Decision-making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals' causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments, and identify the limitations of this approach. We then explore the possibility of aggregating causal (...) class='Hi'>judgments independently of probabilistic ones. Formally, we introduce the problem of causal-network aggregation. Finally, we revisit the aggregation of probabilistic judgments when this is constrained by prior aggregation of qualitative causal judgments. (shrink)

People more frequently select norm-violating factors, relative to norm- conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments—i.e., judgments about the cause of some future outcome. Four experiments show that people more frequently select norm- violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that (...) the abnormal-selection effects are not primarily explained by the perception of agency (Experiment 4). We discuss these results in relation to recent efforts to model causal judgment. (shrink)

Are causal explanations (e.g., “she switched careers because of the COVID pandemic”) treated differently from the corresponding claims that one factor caused another (e.g., “the COVID pandemic caused her to switch careers”)? We examined whether explanatory and causal claims diverge in their responsiveness to two different types of information: covariation strength and mechanism information. We report five experiments with 1,730 participants total, showing that compared to judgments of causal strength, explanatory judgments tend to bemoresensitive to (...) mechanism andlesssensitive to covariation – even though explanatory judgments respond to both types of information. We also report exploratory comparisons to judgments of understanding, and discuss implications of our findings for theories of explanation, understanding, and causal attribution. These findings shed light on the potentially unique role of explanation in cognition. (shrink)

How do people come to opposite causal judgments about societal problems, such as whether a public health policy reduced COVID‐19 cases? The current research tests an understudied cognitive mechanism in which people may agree about what actually happened (e.g., that a public health policy was implemented and COVID‐19 cases declined), but can be made to disagree about the counterfactual, or what would have happened otherwise (e.g., whether COVID‐19 cases would have declined naturally without intervention) via comparison cases. Across (...) two preregistered studies (total N = 480), participants reasoned about the implementation of a public policy that was followed by an immediate decline in novel virus cases. Study 1 shows that people's judgments about the causal impact of the policy could be pushed in opposite directions by emphasizing comparison cases that imply different counterfactual outcomes. Study 2 finds that people recognize they can use such information to influence others. Specifically, in service of persuading others to support or reject a public health policy, people systematically showed comparison cases implying the counterfactual outcome that aligned with their position. These findings were robust across samples of U.S. college students and politically and socioeconomically diverse U.S. adults. Together, these studies suggest that implied counterfactuals are a powerful tool that individuals can use to manufacture others’ causal judgments and warrant further investigation as a mechanism contributing to belief polarization. (shrink)

Several factors have been put forward to explain the variability of moral judgments for superficially analogous moral dilemmas, in particular in the paradigm of trolley cases. In this paper we elaborate on Mikhail's view that (i) causal analysis is at the core of moral judgments and that (ii) causal judgments can be quantified by linguistic methods. According to this model, our moral judgments depend both on utilitarian considerations (whether positive effects outweigh negative effects) and (...) on a representation of the causal structure of the action (whether the negative effects are essentially side-effects rather than main goals). However, the exact contribution of each factor, as well as the precise way in which causal considerations interact with utilitarian considerations, has yet to be quantified and investigated. We present several variations on trolley dilemmas in which subjects had to assess the morality of the action and to evaluate their preference between two competing descriptions of the scene (.. (shrink)

Cognitive Science, Volume 46, Issue 5, May 2022.

Many philosophical theories of causation are egalitarian, rejecting a distinction between causes and mere causal conditions. We sought to determine the extent to which people's causal judgments discriminate, selecting as causes counternormal events—those that violate norms of some kind—while rejecting non-violators. We found significant selectivity of this sort. Moreover, priming that encouraged more egalitarian judgments had little effect on subjects. We also found that omissions are as likely as actions to be judged as causes, and that (...) counternormative selectivity appears to apply equally to actions and omissions. (shrink)

The application of the formal framework of causal Bayesian Networks to children’s causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children’s causal structure and intervention judgments were consistent (...) with one another. In Experiment 1, children aged between 4 and 8 years made causal structure judgments on a three-component causal system followed by counterfactual intervention judgments. In Experiment 2, children’s causal structure judgments were followed by intervention judgments phrased as future hypotheticals. In Experiment 3, we explicitly told children what the correct causal structure was and asked them to make intervention judgments. The results of the three experiments suggest that the representations that support causal structure judgments do not easily support simple judgments about interventions in children. We discuss our findings in light of strong interventionist claims that the two types of judgments should be closely linked. (shrink)

