More Like this

1. Developmental changes in the categorization of speech and song

   https://doi.org/10.1111/desc.13346  

   Vanden Bosch der Nederlanden, Christina M. ; Qi, Xin ; Sequeira, Sarah ; Seth, Prakhar ; Grahn, Jessica A. ; Joanisse, Marc F. ; Hannon, Erin E. ( November 2022 , Developmental Science)

   Music and language are two fundamental forms of human communication. Many studies examine the development of music‐ and language‐specific knowledge, but few studies compare how listeners know they are listening to music or language. Although we readily differentiate these domains, how we distinguish music and language—and especially speech and song— is not obvious. In two studies, we asked how listeners categorize speech and song. Study 1 used online survey data to illustrate that 4‐ to 17‐year‐olds and adults have verbalizable distinctions for speech and song. At all ages, listeners described speech and song differences based on acoustic features, but compared with older children, 4‐ to 7‐year‐olds more often used volume to describe differences, suggesting that they are still learning to identify the features most useful for differentiating speech from song. Study 2 used a perceptual categorization task to demonstrate that 4–8‐year‐olds and adults readily categorize speech and song, but this ability improves with age especially for identifying song. Despite generally rating song as more speech‐like, 4‐ and 6‐year‐olds rated ambiguous speech–song stimuli as more song‐like than 8‐year‐olds and adults. Four acoustic features predicted song ratings: F0 instability, utterance duration, harmonicity, and spectral flux. However, 4‐ and 6‐year‐olds’ song ratings were better predicted by F0 instability than by harmonicity and utterance duration. These studies characterize how children develop conceptual and perceptual understandings of speech and song and suggest that children under age 8 are still learning what features are important for categorizing utterances as speech or song.

   Children and adults conceptually and perceptually categorize speech and song from age 4.

   Listeners use F0 instability, harmonicity, spectral flux, and utterance duration to determine whether vocal stimuli sound like song.

   Acoustic cue weighting changes with age, becoming adult‐like at age 8 for perceptual categorization and at age 12 for conceptual differentiation.

   Young children are still learning to categorize speech and song, which leaves open the possibility that music‐ and language‐specific skills are not so domain‐specific.

    

   more » « less
2. Listening to Waves: Engaging Underrepresented Students Through the Science of Sound and Music

   Minces V., Booker A. ( July 2021 , Connected science learning)

   Music is a central part of adolescent life, and the connections between music, science, and math are vast and deep-rooted in history. In particular, the relationship between sound and the science of waves. This positions musical sound as an ideal avenue for students to explore and connect with science. Listening to Waves (LTW) is a program that introduces adolescents to the physics and technology of music and sound with the goal of improving their attitudes toward science. For this, LTW creates web applications designed to explore and create sound in a playful manner and integrates those applications with hands-on exploration of the physical sonic world. In the case study described in this article, LTW partnered with a large middle school serving low-income and underrepresented students, trained the teachers to use the web applications and associated curriculum (Minces 2021), and worked directly with eighth-grade student participants. Students enjoyed the program and participated enthusiastically. Pre-post surveys indicate that program participation improved the students’ attitudes toward science, including their intention to pursue a science career and their perception of themselves as capable of doing science. 

   more » « less
3. Music and Brain Circuitry: Strategies for Strengthening Evidence-Based Research for Music-Based Interventions

   https://doi.org/10.1523/JNEUROSCI.1135-22.2022  

   Chen, Wen Grace ; Iversen, John Rehner ; Kao, Mimi H. ; Loui, Psyche ; Patel, Aniruddh Dhiren ; Zatorre, Robert J. ; Edwards, Emmeline ( November 2022 , The Journal of Neuroscience)

