Abstract

Emotion recognition from a given music track has heavily relied on acoustic features, social tags, and metadata but is seldom focused on lyrics. There are no datasets of Indian language songs that contain both valence and arousal manual ratings of lyrics. We present a new manually annotated dataset of Telugu songs' lyrics collected from Spotify with valence and arousal annotated on a discrete scale. A fairly high inter-annotator agreement was observed for both valence and arousal. Subsequently, we create two music emotion recognition models by using two classification techniques to identify valence, arousal and respective emotion quadrant from lyrics. Support vector machine (SVM) with term frequency-inverse document frequency (TF-IDF) features and fine-tuning the pre-trained XLMRoBERTa (XLM-R) model were used for valence, arousal and quadrant classification tasks. Fine-tuned XLMRoBERTa performs better than the SVM by improving macro-averaged F1-scores of 54.69%, 67.61%, 34.13% to 77.90%, 80.71% and 58.33% for valence, arousal and quadrant classifications, respectively, on 10-fold cross-validation. In addition, we compare our lyrics annotations with Spotify's annotations of valence and energy (same as arousal), which are based on entire music tracks. The implications of our findings are discussed. Finally, we make the dataset publicly available with lyrics, annotations and Spotify IDs.

Abstract

Cocaine use disorder (CUD) is described as a compulsive urge to seek and consume cocaine despite the inimical consequences. MRI studies from different modalities have shown that CUD patients exhibit structural and/or functional connectivity pathology among several brain regions. Nevertheless, both connectivities are commonly studied and analyzed separately, which may potentially obscure its relationship between them, and with the clinical pathology. Here, we compare and contrast structural and functional brain networks in CUD patients and healthy controls (HC) using multimodal fusion. The sample consisted of 63 (8 females) CUD patients and 42 (9 females) healthy controls (HC), recruited as part of the SUDMEX CONN database. For this, we computed a battery of graph-based measures from multi-shell diffusion-weighted imaging and resting state fc-fMRI to quantify local and global connectivity. Then we used multimodal canonical component analysis plus joint independent component analysis (mCCA+jICA) to compare between techniques, and evaluate group differences and its association with clinical alteration. Unimodal results showed a striatal decrease in the participation coefficient, but applied supervised data fusion revealed other regions with cocaine-related alterations in joint functional communication. When performing multimodal fusion analysis, we observed a higher centrality of the interrelationship and a lower participation coefficient in patients with CUD. In contrast to the unimodal approach, the multimodal fusion method was able to reveal latent information about brain regions involved in impairment due to cocaine abuse. The present results could help in understanding the pathology of CUD in order to develop better pre-treatment/post-treatment intervention designs. Keywords: Cocaine use disorder; Imaging; Graph theory; sMRI; fMRI; Multimodal fusion; mCCA + jICA

Abstract

Music processing is a complex and intriguing phenomenon that has garnered extensive research interest in recent years. In this study, we investigated neural representations of music features by analyzing blood-oxygen-level-dependent (BOLD) signal fMRI activations of selected regions of interest during a continuous listening task. Additionally, we compared these activations to the hidden layer activations of a special class of deep neural networks (DNNs). Using representational similarity analysis, we found a strong correlation between low-level music feature encoding in the human brain regions of the Superior Temporal Gyrus (STG) and Heschl’s gyrus (HG) to the hidden layers of the DNN encoder. Our results suggest that self-supervised DNNs can serve as a reliable architecture to study the music processing domain, which is crucial considering the absence of naturalistic listening conditions in prior research Keywords: neural networks, representation similarity analysis, music cognition, music encoding, neuromusicology

Abstract

Cooperative human-human communication becomes challenging when restrictions such as difference in communication modality and limited time are imposed. We use the popular cooperative social game Pictionary as an online multimodal test bed to explore the dynamics of human-human interactions in such settings. As a part of our study, we identify attributes of player interactions that characterize cooperative gameplay. We found stable and role-specific playing style components that are independent of game difficulty. In terms of gameplay and the larger context of cooperative partially observable communication, our results suggest that too much interaction or unbalanced interaction negatively impacts game success. Additionally

Abstract

Individuals choose varying music listening strategies to fulfill particular mood-regulation goals. However, ineffective musical choices and a lack of cognizance of the effects thereof can be detrimental to their well-being and may lead to adverse outcomes like anxiety or depression. In our study, we use the social media platform Reddit to perform a large-scale analysis to unearth the several music-mediated mood-regulation goals that individuals opt for in the context of depression. A mixed-methods approach involving natural language processing techniques followed by qualitative analysis was performed on all music-related posts to identify the various music-listening strategies and group them into healthy and unhealthy associations. Analysis of the music content (acoustic features and lyrical themes) accompanying healthy and unhealthy associations showed significant differences. Individuals resorting to unhealthy strategies gravitate towards low-valence tracks. Moreover, lyrical themes associated with unhealthy strategies incorporated tracks with low optimism, high blame, and high self-reference. Our findings demonstrate that being mindful of the objectives of using music, the subsequent effects thereof, and aligning both for well-being outcomes is imperative for comprehensive understanding of the effectiveness of music

Abstract

Individual differences are known to modulate brain responses to music. Recent neuroscience research suggests that each individual has unique and fundamentally stable functional brain connections irrespective of task. Our study aims to identify individual differences such as gender and musical expertise from brain responses to music. Thirty-six participants’ functional Magnetic Resonance Imaging (fMRI) responses were measured while listening to three 8-min-long musical pieces representing different musical styles. They were analyzed using various temporal and spectral functional connectivity (FC) measures. These FC measures were compared to identify the one that captured the most variation associated with gender and musical expertise. Subsequently, the measures were used to classify distinct population groupings using a binary Support Vector Machine (SVM). Our results revealed that Coherence, a spectral-domain FC measure, captured the maximum variation for musical stimuli. However, in classifying individuals based on gender and musical expertise, a composite measure outperformed any single measure and had abovechance accuracy. This suggests that each FC measure captures different aspects of the relationship between brain regions and is influenced by the level of analysis (group or individual) and the task, such as similarity analysis or classification. Further ramifications of the findings are discussed.

