Citation

Lee JC, Greenaway JK, Don HJ, Livesey EJ. J. Exp. Psychol. Learn. Mem. Cogn. 2024; ePub(ePub): ePub.

Copyright

(Copyright © 2024, American Psychological Association)

DOI

10.1037/xlm0001365

PMID

38913724

Abstract

The learned predictiveness effect refers to the tendency for predictive cues to attract greater attention and show faster learning in subsequent tasks. However, in typical designs, the predictiveness of each cue (its objective cue-outcome correlation) is confounded with the degree to which it is informative for making the correct response on each trial (a feature we term choice relevance). In four experiments, we tested the unique contributions of cue-outcome correlation and choice relevance to the learned predictiveness effect by manipulating the outcome choices available on each trial. Experiments 1A and 1B compared two sets of partially predictive cues and found that participants learned more in a transfer phase about the set of cues that were previously choice-relevant. Experiments 2A and 2B used a design in which the cue-outcome correlation was stronger for one set of cues (perfect predictors) than the other set (imperfect predictors). Manipulating the choice relevance of the imperfect predictors in this design did not influence the magnitude of the learning bias toward the perfect predictor. Unlike cue-outcome correlation, choice relevance did not seem to correspond to biases in eye-gaze, suggesting that it operates via a distinct mechanism. Simulations with a modified EXIT model successfully predicted cue-outcome correlation and choice relevance effects by assuming that participants update learning for present outcomes only, but incorrectly predicted additive effects. We conclude that cue-outcome correlation and choice relevance are important factors that can lead to biases in future learning; both were individually sufficient but neither was necessary. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Language: en