Automatic musical predictions in the brain as indexed by the Mismatch Negativity -- From acoustic deviants to cognitive musical errors

Automatic musical predictions in the brain as indexed by the Mismatch Negativity -- From acoustic deviants to cognitive musical errors | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 February 2026 V1 Latest version Share on Automatic musical predictions in the brain as indexed by the Mismatch Negativity -- From acoustic deviants to cognitive musical errors Authors : Elvira Brattico 0000-0003-0676-6464 [email protected] , Giovanni Marco Lorusso 0009-0007-9768-5243 , Francesco Carlomagno , and Giulio Carraturo Authors Info & Affiliations https://doi.org/10.22541/au.177032360.01736912/v1 385 views 124 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Since the early 1990s, mismatch negativity (MMN) has been widely used to investigate sensory memory traces and predictive processing of musically relevant acoustic features, including timbre, relational pitch, and rhythm. Evidence consistently demonstrates that the human brain possesses innate musical abilities, which can be further refined through training. The interplay between innate predispositions and experience is supported by robust associations between MMN parameters and behavioral listening skills in both musically trained and untrained children and adults. These findings align with the interpretation of MMN as a prediction-error signal reflecting the precision of internal predictive models: MMN amplitudes decrease with increasing contextual complexity and show more frontal scalp distributions in musicians. As stimulation paradigms have become increasingly ecologically valid, distinguishing low-level sensory prediction errors indexed by MMN from higher-level frontal prediction errors has grown more challenging, supporting an integrative perspective in which generative models dynamically merge bottom-up sensory input with top-down priors across hierarchical levels of the central nervous system. In this review, we summarize evidence for a “musical intelligence” of the automatic MMN mechanism, highlighting its role within widespread cortical predictive coding processes. Running head: Automatic musical predictions in the brain Automatic musical predictions in the brain as indexed by the Mismatch Negativity – From acoustic deviants to cognitive musical errors Elvira Brattico 1,2,* , Giovanni Marco Lorusso 3 , Francesco Carlomagno 4 , & Giulio Carraturo 2 1 Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Denmark 2 Department of Education, Psychology, Communication, University of Bari, Italy 3 School of Medicine, University of Bari, Italy 4 Electrical and Information Engineering Department, Polytechnics of Bari, Italy Word count: 16112 of 20572 Figure count: 2 *Corresponding author: Prof. Elvira Brattico Department of Clinical Medicine Aarhus University Universitetsbyen 3, Building 1710 8000 Aarhus C, Denmark Email: [email protected] Keywords: mismatch negativity (MMN); prediction; music; auditory processing; auditory cortex Abstract Since the early 1990s, mismatch negativity (MMN) has been widely used to investigate sensory memory traces and predictive processing of musically relevant acoustic features, including timbre, relational pitch, and rhythm. Evidence consistently demonstrates that the human brain possesses innate musical abilities, which can be further refined through training. The interplay between innate predispositions and experience is supported by robust associations between MMN parameters and behavioral listening skills in both musically trained and untrained children and adults. These findings align with the interpretation of MMN as a prediction-error signal reflecting the precision of internal predictive models: MMN amplitudes decrease with increasing contextual complexity and show more frontal scalp distributions in musicians. As stimulation paradigms have become increasingly ecologically valid, distinguishing low-level sensory prediction errors indexed by MMN from higher-level frontal prediction errors has grown more challenging, supporting an integrative perspective in which generative models dynamically merge bottom-up sensory input with top-down priors across hierarchical levels of the central nervous system. In this review, we summarize evidence for a “musical intelligence” of the automatic MMN mechanism, highlighting its role within widespread cortical predictive coding processes. MMN for studying prediction processes in the central auditory system and beyond In the late ‘70s, the discovery of the mismatch negativity (MMN), a pre-attentive electrophysiological brain response, classically observed as a negative deflection of the event-related potential of the electroencephalogram (EEG), peaking approximately at 100–200 ms from the stimulus onset. It was first discovered using infrequent sound locations that were deviant from repetitive, standard ones in a classical oddball paradigm (Näätänen et al., 1978). Another milestone study showed that MMN amplitude and latency scale with the magnitude of deviation and correlate with behavioral discrimination performance (Sams et al., 1985), supporting its interpretation as an index of perceptual sensitivity. Initially, Risto Näätänen proposed that the MMN might constitute an index of an automatic sensory memory trace reflecting an automatic comparison between incoming stimuli and a short-lived echoic memory representation of regular input (Näätänen, 2020). This account was later extended by the model adjustment hypothesis, which conceptualizes MMN as arising from continuous updating of an internal model of the auditory environment (Winkler et al., 1996), emphasizing interactions between short- and long-term representations. A major theoretical debate in MMN literature concerns the role of stimulus-specific adaptation (SSA). According to SSA accounts, MMN emerges from differential adaptation of neuronal populations tuned to frequent versus rare stimuli (Ulanovsky et al., 2003). SSA has been demonstrated across multiple levels of the auditory pathway, including auditory cortex, thalamus, and inferior colliculus (Malmierca et al., 2009; Nelken & Ulanovsky, 2007). However, animal studies combining single-unit activity with field potentials suggest that while SSA contributes to deviance sensitivity, it does not fully account for the late deviant-related response characteristic of human MMN (von der Behrens et al., 2009). Accordingly, contemporary frameworks distinguish between a “classic” MMN, which includes adaptation effects, and a “genuine” MMN, isolated using control conditions and attributed to higher-order deviance detection and memory-based comparison processes (Näätänen et al., 2005; Widmann et al., 2026). Later interpretations situate MMN within the predictive coding framework, in which the brain is modeled as a hierarchical inference system generating predictions and minimizing prediction errors (Friston & Kiebel, 2009; Garrido et al., 2009). Within this view, MMN reflects a precision-weighted prediction error elicited when sensory input violates learned regularities. Predictive coding subsumes both adaptation (gain or precision optimization) and learning (model updating), providing a unifying account of MMN across hierarchical levels of the auditory system (Carbajal & Malmierca, 2018). Across MMN theories, a crucial common assumption is that MMN depends on the formation of a sensory model or memory trace. When stimulation is temporally sparse, MMN is abolished and only obligatory sensory components (P1–N1–P2) remain, indicating that MMN relies on nonconscious sensory (echoic) memory operating over a timescale of a few seconds (Csépe & Honbolygó, 2024; Fisher & Todd, 2025). Theoretical advances have been paralleled by the development of diverse stimulation paradigms, especially in auditory research, as illustrated in Table 1. Beyond the classical oddball design, roving and multifeature paradigms probe rapid updating and parallel deviance processing. Importantly, abstract MMN paradigms demonstrate that MMN can be elicited by violations of relational rules rather than physical features, requiring extraction of invariant relationships across variable stimuli (Paavilainen, 2013). This extension has fueled debate regarding whether MMN should be restricted to short-term sensory regularities or extended to expectations grounded in long-term representations, such as linguistic grammar (Pulvermüller et al., 2013). Hierarchical aspects of MMN generation are further highlighted by the local–global paradigm, which dissociates automatic, sensory-bound deviance detection from higher-order regularity violations. Local deviants elicit classical MMN responses originating in auditory cortices, whereas global deviants engage distributed fronto-parietal networks and typically require attention, reflecting later stages of predictive processing (Bekinschtein et al., 2009). In sum, as a result of over five decades of research, MMN-like responses have been observed in auditory, visual, and somatosensory domains, as well as for speech, music, and numerical regularities, underscoring its role in early, automatic prediction of environmental regularities. MMN literature has explored the main topics of cognitive neuroscience, spanning learning across multiple timescales from neural adaptation and refractoriness to neuroplasticity in frontal regions, up to the search of reliable biomarkers of neurological and psychiatric conditions. Table 1. Summary of main stimulation paradigms for eliciting the mismatch negativity (MMN) response in relation to their theoretical frameworks and neural generators. Classical oddball Rare deviant among repetitive identical standards Short-term sensory memory; feature comparison Memory-trace hypothesis; SSA; classic MMN; predictive coding (low-level) Bilateral auditory cortex Roving standard Standards change after several repetitions Rapid model updating; flexibility Model adjustment; predictive coding; reduced adaptation confounds Auditory cortex, secondary auditory areas Multifeature Multiple deviant types within one sequence Parallel deviance detection Predictive coding; efficiency of model updating Auditory cortex Physical deviant MMN Violation of a physical feature (e.g., pitch, duration) Sensory feature discrimination SSA; memory comparison; classic MMN Primary and secondary auditory cortex Abstract MMN Violation of an invariant relational rule Rule extraction; relational encoding Model adjustment; predictive coding; higher-order memory representations Auditory + frontal cortices Local–global paradigm (local) Within-sequence deviation (e.g., AAAAB) Automatic deviance detection Predictive coding (low hierarchical level) Auditory cortex Local–global paradigm (global) Violation of block-level regularity Higher-order prediction; awareness Hierarchical predictive coding; global workspace models Frontal and parietal networks Cross-modal MMN Deviance in visual or somatosensory input Modality-general regularity detection Domain-general predictive processing Modality-specific sensory cortices + frontal areas Is the MMN an index of musical predictions in the auditory cortex? As the foundational and most extensive body of MMN research has focused on the auditory modality, extending MMN investigations to acoustic features specific to the music domain has been a natural progression. Music is a social and cultural construct, that emerged in human history approximately 40,000 years ago, based on tangible archaeological evidence such as fossilized bone flutes (Conard et al., 2009; Tuniz et al., 2012). It is plausible, however, that musical practices originated even earlier, perhaps through vocalization or perishable instruments (Mithen, 2011). Notably, the appearance of music is coeval with other milestones in human cognitive development, particularly emergence of cave paintings (Curtis, 2008). This synchronicity points to a sudden and dramatic leap in the capabilities of the human mind: the development of symbolic and recursive thought (d’Errico et al., 2003). Music, much like other forms of art, is a testament to the human capacity to create representations—to use auditory signs, patterns, and structures to refer not just to immediate reality, but also to abstract concepts, emotions, and narratives (Honing et al., 2015). The ability to abstract and communicate meaning through organized sound is a fundamental marker of modern human cognition, distinguishing Homo sapiens from other species and laying the groundwork for complex communication systems and societal structures (Nakamura, 2013). In this context, music should be seen as a key component of the “Great Leap Forward” in human evolution: it exemplifies the brain’s newfound ability to organize and manipulate information in complex, emotionally resonant ways (Bickerton, 2009). Crucially, even the most culturally variable elements of music rely on biologically grounded, genetically constrained perceptual and cognitive functions, underscoring the deep interdependence of biology and culture in musical behavior. The dichotomy between biology and culture in music perception and production is well described by the distinction between music , intended as a cultural and social construct, and musicality , which enables humans to understand music and which is defined as a “natural, spontaneously developing trait based on and constrained by biology and cognition” (Honing et al., 2015). Across millennia, it has diversified across the world into numerous traditions, styles, and conventions, that are acquired through implicit or explicit learning via acculturation. This process shapes expectations about which musical elements are most likely to occur within a given cultural or stylistic context. Indeed, prehistoric music provides compelling evidence for the existence of fundamental, inborn human capacities that, across millennia, have enabled humans to organize and create complex acoustic phenomena. Throughout history and across diverse cultures, composers and musicians have systematically manipulated and structured musical sounds along core musical dimensions, ranging from the most sensory to the most cognitive. These include spectral aspects such as timbre (the quality that allows us to distinguish a flute from a violin even when they play the same pitch), pitch organization through scales , intervals , chords , and melody (the linear organization of pitch), harmony (the simultaneous or vertical organization and relationships among pitches), and rhythm (the temporal structuring of sound involving beat perception, meter, and hierarchical timing) (cf. Figure 1). Within this framework, culturally specific musical structures can be understood as diverse expressions built upon shared biological foundations. For example, the perception of melody reflects a core component of musicality: an innate capacity to track relative pitch changes and to group them into coherent sequences. While melodic systems vary widely across cultures, this ability likely evolved alongside, or was co-opted from, mechanisms supporting the perception of prosody in speech, enabling music as a cultural artifact to exploit pre-existing auditory and cognitive resources. Similarly, harmony—though a historically and culturally contingent feature, particularly prominent in Western musical traditions—draws on biological sensitivities to consonance and dissonance. These sensitivities arise from psychoacoustic constraints linked to the physical properties of vibrating bodies, simple frequency ratios, and the response characteristics of the basilar membrane to complex tones, which musical cultures selectively formalize into harmonic systems. Rhythm provides a further illustration of the interaction between musicality and music. As perhaps the most ancient and cross-culturally widespread musical dimension, rhythmic structure engages evolutionarily conserved motor control and temporal prediction systems that support beat perception, movement synchronization (entrainment), and hierarchical metric organization. Cultural traditions elaborate these shared capacities into diverse rhythmic patterns and meters. In turn, even the most basic musical dimensions, such as timbre—which involves the sensory analysis of a sound’s attack, decay, and spectral distribution of overtones (harmonics)—are shaped by learning and culture. For instance, listeners of traditional Chinese music learn to recognize and appreciate the distinctive timbral qualities of string