Psychological distance and judgments of causal impact

When assessing causal impact, individuals have to consider two pieces of information: the magnitude of the cause that resulted in an effect, and the magnitude of the resulting effect. In the present research, participants judged the causal impact of cause–effect relationships in which the magnitude of causes and effects varied independently. Participants mainly relied on effect magnitude, rating causal impact to be much higher when strong (vs. weak) effects emerged. When participants took cause magnitude into account (which they did, but to a lesser extent), their judgments reflected a covariation rule (i.e., causal impact being maximal for strong causes generating strong effects) rather than a ratio rule (i.e., causal impact being maximal for weak causes generating strong effects). These distinct views on causal impact were moderated by psychological distance: Effect magnitude dominated judgments of proximal events, whereas cause magnitude had relatively more impact on causal judgments of distal events.     

A dissociation between causal judgment and outcome recall

It has been suggested that causal learning in humans is similar to Pavlovian conditioning in animals. According to this view, judgments of cause reflect the degree to which an association exists between the cause and the effect. Inferential accounts, by contrast, suggest that causal judgments are reasoning based rather than associative in nature. We used a direct measure of associative strength, identification of the outcome with which a cause was paired (cued recall), to see whether associative strength translated directly into causal ratings. Causal compounds AB+ and CD+ were intermixed with A+ and C- training. Cued-recall performance was better for cue B than for cue D; thus, associative strength was inherited by cue B from the strongly associated cue A (augmentation). However, the reverse was observed on the causal judgment measure: Cue B was judged to be less causal than D (cue competition). These results support an inferential over an associative account of causal judgments.     

The impact of object animacy on the appraisal of causality

The goal of this study was to examine whether animacy of objects affected the appraisal of a causal relationship when one object was observed to propel the other either immediately, after a delay, or at a distance. Participants rated the degree of causal interaction either before or after an extended experience with observing the interactions. We expected that spatial distance would have little effect when objects were seen as animate (because social interactions often span spatial gaps) and would have a degrading effect when objects were appraised as inanimate. Motion animacy appeared to attenuate the impact of a gap and to decrease initial causal judgments for direct collisions. Explicitly informing participants about the nature of the objects had a strong impact. Experience with the causal task affected ratings to a greater extent when the objects were explicitly described as nonliving rather than living.     

Asymptotic judgment of cause in a relative validity paradigm

We report three experiments in which we tested asymptotic and dynamic predictions of the Rescorla-Wagner (R-W) model and the asymptotic predictions of Cheng's probabilistic contrast model (PCM) concerning judgments of causality when there are two possible causal candidates. We used a paradigm in which the presence of a causal candidate that is highly correlated with an effect influences judgments of a second, moderately correlated or uncorrelated cause. In Experiment 1, which involved a moderate outcome density, judgments of a moderately positive cause were attenuated when it was paired with either a perfect positive or perfect negative cause. This attenuation was robust over a large set of trials but was greater when the strong predictor was positive. In Experiment 2, in which there was a low overall density of outcomes, judgments of a moderately correlated positive cause were elevated when this cause was paired with a perfect negative causal candidate. This elevation was also quite robust over a large set of trials. In Experiment 3, estimates of the strength of a causal candidate that was uncorrelated with the outcome were reduced when it was paired with a perfect cause. The predictions of three theoretical models of causal judgments are considered. Both the R-W model and Cheng's PCM accounted for some but not all aspects of the data. Pearce's model of stimulus generalization accounts for a greater proportion of the data.     

The strength-based mirror effect in subjective strength ratings: The evidence for differentiation can be produced without differentiation

Criss (Cognitive Psychology 59:297?C319, 2009) reported that subjective ratings of memory strength showed a mirror effect pattern in which strengthening the studied words increased ratings for targets and decreased ratings for lures. She interpreted the effect on lure items as evidence for differentiation, a process whereby lures produce a poorer match to strong than to weak memory traces. However, she also noted that participants might use different mappings between memory evidence and levels of the rating scale when they expected strong versus weak targets; that is, the effect might be produced by decision processes rather than differentiation. We report two experiments designed to distinguish these accounts. Some participants studied pure lists of weak or strong items (presented once or five times, respectively), while others studied mixed lists of half weak and half strong items. The participants from both groups had pure-strength tests: Only strong or only weak items were tested, and the participants were informed of which it would be before the test. The results showed that strength ratings for lures were lower when strong versus weak targets were tested, regardless of whether the study list was pure or mixed. In the mixed-study condition, the effect was produced even after identical study lists, and thus the same degree of differentiation in the studied traces. Therefore, our results suggest that the strength-rating mirror effect is produced by changes in decision processes.     

