title: Predicting musically induced emotions from physiological inputs: linear and neural network models.
abstract: 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.
authors: SMART Lab, Department of Psychology, Ryerson University Toronto, ON, Canada ; Communication Team, Toronto Rehabilitation Institute Toronto, ON, Canada.
authors: Frank A
authors: FA
authors: Russo
authors: Naresh N
authors: NN
authors: Vempala
authors: Gillian M
authors: GM
authors: Sandstrom
keywords: computational modeling; emotion; music cognition; neural networks; physiological responses
references: Appl Psychophysiol Biofeedback. 2007 Mar;32(1):31-50
references: 17333313
references: Can J Exp Psychol. 1997 Dec;51(4):336-53
references: 9606949
references: Emot Rev. 2011 Jan;3(1):8-16
references: 21479078
references: PLoS One. 2009 Oct 16;4(10):e7487
references: 19834599
references: Int J Psychophysiol. 2006 Apr;60(1):34-43
references: 15993964
references: Cognition. 2000 Jul 14;76(1):13-58
references: 10822042
references: Behav Brain Sci. 2008 Oct;31(5):559-75; discussion 575-621
references: 18826699
references: Int J Psychophysiol. 2006 Jul;61(1):5-18
references: 16439033
references: Int J Psychophysiol. 2006 Jul;61(1):57-69
references: 16460823
references: Cognition. 1993 Jul;48(1):71-99
references: 8403835
references: J Pers Soc Psychol. 2003 Jun;84(6):1236-56
references: 12793587
references: IEEE Trans Pattern Anal Mach Intell. 2008 Dec;30(12):2067-83
references: 18988943
references: Psychol Rev. 1992 Jul;99(3):550-3
references: 1344638
references: J Behav Ther Exp Psychiatry. 1994 Mar;25(1):49-59
references: 7962581
references: Emotion. 2008 Aug;8(4):494-521
references: 18729581
references: Emotion. 2011 Aug;11(4):921-37
references: 21859207