128 female Ss were asked to make 4 judgments about a young woman after reading her "job application portfolio." Five characteristics of the young woman were manipulated orthogonally. Ss were asked to report how each of the 5 manipulated factors had influenced each of their judgments. "Observer Ss," who had access only to very impoverished descriptions of each of the 5 factors, were asked to predict how each of the factors would influence each of the judgments. Results (...) show that S reports about the effects of the factors on the judgments were in general highly inaccurate; observer predictions were extremely similar to S reports; for the single judgment for which Ss showed substantial accuracy, observer predictions were as accurate as S reports. Results indicate that whatever introspective access to cognitive processes may exist, it is not sufficient to produce generally accurate reports about such processes, nor even to produce reports that differ much from those of poorly informed outside observers. (shrink)

It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as (...) mechanism clues and repeated interventions are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for causal judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to causal understanding, not multiple instance clues such as empirical association, contingency, and conditional probabilities. (shrink)

The present studies investigate how the intentions of third parties influence judgments of moral responsibility for other agents who commit immoral acts. Using cases in which an agent acts under some situational constraint brought about by a third party, we ask whether the agent is blamed less for the immoral act when the third party intended for that act to occur. Study 1 demonstrates that third-party intentions do influence judgments of blame. Study 2 finds that third-party intentions only (...) influence moral judgments when the agent's actions precisely match the third party's intention. Study 3 shows that this effect arises from changes in participants' causal perception that the third party was controlling the agent. Studies 4 and 5, respectively, show that the effect cannot be explained by changes in the distribution of blame or perceived differences in situational constraint faced by the agent. (shrink)

Researchers recently demonstrated a strong direct relationship between judgments about what a person knows and judgments about how a person should act. But it remains unknown whether actionability judgments cause knowledge judgments, or knowledge judgments cause actionability judgments. This paper uses causal modeling to help answer this question. Across two experiments, we found evidence that actionability judgments cause knowledge judgments.

Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar (...) principles of counterfactual logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach. (shrink)

This paper first outlines a new taxonomy of different views concerning the relationship between normative judgments and motivation. In this taxonomy, according to the Type A views, a positive normative judgment concerning an action consists at least in part of motivation to do that action. According to the Type B views, motivation is never a constituent of a positive normative judgment even if such judgments have, due to the kind of states they are, a causal power to (...) produce motivation in an agent. Finally, according to the Type C views, a normative judgment can produce motivation only with the help of a third mental state or a distinct substantial local disposition. This paper then outlines a novel evolutionary argument for the Type B views. If we assume that normative judgments' ability to shape our motivations enabled efficient planning and cooperation , the psychological mechanism responsible for this adaptation should be understood as a proximal mechanism. This paper argues that it is then more likely that we evolved to make normative judgments that have direct causal powers to influence our motivations because such Type B mechanisms are more reliable than the Type C mechanisms. It also suggests that the Type A views are either empirically false or collapse into the Type B views. (shrink)

In many educational systems, ethnic minority students score lower in their academic achievement, and consequently, teachers develop low expectations regarding this student group. Relatedly, teachers’ implicit attitudes, explicit expectations, and causal attributions also differ between ethnic minority and ethnic majority students—all in a disadvantageous way for ethnic minority students. However, what is not known so far, is how attitudes and causal attributions contribute together to teachers’ judgments. In the current study, we explored how implicit attitudes and (...) class='Hi'>causal attributions contribute to preservice teachers’ judgments of the low educational success of an ethnic minority student. Results showed that both implicit attitudes and causal attributions predicted language proficiency and intelligence judgments. Negative implicit attitudes, assessed with the IRAP, and internal stable causal attributions led to lower judgments of language proficiency, whereas lower judgments of intelligence were predicted by positive implicit attitudes and higher judgments of intelligence by external stable attributions. Substantial differences in the prediction of judgments could be found between the IRAP and BIAT as measures of implicit attitudes. (shrink)

