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Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based on the Bell-like test, which is well-known in quantum physics. We propose quantum probability theory in the hyperspace analog to language (HAL) framework exploiting a Hilbert space for word and document vector specification. The quantum approach allows for accounting for specific user preferences in different contexts. To verify the algorithm proposed, we use a dataset of synthetic advertising text documents from travel agencies generated by the OpenAI GPT-4 model. We show that the "entanglement" in two-word document search and retrieval can be recognized as the frequent occurrence of two words in incompatible query contexts. We have found that the user preferences and word ordering in the query play a significant role in relatively small sizes of the HAL window. The comparison with the cosine similarity metrics demonstrates the key advantages of our approach based on the user-enforced contextual and semantic relationships between words and not just their superficial occurrence in texts. Our approach to retrieving and ranking documents allows for the creation of new information search engines that require no resource-intensive deep machine learning algorithms. [ABSTRACT FROM AUTHOR]

Inspired by the principles of quantum mechanics, we constructed a model of students' misconceptions about heat and temperature, conceptualized as a quantum system represented by a density matrix. Within this framework, the presence or absence of misconceptions is delineated as pure states, while the probability of mixed states is also considered, providing valuable insights into students' cognition based on the mental models they employ when holding misconceptions. Using the analysis model previously employed by Lei Bao and Edward Redish, we represented these results in a density matrix. In our research, we utilized the Zeo and Zadnik Thermal Concept Evaluation among 282 students from a private university in Northeast Mexico. Our objective was to extract information from the analysis of multiple-choice questions designed to explore preconceptions, offering valuable educational insights beyond the typical Correct–Incorrect binary analysis of classical systems. Our findings reveal a probability of 0.72 for the appearance of misconceptions, 0.28 for their absence, and 0.43 for mixed states, while no significant disparities were observed based on gender or scholarship status, a notable difference was observed among programs (p < 0.05). These results are consistent with the previous literature, confirming a prevalence of misconceptions within the student population. [ABSTRACT FROM AUTHOR]

Inspired by the principles of quantum mechanics, we constructed a model of students’ misconceptions about heat and temperature, conceptualized as a quantum system represented by a density matrix. Within this framework, the presence or absence of misconceptions is delineated as pure states, while the probability of mixed states is also considered, providing valuable insights into students’ cognition based on the mental models they employ when holding misconceptions. Using the analysis model previously employed by Lei Bao and Edward Redish, we represented these results in a density matrix. In our research, we utilized the Zeo and Zadnik Thermal Concept Evaluation among 282 students from a private university in Northeast Mexico. Our objective was to extract information from the analysis of multiple-choice questions designed to explore preconceptions, offering valuable educational insights beyond the typical Correct–Incorrect binary analysis of classical systems. Our findings reveal a probability of 0.72 for the appearance of misconceptions, 0.28 for their absence, and 0.43 for mixed states, while no significant disparities were observed based on gender or scholarship status, a notable difference was observed among programs (p < 0.05). These results are consistent with the previous literature, confirming a prevalence of misconceptions within the student population.

Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based on the Bell-like test, which is well-known in quantum physics. We propose quantum probability theory in the hyperspace analog to language (HAL) framework exploiting a Hilbert space for word and document vector specification. The quantum approach allows for accounting for specific user preferences in different contexts. To verify the algorithm proposed, we use a dataset of synthetic advertising text documents from travel agencies generated by the OpenAI GPT-4 model. We show that the “entanglement” in two-word document search and retrieval can be recognized as the frequent occurrence of two words in incompatible query contexts. We have found that the user preferences and word ordering in the query play a significant role in relatively small sizes of the HAL window. The comparison with the cosine similarity metrics demonstrates the key advantages of our approach based on the user-enforced contextual and semantic relationships between words and not just their superficial occurrence in texts. Our approach to retrieving and ranking documents allows for the creation of new information search engines that require no resource-intensive deep machine learning algorithms.