Causal status as a determinant of feature centrality

One of the major problems in categorization research is the lack of systematic ways of constraining feature weights. We propose one method of operationalizing feature centrality, a causal status hypothesis which states that a cause feature is judged to be more central than its effect feature in categorization. In Experiment 1, participants learned a novel category with three characteristic features that were causally related into a single causal chain and judged the likelihood that new objects belong to the category. Likelihood ratings for items missing the most fundamental cause were lower than those for items missing the intermediate cause, which in turn were lower than those for items missing the terminal effect. The causal status effect was also obtained in goodness-of-exemplar judgments (Experiment 2) and in free-sorting tasks (Experiment 3), but it was weaker in similarity judgments than in categorization judgments (Experiment 4). Experiment 5 shows that the size of the causal status effect is moderated by plausibility of causal relations, and Experiment 6 shows that effect features can be useful in retrieving information about unknown causes. We discuss the scope of the causal status effect and its implications for categorization research.     

How two causes are different from one: The use of (un)conditional information in Simpson's paradox

In a causally complex world, two (or more) factors may simultaneously be potential causes of an effect. To evaluate the causal efficacy of a factor, the alternative factors must be controlled for (or conditionalized on). Subjects judged the causal strength of two potential causes of an effect that covaried with each other, thereby setting up a Simpson's paradox--a situation in which causal judgments should vary widely depending on whether or not they are conditionalized on the alternative potential cause. In Experiments 1 (table format) and 2 (trial-by-trial format), the subjects did conditionalize their judgments for one causal factor on a known alternative cause. The subjects also demonstrated that they knew what information was needed to properly make causal judgments when two potential causes are available. In Experiment 3 (trial-by-trial), those subjects who were not told about the causal mechanism by which the alternative cause operated were less likely to conditionalize on it. However, the more a subject recognized the covariation between the alternative cause and the effect, the more the subject conditionalized on it. Such behavior may arise from the interaction between bottom-up and top-down processing.     

Inference-based retrospective revaluation in human causal judgments requires knowledge of within-compound relationships

In an allergist causal-judgment task, food compounds were followed by an allergic reaction (e.g., AB+), and then 1 cue (A) was revalued. Experiment 1, in which participants who were instructed that whatever was true about one element of a causal compound was also true of the other, showed a reverse of the standard retrospective revaluation effect. That is, ratings of B were higher when A was causal (A+) than when A was safe (A-). This effect was taken to reflect inferential reasoning, not an associative mechanism. In Experiment 2, within-compound associations were found to be necessary to produce this inference-based revaluation. Therefore, evidence that within-compound associations are necessary for retrospective revaluation is consistent with the inferential account of causal judgments.     

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.     

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.     

Accounting for occurrences: a new view of the use of contingency information in causal judgment

When people make causal judgments from contingency information, a principal aim is to account for occurrences of the outcome. When 2 causes are under consideration, the capacity of either to account for occurrences is judged from how likely the cause is to be present when the outcome occurs and from the rate at which the outcome occurs when that cause alone is present, which gives an estimate of the strength of the cause. These propositions are formalized in a weighted averaging model, which successfully predicted several judgmental phenomena not predicted by other models of causal judgment. These include a tendency for judgment of one cause (A) to be reduced as the number of occurrences of when only the other one (B) increases and a tendency for A to receive higher judgments than B if A is better able to account for occurrences than B is even if B has a higher contingency with the outcome than A does. Overshadowing, a tendency for judgments of B to be depressed if A has a higher contingency, is weak or absent when B is better able to account for occurrences than A. Results of several experiments support these and related predictions derived from the accounting for occurrences hypothesis.     

Belief-based and covariation-based cues affect causal discounting.

Causal discounting occurs when the perceived efficacy of a putative cause is reduced by the presence of a stronger causal candidate. Previous studies of causal discounting have defined the strength of causal candidates in terms of the degree to which the cause and the effect covary (e.g., Baker, Mercier, Vallee-Tourangeau, Frank, & Pan, 1993). In contrast, in the present study, causal strength was defined in terms of both covariation- and belief-based cues. Seventy-two participants made causality judgments for a fictional causal candidate both in isolation and when paired with either a stronger or a weaker cause. The results demonstrated that the degree to which a causal candidate is discounted depends not only on the degree to which an alternative cause covaries with the effect, but also on whether the alternative is a believable or unbelievable candidate. Indeed, it was observed that a highly believable alternative will produce the discounting effect, even if it is a weaker covariate than the original candidate. These findings suggest the need to incorporate both belief-based and covariation-based cues into models of causal attribution.     