Experimental studies suggest that people’s ordinary causal judgments are affected not only by statistical considerations but also by moral considerations. One way to explain these results would be to construct a model according to which people are trying to make a purely statistical judgment but moral considerations somehow distort their intuitions. The present paper offers an alternative perspective. Specifically, the author proposes a model according to which the very same underlying mechanism accounts for the influence of both statistical (...) and moral considerations. On this model, it appears that ordinary causal judgments are quite different from the sorts of judgments one might find in the systematic sciences.Keywords: Causation; Causal cognition; Experimental philosophy; Cognitive science. (shrink)

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given (...) A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory led to transitive causal judgments. On the other hand, chains schematized as multiple chunks led to intransitive judgments despite strong intermediate links. Normative accounts of causal intransitivity could not explain these intransitive judgments. (shrink)

It has long been known that people’s causal judgments can have an impact on their moral judgments. To take a simple example, if people conclude that a behavior caused the death of ten innocent children, they will therefore be inclined to regard the behavior itself as morally wrong. So far, none of this should come as any surprise. But recent experimental work points to the existence of a second, and more surprising, aspect of the relationship between (...) class='Hi'>causal judgment and moral judgment. It appears that the relationship can sometimes go in the opposite direction. That is, it appears that our moral judgments can sometimes impact our causal judgments. (Hence, we might first determine that a behavior is morally wrong and then, on that basis, arrive at the conclusion that it was the cause of various outcomes.). (shrink)

A growing body of research has examined how people judge the persistence of identity over time—that is, how they decide that a particular individual is the same entity from one time to the next. While a great deal of progress has been made in understanding the types of features that people typically consider when making such judgments, to date, existing work has not explored how these judgments may be shaped by normative considerations. The present studies demonstrate that normative (...) beliefs do appear to play an important role in people's beliefs about persistence. Specifically, people are more likely to judge that the identity of a given entity remains the same when its features improve than when its features deteriorate. Study 1 provides a basic demonstration of this effect. Study 2 shows that this effect is moderated by individual differences in normative beliefs. Study 3 examines the underlying mechanism, which is the belief that, in general, various entities are essentially good. Study 4 directly manipulates beliefs about essence to show that the positivity bias regarding essences is causally responsible for the effect. (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)

A central question in cognitive research concerns how new properties are generalized to categories. This article introduces a model of how generalizations involve a process of causal inference in which people estimate the likely presence of the new property in individual category exemplars and then the prevalence of the property among all category members. Evidence in favor of this causal‐based generalization (CBG) view included effects of an existing feature’s base rate (Experiment 1), the direction of the causal (...) relations (Experiments 2 and 4), the number of those relations (Experiment 3), and the distribution of features among category members (Experiments 4 and 5). The results provided no support for an alternative view that generalizations are promoted by the centrality of the to‐be‐generalized feature. However, there was evidence that a minority of participants based their judgments on simpler associative reasoning processes. (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)

& Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and (...) misses some important probabilistic implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception. (shrink)

Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning (...) via interactions with continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss--Markov (``\OU'') processes and show how such \textit{OU networks} can express complex dynamic phenomena such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world. (shrink)

We report three experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a (...) class='Hi'>causal generalization’s actual scope of application. In Experiment 3, we offer evidence that stable causal relationships may be seen as better guides to action. Collectively, these experiments document a previously underappreciated factor that shapes people’s causal reasoning: the stability of the causal relationship. (shrink)

Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments (...) isolate the contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions. (shrink)

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model—the mutation sampler—that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis–Hastings sampling algorithm. Across a diverse array of (...) tasks and conditions encompassing over 1,700 participants, we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process‐level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it. (shrink)

Some argue that moral judgments apply to fantasies because they can lead to action. Others argue that we should not assume that fantasies will lead to action and should not judge them morally unless they do. Still others argue that evaluating fantasies through their possible connections to action is misguided since fantasies contribute to our characters. I argue for the liberal position that fantasies that do not contribute causally to immoral acts are not subject to moral judgments. I (...) make that argument by, first, distinguishing several categories of clear fantasies and demonstrating why the liberal position is correct for each. Then, I examine a recent development that blurs the fantasy/reality distinction: virtual worlds such as Second Life, an on-line, interactive environment in which millions of users worldwide create virtual identities and lives. Some of the activities of these users create an interesting challenge for the liberal position. (shrink)