instruments such as the guqin or guzheng , illustrating how culturally transmitted preferences interact with biologically grounded perceptual mechanisms. Investigating the mismatch negativity (MMN) in musical contexts is therefore particularly informative, as it allows researchers to probe the degree to which neural predictive processes of music operate automatically in a biologically-driven way, as well as the extent to which they are shaped by experience and acculturation (Vuust et al., 2022). MMN responses may reflect prediction errors arising from biologically grounded auditory constraints, short-term regularities learned during an experimental session, or long-term expectations acquired through cultural exposure. Accordingly, MMN research can help distinguish which music-related predictive processes depend primarily on automatic mechanisms in early auditory cortex and which recruit higher-level associative or frontal regions associated with attentional control. In this way, music-based MMN paradigms provide an ecologically valid yet tightly controlled framework for examining how the brain generates, updates, and signals prediction errors in complex auditory environments. In the following sections, we will illustrate the main findings concerning the main elements of music, from the most biologically grounded to the most culturally varied: timbre, pitch organization (scales, intervals, motifs, chords, and melody), harmony, and rhythm (from beat to meter). First, we will summarize findings with adults, then studies with newborns and experts to understand the role of inborn musicality predispositions and expertise and finally, if evidence exists, neural generators. Figure 1. The figure integrates evidence from electrophysiological source localization studies showing how mismatch responses (MMN/MMNm) reflect hierarchical auditory processing mechanisms in music and speech, spanning early sensory cortices and higher-order frontal regions. Top Left : Minimum norm current estimation (MCE) source reconstructions of early negative responses elicited by out-of-tune and out-of-key pitches during passive listening. Cortical sources were estimated from grand-averaged, reference-free difference waveforms at frontal negative peaks within a latency window of 180–380 ms. The maps illustrate the relative strength of estimated cortical activity, expressed as a percentage at the latency of interest, highlighting distributed auditory cortical involvement in the detection of pitch-related violations. Modified from Brattico et al., 2006. Bottom Left . Neural generators of the magnetic mismatch negativity (MMNm) effect, derived from one-sample t-tests across participants. Color maps depict t-statistics thresholded at p = 0.05 after correction for multiple comparisons. Only the MMNm component is shown here, reflecting early automatic detection of auditory deviations. Data from musicians and non-musicians were pooled, as no statistically significant group differences were observed for this response. Modified from Quiroga-Martinez et al., 2020. Top Right . Distributed cortical sources underlying the speech-evoked MMN, computed using an sLORETA inverse solution and projected onto an inflated MNI cortical surface. Source activity at approximately 250 ms post stimulus onset reveals engagement of superior temporal regions associated with early auditory mismatch processing, as well as inferior frontal regions (insula/Broca’s area) implicated in higher-order speech encoding. Differential patterns of frontal involvement illustrate how speech-in-noise processing recruits compensatory mechanisms beyond sensory auditory cortices. Modified from Bidelman & Dexter 2015. Bottom Right . Cortical sources of the magnetic mismatch negativity elicited by spoken words in a passive oddball paradigm, time-locked to the recognition point of the deviant stimulus. A superior temporal source peaks at approximately 130–140 ms, followed closely by an inferior frontal source at 150–160 ms. This spatio-temporal activation sequence has been interpreted as reflecting the activation of word-related neuronal assemblies. Modified from Pulvermüller & Shtyrov, 2006. Together, the four panels illustrate how mismatch responses index multiple hierarchical stages of auditory processing, from early sensory detection of acoustic irregularities in superior temporal cortices to later engagement of inferior frontal regions associated with abstract, learned representations in both music and speech. 3. Predicting timbre by laypersons and experts Timbral deviants in oddball paradigms were originally operationalized used controlled, synthetic auditory stimulation designed to manipulate timbre-related acoustic features, such as brightness, spectral centroid, attack time , inharmonicity or roughness, while keeping other sound dimensions constant. For instance, larger MMN responses are evoked by spectrally complex sounds as opposed to pure tones (Tervaniemi et al., 2000). More recently, Quiroga-Martinez and colleagues (Quiroga-Martinez et al., 2022) employed a multi-feature oddball paradigm using complex tones that systematically varied in harmonicity and spectral structure. The standard stimuli consisted of harmonic sounds with a stable spectral envelope, while timbre deviants were created by introducing inharmonic partials or spectral distortions, thereby violating learned regularities of natural sound structure without changing basic parameters such as intensity or duration. This design allowed to test how violations of sound plausibility and timbre expectations modulate MMN and subsequent P3a responses. Results showed that harmonic sounds (e.g., piano tones) elicited larger and earlier MMN and P3a responses compared with inharmonic sounds (e.g., hi-hat cymbal tones), consistent with the idea that harmonicity enhances the precision of auditory predictions and thus amplifies prediction-error signals. A related study used a roving oddball paradigm with sequences of complex tones in which the degree of inharmonicity was parametrically manipulated (Basiński et al., 2025). In this paradigm, a given timbre served as the standard for several repetitions before becoming a deviant when a new timbre with a different harmonic structure was introduced. By progressively increasing inharmonicity, the study examined how the auditory system updates predictive models of timbre and how prediction-error responses scale with deviations from harmonic, music-like sound statistics: when inharmonic sounds maintained a certain spectral jitter pattern, they generated MMN responses of similar amplitude to harmonic sounds but increased attention-switch P3a responses, whereas, when the spectral jitter pattern changed unpredictably between consecutive sounds—thereby increasing sequential uncertainty—MMN responses disappeared. These findings suggested that prediction errors in the auditory system depend on sequential and not spectral uncertainty. More ecological studies have manipulated the identity of musical instruments (either via synthetically generated or samples from natural instruments). One of the first studies in this respect (Goydke et al., 2004) introduced an oddball paradigm consisting of a sequence of violin sounds infrequently varying in timbre (flute) or emotional tone (sad or happy) or in pitch: all variations, including the subtle timbral differences modulating the emotional expression, produced an MMN response. In turn, an EEG study (Caclin et al., 2006) used synthetic instrumental sounds to probe timbre-relevant acoustic features (e.g., attack time, spectral centroid/brightness, fine structure) and demonstrated that distinct timbre features can each support MMN, that is, feature-based representations of timbre in auditory sensory memory. Moreover, Christmann and colleagues (Christmann et al., 2014) presented listeners with sequences of natural instrumental tones and equally complex spectrally rotated sounds that preserved overall acoustic complexity but lacked the specific harmonic overtone structure that defines natural timbre. By embedding deviant sounds among frequent ones, they obtained MMN responses that occurred earlier for the natural instrumental tones than those evoked by the spectrally rotated sounds. This indicates that the auditory system processes the timbre of real and familiar musical instrument sounds more rapidly than equally complex but unnatural sounds. As ventured by Tervaniemi (Tervaniemi, 2022), future research might succeed in recording MMN responses even from slight variations within real music. Technically, such naturalistic free-listening paradigm is already feasible through music information retrieval (MIR) technology (Lartillot et al., 2008), which allows researchers to identify and time-lock acoustical or musical events within recorded or live-performed music. Using MIR toolboxes, specific sounds—whether expected or surprising in context—can be marked with trigger pulses for ERP analysis. Previous studies have demonstrated the viability of this approach: Poikonen et al. (Poikonen et al., 2016) applied MIR-based analyses to musical features such as timbre, harmony, and dynamics, comparing N100 and P200 responses across compositions, while Haumann et al. (Haumann et al., 2021) further validated the method using diverse musical excerpts and focusing on early auditory ERP components (P1–N1–P2). The automatic ability of the auditory cortex to automatically react to prediction errors of instrumental timbre does exist since early infancy, demonstrating the biological roots of this ability. At the same time, timbre automatic predictions can be enhanced even by passive exposure in early infancy, as evidenced by an MMN experiment with two groups of 4-months newborns listening for over two hours in the course of a week to guitar or marimba tones, showing more pronounced mismatch responses to slight pitch deviations played in the timbre of exposure (Trainor et al., 2011). In turn, EEG and MEG studies showed that musical expertise influences the MMN/MMNm responses to changes in timbre and in other simple acoustic features, such as reflecting strengthened automatic encoding of low-level acoustic information. Even prenatal musical exposure can induce lasting neural effects in the pre-attentive auditory coding of musical features in infants (Partanen et al., 2013). Fetuses exposed to music during the last weeks of gestation show at 4–5 months larger MMNs in response to timbre (and pitch) deviations compared to unexposed children, indicating an increase in sensitivity to acoustic prediction errors. These results suggest that the fetal brain can form stable neural traces, which persist in subsequent years and reflect not only early auditory plasticity but also the involvement of long-term episodic memory. Overall, the study supports the idea that the MMN represents a sensitive functional indicator of musical predictive coding, capable of reflecting both long-term auditory memory and the effects of early musical experience, even before birth. 4. MMN to pitch organization and its neural generators: findings in adults, newborns and experts Pitch-related MMN responses are robustly elicited by deviations in frequency, melodic contour, or tonal structure. Early studies demonstrated that simple pitch changes (e.g., frequency deviants) of isolated tones reliably evoke MMN, indicating that the auditory cortex maintains a sensory memory trace for pitch information (Näätänen et al., 2007). This pitch MMN is typically maximal at frontocentral scalp sites and is generated primarily in the auditory cortex, with contributions from frontal areas involved in change detection. A ground-breaking study on pitch MMN was conducted with MEG and showed the presence of a magnetic MMN at around 200 ms in response to an occasional pitch change inserted within repeating music-like patterns made of very brief sinusoidal tones (Alho et al., 1993). Source analysis localized the MMNm to the supratemporal auditory cortex, indicating that this region supports short-term memory representations not only for simple sounds but also for complex, temporally structured patterns. Several studies later showed, on one hand, an inborn capacity of the central auditory system supporting MMN processes to automatically predict and minimize prediction errors related to musical pitch. For instance, Lauren Trainor’s lab (He et al., 2007) recorded infant mismatch responses (MMR) to pitch changes in piano tones, providing further evidence of inborn musicality abilities of the human brain. On the other hand, automatic predictive processing of pitch both in isolated sounds and in tone patterns can be shaped by musical experience, indicating that the MMN system draws not only on sensory memory but also on more permanent memory representations. Näätänen et al. (Näätänen et al., 1993) showed that when participants could not initially discriminate between two patterns made of very brief sinusoidal tones, which slightly differed in a single tone, no MMN was elicited. After training enabled successful discrimination, the deviant pattern reliably generated an MMN that increased with performance accuracy. No such change occurred in participants who were already proficient. MEG studies localized this training-dependent MMN to the auditory cortex (Alho et al., 1996; Tervaniemi et al., 2001). Later work demonstrated that these effects can persist for days, indicating long-term plasticity in pre-attentive auditory processing (Atienza et al., 2002; Atienza & Cantero, 2001; Menning et al., 2000; Tremblay et al., 1998). Animal studies further showed that patterns learned during wakefulness can be reactivated during REM sleep when they are re-presented (Hennevin et al., 2007). Similarly, in adult humans, an MMN can be elicited during REM sleep in response to subtle changes in a complex auditory pattern, provided that participants had previously learned to discriminate the patterns (Atienza & Cantero, 2001): importantly, before training, there was no MMN in awake participants but after learning, comparable MMN responses emerged during both wakefulness and REM sleep. All together, these findings suggest that neural changes induced during perceptual learning remain accessible during REM sleep up to two days later. Other studies confirmed the relation between MMN parameters and sleep-dependent consolidation of auditory information in long-term memory either during siesta or nocturnal sleep (for a review, see Atienza et al., 2002). Importantly, MMN further reflects the existence of stable neural representations deriving from long-term learning by musicians. One of the first EEG studies relating the MMN parameters to music training was conducted by Koelsch et al. (1999) and focused on professional violinists compared with non-musicians. The peculiarity of the study was that repeated complex sounds were randomly interspersed with others different only in 1% of the frequency, under both ignore and attend conditions, to test the hypothesis that violinists would show a neural advantage to mistuning. The results confirmed this hypothesis with an MMN elicited only in violinists and not in non-musicians. Moreover, this expertise-related effect appeared strictly dependent on stimulus structure: the differential MMN response emerged when pitch deviations were embedded within harmonic sounds, whereas when the same deviations were presented as simple tones, both groups exhibited comparable MMN responses. This suggests that the violinists’ MMN reflected enhanced automatic processing of musically-relevant harmonic relations rather than a general increased processing sensitivity to simple pitch changes. Moreover, Arndt and colleagues (Arndt et al., 2020) investigated whether musical expertise provides neural processing advantages in pitch discrimination by comparing 50 musicians and 50 non-musicians using behavioral tasks and a passive EEG oddball paradigm. Behaviorally, musicians demonstrated superior “basic auditory abilities” with significantly smaller individual Just Noticeable Difference (JND) thresholds compared to non-musicians, although no significant differences in response times were observed between the groups. During the passive EEG session, both groups exhibited MMN and P3a responses to larger, fixed deviants of 535 Hz and 558 Hz. However, a critical discrepancy emerged in the individual JND condition: while non-musicians showed MMN and P3a because their thresholds were physically distinct enough from the 528 Hz standard to trigger an automatic