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Predictive uncertainty in auditory sequence processing

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Predictive uncertainty in auditory sequence processing. / Hansen, Niels Chr.; Pearce, Marcus T.

In: Frontiers in Psychology, Vol. 5, No. 1052, 1052, 23.09.2014, p. 1-17.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Hansen, NC & Pearce, MT 2014, 'Predictive uncertainty in auditory sequence processing' Frontiers in Psychology, vol. 5, no. 1052, 1052, pp. 1-17. https://doi.org/10.3389/fpsyg.2014.01052

APA

Hansen, N. C., & Pearce, M. T. (2014). Predictive uncertainty in auditory sequence processing. Frontiers in Psychology, 5(1052), 1-17. [1052]. https://doi.org/10.3389/fpsyg.2014.01052

CBE

MLA

Vancouver

Hansen NC, Pearce MT. Predictive uncertainty in auditory sequence processing. Frontiers in Psychology. 2014 Sep 23;5(1052):1-17. 1052. https://doi.org/10.3389/fpsyg.2014.01052

Author

Hansen, Niels Chr. ; Pearce, Marcus T. / Predictive uncertainty in auditory sequence processing. In: Frontiers in Psychology. 2014 ; Vol. 5, No. 1052. pp. 1-17.

Bibtex

@article{100f56ba84ba46b6a6cc1669d82f1119,
title = "Predictive uncertainty in auditory sequence processing",
abstract = "Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty—a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music.",
keywords = "statistical learning, information theory, entropy, expectation, auditory cognition, music, melody",
author = "Hansen, {Niels Chr.} and Pearce, {Marcus T}",
year = "2014",
month = "9",
day = "23",
doi = "10.3389/fpsyg.2014.01052",
language = "English",
volume = "5",
pages = "1--17",
journal = "Frontiers in Psychology",
issn = "1664-1078",
publisher = "Frontiers Media S.A",
number = "1052",

}

RIS

TY - JOUR

T1 - Predictive uncertainty in auditory sequence processing

AU - Hansen, Niels Chr.

AU - Pearce, Marcus T

PY - 2014/9/23

Y1 - 2014/9/23

N2 - Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty—a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music.

AB - Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty—a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using probability estimates from an unsupervised, variable-order Markov model, 12 melodic contexts high in entropy and 12 melodic contexts low in entropy were selected from two musical repertoires differing in structural complexity (simple and complex). Musicians and non-musicians listened to the stimuli and provided explicit judgments of perceived uncertainty (explicit uncertainty). We also examined an indirect measure of uncertainty computed as the entropy of expectedness distributions obtained using a classical probe-tone paradigm where listeners rated the perceived expectedness of the final note in a melodic sequence (inferred uncertainty). Finally, we simulate listeners' perception of expectedness and uncertainty using computational models of auditory expectation. A detailed model comparison indicates which model parameters maximize fit to the data and how they compare to existing models in the literature. The results show that listeners experience greater uncertainty in high-entropy musical contexts than low-entropy contexts. This effect is particularly apparent for inferred uncertainty and is stronger in musicians than non-musicians. Consistent with the Statistical Learning Hypothesis, the results suggest that increased domain-relevant training is associated with an increasingly accurate cognitive model of probabilistic structure in music.

KW - statistical learning

KW - information theory

KW - entropy

KW - expectation

KW - auditory cognition

KW - music

KW - melody

U2 - 10.3389/fpsyg.2014.01052

DO - 10.3389/fpsyg.2014.01052

M3 - Journal article

VL - 5

SP - 1

EP - 17

JO - Frontiers in Psychology

JF - Frontiers in Psychology

SN - 1664-1078

IS - 1052

M1 - 1052

ER -