Computational creativity modeling, including concept combination, enables us to foster deeper abilities of Artificial Intelligence (AI) agents. Although concept combination has been addressed in a lot of computational creativity studies, findings show incompatibility amongst empirical data of concept combination and the results of the used methods. In addition, even though recent neuroscientific studies show the crucial impact of retrieving concepts' relations explicitly stored in episodic memory, it has been underestimated in modeling creative processes. In this paper, a quantum cognition-based approach is used to consider the context more efiectively and resolve logical inconsistencies. Also, episodic memory is leveraged as the basis for the concept combination modeling process based on the created context. The result of the proposed process is a set of meaningful concepts and expressions as a combination of stimuli and related episodes, which are used to depict a visual collage as an image. The significant improvement in the quality of results in comparison with the existing methods suggests that quantum-like modeling can be considered the foundation for developing AI agents capable of creating artistic images or assisting a person during a creative process. [ABSTRACT FROM AUTHOR]

One of the most puzzling questions in neuroscience is the nature of emotions and their role in consciousness. The brain's significant energy investment in maintaining the resting state indicates its essential role as the ground state of consciousness, the source of the sense of self. Emotions, the brain's homeostatic master regulators, continuously measure and motivate the recovery of the psychological equilibrium. Moreover, perception's information-energy exchange with the environment gives rise to a closed thermodynamic cycle, the reversible Carnot engine. The Carnot cycle forms an exothermic process; low entropy and reversible resting state turn the focus to the past, causing regret and remorse. The endothermic reversed Carnot cycle creates a high entropy resting state with irreversible activations generating novelty and intellect. We propose that the cycle's direction represents psychological spin, where the endothermic cycle's energy accumulation forms up-spin, and the energy-wasting exothermic cycle represents down-spin. Psychological spin corresponds to attitude, the determining factor in cognitive function and social life. By applying the Pauli exclusion principle for consciousness, we can explain the need for personal space and the formation of hierarchical social structures and animals' territorial needs. Improving intuition about the brain's intelligent computations may allow new treatments for mental diseases and novel applications in robotics and artificial intelligence. [ABSTRACT FROM AUTHOR]

One of the most puzzling questions in neuroscience is the nature of emotions and their role in consciousness. The brain’s significant energy investment in maintaining the resting state indicates its essential role as the ground state of consciousness, the source of the sense of self. Emotions, the brain’s homeostatic master regulators, continuously measure and motivate the recovery of the psychological equilibrium. Moreover, perception’s information-energy exchange with the environment gives rise to a closed thermodynamic cycle, the reversible Carnot engine. The Carnot cycle forms an exothermic process; low entropy and reversible resting state turn the focus to the past, causing regret and remorse. The endothermic reversed Carnot cycle creates a high entropy resting state with irreversible activations generating novelty and intellect. We propose that the cycle’s direction represents psychological spin, where the endothermic cycle’s energy accumulation forms up-spin, and the energy-wasting exothermic cycle represents down-spin. Psychological spin corresponds to attitude, the determining factor in cognitive function and social life. By applying the Pauli exclusion principle for consciousness, we can explain the need for personal space and the formation of hierarchical social structures and animals’ territorial needs. Improving intuition about the brain’s intelligent computations may allow new treatments for mental diseases and novel applications in robotics and artificial intelligence.

We consider a discrete-time model of a population of agents participating in a minority game using a quantum cognition in an approach with binary choices. As the agents make decisions based on both their present and past states, the model is inherently two-dimensional, but can be reduced to a one-dimensional system governed by a bi-valued function. Through this reduction, we prove how the complex bifurcation structure in the model's 2D parameter space can be explained by a few codimension-2 bifurcation points of a type not yet reported in the literature. These points act as organizing centers for period-adding structures that partially overlap, leading to bistability. [ABSTRACT FROM AUTHOR]

This exploratory study investigates a human agent's evolving judgements of reliability when interacting with an AI system. Two aims drove this investigation: (1) compare the predictive performance of quantum vs. Markov random walk models regarding human reliability judgements of an AI system and (2) identify a neural correlate of the perturbation of a human agent's judgement of the AI's reliability. As AI becomes more prevalent, it is important to understand how humans trust these technologies and how trust evolves when interacting with them. A mixed-methods experiment was developed for exploring reliability calibration in human–AI interactions. The behavioural data collected were used as a baseline to assess the predictive performance of the quantum and Markov models. We found the quantum model to better predict the evolving reliability ratings than the Markov model. This may be due to the quantum model being more amenable to represent the sometimes pronounced within-subject variability of reliability ratings. Additionally, a clear event-related potential response was found in the electroencephalographic (EEG) data, which is attributed to the expectations of reliability being perturbed. The identification of a trust-related EEG-based measure opens the door to explore how it could be used to adapt the parameters of the quantum model in real time. [ABSTRACT FROM AUTHOR]

