Can quantum probability provide a new direction for cognitive modeling?

Quantum Logic Automata Generate Class IV-like patterns and 1/f noise

Owing to recent advancements in brain science and AI, researchers tend to focus on the concept of self-organized criticality or the edge of chaos. On the other hand, quantum cognition, which is rooted in quantum mechanics, is promising for resolving various cognitive illusions. However, until recently, no connection between criticality and quantum mechanics was proposed. Gunji et al. (2024) recently introduced a linkage termed quantum logic automata, which encompasses not only quantum logic but also criticality characterized by power-law distributions. While quantum logic automata can be derived from various structures, only one of them has been proposed and discussed. Here, we define another type of quantum logic automata involving quantum logic and demonstrate that symmetric quantum logic automata lead to complex Class IV-like patterns and power-law distributions. Our findings support the association between criticality and quantum theory.

Phase transitions, KMS condition and decision making: an introductory model

We consider a simple model of interacting agents asked to choose between 'yes' and 'no' to some given question. The agents are described in terms of spin variables, and they interact according to a mean field Heisenberg model. We discuss under which conditions the agents can come out with a common choice. This is made using, in a social context, the notion of KMS states and phase transitions. This article is part of the theme issue 'Thermodynamics 2.0: Bridging the natural and social sciences (Part 2)'.

Quantum formalism for the dynamics of cognitive psychology

The cognitive state of mind concerning a range of choices to be made can be modelled efficiently by use of an element of a high-dimensional Hilbert space. The dynamics of the state of mind resulting from information acquisition can be characterised by the von Neumann-Lüders projection postulate of quantum theory. This is shown to give rise to an uncertainty-minimising dynamical behaviour equivalent to Bayesian updating, hence providing an alternative approach to representing the dynamics of a cognitive state, consistent with the free energy principle in brain science. The quantum formalism, however, goes beyond the range of applicability of classical reasoning in explaining cognitive behaviour, thus opening up new and intriguing possibilities.

Microtubules as a potential platform for energy transfer in biological systems: a target for implementing individualized, dynamic variability patterns to improve organ function

Variability characterizes the complexity of biological systems and is essential for their function. Microtubules (MTs) play a role in structural integrity, cell motility, material transport, and force generation during mitosis, and dynamic instability exemplifies the variability in the proper function of MTs. MTs are a platform for energy transfer in cells. The dynamic instability of MTs manifests itself by the coexistence of growth and shortening, or polymerization and depolymerization. It results from a balance between attractive and repulsive forces between tubulin dimers. The paper reviews the current data on MTs and their potential roles as energy-transfer cellular structures and presents how variability can improve the function of biological systems in an individualized manner. The paper presents the option for targeting MTs to trigger dynamic improvement in cell plasticity, regulate energy transfer, and possibly control quantum effects in biological systems. The described system quantifies MT-dependent variability patterns combined with additional personalized signatures to improve organ function in a subject-tailored manner. The platform can regulate the use of MT-targeting drugs to improve the response to chronic therapies. Ongoing trials test the effects of this platform on various disorders.

Wave-like patterns in parameter space interpreted as evidence for macroscopic effects resulting from quantum or quantum-like processes in the brain

Data from eight numerosity estimation experiments reliably exhibit wave-like patterns in plots of the standard deviations of the response times along the abstract parameter of the magnitude of the error in the numerosity estimation. An explanation for this phenomenon is proposed in terms of an analogy between response times and error magnitude on one hand, and energy and position of quantum particles on the other, constructed using an argument for an overlap between the mathematical apparatus describing Hopfield-type neural networks and quantum systems, established by some researchers. Alternative explanations are presented within the traditional explanatory framework of oscillations due to neural firing, involving hypothetical mechanisms for converting oscillation patterns in time to oscillation patterns in the space of an abstract parameter, such as the magnitude of the error during numerosity estimation. The viability of the proposal of causal influences propagating from the microscale of quantum phenomena to the macroscale of human behavior, needed for the first type of explanation, is exemplified by the phenomenon of magnetoreception in some species of birds, which is allegedly quantum in nature.

Quantum core affect. Color-emotion structure of semantic atom

Psychology suffers from the absence of mathematically-formalized primitives. As a result, conceptual and quantitative studies lack an ontological basis that would situate them in the company of natural sciences. The article addresses this problem by describing a minimal psychic structure, expressed in the algebra of quantum theory. The structure is demarcated into categories of emotion and color, renowned as elementary psychological phenomena. This is achieved by means of quantum-theoretic qubit state space, isomorphic to emotion and color experiences both in meaning and math. In particular, colors are mapped to the qubit states through geometric affinity between the HSL-RGB color solids and the Bloch sphere, widely used in physics. The resulting correspondence aligns with the recent model of subjective experience, producing a unified spherical map of emotions and colors. This structure is identified as a semantic atom of natural thinking-a unit of affectively-colored personal meaning, involved in elementary acts of a binary decision. The model contributes to finding a unified ontology of both inert and living Nature, bridging previously disconnected fields of research. In particular, it enables theory-based coordination of emotion, decision, and cybernetic sciences, needed to achieve new levels of practical impact.

