Bridging the gap between subjective probability and probability judgments: The quantum sequential sampler.

One of the most important challenges in decision theory has been how to reconcile the normative expectations from Bayesian theory with the apparent fallacies that are common in probabilistic reasoning. Recently, Bayesian models have been driven by the insight that apparent fallacies are due to sampling errors or biases in estimating (Bayesian) probabilities. An alternative way to explain apparent fallacies is by invoking different probability rules, specifically the probability rules from quantum theory. Arguably, quantum cognitive models offer a more unified explanation for a large body of findings, problematic from a baseline classical perspective. This work addresses two major corresponding theoretical challenges: first, a framework is needed which incorporates both Bayesian and quantum influences, recognizing the fact that there is evidence for both in human behavior. Second, there is empirical evidence which goes beyond any current Bayesian and quantum model. We develop a model for probabilistic reasoning, seamlessly integrating both Bayesian and quantum models of reasoning and augmented by a sequential sampling process, which maps subjective probabilistic estimates to observable responses. Our model, called the Quantum Sequential Sampler, is compared to the currently leading Bayesian model, the Bayesian Sampler (J. Zhu et al., 2020) using a new experiment, producing one of the largest data sets in probabilistic reasoning to this day. The Quantum Sequential Sampler embodies several new components, which we argue offer a more theoretically accurate approach to probabilistic reasoning. Moreover, our empirical tests revealed a new, surprising systematic overestimation of probabilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Deletion of murine astrocytic vesicular nucleotide transporter increases anxiety and depressive-like behavior and attenuates motivation for reward.

Astrocytes are multi-functional glial cells in the central nervous system that play critical roles in modulation of metabolism, extracellular ion and neurotransmitter levels, and synaptic plasticity. Astrocyte-derived signaling molecules mediate many of these modulatory functions of astrocytes, including vesicular release of ATP. In the present study, we used a unique genetic mouse model to investigate the functional significance of astrocytic exocytosis of ATP. Using primary cultured astrocytes, we show that loss of vesicular nucleotide transporter (Vnut), a primary transporter responsible for loading cytosolic ATP into the secretory vesicles, dramatically reduces ATP loading into secretory lysosomes and ATP release, without any change in the molecular machinery of exocytosis or total intracellular ATP content. Deletion of astrocytic Vnut in adult mice leads to increased anxiety, depressive-like behaviors, and decreased motivation for reward, especially in females, without significant impact on food intake, systemic glucose metabolism, cognition, or sociability. These behavioral alterations are associated with significant decreases in the basal extracellular dopamine levels in the nucleus accumbens. Likewise, ex vivo brain slices from these mice show a strong trend toward a reduction in evoked dopamine release in the nucleus accumbens. Mechanistically, the reduced dopamine signaling we observed is likely due to an increased expression of monoamine oxidases. Together, these data demonstrate a key modulatory role of astrocytic exocytosis of ATP in anxiety, depressive-like behavior, and motivation for reward, by regulating the mesolimbic dopamine circuitry.

Can we change automatic processes: the influence of social priming on alcohol attentional bias.

The Stroop Effect has been linked to social concept priming, suggesting that the latter may trigger automatic behaviour aligned with the primed concept. This study examined the effects of social priming on alcohol attentional bias, with a sample of mostly light drinkers; it used a social priming task and an alcohol-Stroop test to measure participants' response times (RTs) before and after they had been socially primed. Participants were separated into one of three social priming conditions: Neutral, Alcohol Addiction, and Alcohol Preoccupation. A mixed ANOVA was run to determine whether participants' RTs to alcohol-related stimuli, "rather than to neutral sitmuli," slowed significantly after the alcohol interference tasks, relative to the neutral interference task, suggesting an alcohol attentional bias had been induced by the social priming exercise. Key interaction terms did not reveal such an interaction, but rather a general slowing down (for both neutral and alcohol stimuli), in the Alcohol conditions, relative to the Neutral one. As a result, we can conclude that while we did not induce an alcohol-specific bias in participants, we did discover a generalised interference effect, following alcohol-related social priming tasks.

Quantum Circuit Components for Cognitive Decision-Making.

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.

Winning a CHSH Game without Entangled Particles in a Finite Number of Biased Rounds: How Much Luck Is Needed?

Quantum games, such as the CHSH game, are used to illustrate the puzzle and power of entanglement. These games are played over many rounds and in each round, the participants, Alice and Bob, each receive a question bit to which they each have to give an answer bit, without being able to communicate during the game. When all possible classical answering strategies are analyzed, it is found that Alice and Bob cannot win more than 75% of the rounds. A higher percentage of wins arguably requires an exploitable bias in the random generation of the question bits or access to "non-local" resources, such as entangled pairs of particles. However, in an actual game, the number of rounds has to be finite and question regimes may come up with unequal likelihood, so there is always a possibility that Alice and Bob win by pure luck. This statistical possibility has to be transparently analyzed for practical applications such as the detection of eavesdropping in quantum communication. Similarly, when Bell tests are used in macroscopic situations to investigate the connection strength between system components and the validity of proposed causal models, the available data are limited and the possible combinations of question bits (measurement settings) may not be controlled to occur with equal likelihood. In the present work, we give a fully self-contained proof for a bound on the probability to win a CHSH game by pure luck without making the usual assumption of only small biases in the random number generators. We also show bounds for the case of unequal probabilities based on results from McDiarmid and Combes and numerically illustrate certain exploitable biases.

