The forgotten role of absorption in music reward

Enhancing musical pleasure through shared musical experience

Music and social interactions represent two of the most important sources of pleasure in our lives, both engaging the mesolimbic dopaminergic system. However, there is limited understanding regarding whether and how sharing a musical activity in a social context influences and modifies individuals' rewarding experiences. Here, we aimed at (1) modulating the pleasure derived from music under different social scenarios and (2) further investigating its impact on reward-related prosocial behavior and memory. Across three online experiments, we simulated a socially shared music listening and found that participants' music reward was significantly modulated by the social context, with higher reported pleasure for greater levels of social sharing. Furthermore, the increased pleasure reported by the participants positively influenced prosocial behavior and memory outcomes, highlighting the facilitating role of socially boosted reward. These findings provide evidence about the rewarding nature of socially driven music experiences, with important potential implications in educational and clinical settings.

Improvisation and live accompaniment increase motor response and reward during a music playing task

Music provides a reward that can enhance learning and motivation in humans. While music is often combined with exercise to improve performance and upregulate mood, the relationship between music-induced reward and motor output is poorly understood. Here, we study music reward and motor output at the same time by capitalizing on music playing. Specifically, we investigate the effects of music improvisation and live accompaniment on motor, autonomic, and affective responses. Thirty adults performed a drumming task while (i) improvising or maintaining the beat and (ii) with live or recorded accompaniment. Motor response was characterized by acceleration of hand movements (accelerometry), wrist flexor and extensor muscle activation (electromyography), and the drum strike count (i.e., the number of drum strikes played). Autonomic arousal was measured by tonic response of electrodermal activity (EDA) and heart rate (HR). Affective responses were measured by a 12-item Likert scale. The combination of improvisation and live accompaniment, as compared to all other conditions, significantly increased acceleration of hand movements and muscle activation, as well as participant reports of reward during music playing. Improvisation, regardless of type of accompaniment, increased the drum strike count and autonomic arousal (including tonic EDA responses and several measures of HR), as well as participant reports of challenge. Importantly, increased motor response was associated with increased reward ratings during music improvisation, but not while participants were maintaining the beat. The increased motor responses achieved with improvisation and live accompaniment have important implications for enhancing dose of movement during exercise and physical rehabilitation.

Predictive coding in musical anhedonia: A study of groove

Groove, or the pleasurable urge to move to music, offers unique insight into the relationship between emotion and action. The predictive coding of music model posits that groove is linked to predictions of music formed over time, with stimuli of moderate complexity rated as most pleasurable and likely to engender movement. At the same time, listeners vary in the pleasure they derive from music listening: individuals with musical anhedonia report reduced pleasure during music listening despite no impairments in music perception and no general anhedonia. Little is known about musical anhedonics' subjective experience of groove. Here we examined the relationship between groove and music reward sensitivity. Participants (n = 287) heard drum-breaks that varied in perceived complexity, and rated each for pleasure and wanting to move. Musical anhedonics (n = 13) had significantly lower ratings compared to controls (n = 13) matched on music perception abilities and general anhedonia. However, both groups demonstrated the classic inverted-U relationship between ratings of pleasure & move and stimulus complexity, with ratings peaking for intermediately complex stimuli. Across our entire sample, pleasure ratings were most strongly related with music reward sensitivity for highly complex stimuli (i.e., there was an interaction between music reward sensitivity and stimulus complexity). Finally, the sensorimotor subscale of music reward was uniquely associated with move, but not pleasure, ratings above and beyond the five other dimensions of musical reward. Results highlight the multidimensional nature of reward sensitivity and suggest that pleasure and wanting to move are driven by overlapping but separable mechanisms.

Musical anhedonia, timbre, and the rewards of music listening

Pleasure in music has been linked to predictive coding of melodic and rhythmic patterns, subserved by connectivity between regions in the brain's auditory and reward networks. Specific musical anhedonics derive little pleasure from music and have altered auditory-reward connectivity, but no difficulties with music perception abilities and no generalized physical anhedonia. Recent research suggests that specific musical anhedonics experience pleasure in nonmusical sounds, suggesting that the implicated brain pathways may be specific to music reward. However, this work used sounds with clear real-world sources (e.g., babies laughing, crowds cheering), so positive hedonic responses could be based on the referents of these sounds rather than the sounds themselves. We presented specific musical anhedonics and matched controls with isolated short pleasing and displeasing synthesized sounds of varying timbres with no clear real-world referents. While the two groups found displeasing sounds equally displeasing, the musical anhedonics gave substantially lower pleasure ratings to the pleasing sounds, indicating that their sonic anhedonia is not limited to musical rhythms and melodies. Furthermore, across a large sample of participants, mean pleasure ratings for pleasing synthesized sounds predicted significant and similar variance in six dimensions of musical reward considered to be relatively independent, suggesting that pleasure in sonic timbres play a role in eliciting reward-related responses to music. We replicate the earlier findings of preserved pleasure ratings for semantically referential sounds in musical anhedonics and find that pleasure ratings of semantic referents, when presented without sounds, correlated with ratings for the sounds themselves. This association was stronger in musical anhedonics than in controls, suggesting the use of semantic knowledge as a compensatory mechanism for affective sound processing. Our results indicate that specific musical anhedonia is not entirely specific to melodic and rhythmic processing, and suggest that timbre merits further research as a source of pleasure in music.

