PPM-Decay: A computational model of auditory prediction with memory decay
dc.contributor.author | Harrison, PMC | |
dc.contributor.author | Bianco, R | |
dc.contributor.author | Chait, M | |
dc.contributor.author | Pearce, MT | |
dc.date.accessioned | 2021-01-07T10:15:57Z | |
dc.date.available | 2020-09-04 | |
dc.date.available | 2021-01-07T10:15:57Z | |
dc.date.issued | 2020-11 | |
dc.identifier.issn | 1553-734X | |
dc.identifier.other | ARTN e1008304 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/69538 | |
dc.description.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). | en_US |
dc.publisher | PLoS | en_US |
dc.relation.ispartof | PLOS COMPUTATIONAL BIOLOGY | |
dc.rights | This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.title | PPM-Decay: A computational model of auditory prediction with memory decay | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2020 Harrison et al. | |
dc.identifier.doi | 10.1371/journal.pcbi.1008304 | |
pubs.author-url | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000589607000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
pubs.issue | 11 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 16 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | EPSRC and AHRC Centre for Doctoral Training in Media and Arts Technology::Engineering and Physical Sciences Research Council | en_US |
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