Temporal Bell inequalities in cognition.

There is widespread evidence that human memory is constructive, so that recollective processes may alter the information retrieved or impact on subsequent recollections. We examine a framework for narrowing down the nature of such processes, from physics. In Physics, the Temporal Bell (TB) inequality offers a general test of the sensitivity of the context of previous measurements in sequential measurement scenarios, as predicted by quantum theory. We present an empirical memory paradigm that allows a test of the TB inequality, using a novel kind of "change judgment," whereby participants are asked to decide whether there has been a change in a question across different time points of a scenario. Across two experiments, we were able to observe evidence for the violation of a TB inequality in one case, offering evidence for quantum-like processes in memory. The present results complement other recent work purporting the relevance of quantum-like representations in memory and raise questions regarding the adaptive value of such representations.

A Quantum Geometric Framework for Modeling Color Similarity Judgments.

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.

How healthcare structures and communication delivery influence trust: a parallel-group randomized controlled trial.

Background: According to recent polling, public trust in the healthcare sector remains low relative to other industries globally. The implications of low healthcare trust permeate throughout the industry in a number of ways, most visibly by discouraging therapy compliance. Methods: This study investigated four putative determinants of trust in healthcare-related scenarios: individuals vs. collective groups as communicators of healthcare advice; expert vs. laypeople as providers of healthcare communication; public vs. private healthcare sector; and positive vs. negative information. Two hundred seventy-four participants were recruited via Prolific Academic and were presented with four statements in random order, related to a positive reflection of the public healthcare sector, a negative reflection of the public healthcare sector, a positive reflection of the private healthcare sector and a negative reflection of the private healthcare sector. According to these reflection, participants were repeatedly asked to rate the system on its trustworthiness. Trust outcomes were constructed using a four-dimension framework, consisting of benevolence, reliability, competence and predictability. Results: Claims relating to the public sector had a significantly stronger impact on benevolence and reliability than claims relating to the private sector; claims from individuals had a significantly stronger impact on all trust variables than claims from collectives; and claims from laypeople had a significantly greater impact on reliability and competence ratings than claims from experts. Conclusions: The findings in this study offer insight into the patterns with which trust decisions are made in healthcare contexts. More importantly, this research offers a novel perspective of how different factors interact to affect the various facets of trust. These results provide a foundation for future study in this evolving area, and offer insights into designing effective communication strategies that cultivate greater levels of individual trust in the healthcare sector. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-021-01620-z.

Trust during COVID-19: Which factors matter most?

The objective of this experiment was to offer a preliminary exploration into the key factors underlying trust in healthcare systems around the world, in light of the COVID-19 global pandemic. Participants were recruited across ten countries and were asked to complete a two-part questionnaire, in which they rated their country's healthcare system on a scale from 1-5, according to ten trust-related factors, translated specifically to pertain to healthcare, and 4 key pillars of trust: benevolence, reliability, competence and predictability. Correlation analyses between these two separate measures revealed that honesty, consistency, and reasonableness were the most impactful factors underlying trust across the entire population. All other findings are detailed in the main text. This study allowed us to arrive at a preliminary determination of the most impactful factors underlying trust, both at a global and national level.

Quantifying and Interpreting Connection Strength in Macro- and Microscopic Systems: Lessons from Bell's Approach.

Bell inequalities were created with the goal of improving the understanding of foundational questions in quantum mechanics. To this end, they are typically applied to measurement results generated from entangled systems of particles. They can, however, also be used as a statistical tool for macroscopic systems, where they can describe the connection strength between two components of a system under a causal model. We show that, in principle, data from macroscopic observations analyzed with Bell' s approach can invalidate certain causal models. To illustrate this use, we describe a macroscopic game setting, without a quantum mechanical measurement process, and analyze it using the framework of Bell experiments. In the macroscopic game, violations of the inequalities can be created by cheating with classically defined strategies. In the physical context, the meaning of violations is less clear and is still vigorously debated. We discuss two measures for optimal strategies to generate a given statistic that violates the inequalities. We show their mathematical equivalence and how they can be computed from CHSH-quantities alone, if non-signaling applies. As a macroscopic example from the financial world, we show how the unfair use of insider knowledge could be picked up using Bell statistics. Finally, in the discussion of realist interpretations of quantum mechanical Bell experiments, cheating strategies are often expressed through the ideas of free choice and locality. In this regard, violations of free choice and locality can be interpreted as two sides of the same coin, which underscores the view that the meaning these terms are given in Bell's approach should not be confused with their everyday use. In general, we conclude that Bell's approach also carries lessons for understanding macroscopic systems of which the connectedness conforms to different causal structures.

