Music in the brain

Music is ubiquitous across human cultures — as a source of affective and pleasurable experience, moving us both physically and emotionally — and learning to play music shapes both brain structure and brain function. Music processing in the brain — namely, the perception of melody, harmony and rhythm — has traditionally been studied as an auditory phenomenon using passive listening paradigms. However, when listening to music, we actively generate predictions about what is likely to happen next. This enactive aspect has led to a more comprehensive understanding of music processing involving brain structures implicated in action, emotion and learning. Here we review the cognitive neuroscience literature of music perception. We show that music perception, action, emotion and learning all rest on the human brain’s fundamental capacity for prediction — as formulated by the predictive coding of music model. This Review elucidates how this formulation of music perception and expertise in individuals can be extended to account for the dynamics and underlying brain mechanisms of collective music making. This in turn has important implications for human creativity as evinced by music improvisation. These recent advances shed new light on what makes music meaningful from a neuroscientific perspective

Rhythmic complexity and predictive coding::A novel approach to modeling rhythm and meter perception in music

Musical rhythm, consisting of apparently abstract intervals of accented temporal events, has a remarkable capacity to move our minds and bodies. How does the cognitive system enable our experiences of rhythmically complex music? In this paper, we describe some common forms of rhythmic complexity in music and propose the theory of predictive coding (PC) as a framework for understanding how rhythm and rhythmic complexity are processed in the brain. We also consider why we feel so compelled by rhythmic tension in music. First, we consider theories of rhythm and meter perception, which provide hierarchical and computational approaches to modeling. Second, we present the theory of PC, which posits a hierarchical organization of brain responses reflecting fundamental, survival-related mechanisms associated with predicting future events. According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Third, we develop a PC model of musical rhythm, in which rhythm perception is conceptualized as an interaction between what is heard (“rhythm”) and the brain’s anticipatory structuring of music (“meter”). Finally, we review empirical studies of the neural and behavioral effects of syncopation, polyrhythm and groove, and propose how these studies can be seen as special cases of the PC theory. We argue that musical rhythm exploits the brain’s general principles of prediction and propose that pleasure and desire for sensorimotor synchronization from musical rhythm may be a result of such mechanisms

From a musical protolanguage to rhythm and tonality

Treballs Finals del Màster en Ciència Cognitiva i Llenguatge, Facultat de Filosofia, Universitat de Barcelona, Curs: 2014-2015, Tutora: Joana Rosselló Ximenes[eng] Music and language are two faculties that have only evolved in humans, and by mutual interaction. As Darwin (1871) suggested, before speaking, our ancestors were able to sing in a way structurally and functionally similar to what birds do. At that stage, a musical protolanguage with beat yielded a common basis for music and language. Hierarchical recursion along with grammar and lexical meaning joined this musical protolanguage and gave rise to language. Linguistic recursion, in turn, made meter possible. Rhythm therefore would have preceded tonality. Subsequently, in parallel to the emergence of grammar, harmony and tonality were added to the meter. That beat is more primitive than meter is suggested by the fact that some animals perceive but do not externalize it. Crucially, they are all vocal learners. Externalization, either in musical rhythm or language, requires a complex social behaviour, which for rhythm is already present in the drumming behaviour of certain primates. The role of vocalizations, in turn, goes even further: their harmonic spectrum underpinned the tones of our musical scales. Thus, driven to a large extent by language, music has turned out to be as we know it nowadays.[cat] La música i el llenguatge són dues facultats exclusivament humanes que han evolucionat alimentantse mútuament. Com Darwin (1871) ja va suggerir, abans de parlar, els nostres ancestres tenien cants similars funcionalment i estructuralment al cant dels ocells. En aquest estadi, un protollenguatge musical amb pulsació es consolidà com a base comuna de la música i el llenguatge. La recursió jeràrquica, juntament amb la gramàtica i el significat lèxic, es van afegir a aquest protollenguatge musical i van donar lloc al llenguatge. Aquesta recursió lingüística féu possible el metre. El ritme, doncs, va precedir la tonalitat. Ulteriorment, en paral·lel al sorgiment de la gramàtica, l’harmonia i la tonalitat s’afegeixen al metre (compàs). Que la pulsació és més primitiva ho indica el fet que certs animals la perceben però no l’externalitzen espontàniament. Crucialment, tots són vocal learners. L’externalització, tant del ritme com del llenguatge, requereix una conducta social complexa, que ja s’observa en el conducta percutiva (drumming) de certs primats. El paper de les vocalitzacions, per la seva banda, va encara més enllà: l’espectre harmònic que presenten és el fonament de les notes a les escales musical. Així doncs, a remolc del llenguatge, és com s’arriba a la música tal i com l’entenem avui en dia.[spa] La música y el lenguaje son dos capacidades exclusi vamente humanas que han evolucionado alimentándose mutuamente. Como Darwin (1871) ya sug irió, antes de hablar, nuestros ancestros, tenían cantos similares funcionalmente y estructura lmente al canto de los pájaros. En este estadio, un protolenguaje musical con pulsación se consolidó com o la base común de la música y el lenguaje. La recursión jerárquica, junto con la gramática y el sig nificado léxico, se añadieron a este protolenguaje musical y dieron lugar al lenguaje. Esta recursión l ingüística hace posible el metro. El ritmo, pues, precedió la tonalidad. Ulteriormente, en paralelo a l surgimiento de la gramática, la armonía y la tonalidad se añaden al metro (compás). Que la pulsa ción es más primitiva lo indica el hecho de que ciertos animales la perciben pero no la externaliza n espontáneamente. Crucialmente, todos son vocal learners . La externalización, tanto del ritmo como del leng uaje, requiere una conducta social compleja, que ya se observa en la conducta percutiva ( drumming ) de ciertos primates. El papel de las vocalizaciones, por su parte, va aún más allá: el e spectro armónico que presentan es la base de las notas en las escaleras musicales. Así, a remolque d el lenguaje, es como se llega a la música tal y como la entendemos hoy en dí

