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Explicitly predicting outcomes enhances learning of expectancy-violating information.

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Greve, Andrea 
Jolles, Dietsje 
Theobald, Maria 
Galeano-Keiner, Elena M 


Predictive coding models suggest that the brain constantly makes predictions about what will happen next based on past experiences. Learning is triggered by surprising events, i.e., a prediction error. Does it benefit learning when these predictions are made deliberately, so that an individual explicitly commits to an outcome before experiencing it? Across two experiments, we tested whether generating an explicit prediction before seeing numerical facts boosts learning of expectancy-violating information relative to doing so post hoc. Across both experiments, predicting boosted memory for highly unexpected outcomes, leading to a U-shaped relation between expectedness and memory. In the post hoc condition, memory performance decreased with increased unexpectedness. Pupillary data of Experiment 2 further indicated that the pupillary surprise response to highly expectancy-violating outcomes predicted successful learning of these outcomes. Together, these findings suggest that generating an explicit prediction increases learners' stakes in the outcome, which particularly benefits learning of those outcomes that are different than expected.


Funder: DIPF | Leibniz-Institut für Bildungsforschung und Bildungsinformation (3435)


Active learning, Prediction error, Pupillometry, Surprise, Violation of expectation, Humans, Learning, Brain

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Psychon Bull Rev

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Springer Science and Business Media LLC
MRC (unknown)
Medical Research Council (MC_UU_00005/6)