Non-contingent learning data can lead to the (seemingly) illusory perception of causality if the potential cause and effect often co-occur – an effect known as “outcome-density bias.” Bayesian models of causal induction explain this effect as the result of a rational learning process in which the data do not fully override non-zero causal priors. Convincing evidence for this rational explanation requires the demonstration of an experimentally manipulated effect of causal priors, which has been lacking. We successfully manipulated participants’ (𝑁 = 300) causal priors through visually conveyed causal mechanism information. This manipulation influenced the outcome-density effect in the predicted way: in a condition inducing low causal priors, the effect disappeared. The results demonstrate the effectiveness of visual mechanism information in manipulating priors and strengthen the Bayesian view of causal induction.

Non-contingent learning data can produce an apparent “illusory” or “false” perception of causality (“causal illusion”), especially if a potential cause and effect frequently co-occur — a phenomenon known as the “outcome-density bias.” In contrast to the prominent bias view, Bayesian models of causal induction explain these causal illusions as the result of a rational learning process in which observed data fail to fully override non-zero causal priors. Convincing evidence for this rational explanation requires an experimentally manipulated effect of causal priors — which has so far been lacking. We report four experiments (N=1860) supporting the Bayesian interpretation. We successfully manipulated participants’ causal priors either through visually conveyed causal mechanism information or through statistical base-rate information, and found that both manipulations influenced the degree to which participants reported a causal relation after having processed non-contingent learning data. The results were in line with Bayesian updating: lower causal priors yielded lower post-learning beliefs in a causal connection, effectively reducing the “causal illusion.” The results strengthen computational models implementing a rational Bayesian view of causal induction.

Non-contingent learning data can lead to the (seemingly) illusory perception of causality if the potential cause and effect often co-occur (i.e., the effect prevalence is high) -- an effect known as "outcome-density bias". Bayesian models of causal induction explain this effect as the result of a rational learning process in which the data do not fully override held non-zero causal priors. Convincing evidence of this rational explanation requires the demonstration of an experimentally manipulated effect of causal priors, which has been lacking. We successfully manipulated participants' (N =300) causal priors through a combination of statistical and mechanism information. This manipulation moderated the outcome-density effect in the predicted way and, in one condition, even eliminated it entirely by inducing causal priors close to zero. The results strengthen the rational Bayesian view of causal induction and support computational models that formalize this view.

This article investigates people’s judgments of actual causation in the context of a previously neglected property of causal structures—their reversibility, that is, whether an effect persists or returns to its original state if its causes are removed. Causal reversibility, and its potential impact on causal judgment, was recently analyzed theoretically by Ross and Woodward (2022). They hypothesized that reversibility might affect people’s evaluation of causes in late-preemption scenarios. The typical finding in preemption scenarios is that events happening earlier are considered to be actual causes, while events happening later are regarded as non-causes. The hypothesis is that this robust intuition depends on causal reversibility and that in reversible structures later events are regarded as actual causes. Across three main experiments and one supplementary study (N = 590), it is shown that reversibility has the predicted effect: later causes are perceived to make an actual causal contribution to the effect. It is also shown that Henne et al. (2023), in a first study, did not find evidence for Ross and Woodward’s hypothesis because they did not test whether people regard later causes in preemption-like sequences of reversible structures as maintainers and not as triggers of their effect. Because they used test questions that asked explicitly for triggering rather than maintaining or were at least ambiguous, their results seemed to show that people think that later events have no causal impact. Maintaining is a relevant causal concept deserving more attention in both philosophical theories and psychological studies on causal cognition.

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.

Humans are capable explainers and lay people tend to share the same explanatory virtues held in high regard by philosophers and scientists. However, a recent line of studies found a striking deviation from normativity in lay people's explanations, termed the "narrow latent scope bias". When competing explanations with identical a priori probabilities fit observed evidence equally well - but differ in the number of unobserved pieces of evidence they predict (latent scope) - reasoners seem to prefer explanations that predict fewer unobserved pieces of evidence (narrow latent scope). This tendency has been described as a robust explanatory reasoning bias. The present paper empirically demonstrates across six experiments (N=2200) that this bias is less robust than has been claimed, and influenced by nuanced pragmatic inferences on the side of participants. Pragmatic factors shown to influence the bias are assumptions about how easily an unobserved piece of evidence should have been observed if it was present ("feature diagnosability"), and the formulation of the test question being asked. Across studies, genuine narrow latent scope biases resulting from fallacious reasoning were found only in a fraction of participants. It is also demonstrated that the magnitude of the bias depends on response options: it is stronger if participants are forced to commit an error, but at best weak if they are allowed to give the correct answer.

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.

Most psychological studies on causal cognition have focused on how people make predictions from causes to effects or how they assess causal strength for general causal relationships (e.g., “smoking causes cancer”). In the past years, there has been a surge of interest in other types of causal judgments, such as diagnostic inferences or causal selection. Our focus here is on how people assess singular causation relations between cause and effect events that occurred at a particular spatiotemporal location (e.g., “Mary’s having taking this pill caused her sickness.”). The analysis of singular causation has received much attention in philosophy, but relatively few psychological studies have investigated how lay people assess these relations. Based on the power PC model of causal attribution proposed by Cheng and Novick (2005), we have developed and tested a new computational model of singular causation judgments integrating covariation, temporal, and mechanism information. We provide an overview of this research and outline important questions for future research.

