Promoting climate actions: A cognitive-constraints approach.

The present paper reports an experiment with a two-year-delayed (M = 695 days) follow-up that tests an approach to raising willingness to take political and personal climate actions. Many Americans still do not view climate change as a threat requiring urgent action. Moreover, among American conservatives, higher science literacy is paradoxically associated with higher anthropogenic climate-change skepticism. Our experimental materials were designed to harness the power of two central cognitive constraints - coherence and causal invariance, which map onto two narrative proclivities that anthropologists have identified as universal - to promote climate action across the political spectrum. Towards that goal, the essential role of these constraints in the causal-belief-formation process predicts that climate-change information would be more persuasive when it is embedded in a personal climate-action narrative, the evocation of which can benefit from exposure to parsimonious scientific explanations of indisputable everyday observations, juxtaposed with reasoners' own, typically less coherent explanations, occurring in a context that engages their moral stance. Our brief one-time intervention, conducted in ten U.S. states with the highest level of climate skepticism, showed that across the political spectrum, our materials raised appreciation of science, openness to alternative views, and willingness to take climate actions in the immediate assessment. It also raised how likely were reports two years later of having taken those actions or would have taken them had the opportunity existed, suggesting a long-lasting effect. Our approach adopts the framework that conceptions of reality are representations, and adaptive solutions in that infinite space of representations require cognitive constraints to narrow the search.

Causal invariance as a tacit aspiration: Analytic knowledge of invariance functions.

For causal knowledge to be worth learning, it must remain valid when that knowledge is applied. Because unknown background causes are potentially present, and may vary across the learning and application contexts, extricating the strength of a candidate cause requires an assumption regarding the decomposition of the observed outcome into the unobservable influences from the candidate and from background causes. Acquiring stable, useable causal knowledge is challenging when the search space of candidate causes is large, such that the reasoner's current set of candidates may fail to include a cause that generalizes well to an application context. We have hypothesized that an indispensable navigation device that shapes our causal representations toward useable knowledge involves the concept of causal invariance - the sameness of how a cause operates to produce an effect across contexts. Here, we tested our causal invariance hypothesis by making use of the distinct mathematical functions expressing causal invariance for two outcome-variable types: continuous and binary. Our hypothesis predicts that, given identical prior domain knowledge, intuitive causal judgments should vary in accord with the causal-invariance function for a reasoner's perceived outcome-variable type. The judgments are made as if the reasoner aspires to formulate causally invariant knowledge. Our experiments involved two cue-competition paradigms: blocking and overexpectation. Results show that adult humans tacitly use the appropriate causal-invariance functions for decomposition. Our analysis offers an explanation for the apparent elusiveness of the blocking effect and the adaptiveness of intuitive causal inference to the representation-dependent reality in the mind.

How do humans want causes to combine their effects? The role of analytically-defined causal invariance for generalizable causal knowledge.

The present paper reports two experiments (N = 232, 254) addressing the question: How do reasoners reconcile the desire to have useable (i.e., invariant) causal knowledge - knowledge that holds true when applied in new circumstances/contexts - with the reality that causes often interact with other causes present in the context? The experiments test two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners' ability to learn rules (functions) describing how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function-learning view predicts that participants would generalize an acquired combination rule based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view goes beyond empirical learning: it allows for the possibility that one's current representation may not yield useable causal knowledge. This view posits that the human causal-induction process incorporates invariant knowledge as an aspiration, entailing that observed deviation from causal invariance when the knowledge is applied serves as a signal for a need to revise causal knowledge: Only invariance across contexts with potentially new causal factors justifies generalization across them. The invariance view therefore predicts that reasoners would revise their representation so that they have whole causes - potentially consisting of interacting components - that do not interact with each other, even when in their relevant experience all (pre-defined) causes interact. Across both experiments, our results favor the causal-invariance view: Participants generalize their empirically learned function (which may involve interactions) to new stimuli, but switch to the analytic causal-invariance function for both old and new stimuli at the level of the whole cause, indicating that how humans want causes to combine their effects shapes the knowledge they induce.

Causal learning and inference as a rational process: the new synthesis.

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

Analytic Causal Knowledge for Constructing Useable Empirical Causal Knowledge: Two Experiments on Pre-schoolers.

