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 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.

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.

Causal beliefs about depression in different cultural groups-what do cognitive psychological theories of causal learning and reasoning predict?

Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analyzed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given.