Information‐Theoretic Measures Predict the Human Judgment of Rhythm Complexity

Fleurian, Rémi de; Blackwell, Tim; Ben-Tal, Oded; Müllensiefen, Daniel

doi:10.1111/cogs.12347

Cited by 13 publications

(16 citation statements)

References 36 publications

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“…Limitations in processing time and memory space constrain the amount of stimulus information (i.e. the distinct interval durations) that can be efficiently extracted and accurately encoded (Drake & Bertrand, ; de Fleurian, Blackwell, Ben‐Tal, & Müllensiefen, ; Snyder, ). Following this, we would expect weaker predictive models for more complex rhythmic patterns, and hence a down‐weighted neural response to their violations.…”

Section: Discussionmentioning

confidence: 99%

Weighting of neural prediction error by rhythmic complexity: A predictive coding account using mismatch negativity

Lumaca

Haumann

Brattico

et al. 2019

Eur J of Neuroscience

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The human brain's ability to extract and encode temporal regularities and to predict the timing of upcoming events is critical for music and speech perception. This work addresses how these mechanisms deal with different levels of temporal complexity, here the number of distinct durations in rhythmic patterns. We use electroencephalography (EEG) to relate the mismatch negativity (MMN), a proxy of neural prediction error, to a measure of information content of rhythmic sequences, the Shannon entropy. Within each of three conditions, participants listened to repeatedly presented standard rhythms of five tones (four inter‐onset intervals) and of a given level of entropy: zero (isochronous), medium entropy (two distinct interval durations), or high entropy (four distinct interval durations). Occasionally, the fourth tone was moved forward in time that is it occurred 100 ms (small deviation) or 300 ms early (large deviation). According to the predictive coding framework, high‐entropy stimuli are more difficult to model for the brain, resulting in less confident predictions and yielding smaller prediction errors for deviant sounds. Our results support this hypothesis, showing a gradual decrease in MMN amplitude as a function of entropy, but only for small timing deviants. For large timing deviants, in contrast, a modulation of activity in the opposite direction was observed for the earlier N1 component, known to also be sensitive to sudden changes in directed attention. Our results suggest the existence of a fine‐grained neural mechanism that weights neural prediction error to the complexity of rhythms and that mostly manifests in the absence of directed attention.

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Section: Discussionmentioning

confidence: 99%

Weighting of neural prediction error by rhythmic complexity: A predictive coding account using mismatch negativity

Lumaca

Haumann

Brattico

et al. 2019

Eur J of Neuroscience

View full text Add to dashboard Cite

show abstract

“…First, the visual and auditory cortices vastly differ in the parameters defining efficiency in computing representations from continuous input streams (Conway & Christiansen, 2006;Emberson, Conway, & Christiansen, 2011;Frost et al, 2015). From a pure information theory perspective, entropy can be assessed in any input regardless of modality.…”

Section: Discussionmentioning

confidence: 99%

“…The RI hypothesis generates two straightforward predictions. First, if indeed RI drives SL performance, then a clear monotonic function, where higher levels of RI lead to worse SL performance, should be found (as documented in other domains in the Cognitive Sciences, e.g., de Fleurian, Blackwell, Ben‐Tal, & Müllensiefen, ; Fan, ; Miller, ). Whereas, in principle, the shape of this function is not unequivocally clear a priori, in the context of VSL performance, a logarithmic function seems the most probable: At lower levels of RI (i.e., streams with lower amounts of information to be extracted) we expect a substantial impact of changes in the amount of information on SL performance, which is expected to decrease at higher levels of RI as SL performance gets closer to chance level.…”

Section: Introductionmentioning

confidence: 92%

What Determines Visual Statistical Learning Performance? Insights From Information Theory

2019

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In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co-occurrences between them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct-the rate of information in the sensory input-explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large-scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning.

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“…The preference also affects musical memory, which is better for culturally similar music in comparison to unknown music (Demorest et al, 2008). Also the recognition of rhythm is influenced by culture, which is why foreign rhythms seem more complex and are more difficult to recognize (Cross, 2001).…”

Section: Culturally-dependent Human Melody Predictionmentioning

confidence: 99%

“…Several studies analyze the cultural influence on rhythmic and melodic complexity (Eerola et al, 2006;de Fleurian et al, 2017). Groups of participants with different cultural backgrounds (African, Western) perceived music of their own culture as less complex.…”

Section: Culturally-dependent Human Melody Predictionmentioning

confidence: 99%

A Comparison of Human and Computational Melody Prediction Through Familiarity and Expertise

et al. 2020

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Melody prediction is an important aspect of music listening. The success of prediction, i.e., whether the next note played in a song is the same as the one predicted by the listener, depends on various factors. In the paper, we present two studies, where we assess how music familiarity and music expertise influence melody prediction in human listeners, and, expressed in appropriate data/algorithmic ways, computational models. To gather data on human listeners, we designed a melody prediction user study, where familiarity was controlled by two different music collections, while expertise was assessed by adapting the Music Sophistication Index instrument to Slovenian language. In the second study, we evaluated the melody prediction accuracy of computational melody prediction models. We evaluated two models, the SymCHM and the Implication-Realization model, which differ substantially in how they approach melody prediction. Our results show that both music familiarity and expertise affect the prediction accuracy of human listeners, as well as of computational models.

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Information‐Theoretic Measures Predict the Human Judgment of Rhythm Complexity

Cited by 13 publications

References 36 publications

Weighting of neural prediction error by rhythmic complexity: A predictive coding account using mismatch negativity

Weighting of neural prediction error by rhythmic complexity: A predictive coding account using mismatch negativity

What Determines Visual Statistical Learning Performance? Insights From Information Theory

A Comparison of Human and Computational Melody Prediction Through Familiarity and Expertise

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