Generating New Musical Preferences From Multilevel Mapping of Predictions to Reward

Much of what we know and love about music hinges on our ability to make successful predictions, which appears to be an intrinsically rewarding process. Yet the exact process by which learned predictions become pleasurable is unclear. Here we created novel melodies in an alternative scale different from any established musical culture to show how musical preference is generated de novo. Across nine studies (n = 1,185), adult participants learned to like more frequently presented items that adhered to this rapidly learned structure, suggesting that exposure and prediction errors both affected self-report liking ratings. Learning trajectories varied by music-reward sensitivity but were similar for U.S. and Chinese participants. Furthermore, functional MRI activity in auditory areas reflected prediction errors, whereas functional connectivity between auditory and medial prefrontal regions reflected both exposure and prediction errors. Collectively, results support predictive coding as a cognitive mechanism by which new musical sounds become rewarding.

Music Therapy as a Complementary Treatment in Patients with Dementia Associated to Alzheimer's Disease: A Systematic Review

Background

Alzheimer's disease (AD) is a complex condition that affects various aspects of a patient's life. Music therapy may be considered a beneficial supplementary tool to traditional therapies, that not fully address the range of AD manifestations.

Objective

The purpose of this systematic review is to investigate whether music therapy can have a positive impact on AD patients and on which symptoms.

Methods

The main research databases employed have been PubMed and Cochrane, using the keywords "dementia", "music therapy", "Alzheimer", "fMRI", "music", and "EEG".

Results

After removing duplicates and irrelevant studies, 23 were screened using set criteria, resulting in the final inclusion of 15 studies. The total number of participants included in these studies has been of 1,196 patients. For the fMRI analysis the search resulted in 28 studies on PubMed, two of which were included in the research; the total number of participants was of 124 individuals. The studies conducted with EEG were found using PubMed. The initial search resulted in 15 studies, but after a more accurate evaluation only 2 have been included in the analysis.

Conclusions

Even though the data currently available is not sufficient to draw conclusions supported by robust statistical power, the impact of music therapy on AD neuropsychiatric symptoms deserves great interest. Further research should be ushered, possibly multicentric studies, led with neuroimaging and other recent techniques, which can eventually open views on the music role in improving the cognitive status in AD.

Functional Organization of Auditory and Reward Systems in Aging

The intrinsic organization of functional brain networks is known to change with age, and is affected by perceptual input and task conditions. Here, we compare functional activity and connectivity during music listening and rest between younger (n = 24) and older (n = 24) adults, using whole-brain regression, seed-based connectivity, and ROI-ROI connectivity analyses. As expected, activity and connectivity of auditory and reward networks scaled with liking during music listening in both groups. Younger adults show higher within-network connectivity of auditory and reward regions as compared with older adults, both at rest and during music listening, but this age-related difference at rest was reduced during music listening, especially in individuals who self-report high musical reward. Furthermore, younger adults showed higher functional connectivity between auditory network and medial prefrontal cortex that was specific to music listening, whereas older adults showed a more globally diffuse pattern of connectivity, including higher connectivity between auditory regions and bilateral lingual and inferior frontal gyri. Finally, connectivity between auditory and reward regions was 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 for older adults and improve our understanding of functional network dynamics of the brain at rest and during a cognitively engaging task.

Functional Organization of Auditory and Reward Systems in Aging

The intrinsic organization of functional brain networks is known to change with age, and is affected by perceptual input and task conditions. Here, we compare functional activity and connectivity during music listening and rest between younger (N=24) and older (N=24) adults, using whole brain regression, seed-based connectivity, and ROI-ROI connectivity analyses. As expected, activity and connectivity of auditory and reward networks scaled with liking during music listening in both groups. Younger adults show higher within-network connectivity of auditory and reward regions as compared to older adults, both at rest and during music listening, but this age-related difference at rest was reduced during music listening, especially in individuals who self-report high musical reward. Furthermore, younger adults showed higher functional connectivity between auditory network and medial prefrontal cortex (mPFC) that was specific to music listening, whereas older adults showed a more globally diffuse pattern of connectivity, including higher connectivity between auditory regions and bilateral lingual and inferior frontal gyri. Finally, connectivity between auditory and reward regions was 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 for older adults, and improve our understanding of functional network dynamics of the brain at rest and during a cognitively engaging task.

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

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.