We propose a causal model theory to explain asymmetries in judgments of the intentionality of a foreseen side-effect that is either negative or positive (Knobe, 2003). The theory is implemented as a Bayesian network relating types of mental states, actions, and consequences that integrates previous hypotheses. It appeals to two inferential routes to judgment about the intentionality of someone else's action: bottom-up from action to desire and top-down from character and disposition. Support for the theory comes from three (...) experiments that test the prediction that bottom-up inference should occur only when the actor's primary objective is known. The model fits intentionality judgments reasonably well with no free parameters. (shrink)

Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and (...) how these factors differ in their predictive power for both causal and conditional claims. (shrink)

How do people judge the degree of causal responsibility that an agent has for the outcomes of her actions? We show that a relatively unexplored factor -- the robustness of the causal chain linking the agent’s action and the outcome -- influences judgments of causal responsibility of the agent. In three experiments, we vary robustness by manipulating the number of background circumstances under which the action causes the effect, and find that causal responsibility judgments (...) increase with robustness. In the first experiment, the robustness manipulation also raises the probability of the effect given the action. Experiments 2 and 3 control for probability-raising, and show that robustness still affects judgments of causal responsibility. In particular, Experiment 3 introduces an Ellsberg type of scenario to manipulate robustness, while keeping the conditional probability and the skill deployed in the action fixed. Experiment 4, replicates the results of Experiment 3, while contrasting between judgments of causal strength and of causal responsibility. The results show that in all cases, the perceived degree of responsibility increases with the robustness of the action-outcome causal chain. (shrink)

Theories of how organisms learn about cause-effect relations have a history dating back at least to the associationist/mechanistic hypothesis of David Hume. Some contemporary theories of causal learning are descendants of Hume's mechanistic models of conditioning, but others impute principled, rule-based reasoning. Since even primitive animals are conditionable, it is clear that there are built-in mechanical algorithms that respond to cause/effect relations. The evidence suggests that humans retain the use of such algorithms, which are surely adaptive when causal (...) judgments must be rapidly made. But we know very little about what these algorithms are and about when and with what ratiocinative procedures they are sometimes replaced. Nor do we know how the concept of causation originates in humans. To clarify some of these issues, this paper surveys the literature and explores the behavioral predictions made by two contrasting theories of causal learning: the mechanical Rescorla-Wagner model and the sophisticated reasoning codified in Bayes' Theorem. (shrink)

The notion of authenticity, i.e., being “genuine,” “real,” or “true to oneself,” is sometimes held as critical to a person’s autonomy, so that inauthenticity prevents the person from making autonomous decisions or leading an autonomous life. It has been pointed out that authenticity is difficult to observe in others. Therefore, judgments of inauthenticity have been found inadequate to underpin paternalistic interventions, among other things. This article delineates what justifies judgments of inauthenticity. It is argued that for persons who (...) wish to live according to the prevailing social and moral standards and desires that are seriously undesirable according to those standards, it is justified to judge that a desire is inauthentic to the extent that it is due to causal factors that are alien to the person and to the extent that it deviates from the person’s practical identity. The article contributes to a tradition of thinking about authenticity which is known mainly from Frankfurt and Dworkin, and bridges the gap between theoretical ideals of authenticity and real authenticity-related problems in practical biomedical settings. (shrink)

Two separate research programs have revealed two different factors that feature in our judgments of whether some entity persists. One program—inspired by Knobe—has found that normative considerations affect persistence judgments. For instance, people are more inclined to view a thing as persisting when the changes it undergoes lead to improvements. The other program—inspired by Kelemen—has found that teleological considerations affect persistence judgments. For instance, people are more inclined to view a thing as persisting when it preserves its (...) purpose. Our goal in this paper is to determine what causes persistence judgments. Across four studies, we pit normative considerations against teleological considerations. And using causal modeling procedures, we find a consistent, robust pattern with teleological and not normative considerations directly causing persistence judgments. Our findings put teleology in the driver’s seat, while at the same time shedding further light on our folk notion of an object. (shrink)