Electrophysiological correlates of melodic processing in congenital amusia

Omigie, Diana; Pearce, Marcus T.; Williamson, Victoria J.; Stewart, Lauren

doi:10.1016/j.neuropsychologia.2013.05.010

Cited by 128 publications

(127 citation statements)

References 48 publications

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“…In previous research, IDyOM has been found to account accurately for listeners' expectations and uncertainty while listening to music (Pearce, 2005;Omigie et al, 2012;Omigie et al, 2013;Egermann et al, 2013;Hansen & Pearce, 2014). The present research extends IDyOM to modelling similarity perception.…”

Section: Discussionsupporting

confidence: 64%

“…Pearce, 2005;Pearce, Ruiz, Kapasi, Wiggins, & Bhattacharya, 2010;Omigie, Pearce, & Stewart, 2012;Omigie, Pearce, & Stewart, 2013;Egermann, Pearce, Wiggins, & McAdams, 2013;Hansen & Pearce, 2014). Information content and entropy provide more accurate models of listeners' pitch expectations and uncertainty, respectively, than rule-based models (e.g.…”

Section: Compression-based Similarity Measuresmentioning

confidence: 99%

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Compression-based Modelling of Musical Similarity Perception

Pearce

Müllensiefen

2017

Journal of New Music Research

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Similarity is an important concept in music cognition research since the similarity between (parts of) musical pieces determines perception of stylistic categories and structural relationships between parts of musical works. The purpose of the present research is to develop and test models of musical similarity perception inspired by a transformational approach which conceives of similarity between two perceptual objects in terms of the complexity of the cognitive operations required to transform the representation of the first object into that of the second, a process which has been formulated in informationtheoretic terms. Specifically, computational simulations are developed based on compression distance in which a probabilistic model is trained on one piece of music and then used to predict, or compress, the notes in a second piece. The more predictable the second piece according to the model, the more efficiently it can be encoded and the greater the similarity between the two pieces. The present research extends an existing information-theoretic model of auditory expectation (IDyOM) to compute compression distances varying in symmetry and normalisation using high-level symbolic features representing aspects of pitch and rhythmic structure. Comparing these compression distances with listeners' similarity ratings between pairs of melodies collected in three experiments demonstrates that the compression-based model provides a good fit to the data and allows the identification of representations, model parameters and compression-based metrics that best account for musical similarity perception. The compression-based model also shows comparable performance to the best-performing algorithms on the MIREX 2005 melodic similarity task.

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Section: Discussionsupporting

confidence: 64%

Section: Compression-based Similarity Measuresmentioning

confidence: 99%

Compression-based Modelling of Musical Similarity Perception

Pearce

Müllensiefen

2017

Journal of New Music Research

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“…It was designed for modeling dynamically changing auditory expectations, based on long-term and short-term statistical learning, which evolve as a piece of music unfolds. Empirical research has demonstrated that IDyOM accurately simulates listeners' predictive processing of melody in many perceptual tasks involving pitch expectation (Pearce, 2005; Pearce et al, 2010; Omigie et al, 2012, 2013), uncertainty (Hansen and Pearce, 2014), segmentation (Pearce et al, 2010) and emotional response (Egermann et al, 2013; Gingras et al, 2015). …”

Section: The Probabilistic Modelmentioning

confidence: 99%

A Probabilistic Model of Meter Perception: Simulating Enculturation

2017

Self Cite

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Enculturation is known to shape the perception of meter in music but this is not explicitly accounted for by current cognitive models of meter perception. We hypothesize that the induction of meter is a result of predictive coding: interpreting onsets in a rhythm relative to a periodic meter facilitates prediction of future onsets. Such prediction, we hypothesize, is based on previous exposure to rhythms. As such, predictive coding provides a possible explanation for the way meter perception is shaped by the cultural environment. Based on this hypothesis, we present a probabilistic model of meter perception that uses statistical properties of the relation between rhythm and meter to infer meter from quantized rhythms. We show that our model can successfully predict annotated time signatures from quantized rhythmic patterns derived from folk melodies. Furthermore, we show that by inferring meter, our model improves prediction of the onsets of future events compared to a similar probabilistic model that does not infer meter. Finally, as a proof of concept, we demonstrate how our model can be used in a simulation of enculturation. From the results of this simulation, we derive a class of rhythms that are likely to be interpreted differently by enculturated listeners with different histories of exposure to rhythms.

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“…Furthermore, they focused on computing and comparing the information-theoretic properties of entire corpora, rather than building dynamic predictive models that learn incrementally through exposure (Cohen, 1962;Pearce & Wiggins, 2012). In recent years, sophisticated, dynamic probabilistic models such as information dynamics of music (IDyOM; Pearce, 2005; see also Section 3.1) have successfully derived information-theoretic properties of auditory sequences that accurately account for listeners' expectations in many listening tasks (Egermann, Pearce, Wiggins, & McAdams, 2013;Hansen & Pearce, 2014;Omigie, Pearce, & Stewart, 2012;Omigie, Pearce, Williamson, & Stewart, 2013;Pearce, 2005;Pearce, M€ ullensiefen, & Wiggins, 2010;Pearce, Ruiz, et al, 2010). Recent research has also developed more sophisticated informationtheoretic measures that systematically distinguish different ways in which a stimulus can be unpredictable (Abdallah & Plumbley, 2009, 2010, 2012.…”

Section: Information-theoretic Accounts Of Auditory Processingmentioning

confidence: 99%

Information‐Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory

2017

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A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners' memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners' expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session.

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Electrophysiological correlates of melodic processing in congenital amusia

Cited by 128 publications

References 48 publications

Compression-based Modelling of Musical Similarity Perception

Compression-based Modelling of Musical Similarity Perception

A Probabilistic Model of Meter Perception: Simulating Enculturation

Information‐Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory

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