After reviewing the physicalistic or metaphorical accounts to musical and visual forces by Arnheim and Larson, respectively, which were inspired by the basic tenets of gestalt psychology, I present a novel, naturalistic, mathematical framework, based on symmetry principles and gauge theory. In musicology, this approach has already been applied to the phenomenon of tonal attraction, leading to a deformation of the circle of fifths. The underlying gauge symmetry turns out as the SO(2) Lie group of a musical quantum model. Here, I present an alternative description in terms of Riemannian geometry. Its essential constraint of invariance of the infinitesimal line element leads to a deformation of the circle of fifths into a heart of fifths. In vision, the same approach is applied to Fraser's twisted cord illusion where concentric circles are deformed to squircle objects by means of an optical gauge field induced through a checkerboard background. [ABSTRACT FROM AUTHOR]

We present a new quantum-markovian model of two-alternative forced choice (2AFC) decision-making. We treatthe decision-making process as an accumulation of evidencebetween two competing alternatives, analogous to the drift dif-fusion model (DDM), in which the stimulus acts as a gener-ative process, emitting bits of information that are treated asquantum particles. The particles are acted on by a landscapedetermined by the agent’s experience with the task or stimu-lus, signal strength, and allocated cognitive control. We de-rive closed form expressions for success rates under both theinterrogation and free response paradigms. Under the free re-sponse paradigm, we show that this model reduces to a Markovprocess with closed form response time (RT) distributions thattake the form of inverse gaussians (IGs) with periodic noisecharacteristic to the task set. In the limit of long RT, the RTdistributions become smooth, recovering true IG distributionsanalogous to the standard DDM.

This paper provides the foundations of a uni-fied cognitive decision-making framework (QulBIT)which is derived from quantum theory. The mainadvantage of this framework is that it can caterfor paradoxical and irrational human decision mak-ing. Although quantum approaches for cognitionhave demonstrated advantages over classical prob-abilistic approaches and bounded rationality mod-els, they still lack explanatory power. To addressthis, we introduce a novel explanatory analysis ofthe decision-maker’s belief space. This is achievedby exploiting quantum interference effects as a wayof both quantifying and explaining the decision-maker’s uncertainty. We detail the main modulesof the unified framework, the explanatory analy-sis method, and illustrate their application in situ-ations violating the Sure Thing Principle.

This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed in a survey affects whether participants answer 'yes' or 'no', so the population that answers 'yes' to both questions cannot be modeled as the intersection of two fixed sets. It can, however, be modeled as a sequence of projections carried out in different orders. This and other examples have been described successfully using quantum probability, which relies on comparing angles between subspaces rather than volumes between subsets. Now in the early 2020s, quantum computers have reached the point where some of these quantum cognitive models can be implemented and investigated on quantum hardware, by representing the mental states in qubit registers, and the cognitive operations and decisions using different gates and measurements. This paper develops such quantum circuit representations for quantum cognitive models, focusing particularly on modeling order effects and decision-making under uncertainty. The claim is not that the human brain uses qubits and quantum circuits explicitly (just like the use of Boolean set theory does not require the brain to be using classical bits), but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modeling. Key quantum properties include superposition, entanglement, and collapse, as these mathematical elements provide a common language between cognitive models, quantum hardware, and circuit implementations. [ABSTRACT FROM AUTHOR]

Metaphor comprehension is a creative process that may lead to the emergence of novel meaning. Several studies have examined the emergence according to the interaction between the topic and vehicle. We focused on the other type of emergence in metaphor comprehension: the interaction between the literal and figurative meanings. This article hypothesized that the whole meaning of some metaphorical sentences can be regarded as a superposition state of their literal and figurative meanings, which cannot be reduced to the simple composition of each meaning. To test this hypothesis, we applied QQ equality to metaphor comprehension and conducted an experiment using 21 metaphorical sentences and 1,000 participants. The model comparisons suggested that about 15% of comprehension of metaphorical sentences can be regarded as resulting from a superposition state of literal and metaphorical understanding. This result sheds new light on the emergent function and cognitive state surrounding metaphor comprehension. [ABSTRACT FROM AUTHOR]

