The Inverse Base Rate Effect (IBRE; Medin and Edelson (1988)) is a non-rational behavioural phenomenon in predictive learning. In the IBRE, participants learn that a stimulus compound AB leads to one outcome and that another compound AC leads to a different outcome. Importantly, AB and its outcome are presented three times as often as AC (and its outcome). On test, when asked which outcome to expect on presentation of the novel compound BC, participants preferentially select the rarer outcome, previously associated with AC. This is irrational because, objectively, the common outcome is more likely. Usually, the IBRE is attributed to greater attention paid to cue C than to cue B, and so is an excellent test for attentional learning models. The current experiment tested a simple model of attentional learning proposed by Le Pelley, Mitchell, Beesley, George, and Wills (2016) where attention paid to a stimulus is determined by its associative strength. This model struggles to capture the IBRE, but a potential solution suggested by the authors appeals to the role of experimental context. In the present paper, we derive three predictions from their account concerning the effect of changing to a novel experimental context at test, and examine these predictions empirically. Only one of the predictions was supported, concerning the effect of a context shift on responding to a novel cue, was supported. In contrast, Kruschke (2001b)'s EXIT model, in which attention and associative strength can vary independently, captured the data with a high degree of quantitative accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.