Quantum decision making in automatic driving

The behavior intention estimation and interaction between Autonomous Vehicles (AV) and human traffic participants are the key problems in Automatic Driving System (ADS). When the classical decision theory studies implicitly assume that the behavior of human traffic participants is completely rational. However, according to the booming quantum decision theory in recent years and actual traffic cases, traffic behaviors and other human behaviors are often irrational and violate the assumptions of classical cognitive and decision theory. This paper explores the decision-making problem in the two-car game scene based on quantum decision theory and compares it with the current mainstream method of studying irrational behavior-Cumulative Prospect Theory (CPT) model. The comparative analysis proved that the Quantum Game Theory (QGT) model can explain the separation effect which the classical probability model can’t reveal, and it has more advantages than CPT model in dealing with game scene decision-making. When two cars interact with each other, the QGT model can consider the interests of both sides from the perspective of the other car. Compared with the classical probability model and CPT model, the QGT is more realistic in the behavior decision-making of ADS.

A VIKOR-Based Linguistic Multi-Attribute Group Decision-Making Model in a Quantum Decision Scenario

Quantum decision theory has been successfully applied to multi-attribute group decision-making (MAGDM) to model decision-makers’ interference and superposition effects in recent years. Existing quantum models assume that interference effects among decision-makers are symmetric. However, asymmetric interference effects have been ignored. We propose a VIKOR-based linguistic distribution assessments (LDAs) model considering asymmetric interference effects in a quantum decision scenario. Firstly, we combine VIKOR with LDAs to obtain a compromise solution in a fuzzy multi-attribute decision scenario with conflicting attributes. Secondly, an aggregation framework based on quantum probability theory is constructed to explore group preferences considering asymmetric interference effects among decision-makers. Finally, the model is compared with other methods to confirm its validity and stability.

Ambivalence in decision making: An eye tracking study

An intuition of ambivalence in cognition is particularly strong for complex decisions, for which the merits and demerits of different options are roughly equal but hard to compare. We examined information search in an experimental paradigm which tasked participants with an ambivalent question, while monitoring attentional dynamics concerning the information relevant to each option in different Areas of Interest (AOIs). We developed two dynamical models for describing eye tracking curves, for each response separately. The models incorporated a drift mechanism towards the various options, as in standard drift diffusion theory. In addition, they included a mechanism for intrinsic oscillation, which competed with the drift process and undermined eventual stabilization of the dynamics. The two models varied in the range of drift processes postulated. Higher support was observed for the simpler model, which only included drifts from an uncertainty state to either of two certainty states. In addition, model parameters could be weakly related to the eventual decision, complementing our knowledge of the way eye tracking structure relates to decision (notably the gaze cascade effect).

Logic, Probability, and Pragmatics in Syllogistic Reasoning

Syllogistic reasoning lies at the intriguing intersection of natural and formal reasoning of language and logic. Syllogisms comprise a formal system of reasoning yet make use of natural language quantifiers (e.g., all, some) and invite natural language conclusions. The conclusions people tend to draw from syllogisms, however, deviate substantially from the purely logical system. Are principles of natural language understanding to blame? We introduce a probabilistic pragmatic perspective on syllogistic reasoning: We decompose reasoning with natural language arguments into two subproblems: language comprehension and language production. We formalize models of these processes within the Rational Speech Act framework and explore the pressures that pragmatic reasoning places on the production of conclusions. We test our models on a recent, large data set of syllogistic reasoning and find that the selection process of conclusions from syllogisms are best modeled as a pragmatic speaker who has the goal of aligning the beliefs of a naive listener with those of their own. We compare our model to previously published models that implement two alternative theories-Mental Models and Probability Heuristics-finding that our model quantitatively predicts the full distributions of responses as well as or better than previous accounts, but with far fewer parameters. Our results suggest that human syllogistic reasoning may be best understood not as a poor approximation to ideal logical reasoning, but rather as rational probabilistic inference in support of natural communication.

Research on quantum cognition in autonomous driving

Autonomous vehicles for the intention of human behavior of the estimated traffic participants and their interaction is the main problem in automatic driving system. Classical cognitive theory assumes that the behavior of human traffic participants is completely reasonable when studying estimation of intention and interaction. However, according to the quantum cognition and decision theory as well as practical traffic cases, human behavior including traffic behavior is often unreasonable, which violates classical cognition and decision theory. Based on the quantum cognitive theory, this paper studies the cognitive problem of pedestrian crossing. Through the case analysis, it is proved that the Quantum-like Bayesian (QLB) model can consider the reasonability of pedestrians when crossing the street compared with the classical probability model, being more consistent with the actual situation. The experiment of trajectory prediction proves that the QLB model can cover the edge events in interactive scenes compared with the data-driven Social-LSTM model, being more consistent with the real trajectory. This paper provides a new reference for the research on the cognitive problem of intention on bounded rational behavior of human traffic participants in autonomous driving.