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).

Rethinking Rationality.

We seek to understand rational decision making and if it exists whether finite (bounded) agents may be able to achieve its principles. This aim has been a singular objective throughout much of human science and philosophy, with early discussions identified since antiquity. More recently, there has been a thriving debate based on differing perspectives on rationality, including adaptive heuristics, Bayesian theory, quantum theory, resource rationality, and probabilistic language of thought. Are these perspectives on rationality mutually exclusive? Are they all needed? Do they undermine an aim to have rational standards in decision situations like politics, medicine, legal proceedings, and others, where there is an expectation and need for decision making as close to "optimal" as possible? This special issue brings together representative contributions from the currently predominant views on rationality, with a view to evaluate progress on these and related questions.

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.

Context effects in similarity judgments.

Tversky's (1977) famous demonstration of a diagnosticity effect indicates that the similarity between the same two stimuli depends on the presence of contextual stimuli. In a forced choice task, the similarity between a target and a choice, appears to depend on the other choices. Specifically, introducing a distractor grouped with one of the options would reduce preference for the grouped option. However, the diagnosticity effect has been difficult to replicate, casting doubt on its robustness and our understanding of contextual effects in similarity generally. We propose that the apparent brittleness of the diagnosticity effect is because it is in competition with an opposite attraction effect. Even though in both the similarity and decision-making literatures there are indications for such a competition, we provide the first direct experimental demonstration of how an attraction effect can give way to a diagnosticity one, as a distractor option is manipulated. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

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.

Top-down and bottom-up attentional biases for smoking-related stimuli: Comparing dependent and non-dependent smokers.

INTRODUCTION: Substance use causes attentional biases for substance-related stimuli. Both bottom-up (preferential processing) and top-down (inhibitory control) processes are involved in attentional biases. We explored these aspects of attentional bias by using dependent and non-dependent cigarette smokers in order to see whether these two groups would differ in terms of general inhibitory control, bottom-up attentional bias, and top-down attentional biases. This enables us to see whether consumption behaviour would affect these cognitive responses to smoking-related stimuli. METHODS: Smokers were categorised as either dependent (N = 26) or non-dependent (N = 34) smokers. A further group of non-smokers (N = 32) were recruited to act as controls. Participants then completed a behavioural inhibition task with general stimuli, a smoking-related eye tracking version of the dot-probe task, and an eye-tracking inhibition task with smoking-related stimuli. RESULTS: Results indicated that dependent smokers had decreased inhibition and increased attentional bias for smoking-related stimuli (and not control stimuli). By contrast, a decreased inhibition for smoking-related stimuli (in comparison to control stimuli) was not observed for non-dependent smokers. CONCLUSIONS: Preferential processing of substance-related stimuli may indicate usage of a substance, whereas poor inhibitory control for substance-related stimuli may only emerge if dependence develops. The results suggest that how people engage with substance abuse is important for top-down attentional biases.

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.

Project DyAdd: Non-linguistic Theories of Dyslexia Predict Intelligence.

Two themes have puzzled the research on developmental and learning disorders for decades. First, some of the risk and protective factors behind developmental challenges are suggested to be shared and some are suggested to be specific for a given condition. Second, language-based learning difficulties like dyslexia are suggested to result from or correlate with non-linguistic aspects of information processing as well. In the current study, we investigated how adults with developmental dyslexia or ADHD as well as healthy controls cluster across various dimensions designed to tap the prominent non-linguistic theories of dyslexia. Participants were 18-55-year-old adults with dyslexia (n = 36), ADHD (n = 22), and controls (n = 35). Non-linguistic theories investigated with experimental designs included temporal processing impairment, abnormal cerebellar functioning, procedural learning difficulties, as well as visual processing and attention deficits. Latent profile analysis (LPA) was used to investigate the emerging groups and patterns of results across these experimental designs. LPA suggested three groups: (1) a large group with average performance in the experimental designs, (2) participants predominantly from the clinical groups but with enhanced conditioning learning, and (3) participants predominantly from the dyslexia group with temporal processing as well as visual processing and attention deficits. Despite the presence of these distinct patterns, participants did not cluster very well based on their original status, nor did the LPA groups differ in their dyslexia or ADHD-related neuropsychological profiles. Remarkably, the LPA groups did differ in their intelligence. These results highlight the continuous and overlapping nature of the observed difficulties and support the multiple deficit model of developmental disorders, which suggests shared risk factors for developmental challenges. It also appears that some of the risk factors suggested by the prominent non-linguistic theories of dyslexia relate to the general level of functioning in tests of 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.