Brain Responses Track Patterns in Sound

This thesis uses specifically structured sound sequences, with electroencephalography (EEG) recording and behavioural tasks, to understand how the brain forms and updates a model of the auditory world. Experimental chapters 3-7 address different effects arising from statistical predictability, stimulus repetition and surprise. Stimuli comprised tone sequences, with frequencies varying in regular or random patterns. In Chapter 3, EEG data demonstrate fast recognition of predictable patterns, shown by an increase in responses to regular relative to random sequences. Behavioural experiments investigate attentional capture by stimulus structure, suggesting that regular sequences are easier to ignore. Responses to repetitive stimulation generally exhibit suppression, thought to form a building block of regularity learning. However, the patterns used in this thesis show the opposite effect, where predictable patterns show a strongly enhanced brain response, compared to frequency-matched random sequences. Chapter 4 presents a study which reconciles auditory sequence predictability and repetition in a single paradigm. Results indicate a system for automatic predictability monitoring which is distinct from, but concurrent with, repetition suppression. The brain’s internal model can be investigated via the response to rule violations. Chapters 5 and 6 present behavioural and EEG experiments where violations are inserted in the sequences. Outlier tones within regular sequences evoked a larger response than matched outliers in random sequences. However, this effect was not present when the violation comprised a silent gap. Chapter 7 concerns the ability of the brain to update an existing model. Regular patterns transitioned to a different rule, keeping the frequency content constant. Responses show a period of adjustment to the rule change, followed by a return to tracking the predictability of the sequence. These findings are consistent with the notion that the brain continually maintains a detailed representation of ongoing sensory input and that this representation shapes the processing of incoming information

Not Cure But Heal: Music and Medicine

Despite evidence for music-specific mechanisms at the level of pitch-pattern representations, the most fascinating aspect of music is its transmodality. Recent psychological and neuroscientific evidence suggests that music is unique in the coupling of perception, cognition, action, and emotion. This potentially explains why music has been since time immemorial almost inextricably linked to healing processes and should continue to be

How does the brain extract acoustic patterns? A behavioural and neural study

In complex auditory scenes the brain exploits statistical regularities to group sound elements into streams. Previous studies using tones that transition from being randomly drawn to regularly repeating, have highlighted a network of brain regions involved during this process of regularity detection, including auditory cortex (AC) and hippocampus (HPC; Barascud et al., 2016). In this thesis, I seek to understand how the neurons within AC and HPC detect and maintain a representation of deterministic acoustic regularity. I trained ferrets (n = 6) on a GO/NO-GO task to detect the transition from a random sequence of tones to a repeating pattern of tones, with increasing pattern lengths (3, 5 and 7). All animals performed significantly above chance, with longer reaction times and declining performance as the pattern length increased. During performance of the behavioural task, or passive listening, I recorded from primary and secondary fields of AC with multi-electrode arrays (behaving: n = 3), or AC and HPC using Neuropixels probes (behaving: n = 1; passive: n = 1). In the local field potential, I identified no differences in the evoked response between presentations of random or regular sequences. Instead, I observed significant increases in oscillatory power at the rate of the repeating pattern, and decreases at the tone presentation rate, during regularity. Neurons in AC, across the population, showed higher firing with more repetitions of the pattern and for shorter pattern lengths. Single-units within AC showed higher precision in their firing when responding to their best frequency during regularity. Neurons in AC and HPC both entrained to the pattern rate during presentation of the regular sequence when compared to the random sequence. Lastly, development of an optogenetic approach to inactivate AC in the ferret paves the way for future work to probe the causal involvement of these brain regions