The forgotten role of absorption in music reward

Cardona, Gemma; Ferreri, Laura; Lorenzo‐Seva, Urbano; Russo, Frank A.; Rodrı́guez-Fornells, Antoni

doi:10.1111/nyas.14790

Cited by 21 publications

(30 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Methodsmentioning

confidence: 99%

“…These, along with their altered versions, resulted in eight unique melodies presented to participants in this study. Participants also completed an updated version of the BMRQ: the extended Barcelona Music Reward Questionnaire (eBMRQ), which includes an additional sixth factor (4 additional items) which measures experiences of absorption in music listening (Cardona et al, 2022). Procedure Participants underwent the same procedure as previous studies, with the exception that melodies were presented either 0, 4, 10, or 14 times during the exposure phase and that there was only one melody assigned to each condition.…”

Section: Study 5 Participantsmentioning

confidence: 99%

See 1 more Smart Citation

Generating New Musical Preferences from Multi-level Mapping of Predictions to Reward

Kathios

Sachs

Zhang

et al. 2022

Preprint

View full text Add to dashboard Cite

Prediction learning is considered a fundamental feature of biological systems that underlies perception, action, and reward. For cultural artifacts like music, isolating the genesis of reward from prediction is challenging, since predictions are acquired implicitly throughout life. Here, we examined the trajectory of listeners' preferences for melodies in a novel musical scale, where local and global predictions were independently manipulated. Across seven studies (n = 842 total) in two cultures, participants preferred melodies that were presented more during exposure (globally predictable) and that followed schematic expectations (locally predictable). Learning trajectories depended on music reward sensitivity. Furthermore, fMRI showed that while auditory cortical activation reflects predictions, functional connectivity between auditory and reward areas encodes preference. The results are the first to show a hierarchical, relatively culturally-independent process by which predictions map onto reward. Collectively, our findings propose a novel mechanism by which the human brain links predictions with reward value.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Study 5 Participantsmentioning

confidence: 99%

Generating New Musical Preferences from Multi-level Mapping of Predictions to Reward

Kathios

Sachs

Zhang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, from the perspective of people's subjective feelings, music affects people's hearing through the sound effect, and then the audience makes a judgment on this kind of hearing, is happy, sad, thinking, or sad, and then brings an impact on people's intuition and other feelings, and even affects people's feelings and thoughts. Marxism holds that everything is interrelated and complementary with other things except for its own independence, and music is the same [ 3 , 4 ]. In the analysis and research of music, music cannot be independent but should be combined with related things for analysis, which plays an important role in our comprehensive and in-depth understanding of music.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Erhu Performance Effect in Public Health Music Works Based on Artificial Intelligence Technology

Yang

2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

With the rise of Erhu teaching in recent years, a large number of people have joined the team to learn Erhu playing. However, due to the high cost of teaching and the unique one-to-one teaching mode between teachers and students, Erhu education resources are very scarce. Learning Erhu performance has become a luxury activity. Nowadays, with the rise of artificial intelligence, computer music is developing rapidly. Music has two important aspects: composition and performance. Different kinds of instruments convey different styles, and players inject different rhythms and dynamics into their performance, thus producing rich expressive force. The development of image style conversion, which opens people’s evaluation of music performance, is an important issue in many fields of artificial intelligence (it is also known as intelligence, machine intelligence, referring to the intelligence shown by the machine made by people. Usually, artificial intelligence refers to the technique of presenting human intelligence through ordinary computer programs). For an Erhu song, there are various factors that affect its effectiveness, and there are many indexes to evaluate it, such as sense of rhythm, expressive force, musical sense, style, and so on. Using a computer to simulate the evaluation process is essential to find out the mathematical relationship between the factors that affect the performance of music and the evaluation indexes. Neural network is a kind of mathematical model proposed by simulating the way of thinking of human brain in artificial intelligence. It has the advantages of not having strict requirements on data distribution, nonlinear data processing method, strong robustness, and dynamics and is very suitable for the mathematical model of evaluation system. In addition, the neural network also has a strong theoretical basis, and their application in various industries has developed basically mature. This paper tries to introduce a deep neural network mathematical model into the evaluation system of Erhu performance, and the experimental results prove the reliability and practicality of the method in this paper. It can provide a method basis and theoretical reference for evaluation of Erhu performance effect.

show abstract

“…Here we characterize for the first time age-related differences in the neural substrates of musical reward. These data provide insight into the mechanisms underlying observed differences in musical reward in young and older adults (Belfi et al, 2020;Cardona et al, 2022;Mas-Herrero et al, 2013), characterize the contributions of auditory and reward networks during Univariate analyses showed that both young and older adults activated multiple regions within the auditory network during music listening, as well as the mPFC during music listening that was rated as loved. The engagement of mPFC when listening to preferred music is consistent with its observed role in musical reward processing (Salimpoor et al, 2013).…”

Section: Discussionmentioning

confidence: 95%

“…The copyright holder for this preprint this version posted January 2, 2023. ; https://doi.org/10.1101/2023.01.01.522417 doi: bioRxiv preprint adults than in young adults (Belfi et al, 2021;Cardona et al, 2022). Addressing these gaps in our understanding of age-related differences in reward and functional network dynamics will have implications for the design of music-based interventions for healthy aging.…”

Section: Introductionmentioning

confidence: 99%

Functional Organization of Auditory and Reward Systems in Aging

Belden

Quinci

Geddes

et al. 2023

Preprint

View full text Add to dashboard Cite

The intrinsic organization of functional brain networks are known to change with age, and are affected by perceptual input and task conditions. Here, we compare functional activity and connectivity of auditory and reward systems during music listening and rest between young (N=24) and older (N=24) adults, using whole brain regression, seed-based connectivity, and ROI-ROI connectivity analyses. We found that although auditory and reward regions follow expected patterns of activation in response to music listening across both groups, younger adults show higher within-network connectivity of auditory and reward regions as compared to older adults during both rest and music listening. We also show that this age-related difference was reduced during music listening, especially in individuals showing high self-reported musical reward. Furthermore, young adults showed higher connectivity between auditory network and medial prefrontal cortex (mPFC) that was specific to music listening and varied as a function of self-reported musical liking. Meanwhile, older adults showed a more globally diffuse pattern of connectivity, including higher connectivity to bilateral lingual and inferior frontal gyri. Finally, connectivity between auditory and reward regions was higher during well-liked musical stimuli across all participants, and especially higher when listening to music selected by the participant. These results highlight the roles of aging and reward sensitivity on auditory and reward networks. Results may inform the design of music-based interventions in aging populations, and improve our understanding of functional network dynamics as a whole.

show abstract

The forgotten role of absorption in music reward

Cited by 21 publications

References 94 publications

Generating New Musical Preferences from Multi-level Mapping of Predictions to Reward

Generating New Musical Preferences from Multi-level Mapping of Predictions to Reward

[Retracted] Analysis of Erhu Performance Effect in Public Health Music Works Based on Artificial Intelligence Technology

Functional Organization of Auditory and Reward Systems in Aging

Contact Info

Product

Resources

About