Great oaks from giant acorns grow: How causal‐impact judgments depend on the strength of a cause

Causal impact is maximal when weak causes have strong effects. Do people understand this logic when they assess causal impact? In four experiments, participants judged the causal impact of strong or weak dietary treatments leading to strong or weak health effects in fictitious health studies. Rather than following the ratio of effect strength to treatment strength, judgments were influenced by three aspects of the detectability of a cause–effect relation. First, because detectability depends on the effect being strong more than on the cause being subtle, causal judgments were mainly determined by effect strength, whereas the strength of the causal treatment necessary to induce an effect was often neglected. Second, if causal input was not ignored, judgments increased when the maximal covariation between a strong causal treatment and a strong effect rendered the causal link most detectable. Or, third, causal judgments increased when a plausible causal schema facilitated detection. Consistent with sampling models of judgment and decision making, causal‐impact ratings were driven by an uncritical assessment of a detectable difference in a study sample. However, ratings were insensitive to the logical implications of the underlying causal treatment that was necessary to induce a detectable effect. Copyright © 2010 John Wiley & Sons, Ltd.     

Associative and causal reasoning accounts of causal induction: Symmetries and asymmetries in predictive and diagnostic inferences

Associative and causal reasoning accounts are probably the two most influential types of accounts of causal reasoning processes. Only causal reasoning accounts predict certain asymmetries between predictive (i.e., reasoning from causes to effects) and diagnostic (i.e., reasoning from effects to causes) inferences regarding cue-interaction phenomena (e.g., the overshadowing effect). In the experiments reported here, we attempted to delimit the conditions under which these asymmetries occur. The results show that unless participants perceived the relevance of causal information to solving the task, predictive and diagnostic inferences were symmetrical. Specifically, Experiments 1A and 1B showed that implicitly stressing the relevance of causal information by having participants review the instructions favored the presence of asymmetries between predictive and diagnostic situations. In addition, Experiment 2 showed that explicitly stressing the relevance of causal information by stating the importance of the causal role of events after the instructions were given also favored the asymmetry.     

用不同外部表征方式集中呈现信息条件下的因果力判断

在分别用文字陈述、表格和图形三种外部表征方式集中呈现因果信息的条件下,用直接估计因果力大小的实验范式考察单一因果关系因果力估计的特点,检验概率对比模型,效力PC理论和pCI规则。让287名大学生被试估计不同化学药物影响动物基因变异的能力。结果发现,对单一因果关系因果力估计具有以下4个特点：⑴不对称性：在预防原因条件下的因果力估计较多符合效力PC理论,而在产生原因条件下的因果力估计一般符合概率对比模型;⑵文字陈述、表格和图形三种信息外部表征方式,不影响产生原因条件下的因果力估计,但影响预防原因条件下的因果力估计。在预防原因条件下,与文字陈述和表格表征相比,图形表征会促使更多被试按效力PC理论来做因果力估计;⑶没有被试使用pCI规则;⑷被试估计因果力所使用的规则存在明显的个体差异。     

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5).     

因果力比较范式下对效力PC理论的检验

在用图形方式集中呈现信息的条件下,用因果力大小比较的实验范式检验效力PC理论。233名大学生被试对不同化学药物影响动物基因变异的能力做大小比较判断。结果发现,对单一因果关系因果力大小的比较判断具有以下3个特点：（1）不对称性：在预防原因条件下的因果力判断一般符合效力PC理论,而在产生原因条件下的因果力判断一般不符合效力PC理论;（2）在同时变化协变值DP和结果基率P(E|~C)的产生原因条件下,多数被试使用DP规则。这与概率对比模型的预测相一致,而不支持效力PC理论;（3）在固定协变值DP而只变化结果基率P(E|~C)的产生原因条件下,多数被试使用变异比RP规则。这是目前所有的因果推理理论都不能解释的现象。     

The influence of the number of relevant causes on the processing of covariation information in causal reasoning

Research on causal reasoning has focused on the influence of covariation between candidate causes and effects on causal judgments. We suggest that the type of covariation information to which people attend is affected by the task being performed. For this, we manipulated the test questions for the evaluation of contingency information and observed its influence on both contingency learning and subsequent causal selections. When people select one cause related to an effect, they focus on conditional contingencies assuming the absence of alternative causes. When people select two causes related to an effect, they focus on conditional contingencies assuming the presence of alternative causes. We demonstrated this use of contingency information in four experiments.     

