Modulation of Negative Affect Predicts Acceptance of Music Streaming Services, While Personality Does Not

Personality characteristics, music-listening, and well-being: a systematic and scoping review

When researchers and clinicians try to identify appropriate and effective ways to improve well-being among different populations, music is often included in interventions or research projects. The existing literature has consistently reported interactions between the effects of personality traits and music listening. However, the interrelations between individual traits and well-being after music listening are still partially unclear. This systematic review, registered on PROSPERO, investigates the relationship between everyday music listening, individual characteristics - operationalized as stable and transient personality traits - and well-being. Articles were searched on PubMed, Scopus, and Google Scholar, with a final result of 115 records. After eligibility screening, eight studies were included in the review. The results showed that both stable and transient personality traits influence music's effect on well-being. As a result of the scoping review, a theoretical perspective combining the emotional dimension, the principle of emotional congruence, and the dimension of music immersion and well-being was identified. Based on this knowledge, music listening could be systematically introduced into daily routines to improve everyday well-being and prevent or reduce stressful states.

Graph theoretical brain connectivity measures to investigate neural correlates of music rhythms associated with fear and anger

The present study tests the hypothesis that emotions of fear and anger are associated with distinct psychophysiological and neural circuitry according to discrete emotion model due to contrasting neurotransmitter activities, despite being included in the same affective group in many studies due to similar arousal-valance scores of them in emotion models. EEG data is downloaded from OpenNeuro platform with access number of ds002721. Brain connectivity estimations are obtained by using both functional and effective connectivity estimators in analysis of short (2 sec) and long (6 sec) EEG segments across the cortex. In tests, discrete emotions and resting-states are identified by frequency band specific brain network measures and then contrasting emotional states are deep classified with 5-fold cross-validated Long Short Term Memory Networks. Logistic regression modeling has also been examined to provide robust performance criteria. Commonly, the best results are obtained by using Partial Directed Coherence in Gamma (31.5 - 60.5 H z ) sub-bands of short EEG segments. In particular, Fear and Anger have been classified with accuracy of 91.79%. Thus, our hypothesis is supported by overall results. In conclusion, Anger is found to be characterized by increased transitivity and decreased local efficiency in addition to lower modularity in Gamma-band in comparison to fear. Local efficiency refers functional brain segregation originated from the ability of the brain to exchange information locally. Transitivity refer the overall probability for the brain having adjacent neural populations interconnected, thus revealing the existence of tightly connected cortical regions. Modularity quantifies how well the brain can be partitioned into functional cortical regions. In conclusion, PDC is proposed to graph theoretical analysis of short EEG epochs in presenting robust emotional indicators sensitive to perception of affective sounds.