We investigate the hypothesis that within a combination of a 'number concept' plus a 'substantive concept', such as 'eleven animals', the identity and indistinguishability present on the level of the concepts, i.e., all eleven animals are identical and indistinguishable, gives rise to a statistical structure of the Bose–Einstein type similar to how Bose–Einstein statistics is present for identical and indistinguishable quantum particles. We proceed by identifying evidence for this hypothesis by extracting the statistical data from the World-Wide-Web utilizing the Google Search tool. By using the Kullback–Leibler divergence method, we then compare the obtained distribution with the Maxwell–Boltzmann as well as with the Bose–Einstein distributions and show that the Bose–Einstein's provides a better fit as compared to the Maxwell–Boltzmann's. [ABSTRACT FROM AUTHOR]

This study utilises an experiment famous in quantum physics,the Stern-Gerlach experiment, to inform the structure of an ex-perimental protocol from which a quantum cognitive decisionmodel can be developed. The ’quantumness’ of this modelis tested by computing a discrete quasi-probabilistic Wignerfunction. Based on theory from quantum physics, our hypothe-sis is that the Stern-Gerlach protocol will admit negative valuesin the Wigner function, thus signalling that the cognitive de-cision model is quantum. A crowdsourced experiment of twoimages was used to collect decisions around three questions re-lated to image trustworthiness. The resultant data was used toinstantiate the quantum model and compute the Wigner func-tion. Negative values in the Wigner functions of both imageswere encountered, thus substantiating our hypothesis. Find-ings also revealed that the quantum cognitive model was amore accurate predictor of decisions when compared to pre-dictions computed using Bayes’ rule.

Since Tversky argued that similarity judgments violate the three metric axioms, asymmetrical similarity judgments have been particularly challenging for standard, geometric models of similarity, such as multidimensional scaling. According to Tversky, asymmetrical similarity judgments are driven by differences in salience or extent of knowledge. However, the notion of salience has been difficult to operationalize, especially for perceptual stimuli for which there are no apparent differences in extent of knowledge. To investigate similarity judgments between perceptual stimuli, across three experiments, we collected data where individuals would rate the similarity of a pair of temporally separated color patches. We identified several violations of symmetry in the empirical results, which the conventional multidimensional scaling model cannot readily capture. Pothos et al. proposed a quantum geometric model of similarity to account for Tversky's findings. In the present work, we extended this model to a more general framework that can be fit to similarity judgments. We fitted several variants of quantum and multidimensional scaling models to the behavioral data and concluded in favor of the quantum approach. Without further modifications of the model, the best‐fit quantum model additionally predicted violations of the triangle inequality that we observed in the same data. Overall, by offering a different form of geometric representation, the quantum geometric framework of similarity provides a viable alternative to multidimensional scaling for modeling similarity judgments, while still allowing a convenient, spatial illustration of similarity. [ABSTRACT FROM AUTHOR]

The psychology of judgment and decision making can provide useful guidance to the task of medical decision making. More specifically, we describe how a new approach to judgment and decisions, based on quantum probability theory, can shed new light on seemingly irrational judgments, as well as indicate ways to ameliorate these judgment errors. Five different types of probability judgment errors that occur in medical decisions are reviewed. For each one, we provide a simple account using theory from quantum cognition. We conclude by drawing the implications of quantum cognition for ameliorating these common medical probability judgment errors.

This exploratory study investigates a human agent’s evolving judgements of reliability when interacting with an AI system. Two aims drove this investigation: (1) compare the predictive performance of quantum vs. Markov random walk models regarding human reliability judgements of an AI system and (2) identify a neural correlate of the perturbation of a human agent’s judgement of the AI’s reliability. As AI becomes more prevalent, it is important to understand how humans trust these technologies and how trust evolves when interacting with them. A mixed-methods experiment was developed for exploring reliability calibration in human–AI interactions. The behavioural data collected were used as a baseline to assess the predictive performance of the quantum and Markov models. We found the quantum model to better predict the evolving reliability ratings than the Markov model. This may be due to the quantum model being more amenable to represent the sometimes pronounced within-subject variability of reliability ratings. Additionally, a clear event-related potential response was found in the electroencephalographic (EEG) data, which is attributed to the expectations of reliability being perturbed. The identification of a trust-related EEG-based measure opens the door to explore how it could be used to adapt the parameters of the quantum model in real time.