Causal competition based on generic priors

Although we live in a complex and multi-causal world, learners often lack sufficient data and/or cognitive resources to acquire a fully veridical causal model. The general goal of making precise predictions with energy-efficient representations suggests a generic prior favoring causal models that include a relatively small number of strong causes. Such “sparse and strong” priors make it possible to quickly identify the most potent individual causes, relegating weaker causes to secondary status or eliminating them from consideration altogether. Sparse-and-strong priors predict that competition will be observed between candidate causes of the same polarity (i.e., generative or else preventive) even if they occur independently. For instance, the strength of a moderately strong cause should be underestimated when an uncorrelated strong cause also occurs in the general learning environment, relative to when a weaker cause also occurs. We report three experiments investigating whether independently-occurring causes (either generative or preventive) compete when people make judgments of causal strength. Cue competition was indeed observed for both generative and preventive causes. The data were used to assess alternative computational models of human learning in complex multi-causal situations.     

Time and Singular Causation—A Computational Model

Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co-occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model.     

Forward and backward blocking of causal judgment is enhanced by additivity of effect magnitude

When two causes for a given effect are simultaneously presented, it is natural to expect an effect of greater magnitude. However many laboratory tasks preclude such an additivity rule by imposing a ceiling on effect magnitude-for example, by using a binary outcome. Under these conditions, a compound of two causal cues cannot be distinguished from a compound of one causal cue and one noncausal cue. Two experiments tested the effect of additivity on cue competition. Significant but weak forward blocking and no backward blocking were observed in a conventional "allergy" causal judgment task. Explicit pretraining of magnitude additivity produced strong and significant forward and backward blocking. Additivity pretraining was found to be unnecessary for another cue competition effect, release from overshadowing, which does not logically depend on additivity. The results confirm that blocking is constrained when effect magnitude is constrained and provide support for an inferential account of cue competition.     

样例学习条件下的因果力估计

在逐个呈现因果样例的条件下,考察单一因果关系因果力估计的特点,同时检验联想解释,概率对比模型,权重DP模型,效力PC理论和pCI规则。实验让65名大学生被试估计不同化学药物影响动物基因变异的能力。实验结果表明：（1）对产生原因的因果力估计符合权重DP模型;（2）对预防原因的因果力估计较多符合效力PC理论;（3）因果力估计具有复杂多样性,难以用统一的模式加以描述和概括。     

The tight coupling between category and causal learning

The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two interconnected causal relations forming a causal chain (Experiment 1) or a common-cause model (Experiments 2a, b). One of the three events (i.e., the intermediate event of the chain, or the common cause) was presented as a set of uncategorized exemplars. Although participants were not provided with any feedback about category labels, they tended to induce categories in the first phase that maximized the predictability of their causes or effects. In the second causal learning phase, participants had the choice between transferring the newly learned categories from the first phase at the cost of suboptimal predictions, or they could induce a new set of optimally predictive categories for the second causal relation, but at the cost of proliferating different category schemes for the same set of events. It turned out that in all three experiments learners tended to transfer the categories entailed by the first causal relation to the second causal relation.     

Temporal perception in joint action: This is MY action

Here we investigated the temporal perception of self- and other-generated actions during sequential joint actions. Participants judged the perceived time of two events, the first triggered by the participant and the second by another agent, during a cooperative or competitive interaction, or by an unspecified mechanical cause. Results showed that participants perceived self-generated events as shifted earlier in time (anticipation temporal judgment bias) and non-self-generated events as shifted later in time (repulsion temporal judgment bias). This latter effect was observed independently from the kind of cause (i.e., agentive or mechanical) or interaction (i.e., cooperative or competitive). We suggest that this might represent a mental process which allows discriminating events that cannot plausibly be linked to one’s own action. When an event immediately follows a self-generated one, temporal judgment biases operate as self-serving biases in order to separate self-generated events from events of another physical causality.     

Expectations and interpretations during causal learning

In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A-1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings.     

Making probability judgments of future product failures: The role of mental unpacking

When consumers mentally unpack (i.e., imagine) the reasons for product failure, their probability judgments of future product failures are higher than when no mental unpacking is undertaken. However, increasing the level of mental unpacking does not lead to monotonically increasing effects on probability judgments but results in inverted U-shaped relationships. Using a two-factor structure, we propose that when consumers undertake mental unpacking, there will be two conflicting processes; while imagining causes for an event will lead to greater perceived probability, the greater difficulty in generating reasons for an event will lead to lower perceived probability.     

