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. Our view incorporates the notion held by dispositional theories that causes produce effects in virtue of an underlying causal capacity. Going beyond existing normative dispositional theories, however, we argue that reasoners' concept of causality involves the idea that continuous causes spread their capacity across their different causal pathways, analogous to fluids running through pipe systems. Such a representation leads to the prediction of a structure-dependent dilution of causal strength: the more links are served by a cause, the weaker individual links are expected to be. A series of experiments corroborate the theory. For continuous causes with continuous effects, but not in causal structures with genuinely binary variables that can only be present or absent, reasoners tend to think that link strength decreases with the number of links served by a cause. The effect reflects a default notion reasoners have about causality, but it is moderated by assumptions about the amount of causal capacity causes are assumed to possess, and by mechanism knowledge about how a cause generates its effect(s). We discuss the theoretical and empirical implications of our findings.

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. (3) Results: Overall, there was no clear answering pattern across all scenarios. For a variation of triage and pandemic restrictions, most participants selected a mean value, which can be interpreted as deferring the choice. For the other scenarios, most participants used the extremes of the scale, thereby reflecting a clear opinion of the public regarding the moral issue. In addition, moral reasoning varied across the two countries, assessment periods, fear, and age. (4) Conclusions: By using scenarios that were taken from real-life experiences, the current study addresses criticism that moral research mostly relies on unrealistic scenarios that lack in external validity, plausibility, and proximity to everyday situations. In addition, it shows how lay people regard measures of public health and societal decision-making.

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. Here, we investigate which aspects of causal relations are particularly influential when the moral permissibility of actions and the moral responsibility of agents for accidental harms are assessed. Causal strength and causal structure are two independent properties of causal models that may affect moral judgments. We investigated whether the length of a causal chain between acts and accidental harms, a structural feature of causal relations, affects people's moral evaluation of action and agent. In three studies (N = 2285), using a combination of vignettes and causal learning paradigms, we found that longer chains lead to more lenient moral evaluations of actions and agents. Moreover, we show that the reason for this finding is that harms are perceived to be less likely, and therefore less foreseeable for agents, when the relation is indirect rather than direct. When harms are considered equally likely and equally foreseeable, causal structure largely ceases to affect moral judgments. The findings demonstrate a tight coupling between causal representations, mental state inferences, and moral judgments, and show that reasoners process and integrate these components in a largely rational manner.

Editorial.

During its 128 years of operation, Psychological Review 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.g., systematic evaluations of alternative theories) and/or developments (e.g., the generation of novel theories) in the psychological sciences. Thus, the objectives of any incoming editor and editorial board remain steadfast: (a) to maintain and enhance the standing of Psychological Review in the field and (b) to correspondingly align its scope, content, and operations with any changes in the Association, the field of psychology in particular, and science and society in general. The journal's new senior editorial team is excited to navigate Psychological Review through the ever-changing landscape of psychology at this time of multiple challenges, referred to by the United Nations Secretary General António Guterres as "the greatest cascade of crises in our lifetime." Although we are initiating a number of changes, we will do our best to maintain Psychological Review's excellence. This will involve our capacity to reflect on and disseminate new theoretical developments, enriched and inspired by current trends in science in general and in psychological science in particular, while maintaining an overarching commitment to advancing the field through the incorporation of diverse perspectives. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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. Our goal is to study other types of moral judgments in a larger set of cases. We propose a computational model based on sampling and integrating subjective utilities. Our model captures moral permissibility judgments about actions with multiple effects across a range of scenarios involving humans, animals, and plants, and is able to account for some response patterns that might otherwise be associated with deontological ethics. While our model can be embedded in a number of competing contemporary theories of moral reasoning, we argue that it would most fruitfully be combined with a causal model theory.

