Despite the increase in studies investigating people’s explana- tory preferences in the domains of psychology and philoso- phy, little is known about their preferences in more applied do- mains, such as the criminal justice system. We show that when people evaluate competing legal accounts of the same evidence that vary in complexity, their explanatory preferences are af- fected by: i) whether they are required to draw causal mod- els of the evidence, and ii) the actual structure that is drawn. Although previous research has shown that people can reason correctly about causality, ours is one of the first studies that shows that generating and drawing causal models directly af- fects people’s evaluations of explanations.

The present study explored how evaluations of a defendant’s character can influence mock jurors’ judgments using a beliefupdating paradigm. Participants (N=143) were shown a trial transcript in which we manipulated the defendant’s character by introducing an irrelevant moral behavior observed before the crime as well as prior conviction. We found that bad defendants were consistently judged to be more deserving of punishment than good defendants. While character information influenced judgments of guilt, blame, and intentionality immediately after it was presented, the effect diminished as participants received more information about the case, and ultimately did not shift their verdicts. In general, participants also mitigated moral judgments for good defendants rather than exacerbate judgments for bad defendants. Thematic analysis of judgment rationales also revealed that participants reasoned about actions, norms, and mental states when evaluating blame and punishment. We discuss the implications of this study in moral and legal decision-making.

Epistemic states, what an agent knows or beliefs, play a crucial role in people's moral evaluations of the agent's actions. Whether and to what extent epistemic states also influence an agent's perceived causal contribution to an outcome remains the subject of debate. In three experiments, we investigate people's causal and counterfactual judgments about ignorant causal agents. We find that agent's epistemic states, the conditions of their ignorance as well as their epistemic actions influence how causal an agent is perceived, but also the kind of counterfactual alternatives people consider. We take these findings to indicate the crucial role of epistemic states in causal cognition and counterfactual models of causation.

Current causal theories argue that the statistical normality or abnormality of an action makes a difference to people’s causal judgements. In this paper, we present two experiments that explore the role of statistical norms in causal cognition. In our first experiment, we provide a preliminary test of two competing theories that aim to explain the effects of normality in causal cognition – the actual causal strength measure (Icard Kominsky & Knobe, 2017) and the correspondence hypothesis about causal judgements (Harinen, 2017). In addition, we control for an often neglected factor, the epistemic states of agents. Our second experiment investigates the effect of statistical normality in the same context, but with a probabilistic rather than deterministic causal structure. Our results favour Icard et al.’s (2017) model of causal strength, but show that the statistical normality of an action loses its influence when the occurrence of the outcome is probabilistic. We discuss the implications of our findings for current causal theories

Current causal theories aim to incorporate the effect of statistical and prescriptive norms on causal judgements, stating that norm-violating actions are judged as more causal than norm-conforming ones. In this paper, we present two experiments that undermine this claim, showing that people attribute increased causality to agents who conform to the norm of frequent behaviour. Furthermore, we find that the time point at which a moral norm is introduced does not make a difference to causal attributions, but that the frequency of a norm violation further accentuates its causal rating. Because these findings present a challenge to current norm theories of causation, we argue for an extended counterfactual model of causal attribution.

When several causes contributed to an outcome, we often single out one causal factor as being “more of a cause” than others. What explains this selection? Existing research suggests that people’s judgements of actual causation can be influenced by the degree to which they regard certain events as norm-deviant, or “abnormal” (Hart & Honoré, 1963; Kahneman & Miller, 1986; Hitchcock & Knobe, 2009; Halpern & Hitchcock 2015). In this paper, we argue that statistical abnormality influences causal judgements about human agents by changing the agents’ epistemic states (Epistemic Hypothesis). In Experiment 1, we replicate previous findings that people assign more causal strength to a statistically abnormally acting agent, but show that they also assign them more knowledge about the behaviour of their peers. In Experiment 2, we show that in case of equal epistemic uncertainty, people do not differentiate between statistically abnormal and normal causal agents. In Experiment 3, we explore the difference between type and token abnormality, and find that a token abnormal, but type normal behaviour still influences causal judgments, with people’s epistemic judgments mirroring these causal judgments. We discuss the implications of this research for current norm-frameworks in causal cognition.