Abstract

Music listening is a dynamic process that entails complex interactions between sensory, cognitive, and emotional processes. The naturalistic paradigm provides a means to investigate these processes in an ecologically valid manner by allowing experimental settings that mimic real-life musical experiences. In this paper, we highlight the importance of the naturalistic paradigm in studying dynamic music processing and discuss how it allows for investigating both the segregation and integration of brain processes using model-based and model-free methods. We further suggest that studying individual difference-modulated music processing in this paradigm can provide insights into the mechanisms of brain plasticity, which can have implications for the development of interventions and therapies in a personalized way. Finally, despite the challenges that the naturalistic paradigm poses, we end with a discussion on future prospects of music and neuroscience research, especially with the continued development and refinement of naturalistic paradigms and the adoption of open science practices.

Abstract

Background The Empathizing-Systemizing theory proposes that individuals with Autism Spectrum Disorder are systemizers and prefer Intense music characterized as loud , distorted, electric and percussive musical attributes and aggressive emotional attributes, typical of genres such as heavy metal, hard rock. However, there have been no studies that have tested this theory in a cross cultural setting. Aim The aim of this cross-cultural study is to gather music preferences by Indians and Non-Indians (US and Europe) and verify Baron-Cohen’s hypothesis. Method The Indian population of 20 participants (n=20; male, aged - 18-24 years), were contacted directly while for the Non-Indian population of 30 participants (n=25; US, n=5; Europe, male, aged 18-24) , we used social media platform Reddit (r/asperges and r/autism sub-reddit groups). Semi structured interviews were conducted to gather their demographic information, music they liked and disliked and how listening to music helps them in everyday life. Subsequently a mix of 10 songs each were curated comprising 5 songs they liked and out of the remaining 5 songs , 3 are of the genre they liked and 2 of the genre they disliked, taken from other individual’s playlist in a controlled setting. While playing each song, their likert responses (Like, Neutral, Dislike) were recorded per stimulus. Music Preferences were assessed based on the MUSIC model framework. Results Out of 20 participants, 18 preferred a mix of relaxing, non-aggressive, slow, quiet and instrumental musical attributes categorized as mix of Mellow and Sophisticated factor which helped in improved sleep and social skills and disliked music with loud and rebellious musical attributes categorized as Intense factor which shows that music serves for emotion regulation towards positive emotions in Indian population. In comparison , out of the 30 participants 22 preferred music categorized as Intense factor which brought in a calming factor when stressed and disliked music categorized as a mix of Mellow and Sophisticated factors which shows that music serves to discharge negative emotions in the Non-Indian group. Implications These results provide support to the Empathizing–Systemizing Theory for Non-Indian population but not for Indian population. It also suggests that cultural differences affect emotional arousal. These findings are helpful to build a recommendation system based on an individual’s preferences that can help in arousal regulation, sensory modulation difficulties commonly found among individuals with ASD.

Abstract

The experience often described as feeling moved, understood chiefly as a social-relational emotion with social bonding functions, has gained significant research interest in recent years. Although listening to music often evokes what people describe as feeling moved, very little is known about the appraisals or musical features contributing to the experience. In the present study, we investigated experiences of feeling moved in response to music using a continuous rating paradigm. A total of 415 US participants completed an online experiment where they listened to seven moving musical excerpts and rated their experience while listening. Each excerpt was randomly coupled with one of seven rating scales (perceived sadness, perceived joy, feeling moved or touched, sense of connection, perceived beauty, warmth [in the chest], or chills) for each participant. The results revealed that musically evoked experiences of feeling moved are associated with a similar pattern of appraisals, physiological sensations, and trait correlations as feeling moved by videos depicting social scenarios (found in previous studies). Feeling moved or touched by both sadly and joyfully moving music was associated with experiencing a sense of connection and perceiving joy in the music, while perceived sadness was associated with feeling moved or touched only in the case of sadly moving music. Acoustic features related to arousal contributed to feeling moved only in the case of joyfully moving music. Finally, trait empathic concern was positively associated with feeling moved or touched by music. These findings support the role of social cognitive and empathic processes in music listening, and highlight the social-relational aspects of feeling moved or touched by music.

Abstract

Music listening is a great resource for mental well-being, pleasure, and self-regulation, but it may also be maladaptive. Depression, for instance, has been shown to relate to music use that is characterized by rumination, avoidance, and mood worsening. However, we know little of the role of individual differences in such maladaptive music use. Hence, this study focused on examining the role of personality, empathic traits, emotional contagion, and the types of musical reward as predictors of maladaptive music listening. Participants (N=318) answered an online survey comprising measures for the abovementioned traits in addition to the Healthy-Unhealthy Music Scale (HUMS) as a measure of maladaptive music use. Results demonstrated that Unhealthy musical engagement was predicted by a variety of traits representing general negative emotionality (e.g., Neuroticism, Personal Distress, contagion for negative emotions). Structural equation modeling highlighted the importance of the empathic trait Personal Distress in mediating Unhealthy musical engagement. Finally, we deliberate if maladaptive strategies are indeed “maladaptive” for such individuals or merely a coping mechanism, which is indeed adaptive for them, aiding to combat depressive and anxious states thereby preventing them from “tipping over” into depression.