PPM-Decay: A computational model of auditory prediction with memory decay.
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1008304 |
| _version_ | 1819008102746292224 |
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| author | Peter M C Harrison Roberta Bianco Maria Chait Marcus T Pearce |
| author_facet | Peter M C Harrison Roberta Bianco Maria Chait Marcus T Pearce |
| author_sort | Peter M C Harrison |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-12-21T00:35:08Z |
| format | Article |
| id | doaj.art-1ab25795fca946e1928d52f545bc0d46 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T00:35:08Z |
| publishDate | 2020-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-1ab25795fca946e1928d52f545bc0d462022-12-21T19:21:49ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-11-011611e100830410.1371/journal.pcbi.1008304PPM-Decay: A computational model of auditory prediction with memory decay.Peter M C HarrisonRoberta BiancoMaria ChaitMarcus T PearceStatistical 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).https://doi.org/10.1371/journal.pcbi.1008304 |
| spellingShingle | Peter M C Harrison Roberta Bianco Maria Chait Marcus T Pearce PPM-Decay: A computational model of auditory prediction with memory decay. PLoS Computational Biology |
| title | PPM-Decay: A computational model of auditory prediction with memory decay. |
| title_full | PPM-Decay: A computational model of auditory prediction with memory decay. |
| title_fullStr | PPM-Decay: A computational model of auditory prediction with memory decay. |
| title_full_unstemmed | PPM-Decay: A computational model of auditory prediction with memory decay. |
| title_short | PPM-Decay: A computational model of auditory prediction with memory decay. |
| title_sort | ppm decay a computational model of auditory prediction with memory decay |
| url | https://doi.org/10.1371/journal.pcbi.1008304 |
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