response, musicians showed no MMN or P3a for their own JND deviants despite their demonstrated behavioral ability to distinguish them. These findings suggest that for minimal frequency differences near the physical threshold, the MMN may not be entirely independent of attention, as the automatic detection of such subtle deviations might require a degree of attentional focus to be triggered. In turn, an EEG study employing complex tones differing from the standard by small (0.8%), medium (2%), or large (4%) pitch increments measured different kinds of musicians as opposed to nonmusicians under both ignore (reading) and attentive conditions (Tervaniemi et al., 2005). While musicians showed superior behavioral performance and enhanced N2b and P3 components during attentive listening, MMN amplitudes and P3a responses recorded under ignore conditions did not significantly differ for any deviance magnitude. These findings were interpreted as demonstrating that musical expertise might not enhance pitch-change processing pre-attentively but only at later, attention-dependent processing stages. An alternative explanation concerns the different types of listening expertise possessed by musicians recruited in this study (as opposed to the previous study by Koelsch and colleagues (Koelsch et al., 1999), focusing on violinists), including both the ones used to tune their instruments vs. musicians playing instruments with fixed tuning. Another relevant EEG study compared singers with instrumental musicians and individuals without musical training (Nikjeh et al., 2008). Participants completed a behavioral pitch-discrimination task and, during EEG recordings, were presented with spectrally complex sounds containing pitch deviations of 1.5%, 3%, and 6%. Performance measures revealed that both groups of musicians detected pitch changes at substantially lower thresholds (1.4%) than nonmusicians (3.2%, roughly corresponding to half a semitone). MMN data indicated that singers showed the greatest sensitivity to the smallest pitch deviation (1.5%). By contrast, analysis of the P3a component—an index of automatic attentional engagement—demonstrated the shortest response latencies in instrumentalists, followed by singers, with nonmusicians showing the slowest responses. Overall, these results obtained indicate that the demands imposed by different musical instruments shape not only the fidelity of pitch encoding but also downstream neural mechanisms supporting attentional processing. Even more remarkably, the global relation between sounds even when separated in time can be registered by the MMN system. Herholz and colleagues (Herholz et al., 2009) used MEG to investigate how the brain detects violations of tonal regularities based on global statistical recurrencies rather than local interval relations, and how this process might differ between musicians and nonmusicians. In the experiment, participants passively listened to a continuous sequence of two tones (A and B) arranged so that the pattern AAAB occurred frequently, while other longer variations (e.g., AAAAB) occurred with progressively lower probabilities, producing prediction errors when the expected higher tone (B) did not follow three lower tones (A). A clear pattern MMN was elicited when the predominant pattern was violated, even though acoustically the violating sound was as frequent as the pattern itself, demonstrating that the brain pre-attentively extracts and predicts global sequential structure. Musicians and nonmusicians both showed this pattern MMN, but with a clearer left lateralization in musicians, suggesting enhanced neural specialization for integrating sequential information over longer time scales. A further study involving both musicians and non-musicians (van Zuijen et al., 2005) compared the predictive processes related to extracting regularity from the number of elements of a tonal pattern, as in the previous research, or on the timing between the tone of the pattern. The significant MMN responses to occasional variations in the temporal or numerical regularities of the patterns indicate that the auditory system can extract both these regularities automatically since participants were distracted from attentively listening to the sounds. However, the strength and reliability of this processing differed between groups. Musicians exhibited larger and more robust MMN responses, particularly for temporal regularity violations, indicating enhanced sensitivity to timing-based structure in sound sequences. Overall, the study supports the idea that auditory organization is strongly guided by temporal structure and that long-term musical experience fine-tunes early, automatic stages of auditory processing. It also demonstrates that numerical aspects of sound sequences can be encoded preattentively, though temporal regularities appear to be more salient, especially for musicians. Hence, even when standard sounds are constantly changing but with their pitch relations kept constant, a change in these abstract relations can trigger an MMN response. In this respect, another landmark study showed that an MMN could be elicited by changes in the direction (ascending or descending) of a music-like scale made of artificial Shepard tones (tones without a fundamental frequency) (Tervaniemi et al., 1994). The study is notable since it showed that sensory memory can encode stimuli for longer periods than previously supposed (at least 450ms), suggesting that sensory memory operates an anticipatory and predictive encoding of the sound pattern, generating expectations for incoming stimuli in the short time span. Moving further towards a musically more realistic stimulation, Vuust and colleagues (Vuust et al., 2011) adapted the “multifeature” paradigm—first introduced by Näätänen and colleagues (Näätänen et al., 2004). The original paradigm featured an equally probable alternation between a repeated isolated sound and its feature variations. Vuust and colleagues transformed this into a sequence of four-tone patterns, transposed into all musical keys, creating a sound closer to a musical accompaniment often encountered from classical music up to even contemporary pop/rock. Importantly, six types of feature changes were inserted in the third tone of every second pattern, all eliciting significant MMN responses in a fraction of the recording time necessary for previous studies. Subsequently, the feature changes were continuously occurring at the third note of the 4-note pattern, in the so-called no-standard multifeature paradigm, also successfully producing MMNs to musical feature violations in about ten minutes of recordings (Kliuchko et al., 2016). These paradigmatic innovations allowed to study neural predictive processes related to intervals, isolating the genuine MMN response from stimulus specific adaptation that relies on repetition of the same stimulus. The interval is an essential atomic element of a musical culture, called melodic when two sounds are played subsequently or harmonic when they are played synchronously. Acoustically, intervals can be described as ratios of the fundamental frequencies of two tones, with integer ratios characterizing pleasant or consonant intervals and more complex ratios typical of dissonant intervals (for instance, a dissonant minor second has a ratio of 16:15 whereas a perfect fifth of 3:2). Wagner et al. (Wagner et al., 2018) showed that harmonic interval size deviations in Western tonal music (e.g., rare major thirds amidst fifths) elicit a clear MMN in an oddball EEG paradigm, even in non-musicians. This indicates that pre-attentive cortical processes distinguish interval size differences in music without focused attention. Interestingly, this effect was asymmetric: some interval changes elicited stronger MMN than others, suggesting that interval consonance/dissonance influences automatic perceptual discrimination. Subsequently, Fujioka et al. (Fujioka et al., 2004) investigated how melodic contour (the overall directional shape of a melody) and interval structure (the precise pitch relationships between tones) are represented at an automatic, pre-attentive level of auditory processing using the MMNm. Using a passive listening paradigm with short melodic sequences, the study demonstrated that the auditory cortex encodes both the contour and the intervals without requiring focused attention, reflected by clear MMNm to altered contour or interval size. These findings provide evidence that melodic perception is hierarchically organized, with both contour and interval information contributing to early auditory prediction mechanisms. Overall, deviations from a melodic pattern or from a musical key structure can elicit MMN responses, suggesting that the auditory system encodes higher-order regularities in pitch sequences (Tervaniemi, 1999). These findings support the view that MMN reflects not only low-level sensory processing but also the formation of predictive models for musical pitch. The neural processing of relational pitch depends on musical training, as evidenced by reliable MMNs to melodic contour and interval violations during passive listening (Fujioka et al., 2004), as well as MEG findings showing that training facilitates the encoding of abstract sequential regularities, with musicians exhibiting learning-related increases in pattern MMNm amplitude (Herholz et al., 2011) and earlier MMNm responses to tonal pattern violations (Kuchenbuch et al., 2012). Another atomic element of pitch organization of music is the chord, namely a combination of three or more pitches sounding simultaneously. Another pioneer MEG study showed the elicitation of an MMN in response to chord categories typical of Western tonal music, namely minor chords, inserted in a sequence of major chords. Moreover, the MMN was more medially localized in the auditory regions to chords and melodic patterns than to isolated musical tones (Alho et al., 1996). The sensory-cortical system supporting MMN generation is even able to discriminate between different chord categories, such as major, minor or dissonant chords, such that an MMN appears when the deviant chord belongs to another category, but not when the chord is an inversion of the same (major) category (Virtala et al., 2011). In Mari Tervaniemi’s lab MMR were recorded to chord categories even in newborns (Virtala et al., 2013). A primitive musicality and a sense of harmony might, thus, be inscribed already in the pre-attentive auditory-cortex prediction mechanism. At the same time, music training can shape the abilities to predict chord categories, as shown by enhanced MMN in musicians to chord changes (Virtala et al., 2014). Remarkably, these MMN-related models are reinforced and more precise if the sounds conform to musical schemata of own culture that are stored in long-term memory (Brattico et al., 2001): for instance, when a pitch change is inserted in a five-tone motif built on the familiar (diatonic) musical scale the resulting MMN is larger than when the same change is encountered in an artificial scale motif. In other words, the melodic context of the pitch change is less familiar and more uncertain, leading to a lower precision of the pitch models that drive predictive processes and related MMN-error signals. More recently, Quiroga et al. (Quiroga-Martinez et al., 2019) investigated whether the MMNm and behavioral responses could be modulated by the degree of certainty/uncertainty of a musical context. In the MEG dewar, participants were concentrated on a primary silenced-movie-watching task while being presented with ever-varying tonal melodies whereas afterwards they were asked to report whether there were unusual notes in the melodic sequence and how confident they were in their answer. Results showed that both prediction error MMNm responses and degree of accuracy and confidence were reduced in contexts characterized by greater uncertainty (high-entropy) compared to less uncertain contexts (low-entropy). This reduction was observed exclusively for pitch errors and not for duration or intensity or location errors, thus with those features that were most relevant to the melodic context. An earlier study (Brattico et al., 2006) was even bolder, resembling even more an ecological music listening condition: the critical notes were inserted within 40 unfamiliar, temporally and acoustically varying melodies (played by clarinet, nylon-string guitar and jazz guitar), and were placed at a variable temporal position (2–4 s after onset). Three types of violations were used, each eliciting an MMN-like response: a congruous (in-key) pitch belonging to the diatonic scale of the melody, an out-of-key pitch which was a semitone interval from the preceding note violating the tonality of the melody, and an out-of-key pitch namely a error of tuning (quartertone). More recently, Quiroga-Martinez and colleagues examined how predictive uncertainty affects the magnetic MMN to acoustic errors of pitch sequences and how these processes are modulated by musical expertise (Quiroga-Martinez et al., 2020). The strength of the magnetic MMN to pitch and slide errors decreased significantly when sounds were embedded in high-entropy melodies, i.e., less predictable and more complex contexts. This effect of contextual uncertainty was obtained even in music experts, although they showed overall larger MMN compared to non-musicians, reflecting the superior precision of their long-term predictive models. In parallel, in the same study, behavioral data were collected to reveal whether the active detection of pitch errors in melodies would be affected by five different levels of uncertainty, from very low to medium. Participants were required to identify “out-of-tune” notes and report their level of confidence in their answers. The results confirmed the superior accuracy and confidence of musicians in discriminating these mistunined notes. However, consistent with the MEG data, detection ability and subjective confidence decreased in both groups as melodic uncertainty increased, with expertise failing to significantly mitigate this effect. Researchers interpreted the combined results from these two methodologies suggesting twofold mechanisms for modulating predictive precision. On one hand, expertise would refine long-term mental representations, increasing the amplitude of the MMNm; on the other hand, the contextual uncertainty of the stimulus would act dynamically by reducing the gain of prediction error responses. Thus, it seems that the environment’s statistical uncertainty has a robust impact on auditory perception that cannot be overridden even by intensive musical training, at least in the case of acoustic errors and melodic contexts of medium complexity. Other studies did not find group differences, or even found reduced MMN amplitudes in musicians when musical sounds are very familiar (Boh et al., 2011; Bonetti et al., 2018), which has been interpreted as neural efficiency in processing overlearned categories. For instance, musicians may exhibit more subadditive MMNm responses to contextually relevant pitch-related feature combinations, resulting in reduced overall neural activity due to more integrated and efficient predictive processing rather than diminished sensitivity (Hansen et al., 2022). Similarly, evidence from beat processing indicates that when temporal structure is highly salient and redundantly cued, musicians and non-musicians show comparable MMN responses, suggesting that some predictive mechanisms may be universal and robust to training (Bouwer et al., 2014). Moreover, in an MEG study (Brattico et al., 2009), musicians showed a significantly stronger MMNm source compared to non-musicians only in the presence of non-prototypical chords (dissonant or mistuned) interspersed withing a sequence of major chords, whereas for familiar, prototypical (minor) chords, no differences were found between the groups. This supports the idea that musical training induces selective auditory plasticity, a refinement of the brain in rapidly identifying “foreign” or complex sounds relative to the context, rather than a mere generalized accuracy. Source localization studies have indicated that these MMN responses arise primarily from neuronal populations in the supratemporal plane of the temporal lobe, with additional frontal contributions, suggesting that years of musical training plastically reshape auditory cortical networks to automatically detect subtle structural regularities and violations in sound sequences, even in the absence of attention (Quiroga-Martinez et al., 2021). Another study employed MEG and a beamforming analysis to localize the neural generators of the musically elicited MMN with high temporal resolution (Lappe et al., 2013a). The study tested non-musicians who had completed two weeks of intensive piano training to ensure the formation of stable long-term memory traces for the musical stimuli, which consisted of six-tone melodic arpeggios where the final note was occasionally lowered by a minor third. The analysis identified a widely distributed neural network activated between 100 and 200 ms after the deviant tone, involving the superior temporal (STC), inferior frontal (IFC), superior frontal (SFC), and orbitofrontal (OFC) cortices, with particularly pronounced activation in the right hemisphere. A pivotal finding of this research was that neural activation in the IFC occurred slightly earlier than in the STC, contradicting the traditional view that frontal activity is always initiated by the auditory cortex. This temporal sequence suggests that the musically elicited MMN is not merely a bottom-up sensory response but involves significant top-down processes. The researchers concluded that when listeners are familiar with a musical structure, frontal areas draw upon stored structural knowledge to generate predictions; when a deviant tone violates these expectations, the system immediately recruits this frontal-temporal network to signal a prediction error. 