Why Does Rubin's Vase Differ Radically From Optical Illusions? Framing Effects Contra Cognitive Illusions

Many researchers use the term "context" loosely to denote diverse kinds of reference points. The issue is not about terminology but rather about the common conflation of one kind of reference points, such as rules of perception, which is responsible for optical illusions, with another kind, known as "context" or "frame," as exemplified in Rubin's vase. Many researchers regard Rubin's vase as a special kind of optical illusions. This paper rather argues that the two phenomena are radically different. Optical illusions are occasional mistakes that people quickly recognize and eagerly correct, while the different figures of Rubin's vase are not mistakes but, rather, the outcomes of different perspectives that do not need correction. The competing figures in Rubin's vase can, at best, in light of more information, be more warranted or unwarranted. This paper discusses at length one ramification of the proposed distinction. The framing effects, such as loss/gain frame, are the products of contexts and, hence, resemble greatly the figures in Rubin's vase. In contrast, cognitive illusions generated occasionally by the rules of thumb (heuristics) are mistakes and, hence, resemble optical illusions. The proposed distinction carries other ramifications regarding, e.g., happiness studies, moral judgments, and the new philosophy of science.

Quantum decision corrections for the neuroeconomics of irrational movement control and goal attainment

Quantum decision theory corrects categorical and propositional logic pathologies common to classic statistical goal-oriented reasoning, such as rational neuroeconomics-based optimal foraging. Within this ecosalient framework, motivation, perception, learning, deliberation, brain computation, and conjunctive risk-order errors may be understood for subjective utility judgments underlying either rational or irrational canonical decisions-actions used to choose, procure, and consume rewarding nutrition with variable fitness.

Quantum Cognition

Uncertainty is an intrinsic part of life; most events, affairs, and questions are uncertain. A key problem in behavioral sciences is how the mind copes with uncertain information. Quantum probability theory offers a set of principles for inference, which align well with intuition about psychological processes in certain cases: cases when it appears that inference is contextual, the mental state changes as a result of previous judgments, or there is interference between different possibilities. We motivate the use of quantum theory in cognition and its key characteristics. For each of these characteristics, we review relevant quantum cognitive models and empirical support. The scope of quantum cognitive models encompasses fallacies in decision-making (such as the conjunction fallacy or the disjunction effect), question order effects, conceptual combination, evidence accumulation, perception, over-/underdistribution effects in memory, and more. Quantum models often formalize psychological ideas previously expressed in heuristic terms, allow unified explanations of previously disparate findings, and have led to several surprising, novel predictions. We also cast a critical eye on quantum models and consider some of their shortcomings and issues regarding their further development. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Distinguishing Self, Other, and Autonomy From Visual Feedback: A Combined Correlation and Acceleration Transfer Analysis

In cognitive science, Theory of Mind (ToM) is the mental faculty of assessing intentions and beliefs of others and requires, in part, to distinguish incoming sensorimotor (SM) signals and, accordingly, attribute these to either the self-model, the model of the other, or one pertaining to the external world, including inanimate objects. To gain an understanding of this mechanism, we perform a computational analysis of SM interactions in a dual-arm robotic setup. Our main contribution is that, under the common fate principle, a correlation analysis of the velocities of visual pivots is shown to be sufficient to characterize "the self" (including proximo-distal arm-joint dependencies) and to assess motor to sensory influences, and "the other" by computing clusters in the correlation dependency graph. A correlational analysis, however, is not sufficient to assess the non-symmetric/directed dependencies required to infer autonomy, the ability of entities to move by themselves. We subsequently validate 3 measures that can potentially quantify a metric for autonomy: Granger causality (GC), transfer entropy (TE), as well as a novel "Acceleration Transfer" (AT) measure, which is an instantaneous measure that computes the estimated instantaneous transfer of acceleration between visual features, from which one can compute a directed SM graph. Subsequently, autonomy is characterized by the sink nodes in this directed graph. This study results show that although TE can capture the directional dependencies, a rectified subtraction operation denoted, in this study, as AT is both sufficient and computationally cheaper.

A quantum mechanics-based framework for knowledge-based innovation

Purpose

Because the discontinuous and uncertain characteristics of knowledge-based innovation cannot be reasonably interpreted by conventional management approaches, quantum mechanics which begins with uncertainty and concerns with a dynamic process of the complex system, has been exploratorily used in the management field. Although the theoretical new insights are provided by pioneering studies, quantitative research is in short supply. This paper aims to propose a quantum mechanics-based framework for quantitative research, thus extending the application of quantum mechanics in the knowledge management area from a dynamic system evolutionary standpoint.

Design/methodology/approach

Based on the similarity comparison between knowledge-based system evolution and atomic motion, the authors construct the atom-like structure of the knowledge-based system and elaborate the evolutionary mechanism of the knowledge-based system, thereby establishing the quantitative model. Apple and Zhongxing Telecom Equipment were selected for an empirical study to demonstrate the usefulness of the models for research on knowledge-based innovation and explore the unique knowledge-based innovation characteristics of the two firms.

Findings

First, the transition force of dynamic knowledge shows an inverted U shape; accumulating dynamic knowledge to a moderate degree not only facilitates transforming dynamic knowledge into static knowledge but also balances the relationship between the influence of knowledge force range and dynamic knowledge transformation. Second, existing knowledge is gradually substituted by new knowledge and knowledge density at a high knowledge energy level distinctly increases with a narrower bandwidth. Third, the investment loss is associated with resource configuration, resource utilization and the amount of accumulative dynamic knowledge before investment. Knowledge loss is negatively correlated with the knowledge compatibility coefficient.

