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When more observations are better than less: a connectionist account of the acquisition of causal strength
Author(s) -
Van Overwalle Frank,
Van Rooy Dirk
Publication year - 2001
Publication title -
european journal of social psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.609
H-Index - 111
eISSN - 1099-0992
pISSN - 0046-2772
DOI - 10.1002/ejsp.29
Subject(s) - connectionism , optimal distinctiveness theory , psychology , causality (physics) , attribution , sample (material) , sample size determination , statistics , cognitive psychology , social psychology , cognition , mathematics , chemistry , physics , chromatography , quantum mechanics , neuroscience
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.