Computational Auditory Perception Research Group, Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany.
Cognitive Science Research Group, Queen Mary University of London, London, UK.
PLoS Comput Biol. 2020 Nov 4;16(11):e1008304. doi: 10.1371/journal.pcbi.1008304. eCollection 2020 Nov.
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).
统计学习和概率预测是听觉认知中的基本过程。这些过程的一个突出的计算模型是部分匹配预测（PPM），这是一种可变阶马尔可夫模型，通过从训练序列中内化 n 元组来学习。然而，PPM 作为认知模型存在局限性：特别是，它具有完美的记忆，平等地加权所有历史观察结果，这与人类认知中观察到的记忆容量限制和最近效应不一致。我们通过 PPM-Decay 解决了这些限制，这是 PPM 的一个新变体，它引入了一个可定制的记忆衰减核。在三项研究中——一项是使用人工生成的序列，一项是使用西方音乐的和弦序列，一项是使用来自听觉模式检测实验的新行为数据——我们展示了这种衰减核如何提高模型对随时间变化的底层统计数据的序列的预测性能，并使模型能够捕获记忆限制对听觉模式检测的影响。由此产生的模型可在我们的新开源 R 包 ppm 中获得（https://github.com/pmcharrison/ppm）。
