Causal Judgments Research Articles

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.

Sensitizing jurors to eyewitness evidence using a counterfactual mindset induction

SummaryCounterfactual thinking is a form of mental simulation that informs causal judgments regarding the role antecedent events played in producing present outcomes. We examined whether inducing participants to think counterfactually about a case involving eyewitness evidence would sensitize them to variations in eyewitness evidence quality. Participants read a transcript of a murder trial in which we manipulated the quality of witnessing and identification conditions associated with the prosecution's eyewitness. A counterfactual mindset induction manipulation was embedded in the defense's closing arguments. The counterfactual induction produced a sensitization effect. Participants in the counterfactual condition rated the prosecution's eyewitness as weaker and convicted less frequently when the eyewitness evidence conditions were unfavorable to accuracy. However, evaluations and convictions did not increase when these conditions were favorable to accuracy. Verdicts among participants in the control condition did not differ as a function of the eyewitness evidence manipulation.

Reconstruction of Human Resource Management Under Big Data and Artificial Intelligence

In the big data and artificial intelligence (AI) era, enterprise human resource management can be carried out based on the management innovation of big data and AI management innovation to adapt to the new situations such as the increased diversification of labor relations, the decentralization and flattening of organizational management, and the individualized values of the new generation of employees, etc. Based on the maturity model of human resource management, the data demand of enterprise human resource management will present a development trend from individual, phenomenal, accurate and causal judgment to collective, regular, relevant and perceptual prediction, along with the transformation of human resource concept and management mindset. Compared with the “Small data”, big data and AI will drive the innovation of human resource management in enterprises from three aspects as follows: implementing the tagging of human resources to form “Digital portrait” of talents at the basic level, building the decision and feedback system of human resources at the implementation level, and facilitating the transformation of the management mindset from management and control to value empowerment at the strategic level.

Freedom to act enhances the sense of agency, while movement and goal-related prediction errors reduce it.

The Sense of Agency (SoA) is the experience of controlling one's movements and their external consequences. Accumulating evidence suggests that freedom to act enhances SoA, while prediction errors are known to reduce it. Here, we investigated if prediction errors related to movement or to the achievement of the goal of the action exert the same influence on SoA during free and cued actions. Participants pressed a freely chosen or cued-colored button, while observing a virtual hand moving in the same or in the opposite direction-i.e., movement-related prediction error-and pressing the selected or a different color-i.e., goal-related prediction error. To investigate implicit and explicit components of SoA, we collected indirect (i.e., Synchrony Judgments) and direct (i.e., Judgments of Causation) measures. We found that participants judged virtual actions as more synchronous when they were free to act. Additionally, movement-related prediction errors reduced both perceived synchrony and judgments of causation, while goal-related prediction errors impaired exclusively the latter. Our results suggest that freedom to act enhances SoA and that movement and goal-related prediction errors lead to an equivalent reduction of SoA in free and cued actions. Our results also show that the influence of freedom to act and goal achievement may be limited, respectively, to implicit and explicit SoA, while movement information may affect both components. These findings provide support to recent theories that view SoA as a multifaceted construct, by showing that different action cues may uniquely influence the feeling of control.

Proximate Cause Explained: An Essay in Experimental Jurisprudence

Among the oldest debates in American jurisprudence concerns the concept of “proximate cause.” According to so-called formalists, the legal concept of “proximate cause” is the same as the ordinary concept of “cause.” The legal question of whether a cause is proximate for the purposes of establishing tort liability, therefore, is an objective matter about the external world determinable by familiar descriptive inquiry. By contrast, legal realists think that issues of proximate causation are disguised normative questions about responsibility. As the realists William Prosser and Robert Keeton put it, “Proximate cause is better called ‘responsible cause’.” Recent work in cognitive science has afforded us new insights into the way people make causal judgments that were unavailable at the time of the original debate between formalists and realists. We now have access to the results of systematic experimental studies that examine the way people ordinarily think about causation and morality. This work opens up the possibility of a very different approach to understanding the role of causation in the law — one which combines the attractive features of both formalism and realism without accepting their implausible consequences. In addition to providing a model for interpreting the case law of proximate cause, this paper also exemplifies a new way of doing legal theory — a method we call “experimental jurisprudence.” Experimental jurisprudence is the study of jurisprudential questions using empirical methods. Jurisprudential disputes about proximate cause are especially ripe for empirical analysis because the debate revolves around whether the legal concept of proximate cause is the same as the ordinary concept of causation. Interrogating the ordinary concept of causation, therefore, should shed light on this question.

