Overestimating causality: Attributional effects of confirmatory processing.

People may routinely overestimate the strength of a hypothesized cause of an outcome. In 4 experiments, Ss exaggerated the extent to which an outcome resulted from a focal cause. The tendency was attenuated when Ss were prompted to explain the outcome or consider alternative causes before the judgment. Surprisingly, stronger causal inferences were drawn when Ss were highly accountable for their judgments. The results indicate that causal inferences are often mediated by the confirmatory processes that have been delineated in previous hypothesis testing research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

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. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Conditional reasoning, causality, and the structure of semantic memory: strength of association as a predictive factor for content effects

Available evidence indicates that responses to conditional inferences using concrete causal premises is affected by the relative number of available alternate causes (Cummins, D.D., 1995. Memory and Cognition 23 (5), 646-658). We propose that another important factor that may influence the kinds of inferences made to causal conditionals is the relative strength of association between such causes and the consequent term. We present a study with adult participants that examines the effect of strength of association on performance on a conditional reasoning task using causal premises for which there exist one highly associated potential cause for the given consequent term. We predicted that adults would produce a greater proportion of biconditional responses to invalid forms with strongly associated premises than weakly associated ones, while valid forms would not be affected by strength of association. The results are consistent with this hypothesis.     

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

Examined the extent to which individuals' a priori beliefs contribute to the degree of causal discounting and how those beliefs are combined with covariation-based cues. Ss were 72 university students who made causality judgments on multiple causal candidates for a single given effect in 6 stories. Each candidate varied in terms of the degree to which it covaried with the effect and the degree to which it was a believable precursor to the observed effect. The 1st cause, was of neutral belief and was moderately contingent with the effect. The 2nd cause was either highly believable or highly unbelievable and was either less, the same, or more contingent than the 1st cause. Ss made their judgments before and after being presented with the 2nd cause. The results indicate 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. Specifically, the results show that a highly believable alternative will produce the discounted 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. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Recollection-based prospective metamemory judgments are more accurate than those based on confidence: Judgments of remembering and knowing (JORKS).

Five experiments were conducted to examine whether the nature of the information that is monitored during prospective metamemory judgments affected the relative accuracy of those judgments. We compared item-by-item judgments of learning (JOLs), which involved participants determining how confident they were that they would remember studied items, with judgments of remembering and knowing (JORKs), which involved participants determining whether studied items would later be accompanied by contextual details (i.e., remembering) or would not (i.e., knowing). JORKs were more accurate than JOLs when remember–know or confidence judgments were made at test and when cued recall was the outcome measure, but not for yes–no recognition. We conclude that the accuracy of metamemory judgments depends on the nature of the information monitored during study and test and that metamemory monitoring can be improved if participants are asked to base their judgments on contextual details rather than on confidence. These data support the contention that metamemory decisions can be based on qualitatively distinct cues, rather than an overall memory strength signal. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

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. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

How causal knowledge affects classification: A generative theory of categorization.

Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Selective associations and causality judgments: Presence of a strong causal factor may reduce judgments of a weaker one.

In 5 experiments, humans played video games in which 2 events or causes covaried with an outcome. In Exps 1 and 2, a highly correlated cause (a plane) of an outcome (success at traversing a minefield) reduced judgments of the strength of a weaker cause (camouflaging or painting a tank). In Exp 3, similar results were found when both causes were negatively correlated with the outcome. In Exp 4, strong positive or negative contingencies caused the Ss to reduce judgments of contingencies of the opposite polarity. These results can be accounted for by associative or connectionist models from animal learning such as the Rescorla-Wagner model. In Exp 5, this type of model was contrasted with a representational model in which Ss are claimed to monitor accurately the various contingencies but use a rule in which the presence of a strong contingency causes them to discount weaker contingencies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Culpable causation.

"Culpable causation" refers to the influence of the perceived blameworthiness of an action on judgments of its causal impact on a harmful outcome. Four studies were conducted to show that when multiple forces contribute to an unfortunate outcome, people select the most blameworthy act as the prepotent causal factor. In Study 1, an actor was cited more frequently as the primary cause of an accident when his reason for speeding was to hide a vial of cocaine than when it was to hide his parents' anniversary gift. In Study 2, of the 4 acts that produced an unfortunate outcome, the most blameworthy act was cited as the factor with the greatest causal impact. Study 3 found that greater causal influence was perceived throughout a causal chain when the act that engaged the chain was positive rather than negative. Finally, Study 4 found that both traditional causal factors (i.e., necessity and sufficiency) and culpable factors influenced perceived causation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Structure of causes for success and failure: A multidimensional scaling analysis of preference judgments.

