@proceedings {2022,
	title = {Comparing Musicians and Non-musicians{\textquoteright} Expectations in Music and Vision},
	year = {2022},
	month = {09/2022},
	pages = {1-8},
	publisher = {ACM},
	address = {Austria and online},
	abstract = {The role of expectation in music has been of research interest for decades. Expectation mechanisms have also received considerable attention in vision, due in part to the widespread interest in predictive coding. Past research has uncovered different types of expectations that may be formed when exposed to a sequential stimulus, such as schematic expectation (based on general knowledge) and dynamic expectation (based on properties within the current stimulus). Yet to our knowledge, a direct comparison of the relative contribution of these types of expectation has not been performed within the same subjects through careful manipulation of stimuli, nor has this comparison been made across the musical and visual domains. This listener study aims to uncover the relative influence of dynamic and schematic expectations in musical and visual stimuli, and investigate the role of expertise in forming expectations by testin g both musicians and non-musicians. Our findings suggest that musicians are indeed more sensitive than non-musicians to the dynamic and schematic properties of musical stimuli, and they generally produce a wider range of expectedness ratings than non-musicians. Interestingly, musicians also interpret schematic information in the visual condition differently than non-musicians, suggesting that musical training may have influenced their expectation mechanisms more generally.},
	keywords = {cross-modal comparison, Expectation, music, musician vs non-musician, vision},
	author = {Kat R Agres and Ting Yuan Tay and Marcus Pearce}
}
@conference {2022,
	title = {Computational Music Systems for Emotional Health and Wellbeing: A Review},
	booktitle = {ACM Special Interest Group on Computer{\textendash}Human Interaction (SIGHCI) 2022},
	year = {2022},
	month = {12/2022},
	abstract = {Music is a powerful stimulus, and both active and receptive methods of engaging with music provide affordances for improving physical, mental and social health. The emergence of sophisticated computational methods also underscores the potential for novel music technologies to address a wider range of wellbeing outcomes. In this review, we focus on describing the current state of the literature on computational approaches to music generation for health and wellbeing and identifying possible future directions for research in this area.},
	author = {Phoebe Chua and Chitralekha Gupta and Kat R. Agres and Suranga Nanayakkara}
}
@article {9999,
	title = {Change detection and schematic processing in music},
	journal = {Psychology of Music},
	volume = {47},
	year = {2019},
	pages = {173-193},
	abstract = {Research in vision has highlighted the importance of gist representations in change detection and memory. This paper puts forth the hypothesis that schematic processing and gist provide an account for change detection in music as well, where a musical gist is an abstracted memory representation for schematically consistent tones. The present experiments illuminate the content of gist memory representations by testing when listeners can detect single-tone changes in pairs of melodies. In Experiment 1, musicians and non-musicians listened to melodies varying in tonal structure. Less structure resulted in compromised change detection in both groups. Most often, musicians displayed more accurate change detection than non-musicians, but surprisingly, when schematic processing could not contribute to memory encoding, musicians performed worse than their untrained counterparts. Experiment 2 utilized a full-factorial design to examine tonality, interval of pitch change, metrical position, and rhythm. Tonality had a particularly large effect on performance, with non-scale tones generally aiding change detection. Listeners were unlikely, however, to detect schematically-inconsistent tones when only brief melodic context was available. The results uphold the hypothesis that memory for melodies relies on schematic processing, with change detection dependent upon whether the change alters the schematic gist of the melody.},
	doi = {DOI:10.1177/0305735617751249},
	author = {Kat Agres}
}
@article {2019,
	title = {A closed-loop, music-based brain-computer interface for emotion mediation},
	journal = {PLoS ONE},
	volume = {14},
	year = {2019},
	month = {03/2019},
	pages = {e0213516},
	type = {Research Article},
	abstract = {Emotions play a critical role in rational and intelligent behavior; a better fundamental knowledge of them is indispensable for understanding higher order brain function. We propose a non-invasive brain-computer interface (BCI) system to feedback a person{\textquoteright}s affective state such that a closed-loop interaction between the participant{\textquoteright}s brain responses and the musical stimuli is established. We realized this concept technically in a functional prototype of an algorithm that generates continuous and controllable patterns of synthesized affective music in real-time, which is embedded within a BCI architecture. We evaluated our concept in two separate studies. In the first study, we tested the efficacy of our music algorithm by measuring subjective affective responses from 11 participants. In a second pilot study, the algorithm was embedded in a real-time BCI architecture to investigate affective closed-loop interactions in 5 participants. Preliminary results suggested that participants were able to intentionally modulate the musical feedback by self-inducing emotions (e.g., by recalling memories), suggesting that the system was able not only to capture the listener{\textquoteright}s current affective state in real-time, but also potentially provide a tool for listeners to mediate their own emotions by interacting with music. The proposed concept offers a tool to study emotions in the loop, promising to cast a complementary light on emotion-related brain research, particularly in terms of clarifying the interactive, spatio-temporal dynamics underlying affective processing in the brain.},
	keywords = {brain-computer interface, EEG, emotion mediation, music BCI, neurofeedback},
	doi = {https://doi.org/10.1371/journal.pone.0213516},
	author = {Stefan Ehrlich and Kat Agres and Cuntai Guan and Gordon Cheng}
}
@proceedings {2019,
	title = {A Comparative Study of Mental States in 2D and 3D Virtual Environments Using EEG},
	year = {2019},
	month = {10/2019},
	publisher = {IEEE},
	address = {Bari, Italy},
	abstract = {There is growing evidence that virtual reality (VR) can be used as an optional therapy method for stress relief and treatment of mental disorders such as a variety of anxiety disorders, depression and psychosis. However, more systematic studies to quantify the effects of VR for emotion elicitation and compare the effects of 3D and 2D environment for the same is necessary to design feasible and effective treatments. In this study, we design a cross-over experiment protocol comprising of two emotion eliciting environments relaxation and arousal - that is presented to the participant either in a 2D monitor or a 3D head-mounted (HMD) VR display. The EEG data is collected during the experiment and analyzed offline to classify emotion elicited by low and high arousal environments using SVM classification of band power features. A 10-fold cross validation is performed and classification accuracy for each subject is computed for 3D-VR and 2D-screen display. The average classification accuracies over subjects are obtained as 66.88\% and 59.27\% in 3D-VR and 2D-screen group respectively. The performance difference between 3D-VR and 2D screen is statistically significant (p < 0.05), indicating that 3D-VR generates more distinct EEG patterns associated with emotion elicitation. Further, significant differences in band powers from alpha, theta and beta bands are also observed between both groups. The results presented in this paper support the use of VR as an effective tool to study emotion elicitation and regulation, compared to a 2D screen.},
	author = {Pinar Bilgin and Kat Agres and Neethu Robinson and Aung Aung Phyo Wai and Cuntai Guan}
}
@conference {27,
	title = {Conceptualizing Creativity: From Distributional Semantics to Conceptual Spaces},
	booktitle = {Proceedings of the Sixth International Conference on Computational Creativity},
	year = {2015},
	author = {Agres, Kat and McGregor, Stephen and Purver, Matthew and Wiggins, Geraint}
}
@conference {33,
	title = {Co-occurrences of harmonics bootstrap pitch and tonality perception in music: Evidence from a statistical unsupervised learning model},
	booktitle = {CogSci 2015: The annual meeting of the Cognitive Science Society},
	year = {2015},
	address = {Pasadena, CA, USA},
	author = {K. Agres and C. E. Cancino Chac{\'o}n and M. Grachten and S. Lattner}
}
