Asymptotic judgment of cause in a relative validity paradigm

Judgement of Two Causal Candidates from Contingency Information: II. Effects of Information about One Cause on Judgements of the Other Cause

When judgements are being made about two causes there are eight possible kinds of contingency information: occurrences and nonoccurrences of the outcome when both causes are present, when Cause 1 alone is present, when Cause 2 alone is present, and when neither cause is present. It is proposed that contingency information is used to some extent to judge proportionate strength, which is the proportion of occurrences of the outcome that each cause can account for. This leads to a prediction that judgements of one cause will be influenced by information about occurrences, but not nonoccurrences, of the outcome when only the other cause is present. In six experiments consistent support was found for this prediction when the cause being judged had a positive relation with the outcome, but no consistent tendency was found when the cause being judged had a negative relation with the outcome. The effects found for causes with positive contingency cannot be explained by the Rescorla–Wagner model of causal judgement nor by the hypothesis that causal judgements are based on conditional contingencies.

Reasoning about redundant and non-redundant alternative causes of a single outcome: Blocking or enhancement caused by the stronger cause

Perceptions of the effectiveness of a moderate probabilistic cause are influenced by the presence of stronger alternative causes. One important idea is that this influence occurs because the strong cause renders the weaker one statistically redundant. Alternatively, the causes might be contrasted to each other, so the stronger cause may simply overpower perceptions of the weaker one. Causes may have the same polarity (e.g., two generative/excitatory causes or two preventive/inhibitory causes) or be of opposite polarity (e.g., a generative cause versus a preventive or inhibitory cause). Previously, we found that the presence of a stronger redundant alternative cause of the same polarity reduces causal judgements of the moderate cause (i.e., blocking occurs) but a stronger cause of the opposite polarity enhances judgements of the moderate cause (i.e., enhancement). Experiments 1 and 2 further explored these cue competition effects with redundant and non-redundant alternative causes (i.e., correlated versus independent alternatives). We generally found that blocking and enhancement occur with both redundant and non-redundant alternative causes. This is inconsistent with an information processing view of cue competition that relies on statistical redundancy to account for blocking. Although these results are inconsistent with a redundancy information processing account of cue competition and are consistent with our earlier contrast account, we demonstrate here that a simple associative model can account for the sometimes apparently contradictory effects of cue competition.

Models of covariation-based causal judgment: a review and synthesis

Causal judgment is assumed to play a central role in prediction, control, and explanation. Here, we consider the function or functions that map contingency information concerning the relationship between a single cue and a single outcome onto causal judgments. We evaluate normative accounts of causal induction and report the findings of an extensive meta-analysis in which we used a cross-validation model-fitting method and carried out a qualitative analysis of experimental trends in order to compare a number of alternative models. The best model to emerge from this competition is one in which judgments are based on the difference between the amount of confirming and disconfirming evidence. A rational justification for the use of this model is proposed.

Judgments of causal efficacy under constant and changing interevent contingencies

How do people judge constant and varying interevent contingencies? In two experiments, 150 college students rated the efficacy of a potential cause (an experimental fertilizer) of an effect (a plant's blooming). The prevailing probabilistic interevent relation could remain constant for the entirety of the problem or it could change without warning at the midway point: by contingency reversal, by shifting from noncontingency to contingency, or by shifting from contingency to noncontingency. Participants' trial-by-trial ratings sensitively tracked the prevailing positive, negative, and noncontingent interevent relations, even those that entailed an unsignaled change in contingency. Changes in specific cells of the 2 x 2 contingency table differentially affected participants' response to the altered interevent relations. All of this evidence was well described by an associative account of contingency and causal judgments.

Cognitive modeling with spreadsheets

Spreadsheet implementations of two different types of cognitive models--a neural network model and a statistical model--are presented. The two examples illustrate how to employ the facilities of spreadsheets, the spreadsheet data structure, array functions, the built-in function library, and the integrated optimizer, for building cognitive models. The two presented models are new extensions of existing models. They are used for simulating data from experiments illustrating that the extended versions are able to explain experimental results that could not be simulated by the original models. The whole simulation study demonstrates that spreadsheets are a handy tool, especially for researchers without programming knowledge who want to build cognitive models and for instructors teaching cognitive modeling.