   The neuroscience of music and music-based interventions (MBIs) is a fascinating but challenging research field. While music is a ubiquitous component of every human society, MBIs may encompass listening to music, performing music, music-based movement, undergoing music education and training, or receiving treatment from music therapists. Unraveling the brain circuits activated and influenced by MBIs may help us gain better understanding of the therapeutic and educational values of MBIs by gathering strong research evidence. However, the complexity and variety of MBIs impose unique research challenges. This article reviews the recent endeavor led by the National Institutes of Health to support evidence-based research of MBIs and their impact on health and diseases. It also highlights fundamental challenges and strategies of MBI research with emphases on the utilization of animal models, human brain imaging and stimulation technologies, behavior and motion capturing tools, and computational approaches. It concludes with suggestions of basic requirements when studying MBIs and promising future directions to further strengthen evidence-based research on MBIs in connections with brain circuitry. SIGNIFICANCE STATEMENT Music and music-based interventions (MBI) engage a wide range of brain circuits and hold promising therapeutic potentials for a variety of health conditions. Comparative studies using animal models have helped in uncovering brain circuit activities involved in rhythm perception, while human imaging, brain stimulation, and motion capture technologies have enabled neural circuit analysis underlying the effects of MBIs on motor, affective/reward, and cognitive function. Combining computational analysis, such as prediction method, with mechanistic studies in animal models and humans may unravel the complexity of MBIs and their effects on health and disease. 

   more » « less
4. Age-related variability in network engagement during music listening

   https://doi.org/10.1162/netn_a_00333  

   Faber, Sarah E. M. ; Belden, Alexander G. ; Loui, Psyche ; McIntosh, Randy ( December 2023 , Network Neuroscience)

   Listening to music is an enjoyable behaviour that engages multiple networks of brain regions. As such, the act of music listening may offer a way to interrogate network activity, and to examine the reconfigurations of brain networks that have been observed in healthy aging. The present study is an exploratory examination of brain network dynamics during music listening in healthy older and younger adults. Network measures were extracted and analyzed together with behavioural data using a combination of hidden Markov modelling and partial least squares. We found age- and preference-related differences in fMRI data collected during music listening in healthy younger and older adults. Both age groups showed higher occupancy (the proportion of time a network was active) in a temporal-mesolimbic network while listening to self-selected music. Activity in this network was strongly positively correlated with liking and familiarity ratings in younger adults, but less so in older adults. Additionally, older adults showed a higher degree of correlation between liking and familiarity ratings consistent with past behavioural work on age-related dedifferentiation. We conclude that, while older adults do show network and behaviour patterns consistent with dedifferentiation, activity in the temporal-mesolimbic network is relatively robust to dedifferentiation. These findings may help explain how music listening remains meaningful and rewarding in old age.

    

   more » « less
5. The Music of Silence: Part II: Music Listening Induces Imagery Responses

   https://doi.org/10.1523/JNEUROSCI.0184-21.2021  

   Di Liberto, Giovanni M. ; Guilhem Marion, Guilhem ; Shamma, Shihab A. ( January 2021 , The journal of neuroscience)

   During music listening, humans routinely acquire the regularities of the acoustic sequences and use them to anticipate and interpret the ongoing melody. Specifically, in line with this predictive framework, it is thought that brain responses during such listening reflect a comparison between the bottom-up sensory responses and top-down prediction signals generated by an internal model that embodies the music exposure and expectations of the listener. To attain a clear view of these predictive responses, previous work has eliminated the sensory inputs by inserting artificial silences (or sound omissions) that leave behind only the corresponding predictions of the thwarted expectations. Here, we demonstrate a new alternate approach in which we decode the predictive electroencephalography (EEG) responses to the silent intervals that are naturally interspersed within the music. We did this as participants (experiment 1, 20 participants, 10 female; experiment 2, 21 participants, 6 female) listened or imagined Bach piano melodies. Prediction signals were quantified and assessed via a computational model of the melodic structure of the music and were shown to exhibit the same response characteristics when measured during listening or imagining. These include an inverted polarity for both silence and imagined responses relative to listening, as well as response magnitude modulations that precisely reflect the expectations of notes and silences in both listening and imagery conditions. These findings therefore provide a unifying view that links results from many previous paradigms, including omission reactions and the expectation modulation of sensory responses, all in the context of naturalistic music listening. 

   more » « less