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Greve A, Cooper E, Kaula A, et al. Does prediction error drive one-shot declarative learning?. J Mem Lang. 2017;94:149-165doi: 10.1016/j.jml.2016.11.001.
Greve, A., Cooper, E., Kaula, A., Anderson, M. C., & Henson, R. (2017). Does prediction error drive one-shot declarative learning?. Journal of memory and language, 94149-165. https://doi.org/10.1016/j.jml.2016.11.001
Greve, Andrea, et al. "Does prediction error drive one-shot declarative learning?." Journal of memory and language vol. 94 (2017): 149-165. doi: https://doi.org/10.1016/j.jml.2016.11.001
Greve A, Cooper E, Kaula A, Anderson MC, Henson R. Does prediction error drive one-shot declarative learning?. J Mem Lang. 2017 Jun;94:149-165. doi: 10.1016/j.jml.2016.11.001. PMID: 28579691; PMCID: PMC5381756.
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J Mem Lang. 2017 Jun;94:149-165. doi: 10.1016/j.jml.2016.11.001.

Does prediction error drive one-shot declarative learning?.

Journal of memory and language

Andrea Greve, Elisa Cooper, Alexander Kaula, Michael C Anderson, Richard Henson

Affiliations

  1. MRC Cognition & Brain Sciences Unit, Cambridge, England, United Kingdom.

PMID: 28579691 PMCID: PMC5381756 DOI: 10.1016/j.jml.2016.11.001

Abstract

The role of prediction error (PE) in driving learning is well-established in fields such as classical and instrumental conditioning, reward learning and procedural memory; however, its role in human one-shot declarative encoding is less clear. According to one recent hypothesis, PE reflects the divergence between two probability distributions: one reflecting the prior probability (from previous experiences) and the other reflecting the sensory evidence (from the current experience). Assuming unimodal probability distributions, PE can be manipulated in three ways: (1) the distance between the mode of the prior and evidence, (2) the precision of the prior, and (3) the precision of the evidence. We tested these three manipulations across five experiments, in terms of peoples' ability to encode a single presentation of a scene-item pairing as a function of previous exposures to that scene and/or item. Memory was probed by presenting the scene together with three choices for the previously paired item, in which the two foil items were from other pairings within the same condition as the target item. In Experiment 1, we manipulated the evidence to be either consistent or inconsistent with prior expectations, predicting PE to be larger, and hence memory better, when the new pairing was inconsistent. In Experiments 2a-c, we manipulated the precision of the priors, predicting better memory for a new pairing when the (inconsistent) priors were more precise. In Experiment 3, we manipulated both visual noise and prior exposure for unfamiliar faces, before pairing them with scenes, predicting better memory when the sensory evidence was more precise. In all experiments, the PE hypotheses were supported. We discuss alternative explanations of individual experiments, and conclude the Predictive Interactive Multiple Memory Signals (PIMMS) framework provides the most parsimonious account of the full pattern of results.

Keywords: Associative memory; Encoding; One-shot learning; Prediction error

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