Beyond the information (not) given: Associative mechanisms versus representations of uncertainty in extinction in laboratory rats (Rattus norvegicus).

Associative learning models typically reflect statistical relationships between experienced events. Causal models can go beyond this information to specify the ways in which events are related. This meta-representational aspect of causal models allows them to reflect uncertainty about relationships between events: for example, if a light initially leads to sucrose but subsequently the light is experienced without sucrose, this might first support formation of a light-causes-sucrose model and subsequently lead to uncertainty over whether the model remained accurate.

The perceived dilution of causal strength.

Dependency theories of causal reasoning, such as causal Bayes net accounts, postulate that the strengths of individual causal links are independent of the causal structure in which they are embedded; they are inferred from dependency information, such as statistical regularities. We propose a psychological account that postulates that reasoners' concept of causality is richer. It predicts a systematic influence of causal structure knowledge on causal strength intuitions.

How German and Italian Laypeople Reason about Distributive Shortages during COVID-19.

(1) Background: The COVID-19 pandemic provided a unique opportunity to investigate how moral reasoning is influenced by individuals' exposure to a crisis and by personal, societal and temporal proximity. We examined how Italians and Germans judged different behaviors that arose because of the pandemic, which affected health and societal matters. (2) Methods: Over the course of four months and three assessment periods, we used an observational online survey to assess participants' judgments regarding seven scenarios that addressed distributive shortages during the pandemic.

How causal structure, causal strength, and foreseeability affect moral judgments.

Causal analysis lies at the heart of moral judgment. For instance, a general assumption of most ethical theories is that people are only morally responsible for an outcome when their action causally contributed to it. Considering the causal relations between our acts and potential good and bad outcomes is also of crucial importance when we plan our future actions.

Editorial.

During its 128 years of operation, has exerted a powerful and consistent influence on the field under its long-term sponsor, the American Psychological Association (APA). Notwithstanding changes in ownership, it has always been what it is now-the flagship of the Association and the field. Since its inception, the journal has focused on theoretical analyses (e.

Conditionals and the hierarchy of causal queries.

Recent studies indicate that indicative conditionals like "If people wear masks, the spread of Covid-19 will be diminished" require a probabilistic dependency between their antecedents and consequents to be acceptable (Skovgaard-Olsen et al., 2016). But it is easy to make the slip from this claim to the thesis that indicative conditionals are acceptable only if this probabilistic dependency results from a causal relation between antecedent and consequent.

How to weigh lives. A computational model of moral judgment in multiple-outcome structures.

When is it allowed to carry out an action that saves lives, but leads to the loss of others? While a minority of people may deny the permissibility of such actions categorically, most will probably say that the answer depends, among other factors, on the number of lives saved versus lives lost. Theories of moral reasoning acknowledge the importance of outcome trade-offs for moral judgments, but remain silent on the precise functional form of the psychological mechanism that determines their moral permissibility. An exception is Cohen and Ahn's (2016) subjective-utilitarian theory of moral judgment, but their model is currently limited to decisions in two-option life-and-death dilemmas.

The role of mechanism knowledge in singular causation judgments.

Singular causation queries (e.g., "Did Mary's taking contraceptives cause her thrombosis?") are ubiquitous in everyday life and crucial in many professional disciplines, such as medicine or law.

Interpolating causal mechanisms: The paradox of knowing more.

Causal knowledge is not static; it is constantly modified based on new evidence. The present set of seven experiments explores 1 important case of causal belief revision that has been neglected in research so far: causal interpolations. A simple prototypic case of an interpolation is a situation in which we initially have knowledge about a causal relation or a positive covariation between 2 variables but later become interested in the mechanism linking these 2 variables.

Time and Singular Causation-A Computational Model.

Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co-occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries.

How Should Autonomous Cars Drive? A Preference for Defaults in Moral Judgments Under Risk and Uncertainty.

Autonomous vehicles (AVs) promise to make traffic safer, but their societal integration poses ethical challenges. What behavior of AVs is morally acceptable in critical traffic situations when consequences are only probabilistically known (a situation of risk) or even unknown (a situation of uncertainty)?  How do people retrospectively evaluate the behavior of an AV in situations in which a road user has been harmed? We addressed these questions in two empirical studies (N = 1,638) that approximated the real-world conditions under which AVs operate by varying the degree of risk and uncertainty of the situation. In Experiment 1, subjects learned that an AV had to decide between staying in the lane or swerving.