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).

All objects occupy different positions in space and can also differ in terms of their orientation, sense, size, and shape. Here, we ask the question of how people spontaneously categorize geometrical objects. We propose that people consider objects’ physical properties when categorizing them. Our new theory assumes that a critical factor guiding object categorization is the interaction between objects’ geometrical features and gravity. In 3D contexts, gravity endows some geometrical features (e.g., orientation) with more informativeness because they predict differences in objects’ levels of energy (i.e., their gravitational potential energy). Consequently, some geometrical objects (e.g., objects with different orientations) can be considered as being of different “physical” kinds. We predict and show across several behavioral studies that humans are more likely to generalize information between objects of the same physical kind than between objects of a different physical kind. For 2D contexts, we predict and show that people treat differences in objects’ geometrical features more equally because objects in 2D contexts are deprived of physical properties. Our findings confirm the central role that gravity plays in how people categorize geometrical objects in different contexts.

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).

The categorization of geometric objects is one of the most fundamental problems all intelligent systems have to deal with in dynamic environments in which objects' geometrical configuration constantly changes. Animals, including humans, do not treat all geometrical differences equally: they ignore some geometrical features when it comes to generalization but not others. So far, no theory has been presented that explains this cognitive phenomenon. We here propose and empirically test such a theory. The theory identifies and relies on the invariant referents existing in 3D (i.e., gravity) and 2D (e.g., any 2D frame) environments to predict the geometrical differences reasoners consider as important or irrelevant for object categorization. We test and confirm a novel central prediction of the theory, namely that human reasoners categorize objects differently in 3D and 2D environments. These findings cast new light on core cognitive abilities that minds use to make sense of the world.

Most psychological studies focused on how people reason about generative causation, in which a cause produces an effect. We here study the prevention of effects both on the general and singular level. A general prevention query might ask how strongly a vaccine is expected to reduce the risk of contracting COVID-19, whereas a singular prevention query might ask whether the absence of COVID-19 in a specific vaccinated person actually resulted from this person’s vaccination. We propose a computational model answering how knowledge about the general strength of a preventive cause can be used to assess whether a preventive link is instantiated in a singular case. We also discuss how psychological models of causal strength learning relate to mathematical models of vaccination efficacy used in medical research. The results of an experimentsuggest that many, but not all people differentiate between preventive strength and singular prevention queries, in line with the formal model.

When do people say that an event that did not happen was a cause? We extend the counterfactual simulation model (CSM) of causal judgment (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2021) and test it in a series of three experiments that look at people's causal judgments about omissions in dynamic physical interactions. The problem of omissive causation highlights a series of questions that need to be answered in order to give an adequate causal explanation of why something happened: what are the relevant variables, what are their possible values, how are putative causal relationships evaluated, and how is the causal responsibility for an outcome attributed to multiple causes? The CSM predicts that people make causal judgments about omissions in physical interactions by using their intuitive understanding of physics to mentally simulate what would have happened in relevant counterfactual situations. Prior work has argued that normative expectations affect judgments of omissive causation. Here we suggest a concrete mechanism of how this happens: expectations affect what counterfactuals people consider, and the more certain people are that the counterfactual outcome would have been different from what actually happened, the more causal they judge the omission to be. Our experiments show that both the structure of the physical situation as well as expectations about what will happen affect people's judgments.

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 eventscandewas 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.

The communicative goal behind a causal claim like “Smoking causes heart attacks” is to inform recipients about the existence of a causal link between the factors mentioned in the proposition. Different terminologies can be used to accomplish this goal. Sometimes people use formulations of the form “C causes E”, like in the tobacco warning above, and sometimes they use other formulations, such as modal propositions like “C can cause / lead to E.”, or statements like “C increases the risk of E.”. We investigate the hypothesis that different causal structure claims, by means of different terminologies, not only communicate the existence of a causal link but also implicitly elicit intuitions about that link’s strength. Experiment 1 revealed that claims like “C causes E” imply a stronger link than, for example, modal formulations like “C can cause E”. Experiment 2 tested implications of this finding for research on causal structure learning.

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.

Consider the following causal explanation: The ball went through the goal because the defender didn’t block it. There are at least two problems with citing omissions as causal explanations. First, how do we choose the relevant candidate omission (e.g. why the defender and not the goalkeeper). Second, how do we determine what would have happened in the relevant counterfactual situation (i.e. maybe the shot would still have gone through the goal even if it had been blocked). In this paper, we extend the counterfactual simulation model (CSM) of causal judgment (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2014) to handle the second problem. In two experiments, we show how people’s causal model of the situation affects their causal judgments via influencing what counterfactuals they consider. Omissions are considered causes to the extent that the outcome in the relevant counterfactual situation would have been different from what it actually was.