5. Predicting rhythm Rhythm is a fundamental element of music in the temporal domain, referring to the temporal organization of sounds around a recurring pulse or meter that enables precise predictions about when events will occur. This temporal structure engages auditory–motor networks even throughout passive listening, giving rise to the spontaneous tendency in humans to move or tap to a beat. As we remarked above, within the predictive coding framework, MMN is interpreted as a neural signature of prediction error arising when incoming sensory input deviates from hierarchically organized expectations about the auditory environment, with lower levels encoding local acoustic features (e.g., pitch) and higher levels representing abstract regularities (e.g., tonal structure). Rhythm represents an exemplary test case for the predictive coding hypothesis, because temporal priors guide expectations about when sounds will occur in a hierarchically organized manner: meter functions as a higher-order prior that constrains predictions about event timing, while deviations elicit prediction errors that propagate up the cortical hierarchy. Whereas grasping meter requires integration across local rhythmic events, beat perception relies on priors derived from integrating temporally distant events. Accordingly, rhythmic MMN does not reflect simple deviations in physical stimulus properties, but rather violations of temporal predictions derived from inferred beat and meter. An early EEG study by Rüsseler et al. (Rüsseler et al., 2001) used omission and timing-deviation paradigms to discern how the human brain models temporal regularities and automatically responds to deviations from them. They found more robust MMN responses in musicians to violations of temporal regularities. Ladinig and colleagues (Ladinig et al., 2009) investigated the brain’s ability to perceive metrical regularity using simple tone sequences. These sequences featured slight variations in loudness and timing, which naturally created a sense of grouping or a “pulse”—akin to feeling a beat in music. The researchers occasionally introduced subtle violations to this regular pattern, such as altering a tone’s loudness or position, which shifted the accent and created a “syncopation” or a disrupted beat. By comparing conditions where listeners paid attention to the sounds versus when they did not, the study demonstrated that the brain automatically extracts and predicts metrical regularity, even in individuals without musical training and without focused attention. Evidence for the pre-attentive nature of rhythm processing has been particularly strong for metrically simple structures. Bouwer et al. (Bouwer et al., 2014) demonstrated that beat processing occurs automatically when rhythms possess clear accents and simple metrical organization, as indexed by MMN responses independent of attentional focus. Similarly, these findings support the idea that meter emerges as a perceptual property of rhythmic input through automatic grouping and temporal prediction mechanisms. Remarkably, early electrophysiological correlates of rhythm and meter processing have been identified even before the classical MMN time window (Geiser et al., 2009): early ERP components sensitive to metrical structure, suggesting that temporal regularities influence auditory processing at multiple hierarchical stages, from early sensory encoding to higher-order predictive integration. The automaticity of rhythm perception is further underscored by developmental evidence. The automaticity of rhythmic predictions has been tested with EEG even in sleeping neonates, using stimulation consisting of rhythmic patterns with infrequent omissions of metrically relevant beats, which elicited MMR (Winkler et al., 2009). These results demonstrate the sensitivity to beat violations even when the rhythm contained natural variations in constituent sounds and that since birth humans possess the neural ability not only to detect periodicity but also track the hierarchical temporal structure of rhythms. Beat perception, thus, seems to rely on temporal predictive skills that are either innate or develop extremely early, forming a foundational scaffold for later musical and linguistic timing skills. On the other hand, experience shapes neural predictions of rhythm as demonstrated by a superior sensitivity to temporal features of sounds in music experts. Remarkably, this sensitivity is visible not only in the music domain but also in the language domain and specifically in abstract phonological representation of speech, as evidenced by enhanced MMN to syllables varying in durational and voicing features in musically trained 9-year old children vs. non-musically trained peers (Chobert et al., 2011), musical expertise facilitates a positive transfer of training from the musical domain to the abstract phonological representations of speech. Moreover, early work by Vuust and colleagues (Vuust et al., 2005) showed that musicians exhibit enhanced and left-lateralized MMN responses to metrically incongruent rhythms whereas musically untrained individuals displayed right-lateralized MMN, suggesting that long-term musical training refines internal temporal models and strengthens predictive precision affecting even hemispheric specialization for processing musically-relevant temporal features. A further MEG study by Vuust et al. (Vuust et al., 2009) further supported the ability of musicians’ brain to identify metrical incongruities, as indexed by the MMN. Participants listened to drum-based rhythmic sequences establishing a regular metrical context, within which both metrically incongruent beats and subtler syncopated deviations were introduced. While both groups showed MMNm responses to clear metrical violations, only musicians showed strong and earlier MMNm responses to syncopations, reinforcing the notion that meter functions as a higher-order temporal schema rather than a mere sequence of intervals. Longitudinal research further supports a causal role of musical practice, showing training-induced gains in duration and rhythm-related MMN responses (Chobert et al., 2014; Lappe et al., 2011; Moreno et al., 2009), even among children (Putkinen, Tervaniemi, Saarikivi, de Vent, et al., 2014). For instance, in a two-years longitudinal study, Chobert and colleagues (Chobert et al., 2012) provided further support of their previous observations on superior temporal predictions for speech sounds after music training, obtained with a cross-sectional design: 24 children (ages 8-10) with no prior training were randomly assigned to either music or painting training and only the music group showed significantly enlarged MMN amplitudes for duration and voicing deviants. The researchers concluded that active musical training, not innate predispositions, yielded these improvements. Another longitudinal study examined if the integration of motor and auditory systems during training accelerates cortical plasticity in relation to rhythmic processing of musical sounds (Lappe et al., 2011): non-musicians underwent two weeks of training and were divided in two groups, depending on the content of their training. The Sensorimotor-Auditory (SA) group learned to play piano sequences, while the Auditory (A) group merely listened to and evaluated the rhythmic accuracy of the SA group’s performances. The SA group showed a significantly greater increase in MMN and P2 amplitudes to rhythmic deviants compared to the A group. Behaviorally, only the SA group showed significant improvement in detecting temporal errors. Thus, multimodal integrated training (playing an instrument) was more effective at driving plastic changes in the auditory cortex than purely auditory exposure, likely due to the bidirectional connections between motor and auditory brain areas. A semi-longitudinal approach was further adopted to track the development of auditory discrimination profiles in 117 children, either musically trained or untrained, tested at ages 9, 11, and 13 (Putkinen, Tervaniemi, Saarikivi, Ojala, et al., 2014). While no group differences were found at age 9, by age 11, the music group displayed enlarged MMNs for rhythm, timbre, and tuning, and by age 13, even for melodic modulations. The researchers concluded that the neural advantages observed result from accumulated training rather than pre-existing differences. Neurophysiological studies have also highlighted functional differentiation between rhythmic and melodic deviations. By implementing source reconstruction of two fast-training MEG studies, one including a stimulation paradigm with melodic violations and another with rhythmic violations, Lappe and colleagues (Lappe et al., 2013b) demonstrated that rhythmic MMN responses recruit cortical areas distinct from those elicited by melodic deviations, with rhythmic MMN responses showing stronger involvement of motor-related and timing networks. This dissociation was further supported by a dedicated MEG experiment, directly comparing MMN responses to melody or rhythm violations in the same sample of music experts (Lappe et al., 2016): MMN responses to a rhythmic deviation not only were shorter and larger than MMN responses to a melodic deviation but were also generated by difference neuronal assemblies. Specifically, melodic MMN recruited the anterior part of the auditory cortex, the inferior prefrontal cortex and the supplementary motor area whereas rhythmic MMN recruited a neural network comprising auditory cortex, the inferior parietal lobule, the superior parietal and postcentral cortices as well as the supplementary motor area. A more pronounced involvement of supratemporal, frontal motor and parietal areas. These findings support a dual stream model of auditory processing according to which spatial and temporal features are processed in posterior-dorsal auditory and parietal areas and spectral acoustic features are elaborated in ventral anterior auditory regions (Belin & Zatorre, 2000; Rauschecker & Scott, 2009; Rauschecker & Tian, 2000). While pre-attentive processes play a dominant role, attention significantly modulates neural responses to rhythmic complexity. For instance, complex rhythms elicit stronger and more widespread neural responses when attended, particularly involving sensorimotor and frontal networks (Chapin et al., 2010). This indicates that while basic temporal predictions can be generated automatically, attentional engagement enhances the integration of rhythm with higher-order cognitive and motor systems, especially for less regular patterns. In a phylogenetic perspective, recent studies on cultural transmission expanded the view of the MMN as an index of the cultural transmission of regular rhythmic structures, confirming the intersection between biological predispositions, learning and even cultural communication (Lumaca et al., 2018). Specifically, in an iterated learning paradigm, participants listened to initially random rhythmic patterns, reproduced them, and their reproductions were then presented to subsequent participants, thus simulating a cultural transmission through artificial ‘generations’. In a preceding separate session, the MMN was recorded to evaluate the participants’ sensitivity to temporal deviations in rhythms. Two experiments consistently showed that, as the patterns are transmitted, they progressively become more isochronous, i.e., more regular and predictable in their timing. This change was not random: it was systematically influenced by the neural sensitivity to temporal errors, as assessed with the MMN parameters. Participants who showed a larger MMN to temporal deviations also tended to reproduce more regular rhythms and accelerate the emergence of isochrony in the cultural transmission process. Hence, individual neural predispositions - in particular, the precision with which the brain detects and predicts the timing of sounds - seem to shape the structure of the rhythms that emerge and stabilize culturally. Rhythmic regularity would therefore not only be a product of cultural convention or social learning but would emerge in part because of neurocognitive constraints related to temporal prediction, which the MMN allows us to observe directly and pre-attentively. In sum, rhythm-based expectations are encoded early, automatically, and hierarchically, while also being modulated by attention, musical expertise, and developmental stage. Within a predictive coding framework, rhythm-based MMN responses index precision-weighted prediction errors arising from violations of internally generated temporal models, positioning rhythm perception as a paradigmatic example of hierarchical auditory prediction. 5.1 Interim conclusions Overall, the MMN studies on pitch- and rhythm-related features convincingly demonstrated that the MMN reflects anticipatory processes related to any acoustic feature relevant to the musical discourse, and that these processes are modulated by existing knowledge of musical scale conventions. Findings have been read in the light of the predictive coding theory, according to which the human brain is conceived as a hierarchical predictive system, constantly capable of integrating predictions about sensory inputs (a top-down process) and comparing them with the sensory input. Discrepancies between prediction and input generate responses defined as prediction errors, which are transmitted with a bottom-up logic to hierarchically superior centers, with the aim of updating internal models. In this process, it is possible to distinguish first-order predictions, related to the content of the stimuli (e.g., melody, pitch, rhythm), and second-order predictions, related to the reliability or uncertainty of the context. These second-order predictions would modulate the weight or precision of prediction errors, or, in other words, the sensitivity to them: in predictable contexts, prediction errors have a greater impact and present with stronger electrophysiological correlates, while in uncertain/unpredictable contexts, their weight decreases (Vuust et al., 2022). This theoretical approach unifies perception, action, learning, and emotion, providing a coherent basis for understanding phenomena related to music listening and practice such as the pleasure (or tension) derived from “unexpected deviations”, the sense of rhythm, the ability to anticipate subsequent notes in a melodic sequence, and individual differences in response to musical stimuli. Hence, MMN paradigms are functional for a fast and reliable characterization of an integrated auditory profile, allowing the study individual differences in the processing of musical stimuli as well as possible auditory or neurocognitive abilities and even deficits. Furthermore, the modulation of MMN amplitude as a function of deviation magnitude once again supports the predictive coding theory, in which the brain generates expectations about sound parameters and detects discrepancies automatically. 