Mutability and propensity in causal selection

We examined differences in causal ratings of 1 factor depending on the mutability (defined as the ease with which a factor can be imagined to be different) and causal propensity (defined as the likelihood that the event would occur in the presence of a factor) of another factor that conjoined to produce the event. In 3 studies, causal ratings of the target factor depended on the interaction of mutability and propensity of the other factor. When the other factor was high in mutability, ratings of the target decreased as the propensity of the contributing factor increased, but when the other was low in mutability, ratings of the target increased as the propensity of the contributing factor increased. Mediation analysis indicated that mutability and propensity affected causal ratings by determining the comparison against which the event was considered. Comparison judgments also mediated beliefs about which factor should have adjusted to the other.     

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.     

Interaction between previous beliefs and cue predictive value in covariation-based causal induction

The main aim of this work was to show the impact of preexisting causal beliefs on causal induction from cause-effect co-occurrence information, when several cues compete with each other for predicting the same effect. Two different causal scenarios -- one social (a), the other medical (b) -- were used to check the generality of the effects. In Experiments 1a and 1b, participants were provided information on the co-occurrence of a two-cause compound and an effect, but not about the potential relationship between each cause by its own and the effect. As expected, prior beliefs -- induced by means of instructions -- strongly modulated the causal strength assigned to each element of the compound. In Experiments 2a and 2b, covariation evidence was provided, not only about the predictive value of the two-cause compound, but also about one of the elements of the compound. When this evidence was available, prior beliefs had less impact on judgments, and these were mostly guided by the relative predictive value of the cue. These results demonstrate the involvement of inferential integrative mechanisms in the generation of causal knowledge and show that single covariation detection mechanisms -- either rule-based or associative -- are insufficient to account for human causal judgment. At the same time, the fact that the power of new covariational evidence to change prior beliefs depended on the availability of information on the relative (conditional) predictive value of the target candidate cause suggests that causal knowledge derived from information on causal mechanisms and from covariation probably share a common representational basis.     

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.     

Effect of local context of responding on human judgment of causality

Two experiments examined the effect of various relationships between a response (pressing the space bar of a computer) and an outcome (a triangle flashing on a screen) on judgments of the causal effectiveness of the response. In Experiment 1, when responses were required to be temporarily isolated from each other prior to an outcome, ratings of the causal effectiveness of the responses were higher than in a condition in which the probability of an outcome following a response was the same but in which no temporal isolation was required. In Experiment 2, when a number of responses were required to be emitted temporally close to the outcome, ratings of the causal effectiveness of the responses were lower than in a condition in which the probability of an outcome following a response was the same but in which no temporal proximity was required. These results suggest that, in addition to the overall probability that an outcome will follow a response, the local context of responding at the time an outcome is presented is critical in influencing ratings of causal effectiveness.     

Non-bayesian inference: causal structure trumps correlation

The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results.     

Discounting and conditionalization

When people are asked to judge the strengths of two potential causes of an effect, they often demonstrate discounting--devaluing the strength of a target cause when it is judged in the presence of a strong (relative to a weak) alternative cause. Devaluing the target cause sometimes results from conditionalization--holding alternative causes constant while evaluating the target cause. Yet discounting not attributable to conditionalization also occurs. We sought to dissociate conditionalization and discounting (beyond that accounted for by conditionalization) by having subjects perform either a spatial or a verbal working memory task while learning a causal relation. Conditionalization was disrupted by the verbal task but not the spatial task; however, discounting was disrupted by the spatial task but not the verbal task. Conditionalization and discounting are therefore cognitively dissociable processes in human causal inference.     

Similarity and causality in the use of social analogies

The present paper examines whether causal relations are necessary for the use of social analogies. Although causal relations increase the use of social analogies, it is not known whether they are necessary. Establishing this is important for understanding both analogical reasoning and learning in novel situations where people lack knowledge of the causal relations. Study 1 demonstrated that, in the absence of an explicit causal relation, as the similarity between a target individual and a previously encountered individual (the base) increased, people thought the target was increasingly likely to perform the same behavior as the base. Thus, a causal relation is not necessary. Two additional studies used asymmetries in similarity judgments to provide additional evidence that when reasoning analogically people relied on similarity. Manipulating whether subjects focused primarily on the target or the base completely reversed asymmetries both in judgments of how similar the target was to the base and in predictions of how likely the target was to perform the same behavior as the base. The asymmetries for similarity and prediction were completely parallel. Thus, in the absence of an explicit causal relation, use of the analogy was based on judgments of global similarity. The implications of these asymmetries in similarity judgments and predictions for other judgments, such as stereotyping, are also discussed.