Deep AI and big data have reshaped the IT field. Since AI refers to the simulation of human intelligence processes by computer systems for problem solving, the current landscape of IT provides an excellent opportunity for us to re-examine how human cognition has responded to new developments in IT. In this paper, we discuss several issues related to human cognition in IT at AI-enriched big data era. First, we note that deep AI reflects human beings' needs for causality and explanations, and review a couple of related aspects. We then examine human cognition in IT from two perspectives: On the theoretical side, recent developments in quantum computing has the potential to reveal complex nature of human cognition in the form of quantum cognition, and on the practical side, cognitive bias may play a negative role, as demonstrated in the way of dealing with fake news. The complexity of human cognition in IT leads to a proposed multi-layer concept description which is based on our view that a complete concept definition is impossible without incorporating human cognitive feedback in the loop. We conclude this paper by emphasizing the need for integrated study of human cognition in IT. [ABSTRACT FROM AUTHOR]

We show how a behavioral form of capital theory can be derived using canonical quantization. In particular, we introduce quantum cognition into capital theory by applying Dirac's canonical quantization approach to Weitzman's Hamiltonian formulation of capital theory, the justification for the use of quantum cognition being the incompatibility of questions encountered in the investment decision-making process. We illustrate the utility of this approach by deriving the capital-investment commutator for a canonical dynamic investment problem. [ABSTRACT FROM AUTHOR]

In the present paper, we aim to develop a formal quantum logic theory of the interplay between conscious and unconscious processes of the human mind, a goal that has already been envisaged in quantum cognition; in doing so, we will show how the interplay between formal language and metalanguage allows for characterizing pure quantum states as infinite singletons: in the case of the spin observable, we obtain an equation defining a modality that is then re-interpreted as an abstract projection operator. By including a temporal parameter in the equations and by defining a modal negative operator, we derive an intuitionistic-like negation, for which the non-contradiction law is seen as an equivalent of the quantum uncertainty. Building on the psychoanalytic theory of Bi-Logic by Matte Blanco, we use modalities in interpreting the emergence of conscious representations from an unconscious one, and we demonstrate that this description fits well with Freud's view of the role of negation in mental processes. Psychoanalysis, where affect plays a prominent role in shaping not only conscious, but also unconscious representations, is therefore seen as a suitable model to expand the domain of quantum cognition to the broader field of affective quantum cognition. [ABSTRACT FROM AUTHOR]

We re-examine the long-held postulate that there are two modes of thought, and develop a more fine-grained analysis of how different modes of thought affect conceptual change. We suggest that cognitive development entails the fine-tuning of three dimensions of thought: abstractness, divergence, and context-specificity. Using a quantum cognition modeling approach, we show howthese three variables differ, and explain why they would have a distinctively different impacts on thought processes and mental contents. We suggest that, through simultaneous manipulation of all three variables, one spontaneously, and on an ongoing basis, tailors one's mode of thought to the demands of the current situation. The paper concludes with an analysis based on results from an earlier study of children's mental models of the shape of the Earth. The example illustrates how, through reiterated transition between mental states using these three variables, thought processes unfold, and conceptual change ensues. While this example concerns children, the approach applies more broadly to adults as well as children. [ABSTRACT FROM AUTHOR]

For two decades, the formalism of quantum mechanics has been successfully used to describe human decision processes, situations of heuristic reasoning, and the contextuality of concepts and their combinations. The phenomenon of 'categorical perception' has put us on track to find a possible deeper cause of the presence of this quantum structure in human cognition. Thus, we show that in an archetype of human perception consisting of the reconciliation of a bottom up stimulus with a top down cognitive expectation pattern, there arises the typical warping of categorical perception, where groups of stimuli clump together to form quanta, which move away from each other and lead to a discretization of a dimension. The individual concepts, which are these quanta, can be modeled by a quantum prototype theory with the square of the absolute value of a corresponding Schrödinger wave function as the fuzzy prototype structure, and the superposition of two such wave functions accounts for the interference pattern that occurs when these concepts are combined. Using a simple quantum measurement model, we analyze this archetype of human perception, provide an overview of the experimental evidence base for categorical perception with the phenomenon of warping leading to quantization, and illustrate our analyses with two examples worked out in detail. [ABSTRACT FROM AUTHOR]

