@article {2021,
	title = {Music, Computing, and Health: A roadmap for the current and future roles of music technology for health care and well-being},
	journal = {Music \& Science},
	year = {2021},
	abstract = {The fields of music, health, and technology have seen significant interactions in recent years in
developing music technology for health care and well-being. In an effort to strengthen the
collaboration between the involved disciplines, the workshop {\textquoteleft}Music, Computing, and Health{\textquoteright} was
held to discuss best practices and state-of-the-art at the intersection of these areas with
researchers from music psychology and neuroscience, music therapy, music information retrieval,
music technology, medical technology (medtech) and robotics. Following the discussions at the
workshop, this paper provides an overview of the different methods of the involved disciplines
and their potential contributions to developing music technology for health and well-being.
Furthermore, the paper summarizes the state of the art in music technology that can be applied in
various health scenarios and provides a perspective on challenges and opportunities for
developing music technology that 1) supports person-centered care and evidence-based
treatments, and 2) contributes to developing standardized, large-scale research on music-based
interventions in an interdisciplinary manner. The paper provides a resource for those seeking to
engage in interdisciplinary research using music-based computational methods to develop
technology for health care, and aims to inspire future research directions by evaluating the state
of the art with respect to the challenges facing each field.},
	keywords = {Computing, Health, Music Information Retrieval, Music Psychology, Music Therapy, Roadmap, Technology},
	doi = {https://doi.org/10.1177/2059204321997709},
	author = {Agres, Kat R and Schaefer, Rebecca and Volk, Anja and van Hooren, Susan and Holzapfel, Andre and DallaBella, Simone and Müller, Meinard and de Witte, Martina and Herremans, Dorien and Ramirez-Melendez, Rafael and Neerincx, Mark and Ruiz, Sebastian and Meredith, David and Dimitriadis, Theo and Magee, Wendy}
}
@article {2019,
	title = {Age dependent statistical learning trajectories reveal differences in information weighting},
	journal = {Psychology and Aging},
	year = {2020},
	abstract = {Statistical learning (SL) is the ability to generate predictions based on probabilistic dependencies in the environment, an ability that is present throughout life. However, SL in older adults has received far less attention. Here, we explore statistical learning in healthy adults (40 younger, and 40 older). The novel paradigm tracks learning trajectories and shows age-related differences in overall performance, yet similarities in learning rates. Bayesian models reveal further differences between younger and older adults in dealing with uncertainty in this probabilistic SL task. We test computational models of three different learning strategies: (1) Win-Stay, Lose-Shift, (2) Delta Rule Learning, (3) Information Weights to explore whether they capture age-related differences in performance and learning in the present task. A likely candidate mechanism emerges in the form of age-dependent differences in information weights, in which young adults more readily change their behaviour, but also show disproportionally strong reactions towards erroneous predictions. Taken together, the present pattern of results points towards age-related differences in information processing, with lower but more balanced information weights in older adults.  },
	keywords = {Cognitive Assessment, Continuous Paradigm, Elderly, Statistical Learning},
	doi = {10.31234/osf.io/kuy6p},
	url = {psyarxiv.com/kuy6p},
	author = {Herff, Steffen A and Zhen, Shanshan and Yu, Rongjun and Agres, Kat R}
}
@article {2016,
	title = {Workshop on Auditory Neuroscience, Cognition, and Modeling},
	journal = {Psychomusicology: Music, Mind, and Brain},
	volume = {26},
	year = {2016},
	month = {09/2016},
	pages = {288-292},
	abstract = {Early 2016 saw the first international Workshop on Auditory Neuroscience, Cognition and Modeling, hosted by Queen Mary University of London. The workshop aimed to bring together cognitive scientists, neuroscientists, and computer scientists working on sound, music, and speech processing. Engendering broad interest, 96 participants from a wide range of backgrounds attended for a day of talks, poster presentations, and lively discussion. This report provides an overview of the event, summarizing the 3 keynote talks, and highlighting the major themes from the oral presentation and poster sessions. A summary is provided in closing, touching upon main outcomes and considerations for future research within this alignment of fields.},
	keywords = {auditory neuroscience, computational modeling, music cognition, speech perception, WANCM},
	author = {Agres, Kat R and Sauv{\'e}, Sarah A}
}
@conference {26,
	title = {The sparsity of simple recurrent networks in musical structure learning},
	booktitle = {Proceedings of the 31th Annual Conference of the Cognitive Science Society},
	year = {2009},
	publisher = {Citeseer},
	organization = {Citeseer},
	author = {Agres, Kat R and DeLong, Jordan E and Spivey, Michael}
}
@conference {25,
	title = {Musical change deafness: The inability to detect change in a non-speech auditory domain},
	booktitle = {In B. C. Love, K. McRae, \& V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 969-974)},
	year = {2008},
	author = {Agres, Kat R and Krumhansl, Carol L}
}
@article {28,
	title = {Musical expectancy: The influence of musical structure on emotional response},
	journal = {Behavioral and Brain Sciences},
	volume = {31},
	year = {2008},
	month = {10/2008},
	pages = {584{\textendash}585},
	abstract = {When examining how emotions are evoked through music, the role of musical expectancy is often surprisingly under-credited. This mechanism, however, is most strongly tied to the actual structure of the music, and thus is important when considering how music elicits emotions. We briefly summarize Leonard Meyer{\textquoteright}s theoretical framework on musical expectancy and emotion and cite relevant research in the area.},
	doi = {https://doi.org/10.1017/S0140525X08005384},
	author = {Krumhansl, Carol L and Agres, Kat R}
}
