The Influence of Streamlined Music on Cognition and Mood
Recent advances in sound engineering have led to the development of so-called streamlined music designed to reduce exogenous attention and improve endogenous attention. Although anecdotal reports suggest that streamlined music does indeed improve focus on daily work tasks and may improve mood, the specific influences of streamlined music on cognition and mood have yet to be examined. In this paper, we report the results of a series of online experiments that examined the impact of one form of streamlined music on cognition and mood. The tested form of streamlined music, which was tested primarily by listeners who felt they benefited from this type of music, significantly outperformed plain music on measures of perceived focus, task persistence, precognition, and creative thinking, with borderline effects on mood. In contrast, this same form of streamlined music did not significantly influence measures assessing visual attention, verbal memory, logical thinking, self-efficacy, perceived stress, or self-transcendence. We also found that improvements in perceived focus over a 2-month period were correlated with improvements in emotional state, including mood. Overall the results suggest that at least for individuals who enjoy using streamlined music as a focus tool, streamlined music can have a beneficial impact on cognition without any obvious costs, while at the same time it may potentially boost mood.
Code (0)
등록된 구현이 없습니다.
Tasks
FormSimilar Papers 제목 키워드 기반
Mood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems
Recommendation systems are essential in modern music streaming platforms due to the vast amount of available content. While collaborative filtering is widely used to suggest items based on the preferences of others with …
Collaborative FilteringRecommendation SystemsEmotion RecognitionMood-based On-Car Music Recommendations
Driving and music listening are two inseparable everyday activities for millions of people today in the world. Considering the high correlation between music, mood and driving comfort and safety, it makes sense to use ap…
Recommendation SystemsEmotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network
Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users' preferences for m…
Music RecommendationRecommendation SystemsMulti-task Learning with Metadata for Music Mood Classification
Mood recognition is an important problem in music informatics and has key applications in music discovery and recommendation. These applications have become even more relevant with the rise of music streaming. Our work i…
ClassificationMulti-Task LearningMusic theme recognition using CNN and self-attention
We present an efficient architecture to detect mood/themes in music tracks on autotagging-moodtheme subset of the MTG-Jamendo dataset. Our approach consists of two blocks, a CNN block based on MobileNetV2 architecture an…