of typicality and membership weight for conjunction and disjunction.[31] [32] By and large, quantum cognition has drawn on quantum theory in three ways to model concepts.

  1. Exploit the contextuality of quantum theory to account for the contextuality of concepts in cognition and language and the phenomenon of emergent properties when concepts combine [11][33][34][35][36]
  2. Use quantum entanglement to model the semantics of concept combinations in a non-decompositional way, and to account for the emergent properties/associates/inferences in relation to concept combinations[37]
  3. Use quantum superposition to account for the emergence of a new concept when concepts are combined, and as a consequence put forward an explanatory model for the Pet-Fish problem situation, and the overextension and underextension of membership weights for the conjunction and disjunction of concepts.[25][33][34]

The large amount of data collected by Hampton [31][32] on the combination of two concepts can be modeled in a specific quantum-theoretic framework in Fock space where the observed deviations from classical set (fuzzy set) theory, the above mentioned over- and under- extension of membership weights, are explained in terms of contextual interactions, superposition, interference, entanglement and emergence.[25][38][39][40] And, more, a cognitive test on a specific concept combination has been performed which directly reveals, through the violation of Bell’s inequalities, quantum entanglement between the component concepts.[41][42]

Human memory[edit]

The hypothesis that there may be something quantum-like about the human mental function was put forward with “Spooky Activation at Distance” formula which attempted to model the effect that when a word’s associative network is activated during study in memory experiment, it behaves like a quantum-entangled system.[9] Models of cognitive agents and memory based on quantum collectives have been proposed by Subhash Kak.[43][44]

Semantic analysis and information retrieval[edit]

The research in (iv) had a deep impact on the understanding and initial development of a formalism to obtain semantic information when dealing with concepts, their combinations and variable contexts in a corpus of unstructured documents. This conundrum of natural language processing (NLP) and information retrieval (IR) on the web – and data bases in general – can be addressed using the mathematical formalism of quantum theory. As basic steps, (a) the seminal book "The Geometry of Information Retrieval" by K. Van Rijsbergen[45] introduced a quantum structure approach to IR, (b) Widdows and Peters utilised a quantum logical negation for a concrete search system,[36][46] and Aerts and Czachor identified quantum structure in semantic space theories, such as latent semantic analysis.[47] Since then, the employment of techniques and procedures induced from the mathematical formalisms of quantum theory – Hilbert space, quantum logic and probability, non-commutative algebras, etc. – in fields such as IR and NLP, has produced significant results.

Human Perception[edit]

Bi-stable perceptual phenomena is a fascinating topic in the area of perception. If a stimulus has an ambiguous interpretation, such as a Necker cube, the interpretation tends to oscillate across time. Quantum models have been developed to predict the time period between oscillations and how these periods change with frequency of measurement.[48] Quantum theory has also been used for modeling Gestalt perception, to account for interference effects obtained with measurements of ambiguous figures (see next section).[49][50][51][52]

Gestalt perception[edit]

Similarities between Gestalt perception and quantum theory have been pointed out, among others, by Anton Amann. In an article discussing the application of Gestalt to chemistry, Amann writes: "Quantum mechanics does not explain Gestalt perception, of course, but in quantum mechanics and Gestalt psychology there exist almost isomorphic conceptions and problems:

  • Similarly as with the Gestalt concept, the shape of a quantum object does not a priori exist but it depends on the interaction of this quantum object with the environment (for example: an observer or a measurement apparatus).
  • Quantum mechanics and Gestalt perception are organized in a holistic way. Subentities do not necessarily exist in a distinct, individual sense.
  • In quantum mechanics and Gestalt perception objects have to be created by elimination of holistic correlations with the 'rest of the world'."[53]

Amann comments: "The structural similarities between Gestalt perception and quantum mechanics are on a level of a parable, but even parables can teach us something, for example, that quantum mechanics is more than just production of numerical results or that the Gestalt concept is more than just a silly idea, incompatible with atomistic conceptions."[53]

Theoretical physicist Elio Conte et al (2007) have proposed quantum cognition models to account for Gestalt phenomena. That is, they have set up abstract, mathematically-formulated models that are intended to describe the time dynamics of cognitive associations. The mathematical formulation which they used is borrowed from quantum mechanics (one model is formulated mathematically in terms of a Hilbert space of quantum states evolving according to a Schrödinger equation,[49] another is constructed using algebraic techniques[54]). In this context, Conte has also discussed psychology experiments.

