Harrison, PMC;
Bianco, R;
Chait, M;
Pearce, MT;
(2020)
PPM-Decay: A computational model of auditory prediction with memory decay.
PLOS Computational Biology
, 16
(11)
, Article e1008304. 10.1371/journal.pcbi.1008304.
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Abstract
Statistical learning and probabilistic prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n-grams from training sequences. However, PPM has limitations as a cognitive model: in particular, it has a perfect memory that weights all historic observations equally, which is inconsistent with memory capacity constraints and recency effects observed in human cognition. We address these limitations with PPM-Decay, a new variant of PPM that introduces a customizable memory decay kernel. In three studies—one with artificially generated sequences, one with chord sequences from Western music, and one with new behavioral data from an auditory pattern detection experiment—we show how this decay kernel improves the model’s predictive performance for sequences whose underlying statistics change over time, and enables the model to capture effects of memory constraints on auditory pattern detection. The resulting model is available in our new open-source R package, ppm (https://github.com/pmcharrison/ppm).
Type: | Article |
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Title: | PPM-Decay: A computational model of auditory prediction with memory decay |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1371/journal.pcbi.1008304 |
Publisher version: | https://doi.org/10.1371/journal.pcbi.1008304 |
Language: | English |
Additional information: | Copyright © 2020 Harrison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | Memory, Forecasting, Learning, Reaction time, Markov models, Human performance, Algorithms, Music cognition |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > The Ear Institute |
URI: | https://discovery.ucl.ac.uk/id/eprint/10115193 |
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