Research limitations/implications

The authors use the advanced method in quantum mechanics to legitimately unveil the emergence mechanism of knowledge-based innovation. Meanwhile, the authors capture the non-linear transformation relationship of heterogeneous knowledge and expose the change in ways of both investment loss and knowledge loss that cannot be quantified by conventional models. In doing so, the authors not only reveal the principle of qualitative knowledge change but also offer practical implications for developing flexible and targeted innovation strategies.

Originality/value

First, by proposing a complete quantum mechanics-based framework, the authors not only supplement the quantitative research contents to knowledge-based innovation literature which proposed calls to conduct research in way of quantum mechanics but also overcome the difficulties of knowledge-based system conceptualization and measurement. Second, the authors reveal the uncertain change of knowledge transformation and measure the loss of investment and knowledge, which contribute to identifying defects of firms in knowledge-based innovation. Third, the authors explore the internal mechanism that led to knowledge-based innovation exhibits non-linear characteristics and capture unique dynamic relationships between different variables which affect the emergence of knowledge-based innovation.

Studies in Ecological Rationality

Ecological rationality represents an alternative to classic frameworks of rationality. Extending on Herbert Simon's concept of bounded rationality, it holds that cognitive processes, including simple heuristics, are not per se rational or irrational, but that their success rests on their degree of fit to relevant environmental structures. The key is therefore to understand how cognitive and environmental structures slot together. In recent years, a growing set of analyses of heuristic-environment systems has deepened the understanding of the human mind and how boundedly rational heuristics can result in successful decision making. This article is concerned with three conceptual challenges in the study of ecological rationality. First, do heuristics also succeed in dynamic contexts involving competitive agents? Second, can the mind adapt to environmental structures via an unsupervised learning process? Third, how can research go beyond mere descriptions of environmental structures to develop theories of the mechanisms that give rise to those structures? In addressing these questions, we illustrate that a successful theory of rationality will focus on the adaptive aspects of the mind and will need to account for three components: the mind's information processing, the environment to which the mind adapts, and the intersection between the environment and the mind.

Approaching Bounded Rationality: From Quantum Probability to Criticality

The bounded rationality mainstream is based on interesting experiments showing human behaviors violating classical probability (CP) laws. Quantum probability (QP) has been shown to successfully figure out such issues, supporting the hypothesis that quantum mechanics is the central fundamental pillar for brain function and cognition emergence. We discuss the decision-making model (DMM), a paradigmatic instance of criticality, which deals with bounded rationality issues in a similar way as QP, generating choices that cannot be accounted by CP. We define this approach as criticality-induced bounded rationality (CIBR). For some aspects, CIBR is even more satisfactory than QP. Our work may contribute to considering criticality as another possible fundamental pillar in order to improve the understanding of cognition and of quantum mechanics as well.

The Brain and the New Foundations of Mathematics

Many concepts in mathematics are not fully defined, and their properties are implicit, which leads to paradoxes. New foundations of mathematics were formulated based on the concept of innate programs of behavior and thinking. The basic axiom of mathematics is proposed, according to which any mathematical object has a physical carrier. This carrier can store and process only a finite amount of information. As a result of the D-procedure (encoding of any mathematical objects and operations on them in the form of qubits), a mathematical object is digitized. As a consequence, the basis of mathematics is the interaction of brain qubits, which can only implement arithmetic operations on numbers. A proof in mathematics is an algorithm for finding the correct statement from a list of already-existing statements. Some mathematical paradoxes (e.g., Banach–Tarski and Russell) and Smale’s 18th problem are solved by means of the D-procedure. The axiom of choice is a consequence of the equivalence of physical states, the choice among which can be made randomly. The proposed mathematics is constructive in the sense that any mathematical object exists if it is physically realized. The consistency of mathematics is due to directed evolution, which results in effective structures. Computing with qubits is based on the nontrivial quantum effects of biologically important molecules in neurons and the brain.

Constructive Biases in Clinical Judgment

With a pair of oppositely valenced stimuli, rating the first one sometimes leads to a more extreme evaluation for the second (e.g., if the second is negatively valenced, rating the first stimulus would lead to a more negative rating for the second). We considered an evaluation bias in the case of clinical diagnosis relating to eating disorders. A population sample which included experienced clinical psychologists and psychiatrists showed partial evidence of an evaluation bias, when judging descriptions of individuals designed to be consistent with eating disorders or not. Quantum probability theory, the probability rules from quantum mechanics without any of the physics, is particularly well-suited to modeling the evaluation bias (and constructive influences generally), because a measurement (or judgment) can change the state of the system. We applied a previous quantum model to the present result, an extension of the model embodying noisy processes, and belief adjustment model. We discuss how model fits inform an examination of rationality in the observed behavior.