It appears that the free energy minimization principle conflicts with quantum cognition since the former adheres to a restricted view based on experience while the latter allows deviations fromsuch a restricted view. While free energy minimization, which incorporates Bayesian inference, leads to a Boolean lattice of propositions (classical logic), quantum cognition, which seems to be very dissimilar to Bayesian inference, leads to an orthomodular lattice of propositions (quantumlogic). Thus, we address this challenging issue to bridge and connect the free energyminimization principle with the theory of quantum cognition. In this work, we introduce "excess Bayesian inference" and showthat this excess Bayesian inference entails an underlying orthomodular lattice, while classic Bayesian inference entails a Boolean lattice. Excess Bayesian inference is implemented by extending the key idea of Bayesian inference beyond classic Bayesian inference and its variations. It is constructed by enhancing the idea of active inference and/or embodied intelligence. The appropriate lattice structure of its logic is obtained from a binary relation transformed from a distribution of the joint probabilities of data and hypotheses by employing a rough-set lattice technique in accordance with quantum cognition logic. [ABSTRACT FROM AUTHOR]

The concept of intelligent agents is—roughly speaking—based on an architecture and a set of behavioral programs that primarily serve to solve problems autonomously. Increasing the degree of autonomy and improving cognitive performance, which can be assessed using cognitive and behavioral tests, are two important research trends. The degree of autonomy can be increased using higher-level psychological modules with which needs and motives are taken into account. In our approach we integrate these modules in architecture for an embodied, enactive multi-agent system, such that distributed problem solutions can be achieved. Furthermore, after uncovering some weaknesses in the cognitive performance of traditionally designed agents, we focus on two major aspects. On the one hand, the knowledge processing of cognitive agents is based on logical formalisms, which have deficiencies in the representation and processing of incomplete or uncertain knowledge. On the other hand, in order to fully understand the performance of cognitive agents, explanations at the symbolic and subsymbolic levels are required. Both aspects can be addressed by quantum-inspired cognitive agents. To investigate this approach, we consider two tasks in the sphere of Shannon's famous mouse-maze problem: namely classifying target objects and ontology inference. First, the classification of an unknown target object in the mouse-maze, such as cheese, water, and bacon, is based on sensory data that measure characteristics such as odor, color, shape, or nature. For an intelligent agent, we need a classifier with good prediction accuracy and explanatory power on a symbolic level. Boolean logic classifiers do work on a symbolic level but are not adequate for dealing with continuous data. Therefore, we demonstrate and evaluate a quantum-logic-inspired classifier in comparison to Boolean-logic-based classifiers. Second, ontology inference is iteratively achieved by a quantum-inspired agent through maze exploration. This requires the agent to be able to manipulate its own state by performing actions and by collecting sensory data during perception. We suggest an algebraic approach where both kinds of behaviors are uniquely described by quantum operators. The agent's state space is then iteratively constructed by carrying out unitary action operators, while Hermitian perception operators act as observables on quantum eigenstates. As a result, an ontology emerges as the simultaneous solution of the respective eigenvalue equations.

Predictive brain theory challenges the general assumption of a brain extracting knowledge from sensations and considers the brain as an organ of inference, actively constructing explanations about reality beyond its sensory evidence. Predictive brain has been formalized through Bayesian updating, where top-down predictions are compared with bottom-up evidence. In this article, we propose a different approach to predictive brain based on quantum probability—we call it Quantum Predictive Brain (QPB). QPB is consistent with the Bayesian framework, but considers it as a special case. The tenet of QPB is that top-down predictions and bottom-up evidence are complementary, as they cannot be co-jointly determined to pursue a univocal model of brain functioning. QPB can account for several high-order cognitive phenomena (which are problematic in current predictive brain theories) and offers new insights into the mechanisms of neural reuse. [ABSTRACT FROM AUTHOR]