Investigated whether B. Weiner's (1979) model of causal attributions applies to perceptions of the causes for success and failure. Instead of the usual similarity judgments, preference judgments were used to reveal the dimensions underlying these perceptions. Female subjects, randomly assigned to a success or failure condition, made preference judgments with regard to 12 causes for success or failure. Multidimensional scaling analysis uncovered internality, stability, and excusability dimensions, thus supporting Weiner's model at least partly. In addition, differences in the relative emphasis given to the dimensions were found between the success and failure conditions: Following success, the internality dimension was the most salient, whereas after failure the stability and excusability dimensions were the most important. Furthermore, after success subjects preferred more internal causes and more causes that seem inappropriate as an excuse. After failure, subjects chose more external and more excusable causes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Correction to Darredeau et al. (2009).

Reports an error in "Competition between multiple causes of a single outcome in causal reasoning" by Christine Darredeau, Irina Baetu, Andrew G. Baker and Robin A. Murphy (Journal of Experimental Psychology: Animal Behavior Processes, 2009[Jan], Vol 35[1], 1-14). The URL provided for the supplemental material was incomplete. The complete URL is http://dx.doi.org/10.1037/a0012699.supp (The following abstract of the original article appeared in record 2009-00257-001.) A strong positive predictor of an outcome modulates the causal judgments of a moderate predictor. To study the empirical basis of this modulation, we compared treatments with one and with two strong competing (i.e., modulating) causes. This allowed us to vary the frequency of outcome occurrences or effects paired with the predictors. We investigated causal competition between positive predictors (those signaling the occurrence of the outcome), between negative predictors (those signaling the absence of the outcome) and between predictors of opposite polarity (positive and negative). The results are consistent with a contrast rather than a reduced associative strength or conditional contingency account, because a strong predictor of opposite polarity enhances rather than reduces causal estimates of moderate predictors. In addition, we found competition effects when the strong predictor predicted fewer outcome occurrences than the moderate predictor, thus implying that cue competition is, at least sometimes, a consequence of contingency rather than total cue-outcome pairings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Structural alignment in induction and similarity.

According to a structural alignment view, representational commonalities contributed to the perception of similarity, whereas nonshared attributes related to commonalities are candidate inferences in induction. This view was tested in 5 experiments. Novel animal pairs varying in the number of attributes and relations they shared were used to assess the relationship between induction and similarity. In Experiments 1 and 2, the number of shared attributes and the presence of 2 kinds of causal relations between attributes varied. Shared attributes increased both similarity and inductive strength judgments. Shared causal relations, possessed by both animals, influenced perceived similarity, but binding causal relations, which connected a shared attribute to a candidate inference in the induction task, were important for inductive strength. In Experiments 3–5, these results were extended through use of a noncausal relation and familiar animal categories. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Competition between multiple causes of a single outcome in causal reasoning.

[Correction Notice: An erratum for this article was reported in Vol 35(2) of Journal of Experimental Psychology: Animal Behavior Processes (see record 2009-05154-015). The URL provided for the supplemental material was incomplete. The complete URL is http://dx.doi.org/10.1037/a0012699.supp] A strong positive predictor of an outcome modulates the causal judgments of a moderate predictor. To study the empirical basis of this modulation, we compared treatments with one and with two strong competing (i.e., modulating) causes. This allowed us to vary the frequency of outcome occurrences or effects paired with the predictors. We investigated causal competition between positive predictors (those signaling the occurrence of the outcome), between negative predictors (those signaling the absence of the outcome) and between predictors of opposite polarity (positive and negative). The results are consistent with a contrast rather than a reduced associative strength or conditional contingency account, because a strong predictor of opposite polarity enhances rather than reduces causal estimates of moderate predictors. In addition, we found competition effects when the strong predictor predicted fewer outcome occurrences than the moderate predictor, thus implying that cue competition is, at least sometimes, a consequence of contingency rather than total cue-outcome pairings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The effect of different sensitivity, specificity and cause-specific mortality fractions on the estimation of differences in cause-specific mortality rates in children from studies using verbal autopsies

