Cognitive scientists have been debating how the folk concept of intentional action works. We suggest a simple account: people consider that an agent did X intentionally to the extent that X was causally dependent on how much the agent wanted X to happen (or not to happen). Combined with recent models of human causal cognition, this definition provides a good account of the way people use the concept of intentional action, and offers natural explanations for puzzling phenomena such as the (...) side-effect effect. We provide empirical support for our theory, in studies where we show that people’s causation and intentionality judgments track each other closely, in everyday situations as well as in scenarios with unusual causal structures. Study 5 additionally shows that the effect of norm violations on intentionality judgments depends on the causal structure of the situation, in a way uniquely predicted by our theory. Taken together, these results suggest that the folk concept of intentional action has been difficult to define because it is made of cognitive building blocks, such as our intuitive concept of causation, whose logic cognitive scientists are just starting to understand. (shrink)

People tend to judge more recent events, relative to earlier ones, as the cause of some particular outcome. For instance, people are more inclined to judge that the last basket, rather than the first, caused the team to win the basketball game. This recency effect, however, reverses in cases of overdetermination: people judge that earlier events, rather than more recent ones, caused the outcome when the event is individually sufficient but not individually necessary for the outcome. In five experiments (N (...) = 5507), we find evidence for the recency effect and the primacy effect for causal judgment. Traditionally, these effects have been a problem for counterfactual views of causal judgment. However, we argue that an extension of a recent counterfactual model of causal judgment explains both the recency and the primacy effect. In line with the predictions of our extended counterfactual model, we also find that, regardless of causal structure, people tend to imagine the counterfactual alternative to the more recent event rather than to the earlier one. Moreover, manipulating this tendency affects causal judgments in the ways predicted by this extended model: asking participants to imagine the counterfactual alternative to the earlier event weakens the interaction between recency and causal structure, and asking participants to imagine the counterfactual alternative to the more recent event strengthens the interaction between recency and causal structure. We discuss these results in relation to work on counterfactual thinking, causal modeling, and late-preemption. (shrink)

When comparing the roles of the lightning strike and the dry climate in causing the forest fire, one might think that the lightning strike is more of a cause than the dry climate, or one might think that the lightning strike completely caused the fire while the dry conditions did not cause it at all. Psychologists and philosophers have long debated whether such causal judgments are graded; that is, whether people treat some causes as stronger than others. To address this (...) debate, we first reanalyzed data from four recent studies. We found that causal judgments were actually multimodal: although most causal judgments made on a continuous scale were categorical, there was also some gradation. We then tested two competing explanations for this gradation: the confidence explanation, which states that people make graded causal judgments because they have varying degrees of belief in causal relations, and the strength explanation, which states that people make graded causal judgments because they believe that causation itself is graded. Experiment 1 tested the confidence explanation and showed that gradation in causal judgments was indeed moderated by confidence: people tended to make graded causal judgments when they were unconfident, but they tended to make more categorical causal judgments when they were confident. Experiment 2 tested the causal strength explanation and showed that although confidence still explained variation in causal judgments, it did not explain away the effects of normality, causal structure, or the number of candidate causes. Overall, we found that causal judgments were multimodal and that people make graded judgments both when they think a cause is weak and when they are uncertain about its causal role. (shrink)

Mike accidentally knocked against a bottle. Seeing that the bottle was about to fall, Jack was just about to catch it when Peter accidentally knocked against him, making Jack unable to catch it. Jack did not grab the bottle, and it fell to the ground and spilled. In double-prevention cases like these, philosophers and nonphilosophers alike tend to judge that Mike knocking into the bottle caused the beer to spill and that Peter knocking into Jack did not cause the beer (...) to spill. This difference in causal judgment is a difficult puzzle for counterfactual theories of causal judgment; if each event had not happened, the outcome would not have, yet there is a difference in people’s causal judgments. In four experiments and three supplemental experiments, we confirm this difference in causal judgments. We also show that differences in people’s counterfactual thinking can explain this difference in their causal judgments and that recent counterfactual models of causal judgment can account for these patterns. We discuss these results in relation to work on counterfactual thinking and causal modeling. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

