Towards a quantum probability theory of similarity judgments

We review recent progress in understanding similarity judgments in cognition by means of quantum probability theory (QP) models. We begin by outlining some features of similarity judgments that have proven difficult to model by traditional approaches. We then briefly present a model of similarity judgments based on QP, and show how it can solve many of the problems faced by traditional approaches. Finally we look at some areas where the quantum model is currently less satisfactory, and discuss some open questions and areas for further work

Quantum probability in decision making from quantum information representation of neuronal states

The recent wave of interest to modeling the process of decision making with the aid of the quantum formalism gives rise to the following question: ‘How can neurons generate quantum-like statistical data?’ (There is a plenty of such data in cognitive psychology and social science.) Our model is based on quantum-like representation of uncertainty in generation of action potentials. This uncertainty is a consequence of complexity of electrochemical processes in the brain; in particular, uncertainty of triggering an action potential by the membrane potential. Quantum information state spaces can be considered as extensions of classical information spaces corresponding to neural codes; e.g., 0/1, quiescent/firing neural code. The key point is that processing of information by the brain involves superpositions of such states. Another key point is that a neuronal group performing some psychological function F is an open quantum system. It interacts with the surrounding electrochemical environment. The process of decision making is described as decoherence in the basis of eigenstates of F. A decision state is a steady state. This is a linear representation of complex nonlinear dynamics of electrochemical states. Linearity guarantees exponentially fast convergence to the decision state

Under pressure: Response urgency modulates striatal and insula activity during decision-making under risk

When deciding whether to bet in situations that involve potential monetary loss or gain (mixed gambles), a subjective sense of pressure can influence the evaluation of the expected utility associated with each choice option. Here, we explored how gambling decisions, their psychophysiological and neural counterparts are modulated by an induced sense of urgency to respond. Urgency influenced decision times and evoked heart rate responses, interacting with the expected value of each gamble. Using functional MRI, we observed that this interaction was associated with changes in the activity of the striatum, a critical region for both reward and choice selection, and within the insula, a region implicated as the substrate of affective feelings arising from interoceptive signals which influence motivational behavior. Our findings bridge current psychophysiological and neurobiological models of value representation and action-programming, identifying the striatum and insular cortex as the key substrates of decision-making under risk and urgency

Protecting eyewitness evidence: Examining the efficacy of a self-administered interview tool

Given the crucial role of eyewitness evidence, statements should be obtained as soon as possible after an incident. This is not always achieved due to demands on police resources. Two studies trace the development of a new tool, the Self-Administered Interview (SAI), designed to elicit a comprehensive initial statement. In Study 1, SAI participants reported more correct details than participants who provided a free recall account, and performed at the same level as participants given a Cognitive Interview. In Study 2, participants viewed a simulated crime and half recorded their statement using the SAI. After a delay of 1 week, all participants completed a free recall test. SAI participants recalled more correct details in the delayed recall task than control participants

Scale-free memory model for multiagent reinforcement learning. Mean field approximation and rock-paper-scissors dynamics

A continuous time model for multiagent systems governed by reinforcement learning with scale-free memory is developed. The agents are assumed to act independently of one another in optimizing their choice of possible actions via trial-and-error search. To gain awareness about the action value the agents accumulate in their memory the rewards obtained from taking a specific action at each moment of time. The contribution of the rewards in the past to the agent current perception of action value is described by an integral operator with a power-law kernel. Finally a fractional differential equation governing the system dynamics is obtained. The agents are considered to interact with one another implicitly via the reward of one agent depending on the choice of the other agents. The pairwise interaction model is adopted to describe this effect. As a specific example of systems with non-transitive interactions, a two agent and three agent systems of the rock-paper-scissors type are analyzed in detail, including the stability analysis and numerical simulation. Scale-free memory is demonstrated to cause complex dynamics of the systems at hand. In particular, it is shown that there can be simultaneously two modes of the system instability undergoing subcritical and supercritical bifurcation, with the latter one exhibiting anomalous oscillations with the amplitude and period growing with time. Besides, the instability onset via this supercritical mode may be regarded as "altruism self-organization". For the three agent system the instability dynamics is found to be rather irregular and can be composed of alternate fragments of oscillations different in their properties.Comment: 17 pages, 7 figur

Do Physicians Know When Their Diagnoses Are Correct?