A Quantum Walk Model for Idea Propagation in Social Network and Group Decision Making

We propose a quantum walk model to investigate the propagation of ideas in a network and the formation of agreement in group decision making. In more detail, we consider two different graphs describing the connections of agents in the network: the line graph and the ring graph. Our main interest is to deduce the dynamics for such propagation, and to investigate the influence of compliance of the agents and graph structure on the decision time and the final decision. The methodology is based on the use of control-U gates in quantum computing. The original state of the network is used as controller and its mirrored state is used as target. The state of the quantum walk is the tensor product of the original state and the mirror state. In this way, the proposed quantum walk model is able to describe asymmetric influence between agents.

Violations of locality and free choice are equivalent resources in Bell experiments

Bell inequalities rest on three fundamental assumptions: realism, locality, and free choice, which lead to nontrivial constraints on correlations in very simple experiments. If we retain realism, then violation of the inequalities implies that at least one of the remaining two assumptions must fail, which can have profound consequences for the causal explanation of the experiment. We investigate the extent to which a given assumption needs to be relaxed for the other to hold at all costs, based on the observation that a violation need not occur on every experimental trial, even when describing correlations violating Bell inequalities. How often this needs to be the case determines the degree of, respectively, locality or free choice in the observed experimental behavior. Despite their disparate character, we show that both assumptions are equally costly. Namely, the resources required to explain the experimental statistics (measured by the frequency of causal interventions of either sort) are exactly the same. Furthermore, we compute such defined measures of locality and free choice for any nonsignaling statistics in a Bell experiment with binary settings, showing that it is directly related to the amount of violation of the so-called Clauser-Horne-Shimony-Holt inequalities. This result is theory independent as it refers directly to the experimental statistics. Additionally, we show how the local fraction results for quantum-mechanical frameworks with infinite number of settings translate into analogous statements for the measure of free choice we introduce. Thus, concerning statistics, causal explanations resorting to either locality or free choice violations are fully interchangeable.

Deep memory distortions

Deep distortions are a new family of memory biases that comprise one of the two basic varieties of false memory. The first and older variety, surface distortions, are specific item or source memories that are erroneous because the events did not happen. The new variety, deep distortions, are emergent properties of multiple specific memories. They are relations among such memories that are false because they violate objective logical rules that real-world events must obey. I discuss four deep distortions for which substantial data have accumulated: overdistribution, super-overdistribution, non-additivity, and impossible conjunctions. These phenomena violate four axioms of classical probability (numerical bound, universal event, additivity, and countable additivity) and two rules that follow from them (empty set and monotonicity). Their psychological significance lies in four facts about them: (a) They demonstrate that although events in the real world are compensatory, our memories of them are not; (b) they establish that we persistently over remember experience; (c) they reveal that surface distortions are by-products of deep distortions; and (d) they pose the theoretical conundrum of how the structure of memory could so thoroughly misrepresent the objective structure of the events we are remembering.

The Compounding Effect of Investors’ Cognition and Risk Absorption Potential on Enhancing the Level of Interest towards Investment in the Domestic Capital Market

It is eminent to understand, be aware of and encourage domestic retail investors towards investment in the capital market in a developing economy such as India for tackling the situation of capital insufficiency and financial instability. Therefore, the study was purposed to find out the different dimensions of cognition that affect investment attitude and the different characteristics of risk absorption affecting the investment decision making. The study also intended to find the direct and the mediating impact of investors’ cognition directly and through risk-absorption scenarios on the level of interest on investment. The study used the causative research design and by using stratified random sampling, received 392 responses from investors with risk-absorption characteristics from four strata of Odisha (a state of India) through a self-constructed questionnaire. Factor analysis was used to find out the factor of cognition and risk absorption. Multiple linear regression was used to find out the effect of both factors of cognition and risk absorption on the intensity of purchase financial product or level of interest in investment. Mediation analysis was used to find the mediating impact showing the direct and indirect impact of cognition on interest in investment and through the factors risk absorption. The study found that the dimensions of cognition (hot, cold, social and meta) have a significant impact on the level of interest towards investment, so financial product sellers must use these dimensions and sources of cognition to bring up interest from the domestic investor to invest in the domestic capital market. It has also been found that the risk-absorption characteristics play a mediating and vital role in the relation between investors’ cognition and level of interest in investment. Therefore, it is imperative to uplift the risk-absorption capacity through different dimensions of cognition and sources of information, which can reflect in a better understanding of the market and investment scenarios.

Information overload for (bounded) rational agents

Bayesian inference offers an optimal means of processing environmental information and so an advantage in natural selection. We consider the apparent, recent trend in increasing dysfunctional disagreement in, for example, political debate. This is puzzling because Bayesian inference benefits from powerful convergence theorems, precluding dysfunctional disagreement. Information overload is a plausible factor limiting the applicability of full Bayesian inference, but what is the link with dysfunctional disagreement? Individuals striving to be Bayesian-rational, but challenged by information overload, might simplify by using Bayesian networks or the separation of questions into knowledge partitions, the latter formalized with quantum probability theory. We demonstrate the massive simplification afforded by either approach, but also show how they contribute to dysfunctional disagreement.