A puzzling finding from research on strategical decision making concerns the effect that predictions have on future actions. Simply stating a prediction about an opponent changes the total probability (pooled over predictions) of a player taking a future action compared to not stating any prediction. This is called an interference effect. We first review five different findings of interference effects from past empirical work using the prisoner's dilemma game. Then we report interference effects obtained from a new experiment in which 493 participants played a six‐stage centipede game against a computer agent. During the first stage of the game, the total probability following prediction for cooperation was higher than making a decision alone; during later stages, the total probability following prediction for cooperation was lower than making a decision alone. These interference effects are difficult to explain using traditional economic models, and instead these results suggest turning to a quantum cognition approach to strategic decision making. Toward this end, we develop a belief‐action entanglement model that provides a good account of the empirical results. A puzzling finding from research on strategic decision making concerns the effect that predictions have on future actions. Simply stating a prediction about an opponent changes the total probability (pooled over predictions) of a player taking a future action as compared to not stating any prediction. These interference effects are difficult to explain using traditional economic models, and instead these results suggest turning to a quantum cognition approach to strategic decision making. [ABSTRACT FROM AUTHOR]

Human decision-making is relevant for concept formation and cognitive illusions. Cognitive illusions can be explained by quantum probability, while the reason for introducing quantum mechanics is based on ad hoc bounded rationality (BR). Concept formation can be explained in a set-theoretic way, although such explanations have not been extended to cognitive illusions. We naturally expand the idea of BR to incomplete BR and introduce the key notion of nonlocality in cognition without any attempts on quantum theory. We define incomplete bounded rationality and nonlocality as a binary relation, construct a lattice from the relation by using a rough-set technique, and define probability in concept formation. By using probability defined in concept formation, we describe various cognitive illusions, such as the guppy effect, conjunction fallacy, order effect, and so on. It implies that cognitive illusions can be explained by changes in the probability space relevant to concept formation. [ABSTRACT FROM AUTHOR]

A predictive model applicable in both neurophysiological and decision-making studies is proposed, bridging the gap between psychological/behavioral and neurophysiological studies. Supposing the electromagnetic waves (brainwaves) are carriers of decisionmaking, and electromagnetic waves with the same frequency, individual amplitude and constant phase triggered by conditions interfere with each other and the resultant intensity determines the probability of the decision. Accordingly, brainwave-interference decisionmaking model is built mathematically and empirically test with neurophysiological and behavioral data. Event-related potential data confirmed the stability of the phase differences in a given decision context. Behavioral data analysis shows that phase stability exists across categorization-decision, two-stage gambling, and prisoner's dilemma decisions. Irrational decisions occurring in those experiments are actually rational as their phases could be quantitatively derived from the phases of the riskiest and safest choices. Model fitting result reveals that the root-mean-square deviations between the fitted and actual phases of irrational decisions are less than 10°, and the mean absolute percentage errors of the fitted probabilities are less than 0.06. The proposed model is similar in mathematical form compared with the quantum modeling approach, but endowed with physiological/psychological connection and predictive ability, and promising in the integration of neurophysiological and behavioral research to explore the origin of the decision. [ABSTRACT FROM AUTHOR]

The psychology of judgment and decision making can provide useful guidance to the task of medical decision making. More specifically, we describe how a new approach to judgment and decisions, based on quantum probability theory, can shed new light on seemingly irrational judgments, as well as indicate ways to ameliorate these judgment errors. Five different types of probability judgment errors that occur in medical decisions are reviewed. For each one, we provide a simple account using theory from quantum cognition. We conclude by drawing the implications of quantum cognition for ameliorating these common medical probability judgment errors. [ABSTRACT FROM AUTHOR]

Predictive brain theory challenges the general assumption of a brain extracting knowledge from sensations and considers the brain as an organ of inference, actively constructing explanations about reality beyond its sensory evidence. Predictive brain has been formalized through Bayesian updating, where top-down predictions are compared with bottom-up evidence. In this article, we propose a different approach to predictive brain based on quantum probability—we call it Quantum Predictive Brain (QPB). QPB is consistent with the Bayesian framework, but considers it as a special case. The tenet of QPB is that top-down predictions and bottom-up evidence are complementary, as they cannot be co-jointly determined to pursue a univocal model of brain functioning. QPB can account for several high-order cognitive phenomena (which are problematic in current predictive brain theories) and offers new insights into the mechanisms of neural reuse.

