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We propose two novel benchmarks for assessing models of probability judgments: the impact of Cognitive Reflection Test (CRT) on probability judgment expressions and 16 ``normality identities" expected to sum to 1 under classical probability theory. We compared three models on these benchmarks – the Probability Plus Noise Model (PPN), the Bayesian Sampler (BS), and the Quantum Sequential Sampler (QSS) – using the largest dataset to date for probability judgments. Our results reveal that higher CRT scores are associated with fewer probabilistic fallacies and identity violations, a trend most accurately captured by the QSS, although we also identified QSS limitations. Regarding the normality identities, the QSS outperformed the PPN and the BS, which had difficulty with both the average values of the normality identities and their dependence on CRT scores. Additionally, we uncovered a unique ``1 crossing" effect for normality identities N8 and N11, an effect PPN and BS cannot capture.

Probability judgments appear to violate basic axioms of probability theory, which seems to contradict with the recent successes of Bayesian models of cognition. To explain these violations, we propose the Quantum Sequential Sampler model, which combines quantum probability for explaining conjunction and disjunction fallacies, and a sequential sampling model that maps subjective quantum probabilities into responses. Our model explains probability judgments by a dynamical process, and achieves state-of-the-art performance in the biggest dataset for probability judgments to-date. Comparing with existing Bayesian models, our model predicts both probability judgments and violations of probability identities better.

An online questionnaire attempted to reveal the heuristics and biases used when participants reflected on their trust ina healthcare system. Participants answered quantitative questions related to six different heuristics and biases, whichrevealed their propensity for exhibiting each heuristic, before rating healthcare systems on seven trust-related metrics: re-source allocation; access to treatment; honesty, integrity and intention; competence; quality; safety; and equality. Multipleregressions tested whether the predictive power of heuristics and biases on trust ratings was significantly moderated bythe relative proportion of patients receiving service in the public and private sector. Results revealed that heuristics andbiases significantly impacted thought processes when arriving at assessments of participants willingness to trust. As trustin major institutions declines, this presents the scientific and medical communities with relevant data to potentially alterpractices and communication approaches in a way that fosters trust.

Eye movements during decision making show systematic patterns such as increased fixations to the chosen option (i.e.gaze cascades) and multiple gaze transitions between fixated options. Existing formalisms, such as multivariate decisionfield theory, only provide limited scope for describing multiple reversals in the attentional focus and it is therefore unclearhow they can be applied to the underlying attentional dynamics. Here, we present an open systems dynamical model fromquantum theory to describe gaze transitions between choice options and the gaze cascade effect. Our model was tested ona decision task, in which participants repeatedly decided among two complex options (i.e. that lacked easily quantifiable,matched characteristics). The model can describe the gaze patterns on the individual trial level. It reveals structure inthe gaze dynamics that is predictive for choice behavior. The explanatory value of this account for studying attentionaldynamics during decision making will be discussed.

Recent studies have linked the Stroop Effect with social priming, suggesting that social concept priming tends to triggerautomatic behaviour aligned with the primed concept (Augustinova & Ferrand, 2014; Goldfarb, Aisenberg, & Henik,2011). This study attempts to test the efficacy of social priming on alcohol attentional bias, integrating a social priminginterference task into an alcohol-Stroop test to measure Stroop interference before and after participants have been sociallyprimed. Results show no significant interaction between stimulus category (alcohol and neutral), experiment block, andsocial priming condition (alcohol addiction, alcohol preoccupation and control) to indicate that social priming had trig-gered expedited, automatic behaviour. Our results do show a significant interaction between experiment block and socialpriming condition (F(6, 426) = 2.166, p = .045), suggesting the alcohol social priming tasks may have induced a greatergeneral interference for participants in those conditions, than for participants receiving the neutral interference task.

There have been several demonstrations of constructive influences from choice paradigms, for example, when a decisionmaker has to commit to one of the available options and abandon the rest. In such cases, an expectation of constructiveinfluences, whereby the preference for the chosen option increases, while the preference for the abandoned ones decreases,is perhaps reasonable (e.g., as a way to reduce cognitive dissonance). However, this reasoning is harder to translateto situations such that there is a simple evaluation. We employ an organizational questionnaire to show that a simpleevaluation of an earlier statement can lead to systematic influences on a later one. Our results generalize our understandingfor when constructive influences may occur. We outline a technical framework for predicting this bias (which we labelevaluation bias), based on quantum theory. Quantum theory is an appropriate framework for modelling constructiveinfluences, because the theory involves a fundamental process of state change when a measurement (evaluation) is made.

