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.

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

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.

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.

We outline a simple way of representing sets of non-normativejudgements that makes them look as similar as possible to nor-mative ones. This representation allows us to view certaintypes of non-normative judgments, such as conjunction falla-cies, as arising from a misestimation of the correlation betweenevents, that might arise when decision-makers have no priorinformation about the frequency of co-occurrence. We sug-gest that decision-makers use the perceived similarity betweenevents to make inferences about correlation, and we describethe results of an experiment showing that judged correlationand violations of independence in probabilistic judgments arestrongly influenced by the perceived similarity between events.

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

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