Identifying expectations about the strength of causal relationships

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.

The perceived dilution of causal strength

Dependency theories of causal reasoning, such as causal Bayes net accounts, postulate that the strengths of individual causal links are independent of the causal structure in which they are embedded; they are inferred from dependency information, such as statistical regularities. We propose a psychological account that postulates that reasoners' concept of causality is richer. It predicts a systematic influence of causal structure knowledge on causal strength intuitions. Our view incorporates the notion held by dispositional theories that causes produce effects in virtue of an underlying causal capacity. Going beyond existing normative dispositional theories, however, we argue that reasoners' concept of causality involves the idea that continuous causes spread their capacity across their different causal pathways, analogous to fluids running through pipe systems. Such a representation leads to the prediction of a structure-dependent dilution of causal strength: the more links are served by a cause, the weaker individual links are expected to be. A series of experiments corroborate the theory. For continuous causes with continuous effects, but not in causal structures with genuinely binary variables that can only be present or absent, reasoners tend to think that link strength decreases with the number of links served by a cause. The effect reflects a default notion reasoners have about causality, but it is moderated by assumptions about the amount of causal capacity causes are assumed to possess, and by mechanism knowledge about how a cause generates its effect(s). We discuss the theoretical and empirical implications of our findings.

From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning

Recent work in cognitive science has uncovered a diversity of explanatory values, or dimensions along which we judge explanations as better or worse. We propose a Bayesian account of these values that clarifies their function and shows how they fit together to guide explanation-making. The resulting taxonomy shows that core values from psychology, statistics, and the philosophy of science emerge from a common mathematical framework and provide insight into why people adopt the explanations they do. This framework not only operationalizes the explanatory virtues associated with, for example, scientific argument-making, but also enables us to reinterpret the explanatory vices that drive phenomena such as conspiracy theories, delusions, and extremist ideologies.

Individual differences in strategy use and performance during fault diagnosis

Background

Research on causal reasoning often uses group-level data analyses that downplay individual differences and simple reasoning problems that are unrepresentative of everyday reasoning. In three empirical studies, we used an individual differences approach to investigate the cognitive processes people used in fault diagnosis, which is a complex diagnostic reasoning task. After first showing how high-level fault diagnosis strategies can be composed of simpler causal inferences, we discussed how two of these strategies—elimination and inference to the best explanation (IBE)—allow normative performance, which minimizes the number of diagnostic tests, whereas backtracking strategies are less efficient. We then investigated whether the use of normative strategies was infrequent and associated with greater fluid intelligence and positive thinking dispositions and whether normative strategies used slow, analytic processing while non-normative strategies used fast, heuristic processing.

Results

Across three studies and 279 participants, uses of elimination and IBE were infrequent, and most participants used inefficient backtracking strategies. Fluid intelligence positively predicted elimination and IBE use but not backtracking use. Positive thinking dispositions predicted avoidance of backtracking. After classifying participants into groups that consistently used elimination, IBE, and backtracking, we found that participants who used elimination and IBE made fewer, but slower, diagnostic tests compared to backtracking users.

Conclusions

Participants’ fault diagnosis performance showed wide individual differences. Use of normative strategies was predicted by greater fluid intelligence and more open-minded and engaged thinking dispositions. Elimination and IBE users made the slow, efficient responses typical of analytic processing. Backtracking users made the fast, inefficient responses suggestive of heuristic processing.

Successful structure learning from observational data

Previous work suggests that humans find it difficult to learn the structure of causal systems given observational data alone. We identify two conditions that enable successful structure learning from observational data: people succeed if the underlying causal system is deterministic, and if each pattern of observations has a single root cause. In four experiments, we show that either condition alone is sufficient to enable high levels of performance, but that performance is poor if neither condition applies. A fifth experiment suggests that neither determinism nor root sparsity takes priority over the other. Our data are broadly consistent with a Bayesian model that embodies a preference for structures that make the observed data not only possible but probable.

Beyond Markov: Accounting for independence violations in causal reasoning

Although many theories of causal cognition are based on causal graphical models, a key property of such models-the independence relations stipulated by the Markov condition-is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people's understanding of the correlational structure of data generated from a causal graph differs from that stipulated by causal graphical model framework. To distinguish these models, experiments assessed how people reason with causal graphs that are larger than those tested in previous studies. A traditional common cause network (Y₁←X→Y₂) was extended so that the effects themselves had effects (Z₁←Y₁←X→Y₂→Z₂). A traditional common effect network (Y₁→X←Y₂) was extended so that the causes themselves had causes (Z₁→Y₁→X←Y₂←Z₂). Subjects' inferences were most consistent with the beta-Q model in which consistent states of the world-those in which variables are either mostly all present or mostly all absent-are viewed as more probable than stipulated by the causal graphical model framework. Substantial variability in subjects' inferences was also observed, with the result that substantial minorities of subjects were best fit by one of the other models (the dual prototype or a leaky gate models). The discrepancy between normative and human causal cognition stipulated by these models is foundational in the sense that they locate the error not in people's causal reasoning but rather in their causal representations. As a result, they are applicable to any cognitive theory grounded in causal graphical models, including theories of analogy, learning, explanation, categorization, decision-making, and counterfactual reasoning. Preliminary evidence that independence violations indeed generalize to other judgment types is presented.

Do people reason rationally about causally related events? Markov violations, weak inferences, and failures of explaining away

Making judgments by relying on beliefs about the causal relationships between events is a fundamental capacity of everyday cognition. In the last decade, Causal Bayesian Networks have been proposed as a framework for modeling causal reasoning. Two experiments were conducted to provide comprehensive data sets with which to evaluate a variety of different types of judgments in comparison to the standard Bayesian networks calculations. Participants were introduced to a fictional system of three events and observed a set of learning trials that instantiated the multivariate distribution relating the three variables. We tested inferences on chains X1→Y→X2, common cause structures X1←Y→X2, and common effect structures X1→Y←X2, on binary and numerical variables, and with high and intermediate causal strengths. We tested transitive inferences, inferences when one variable is irrelevant because it is blocked by an intervening variable (Markov Assumption), inferences from two variables to a middle variable, and inferences about the presence of one cause when the alternative cause was known to have occurred (the normative "explaining away" pattern). Compared to the normative account, in general, when the judgments should change, they change in the normative direction. However, we also discuss a few persistent violations of the standard normative model. In addition, we evaluate the relative success of 12 theoretical explanations for these deviations.