Quantum Mean-Field Games with the Observations of Counting Type

Quantum games and mean-field games (MFG) represent two important new branches of game theory. In a recent paper the author developed quantum MFGs merging these two branches. These quantum MFGs were based on the theory of continuous quantum observations and filtering of diffusive type. In the present paper we develop the analogous quantum MFG theory based on continuous quantum observations and filtering of counting type. However, proving existence and uniqueness of the solutions for resulting limiting forward-backward system based on jump-type processes on manifolds seems to be more complicated than for diffusions. In this paper we only prove that if a solution exists, then it gives an ϵ-Nash equilibrium for the corresponding N-player quantum game. The existence of solutions is suggested as an interesting open problem.

A framework for building cognitive process models

The term process model is widely used, but rarely agreed upon. This paper proposes a framework for characterizing and building cognitive process models. Process models model not only inputs and outputs but also model the ongoing information transformations at a given level of abstraction. We argue that the following dimensions characterize process models: They have a scope that includes different levels of abstraction. They specify a hypothesized mental information transformation. They make predictions not only for the behavior of interest but also for processes. The models' predictions for the processes can be derived from the input, without reverse inference from the output data. Moreover, the presumed information transformation steps are not contradicting current knowledge of human cognitive capacities. Lastly, process models require a conceptual scope specifying levels of abstraction for the information entering the mind, the proposed mental events, and the behavior of interest. This framework can be used for refining models before testing them or after testing them empirically, and it does not rely on specific modeling paradigms. It can be a guideline for developing cognitive process models. Moreover, the framework can advance currently unresolved debates about which models belong to the category of process models.

Broken Physics: A Conjunction-Fallacy Effect in Intuitive Physical Reasoning

One remarkable aspect of human cognition is our ability to reason about physical events. This article provides novel evidence that intuitive physics is subject to a peculiar error, the classic conjunction fallacy, in which people rate the probability of a conjunction of two events as more likely than one constituent (a logical impossibility). Participants viewed videos of physical scenarios and judged the probability that either a single event or a conjunction of two events would occur. In Experiment 1 (n = 60), participants consistently rated conjunction events as more likely than single events for the same scenes. Experiment 2 (n = 180) extended these results to rule out several alternative explanations. Experiment 3 (n = 100) generalized the finding to different scenes. This demonstration of conjunction errors contradicts claims that such errors should not appear in intuitive physics and presents a serious challenge to current theories of mental simulation in physical reasoning.

From Quantum Physics to Quantum Hypnosis: A Quantum Mind Perspective

A novel, heuristic model based upon chaotic complex systems theory and quantum mechanics is proposed to overcome the dichotomy between mind and body. The mind-body interface represents a chaotic system, ruled by the probability principle, as shown in quantum mechanics. Neuronal activity shows many patterns of chaotic behavior, and applications of chaotic patterns seem to be relevant for research regarding the mind-body relationship and the process of trance. A quantum consciousness theory has been proposed, largely controversial, since quantum physics applies to subatomic world and not to macrostructures, such as the brain. Quantum cognition is an emerging field that applies the formalism of quantum theory to model cognitive phenomena such as information processing by the human brain; it overcomes limits and shortcomings of cartesian dualism as well as quantum general theory. As hypnosis is a state of consciousness, it applies to hypnotic cognitive functioning rather than hypnotic structure.

Challenges and Opportunities for Grounding Cognition

According to the grounded perspective, cognition emerges from the interaction of classic cognitive processes with the modalities, the body, and the environment. Rather than being an autonomous impenetrable module, cognition incorporates these other domains intrinsically into its operation. The Situated Action Cycle offers one way of understanding how the modalities, the body, and the environment become integrated to ground cognition. Seven challenges and opportunities are raised for this perspective: (1) How does cognition emerge from the Situated Action Cycle and in turn support it? (2) How can we move beyond simply equating embodiment with action, additionally establishing how embodiment arises in the autonomic, neuroendocrine, immune, cardiovascular, respiratory, digestive, and integumentary systems? (3) How can we better understand the mechanisms underlying multimodal simulation, its functions across the Situated Action Cycle, and its integration with other representational systems? (4) How can we develop and assess theoretical accounts of symbolic processing from the grounded perspective (perhaps using the construct of simulators)? (5) How can we move beyond the simplistic distinction between concrete and abstract concepts, instead addressing how concepts about the external and internal worlds pattern to support the Situated Action Cycle? (6) How do individual differences emerge from different populations of situational memories as the Situated Action Cycle manifests itself differently across individuals? (7) How can constructs from grounded cognition provide insight into the replication and generalization crises, perhaps from a quantum perspective on mechanisms (as exemplified by simulators).