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. Knowledge about general causal regularities is necessary but not sufficient for establishing a singular causation relation because it is possible that co-occurrences consistent with known regularities are in an individual case still just coincidental. Thus, further cues are helpful to establish a singular causation relation. In the present research we focus on information about mechanisms as a potent cue. While previous studies have shown that reasoners consider mechanism information as important when it comes to answering singular causation queries, no formal model has been proposed that explains why this is case. We here present a computational model that explains how causal mechanism information affects singular causation judgments. We also use the model to identify conditions that restrict the utility of mechanism information. We report three experiments testing the implications of our formal analysis. In Experiment 1 we found that reasoners systematically use mechanism information, largely in accordance with our formal model, although we also discovered that some people seem to rely on simpler, computationally less demanding reasoning strategies. The results of Experiments 2 and 3 demonstrate that reasoners have a tentative understanding of the conditions that restrict the utility of causal mechanism information.

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. According to Pearl (2009), understanding a causal relation involves multiple, hierarchically organized conceptual dimensions: prediction, intervention, and counterfactual dependence. In a series of experiments, we test the hypothesis that these conceptual dimensions are differentially encoded in indicative and counterfactual conditionals. If this hypothesis holds, then there are limits as to how much of a causal relation is captured by indicative conditionals alone. Our results show that the acceptance of indicative and counterfactual conditionals can become dissociated. Furthermore, it is found that the acceptance of both is needed for accepting a causal relation between two co-occurring events. The implications that these findings have for the hypothesis above, and for recent debates at the intersection of the psychology of reasoning and causal judgment, are critically discussed. Our findings are consistent with viewing indicative conditionals as answering predictive queries requiring evidential relevance (even in the absence of direct causal relations). Counterfactual conditionals in contrast target causal relevance, specifically. Finally, we discuss the implications our results have for the yet unsolved question of how reasoners succeed in constructing causal models from verbal descriptions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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. Our key finding is that the interpolation of mechanism variables tends to be misrepresented, which leads to the paradox of knowing more: The more people know about a mechanism, the weaker they tend to find the probabilistic relation between the 2 variables (i.e., weakening effect). Indeed, in all our experiments we found that, despite identical learning data about 2 variables, the probability linking the 2 variables was judged higher when follow-up research showed that the 2 variables were assumed to be directly causally linked (i.e., C→E) than when participants were instructed that the causal relation is in fact mediated by a variable representing a component of the mechanism (M; i.e., C→M→E). Our explanation of the weakening effect is that people often confuse discoveries of preexisting but unknown mechanisms with situations in which new variables are being added to a previously simpler causal model, thus violating causal stability assumptions in natural kind domains. The experiments test several implications of this hypothesis. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model.

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. Each action could lead to a collision with another road user, with some known or unknown likelihood. Subjects' decision preferences and moral judgments varied considerably with specified probabilities under risk, yet less so under uncertainty. The results suggest that staying in the lane and performing an emergency stop is considered a reasonable default, even when this action does not minimize expected loss. Experiment 2 demonstrated that if an AV collided with another road user, subjects' retrospective evaluations of the default action were also more robust against unwanted outcome and hindsight effects than the alternative swerve maneuver. The findings highlight the importance of investigating moral judgments under risk and uncertainty in order to develop policies that are societally acceptable even under critical conditions.

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. We argue that the central problem of this model is that it treats causal powers that are probabilistically sufficient to generate the effect on a particular occasion as actual causes of the effect, and thus neglects that sufficient causal powers can be preempted in their efficacy. Also, the model does not take into account that reasoners incorporate uncertainty about the underlying general causal structure and strength of causes when making causal inferences. We propose a new measure of causal attribution and embed it into the structure induction model of singular causation (SISC; Stephan & Waldmann, ). Two experiments support the model.

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. This research focuses on these two failures comparing tasks in which causal scenarios are merely described (via verbal statements of the causal relations) versus experienced (via samples of data that manifest the intervariable correlations implied by the causal relations). Our key finding is that we obtained stronger deviations from normative predictions in the described conditions that highlight the instructed causal model compared to those that presented data. This counterintuitive finding indicate that a theory of causal reasoning and learning needs to integrate normative principles with biases people hold about causal relations.