Many cognitive phenomena of the sort studied by behavioral psychologists show evidence of a threshold effect, where a certain minimum impulse is required in order to produce a change. An example is the phenomenon of preference reversal, where a change in context affects a decision, but only if the effect on perceived utility is sufficiently large. Similar threshold effects play a role in the endowment effect, where the change of context from owning to buying something induces a step change in its perceived value, or the ultimatum game, where people demand a certain minimum threshold amount before a deal can be accepted. The situation is similar to the photoelectric experiment in physics, where a minimum threshold of energy from a photon is required in order to dislodge an electron from an atom. In physics, this quantum of energy is written as the product of Planck’s constant and frequency. This paper uses the concept of entropic force to derive a similar expression for quantum economics. The theory is applied to a range of cognitive and economic phenomena exhibiting a threshold effect.

This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed in a survey affects whether participants answer ‘yes’ or ‘no’, so the population that answers ‘yes’ to both questions cannot be modeled as the intersection of two fixed sets. It can, however, be modeled as a sequence of projections carried out in different orders. This and other examples have been described successfully using quantum probability, which relies on comparing angles between subspaces rather than volumes between subsets. Now in the early 2020s, quantum computers have reached the point where some of these quantum cognitive models can be implemented and investigated on quantum hardware, by representing the mental states in qubit registers, and the cognitive operations and decisions using different gates and measurements. This paper develops such quantum circuit representations for quantum cognitive models, focusing particularly on modeling order effects and decision-making under uncertainty. The claim is not that the human brain uses qubits and quantum circuits explicitly (just like the use of Boolean set theory does not require the brain to be using classical bits), but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modeling. Key quantum properties include superposition, entanglement, and collapse, as these mathematical elements provide a common language between cognitive models, quantum hardware, and circuit implementations.

The mind--body dualistic model is not supported on either epistemological or clinical grounds. A new paradigm based upon chaotic complex systems theory and quantum mechanics is proposed to overcome the dichotomy between mind and body. Further, the mind/body interface represents a chaotic system, based upon the probability principle, as seen in subatomic physics and quantum mechanics, rather than on the deterministic principle of causality. Patterns of chaotic behaviour can be found in neuronal activity and applications of chaotic patterns seem to be pivotal for mind-body research and the process of trance. A controversial quantum consciousness theory has been proposed, since quantum physics applies to subatomic world and not to macrostructures, such as the brain. Quantum cognition is a novel field which applies the mathematical formalism of quantum theory to explore and model cognitive phenomena, overcoming limits and shortcomings of both Cartesian dualism and quantum general theory. A quantum-like paradigm opens new perspectives in conceptualizing heuristically the hypnotic experience, but it also offers a new insight of hypnotic techniques, such as, for instance, indirect Ericksonian techniques, providing a far greater cognitive flexibility than classical techniques in modelling hypnotic processes and responses. [ABSTRACT FROM AUTHOR]

Recently quantum probability theory started to be actively used in studies of human decision-making, in particular for the resolution of paradoxes (such as the Allais, Ellsberg, and Machina paradoxes). Previous studies were based on a cognitive metaphor of the quantum double-slit experiment—the basic quantum interference experiment. In this paper, we report on an economics experiment based on a triple-slit experiment design, where the slits are menus of alternatives from which one can choose. The test of nonclassicality is based on the Sorkin equality (which was only recently tested in quantum physics). Each alternative is a voucher to buy products in one or more stores. The alternatives are obtained from all disjunctions including one, two or three stores. The participants have to reveal the amount for which they are willing to sell the chosen voucher. Interference terms are computed by comparing the willingness to sell a voucher built as a disjunction of stores and the willingness to sell the vouchers corresponding to the singleton stores. These willingness to sell amounts are used to estimate probabilities and to test both the law of total probabilities and the Born Rule. Results reject neither classical nor quantum probability. We discuss this initial experiment and our results and provide guidelines for future studies. [ABSTRACT FROM AUTHOR]