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

Abstract

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.

Causal judgements about two causal candidates: Accounting for occurrences, estimating strength, and the importance of interaction judgements

“…In the .5/.67/.67 dataset there were again six XB' instances, but this time the information about Cue B on its own (B'02, BÁ00) indicates that cue B is strong enough to account for these, so there is no need to attribute them either to X or to an interaction between B and X. For Experiment 2A in Darredeau et al (2009), Equation 1 predicts lower judgements in condition .5/Á.67/0 than in conditions .5/Á.67/Á.67 and conditions .5/Á1/0, which was not found. This result has the same explanation as the predictive failure in Experiment 1A.…”

“…A small number of studies have extended the investigation of discounting to the case where there are three possible causes of an outcome (Baetu, Baker, Darredeau, & Murphy, 2005;Darredeau, Baetu, Baker, & Murphy, 2009). The motivation behind these studies was to test predictions of associative learning models where one cause was judged against information about a single alternative cause or against the same contingency information distributed between two alternative causes.…”

“…First, for experiment 1A in Darredeau et al (2009), Equation 1 predicts no differences between three of the conditions, labelled .5/.67/.67, .5/1/0, and .5/.67/0. In fact, judgements were significantly higher in .5/.67/0 than in the other two conditions.…”

“…Labelling the three causes X, A, and B, as Baetu et al (2005) and Darredeau et al (2009) did, results in the following set of weights for judgements of Cause X, the cause of interest in this research: wXA'0wXB'0.15; wXAÁ0 wXBÁ0.075; wX'0.4; wXÁ0.15; wA'0 wB'0wAB'0.15; w0'0.075; wAÁ0wBÁ 0wABÁ0w0Á00. X', XA', and XB' are confirmatory and all other combinations with nonzero weight are disconfirmatory.…”

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

“…Generally, competition effects have been observed when the strong competing predictor is followed by an outcome of a higher proportion of its presentations, compared to the moderate predictor; and it is also paired with an equivalent or higher proportion of the total outcomes than the moderate target predictor (Darredeau et al 2009).…”