TY - JOUR A1 - Anglada-Tort, Manuel A1 - Masters, Nikhil A1 - Steffens, Jochen A1 - North, Adrian A1 - Müllensiefen, Daniel T1 - The Behavioural Economics of Music: Systematic review and future directions JF - Quarterly Journal of Experimental Psychology N2 - Music-related decision-making encompasses a wide range of behaviours including those associated with listening choices, composition and performance, and decisions involving music education and therapy. Although research programmes in psychology and economics have contributed to an improved understanding of music-related behaviour, historically, these disciplines have been unconnected. Recently, however, researchers have begun to bridge this gap by employing tools from behavioural economics. This article contributes to the literature by providing a discussion about the benefits of using behavioural economics in music-decision research. We achieve this in two ways. First, through a systematic review, we identify the current state of the literature within four key areas of behavioural economics-heuristics and biases, social decision-making, behavioural time preferences, and dual-process theory. Second, taking findings of the literature as a starting point, we demonstrate how behavioural economics can inform future research. Based on this, we propose the Behavioural Economics of Music (BEM), an integrated research programme that aims to break new ground by stimulating interdisciplinary research in the intersection between music, psychology, and economics. Y1 - 2022 SN - 1747-0218 U6 - https://doi.org/10.1177/17470218221113761 SN - 1747-0226 VL - 76 IS - 5 PB - SAGE ER - TY - CHAP A1 - Steffens, Jochen A1 - Himmelein, Hendrik T1 - Induced cognitive load influences unpleasantness judgments of modulated noise T2 - Proceedings of the 24th International Congress on Acoustics Y1 - 2022 CY - Gyeoungju, Südkorea ER - TY - CHAP A1 - Gaudry, Arnaud A1 - Huber, Florian A1 - Flückiger, Julien A1 - Quirós, L A1 - Rutz, Adriano A1 - Kaiser, M A1 - Grondin, A A1 - Marcourt, Laurence A1 - Ferreira Queiroz, E A1 - Wolfender, Jean-Luc A1 - Allard, Pierre-Marie T1 - Short Lecture “Mass spectrometry-based sample vectorization for exploration of large chemodiverse datasets and efficient identification of new antiparasitic compounds” T2 - Planta Medica KW - Massenspektrometrie KW - Naturstoffchemie KW - Antiparasitäres Mittel KW - Arzneimittelforschung Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1758983 SN - 1439-0221 VL - 88 IS - 15 PB - Thieme ER - TY - JOUR A1 - de Jonge, Niek F. A1 - Mildau, Kevin A1 - Meijer, David A1 - Louwen, Joris J. R. A1 - Bueschl, Christoph A1 - Huber, Florian A1 - van der Hooft, Justin J. J. T1 - Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools JF - Metabolomics N2 - Background Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. Aim of review We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. Key scientific concepts of review This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks. KW - Metabolomik KW - Massenspektrometrie KW - Maschinelles Lernen KW - Benchmarking KW - Mass fragmentation spectra Y1 - 2022 U6 - https://doi.org/10.1007/s11306-022-01963-y SN - 1573-3890 VL - 18 IS - 12 PB - Springer Nature ER -