6. Relating MMN to individual differences in musicality After demonstrating that a MMN could be elicited by music-related features, it became urgent to determine whether these MMN responses would be associated with their behavioral correlates, i.e., with individual differences in music discrimination abilities, as assessed with listening tests. As noted earlier, these musicality skills are substantially innate: they emerge even in the absence of formal music training, vary substantially across individuals from early developmental stages, and, crucially, show high heritability (Gingras et al., 2015). Evidence for a genetic contribution to musicality includes familial clustering of musical traits such as absolute pitch and congenital amusia—an innate impairment of music perception and production—indicating that these traits occur in families more frequently than expected by chance. Furthermore, twin studies comparing monozygotic and dizygotic twins have provided quantitative support for this view, demonstrating that pitch perception abilities, commonly assessed using tasks such as the Distorted Tunes Test, exhibit high heritability (approximately 70–80%) with minimal influence from shared environmental factors (Seesjärvi et al., 2016). The first attempt to relate musical skills with their MMN correlates was conducted by Tervaniemi and colleagues (Tervaniemi et al., 1993), focusing on musicians who self-reported to possess absolute pitch (the ability to correctly identify the pitch label of a note even without prior listening to a reference note). By presenting sequences of standard tones interspersed with deviant tones, an MMN was obtained in absolute pitch possessors and non-possessors, with larger amplitude and shorter latency for complex tones (piano) compared to simple tones (sinusoidal), and for larger deviations (semitone) compared to deviations of smaller magnitude (quarter tone). Results indicated that absolute pitch skill must depend on different neurophysiological mechanisms than a greater precision of automatic auditory sensory memory indexed by MMN. Subsequently, EEG recordings with the oddball paradigms were connected with listening tests behaviorally assessing the individual musical aptitude. Findings consistently showed that individuals with higher scores in pitch and melody discrimination performance on musicality tests exhibited more robust MMN responses to subtle changes in sound features, particularly pitch-related deviations. The reliable link between MMN amplitude and behavioral measures of musicality suggests that the prediction mechanisms underlying MMN generation might represent prerequisites for conscious musical abilities (for a review, see Yu et al., 2015). Furthermore, this relationship between musical aptitude and enhanced preattentive processing is not limited to musical sounds but can generalize to speech stimuli, supporting the notion of shared auditory mechanisms underlying music and language processing. Supporting evidence comes, for instance, from Milovanov et al. (Milovanov et al., 2009), who compared duration discrimination of vowels and violin tones in school-age children. More accurate pre-attentive discrimination in both domains was related to higher musicality and foreign language pronunciation skills. The studies described so far employed stimulation paradigms that were often quite long and not properly superimposable or comparable to music. For this reason, Vuust and colleagues (Vuust et al., 2011) adopted the multi-feature paradigm, including 6 different types of acoustic feature changes (pitch, timbre, localization, intensity, rhythm and slide –a continuous, usually rapid transition between two adjacent pitches–) to swiftly assess the neural prerequisites of musicality skills. A further development introduced for each of the six feature deviants (pitch, timbre, intensity, spatial position, slide, and rhythm) three levels of deviation, allowing for the evaluation of the gradual sensitivity of the brain to discrepancies (Vuust et al., 2016). In a separate behavioral session, participants’ musical competence was measured using the Musical Ear Test (MET), a standardized behavioral test that assesses the ability to discriminate melodies and rhythms presented in musical sequences (Wallentin et al., 2010). The MET provides separate scores for the melodic component (MET-melody) and the rhythmic component (MET-rhythm), to correlate differences in behavioral performance with neural responses. The results show that 16 of the 18 feature deviation levels generated a significant MMN, confirming the paradigm’s sensitivity for detecting pre-attentive neural requisites of individual musical skills across multiple features. Furthermore, for pitch, intensity, position, and slide, the MMN amplitude grew proportionally to the magnitude of the deviation, indicating a coding sensitive to quantitative differences in sounds. Some MMN responses correlated with musical competence as measured by the MET: for example, sensitivity to slide deviations showed positive correlations with MET-melody and negative correlations with MET-rhythm, suggesting that different aspects of musical perception are processed separately at the cerebral level. At the other side of the medal, opposite of highly musical individuals are congenital amusics or tone-deaf people, characterized by a performance in musicality tests that is below five standard deviations. Notably, congenital amusia is linked to heritable causes rather than purely environmental ones, since ~39% of first-degree relatives of amusic individuals also show the disorder, compared with (Peretz et al., 2007). Using a modification of the multifeature paradigm, with melodies varying in complexity and familiarity, MMN responses were recorded for pitch, intensity, timbre, location, and rhythm deviants in amusics and matched control listeners (Quiroga-Martinez, Tillmann, et al., 2020). Both groups showed robust MMN responses across most sound features and conditions, although with reduced amplitudes and prolonged latencies when increasing melodic complexity, suggesting sensitivity to context uncertainty even among amusics. Notably, amusics displayed MMN responses across conditions that were delayed by about 20 ms compared to controls, consistent with some difficulty in auditory processing in these individuals. Moreover, pitch MMN responses in the amusic group were not observed in high-complexity and unfamiliar melodic contexts, hinting at pitch-specific impairments under conditions of elevated uncertainty that may require intact frontotemporal processing. Similarly, Peretz et al. (Peretz et al., 2009) examined neural responses to melodic incongruities in individuals with congenital amusia, focusing on whether the brain automatically detects fine-grained pitch deviations that amusics cannot consciously report. Using EEG in a passive listening context, Peretz and colleagues found that amusics exhibited a normal MMN response to pitch errors inserted in random places within melodies, even when those errors were small (quartertone intervals) and not consciously detected in a subsequent behavioral session. The authors infer that early pre-attentive auditory change detection can be intact in congenital amusia. In contrast, later attention-dependent components such as the P3b, which reflect conscious discrimination of pitch changes, were absent or abnormal in amusics. This dissociation suggests that while the early auditory cortex in amusic listeners can register pitch deviations at a pre-attentive level (as indexed by MMN), the deficit likely emerges at later stages of processing where conscious perception and higher-order integration occur. Hence, both studies converge on the idea that pre-attentive pitch change detection (MMN) is relatively spared in congenital amusia, even when behavioral discrimination fails, but that contextual complexity and later stages of processing reveal limitations in how pitch information is used when prediction demands are high or when conscious awareness is required. The contribution of the innate biological repertoire on music-related predictive processes as indexed by MMN has been investigated in sparse studies combining neurophysiological signals with genetic mapping. For instance, an MEG study (Bonetti et al., 2021) in a large sample of healthy adults (N ≈ 108) has related the MMN to deviant musical features to the COMT Val158Met polymorphism (rs4680), related to enzyme’s activity moderating dopamine degradation especially in prefrontal cortex. Heterozygous Val/Met individuals showed enhanced prediction error signals to acoustic features embedded in repetitive melodies in cortical regions including inferior frontal and temporal cortex relative to homozygous Val/Val and Met/Met carriers. These neural deviance effects support the idea that COMT genotype influences automatic deviance-detection of musical features and predictive coding mechanisms in healthy brains. Moreover, findings suggest non-linear genotype effects (enhanced predictive responses in heterozygotes), indicating that simple Val versus Met comparisons may not capture the complexity of dopaminergic modulation on auditory predictive mechanisms. Another isolated investigation combining neurophysiological recordings to genetic mapping explored the relationship between musical competence and the genetic-biological substrate. The Val66Met polymorphism of the gene for brain-derived neurotrophic factor (BDNF) has been associated in numerous studies with cognitive and memory alterations, as well as psychiatric pathologies (in particular, major depressive disorder). In a study combining MEG, MRI and genetic mapping from plasma samples, it was investigated how the BDNF Val66Met polymorphism modulates the plasticity of the auditory system in response to musical training (Bonetti et al., 2023). The central hypothesis was that individuals with the Val/Val genotype, which is associated with higher levels of mature BDNF and increased neural plasticity, would show enhanced MMN responses if they had undergone intensive musical training. To investigate this, the team recorded the neural activity of 74 participants using MEG while they were exposed to the musical multi-feature paradigm (MuMuFe), which includes deviants in pitch, timbre, localization, intensity, slide, and rhythm. The results confirmed that Val/Val musicians displayed significantly stronger MMN amplitudes compared to Met-carriers and non-musicians, with the most pronounced effects found for pitch, slide, and rhythm deviants. Source reconstruction localized these enhanced responses to the auditory cortex and medial temporal lobe, suggesting that BDNF facilitates functional optimization in these regions. The data suggest that the combination of musical training and “favorable” genetics (wild-type Val/Val genotype) facilitates greater auditory plasticity in terms of predictive capacity. Overall, the literature indicates that the MMN provides a sensitive index of musical expertise, reflecting training-related modulations of pre-attentive auditory predictive processing. Indeed, musicians consistently show enhanced MMN responses to musically relevant features such as pitch, harmony (e.g., slightly impure chords), timing (e.g., deviations as small as 20 ms in regularly spaced tonal sequences), meter, and abstract sequential structure, particularly when deviations are embedded within meaningful musical contexts rather than presented as isolated acoustic changes. Remarkably, musicians’ auditory predictive processes are highly sensitive as indexed by MMN elicitation whereas non-musicians might not even show any MMN in presence of tiny errors hidden in musical sequences. Musicians, furthermore, display heightened short-term plasticity for generating internal predictive models during learning. At the same time, null or reduced MMN effects have been found in musicians for highly familiar or redundant stimuli point to neural efficiency rather than reduced sensitivity. Finally, initial imaging genetic evidence points at the role of genetic factors, such as BDNF polymorphisms, to govern the neuroplastic effects of music training on the auditory predictive processes indexed by MMN. 7. Is MMN an index of musical practices and cultures? Considering the sensitivity of MMN parameters and underlying neural generators to both the biological predispositions and the specific demands imposed by music training, it is reasonable to suppose that the MMN might reflect the peculiarities of the different practice styles in musicians, and even index in music amateurs and non-musicians the degree of familiarity with different musical genres or cultures. Inspiration for a possible impact of performing and practice habits in musicians on MMN parameters came from an experiment dating back to 2001 (Tervaniemi et al., 2001), in which participants (musicians and non-musicians) were made to listen to melodies having an “inverted U” contour. After a period of attentive listening, the musicians, particularly those with experience of musical practice by ear, showed an MMN in response to infrequent patterns without the inverted-U contour during passive listening. Non-musicians and musicians accustomed to practicing with sheet music did not show the same response. The MEG recordings on a subset of the musicians localized this response in the auditory cortex, providing supporting evidence for the presence of a perceptual memory even for abstract tone patterns, not only for simple acoustic features. The authors concluded that musical experience, and in particular training in attentive auditory listening as it occurs when frequently practicing improvisation, can facilitate the formation of abstract and automatic representations of tone patterns. These findings were extended by Seppänen and colleagues (Seppänen et al., 2007), who purposely recruited professional musicians who predominantly used “aural” strategies for studying and performing (playing by ear, improvising) and musicians who preferred “non-aural” strategies (score-based practice and performance). While both groups exhibited comparable MMN responses to simple deviations in acoustic features (pitch, duration, loudness), the aural group demonstrated significantly larger MMN amplitudes for violations of melodic contour. This effect was most pronounced in MMN responses recorded during a second passive listening condition that followed an active task requiring attentive discrimination of the erroneous sounds. The MMN enhancement in aural musicians suggests that extensive practice with auditory strategies might promote perceptual learning of transposition-invariant representations of melodic structure, accessible pre-attentively, whereas musicians who rely primarily on visual strategies may develop weaker auditory models for sound patterns. These effects have been observed even in the developmental age. A study focused on children violinists taking Suzuki lessons, namely learning to play the violin by imitating and listening rather than by reading the score (Meyer et al., 2011). Results showed that 7.5-12 years-old children who received Suzuki-method training for few years exhibited significantly larger MMN to violin tones compared to untrained peers. This enhancement reflects increased neural sensitivity to violations in timbre and pitch characteristics of the trained instrument. Continuing along the same line of inquiry, the question arose as to whether the musical style practiced (e.g., jazz, classical, rock/pop) could influence neural sensitivity to changes in musical sound characteristics. Using the multifeature paradigm to measure MMN to mistuning, timbre, rhythm, slide, intensity, and localization, it was found that musicians with intensive rhythmic or improvisatory training, like jazz performers, exhibit robust and faster MMN responses across multiple sound features as opposed to classical and rock/pop musicians and to non-musicians, likely due to the high demands in temporal flexibility, prediction, and rapid auditory, motor integration that characterize jazz improvisation (Vuust et al., 2012). Remarkably, this MMN enhancement was mainly driven by mistuning and slide deviants, hence errors in musical scale priors, i.e., in what pitches were expected during listening. This enhancement in jazz musicians indicates a refinement of the precision of predictive models deriving from the higher familiarity with the sliding note feature in improvisational music as opposed to classical music. Furthermore, the topography of the MMN varied as a function of the practiced style: the MMN was more frontal