The consideration about study design and implementation of vaccine clinic trials

The safety of prophylactic vaccine is one of the key points both in clinical trials and evaluation. Based on the review practice of vaccine registration clinical trials in recent years, we puts forward a series of thoughts and suggestions on the common problems of safety study on vaccine, including the hypothesis of safety study, the consideration of inclusion/exclusion criteria and suspension/termination criteria for study; the routine requirements of safety study and some special issue such as medical laboratory test index, vaccine virus shedding and safety of adjuvant, and further clarifies the requirements of safety observation on sequential enrollment; also emphasized the scientific judgment of causality and the quality of safety monitoring. It can be used for reference by vaccine researchers and to explore appropriate solutions to promote vaccine research & development and risk control in China.

Expectations affect physical causation judgments.

When several causes contributed to an outcome, people often single out one as "the" cause. What explains this selection? Previous work has argued that people select abnormal events as causes, though recent work has shown that sometimes normal events are preferred over abnormal ones. Existing studies have relied on vignettes that commonly feature agents committing immoral acts. An important challenge to the thesis that norms permeate causal reasoning is that people's responses may merely reflect pragmatic or social reasoning rather than arising from causal cognition per se. We tested this hypothesis by asking whether the previously observed patterns of causal selection emerge in tasks that recruit participants' causal reasoning about physical systems. Strikingly, we found that the same patterns observed in vignette studies with intentional agents arise in visual animations of physical interactions. Our results demonstrate how deeply normative expectations affect causal cognition. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

What is and what never should have been: Children’s causal and counterfactual judgments about the same events

Substantial research with adults has characterized the contents of individuals’ counterfactual thoughts. In contrast, little is known about the types of events children invoke in their counterfactual thoughts and how they compare with their causal ascriptions. In the current study, we asked children open-ended counterfactual and causal questions about events in which a character’s action enabled a force of nature to cause a minor mishap. Children aged 3.5–8 years (N = 160) tended to invoke characters’ actions in their counterfactual judgments to explain how an event could have been prevented (e.g., “She should have closed the window”) and tended to invoke forces of nature in their causal judgments (e.g., “The rain got it wet”). Younger children were also significantly more likely than older children to invoke forces of nature in their counterfactuals (e.g., “It shouldn’t have rained”). These results indicate that, similar to reasoning patterns found in adults, children tend to focus on controllable enabling conditions when reasoning counterfactually, but the results also point to some developmental differences. The developmental similarities suggest that counterfactual reasoning may serve a similar function from middle childhood through adulthood.

The distinct contributions of cause–effect order and reasoning type in judgments of causality

ABSTRACTPeople generally perceive a stronger link between smoking and cancer than between cancer and smoking. Generally, prior research on asymmetrical causal reasoning has not distinguished predictive (searching for effects) and diagnostic reasoning (searching for causes) from the order in which causes and effects are presented. Across 6 studies (overall N = 627), we show that order and reasoning have an additive influence on the causality perception: causes, spatially or temporally presented before the effect, strengthen the causality attribution associated to predictive (vs. diagnostic) frames. Moreover, we show that order and reasoning frame are bi-directionally related, as the cause-first order triggers predictive reasoning and vice versa, and people mentally maintain the cause-first order when envisaging a causal relation. Besides its methodological contribution to the causal reasoning literature, this research demonstrates the powerful role of word order in causal reasoning. Implications for the role of word order in communication and risk prevention are discussed.

Immoral Professors and Malfunctioning Tools: Counterfactual Relevance Accounts Explain the Effect of Norm Violations on Causal Selection.

