Information-theoretic Modeling of Perceived Musical Complexity

Sauvé, Sarah A.; Pearce, Marcus T.

doi:10.1525/mp.2019.37.2.165

Cited by 8 publications

(7 citation statements)

References 42 publications

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“…Similarly, the study paradigm employed experts’ complexity ratings to define how difficult or easy it was to predict musical elements in the stimuli. Notably, not all acoustic features are important to the experience of predictability [ 27 , 38 , 60 ]. Hence, comparing liking ratings to complexity judgements, rather than acoustic features such as probability of pitch change, increased the likelihood of observing how people respond to music that is perceived as unpredictable.…”

Section: Discussionmentioning

confidence: 99%

“…If preference for predictability influences music preferences, then the extent to which a listener enjoys a piece of music should be contingent on the listener’s perceived levels of predictability. However, not all acoustic features contribute equally to the experience of predictability [ 27 , 38 , 60 ]. The likelihood of capturing facets relevant to preference might therefore be higher for subjective evaluations of predictability than for an assessment based on acoustic features.…”

Section: Current Studymentioning

confidence: 99%

“…What mechanisms drive individual differences in the sweet spot for predictability? The experience of predictability reflects subjective perceptions of predictability, which only partly relates to objective features such as the number of tones in a song or edges in a painting [ 36 – 38 ]. Accordingly, people vary in their perception of stimuli as predictable or unpredictable [ 36 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

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Sweet spot in music—Is predictability preferred among persons with psychotic-like experiences or autistic traits?

et al. 2022

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People prefer music with an intermediate level of predictability; not so predictable as to be boring, yet not so unpredictable that it ceases to be music. This sweet spot for predictability varies due to differences in the perception of predictability. The symptoms of both psychosis and Autism Spectrum Disorder have been attributed to overestimation of uncertainty, which predicts a preference for predictable stimuli and environments. In a pre-registered study, we tested this prediction by investigating whether psychotic and autistic traits were associated with a higher preference for predictability in music. Participants from the general population were presented with twenty-nine pre-composed music excerpts, scored on their complexity by musical experts. A participant’s preferred level of predictability corresponded to the peak of the inverted U-shaped curve between music complexity and liking (i.e., a Wundt curve). We found that the sweet spot for predictability did indeed vary between individuals. Contrary to predictions, we did not find support for these variations being associated with autistic and psychotic traits. The findings are discussed in the context of the Wundt curve and the use of naturalistic stimuli. We also provide recommendations for further exploration.

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Section: Discussionmentioning

confidence: 99%

Section: Current Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sweet spot in music—Is predictability preferred among persons with psychotic-like experiences or autistic traits?

et al. 2022

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“…No matter how internalization occurs, internal models of the (permissible) musical structures are generated and applied whenever listening to novel music (Deliège, 1987;Agres, 2019). If the internal models somehow confront the (prescribed) rules of the arrangement of the constituent parts of a musical structure, the structure is perceived not only as irregular, but also as more complex and less enjoyable (Sauvé & Pearce 2019;Mihelač & Povh, 2020b). This is because more effort has to be put into the listening process, as the learned musical syntax and its rules have to be adjusted in order to understand the novel structures (Kramer, 1988).…”

Section: Introductionmentioning

confidence: 99%

A Computational Approach to the Detection and Prediction of (Ir)Regularity in Children's Folk Songs

Mihelač

Povh

Wiggins

2023

EMR

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We examine (ir)regularity in the musical structure of 736 monophonic children's folk songs from 22 European countries, by simulating and detecting (ir)regularity with the computational model, IDyOM, and our own algorithm, Ir_Reg, which classifies melodies according to regularity of their musical structure. IDyOM offers a range of viewpoints which allow observation and prediction of various musical features. We used five viewpoints to measure the information content and entropy of musical events in songs. Analysis across the data shows absence of irregular musical structure in children's folk songs from Croatia, Serbia, Turkey, Portugal, Hungary, and Romania. Conversely, absence of regular structure in children's folk songs was found in Great Britain, Norway and Switzerland. Further analysis of (ir)regularity, by individual country, revealed the importance of patterns repeated at pitch in regular songs, and a higher occurrence of transposed repeated patterns in irregular songs. Principal component analysis (PCA) shows the salience of pitch and pitch intervals in the perception of (ir)regular structure. Neither rhythm nor contour affects the perception of regularity. Recurring pulse/meter and arch-like melodic structure were found in the majority of children's folk songs. The study shows that irregularity exists in children's folk songs, and that this genre can be complex.

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“…IDyOM is motivated by the idea that statistical learning applies across domains, and that melodic expectations are just one instance of this domain‐general learning. IDyOM's predictions have been shown to correlate with listener judgments of melodic uncertainty (Hansen & Pearce, 2014) and electrophysiological responses to melodic stimuli (Pearce, Ruiz, Kapasi, Wiggins, & Bhattacharya, 2010) as well as phenomena such as melodic segmentation (Pearce, Müllensiefen, & Wiggins, 2010), expressive timing (Gingras et al., 2016), and perceived musical complexity (Sauvé & Pearce, 2019).…”

Section: Introductionmentioning

confidence: 99%

Pitches that Wire Together Fire Together: Scale Degree Associations Across Time Predict Melodic Expectations

Verosky¹,

Morgan

2021

Cognitive Science

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The ongoing generation of expectations is fundamental to listeners' experience of music, but research into types of statistical information that listeners extract from musical melodies has tended to emphasize transition probabilities and n-grams, with limited consideration given to other types of statistical learning that may be relevant. Temporal associations between scale degrees represent a different type of information present in musical melodies that can be learned from musical corpora using expectation networks, a computationally simple method based on activation and decay.Expectation networks infer the expectation of encountering one scale degree followed in the near (but not necessarily immediate) future by another given scale degree, with previous work suggesting that scale degree associations learned by expectation networks better predict listener ratings of pitch similarity than transition probabilities. The current work outlines how these learned scale degree associations can be combined to predict melodic continuations and tests the resulting predictions on a dataset of listener responses to a musical cloze task previously used to compare two other models of melodic expectation, a variable-order Markov model (IDyOM) and Temperley's music-theoretically motivated model. Under multinomial logistic regression, all three models explain significant unique variance in human melodic expectations, with coefficient estimates highest for expectation networks.These results suggest that generalized scale degree associations informed by both adjacent and nonadjacent relationships between melodic notes influence listeners' melodic predictions above and beyond n-gram context, highlighting the need to consider a broader range of statistical learning processes that may underlie listeners' expectations for upcoming musical events.

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Information-theoretic Modeling of Perceived Musical Complexity

Cited by 8 publications

References 42 publications

Sweet spot in music—Is predictability preferred among persons with psychotic-like experiences or autistic traits?

Sweet spot in music—Is predictability preferred among persons with psychotic-like experiences or autistic traits?

A Computational Approach to the Detection and Prediction of (Ir)Regularity in Children's Folk Songs

Pitches that Wire Together Fire Together: Scale Degree Associations Across Time Predict Melodic Expectations

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