to pitch and location compared to the other deviants in jazz musicians and was more left lateralized to timbre MMN in classical musicians. Therefore, even the specific demands and habits of musical training may shape the brain’s predictive function in relation to musically relevant features, reinforcing the idea that MMN might reflect auditory abilities shaped by experience and practice, and that automatic sound perception can be modeled or modulated by experiential context. Further corroboration is provided by an additional multifeature MMNm investigation encompassing professional classical and jazz musicians, amateur musicians, and non-musicians (Kliuchko et al., 2019). Active jazz training was associated with stronger magnetic MMNs, especially to spectral features and sound location, as compared with amateurs and non-musicians. MMN sources were reconstructed in the auditory cortex and frontal regions, signifying more pronounced automatic fronto-temporal neural signals in response to violated priors. Conversely, music listening alone, devoid of instrumental practice, did not produce a strengthening of neural responses: non-musicians who named jazz as their favorite musical genre displayed a negative correlation between their preference scores and MMN amplitude. This outcome may be accounted for by two potential mechanisms. On the one hand, the consistent exposure to a musical language such as jazz, characterized by a wealth of rhythmic and harmonic deviations and tonal instability, could better anticipate stylistic variations, thus generating a more attenuated prediction error response with a consequently equally attenuated MMN. On the other hand, the preference for jazz could imply a greater activation of sensory-motor circuits even in the absence of motor execution: this sensory-motor interaction would “stabilize” predictions, reducing the need for a strong prediction error response. Collectively, the study demonstrates that musical experience does not model perception in a uniform manner; instead, its effects depend on the audio-motor content of the experience. In addition to auditory predictions, the peculiar demands of a musical practice might affect the neural correlates of preattentive auditory processing in domain-general ways. Nager and colleagues (Nager et al., 2003) compared professional conductors, pianists, and non-musicians in an auditory spatial attention task. While all participants detected target sounds from a central location equally well, conductors showed superior behavioral and electrophysiological selectivity for sounds in peripheral auditory space, consistent with previous findings in blind individuals. Specifically, both the conductor and pianist groups showed significantly greater MMN responses to spatial deviations from unattended locations compared to non-musicians, who showed only rudimentary pre-attentive shift detection. Conductors also showed similar subsequent positivity in P3a, suggesting enhanced automatic orienting to unexpected auditory events. These findings indicate that the unique professional demands of the conductor – the simultaneous monitoring of multiple sound sources in the auditory scene – lead to enhanced pre-attentive spatial processing and suggest that specific professional roles in music may produce qualitatively distinct patterns of neural plasticity. Specific advantages in sound processing associated with instrumental and practice-related demands are not limited to professional musicians but can also extend to amateur performers, such as individuals playing in rock or pop bands (Tervaniemi, Castaneda, et al., 2006). In this study, amateur musicians exhibited larger MMN and P3a responses than nonmusicians selectively in response to sound-location changes, while no group differences were observed for other acoustic features. This pattern suggests a form of practice-dependent plasticity specifically related to the demands of ensemble playing, in which accurate spatial auditory processing is critical for coordinating with other musicians. Moreover, even when musical experience is viewed primarily as an auditory phenomenon, predictive processes are shaped by the specific content of that experience—namely, the musical culture to which an individual is exposed. While musicality is a universal human capacity, its expression varies across cultures through distinct, culture-specific features. As previously mentioned, one key feature that differs is the musical scale, or in psychological terms, the tonal schema, which is acquired through exposure to a particular musical tradition. For example, Western listeners are familiar with the seven-tone diatonic scale and possess tonal schemata corresponding to its pitches, whereas Chinese listeners typically expect melodies to follow the five-tone pentatonic scales characteristic of their traditional music. MMN research on non-Western musical scales remains to date scarce. This contrasts with other domains, such as speech, emotion and multisensory perception, where cultural modulation of early predictive processes has been clearly demonstrated. For example, Liu and colleagues (Liu et al., 2015) showed that Chinese listeners produce larger vMMN responses than English-speaking North Americans when viewing emotional faces accompanied by vocal expressions, indicating greater automatic sensitivity to vocal cues, an effect visible as early as 100–200 ms and shaped by culturally acquired display rules. Such findings underscore that early predictive responses, including MMN, are not culture-independent but can be modulated by long-term exposure to culturally specific perceptual norms. One of the few empirical studies addressing the cultural specificity of early predictive processing in music provides converging support for this view. In a MEG study, Matsunaga and colleagues (Matsunaga et al., 2012) demonstrated that listeners with extensive exposure to both Western and traditional Japanese music engage partially distinct neural subregions when processing out-of-key tones from melodies of these two musical systems. Although the authors do not explicitly label these early responses—peaking around 140 ms—as MMN, they interpret them as reflecting prediction error processing in line with predictive coding theory, related to musical scale conventions. While the temporal characteristics of these prediction error responses were similar across cultures, their spatial distribution differed, suggesting that neural models of tonal structure are instantiated in a culture-dependent manner. This study highlights important cultural influences on hierarchical auditory predictions but leaves open the question of whether culturally acquired musical regularities modulate the precision and weighting of earlier predictive processes indexed by the MMN. To conclude, EEG and MEG investigations demonstrate that musical training produces a constellation of domain-specific neural plasticity as indexed by MMN, shaped by the distinct perceptual, cognitive, and motor demands of each musical practice (Tervaniemi, 2009). Remarkably, the studies on predictive processes for cultural conventions for pitch organization together highlight how the MMN constitutes a sensitive indicator both of the automatic detection of prediction errors and of the retrieval of consolidated sound representations in long-term episodic memory, showing the interaction between perception, memory, and musical expertise. 8. Does MMN reflect only acoustic predictions or also cognitive predictions? As we have reviewed above, the MMN indexes the formation not only of short-term models of the auditory environment but also the existence of long-term memory representations affecting early automatic processing of sounds. This is demonstrated by evidence of MMN modifications as a result of: life-long exposure to the sound features of their musical instruments or musical styles in musicians or amateurs; life-long exposure to the selected sounds of the musical scales and to the temporal patterns of the rhythms in listeners from a particular culture; hours-long exposure to artificial sound features and sound combinations in a perceptual training sessions, even after sleep-dependent consolidation. The detailed nature of musical experience profoundly shapes the formation of “auditory priors” in the brain. Whether music is engaged with through instrument practice, specific playing styles, or exposure to a particular musical genre or culture, the resulting predictive models guide the automatic processing of sounds. This conclusion is supported by MMN modulation observed months after birth following prenatal exposure to a specific melody (Partanen et al., 2013), indicating that even musical regularities learned over hours or months can influence early predictive processes distributed hierarchically across frontotemporal networks. Collectively, these findings move beyond the view of MMN as a mere reflection of sensory memory, suggesting instead a form of musical “primitive intelligence” in auditory and inferofrontal brain networks that automatically tracks temporal and sequential regularities in musical sounds. MMN responses to violations of expected tone order or timing demonstrate that the auditory cortex can rapidly form predictions about both spectrotemporal structure and dynamic musical patterns, independent of frontoparietal attentional resources. In other words, a musical primitive intelligence encompasses not only static sound properties but also the unfolding structure of complex musical sequences. From the perspective of music-related MMN findings, the long-standing debate between stimulus-specific adaptation (SSA) and predictive coding can be reconsidered in a new light. SSA-based models conceptualize MMN as an emergent property of local neuronal fatigue: neurons tuned to frequently presented standards reduce their responsiveness, while neurons selective for rare deviants remain relatively unadapted, producing enhanced responses to deviant stimuli. This account is parsimonious and well supported by single-unit recordings demonstrating SSA across multiple auditory stages, from subcortical nuclei to primary auditory cortex, and can account for MMN-like responses to acoustic features such as timbre, timing, or pitch embedded within musical sequences. However, SSA alone cannot explain MMN sensitivity to relational pitch regularities, its persistence under adaptation-controlled conditions, or its dependence on contextual predictability rather than mere stimulus probability. Importantly, SSA also fails to account for MMN elicited by deviations in continuously varying melodies, which reflect violations of musical regularities acquired implicitly over a lifetime rather than through short-term exposure during the experimental session (Brattico et al., 2001, 2006). Predictive coding accounts address these limitations by conceptualizing MMN as a precision-weighted prediction error generated within a hierarchical generative model of the sensory environment. In this framework, deviance responses are not solely determined by stimulus rarity but by violations of learned expectations, allowing MMN to reflect abstract rules, long-range temporal dependencies, and contextual uncertainty. Importantly, predictive coding naturally accounts for the distinction between “classic” or “physical” vs. “genuine” MMN: adaptation-related effects reflect changes in synaptic gain or precision at lower hierarchical levels, whereas genuine MMN indexes higher-order cortical computations involving memory comparison and model updating. Rather than representing competing explanations, SSA and predictive coding are increasingly viewed as complementary descriptions operating at different levels of analysis. SSA captures mechanistic, circuit-level processes that implement efficient sensory encoding, while predictive coding provides a computational framework that integrates adaptation, learning, and hierarchical inference. From this integrative perspective, SSA constitutes a necessary but insufficient substrate for MMN generation, supplying the neural economy upon which predictive models operate. MMN thus emerges not from adaptation or prediction alone, but from their interaction across hierarchical levels of the auditory system. Indeed, recent views have emphasized the central role of predictive mechanisms and pre-attentive neural responses to explain music perception and cognition (Koelsch, 2011; Koelsch et al., 2019): music activates a complex hierarchical neural architecture that includes auditory, frontal, limbic, and motor systems. Among these, a crucial role is played by the mechanisms dedicated to the automatic detection of deviations from musical regularities and conventions, which include the MMN and another frontally-negative response, the Early Right Anterior Negativity (ERAN), elicited in response to a chord or a melody pitch deviating from what would be expected based on tonal harmony conventions (Koelsch et al., 2000; Koelsch & Jentschke, 2010). These prediction error responses share a similar latency (~200 ms) and a frontocentral scalp distribution, with neural generators in auditory cortex and inferofrontal-parietal regions, and they frequently co-occur or temporally overlap, suggesting integrated predictive circuits across hierarchical levels of music processing (Koelsch, 2011; Koelsch et al., 2019). However, electrophysiological research using EEG and MEG aimed to separate the MMN from the ERAN with respect to the types of violations they index and their underlying neural mechanisms. Stimuli for eliciting the MMN have been typically restricted to acoustic violations of music-relevant priors formed over short-term exposure, such as timbre, intensity or pitch deviations within a musical sequence (Gao et al., 2025; Koelsch et al., 2001) and these simple violations are contrasted to chords or notes that violate the abstract syntactic rules of Western tonal harmony for eliciting the ERAN. This stimulus selection by itself implies that MMN would be sensorial response to acoustic predictions, consistent with SSA view whereas ERAN would reflect schematic, higher-order predictions about musical structure (Gao et al., 2025; Koelsch et al., 2001). Experimental manipulations confirm the functional dissociation and potential additive nature of MMN and ERAN. Ishida and Nittono (Ishida & Nittono, 2022, 2024) orthogonally manipulated music-syntactic (harmonic) and acoustic (intensity or contour) deviants in chord sequences, showing that each type elicited distinct negativities with comparable latencies and topographies. When both deviants occurred simultaneously, the resulting ERP was approximately the sum of the individual components, indicating that music-syntactic and sensory prediction errors operate independently, based on separate regularity representations. Gao and colleagues (Gao et al., 2025) similarly demonstrated that ERAN is specifically tuned to musical violations and recruits right-hemispheric and temporal regions involved in music processing, whereas MMN reflects more general auditory deviance detection with broader bilateral sources. A strict dichotomy according to which MMN indexes only physical, stimulus-driven deviations whereas ERAN reflects exclusively music-syntactic violations is increasingly difficult to sustain. Although the “classic” MMN has often been characterized as reflecting violations of local auditory regularities and low-level, stimulus-driven prediction errors (Koelsch, 2009), accumulating evidence reviewed above demonstrates that MMN is also sensitive to culturally learned musical priors, including scale membership, melodic contour, and probabilistic pitch relationships. We propose that what distinguishes MMN from ERAN is not the physical versus syntactic nature of the violated regularity, but rather the temporal and hierarchical scope over which predictions are integrated. MMN reflects violations detected through relatively local integration of adjacent sound events—typically involving short-range temporal dependencies—whereas ERAN indexes violations that depend on the hierarchical role and positional function of incoming events within longer musical contexts, akin to syntactic processing in language (Garza Villarreal et al., 2011; Leino et al., 2007). This distinction is supported by electrophysiological and source-localization studies (Garza Villarreal et al., 2011; Leino et al., 2007),employing musical cadences of seven chords that contained either harmonically congruous chords, harmonically