Moral Values Reveal the Causality Implicit in Verb Meaning

Prior work has found that moral values that build and bind groups—that is, the binding values of ingroup loyalty, respect for authority, and preservation of purity—are linked to blaming people who have been harmed. The present research investigated whether people's endorsement of binding values predicts their assignment of the causal locus of harmful events to the victims of the events. We used an implicit causality task from psycholinguistics in which participants read a sentence in the form “SUBJECT verbed OBJECT because…” where male and female proper names occupy the SUBJECT and OBJECT position. The participants were asked to predict the pronoun that follows “because”—the referent to the subject or object—which indicates their intuition about the likely cause of the event. We also collected explicit judgments of causal contributions and measured participants' moral values to investigate the relationship between moral values and the causal interpretation of events. Using two verb sets and two independent replications (N = 459, 249, 788), we found that greater endorsement of binding values was associated with a higher likelihood of selecting the object as the cause for harmful events in the implicit causality task, a result consistent with, and supportive of, previous moral psychological work on victim blaming. Endorsement of binding values also predicted explicit causal attributions to victims. Overall, these findings indicate that moral values that support the group rather than the individual reliably predict that people shift the causal locus of harmful events to those affected by the harms.     

A Process Model of Causal Reasoning

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.     

Knowing When Help Is Needed: A Developing Sense of Causal Complexity

Research on the division of cognitive labor has found that adults and children as young as age 5 are able to find appropriate experts for different causal systems. However, little work has explored how children and adults decide when to seek out expert knowledge in the first place. We propose that children and adults rely (in part) on “mechanism metadata,” information about mechanism information. We argue that mechanism metadata is relatively consistent across individuals exposed to similar amounts of mechanism information, and it is applicable to a wide range of causal systems. In three experiments, we show that adults and children as young as 5 years of age have a consistent sense of the causal complexity of different causal systems, and that this sense of complexity is related to decisions about when to seek expert knowledge, but over development there is a shift in focus from procedural information to internal mechanism information.     

The development of causal categorization

Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (Experiment 1) but showed additional influences of causal status when links were probabilistic (Experiment 2). Children's classification was based primarily on causal coherence in both cases. There was no effect of relational centrality in either age group. These results suggest that the generative model (Rehder, 2003a) provides a good account of causal categorization in children as well as adults.     

Just world beliefs, causal beliefs, and acquaintance: Associations with stigma toward eating disorders and obesity

The current study investigated the relationship between just world beliefs and stigmatizing attitudes toward eating disorders and obesity. Further, the associations between stigma and causal beliefs, and between stigma and acquaintance with these conditions, were examined. Participants (n = 447) read four vignettes describing an individual with anorexia nervosa, bulimia nervosa, binge eating disorder, or obesity. After each vignette, participants completed questionnaires assessing stigmatizing attitudes, just world beliefs, causal beliefs, and acquaintance with the condition depicted in the vignette. Stronger just world beliefs were associated with greater stigma toward all three eating disorders, as well as obesity (rs ranging from −.11 to −.18). More stigmatizing attitudes were associated with greater attribution of individual responsibility for the development of the disorder. However, participants with personal experience or who knew someone with the depicted problem did not have lower stigma scores than those who did not. The current study suggests that justification ideologies such as just world beliefs and controllability beliefs may underlie the stigmatization of eating disorders and obesity. These findings provide support for stigma reduction efforts aimed at targeting justification ideologies and altering causal beliefs.     

Transfer effects between moral dilemmas: A causal model theory

Evaluations of analogous situations are an important source for our moral intuitions. A puzzling recent set of findings in experiments exploring transfer effects between intuitions about moral dilemmas has demonstrated a striking asymmetry. Transfer often occurred with a specific ordering of moral dilemmas, but not when the sequence was reversed. In this article we present a new theory of transfer between moral intuitions that focuses on two components of moral dilemmas, namely their causal structure and their default evaluations. According to this theory, transfer effects are expected when the causal models underlying the considered dilemmas allow for a mapping of the highlighted aspect of the first scenario onto the causal structure of the second dilemma, and when the default evaluations of the two dilemmas substantially differ. The theory’s key predictions for the occurrence and the direction of transfer effects between two moral dilemmas are tested in five experiments with various variants of moral dilemmas from different domains. A sixth experiment tests the predictions of the theory for how the target action in the moral dilemmas is represented.     

Inference and coherence in causal-based artifact categorization

In four experiments, we tested conditions under which artifact concepts support inference and coherence in causal categorization. In all four experiments, participants categorized scenarios in which we systematically varied information about artifacts’ associated design history, physical structure, user intention, user action and functional outcome, and where each property could be specified as intact, compromised or not observed. Consistently across experiments, when participants received complete information (i.e., when all properties were observed), they categorized based on individual properties and did not show evidence of using coherence to categorize. In contrast, when the state of some property was not observed, participants gave evidence of using available information to infer the state of the unobserved property, which increased the value of the available information for categorization. Our data offers answers to longstanding questions regarding artifact categorization, such as whether there are underlying causal models for artifacts, which properties are part of them, whether design history is an artifact’s causal essence, and whether physical appearance or functional outcome is the most central artifact property.     

Children Use Temporal Cues to Learn Causal Directionality

The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real‐world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments.