Causal Judgments Research Articles

Models of Variability in Probabilistic Causal Judgments

AbstractMost theories of causal reasoning aim to explain the central tendency of causal judgments. However, experimental studies show that causal judgments are quite variable. In this article, we report the results of an experiment using a novel repeated measures design that demonstrate the existence of meaningful (i.e., not noise-related) within-participant variability. Next, we introduce and assess multiple computational cognitive models that serve as potential accounts of the sources of variability and fit those models to the new empirical data. We find that the Bayesian Mutation Sampler has the best fit to the data and is able to account for a number of unusual features of the response distributions (e.g., bi-modality), supporting the view that the stochastic sampling mechanism it posits reflects the cognitive processes via which people draw causal inferences. Additionally, our findings suggest that incorporating ‘non-reasoning’ processes, such as rounding and guessing, can improve the ability of models of causal reasoning to account for the observed response distributions. Overall, the study highlights the potential of computational modeling of full response distributions to shed light on the underlying mechanisms of human causal reasoning and identifies promising directions for future research.

No evidence that reversibility affects causal judgments in late-preemption cases

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

Modeling confidence in causal judgments.

Counterfactual theories propose that people's capacity for causal judgment depends on their ability to consider alternative possibilities: The lightning strike caused the forest fire because had it not struck, the forest fire would not have ensued. To accommodate a variety of psychological effects on causal judgment, a range of recent accounts have proposed that people probabilistically sample counterfactual alternatives from which they compute a graded measure of causal strength. While such models successfully describe the influence of the statistical normality (i.e., the base rate) of the candidate and alternate causes on causal judgments, we show that they make further untested predictions about how normality influences people's confidence in their causal judgments. In a large (N = 3,020) sample of participants in a causal judgment task, we found that normality indeed influences people's confidence in their causal judgments and that these influences were predicted by a counterfactual sampling model in which people are more confident in a causal relationship when the effect of the cause is less variable among imagined counterfactual possibilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Kant and the other of necessity - contingency

Kant’s critical philosophy is, as is well known, based on the question of the possibility of synthetic judgments a priori. This goes hand in hand with the validity claims of judgments in cognition, morality, law, aesthetics and other philosophical areas that can be shown to be necessary. The degree of necessity differs depending on the context. Central to Kant’s epistemology is the objectively necessary validity of causal judgments, which he grounded anew and in distinction to the subjectively necessary judgments that he attributed to David Hume’s causal theory. Necessity, a priori validity of pure or, for the most part, non-pure synthetic judgements a priori in the context of experience are inconceivable without their other, empiricism and contingency. Contingency plays a less marginal role in Kant’s epistemology in the Critique of Pure Reason, than it might seem at first glance. Contingency is of obvious importance in Kant’s teleology, which he elaborates in the second part of the Critique of the Power of Judgement. Nature is characterized by infinite variety and an infinite number of empirical laws, and therefore contingency is predominant for human cognition. With the (apparent) antinomy of teleological judgement, the reciprocal relationship between (necessary) causal mechanism and finality as teleological expediency Kant shows that the two principles are indispensable for knowledge and observation of nature, even if they each have their own tasks.

Human causality detection and judgment with unsignaled and signaled delayed outcomes.

Four experiments examined human ratings of causal effectiveness, and ability to detect causal relationships, in a nonverbal paradigm. Participants responded on a concurrent random interval, extinction schedule. In the presence of one stimulus, responses produced an outcome (triangle flash); in the presence of the other stimulus, they did not. Following making a judgment of causal effectiveness, two further stimuli were presented simultaneously with one another, and participants had to select one depending on which of the previous two stimuli were associated with effective responses. In all experiments, immediate outcomes were associated with higher causal ratings and better causal detection than outcomes delayed by 3 s. A signal inserted between response and outcome improved ratings and detection (Experiments 2 and 4), even when it was contiguous with the response but not the outcome (Experiments 2 and 3). Stimuli associated with both components (marking cues) did not impact judgments or detection (Experiment 3). Stimuli signaling the availability of an outcome if a response was made (signaled reinforcement) did not improve causal judgments, but did improve detection of stimuli associated with the outcome (Experiment 4). Responses during the delay interfered with detection of the actual relationship when delays were unsignaled (Experiments 1-4), but not with fully or briefly signaled delays (Experiments 2-4), or with signaled reinforcement (Experiment 4). The results suggest a delay stimulus serves to signal the response has been successful and demark the delay period by serving a discriminative function. These findings mirror those seen in nonhuman conditioning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploring the influence of self-identification on perceptual judgments of physical and social causality.

People tend to overestimate the causal contribution of the self to the observed outcome in various situations, a cognitive bias known as the 'illusion of control.' This study delves into whether this cognitive bias impacts causality judgments in animations depicting physical and social causal interactions. In two experiments, participants were instructed to associate themselves and a hypothetical stranger identity with two geometrical shapes (a circle and a square). Subsequently, they viewed animations portraying these shapes assuming the roles of agent and patient in causal interactions. Within one block, the shape related to the self served as the agent, while the shape associated with the stranger played the role of the patient. Conversely, in the other block, the identity-role association was reversed. We posited that the perception of the self as a causal agent might influence explicit judgments of physical and social causality. Experiment 1 demonstrated that physical causality ratings were solely shaped by kinematic cues. In Experiment 2, emphasising social causality, the dominance of kinematic parameters was confirmed. Therefore, contrary to the hypothesis anticipating diminished causality ratings with specific identity-role associations, results indicated negligible impact of our manipulation. The study contributes to understanding the interplay between kinematic and non-kinematic cues in human causal reasoning. It suggests that explicit judgments of causality in simple animations primarily rely on low-level kinematic cues, with the cognitive bias of overestimating the self's contribution playing a negligible role.

Instruction on the Scientific Method Provides (Some) Protection Against Illusions of Causality.

People tend to overestimate the efficacy of an ineffective treatment when they experience the treatment and its supposed outcome co-occurring frequently. This is referred to as the outcome density effect. Here, we attempted to improve the accuracy of participants' assessments of an ineffective treatment by instructing them about the scientific practice of comparing treatment effects against a relevant base-rate, i.e., when no treatment is delivered. The effect of these instructions was assessed in both a trial-by-trial contingency learning task, where cue administration was either decided by the participant (Experiments 1 & 2) or pre-determined by the experimenter (Experiment 3), as well as in summary format where all information was presented on a single screen (Experiment 4). Overall, we found two means by which base-rate instructions influence efficacy ratings for the ineffective treatment: 1) When information was presented sequentially, the benefit of base-rate instructions on illusory belief was mediated by reduced sampling of cue-present trials, and 2) When information was presented in summary format, we found a direct effect of base-rate instruction on reducing causal illusion. Together, these findings suggest that simple instructions on the scientific method were able to decrease participants' (over-)weighting of cue-outcome coincidences when making causal judgements, as well as decrease their tendency to over-sample cue-present events. However, the effect of base-rate instructions on correcting illusory beliefs was incomplete, and participants still showed illusory causal judgements when the probability of the outcome occurring was high. Thus, simple textual information about assessing causal relationships is partially effective in influencing people's judgements of treatment efficacy, suggesting an important role of scientific instruction in debiasing cognitive errors.

Reasoning about actual causation in reversible and irreversible causal structures.

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 noncauses. 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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Probabilistic causal reasoning under time pressure.

While causal reasoning is a core facet of our cognitive abilities, its time-course has not received proper attention. As the duration of reasoning might prove crucial in understanding the underlying cognitive processes, we asked participants in two experiments to make probabilistic causal inferences while manipulating time pressure. We found that participants are less accurate under time pressure, a speed-accuracy-tradeoff, and that they respond more conservatively. Surprisingly, two other persistent reasoning errors-Markov violations and failures to explain away-appeared insensitive to time pressure. These observations seem related to confidence: Conservative inferences were associated with low confidence, whereas Markov violations and failures to explain were not. These findings challenge existing theories that predict an association between time pressure and all causal reasoning errors including conservatism. Our findings suggest that these errors should not be attributed to a single cognitive mechanism and emphasize that causal judgements are the result of multiple processes.

Evidence from the future.

The outcome of any scientific experiment or intervention will naturally unfold over time. How then should individuals make causal inferences from measurements over time? Across three experiments, we had participants observe experimental and control groups over several days posttreatment in a fictional biological research setting. We identify competing perspectives in the literature: contingency-driven accounts predict no effect of the outcome timing while the contiguity principle suggests people will view a treatment as more harmful to the extent that bad treatment outcomes occur earlier rather than later. In contrast, inference of the functional form of a treatment effect can license extrapolation beyond the measurements and lead to different causal inferences. We find participants' causal strength and direction judgments in temporal settings vary with minimal manipulations of instruction framing. When it is implied that the observations are made over a preplanned number of days, causal judgments depend strongly on contiguity. When it is implied that the observation may be ongoing, participants extrapolate current trends into the future and adapt their causal judgments accordingly. When data are revealed sequentially, participants rely on extrapolation regardless of instruction framing. Our results demonstrate human flexibility in interpreting temporal evidence for causal reasoning and emphasize human tendency to generalize from evidence in ways that are acutely sensitive to task framing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Looking at Mental Images: Eye-Tracking Mental Simulation During Retrospective Causal Judgment.

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

Calculated Comparisons: Manufacturing Societal Causal Judgments by Implying Different Counterfactual Outcomes.

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.

Emotion regulation efficacy beliefs: The outsized impact of base rates.

To regulate others' emotions effectively we must learn about the efficacy of our regulation attempts. Deciding whether we made someone else feel better involves a causal judgment about the effect of our intervention on their emotional state. The current study examined whether, like other causal judgments, beliefs about emotion regulation efficacy are disproportionately affected by base rates. In two experiments, we showed that participants' perceived efficacy at helping a target regulate their emotions was more influenced by the target's average emotion levels than the relative effect of regulating versus not regulating the target's emotion. This led participants to conclude that they were helpful both when they were not (Experiment 1) and even when they made the target feel worse (Experiment 2). These findings suggest that our beliefs about the effectiveness of other-directed emotion regulation are notably biased by the average level of emotion expressed by the regulation target. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Relevance of Double Effect Doctrine in The Perspective of Criminal Law Causality Doctrine: A Rebuttal to The Concept Of Conditio Sine Qua Non

This research explores and analyzes how the doctrine of causality, particularly the concept of <em>Conditio Sine Qua Non</em>, and the doctrine of double effect interact and influence the determination of responsibility in criminal law. Using the normative-dogmatic research method, this study systematically analyzes applicable legal rules without empirical data, emphasizing the qualitative application of legal and philosophical principles. The research uses a literature review as the primary data collection tool, examining legal philosophy books, academic journals, and professional publications to draw relevant conclusions. The results indicate that <em>Conditio Sine Qua Non</em>, which focuses on objective causation, needs to be revised for cases involving complex ethical considerations. In contrast, the double effect doctrine introduces an ethical dimension to causality judgments, justifying actions that produce unintended but unavoidable adverse consequences as a side effect of a good goal. The findings suggest that an integrative approach combining both doctrines may lead to a more balanced and comprehensive understanding of criminal liability. This research aims to enrich the academic discourse on causality in criminal law by introducing ethical considerations as part of causal analysis. It offers a new perspective in dealing with criminal cases involving complicated morality and consequences, challenging the existing limitations of causal interpretation. Nevertheless, this study has limitations in terms of its scope of practical application and the need for empirical verification, signaling the need for further research that can evaluate and validate these findings in actual legal practice.

Causal Priors and Their Influence on Judgements of Causality in Visualized Data.

"Correlation does not imply causation' is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations. We collected a corpus of concept pairs from variables in widely used datasets and created visualizations that depict varying correlative associations using three typical statistical chart types. We conducted two MTurk studies on (1) preconceived notions on causal relations without charts, and (2) perceived causal relations with charts, for each concept pair. Our results indicate that people make assumptions about causal relationships between pairs of concepts even without seeing any visualized data. Moreover, our results suggest that these assumptions constitute causal priors that, in combination with visualized association, impact how data visualizations are interpreted. The results also suggest that causal priors may lead to over- or under-estimation in perceived causal relations in different circumstances, and that those priors can also impact users' confidence in their causal assessments. In addition, our results align with prior work, indicating that chart type may also affect causal inference. Using data from the studies, we develop a model to capture the interaction between causal priors and visualized associations as they combine to impact a user's perceived causal relations. In addition to reporting the study results and analyses, we provide an open dataset of causal priors for 56 specific concept pairs that can serve as a potential benchmark for future studies. We also suggest remaining challenges and heuristic-based guidelines to help designers improve visualization design choices to better support visual causal inference.

Don't throw the associative baby out with the Bayesian bathwater: Children are more associative when reasoning retrospectively under information processing demands.

