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PPM-Decay: A computational model of auditory prediction with memory decay

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  • Peter M.C. Harrison, Max Planck Institute for Empirical Aesthetics, Queen Mary University of London
  • ,
  • Roberta Bianco, University College London
  • ,
  • Maria Chait, University College London
  • ,
  • Marcus T. Pearce

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).

Original languageEnglish
Article numbere1008304
JournalPLOS Computational Biology
Number of pages41
Publication statusPublished - Nov 2020

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