The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of causal-invariance decomposition functions is essential for this process. Such knowledge specifies the decomposition of an observed outcome into contributions from constituent causes under the default assumption that the empirical knowledge acquired is invariant across contextual/background causes. The paper reports two psychological experiments (and replication studies) with pre-school-age children on generalization across contexts involving binary cause and effect variables. The critical role of causal invariance for constructing useable causal knowledge predicts that even young children should (tacitly) use the causal-invariance decomposition function for such variables rather than a non-causal-invariance decomposition function common in statistical practice in research involving binary outcomes. The findings support the rational shaping of empirical causal knowledge by the causal-invariance constraint, ruling out alternative explanations in terms of non-causal-invariance decomposition functions, heuristics, and biases. For the same causal structure involving candidate causes and outcomes that are binary variables with a "present" value and an "absent" value, the paper argues against the possibility of multiple rational characterizations of the "sameness of causal influence" that justifies generalization across contexts.

Bayesian generic priors for causal learning.

The article presents a Bayesian model of causal learning that incorporates generic priors--systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes--causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.

Assessing interactive causal influence.

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a). the conditions under which covariation implies conjunctive causation and (b). functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables.

From covariation to causation: a test of the assumption of causal power.

How humans infer causation from covariation has been the subject of a vigorous debate, most recently between the computational causal power account (P. W. Cheng, 1997) and associative learning theorists (e.g., K. Lober & D. R. Shanks, 2000). Whereas most researchers in the subject area agree that causal power as computed by the power PC theory offers a normative account of the inductive process. Lober and Shanks, among others, have questioned the empirical validity of the theory. This article offers a full report and additional analyses of the original study featured in Lober and Shanks's critique (M. J. Buehner & P. W. Cheng, 1997) and reports tests of Lober and Shanks's and other explanations of the pattern of causal judgments. Deviations from normativity, including the outcome-density bias, were found to be misperceptions of the input or other artifacts of the experimental procedures rather than inherent to the process of causal induction.

Inferential dependencies in causal inference: a comparison of belief-distribution and associative approaches.

Causal evidence is often ambiguous, and ambiguous evidence often gives rise to inferential dependencies, where learning whether one cue causes an effect leads the reasoner to make inferences about whether other cues cause the effect. There are 2 main approaches to explaining inferential dependencies. One approach, adopted by Bayesian and propositional models, distributes belief across multiple explanations, thereby representing ambiguity explicitly. The other approach, adopted by many associative models, posits within-compound associations--associations that form between potential causes--that, together with associations between causes and effects, support inferences about related cues. Although these fundamentally different approaches explain many of the same results in the causal literature, they can be distinguished, theoretically and experimentally. We present an analysis of the differences between these approaches and, through a series of experiments, demonstrate that models that distribute belief across multiple explanations provide a better characterization of human causal reasoning than models that adopt the associative approach.

The influence of virtual sample size on confidence and causal-strength judgments.

The authors investigated whether confidence in causal judgments varies with virtual sample size--the frequency of cases in which the outcome is (a) absent before the introduction of a generative cause or (b) present before the introduction of a preventive cause. Participants were asked to evaluate the influence of various candidate causes on an outcome as well as to rate their confidence in those judgments. They were presented with information on the relative frequencies of the outcome given the presence and absence of various candidate causes. These relative frequencies, sample size, and the direction of the causal influence (generative vs. preventive) were manipulated. It was found that both virtual and actual sample size affected confidence. Further, confidence affected estimates of strength, but confidence and strength are dissociable. The results enable a consistent explanation of the puzzling previous finding that observed causal-strength ratings often deviated from the predictions of both of the 2 dominant models of causal strength.

When is a cause the "same"? Coherent generalization across contexts.

Two competing psychological approaches to causal learning make different predictions regarding what aspect of perceived causality is generalized across contexts. Two experiments tested these predictions. In one experiment, the task required a judgment regarding the existence of a simple causal relation; in the other, the task required a judgment regarding the existence of an interaction between a candidate cause and unobserved background causes. The task materials did not mention assessments of causal strength. Results indicate that causal power (Cartwright, 1989; Cheng, 1997) is the mental construct that people carry from one context to another.