Two variables are usually recognised as determinants of human causal learning: the contingency between a candidate cause and effect, and the temporal and/or spatial contiguity between them. A common finding is that reductions in temporal contiguity produce concomitant decrements in causal judgement. This finding had previously (Shanks & Dickinson, 1987) been interpreted as evidence that causal induction is based on associative learning processes. Buehner and May (2002, 2003, 2004) have challenged this notion by demonstrating that the impact of temporal delay (...) depends on expectations about the timeframe between cause and effect. A corollary of this knowledge-mediation account is that in certain situations longer delays could facilitate, while shorter delays should impair, causal learning. Here we present two experiments involving a physical apparatus that demonstrate a detrimental effect of contiguity under certain conditions. In contrast to all previous studies, delays universally promoted causal learning. This evidence is clearly at variance with the notion of a bottom-up contiguity bias in causal induction. A new, more general timeframe bias is discussed. (shrink)

The authors challenge the reigning “causal power framework” as an explanation for whether a particular outcome was actually caused by a specific potential cause. They test a new measure of causal attribution in two experiments by embedding the measure within the Structure Induction model of Singular Causation (SISC, Stephan & Waldmann, 2016).

When two rocks shatter the window at once, what causes the window to shatter? Is the throwing of each individual rock a cause of the window shattering, or are the throwings only causes collectively? This question bears on the analysis of causation, and the metaphysics of macro-causation. I argue that the throwing of each individual rock is a cause of the window shattering, and generally that individual overdeterminers are causes.

Past work has demonstrated that people’s moral judgments can influence their judgments in a number of domains that might seem to involve straightforward matters of fact, including judgments about freedom, causation, the doing/allowing distinction, and intentional action. The present studies explore whether the effect of morality in these four domains can be explained by changes in the relevance of alternative possibilities. More precisely, we propose that moral judgment influences the degree to which people regard certain alternative possibilities as relevant, which (...) in turn impacts intuitions about freedom, causation, doing/allowing, and intentional action. Employing the stimuli used in previous research, Studies 1a, 2a, 3a, and 4a show that the relevance of alternatives is influenced by moral judgments and mediates the impact of morality on non-moral judgments. Studies 1b, 2b, 3b, and 4b then provide direct empirical evidence for the link between the relevance of alternatives and judgments in these four domains by manipulating (rather than measuring) the relevance of alternative possibilities. Lastly, Study 5 demonstrates that the critical mechanism is not whether alternative possibilities are considered, but whether they are regarded as relevant. These studies support a unified framework for understanding the impact of morality across these very different kinds of judgments. (shrink)

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation (...) and (b) functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables. (shrink)

How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main (...) theoretical and empirical issues that arise from this literature and propose a novel model of intuitive judgments of responsibility. This model is a function of both pivotality (whether an agent made a difference to the outcome) and criticality (how important the agent is perceived to be for the outcome, before any actions are taken). The model explains empirical results from previous studies and is supported by a new experiment that manipulates both pivotality and criticality. We also discuss possible extensions of this model to deal with a broader range of causal situations. Overall, our approach emphasizes the close interrelations between causality, counterfactuals, and responsibility attributions. (shrink)

When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as "causal superseding." We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that this causal (...) superseding effect is dependent on a particular event structure, following a prediction of our counterfactual model. Experiment 4 demonstrates that causal superseding can occur with violations of non-moral norms. We propose a model of the superseding effect based on the idea of counterfactual sufficiency. (shrink)

Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been observed in existing studies. (...) More importantly, the measure predicts a new effect ("abnormal deflation"). Two studies show that people's judgments do, in fact, show this new effect. Taken together, the patterns of people's causal judgments thereby provide support for the proposed explanation. (shrink)

Much of the philosophical literature on causation has focused on the concept of actual causation, sometimes called token causation. In particular, it is this notion of actual causation that many philosophical theories of causation have attempted to capture.2 In this paper, we address the question: what purpose does this concept serve? As we shall see in the next section, one does not need this concept for purposes of prediction or rational deliberation. What then could the purpose be? We will argue (...) that one can gain an important clue here by looking at the ways in which causal judgments are shaped by people‘s understanding of norms. (shrink)

Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality. A number of philosophers and computer scientists have also suggested that an appeal to such factors can help deal with problems facing existing accounts of actual causation. This article develops a flexible formal framework for incorporating defaults, typicality, and normality into an account of actual causation. The resulting account takes actual causation to be both graded and (...) comparative. We then show how our account would handle a number of standard cases. 1 Introduction2 Causal Models3 The HP Definition of Actual Causation4 The Problem of Isomorphism5 Defaults, Typicality, and Normality6 Extended Causal Models7 Examples7.1 Omissions7.2 Knobe effects7.3 Causes versus background conditions7.4 Bogus prevention7.5 Causal chains7.6 Legal doctrines of intervening causes7.7 Pre-emption and short circuits8 Conclusion. (shrink)