Causal judgments are widely known to be sensitive to violations of both prescriptive norms (e.g., immoral events) and statistical norms (e.g., improbable events). There is ongoing discussion as to whether both effects are best explained in a unified way through changes in the relevance of counterfactual possibilities, or whether these two effects arise from unrelated cognitive mechanisms. Recent work has shown that moral norm violations affect causal judgments of agents, but not inanimate artifacts used by those agents. These results have been interpreted as showing that prescriptive norm violations only affect causal reasoning about intentional agents, but not the use of inanimate artifacts, thereby providing evidence that the effect of prescriptive norm violations arises from mechanisms specific to reasoning about intentional agents, and thus casting doubt on a unified counterfactual analysis of causal reasoning. Four experiments explore this recent finding and provide clear support for a unified counterfactual analysis. Experiment 1 demonstrates that these newly observed patterns in causal judgments are closely mirrored by judgments of counterfactual relevance. Experiment 2 shows that the relationship between causal and counterfactual judgments is moderated by causal structure, as uniquely predicted by counterfactual accounts. Experiment 3 directly manipulates the relevance of counterfactual alternatives and finds that causal judgments of intentional agents and inanimate artifacts are similarly affected. Finally, Experiment 4 shows that prescriptive norm violations (in which artifacts malfunction) affect causal judgments of inanimate artifacts in much the same way that prescriptive norm violations (in which agents act immorally) affect causal judgments of intentional agents.

Supplemental Material for Expectations Affect Physical Causation Judgments

Supplemental Material for Expectations Affect Physical Causation Judgments

The judgment of causality for deformations of stretchy materials

The judgment of causality for deformations of stretchy materials

A hard to read font reduces the causality bias

AbstractPrevious studies have demonstrated that fluency affects judgment and decision-making. The purpose of the present research was to investigate the effect of perceptual fluency in a causal learning task that usually induces an illusion of causality in non-contingent conditions. We predicted that a reduction of fluency could improve accuracy in the detection of non-contingency and, therefore, could be used to debias illusory perceptions of causality. Participants were randomly assigned to either an easy-to-read or a hard-to-read condition. Our results showed a strong bias (i.e., overestimation) of causality in those participants who performed the non-contingent task in the easy-to-read font, which replicated the standard causality bias effect. This effect was reduced when the same task was presented in a hard-to-read font. Overall, our results provide evidence for a reduction of the causality bias when presenting the problem in a hard-to-read font. This suggests that perceptual fluency affects causal judgments.

Quantitative causal selection patterns in token causation.

When many events contributed to an outcome, people consistently judge some more causal than others, based in part on the prior probabilities of those events. For instance, when a tree bursts into flames, people judge the lightning strike more of a cause than the presence of oxygen in the air—in part because oxygen is so common, and lightning strikes are so rare. These effects, which play a major role in several prominent theories of token causation, have largely been studied through qualitative manipulations of the prior probabilities. Yet, there is good reason to think that people’s causal judgments are on a continuum—and relatively little is known about how these judgments vary quantitatively as the prior probabilities change. In this paper, we measure people’s causal judgment across parametric manipulations of the prior probabilities of antecedent events. Our experiments replicate previous qualitative findings, and also reveal several novel patterns that are not well-described by existing theories.

A counterfactual explanation for the action effect in causal judgment

People’s causal judgments are susceptible to the action effect, whereby they judge actions to be more causal than inactions. We offer a new explanation for this effect, the counterfactual explanation: people judge actions to be more causal than inactions because they are more inclined to consider the counterfactual alternatives to actions than to consider counterfactual alternatives to inactions. Experiment 1a conceptually replicates the original action effect for causal judgments. Experiment 1b confirms a novel prediction of the new explanation, the reverse action effect, in which people judge inactions to be more causal than actions in overdetermination cases. Experiment 2 directly compares the two effects in joint-causation and overdetermination scenarios and conceptually replicates them with new scenarios. Taken together, these studies provide support for the new counterfactual explanation for the action effect in causal judgment.

I know what you did last summer (and how often). Epistemic states and statistical normality in causal judgements

I know what you did last summer (and how often). Epistemic states and statistical normality in causal judgements

What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks.

