Rising rates of stress, anxiety, and depression—fueled by rapid sociocultural and economic shifts, digital overexposure, and the lasting impact of COVID-19—are accelerating investment in scalable tools aimed at enhancing resilience and wellbeing. Music-based digital therapeutics (MDTs) hold promise given music’s unique ability to modulate core dimensions of health—affect, anxiety, and reward, as well as autonomic and social functioning—through a medium that is universal, intuitive, and increasingly accessible. To assess the current state of MDTs targeting stress, anxiety, and depression in adults, we conducted a scoping review using a modified Population, Intervention, Comparison, Outcome (PICO) keyword framework to structure Google search results. Twenty-two commercially available MDTs were identified for inclusion. We organize these MDTs into five principal categories based on underlying treatment strategies: (1) Preference-based music selection; (2) Affective Parameterization; (3) Affect Matching and Compensation; (4) Neural Entrainment; and (5) Biofeedback. We review general evidence supporting each strategy from music neuroscience and therapy research, as well as limited applied research testing specific MDTs. We conclude that, while general evidence supporting musical-based interventions for stress, anxiety, and depression is substantial, evidence for MDTs specifically is presently too limited to draw conclusions about real world effectiveness. Determining whether MDTs are likely to fulfill their potential will require increased focus on rigorous laboratory studies testing specific treatment strategies and randomized double-blind placebo-controlled trials conducted in ecologically valid settings. To support progress in this field, we make recommendations to support the sustainable development of MDTs as evidence-based tools to support mental health and wellbeing.

Auditory beat stimulation in the delta–theta frequency range (0–7 Hz) is gaining interest as a non-invasive intervention for anxiety. This study investigated the effects of a relatively understudied form—monaural beats—and whether they produce acute changes in anxiety and mood when presented alone or embedded harmonically within music. Participants (n = 308) were randomly assigned to one of three 30-min listening conditions: (1) Monaural Beats + Music, (2) Monaural Beats-Only, or (3) a Pure Tone Control. Psychological effects were assessed via changes in self-reported anxiety (State–Trait Anxiety Inventory, state subscale) and mood (bipolar Likert scales for emotional valence, arousal, and energy). The results showed that only the Beats + Music condition significantly reduced anxiety from before to after listening with a medium effect size anxiety from before to after listening (p < 0.001, d = −0.58). Furthermore, only the Beats + Music significantly increased emotional valence from before to after listening (p < 0.001, d = 0.48). Finally, the Beats-Only condition showed a significant reduction in energy from before to after listening (p < 0.05, d = −0.28). These findings indicate that monaural beats can be harmoniously integrated into music without diminishing the anxiolytic properties of the latter, whereas presentation of beats alone has different effects. This suggests that integrating monaural beats within music may be a viable approach for targeted auditory neuromodulation.

Developments in the field of Artificial Intelligence (AI), and particularly large language models (LLMs), have created a 'perfect storm' for observing 'sparks' of Artificial General Intelligence (AGI) that are spurious. Like simpler models, LLMs distill meaningful representations in their latent embeddings that have been shown to correlate with external variables. Nonetheless, the correlation of such representations has often been linked to human-like intelligence in the latter but not the former. We probe models of varying complexity including random projections, matrix decompositions, deep autoencoders and transformers: all of them successfully distill information that can be used to predict latent or external variables and yet none of them have previously been linked to AGI. We argue and empirically demonstrate that the finding of meaningful patterns in latent spaces of models cannot be seen as evidence in favor of AGI. Additionally, we review literature from the social sciences that shows that humans are prone to seek such patterns and anthropomorphize. We conclude that both the methodological setup and common public image of AI are ideal for the misinterpretation that correlations between model representations and some variables of interest are 'caused' by the model's understanding of underlying 'ground truth' relationships. We, therefore, call for the academic community to exercise extra caution, and to be keenly aware of principles of academic integrity, in interpreting and communicating about AI research outcomes.

Machine Learning (ML) models influence all aspects of our lives. They also commonly are integrated in recommender systems, which facilitate users’ decision-making processes in various scenarios, such as e-commerce, social media, news and online learning. Training performed on large volumes of data is what ultimately drives such systems to provide meaningful recommendations. However, a lack of standardized practices has been observed when it comes to data collection and annotation methods for ML datasets. This research paper systematically identifies and synthesizes the state of standardization with regard to data collection and annotation reporting in the recommender systems domain, through a systematic literature view into the 100 most-cited recommender systems papers from the most impactful venues within the Computing and Information Technology field. Multiple facets of the employed techniques are touched upon, such as reported human annotations and annotator diversity, label quality, and the public availability of training datasets. Recurrent use of just a few benchmark datasets, poor documentation practices, and reproducibility issues in experiments are some of the most striking findings uncovered by this study. We discuss the necessity of transitioning from pure reliance on algorithmic performance metrics to prioritizing data quality and fit. Finally, concerns are raised when it comes to biases and socio-psychological factors inherent in the datasets, and further exploration of embedding these early in the design of ML models is suggested.

Self-control is a crucial factor in maintaining an established romantic relationship, but its role in relationship formation is understudied. The current study tested whether trait self-control is related to a more selective approach toward romantic partners. Over 4 years, we organized 11 speed-date events at which a total of 342 single, heterosexual participants met potential partners. Our results indicated that there was no main effect of self-control on selectivity. However, there was an interaction between self-control and sociosexual orientation (SOI) in predicting selectivity. Specifically, self-control was positively related to selectivity for people with a restricted SOI (i.e., interested in a long-term, stable relationship). For people with an unrestricted SOI (i.e., interested in a short-term, sexual relationship), however, self-control was related to lower selectivity. Our findings point to the flexibility of self-control in facilitating goal progress, stimulating people to refrain from—or act on—their impulses, depending on their own personal mating goals.