Social Laser Model for the Bandwagon Effect: Generation of Coherent Information Waves

During recent years our society has often been exposed to coherent information waves of high amplitudes. These are waves of huge social energy. Often they are of destructive character, a kind of information tsunami. However, they can also carry positive improvements in human society, as waves of decision-making matching rational recommendations of societal institutes. The main distinguishing features of these waves are their high amplitude, coherence (homogeneous character of social actions generated by them), and short time needed for their generation and relaxation. Such waves can be treated as large-scale exhibitions of the bandwagon effect. We show that this socio-psychic phenomenon can be modeled based on the recently developed social laser theory. This theory can be used to model stimulated amplification of coherent social actions. “Actions” are treated very generally, from mass protests to votes and other collective decisions, such as, e.g., acceptance (often unconscious) of some societal recommendations. In this paper, we concentrate on the theory of laser resonators, physical vs. social. For the latter, we analyze in detail the functioning of Internet-based echo chambers. Their main purpose is increasing of the power of the quantum information field as well as its coherence. Of course, the bandwagon effect is well known and well studied in social psychology. However, social laser theory gives the possibility to model it by using general formalism of quantum field theory. The paper contains the minimum of mathematics and it can be read by researchers working in psychological, cognitive, social, and political sciences; it might also be interesting for experts in information theory and artificial intelligence.

What are the appropriate axioms of rationality for reasoning under uncertainty with resource-constrained systems?

When constrained by limited resources, how do we choose axioms of rationality? The target article relies on Bayesian reasoning that encounter serious tractability problems. We propose another axiomatic foundation: quantum probability theory, which provides for less complex and more comprehensive descriptions. More generally, defining rationality in terms of axiomatic systems misses a key issue: rationality must be defined by humans facing vague information.

Towards a quantum-like cognitive architecture for decision-making

We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information compared to classical models. This framework can accommodate and predict several cognitive biases reported in Lieder & Griffiths without heavy reliance on heuristics or on assumptions of the computational resources of the mind.

Comparison of Markov versus quantum dynamical models of human decision making

What kind of dynamic decision process do humans use to make decisions? In this article, two different types of processes are reviewed and compared: Markov and quantum. Markov processes are based on the idea that at any given point in time a decision maker has a definite and specific level of support for available choice alternatives, and the dynamic decision process is represented by a single trajectory that traces out a path across time. When a response is requested, a person's decision or judgment is generated from the current location along the trajectory. By contrast, quantum processes are founded on the idea that a person's state can be represented by a superposition over different degrees of support for available choice options, and that the dynamics of this state form a wave moving across levels of support over time. When a response is requested, a decision or judgment is constructed out of the superposition by "actualizing" a specific degree or range of degrees of support to create a definite state. The purpose of this article is to introduce these two contrasting theories, review empirical studies comparing the two theories, and identify conditions that determine when each theory is more accurate and useful than the other. This article is categorized under: Economics > Individual Decision-Making Psychology > Reasoning and Decision Making Psychology > Theory and Methods.

Application of Theory of Quantum Instruments to Psychology: Combination of Question Order Effect with Response Replicability Effect

Recently, quantum formalism started to be actively used outside of quantum physics: in psychology, decision-making, economics, finances, and social science. Human psychological behavior is characterized by a few basic effects; one of them is the question order effect (QOE). This effect was successfully modeled (Busemeyer–Wang) by representing questions A and B by Hermitian observables and mental-state transformations (back action of answering) by orthogonal projectors. However, then it was demonstrated that such representation cannot be combined with another psychological effect, known as the response replicability effect (RRE). Later, this no-go result was generalized to representation of questions and state transformations by quantum instruments of the atomic type. In light of these results, the possibility of using quantum formalism in psychology was questioned. In this paper, we show that, nevertheless, the combination of the QOE and RRE can be modeled within quantum formalism, in the framework of theory of non-atomic quantum instruments.

Primer on quantum cognition

Quantum cognition is a new field in psychology, which is characterized by the application of quantum probability theory to human judgment and decision making behavior. This article provides an introduction that presents several examples to illustrate in a simple and concrete manner how to apply these principles to interesting psychological phenomena. Following each simple example, we present the general mathematical derivations and new predictions related to these applications.

Perspectives on Correctness in Probabilistic Inference from Psychology

Research into decision making has enabled us to appreciate that the notion of correctness is multifaceted. Different normative framework for correctness can lead to different insights about correct behavior. We illustrate the shifts for correctness insights with two tasks, the Wason selection task and the conjunction fallacy task; these tasks have had key roles in the development of logical reasoning and decision making research respectively. The Wason selection task arguably has played an important part in the transition from understanding correctness using classical logic to classical probability theory (and information theory). The conjunction fallacy has enabled a similar shift from baseline classical probability theory to quantum probability. The focus of this overview is the latter, as it represents a novel way for understanding probabilistic inference in psychology. We conclude with some of the current challenges concerning the application of quantum probability theory in psychology in general and specifically for the problem of understanding correctness in decision making.

Bridging the theoretical gap between semantic representation models without the pressure of a ranking: some lessons learnt from LSA

In recent years, latent semantic analysis (LSA) has reached a level of maturity at which its presence is ubiquitous in technology as well as in simulation of cognitive processes. In spite of this, in recent years there has been a trend of subjecting LSA to some criticisms, usually because it is compared to other models in very specific tasks and conditions and sometimes without having good knowledge of what the semantic representation of LSA means, and without exploiting all the possibilities of which LSA is capable other than the cosine. This paper provides a critical review to clarify some of the misunderstandings regarding LSA and other space models. The historical stability of the predecessors of LSA, the representational structure of word meaning and the multiple topologies that could arise from a semantic space, the computation of similarity, the myth that LSA dimensions have no meaning, the computational and algorithm plausibility to account for meaning acquisition in LSA (in contrast to others models based on online mechanisms), the possibilities of spatial models to substantiate recent proposals, and, in general, the characteristics of classic vector models and their ease and flexibility to simulate some cognitive phenomena will be reviewed. The review highlights the similarity between LSA and other techniques and proposes using long LSA experiences in other models, especially in predicting models such as word2vec. In sum, it emphasizes the lessons that can be learned from comparing LSA-based models to other models, rather than making statements about "the best."

