When more observations are better than less: a connectionist account of the acquisition of causal strength



The statistical law of large numbers prescribes that estimates are more reliable and accurate when based on a larger sample of observations. This effect of sample size was investigated on causal attributions. Subjects received fixed levels of consensus and distinctiveness covariation, and attributions were measured after a varying number of trials. Whereas prominent statistical models of causality (e.g. Cheng & Novick, 1990; Försterling, 1992) predict no effect of sample size, adaptive connectionist models (McClelland & Rumelhart, 1988) predict that subjects will incrementally adjust causal ratings in the direction of the true covariation the more observations are made. In three experiments, sample size effects were found consistent with the connectionist prediction. Possible extensions of statistical models were considered and simulated, but none of them accommodated the data as well as connectionist models. Copyright © 2001 John Wiley & Sons, Ltd.