@article {2023,
	title = {AffectMachine-Classical: a novel system for generating affective classical music},
	journal = {Frontiers in Psychology 14:1158172. doi: 10.3389/fpsyg.2023.1158172},
	volume = {14:1158172},
	year = {2023},
	month = {06/2023},
	pages = {1-13},
	type = {Journal article},
	abstract = {This work introduces a new music generation system, called AffectMachine-Classical, that is capable of generating affective Classic music in real-time. AffectMachine was designed to be incorporated into biofeedback systems (such as brain-computer-interfaces) to help users become aware of, and ultimately mediate, their own dynamic affective states. That is, this system was developed for music-based MedTech to support real-time emotion self-regulation in users. We provide an overview of the rule-based, probabilistic system architecture, describing the main aspects of the system and how they are novel. We then present the results of a listener study that was conducted to validate the ability of the system to reliably convey target emotions to listeners. The findings indicate that AffectMachine-Classical is very effective in communicating various levels of Arousal (R2 = 0.96) to listeners, and is also quite convincing in terms of Valence (R2 = 0.90). Future work will embed AffectMachine-Classical into biofeedback systems, to leverage the efficacy of the affective music for emotional wellbeing in listeners.},
	keywords = {affective computing, algorithmic composition, automatic music generation system, emotion regulation, listener validation study, Music MedTech},
	doi = {doi.org/10.3389/fpsyg.2023.1158172},
	url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1158172/full},
	author = {Kat R. Agres and Adyasha Dash and Phoebe Chua}
}
@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}
}
@conference {32,
	title = {An auditory twist on visual search using head movement controlled ambisonics},
	booktitle = {International Computer Music Conference},
	year = {2010},
	publisher = {Ann Arbor, MI: MPublishing, University of Michigan Library},
	organization = {Ann Arbor, MI: MPublishing, University of Michigan Library},
	author = {Agres, Kat and Spencer Topel and Michael Spivey and Stephen Moseson}
}