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. By contrast, the counterfactual reasoning account of causal selection assumes that norms do not alter the representation of the causal model, but rather later causal selection stages. According to this view, reasoners tend to preferentially consider counterfactual states of abnormal rather than normal factors, which leads to the choice of the abnormal factor in a causal selection task. A third view, the accountability hypothesis, claims that the effects of prescriptive norms are generated by the ambiguity of the causal test question. Asking whether an agent is a cause can be understood as a request to assess her causal contribution but also her moral accountability. According to this theory norm effects on causal selection are mediated by accountability judgments that are not only sensitive to the abnormality of behavior but also to mitigating factors, such as intentionality and knowledge of norms. Five experiments are presented that favor the accountability account over the two alternative theories.

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. One class of explanation for the sensitivity to different types of event absence is that rats' representations go beyond their immediate sensory experience and that covering creates uncertainty regarding the status of an event (thus impacting on the underlying causal model of the relationship between events). A second class of explanation, which includes associative mechanisms, assumes that rats represent only their direct sensory experience and that particular features of the covering procedures provide incidental cues that elicit the observed behaviors. We outline a set of consensus predictions from these two classes of explanation focusing on the potential importance of uncertainty about the presentation of an outcome. The example of covering the food-magazine during the extinction of appetitive conditioning is used as a test case for the derivation of diagnostic tests that are not biased by preconceived assumptions about the nature of animal cognition. (PsycINFO Database Record

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). We argue that the presented evidence is in fact consistent with the standard subjective view if conversational pragmatics is taken into account. Three experiments are presented that directly test and support the subjective view. An additional experiment backs up our pragmatic hypothesis by using the uncontroversial case of making a promise.

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. In 2 experiments, we investigated the influence of norm violations on causal selection in both 5-year-old children and adults. In particular, we focused on the role of mental state ascription and blame evaluation as potential mediating factors in this process. To this end, the knowledge status of the agent in question was varied such that she either was or was not aware of her norm transgression. Results revealed that children and adults assigned blame differently: Only adults were sensitive to the knowledge of the agent about norms as a mitigating factor. Crucially, however, despite its different sensitivity to knowledge ascription in children and adults, blame assignment in both age groups affected causal selection in the same ways. The relevance of these findings for alternative theories of causal selection is discussed.

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. We found strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism (sufficiency or necessity; Experiment 1). These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally (Experiment 2) and appear to be domain-general (Experiment 3). Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors.

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"). Moreover, X tends to be attributed more force than Y (force asymmetry), which contradicts the laws of Newtonian mechanics. Recent theories of force asymmetry try to explain these findings as the result of an asymmetrical identification with either the (stronger) agent or the (weaker) patient of the causal interaction. We directly tested this assumption by manipulating attributions of causal agency while holding the properties of the causal interaction constant across conditions. In contrast to previous accounts, we found that force judgments stayed invariant across conditions in which assignments of causal agency shifted from X to Y and that even those subjects who chose Y as the causal agent gave invariantly higher force ratings to X. These results suggest that causal agency and the perception of force are conceptually independent of each other. Different possible explanations are discussed.

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. implementing a business plan) with an intended primary effect (e.g. raise profits) and a foreseen side effect (e.g. harming/helping the environment), subjects tend to interpret the bringing about of the side effect more often as intentional when it is negative (harming the environment) than when it is positive (helping the environment). From a cognitive point of view, it is unclear whether the side-effect effect is driven by the moral status of the side effects specifically, or rather more generally by its normative status. And from a developmental point of view, little is known about the ontogenetic origins of the effect. The present study therefore explored the cognitive foundations and the ontogenetic origins of the side-effect effect by testing 4-to 5-year-old children with scenarios in which a side effect was in accordance with/violated a norm. Crucially, the status of the norm was varied to be conventional or moral. Children rated the bringing about of side-effects as more intentional when it broke a norm than when it accorded with a norm irrespective of the type of norm. The side-effect effect is thus an early-developing, more general and pervasive phenomenon, not restricted to morally relevant side effects.