Quantum-like models of cognition in economics and finances[edit]

The assumption that information processing by the agents of the market follows the laws of quantum information theory and quantum probability was actively explored by many authors, e.g., E. Haven, O. Choustova, A. Khrennikov, see the book of E. Haven and A. Khrennikov,[55] for detailed bibliography. We can mention, e.g., the Bohmian model of dynamics of prices of shares in which the quantum(-like) potential is generated by expectations of agents of the financial market and, hence, it has the mental nature. This approach can be used to model real financial data, see the book of E. Haven and A. Khrennikov (2012).

Application of theory of open quantum systems to decision making and "cell's cognition"[edit]

An isolated quantum system is an idealized theoretical entity. In reality interactions with environment have to be taken into account. This is the subject of theory of open quantum systems. Cognition is also fundamentally contextual. The brain is a kind of (self-)observer which makes context dependent decisions. Mental environment plays a crucial role in information processing. Therefore it is natural to apply theory of open quantum systems to describe the process of decision making as the result of quantum-like dynamics of the mental state of a system interacting with an environment. The description of the process of decision making is mathematically equivalent to the description of the process of decoherence. This idea was explored in a series of works of the multidisciplinary group of researchers at Tokyo University of Science.[56] .[57]

Since in the quantum-like approach the formalism of quantum mechanics is considered as a purely operational formalism, it can be applied to the description of information processing by any biological system, i.e., not only by human beings. Operationally it is very convenient to consider e.g. a cell as a kind of decision maker processing information in the quantum information framework. This idea was explored in a series of papers of the Swedish-Japanese research group using the methods of theory of open quantum systems: genes expressions were modeled as decision making in the process of interaction with environment.[58]

History of quantum cognition[edit]

Here is a short history of applying the formalisms of quantum theory to topics in psychology. Ideas for applying quantum formalisms to cognition first appeared also in the 1990s by Diederik Aerts, Harald Atmanspacher, Robert Bordley, and Andrei Khrennikov. A special issue on Quantum Cognition and Decision appeared in the Journal of Mathematical Psychology (2009, vol 53.), which planted a flag for the field. A few books related to quantum cognition have been published including those by Khrennikov (2004, 2010), Ivancivic and Ivancivic (2010), Busemeyer and Bruza (2012), E. Conte (2012). The first Quantum Interaction workshop was held at Stanford in 2007 organized by Peter Bruza, William Lawless, C. J. van Rijsbergen, and Don Sofge as part of the 2007 AAAI Spring Symposium Series. This was followed by workshops at Oxford in 2008, Saarbrücken in 2009, at the 2010 AAAI Fall Symposium Series held in Washington, D.C., 2011 in Aberdeen, 2012 in Paris, and 2013 in Leicester. Tutorials also were presented annually beginning in 2007 until 2013 at the annual meeting of the Cognitive Science Society. A Special Issue on Quantum models of Cognition appeared in 2013 Topics in Cognitive Science.

Related theories[edit]

It was suggested by theoretical physicists David Bohm and Basil Hiley that mind and matter both emerge from an "implicate order".[59] Bohm and Hiley's approach to mind and matter is supported by philosopher Paavo Pylkkänen.[60]

The mathematical techniques of both Conte's group and Hiley's group involve the use of Clifford algebras. This has the reason that these algebras account for "non-commutativity" (that is, the commutative property is not fulfilled; for intuitive examples of "non-commutativity" see: noncommutative operations in everyday life). A mathematical model with "non-commutativity" can describe systems in which the outcome of two measurements may depend on the order in which these measurements are performed. This feature appears to be particularly pertinent for psychological processes, as it is obvious that an experiment performed on a conscious person may change the state of mind of that person, thereby influencing the outcome of the next experiment.