BACKGROUND: Verbal autopsies (VA) are increasingly being used in developing countries to determine causes of death, but little attention is generally given to the misclassification effects of the VA. This paper considers the effect of misclassification on the estimation of differences in cause-specific mortality rates between two populations. METHODS: The bias in the percentage difference in cause-specific mortality between two populations has been explored under two different models: i) assuming that mortality from all other causes does not differ between the two populations; ii) allowing for a difference in mortality from all other causes. The bias is described in terms of the sensitivity and specificity of the VA diagnosis and the proportion of mortality due to the cause of interest. Methods for adjustment of sample size and adjusting the estimate of effect are also explored. RESULTS: The results are illustrated for a range of plausible values for these parameters. The bias is more extreme as both sensitivity and specificity fall, and is particularly affected even by a small loss of specificity. The bias also increases as the proportion of all deaths due to the cause of interest decreases, and is affected by the size of the true change in mortality due to the cause of interest relative to the change in mortality from other causes. Calculations from existing data suggest prohibitively large sample sizes may often be required to detect important differences in cause-specific mortality rates in studies using existing VA. CONCLUSIONS: Highly specific VA tools are needed before observed differences in cause-specific mortality can be interpreted. Loss of power due to misclassification may obscure real differences in cause-specific mortality.     

Counterfactual potency.

Counterfactual thoughts typically take the form of implied or explicit if–then statements. We propose that the multiplicative combination of “if likelihood” (the degree to which the antecedent condition of the counterfactual is perceived to be likely) and “then likelihood” (the perceived conditional likelihood of the outcome of the counterfactual, given the antecedent condition) determine the strength and impact of counterfactuals. This construct, termed counterfactual potency, is a reliable predictor of the degree of influence of counterfactual thinking upon judgments of regret, causation, and responsibility. Through 4 studies, we demonstrate the predictive power of this construct in a variety of contexts and show that it plays a causal role in determining the strength of the effects of counterfactual thought. Implications of counterfactual potency as a central factor of counterfactual influence are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Race, class, and the attributional process.

Two experiments examined the process and content of attributional thinking in Black and White children who differed in socioeconomic status (SES). In Exp I, 171 7th graders subdivided into middle-SES Black, middle-SES White, low-SES Black, and low-SES White groups imagined that they succeeded or failed at an examination, with the cause of the outcome specified. Their perceptions of the dimensional properties of causes (locus, stability, and controllability), expectancy for success, teacher evaluation, and affective reactions were reported. Similar judgments were made in Exp II, with 148 of the Ss from Exp I, in response to actual rather than hypothetical success and failure, and Ss' causal attributions for their performance were reported. Analyses revealed that Blacks did not display a less adaptive attributional pattern than did Whites following actual performance, and no differences existed in Ss' understanding of the meaning (dimensional placement) of causes. A linkage between the locus of causes and affect also was documented in all race?×?SES groups. In contrast, race and class differences occurred in Ss' perceptions of predicted stability–expectancy and controllability–evaluation causal linkages. (23 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Aging and integration of contingency evidence in causal judgment.

Age differences in causal judgment are consistently greater for preventative/negative relationships than for generative/positive relationships. In this study, a feature analytic procedure (Mandel & Lehman, 1998) was used to determine whether this effect might be due to differences in young and older adults’ integration of contingency evidence during causal induction. To reduce the impact of age-related changes in learning/memory, the authors presented contingency evidence for preventative, noncontingent, and generative relationships in summary form; the meaningfulness of causal context was varied to induce participants to integrate greater or lesser amounts of this evidence. Young adults showed greater flexibility in their integration processes than did older adults. In an abstract causal context, there were no age differences in causal judgment or integration, but in meaningful contexts, young adults’ judgments for preventative relationships were more accurate than older adults’ and young adults assigned more weight to the contingency evidence confirming these relationships. These differences were mediated by age-related changes in processing speed. The decline in this basic cognitive resource may place boundaries on the amount or type of evidence that older adults can integrate for causal judgment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Schedules of reinforcement as determinants of human causality judgments and response rates.

Experiments examined the effect of relationships between a response and an outcome on human judgments of causal effectiveness. In Experiment 1, the time between outcomes obtained on a variable ratio (VR) schedule became the intervals for a yoked variable interval (VI) schedule. Response rates were higher on the VR than on the VI schedule. In Experiment 2, the number of responses required per outcome on a VR schedule were matched to that on a master VI 20-s schedule. Both ratings of causal effectiveness and response rates were higher in the VR schedule. In Experiment 3, tandem VI fixed-ratio (FR) schedules produced higher rates and judgments than equivalent conjunctive VI FR schedule. In Experiment 4, a VI schedule with a reinforcement requirement for a short interresponse time (IRT) produced higher rates and judgments than a simple VI schedule. These results corroborate the view that schedules are a determinant of both response rates and causal judgments. Few current theories of causal judgment predict this pattern of results. (PsycINFO Database Record (c) 2010 APA, all rights reserved)