incongruous chords (Neapolitan subdominant), or harmonically congruous but mistuned chords (fifth raised by 50 cents). Chords that violated harmonic rules elicited a bilateral early right anterior negativity (ERAN), whereas mistuned chords embedded in otherwise congruent sequences elicited a right-lateralized mismatch negativity (MMN). Importantly, ERAN amplitude scaled with the degree of harmonic violation, being larger for violations occurring at the third and seventh chord positions than at the fifth. In contrast, mistuned chords—violating local pitch-relation regularities across the sequence—elicited an MMN whose amplitude was not modulated by the position of the violation. Source reconstruction analyses further indicated that the ERAN originated in inferior frontal regions, encompassing Broca’s area and its right-hemispheric homologue, whereas MMN generators were localized to the auditory cortex. Together, these findings suggest a functional dissociation in which auditory cortices predominantly support the detection of sequential pitch-scale regularities, while prefrontal regions are engaged in processing hierarchical harmonic structure in music. Furthermore, MMN can be elicited by violations of abstract, yet locally defined musical regularities acquired through long-term cultural exposure—such as tonal scale expectations—even in the absence of any physical deviance. This challenges the view that MMN is restricted to the detection of purely “physical” deviations (Kalda & Minati, 2012; Sauvé et al., 2022). Within a predictive-coding framework, MMN and ERAN can therefore be understood as indexing prediction errors at different hierarchical levels rather than reflecting distinct representational domains. MMN appears largely insensitive to second-order predictions and explicit knowledge about upcoming deviants, consistent with high-precision ascending prediction errors generated at early stages of the auditory hierarchy. By contrast, ERAN latency and amplitude are modulated by expectations, confidence, and attentional set, indicating sensitivity to precision weighting and higher-order predictions (Koelsch et al., 2019). Accordingly, MMN reflects culturally shaped but locally computed prediction errors, whereas ERAN indexes violations of abstract musical syntax that require integration over extended temporal windows and hierarchical structures. This reconceptualization preserves the empirical dissociation between MMN and ERAN while rejecting an overly simplistic physical–syntactic dichotomy, situating both responses within a unified hierarchical predictive architecture for music perception. Recent EEG and MEG/MRI studies provide converging evidence for a form of musical primitive intelligence within the MMN system (Bonetti et al., 2022; Putkinen, Tervaniemi, Saarikivi, de Vent, et al., 2014; Tervaniemi et al., 2014). These studies take advantage of a novel melodic multifeature paradigm, in which multiple types of deviations are embedded within an accompanied melody that continuously modulates across tonalities. This paradigm makes it possible to dissociate prediction-error responses to acoustic deviations—violations of elementary physical sound properties—from responses to cognitive deviations, which infringe upon more abstract melodic regularities such as tonal context, scale membership, and interval structure (Tervaniemi et al., 2014). MMNs elicited by these two deviation types delineate distinct auditory profiles across individuals. Whereas acoustic deviations evoke relatively stable MMN responses across participants, cognitive deviations produce substantially greater interindividual variability, indicating that the extraction of melodic regularities differs markedly between listeners and is particularly sensitive to musical experience. Using the same paradigm in children, Putkinen and colleagues (Putkinen, Tervaniemi, Saarikivi, de Vent, et al., 2014) showed that children undergoing musical training exhibited larger and more adult-like MMNs to higher-order melodic deviations, accompanied by an enhanced Late Discriminative Negativity (LDN). This pattern suggests a more robust post-perceptual evaluation of melodic violations following the initial automatic response. Figure 2 . The figure shows cortical source maps illustrating the relationship between musical expertise and the neural generators of the mismatch negativity (MMN) elicited by acoustic and cognitive musical deviants in musicians (modified from Bonetti et al., 2022). Brain templates depict correlation patterns separately for the two deviant types, with acoustic deviants represented in red and cognitive deviants represented in blue. The color scale reflects the strength of the correlation (r‑values) between MMN source activity and musical expertise. Regions showing stronger associations indicate cortical areas where increased musical expertise is linked to enhanced neural responses to auditory deviations. Across the cortex, musical expertise is associated with MMN source activity particularly in medial cingulate regions and the right orbitofrontal cortex, with more pronounced and spatially extended effects observed for cognitive musical deviants compared to acoustic ones. Overall, the combined maps highlight a functional distinction between acoustically driven and cognitively driven musical mismatch responses, suggesting that higher levels of musical expertise preferentially modulate neural mechanisms involved in abstract, learned musical structure rather than low-level acoustic change detection. Extending these findings, a recent whole-brain MEG and MRI study employing the melodic multifeature paradigm (Bonetti et al., 2022) demonstrated that acoustic deviations primarily elicit MMNs localized to the auditory cortex and insula, whereas cognitive deviations generate larger MMNs engaging frontal, cingulate, and orbitofrontal regions (cf. Figure 2). This dissociation supports a hierarchical account in which increasingly complex and culturally acquired musical regularities recruit more distributed and integrative neural networks. Crucially, musical training was found to modulate both the amplitude and the cortical generators of prediction-error responses: in musicians, acoustic MMNs were enhanced in the right auditory cortex and insula, while cognitive MMNs showed increased involvement of the inferior frontal gyrus, cingulate cortex, and supplementary motor areas (cf. Figure 2). These results suggest that musical experience optimizes not only pre-attentive sensitivity to deviations but also the large-scale networks responsible for generating and propagating prediction-error signals. Overall, the study confirms that MMN is a sensitive marker of both basic sensory prediction errors and violations of more abstract, hierarchically organized musical regularities. Moreover, MMN sensitivity to both acoustic and cognitive deviations scaled with years of musical training, indicating that experience strengthens the ability to compare incoming sounds with stored predictive models. Further evidence for an intelligent view of the MMN system comes from studies showing that musical expertise can shape pre-attentive auditory processing even when predictions are internally generated. Using MEG, Yumoto and colleagues (Yumoto et al., 2005) reported that in trained musicians, pitch mismatches between auditory input and pitches expected from visually presented musical notation elicited an imagery-based MMN (iMMN), peaking at approximately 150 ms and localized to the auditory cortex. Although a non-musician control group was not included, these findings demonstrate that internally generated auditory expectations can serve as predictive templates for early deviance detection. Complementary evidence comes from a study comparing expert musicians and non-musicians during silent musical imagery of previously heard sequences (Herholz et al., 2008). Musical imagination activated cortical regions largely overlapping with those engaged during actual perception—most prominently primary and secondary auditory cortices—while musicians additionally recruited prefrontal and parietal associative areas. In experts, these activations were stronger and more extensive, suggesting that musical experience enhances the internal generation of detailed auditory representations. The engagement of auditory cortex during imagination further implies that predictive models draw on long-term episodic memory traces, supporting the view that MMN reflects not only immediate sensory processing but also prediction based on experience. Considering this evidence, music emerges as a privileged domain for examining how the brain generates, evaluates, and updates internal models through prediction-error signaling. Distinct neural markers appear to index violations at different hierarchical levels: sensory MMN for acoustic–perceptual deviations and probabilistic regularities; abstract MMN for violations of dynamically learned relational regularities between sounds; music-syntactic MMN for breaches of higher-order, culturally acquired structures such as scale and tonality; and ERAN for violations of even higher-level syntactic expectations. Together, these findings delineate a hierarchy of predictive processes in music that closely parallels theoretical accounts of musical structure. Music perception thus emerges from the interaction of multiple interconnected systems, in which musical syntax is processed not only by frontal regions traditionally associated with “linguistic” functions, but also by auditory circuits specialized in the automatic detection of novelty and incongruity. 8. Conclusions: automatic predictive processes in the brain for music vs. speech The MMN reflects predictive processes that operate across multiple levels of the auditory hierarchy, from early sensory stages to higher-order evaluative mechanisms. This multilevel organization makes it a particularly informative neural marker for studying how predictions are generated and updated across different auditory domains. Consistent with this view, MMN has been localized—primarily through MEG studies—to sensory cortices, with secondary sources in lateral inferior frontal regions linked to attention switching and contextual updating (Näätänen, 1990, 2020; Näätänen et al., 2003, 2005, 2007). In addition, MMN responses have been observed in subcortical structures such as the inferior colliculus and the hippocampus, suggesting that some prediction processes may originate at lower levels of the auditory system (Csépe & Honbolygó, 2024). The search for neural structures underlying sound perception and production goes back to Paul Broca and Carl Wernicke, whose discoveries of left-hemispheric structures necessary for speech production and comprehension initiated an interest in investigating hemispheric lateralization by neurologists and neuropsychologists first, and later, thanks to neuroimaging advancements, also by cognitive neuroscientists. A comparison between speech and music processing emerged as a central test case for modular and lateralized organization. Jerry Fodor’s landmark book “ The Modularity of the Mind ” (Fodor, 1983) formalized a modular view of the brain, according to which certain mental systems—particularly perceptual and linguistic processes—are domain-specific, informationally encapsulated, fast, and automatic. In this framework, modules are specialized processors with limited access to global cognitive states, distinguishing them from central, non-modular systems such as reasoning and decision-making. Such modular view was translated to the musical domain by Isabelle Peretz and Max Coltheart (Peretz & Coltheart, 2003): according to them, music cognition would rely on a set of domain-specific, functionally independent modules—such as those for pitch, rhythm, and timbre—that can be selectively impaired by brain damage, and dissociated from both language and other cognitive systems. In this context, MMN studies, especially when conducted using MEG or fMRI, very recently summarized by Tervaniemi (Tervaniemi, 2026), have allowed for testing whether preattentive auditory processes for music vs. speech are spatially discernable. A first study with MMNm (Tervaniemi et al., 1999) revealed that the processing of deviations in a musical context preferentially activated the auditory areas of the right hemisphere, while those related to phonemes seem to activate both hemispheres. In both cases, the source localization was observed posteriorly to primary auditory areas, spatially specialized to either musical or phonemic sound processing. A follow-up study (Tervaniemi, Szameitat, et al., 2006) with fMRI employed acoustically comparable stimuli: the pseudoword /ba:ba/ to represent speech and saxophone sounds for the musical domain sharing fundamental frequencies (between 180 and 190 Hz), envelope contour and intensity. The experimental design employed a blocked oddball paradigm where subjects performed a categorization task (distinguishing between speech and music) while being exposed to duration and frequency deviants. Speech sounds predominantly activated more anterior, inferior, and lateral areas of the superior temporal gyrus (STG) whereas musical sounds recruited more posterior, superior, and medial areas, encompassing the Heschl’s gyrus (HG). Moreover, frequency changes in speech sounds activated the right superior temporal sulcus (STS), the left STG, and the right middle frontal gyrus (MFG). This MFG activation suggests that frequency variations in speech are processed as prosodic information, thus requiring additional attentional and executive resources. In contrast, frequency deviants in the musical domain primarily activated the right HG. Similarly, duration deviants in speech showed left-dominant activation in the STG, while those in music activated the right HG. Crucially, the study documented a significant subcortical specialization within the thalamus that was unique to the speech domain, with duration changes activating the left thalamic nuclei and frequency changes the right ones. These findings lead to the conclusion that the human brain possesses a fine-tuned, hierarchically organized neural network capable of selectively encoding sound information based not only on the category (speech vs. music) but also on the specific acoustic parameter involved (pitch or duration). Another study investigated whether duration-related stress (accent) is processed similarly in speech and music (Peter et al., 2012). To address this, the researchers employed an abstract MMN paradigm. In this EEG study, twenty English-speaking adults were tested to monitor their neural responses to long-short and short-long stress patterns applied to both the pseudoword /dada/ and oboe notes. The results revealed a clear divergence in how the brain predicts these patterns across the two domains: while the long-short stress pattern elicited a significant MMN in both speech and music, the short-long pattern produced a significant MMN only for music and was entirely absent for speech. The authors suggest that this linguistic ‘blindness’ to the short-long pattern could be explained by prosodic familiarity: because approximately 90% of English content words follow a long-short stress pattern, the brain lacks the consolidated long-term memory traces required to automatically detect violations in the less familiar short-long pattern. In contrast, Western music utilizes both patterns frequently, allowing for robust neural discrimination in both cases. Furthermore, the study identified an additional neural response, the Late Discriminative Negativity (LDN), which occurred exclusively for the music stimuli with a latency of approximately 300 ms and was more prominent in the right hemisphere. The researchers propose that while the MMN reflects the initial pre-attentive discrimination of the stress pattern, the LDN might represent a subsequent cognitive stage allowing for the transfer of newly extracted rules into long-term memory. Hence, while the neural mechanisms for predicting stress may be shared, their efficiency is domain-specific and influenced by the long-term linguistic and musical experience of the listener. Chen and colleagues (Chen et al., 2022) have recently investigated whether the neural discrimination of pitch in speech and music follows shared or distinct developmental trajectories during the first year of life. The study compared English-speaking adults and infants aged 4, 8, and 12 months using a passive oddball paradigm and