We use quantum probability (QP) theory to investigate individual differences in causal reasoning. By analyzing datasets from Rehder (2014) on comparative judgments, and from Rehder and Waldmann (2016) on absolute judgments, we show that a QP model can both account for individual differences in causal judgments, and why these judgments sometimes violate the properties of causal Bayes nets. We implement this and previously proposed models of causal reasoning (including classical probability models) within the same hierarchical Bayesian inferential framework to provide a detailed comparison between these models, including computing Bayes factors. Analysis of the inferred parameters of the QP model illustrates how these can be interpreted in terms of putative cognitive mechanisms of causal reasoning. Additionally, we implement a latent classification mechanism that identifies subcategories of reasoners based on properties of the inferred cognitive process, rather than post hoc clustering. The QP model also provides a parsimonious explanation for aggregate behavior, which alternatively can only be explained by a mixture of multiple existing models. Investigating individual differences through the lens of a QP model reveals simple but strong alternatives to existing explanations for the dichotomies often observed in how people make causal inferences. These alternative explanations arise from the cognitive interpretation of the parameters and structure of the quantum probability model.

The focus of the present work concerns the nature ofdeontological decisions. We test the hypothesis that it ispossible to specify deontological moral choices based on anunemotional rule, norm or principle and that such moralchoices can be distinguished from emotion-driven ones.Using a novel paradigm for moral choice that we call TheRefugees’ Dilemma, we provide evidence for such a rule-based route to moral choice. We show that participants withhigh scores in a Cognitive Reflection Test (CRT) were morelikely to adopt utilitarian or rule-based responses, as opposedto emotional ones. We also found that rule-based respondentsreported the highest average psychological distance, more sothat even utilitarian respondents. These findings show howemotional and rule-based influences can be separated with theappropriate scenario and challenges the approach of assumingboth influences can be combined into a single deontologicalroute in dual-process models.

Decades of research in decision making have established thatthere are some situations where human judgments cannot bemodelled according to classical probability theory. Yet if weabandon classical (Bayesian) probability theory as an overar-ching framework for constructing cognitive models, what dowe replace it with? In this contribution we outline a way to di-vide the space of possible computational level models of prob-abilistic judgment into a hierarchy of levels of increasing com-plexity, with classical Bayesian probability models occupyingthe lowest level. Each level has a unique experimental sig-nature, and we examine which level is best able to describehuman behavior in a particular probabilistic reasoning task.

Nowadays, several of the situations in which we have to makedecisions are in digital form. In a first experiment (N=1010)we showed that people’s moral judgments depend on theDigital Context (Smartphone vs. PC) in which a dilemma ispresented, becoming more utilitarian (vs. deontological) whenusing Smartphones. To provide additional evidence, we ran asecond (N=250) and a third experiment (N=300), where weintroduced time constraints and we manipulated timeinstructions. Our results provide an extended perspective onDual-Process Models of Moral Judgment, as we showed thatthe use of smartphones, often assumed to be hurried whichwould be consistent with gut-feeling decision-making,increased the likelihood of utilitarian responses and decreaseddeontological ones. This is the first study to look at theimpact of the digital age on moral judgments and the resultspresented have consequences for understanding moral choicein our increasingly virtualized world

It is sometimes considered that there are 2 routes to moral choice, deontological and utilitarian (with debate as to whether each of these routes uniquely reflect emotional vs. analytic components). Yet in moral judgments we are often faced with a third route to choice, based on rules imposed by an outside agency (most typically the legal framework of a country). Whether we agree with these rules or not, we can choose to guide moral choice in this way. Moreover, depending on the situation, we can sometimes choose to prioritize such external rules or not. Sometimes external rules coincide with either deontological or utilitarian biases. But what happens when they do not? Using a novel paradigm for moral judgment, the refugee dilemma, we provide evidence for such a route to moral choice based on external rules. We show in 3 experiments that participants with high scores on a cognitive reflection test or under cognitive load were more likely to adopt utilitarian or rule-based responses, as opposed to emotional ones. We also found that rule-based respondents reported the highest average psychological distance. These findings extend the predominant approach of dual process models. Supplemental materials are available here: https://www.press.uillinois.edu/journals/ajp/media/untangling_decision_routes_in_moral_dilemmas/index.html KEYWORDS: dual process models, ethics, moral judgment, moral psychology, Moral decision making is a key issue for modern democracies. Therefore, understanding the principles underpinning moral judgment is fundamentally important. Consider the recent refugee crisis. The number of forcibly displaced [...]

