On the origin of personal causal theories

Detecting the causal relations among environmental events is an important facet of learning. Certain variables have been identified which influence both human causal attribution and animal learning: temporal priority, temporal and spatial contiguity, covariation and contingency, and prior experience. Recent research has continued to find distinct commonalities between the influence these variables have in the two domains, supporting a neo-Humean analysis of the origins of personal causal theories. The cues to causality determine which event relationships will be judged as causal; personal causal theories emerge as a result of these judgments and in turn affect future attributions. An examination of animal learning research motivates further extensions of the analogy. Researchers are encouraged to study real-time causal attributions, to study additional methodological analogies to conditioning paradigms, and to develop rich learning accounts of the acquisition of causal theories.     

Naive theories and causal deduction

Evidence is presented that implicates two factors in deductive reasoning about causality. The factors are alternative causes and disabling conditions (factors that prevent effects from occurring in the presence of viable causes). A causal analysis is presented in which these factors impact on judgments concerning causal necessity and sufficiency, which in turn determine deductive entailment relations. In Experiment 1, these factors were found to impact causal deductive judgments more strongly than did logical form. In Experiment 2, causal deductive judgments were found to vary as a function of familiarity with a particular causal relationship: The more familiar the causal relationship, the less willing reasoners were to accept conclusions based on them.     

Two causal theories of counterfactual conditionals

Bayes nets are formal representations of causal systems that many psychologists have claimed as plausible mental representations. One purported advantage of Bayes nets is that they may provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This article compares two proposed Bayes net theories as models of people's understanding of counterfactuals. Experiments 1-3 show that neither theory makes correct predictions about backtracking counterfactuals (in which the event of the if-clause occurs after the event of the then-clause), and Experiment 4 shows the same is true of forward counterfactuals. An amended version of one of the approaches, however, can provide a more accurate account of these data.     

Interpersonal reactions to suicide attempt: the role of respondent causal attributions

Interpersonal responses to a depressed person with or without a suicide attempt were examined. It was hypothesized that the depressed person who attempted suicide would receive higher negative attributions and interpersonal rejection, and that attributions would mediate the relationship between exposure to a depressed person and rejection. Contrary to hypotheses, respondents were more willing to interact with, held higher esteem for, and endorsed lower negative attributions for the depressed person who attempted suicide. Mediation hypotheses were supported. Findings suggest that a suicide attempt may promote less negative attributions toward depressed individuals, which in turn dampen negative interpersonal reactions.     

When learning order affects sensitivity to base rates: challenges for theories of causal learning

In three experiments we investigated whether two procedures of acquiring knowledge about the same causal structure, predictive learning (from causes to effects) versus diagnostic learning (from effects to causes), would lead to different base-rate use in diagnostic judgments. Results showed that learners are capable of incorporating base-rate information in their judgments regardless of the direction in which the causal structure is learned. However, this only holds true for relatively simple scenarios. When complexity was increased, base rates were only used after diagnostic learning, but were largely neglected after predictive learning. It could be shown that this asymmetry is not due to a failure of encoding base rates in predictive learning because participants in all conditions were fairly good at reporting them. The findings present challenges for all theories of causal learning.     

The influence of naive causal theories on lay concepts of mental illness

Two experiments, incorporating both real-life (Experiment 1) and artificial (Experiment 2) stimuli, demonstrated that lay concepts of mental disorders can be reliably predicted from subjects' naive causal theories about those disorders. Symptoms that are deeper causes (X, where X causes Y, which causes Z) are more important in lay concepts than intermediate causes (Y), which in turn are more important than terminal effects (Z). In addition, symptoms that cause or are caused by other symptoms are more important in lay concepts than symptoms not participating in any causal relationships. Implications of these results for current models of categorization and for research on lay theories of mental disorders are discussed, and future directions for research are suggested.     

Comparing elemental and configural associative theories in human causal learning: a case for attention

In two causal learning experiments with human participants, the authors compared various associative theories that assumed either elemental (unique cue, modified unique cue, replaced elements model, and Harris' model) or configural processing of stimuli (Pearce's theory and a modification of it). The authors used modified patterning problems initially suggested by Redhead and Pearce (1995). Predictions for all theories were generated by computer simulations. Both configural theories and the unique cue approach failed to account for the observations. The replaced elements model was able to account for part of the data, but only if the replacement parameters could vary across discrimination problems. The Harris model and the modified unique cue approach, assuming that the salience of stimuli decreases with an increasing number of stimuli in a compound, successfully accounted for all of our data. This success implies that attentional factors should be explicitly taken into account in associative learning theory.