Causal reasoning is a fundamental cognitive ability that enables individualsto learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or associative learning. The two experiments and computational models reported here were designed to examine whether 5- and 6-year-olds will retrospectively reevaluate objects-that is, adjust their beliefs about the causal status of some objects presented at an earlier point in time based on the observed causal status of other objects presented at a later point in time-when asked to reason about 3 and 4 objects and under varying degrees of information processing demands. Additionally, the experiments and models were designed to determine whether children's retrospective reevaluations were best explained by associative learning, Bayesian inference, or some combination of both. The results indicated that participants retrospectively reevaluated causal inferences under minimal information-processing demands (Experiment 1) but failed to do so under greater information processing demands (Experiment 2) and that their performance was better captured by an associative learning mechanism, with less support for descriptions that rely on Bayesian inference. RESEARCH HIGHLIGHTS: Five- and 6-year-old children engage in retrospective reevaluation under minimal information-processing demands (Experiment 1). Five- and 6-year-old children do not engage in retrospective reevaluation under more extensive information-processing demands (Experiment 2). Across both experiments, children's retrospective reevaluations were better explained by a simple associative learning model, with only minimal support for a simple Bayesian model. These data contribute to our understanding of the cognitive mechanisms by which children make causal judgements.

Kantian Circularity: Maimon on Causal Scepticism and the Status of the Hypothetical Judgement

AbstractA key theme throughout Maimon’s works is a circularity he diagnoses at the heart of Kant’s response to Hume. The objective validity of Kant’s category of causality ultimately rests, Maimon argues, upon the logical status of the hypothetical judgement – on its inclusion among the forms of pure general logic. In turn, however, the inclusion of the hypothetical within pure general logic itself rests upon the objective validity of causal judgements. This article examines Maimon’s diagnosis and traces it back to a debate that has its origins in Wolff’s German Logic, concerning the relationship between categorical and hypothetical judgements.

Causal Opacity or Causal Translucence?

According to longstanding interpretations in the social and cognitive sciences, rituals are said to be characterized by arbitrary action and the lack of a causal connection between action and desired outcome. The observer who assigns a physical-causal connection has taken the instrumental stance, while one who accepts a group convention is said to take the ritual stance. I argue that in religious rituals at least, including those with magical elements, the gap is bridged and causal intuitions are present, if limited. For example, we rely on a mental heuristic called Representativeness in order to make many causal judgments, and Representativeness tells us that effects usually resemble their causes. This heuristic, studied by Daniel Kahnemann and Amos Tversky, corresponds to J. G. Frazer’s so-called “Law of Similarity” in magic. Representativeness and other forms of magical thinking appear to yield weaker causal inferences than our intuitions about physical processes or the agency of other people. Accordingly, religious rituals are often employed in situations where a goal cannot be achieved in more obvious ways, but some lesser intuition of causal efficacy can still be generated. Illustrative examples are drawn from ancient Greek rituals of offering, oath-taking, and purification.

Associative learning or Bayesian inference? Revisiting backwards blocking reasoning in adults

Causal reasoning is a fundamental cognitive ability that enables humans to learn about the complex interactions in the world around them. However, the cognitive mechanisms that underpin causal reasoning are not well understood. For instance, there is debate over whether Bayesian inference or associative learning best captures causal reasoning in human adults. The two experiments and computational models reported here were designed to examine whether adults engage in one form of causal inference called backwards blocking reasoning, whether the presence of potential distractors affects performance, and how adults' ratings align with the predictions of different computational models. The results revealed that adults engaged in backwards blocking reasoning regardless of whether distractor objects are present and that their causal judgements supported the predictions of a Bayesian model but not the predictions of two different associative learning models. Implications of these results are discussed.

Schizotypy dimensions do not predict overshadowing

When two cues are presented together and reliably predict an outcome (AB-O1) an “overshadowing” effect is typically observed. That is, the relationship between these cues and the outcome is learned about less well than a cue presented on its own with an outcome (e.g., C – O1). The current study sought to explore the relationship between overshadowing and the positive and negative dimensions of schizotypy. A total of 256 participants completed an overshadowing procedure embedded within a causal judgement task and the Short Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE) which measured the different dimensions of schizotypy. A unilateral overshadowing effect was observed, however, none of the dimensions of schizotypy predicted the magnitude of this effect. These results are the first to demonstrate this finding using an appropriately powered sample and reveal that a tendency to experience symptoms of schizophrenia does not impact upon the overshadowing effect.