The impact of domain-specific beliefs on decisions and causal judgments

Extensive evidence suggests that people often rely on their causal beliefs in their decisions and causal judgments. To date, however, there is a dearth of research comparing the impact of causal beliefs in different domains. We conducted two experiments to map the influence of domain-specific causal beliefs on the evaluation of empirical evidence when making decisions and subsequent causal judgments. Participants made 120 decisions in a two-alternative forced-choice task, framed in either a medical or a financial domain. Before each decision, participants could actively search for information about the outcome (“occurrence of a disease” or “decrease in a company's share price”) on the basis of four cues. To analyze the strength of causal beliefs, we set two cues to have a generative relation to the outcome and two to have a preventive relation to the outcome. To examine the influence of empirical evidence, we manipulated the predictive power (i.e., cue validities) of the cues. Both experiments included a validity switch, where the four selectable cues switched from high to low validity or vice versa. Participants had to make a causal judgment about each cue before and after the validity switch. In the medical domain, participants stuck to the causal information in causal judgments, even when evidence was contradictory, while decisions showed an effect of both empirical and causal information. In contrast, in the financial domain, participants mainly adapted their decisions and judgments to the cue validities. We conclude that the strength of causal beliefs (1) is shaped by the domain, and (2) has a differential influence on the degree to which empirical evidence is taken into account in causal judgments and decision making.     

Momentary and integrative response strategies in causal judgment

Associative models of causal learning predict recency effects. Judgments at the end of a trial series should be strongly biased by recently presented information. Prior research, however, presents a contrasting picture of human performance. López, Shanks, Almaraz, and Fernández (1998) observed recency, whereas Dennis and Ahn (2001) found the opposite, primacy. Here we replicate both of these effects and provide an explanation for this paradox. Four experiments show that the effect of trial order on judgments is a function of judgment frequency, where incremental judgments lead to recency while single final judgments abolish recency and lead instead to integration of information across trials (i.e., primacy). These results challenge almost all existing accounts of causal judgment. We propose a modified associative account in which participants can base their causal judgments either on current associative strength (momentary strategy) or on the cumulative change in associative strength since the previous judgment (integrative strategy).     

Accounting for occurrences: An explanation for some novel tendencies in causal judgment from contingency information

Contingency information is information about empirical associations between possible causes and outcomes. In the present research, it is shown that, under some circumstances, there is a tendency for negative contingencies to lead to positive causal judgments and for positive contingencies to lead to negative causal judgments. If there is a high proportion of instances in which a candidate cause (CC) being judged is present, these tendencies are predicted by weighted averaging models of causal judgment. If the proportion of such instances is low, the predictions of weighted averaging models break down. It is argued that one of the main aims of causal judgment is to account for occurrences of the outcome. Thus, a CC is not given a high causal judgment if there are few or no occurrences of it, regardless of the objective contingency. This argument predicts that, if there is a low proportion of instances in which a CC is present, causal judgments are determined mainly by the number of Cell A instances (i.e., CC present, outcome occurs), and that this explains why weighted averaging models fail to predict judgmental tendencies under these circumstances. Experimental results support this argument.     

Asymmetries in predictive and diagnostic reasoning

In this article, we address the apparent discrepancy between causal Bayes net theories of cognition, which posit that judgments of uncertainty are generated from causal beliefs in a way that respects the norms of probability, and evidence that probability judgments based on causal beliefs are systematically in error. One purported source of bias is the ease of reasoning forward from cause to effect (predictive reasoning) versus backward from effect to cause (diagnostic reasoning). Using causal Bayes nets, we developed a normative formulation of how predictive and diagnostic probability judgments should vary with the strength of alternative causes, causal power, and prior probability. This model was tested through two experiments that elicited predictive and diagnostic judgments as well as judgments of the causal parameters for a variety of scenarios that were designed to differ in strength of alternatives. Model predictions fit the diagnostic judgments closely, but predictive judgments displayed systematic neglect of alternative causes, yielding a relatively poor fit. Three additional experiments provided more evidence of the neglect of alternative causes in predictive reasoning and ruled out pragmatic explanations. We conclude that people use causal structure to generate probability judgments in a sophisticated but not entirely veridical way.     

The Motivational Roots of Norms for Environmentally Responsible Behavior

We assessed whether people use a magnitude-matching principle in determining causes for complex social events. We hypothesized that individuals tend to favor causal explanations that match the event in terms of size and scope. In Experiment 1, the magnitude of the consequences of events was manipulated, and participants were presented with two potential causes of modest magnitude and two potential causes of high magnitude. Analyses revealed a relative magnitude-matching effect such that participants were more likely to select high magnitude causes for large magnitude events than modest magnitude events and more likely to select modest magnitude causes for modest magnitude events than large magnitude events. Experiment 2 replicated the magnitude-matching effect with a different event and set of causes, and demonstrated that this effect could be reversed by undermining participants' beliefs in the magnitude-matching principle.