This study explores the alignment between physicians' confidence in their diagnoses and the “correctness” of these diagnoses, as a function of clinical experience, and whether subjects were prone to over-or underconfidence. Design : Prospective, counterbalanced experimental design. Setting : Laboratory study conducted under controlled conditions at three academic medical centers. Participants : Seventy-two senior medical students, 72 senior medical residents, and 72 faculty internists. Intervention : We created highly detailed, 2-to 4-page synopses of 36 diagnostically challenging medical cases, each with a definitive correct diagnosis. Subjects generated a differential diagnosis for each of 9 assigned cases, and indicated their level of confidence in each diagnosis. Measurements And Main Results : A differential was considered “correct” if the clinically true diagnosis was listed in that subject's hypothesis list. To assess confidence, subjects rated the likelihood that they would, at the time they generated the differential, seek assistance in reaching a diagnosis. Subjects' confidence and correctness were “mildly” aligned (Κ=.314 for all subjects, .285 for faculty, .227 for residents, and .349 for students). Residents were overconfident in 41% of cases where their confidence and correctness were not aligned, whereas faculty were overconfident in 36% of such cases and students in 25%. Conclusions : Even experienced clinicians may be unaware of the correctness of their diagnoses at the time they make them. Medical decision support systems, and other interventions designed to reduce medical errors, cannot rely exclusively on clinicians' perceptions of their needs for such support.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74850/1/j.1525-1497.2005.30145.x.pd

Optimal Resource Allocation over Networks via Lottery-Based Mechanisms

We show that, in a resource allocation problem, the ex ante aggregate utility of players with cumulative-prospect-theoretic preferences can be increased over deterministic allocations by implementing lotteries. We formulate an optimization problem, called the system problem, to find the optimal lottery allocation. The system problem exhibits a two-layer structure comprised of a permutation profile and optimal allocations given the permutation profile. For any fixed permutation profile, we provide a market-based mechanism to find the optimal allocations and prove the existence of equilibrium prices. We show that the system problem has a duality gap, in general, and that the primal problem is NP-hard. We then consider a relaxation of the system problem and derive some qualitative features of the optimal lottery structure

A Pluralist Account of Knowledge as a Natural Kind

In an attempt to address some long-standing issues of epistemology, Hilary Kornblith proposes that knowledge is a natural kind the identification of which is the unique responsibility of one particular science: cognitive ethology. As Kornblith sees it, the natural kind thus picked out is knowledge as construed by reliabilism. Yet the claim that cognitive ethology has this special role has not convinced all critics. The present article argues that knowledge plays a causal and explanatory role within many of our more fruitful current theories, diverging from the reliabilist conception even in disciplines that are closely related to cognitive ethology, and thus still dealing with knowledge as a natural as opposed to a social phenomenon, where special attention will be given to cognitive neuroscience. However, rather than discarding the natural kind approach altogether, it is argued that many of Kornblith’s insights can in fact be preserved within a framework that is both naturalist and pluralist

A Conflict Model for Strategists and Managers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67210/2/10.1177_000276427201500604.pd

The Quantum Mind: Alternative Ways of Reasoning with Uncertainty

© 2018, Ontario Institute for Educational Studies (OISE). Human reasoning about and with uncertainty is often at odds with the principles of classical probability. Order effects, conjunction biases, and sure-thing inclinations suggest that an entirely different set of probability axioms could be developed and indeed may be needed to describe such habits. Recent work in diverse fields, including cognitive science, economics, and information theory, explores alternative approaches to decision theory. This work considers more expansive theories of reasoning with uncertainty while continuing to recognize the value of classical probability. In this paper, we discuss one such alternative approach, called quantum probability, and explore its applications within decision theory. Quantum probability is designed to formalize uncertainty as an ontological feature of the state of affairs, offering a mathematical model for entanglement, de/coherence, and interference, which are all concepts with unique onto-epistemological relevance for social theorists working in new and trans-materialisms. In this paper, we suggest that this work be considered part of the quantum turn in the social sciences and humanities. Our aim is to explore different models and formalizations of decision theory that attend to the situatedness of judgment. We suggest that the alternative models of reasoning explored in this article might be better suited to queries about entangled mathematical concepts and, thus, be helpful in rethinking both curriculum and learning theory