Reasoning about underlying causal relations drives responsibility judgments: agents are held responsible for the outcomes they cause through their behaviors. Two main causal reasoning approaches exist: dependence theories emphasize statistical relations between causes and effects, while transference theories emphasize mechanical transmission of energy. Recently, pluralistic or hybrid models, combining both approaches, have emerged as promising psychological frameworks. In this paper, we focus on causal reasoning as involved in third-party judgements of responsibility and on related judgments of intention and control. In (...) particular, we used a novel visual paradigm to investigate the combined effects of two well-known causal manipulations, namely omission and pre-emption, on these evaluations. Our findings support the view that people apply a pluralistic causal reasoning when evaluating individual responsibility for negative outcomes. In particular, we observed diminished responsibility when dependence, transference, or both fail, compared to when these mechanisms are upheld. Responsibility judgement involves a cognitive hybrid of multiple aspects of causal reasoning. However, important differences exist at the interindividual level, with most people weighting transference more than dependence. (shrink)

The theory of mental models and its computer implementations have led to crucial experiments showing that no standard logic—the sentential calculus and all logics that include it—can underlie human reasoning. The theory replaces the logical concept of validity (the conclusion is true in all cases in which the premises are true) with necessity (conclusions describe no more than possibilities to which the premises refer). Many inferences are both necessary and valid. But experiments show that individuals make necessary inferences that are (...) invalid, e.g., Few people ate steak or sole; therefore, few people ate steak. Other crucial experiments show that individuals reject inferences that are not necessary but valid, e.g., He had the anesthetic or felt pain, but not both; therefore, he had the anesthetic or felt pain, or both. Nothing in logic can justify the rejection of a valid inference: a denial of its conclusion is inconsistent with its premises, and inconsistencies yield valid inferences of any conclusions whatsoever including the one denied. So inconsistencies are catastrophic in logic. In contrast, the model theory treats all inferences as defeasible (nonmonotonic), and inconsistencies have the null model, which yields only the null model in conjunction with any other premises. So inconsistences are local. Which allows truth values in natural languages to be much richer than those that occur in the semantics of standard logics; and individuals verify assertions on the basis of both facts and possibilities that did not occur. (shrink)

Recently, Ross and Woodward (2022) argued that the reversibility of an outcome – that is, whether the outcome can be undone – affects causal judgments. One prediction of their account is that reversibility affects causal judgments in latepreemption scenarios, where people typically judge that events that produce the outcome earlier are more causal than preempted alternative events that would have otherwise produced the outcome. Ross and Woodward’s account predicts that when the outcome is reversible, people would judge these events similarly (...) – as in standard overdetermination cases. In three experiments, we found no evidence that reversibility affects causal judgments in late-preemption cases. We discuss how these results affect the experimental philosophy and cognitive science of causation, causal modeling, and experimental jurisprudence. (shrink)

How do people evaluate causal relationships? Do they just consider what actually happened, or do they also consider what could have counterfactually happened? Using eye tracking and Gaussian process modeling, we investigated how people mentally simulated past events to judge what caused the outcomes to occur. Participants played a virtual ball‐shooting game and then—while looking at a blank screen—mentally simulated (a) what actually happened, (b) what counterfactually could have happened, or (c) what caused the outcome to happen. Our findings showed (...) that participants moved their eyes in patterns consistent with the actual or counterfactual events that they mentally simulated. When simulating what caused the outcome to occur, participants moved their eyes consistent with simulations of counterfactual possibilities. These results favor counterfactual theories of causal reasoning, demonstrate how eye movements can reflect simulation during this reasoning and provide a novel approach for investigating retrospective causal reasoning and counterfactual thinking. (shrink)

I argue for responsibility internalism. That is, moral responsibility (i.e., accountability, or being apt for praise or blame) depends only on factors internal to agents. Employing this view, I also argue that no one is responsible for what AI does but this isn’t morally problematic in a way that counts against developing or using AI. Responsibility is grounded in three potential conditions: the control (or freedom) condition, the epistemic (or awareness) condition, and the causal responsibility condition (or consequences). I argue (...) that causal responsibility is irrelevant for moral responsibility, and that the control condition and the epistemic condition depend only on factors internal to agents. Moreover, since what AI does is at best a consequence of our actions, and the consequences of our actions are irrelevant to our responsibility, no one is responsible for what AI does. That is, the so-called responsibility gap exits. However, this isn’t morally worrisome for developing or using AI. Firstly, I argue, current AI doesn’t generate a new kind of concern about responsibility that the older technologies don’t. Then, I argue that responsibility gap is not worrisome because neither responsibility gap, nor my argument for its existence, entails that no one can be justly punished, held accountable, or incurs duties in reparations when AI causes a harm. (shrink)