Preemption in Singular Causation Judgments: A Computational Model.

Causal queries about singular cases are ubiquitous, yet the question of how we assess whether a particular outcome was actually caused by a specific potential cause turns out to be difficult to answer. Relying on the causal power framework (Cheng, ), Cheng and Novick () proposed a model of causal attribution intended to help answer this question. We challenge this model, both conceptually and empirically.

Failures of explaining away and screening off in described versus experienced causal learning scenarios.

Causal Bayes nets capture many aspects of causal thinking that set them apart from purely associative reasoning. However, some central properties of this normative theory routinely violated. In tasks requiring an understanding of explaining away and screening off, subjects often deviate from these principles and manifest the operation of an associative bias that we refer to as the rich-get-richer principle.

How prescriptive norms influence causal inferences.

Recent experimental findings suggest that prescriptive norms influence causal inferences. The cognitive mechanism underlying this finding is still under debate. We compare three competing theories: The culpable control model of blame argues that reasoners tend to exaggerate the causal influence of norm-violating agents, which should lead to relatively higher causal strength estimates for these agents.

Beyond the information (not) given: Representations of stimulus absence in rats (Rattus norvegicus).

Questions regarding the nature of nonhuman cognition continue to be of great interest within cognitive science and biology. However, progress in characterizing the relative contribution of "simple" associative and more "complex" reasoning mechanisms has been painfully slow-something that the tendency for researchers from different intellectual traditions to work separately has only exacerbated. This article reexamines evidence that rats respond differently to the nonpresentation of an event than they do if the physical location of that event is covered.

Lying despite telling the truth.

According to the standard definition of lying an utterance counts as a lie if the agent believes the statement to be false. Thus, according to this view it is possible that a lie states something that happens to be true. This subjective view on lying has recently been challenged by Turri and Turri (2015) who presented empirical evidence suggesting that people only consider statements as lies that are objectively false (objective view).

The role of prescriptive norms and knowledge in children's and adults' causal selection.

A widely discussed discovery has been the influence of norms on causal selection. Confronted with scenarios in which 2 agents contribute equally to an effect, adult participants tend to choose the agent who is violating a norm over an agent who is conforming to a norm as the cause of the outcome. To date, this effect has been established only in adult populations, so its developmental course is unknown.

Sufficiency and Necessity Assumptions in Causal Structure Induction.

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect.

Causal agency and the perception of force.

In the Michotte task, a ball (X) moves toward a resting ball (Y). In the moment of contact, X stops und Y starts moving. Previous studies have shown that subjects tend to view X as the causal agent ("X launches Y") rather than Y ("Y stops X").

The Side-Effect Effect in Children Is Robust and Not Specific to the Moral Status of Action Effects.

Adults' intentionality judgments regarding an action are influenced by their moral evaluation of this action. This is clearly indicated in the so-called side-effect effect: when told about an action (e.g.

Structure induction in diagnostic causal reasoning.

Our research examines the normative and descriptive adequacy of alternative computational models of diagnostic reasoning from single effects to single causes. Many theories of diagnostic reasoning are based on the normative assumption that inferences from an effect to its cause should reflect solely the empirically observed conditional probability of cause given effect. We argue against this assumption, as it neglects alternative causal structures that may have generated the sample data.

Indicators of causal agency in physical interactions: the role of the prior context.

The question how agent and patient roles are assigned to causal participants has largely been neglected in the psychological literature on force dynamics. Inspired by the linguistic theory of Dowty (1991), we propose that agency attributions are based on a prototype concept of human intervention. We predicted that the number of criteria a participant in a causal interaction shares with this prototype determines the strength of agency intuitions.

Agents and causes: dispositional intuitions as a guide to causal structure.

Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented a causal Bayes net model with separate error sources for causes and effects.

Transfer effects between moral dilemmas: a causal model theory.

Evaluations of analogous situations are an important source for our moral intuitions. A puzzling recent set of findings in experiments exploring transfer effects between intuitions about moral dilemmas has demonstrated a striking asymmetry. Transfer often occurred with a specific ordering of moral dilemmas, but not when the sequence was reversed.