Understanding Neurobehavioural Dynamics: A Close-Up View on Psychiatry and Quantum Mechanics

Psychiatric disorders are prevalent throughout the world and causes heavy burden on mankind. Alone in US, billions of dollars are used for treatment purposes annually. Although advances in treatment strategies had witnessed recently, however the efficacy and overall outcome weren’t quite promising. In neurobehavioural sciences, old problems survive through ages and with psychiatric disease, the phenomenon turns intensely complex. While our understanding of brain is mostly based on concepts of particle physics, its functions largely follow the principles of quantum mechanics. The current therapeutics relies on understanding of brain as a material entity that turns to be one of the chief reasons for the unsatisfactory therapeutic outcomes. Collectively, as mankind we are suffering huge loss due to the least effective treatment strategies. Even though we just begin to understand about how brain works, we also do not know much about quantum mechanics and how subatomic particles behave with quantum properties. Though it is apparent that quantum properties like particle and wave function duality coincides with the fundamental aspects of brain and mind duality, thus must share some common basis. Here in this article, an opinion is set that quantum mechanics in association with brain and more specifically psychiatry may take us towards a better understanding about brain, behaviour and how we approach towards treatment.

Decreasing Respondent Heterogeneity by Likert Scales Adjustment via Multipoles

A description of Likert scales can be given using the multipoles technique known in quantum physics and applied to behavioral sciences data. This paper considers decomposition of Likert scales by the multipoles for the application of decreasing the respondents’ heterogeneity. Due to cultural and language differences, different respondents habitually use the lower end, the mid-scale, or the upper end of the Likert scales which can lead to distortion and inconsistency in data across respondents. A big impact of different kinds of respondent is well known, for instance, in international studies, and it is called the problem of high and low raters. Application of a multipoles technique to the row-data smoothing via prediction of individual rates by the histogram of the Likert scale tiers produces better results than standard row-centering in data. A numerical example by marketing research data shows that the results are encouraging: while a standard row-centering produces a poor outcome, the dipole-adjustment noticeably improves the obtained segmentation results.

Can Computers Become Conscious and Overcome Humans?

The idea of machines overcoming humans can be intrinsically related to conscious machines. Surpassing humans would mean replicating, reaching and exceeding key distinctive properties of human beings, for example, high-level cognition associated with conscious perception. However, can computers be compared with humans? Can computers become conscious? Can computers outstrip human capabilities? These are paradoxical and controversial questions, particularly because there are many hidden assumptions and misconceptions about the understanding of the brain. In this sense, it is necessary to first explore these assumptions and then suggest how the specific information processing of brains would be replicated by machines. Therefore, this article will discuss a subset of human capabilities and the connection with conscious behavior, secondly, a prototype theory of consciousness will be explored and machines will be classified according to this framework. Finally, this analysis will show the paradoxical conclusion that trying to achieve conscious machines to beat humans implies that computers will never completely exceed human capabilities, or if the computer were to do it, the machine should not be considered a computer anymore.

Sciences of Observation

Multiple sciences have converged, in the past two decades, on a hitherto mostly unremarked question: what is observation? Here, I examine this evolution, focusing on three sciences: physics, especially quantum information theory, developmental biology, especially its molecular and “evo-devo” branches, and cognitive science, especially perceptual psychology and robotics. I trace the history of this question to the late 19th century, and through the conceptual revolutions of the 20th century. I show how the increasing interdisciplinary focus on the process of extracting information from an environment provides an opportunity for conceptual unification, and sketch an outline of what such a unification might look like.

Asymmetry in Similarity Formation: Extension of Similarity Theory to Open Sets of Features

The current work shows the extension of similarity theory to open sets of features. Increasing similarity between two objects while deleting distinctive features and adding common features results in different effects depending on the original similarity. When the similarity is greater than 0.5, deleting distinctive features is more effective. When the similarity value is less than 0.5, adding common features is more effective. When we compare an object with its ideal, common features are positive and distinctive features are negative, whereas when we compare an object with its anti-ideal, common features are negative and distinctive features are positive. Consequently, greater strength of negative or positive features in increasing similarity depends on both the value of original similarity and whether the object is compared with its ideal or to its anti-ideal. In this way the positive–negative asymmetry is revealed. Theoretical simulations and a brief empirical demonstration of how the model works have shown that for similarity exceeding 0.5 of a compared object to its ideal, deleting negative features had a stronger effect on the positive image of a particular object than adding positive features of the same value. The mechanism presented is universal and refers to estimation and comparison of all natural stimuli, which can be defined by means of open sets of features.