Effects of memory size on melody recognition in a simulation of cohort theory

Vempala, Naresh N.; Maida, Anthony S.

doi:10.1016/j.cogsys.2010.07.003

“…Although artificial neural networks have been applied extensively for classification and detection tasks in domains such as object and speech recognition, they have been relatively underutilized in music cognition (see however, Bharucha, 1987; Stevens and Latimer, 1992; Krumhansl et al, 2000; Vempala and Maida, 2011). In the current study, we applied neural networks as a non-linear regression function to predict valence and arousal ratings using physiological features as inputs.…”

Section: Methodsmentioning

confidence: 99%

Predicting musically induced emotions from physiological inputs: linear and neural network models

Russo¹,

Vempala²,

Sandstrom³

2013

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Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of “felt” emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants—heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

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“…One way of exploring non-linear combinations of physiological features is through the use of artificial neural networks. Although artificial neural networks have been applied extensively for classification and detection tasks in domains such as object and speech recognition, they have been relatively underutilized in music cognition (see however, Bharucha, 1987;Stevens and Latimer, 1992; Krumhansl et al, 2000;Vempala and Maida, 2011). In the current study, we applied neural networks as a non-linear regression function to predict valence and arousal ratings using physiological features as inputs.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predicting musically induced emotions from physiological inputs: linear and neural network models

Russo,

Vempala,

M. Sandstrom

2024

Preprint

0

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<p>Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of “felt” emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants—heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.</p>

show abstract

“…One way of exploring non-linear combinations of physiological features is through the use of artificial neural networks. Although artificial neural networks have been applied extensively for classification and detection tasks in domains such as object and speech recognition, they have been relatively underutilized in music cognition (see however, Bharucha, 1987;Stevens and Latimer, 1992; Krumhansl et al, 2000;Vempala and Maida, 2011). In the current study, we applied neural networks as a non-linear regression function to predict valence and arousal ratings using physiological features as inputs.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predicting musically induced emotions from physiological inputs: linear and neural network models

Russo,

Vempala,

M. Sandstrom

2024

Preprint

0

View full text Add to dashboard Cite

<p>Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of “felt” emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants—heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.</p>

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Effects of memory size on melody recognition in a simulation of cohort theory

Cited by 3 publications

References 21 publications

Predicting musically induced emotions from physiological inputs: linear and neural network models

Predicting musically induced emotions from physiological inputs: linear and neural network models

Predicting musically induced emotions from physiological inputs: linear and neural network models

Predicting musically induced emotions from physiological inputs: linear and neural network models

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