Throughout our lives, we are faced with a variety of causalreasoning problems. Arguably, the most successful models ofcausal reasoning, Causal Graphical Models (CGMs), performwell in some situations, but there is considerable variation inhow well they are able to account for data, both across scenar-ios and between individuals. We propose a model of causalreasoning based on quantum probability (QP) theory that ac-counts for behavior in situations where CGMs fail. WhetherQP or classical models are appropriate depends on the repre-sentation of events constructed by the reasoner. We describean experiment that suggests the representation of events canchange with experience to become more classical, and that therepresentation constructed can vary between individuals, in away that correlates with a simple measure of cognitive ability,The Cognitive Reflection Task.

Rational agents should update their beliefs in the light of newevidence. Equally, changes in belief should depend only onthe quality of the evidence, and not on factors such as the or-der in which the evidence is acquired, or whether intermedi-ate judgements are requested during evidence acquisition. Incontrast we show that requests for intermediate judgments caninhibit belief updating for real decision makers, which repre-sents a new type of decision making fallacy. This behaviour isparadoxical from the point of view of classical Bayesian mod-els, but we show that it is consistent with an a priori, parameterfree prediction of a cognitive model based on quantum theory.

Recent research on moral dynamics (the processes andphenomena –collective or individual– by which moralbehavior and moral attitudes emerge, evolve, spread, erode ordisappear) shows that an individual’s ethical mind-set (i.e.,outcome-based vs. rule-based) moderates the impact of aninitial ethical or unethical act regarding the likelihood ofbehaving ethically on a subsequent occasion. Morespecifically, an outcome-based mind-set facilitates MoralBalancing (behaving ethically or unethically decreases thelikelihood of engaging in the same type of behavior later),whereas a rule-based mind-set facilitates Moral Consistency(engaging in an ethical or unethical behavior increases thelikelihood of engaging in the same type of behavior later).Our objective was to look at the evolution of moral choiceacross a series of scenarios and so explore if these moralpatterns (Balancing vs. Consistency) are maintained overtime. The results of three studies showed that MoralBalancing is not maintained over time. On the other hand,Moral Consistency could be maintained over time, if themind-set was reinforced before making a new moraljudgment (but not otherwise).

We discuss recent progress towards an empirical test of ‘realism’in cognition, ‘realism’ in this context being the propertythat cognitive variables always have well defined (if unknown)values at all times. Our main result is an inequality obeyed byrealist theories, which could be tested by a suitable experiment.We focus our attention in this contribution on two particularissues. The first is the exact notion of realism which is to betested, as this has received less attention in earlier work. Thesecond is an important technical issue about the inequality weuse; in earlier work Atmanspacher and Filk (2010) considereda different expression, and we explain why our inequality ismore suitable for use under realistic experimental conditions

We present progress towards a novel theoretical approach forunderstanding Tversky’s famous ‘diagnosticity’ effect in similarityjudgments, and an initial empirical validation. Our approachuses a model for similarity judgments based on quantumprobability theory. The model predicts a diagnosticity effectunder certain conditions only. Our model also predicts thatchanges to the set of stimuli to be compared can cause the diagnosticityeffect to break down or reverse. In one experiment,we test and confirm one of our key predictions

We develop a quantum probability model that can account forsituations where people’s causal judgments violate theproperties of causal Bayes nets and demonstrate how theparameters of our model can be interpreted to provideinformation about underlying cognitive processes. Weimplement this model within a hierarchical Bayesianinference framework that allows us to systematically identifyindividual differences and also provide a latent classificationof individuals into categories of causal and associativereasoners. Finally, we implement a basic normative causalBayes net within the same inference framework that allows usto directly compare quantum and classical probability modelsusing Bayes factors