@article {2023, title = {Intra- and inter-brain coupling and activity dynamics during improvisational music therapy with a person with dementia: an explorative EEG-hyperscanning single case study}, journal = {Frontiers in Psychology: Cognitive Science}, volume = {14:1155732}, year = {2023}, month = {09/2023}, abstract = {Objective: Real-life research into the underlying neural dynamics of improvisational music therapy, used with various clinical populations, is largely lacking. This single case study explored within-session differences in musical features and in within- and between-brain coupling between a Person with Dementia (PwD) and a music therapist during a music therapy session. Methods: Dual-EEG from a music therapist and a PwD (male, 31 years) was recorded. Note density, pulse clarity and synchronicity were extracted from audio-visual data. Three music therapists identified moments of interest and no interest (MOI/MONI) in two drum improvisations. The Integrative Coupling Index, reflecting time-lagged neural synchronization, and musical features were compared between the MOI and MONI. Results: Between-brain coupling of 2 Hz activity was increased during the MOI, showing anteriority of the therapist{\textquoteright}s neural activity. Within-brain coupling for the PwD was stronger from frontal and central areas during the MOI, but within-brain coupling for the therapist was stronger during MONI. Differences in musical features indicated that both acted musically more similar to one another during the MOI. Conclusion: Within-session differences in neural synchronization and musical features highlight the dynamic nature of music therapy. Significance: The findings contribute to a better understanding of social and affective processes in the brain and (interactive) musical behaviors during specific moments in a real-life music therapy session. This may provide insights into the role of such moments for relational-therapeutic processes.}, keywords = {brain coupling, dementia, EEG, improv, Music Therapy}, doi = {doi:10.3389/fpsyg.2023.1155732}, author = {Clemens Maidhof and Viktor M{\"u}ller and Olivier Lartillot and Kat Agres and Jodie Bloska and Rie Asano and Helen Odell-Miller and J{\"o}rg Fachner} } @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 Mùˆller, 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} } @conference {2021, title = {Sen2Seq: A Conditional seq2seq Framework for Generating Lead Sheets with Sentiment}, booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Virtual}, author = {Dimos Makris and Kat Agres and D Herremans} } @article {2019, title = {Re-Representing Metaphor: Modeling Metaphor Perception Using Dynamically Contextual Distributional Semantics}, journal = {Frontiers in Psychology: Cognitive Science}, volume = {10}, year = {2019}, month = {04/2019}, pages = {1-18}, type = {Original Research}, abstract = {In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process.}, keywords = {computational creativity, computational linguistics, conceptual models, distributional semantics, metaphor, vector space models}, doi = {doi: 10.3389/fpsyg.2019.00765}, author = {Stephen McGregor and Kat Agres and Karolina Rataj and Matthew Purver and Geraint Wiggins} } @proceedings {2018, title = {Modelling Novice and Expert Listeners{\textquoteright} Ability to Detect Changes in Short Melodies}, year = {2018}, address = {Sydney, Australia}, author = {Kat Agres and David Meredith} } @proceedings {2016, title = {Modeling metaphor perception with distributional semantics vector space models}, year = {2016}, month = {08/2016}, abstract = {In this paper, we present a novel application of a computational model of word meaning to capture human judgments of the linguistic properties of metaphoricity, familiarity, and meaningfulness. We present data gathered from human subjects regarding their ratings of these properties over a set of word pairs specifically designed to exhibit varying degrees of metaphoricity. We then investigate whether these properties can be measured in terms of geometric features of a model of distributional lexical semantics. We compare the performance of two models, our own Concept Discovery Model which dynamically constructs context-sensitive subspaces, and a state-of-the-art static distributional semantic model, and find that our dynamic model performs significantly better in its measurement of metaphoricity.}, keywords = {computational creativity, distributional semantics, metaphor, vector space models}, author = {Kat R. Agres and Stephen McGregor and Karolina Rataj and Matthew Purver and Geraint A. Wiggins} } @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} } @article {39, title = {From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation}, journal = {Journal of Artificial General Intelligence}, volume = {6}, year = {2015}, pages = {55-86}, abstract = {We investigate the relationship between lexical spaces and contextually-defined conceptual spaces, offering applications to creative concept discovery. We define a computational method for discovering members of concepts based on semantic spaces: starting with a standard distributional model derived from corpus co-occurrence statistics, we dynamically select characteristic dimensions associated with seed terms, and thus a subspace of terms defining the related concept. This approach performs as well as, and in some cases better than, leading distributional semantic models on a WordNet-based concept discovery task, while also providing a model of concepts as convex regions within a space with interpretable dimensions. In particular, it performs well on more specific, contextualized concepts; to investigate this we therefore move beyond WordNet to a set of human empirical studies, in which we compare output against human responses on a membership task for novel concepts. Finally, a separate panel of judges rate both model output and human responses, showing similar ratings in many cases, and some commonalities and divergences which reveal interesting issues for computational concept discovery.}, keywords = {behavioural validation, computational creativity, concept discovery, conceptual spaces, distributional semantics}, issn = {1946-0163}, doi = {10.1515/jagi-2015-0004}, url = {http://www.degruyter.com/view/j/jagi.2015.6.issue-1/jagi-2015-0004/jagi-2015-0004.xml?format=INT$\#$fn1}, author = {Stephen McGregor and Kat Agres and Matthew Purver and Geraint A. Wiggins} } @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} }