Actual causation is concerned with the question: “What caused what?” Consider a transition between two states within a system of interacting elements, such as an artificial neural network, or a biological brain circuit. Which combination of synapses caused the neuron to fire? Which image features caused the classifier to misinterpret the picture? Even detailed knowledge of the system’s causal network, its elements, their states, connectivity, and dynamics does not automatically provide a straightforward answer to the “what caused what?” question. Counterfactual accounts of actual causation, based on graphical models paired with system interventions, have demonstrated initial success in addressing specific problem cases, in line with intuitive causal judgments. Here, we start from a set of basic requirements for causation (realization, composition, information, integration, and exclusion) and develop a rigorous, quantitative account of actual causation, that is generally applicable to discrete dynamical systems. We present a formal framework to evaluate these causal requirements based on system interventions and partitions, which considers all counterfactuals of a state transition. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking the two consecutive system states. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.

From the structure of experience to concepts of structure: How the concept "cause" is attributed to objects and events.

The pervasive presence of relational information in concepts, and its indirect presence in sensory input, raises the question of how it is extracted from experience. We operationalized experience as a stream of events in which reliable predictive relationships exist among random ones, and in which learners are naïve as to what they will learn (i.e., a statistical learning paradigm). First, we asked whether predictive event pairs would spontaneously be seen as causing each other, given no instructions to evaluate causality. We found that predictive information indeed informed later causal judgments but did not lead to a spontaneous sense of causality. Thus, event contingencies are relevant to causal inference, but such interpretations may not occur fully bottom-up. A second question was how such experience might be used to learn about novel objects. Because events occurred either around or involving a continually present object, we were able to distinguish objects from events. We found that objects can be attributed causal properties by virtue of a higher-order structure, in which the object's identity is linked not to the increased likelihood of its effect, but rather, to the predictive structure among events, given its presence. This is an important demonstration that objects' causal properties can be highly abstract: They need not refer to an occurrence of a sensory event per se, or its link to an object, but rather to whether or not a predictive relationship holds among events in its presence. These learning mechanisms may be important for acquiring abstract knowledge from experience. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Testing the deductive inferential account of blocking in causal learning.

The sensitivity of the blocking effect to outcome additivity pretraining has been used to argue that the phenomenon is the result of deductive inference, and to draw general conclusions about the nature of human causal learning. In two experiments, we manipulated participants' assumptions about the additivity of the outcome using pretraining before a typical blocking procedure. Ratings measuring causal judgments, confidence, and expected severity of the outcome were used concurrently to investigate how pretraining affected assumptions of outcome additivity and blocking. In Experiment 1, additive pretraining led to lower causal ratings and higher confidence ratings of the blocked cue, relative to control cues, consistent with the notion that additive pretraining encourages deductive reasoning. However, Experiments 1 and 2 showed that removing additivity assumptions through nonadditive pretraining had no impact on a statistically reliable blocking effect observed in a blocking procedure with no pretraining. We found no evidence that the blocking effect in the absence of pretraining was related to the participants' assumptions about the additivity of the outcome. Although additive pretraining may enhance blocking by encouraging deductive reasoning about the blocked cue, the evidence suggests that blocking in causal learning is not reliant on this reasoning and that humans do not readily engage in deduction merely because they possess the assumptions that permit its use.

Negative evidence and inductive reasoning in generalization of associative learning.

When generalizing properties from known to novel instances, both positive evidence (instances known to possess a property) and negative evidence (instances known not to possess a property) must be integrated. The current study compared generalization based on positive evidence alone against a mixture of positive evidence and perceptually dissimilar negative evidence in an interdimensional discrimination procedure. In 2 experiments, we compared generalization following training with a single positive stimulus (that predicted shock) against groups where an additional negative stimulus (that did not predict shock) was presented in a causal judgment (Experiment 1) and a fear conditioning (Experiment 2) procedure. In contrast to animal conditioning studies, we found that adding a "distant" negative stimulus resulted in an overall increase in generalization to stimuli varying on the dimension of the positive stimulus, consistent with the inductive reasoning literature. We show that this key qualitative result can be simulated by a Bayesian model that incorporates helpful sampling assumptions. Our results suggest that similar processes underlie generalization in inductive reasoning and associative learning tasks. (PsycINFO Database Record (c) 2019 APA, all rights reserved).