Elio Conte has applied quantum mechanics to human biological dynamics (primarily the human cognitive level).[61][disputed ]

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External links[edit]

Additional Reading[edit]

  • Accardi, L., Khrennikov, A., Ohya, M. (2009) Quantum Markov model for data from Shafir-Tversky experiments in cognitive psychology. Open Systems and Information Dynamics, 16, 371-385.
  • Aerts, D. & Aerts, S. (1995). Applications of quantum statistics in psychological studies of decision processes. Foundations of Science, 1, 85-97.
  • Aerts, D., Aerts, S. and Gabora, L. (2009). Experimental evidence for quantum structure in cognition. In P. D. Bruza, D. Sofge, W. Lawless, C. J. van Rijsbergen and M. Klusch (Eds.), Proceedings of QI 2009-Third International Symposium on Quantum Interaction, Book series: Lecture Notes in Computer Science, 5494, 59-70. Berlin, Heidelberg: Springer.
  • Aerts, D., Broekaert, J. & Gabora, L. (2011). A case for applying an abstracted quantum formalism to cognition. New Ideas in Psychology, 29(1), 136-146.
  • Aerts, D. and D'Hooghe, B. (2009). Classical logical versus quantum conceptual thought: Examples in economics, decision theory and concept theory. In P. D. Bruza, D. Sofge, W. Lawless, C. J. van Rijsbergen and M. Klusch (Eds.), Proceedings of QI 2009-Third International Symposium on Quantum Interaction, Book series: Lecture Notes in Computer Science, 5494, 128-142. Berlin, Heidelberg: Springer.
  • Atmanspacher, H. (1992). Categoreal and a categoreal representation of knowledge. Cognitive Systems, 3, 259-288.
  • Atmanspacher, H., Filk, T., & Romer, H. (2004). Quantum Zeno features of bistable perception. Biological Cybernetics, 90, 33-40.
  • Blutner, R. (2009). Concepts and bounded rationality: An application of Niestegge's approach to conditional quantum probabilities. In L. Accardi, G. Adenier, C. Fuchs, G. Jaeger, A. Khrennikov, L. J. A. & S. Stenholm (Eds.), Foundations of Probability and Physics - 5 (Vol. 1101, pp. 302–310): NY: American Institute of Physics Conference Proceedings.
  • Bordley, R. F. (1998). Quantum mechanical and human violations of compound probability principles: Toward a generalized Heisenberg uncertainty principle. Operations Research, 46, 923-926.
  • Bruza, P. D., Lawless, W., van Rijsbergen, C.J., & Sofge, D., Editors. (2007). Proceedings of the AAAI Spring Symposium on Quantum Interaction, March 27–29. Stanford University, 2007. AAAI Press.
  • Bruza, P. D., Lawless, W., van Rijsbergen, C.J., & Sofge, D., Editors. (2008). Quantum interaction: Proceedings of the Second Quantum Interaction Symposium. London: College Publications.
  • Bruza, P.D., Sofge, D., Lawless, W., Van Risjbergen, K., & Klusch, M., Editors. (2009). Proceedings of the Third Quantum Interaction Symposium. Lecture Notes in Artificial Intelligence, vol. 5494, Springer.
  • Busemeyer, J. R. & Bruza, P. D. (2012). Quantum models of cognition and decision. Cambridge University Press.
  • Busemeyer, J. R., Wang, Z., & Townsend, J. T. (2006). Quantum dynamics of human decision making. Journal of Mathematical Psychology, 50 (3), 220-241.
  • Conte, E. (2012) Advances in application of quantum mechanics in neuroscience and psychology: a Clifford algebraic approach, Nova Science Publishers.
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