Proceedings of the Annual Meeting of the Cognitive Science Society

Previous work proposes that heuristics, such as Take-The-Best, may succeed because of deliberate ignorance of covari-ance in their cue weight estimates as opposed to full-information models (logistic regression). Other studies find thatTake-The-Best performs particularly well compared to full-information models in high covariance as opposed to low co-variance environments. This poses the question of whether heuristics perform well when there is a mismatch between theircovariance prior and the covariance in the environment? We test this by gradually manipulating solely the level of covari-ance among cues. Indeed, Take-The-Best performs better as average covariance increases, while tallying, nave Bayes andlogistic regression worsen. Since both nave Bayes and tallying also disregard covariance but integrate across cues, thisindicates the competitive advantage of Take-The-Best stems from relying on a single cue when redundancy is high. Weextend previous work by Rieskamp and Dieckmann (2012) and imply a reinterpretation of past Take-The-Bests successes.