Psychology research has shown that song lyrics are a rich source of data, yet they are often overlooked in the field of MIR compared to audio. In this paper, we provide an initial assessment of the usefulness of features drawn from lyrics for various fields, such as MIR and Music Psychology. To do so, we assess the performance of lyric-based text features on 3 MIR tasks, in comparison to audio features. Specifically, we draw sets of text features from the field of Natural Language Processing and Psychology. Further, we estimate their effect on performance while statistically controlling for the effect of audio features, by using a hierarchical regression statistical model. Lyric-based features show a small but statistically significant effect, that anticipates further research. Implications and directions for future studies are discussed.

There is evidence that testosterone and cortisol levels are related to the attraction of a romantic partner; testosterone levels relate to a wide range of sexual behaviors and cortisol is a crucial component in the response to stress. To investigate this, we conducted a speed-dating study among heterosexual singles. We measured salivary testosterone and cortisol changes in men and women (n = 79) when they participated in a romantic condition (meeting opposite-sex others, i.e., potential romantic partners), as well as a control condition (meeting same-sex others, i.e., potential friends). Over the course of the romantic speed-dating event, results showed that women's but not men's testosterone levels increased and cortisol levels decreased for both men and women. These findings indicate that men's testosterone and cortisol levels were elevated in anticipation of the event, whereas for women, this appears to only be the case for cortisol. Concerning the relationship between attraction and hormonal change, four important findings can be distinguished. First, men were more popular when they arrived at the romantic speed-dating event with elevated cortisol levels. Second, in both men and women, a larger change in cortisol levels during romantic speed-dating was related to more selectivity. Third, testosterone alone was unrelated to any romantic speed-dating outcome (selectivity or popularity). However, fourth, women who arrived at the romantic speed-dating event with higher testosterone levels were more selective when their anticipatory cortisol response was low. Overall, our findings suggest that changes in the hormone cortisol may be stronger associated with the attraction of a romantic partner than testosterone.

Objectives: This study proposes an alternative hypothetical scenario method capitalizing on the potential of virtual reality (VR). Rather than asking participants to imagine themselves in a specific situation, VR perceptually immerses them in it. We hypothesized that experiencing a scenario in VR would increase feelings of being “present” in the situation, and add to perceived realism compared to the written equivalent. This, in turn, was expected to trigger stronger emotional experiences influencing subsequent behavioral intentions. Methods: In an experiment, participants (N = 153), visitors of a large music festival, either read a “bar fight” scenario or experienced the scenario in VR. Following the scenario, they were presented a series of questions including intention to aggress, perceived risk, anticipated shame/guilt, presence, perceived realism, and anger. Analyses were conducted using analysis of variance, stepwise regression, and mediation analysis using nonparametric bootstrapping. Results: In line with expectations, the results indicate significant differences between conditions with the VR scenario triggering stronger presence, higher realism, and higher intention to aggress. Importantly, presence and anger mediated the relation between condition and intention to aggress. Conclusions: We show that the VR scenario method may provide benefits over written scenarios for the study of criminal decision-making. Implications are discussed.

In music information retrieval, we often make assertions about what features of music are important to study, one of which is vocals. While the importance of vocals in music preference is both intuitive and anticipated by psychological theory, we have not found any survey studies that confirm this commonly held assertion. We address two questions: (1) what components of music are most salient to people’s musical taste, and (2) how do vocals rank relative to other components of music, in regards to whether people like or dislike a song. Lastly, we explore the aspects of the voice that listeners find important. Two surveys of Spotify users were conducted. The first gathered open-format responses that were then card-sorted into semantic categories by the team of researchers. The second asked respondents to rank the semantic categories derived from the first survey. Responses indicate that vocals were a salient component in the minds of listeners. Further, vocals ranked high as a self-reported factor for a listener liking or disliking a track, among a statistically significant ranking of musical attributes. In addition, we open several new interesting problem areas that have yet to be explored in MIR.

We present MatchNMingle, a novel multimodal/multisensor dataset for the analysis of free-standing conversational groups and speed-dates in-the-wild. MatchNMingle leverages the use of wearable devices and overhead cameras to record social interactions of 92 people during real-life speed-dates, followed by a cocktail party. To our knowledge, MatchNMingle has the largest number of participants, longest recording time and largest set of manual annotations for social actions available in this context in a real-life scenario. It consists of 2 hours of data from wearable acceleration, binary proximity, video, audio, personality surveys, frontal pictures and speed-date responses. Participants&#x0027; positions and group formations were manually annotated; as were social actions (eg. speaking, hand gesture) for 30 minutes at 20fps making it the first dataset to incorporate the annotation of such cues in this context. We present an empirical analysis of the performance of crowdsourcing workers against trained annotators in simple and complex annotation tasks, founding that although efficient for simple tasks, using crowdsourcing workers for more complex tasks like social action annotation led to additional overhead and poor inter-annotator agreement compared to trained annotators (differences up to 0.4 in Fleiss&#x0027; Kappa coefficients). We also provide example experiments of how MatchNMingle can be used.