EEG to measure MMR. To ensure domain-specific comparisons, the researchers used acoustically matched stimuli: Mandarin lexical tones for speech and three-note piano melodies with matching pitch contours. The results revealed a clear domain-specific maturation of the auditory system: MMN were obtained in adults for both speech and melodies whereas infants showed for speech, a positive MMR remaining stable at 4, 8, and 12 months and a shift maturation for music MMR shifting from a p-MMR at 4 months to a null response at 8 months, and finally to an adult-like MMN at 12 months. Such dissociation again supports the early presence of independent neural networks for music and speech processing. Overall, the results reported here show that, although music and language share a common predictive coding architecture, the brain operates through different patterns for these two domains, using distinct priorities, different levels of contextual abstraction, and domain-specific learning mechanisms (cf. Figure 1). Music prediction relies more heavily on culturally acquired and experience-dependent spectral, tonal, rhythmic, and structural regularities, while speech prediction is anchored in categorical and linguistically constrained representations. Electric and magnetic MMN responses thus reveal both shared and specialized patterns through which the auditory system anticipates and interprets musical versus vocal input. Winkler and Schröger (2015) proposed a more elaborate account of MMN generation in their Auditory Event Representation System (AERS) predictive coding model. In line with predictive coding theory, the AERS model posits that the auditory system maintains an internal model of the acoustic environment that encodes both current regularities in the auditory input and long-term experiential knowledge. In speech perception, these long-term representations span multiple linguistic levels, including native phonemes, prosodic patterns, and lexical units. Incoming auditory input is first analyzed to extract basic speech-related features such as voicing, formant transitions, pitch, and intensity. These features are subsequently integrated into sensory memory representations (e.g., phonemic and prosodic structures), which are then evaluated against the model’s predictions of the auditory environment. Extending the AERS framework to music perception and informed by the music-MMN literature reviewed above, acoustic features are likewise integrated into long-term representations of scale pitches, intervals, chords and rhythm within the auditory cortex—corresponding to the auditory “atoms” of an individual’s musical system. When incoming input deviates from these predictions, the internal model must be updated, a process indexed by the elicitation of the MMN, and, at later processing stages, by the ERAN. As representations become less strictly sensory and rely on longer temporal integration windows and greater cognitive resources, model updating increasingly engages frontal brain regions, reflecting top-down predictive processes that operate to minimize prediction error across multiple levels of musical cognition. Taken together, the evidence reviewed here supports a view of the musical MMN as a central neural marker of hierarchical predictive processing rather than a mere index of low-level auditory deviance detection. Across development, expertise, and stimulus domains, MMN responses reveal how the auditory system exploits both short-term regularities and long-term, culturally acquired knowledge to generate and update predictions about incoming sound. Music, by virtue of its rich multilevel structure and strong dependence on learning and experience, emerges as a particularly powerful domain for studying predictive coding in the human brain. The dissociations observed between music and speech, as well as between sensory and syntactic levels of musical processing, argue against a strictly modular or domain-encapsulated organization, instead favoring a distributed architecture in which shared predictive mechanisms are differentially weighted and tuned by experience, culture, and developmental stage. Looking ahead, the musical MMN provides a compelling framework for investigating how predictive models of music develop, adapt, and interact with broader cognitive systems across the lifespan. Its sensitivity to learning and expertise makes it a promising biomarker of neuroplasticity, with potential applications in clinical, educational, and rehabilitative contexts. Framed within a hierarchical predictive-coding perspective, MMN and related responses can inform not only models of music perception, but also general theories of how the brain builds and updates internal representations of complex auditory environments. Acknowledgments The authors wish to thank Peter Vuust and Minna Huotilainen for discussions that inspired the preparation of this article. Funding Center for Music in the Brain (MIB) is funded by the Danish National Research Foundation (project number 117), the Lundbeck Foundation (R469-2024-1573) and Købmand Herman Sallings Fond. Conflict of Interest The authors declare that there are no conflicts of interest regarding the publication of this article. Data sharing statement The authors confirm that no new data were generated in the preparation of this review article. References Alho, K., Huotilainen, M., Tiitinen, H., Ilmoniemi, R. J., Knuutila, J., & Näätänen, R. (1993). Memory-related processing of complex sound patterns in human auditory cortex: a MEG study. Neuroreport , 4 (4), 391–394. Alho, K., Tervaniemi, M., Huotilainen, M., Lavikainen, J., Tiitinen, H., Ilmoniemi, R. J., Knuutila, J., & Näätänen, R. (1996). Processing of complex sounds in the human auditory cortex as revealed by magnetic brain responses. Psychophysiology , 33 (4), 369–375. Arndt, C., Schlemmer, K., & van der Meer, E. (2020). Same or different pitch? Effects of musical expertise, pitch difference, and auditory task on the pitch discrimination ability of musicians and non-musicians. Experimental Brain Research , 238 (1), 247–258. Atienza, M., & Cantero, J. L. (2001). Complex sound processing during human REM sleep by recovering information from long-term memory as revealed by the mismatch negativity (MMN). Brain Research , 901 (1-2), 151–160. Atienza, M., Cantero, J. L., & Dominguez-Marin, E. (2002). Mismatch negativity (MMN): an objective measure of sensory memory and long-lasting memories during sleep. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology , 46 (3), 215–225. Basiński, K., Celma-Miralles, A., Quiroga-Martinez, D. R., & Vuust, P. (2025). Inharmonicity enhances brain signals of attentional capture and auditory stream segregation. In bioRxiv . https://doi.org/10.1101/2025.04.17.649377 Bekinschtein, T. A., Dehaene, S., Rohaut, B., Tadel, F., Cohen, L., & Naccache, L. (2009). Neural signature of the conscious processing of auditory regularities. Proceedings of the National Academy of Sciences of the United States of America , 106 (5), 1672–1677. Belin, P., & Zatorre, R. J. (2000). “What”, “where” and “how” in auditory cortex. Nature Neuroscience , 3 (10), 965–966. Bickerton, D. (2009). Syntax for Non-syntacticians: A Brief Primer. In Biological Foundations and Origin of Syntax (pp. 3–14). The MIT Press. Bidelman, G.M., & Dexter, L. (2015). Bilinguals at the “cocktail party”: Dissociable neural activity in auditory–linguistic brain regions reveals neurobiological basis for nonnative listeners’ speech-in-noise recognition deficits. Brain and Language , 143, 32-41. Boh, B., Herholz, S. C., Lappe, C., & Pantev, C. (2011). Processing of complex auditory patterns in musicians and nonmusicians. PloS One , 6 (7), e21458. Bonetti, L., Bruzzone, S. E. P., Paunio, T., Kantojärvi, K., Kliuchko, M., Vuust, P., Palva, S., & Brattico, E. (2023). Moderate associations between BDNF Val66Met gene polymorphism, musical expertise, and mismatch negativity. Heliyon , 9 (5), e15600. Bonetti, L., Bruzzone, S. E. P., Sedghi, N. A., Haumann, N. T., Paunio, T., Kantojärvi, K., Kliuchko, M., Vuust, P., & Brattico, E. (2021). Brain predictive coding processes are associated to COMT gene Val158Met polymorphism. NeuroImage , 233 , 117954. Bonetti, L., Carlomagno, F., Kliuchko, M., Gold, B. P., Palva, S., Haumann, N. T., Tervaniemi, M., Huotilainen, M., Vuust, P., & Brattico, E. (2022). Whole-brain computation of cognitive versus acoustic errors in music: A mismatch negativity study. Neuroimage: Reports , 2 (4), 100145. Bonetti, L., Haumann, N. T., Brattico, E., Kliuchko, M., Vuust, P., Särkämö, T., & Näätänen, R. (2018). Auditory sensory memory and working memory skills: Association between frontal MMN and performance scores. Brain Research , 1700 , 86–98. Bouwer, F. L., Van Zuijen, T. L., & Honing, H. (2014). Beat processing is pre-attentive for metrically simple rhythms with clear accents: an ERP study. PloS One , 9 (5), e97467. Brattico, E., Näätänen, R., & Tervaniemi, M. (2001). Context effects on pitch perception in musicians and nonmusicians: Evidence from event-related-potential recordings. Music Perception , 19 (2), 199–222. Brattico, E., Pallesen, K. J., Varyagina, O., Bailey, C., Anourova, I., Järvenpää, M., Eerola, T., & Tervaniemi, M. (2009). Neural discrimination of nonprototypical chords in music experts and laymen: an MEG study. Journal of Cognitive Neuroscience , 21 (11), 2230–2244. Brattico, E., Tervaniemi, M., Näätänen, R., & Peretz, I. (2006). Musical scale properties are automatically processed in the human auditory cortex. Brain Research , 1117 (1), 162–174. Caclin, A., Brattico, E., Tervaniemi, M., Näätänen, R., Morlet, D., Giard, M.-H., & McAdams, S. (2006). Separate neural processing of timbre dimensions in auditory sensory memory. Journal of Cognitive Neuroscience , 18 (12), 1959–1972. Carbajal, G. V., & Malmierca, M. S. (2018). The neuronal basis of predictive coding along the auditory pathway: From the subcortical roots to cortical deviance detection. Trends in Hearing , 22 , 2331216518784822. Chapin, H. L., Zanto, T., Jantzen, K. J., Kelso, S. J. A., Steinberg, F., & Large, E. W. (2010). Neural responses to complex auditory rhythms: the role of attending. Frontiers in Psychology , 1 , 224. Chen, A., Peter, V., & Burnham, D. (2022). Development of neural discrimination of pitch across speech and music in the first year of life, a mismatch response study. Language, Cognition and Neuroscience , 37 (9), 1153–1168. Chobert, J., François, C., Velay, J.-L., & Besson, M. (2014). Twelve months of active musical training in 8- to 10-year-old children enhances the preattentive processing of syllabic duration and voice onset time. Cerebral Cortex (New York, N.Y.: 1991) , 24 (4), 956–967. Chobert, J., Marie, C., François, C., Schön, D., & Besson, M. (2011). Enhanced passive and active processing of syllables in musician children. Journal of Cognitive Neuroscience , 23 (12), 3874–3887. Christmann, C. A., Lachmann, T., & Berti, S. (2014). Earlier timbre processing of instrumental tones compared to equally complex spectrally rotated sounds as revealed by the mismatch negativity. Neuroscience Letters , 581 , 115–119. Conard, N. J., Malina, M., & Münzel, S. C. (2009). New flutes document the earliest musical tradition in southwestern Germany. Nature , 460 (7256), 737–740. Csépe, V., & Honbolygó, F. (2024). From psychophysiology to brain imaging: forty-five years MMN history of investigating acoustic change sensitivity. Biologia Futura , 75 (1), 117–128. Curtis, G. (2008). The cave painters . Anchor Books. d’Errico, F., Henshilwood, C., Lawson, G., Vanhaeren, M., Tillier, A.-M., Soressi, M., Bresson, F., Maureille, B., Nowell, A., Lakarra, J., Backwell, L., & Julien, M. (2003). Archaeological evidence for the emergence of language, symbolism, and music–an alternative multidisciplinary perspective. Journal of World Prehistory , 17 (1), 1–70. Fisher, D. J., & Todd, J. (2025). From Näätänen to now: Moving the Mismatch Negativity into the Next 50 Years. Clinical EEG and Neuroscience: Official Journal of the EEG and Clinical Neuroscience Society (ENCS) , 56 (1), 3–7. Fodor, J. A. (1983). Modularity of Mind . MIT Press. Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences , 364 (1521), 1211–1221. Fujioka, T., Trainor, L. J., Ross, B., Kakigi, R., & Pantev, C. (2004). Musical training enhances automatic encoding of melodic contour and interval structure. Journal of Cognitive Neuroscience , 16 (6), 1010–1021. Gao, P., Wang, H., Qiu, Y., & Zhang, J. (2025). Beyond the sound: Uncovering the distinct neural mechanisms of ERAN and MMN in music perception. NeuroImage , 321 (121534), 121534. Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009). The mismatch negativity: A review of underlying mechanisms. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology , 120 (3), 453–463. Garza Villarreal, E. A., Brattico, E., Leino, S., Ostergaard, L., & Vuust, P. (2011). Distinct neural responses to chord violations: a multiple source analysis study. Brain Research , 1389 , 103–114. Geiser, E., Ziegler, E., Jancke, L., & Meyer, M. (2009). Early electrophysiological correlates of meter and rhythm processing in music perception. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior , 45 (1), 93–102. Gingras, B., Honing, H., Peretz, I., Trainor, L. J., & Fisher, S. E. (2015). Defining the biological bases of individual differences in musicality. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences , 370 (1664), 20140092. Goydke, K. N., Altenmüller, E., Möller, J., & Münte, T. F. (2004). Changes in emotional tone and instrumental timbre are reflected by the mismatch negativity. Brain Research. Cognitive Brain Research , 21 (3), 351–359. Hansen, N. C., Højlund, A., Møller, C., Pearce, M., & Vuust, P. (2022). Musicians show more integrated neural processing of contextually relevant acoustic features. Frontiers in Neuroscience , 16 , 907540. Haumann, N. T., Lumaca, M., Kliuchko, M., Santacruz, J. L., Vuust, P., & Brattico, E. (2021). Extracting human cortical responses to sound onsets and acoustic feature changes in real music, and their relation to event rate. Brain Research , 1754 (147248), 147248. He, C., Hotson, L., & Trainor, L. J. (2007). Mismatch responses to pitch changes in early infancy. Journal of Cognitive Neuroscience , 19 (5), 878–892. Hennevin, E., Huetz, C., & Edeline, J.-M. (2007). Neural representations during sleep: from sensory processing to memory traces. Neurobiology of Learning and Memory , 87 (3), 416–440. Herholz, S. C., Boh, B., & Pantev, C. (2011). Musical training modulates encoding of higher-order regularities in the auditory cortex. The European Journal of Neuroscience , 34 (3), 524–529. Herholz, S. C., Lappe, C., & Pantev, C. (2009). Looking for a pattern: an MEG study on the abstract mismatch negativity in musicians and nonmusicians. BMC Neuroscience , 10 (1), 42. Honing, H., ten Cate, C., Peretz, I., & Trehub, S. E. (2015). Without it no music: cognition, biology and evolution of musicality. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences , 370 (1664), 20140088. Ishida, K., & Nittono, H. (2022). Relationship between early neural responses to syntactic and acoustic irregularities in music. The European Journal of Neuroscience , 56 (12), 6201–6214. Ishida, K., & Nittono, H. (2024). Relationship between schematic and dynamic expectations of melodic patterns in music perception. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology , 196 (112292), 112292. Kalda, T., & Minati, L. (2012). Detecting scale violations in absence of mismatch requires music-syntactic analysis: a further look at the early right anterior negativity (ERAN). Brain Topography , 25 (3), 285–292. Kliuchko, M., Brattico, E., Gold, B. P., Tervaniemi, M., Bogert, B., Toiviainen, P., & Vuust, P. (2019). Fractionating auditory priors: A neural dissociation between active and passive experience of musical sounds. PloS One , 14 (5), e0216499. Kliuchko, M., Heinonen-Guzejev, M., Vuust, P., Tervaniemi, M., & Brattico, E. (2016). A window into the brain mechanisms associated with noise sensitivity. Scientific Reports , 6 , 39236. Koelsch, S. (2009). Music-syntactic processing and auditory memory: similarities and differences between ERAN and MMN. Psychophysiology , 46 (1), 179–190. Koelsch, S. (2011). Toward a neural basis of music perception - a review and updated model. Frontiers in Psychology , 2 , 110. Koelsch, S., Gunter, T. C., Schröger, E., Tervaniemi, M., Sammler, D., & Friederici, A. D. (2001). Differentiating ERAN and MMN: An ERP study. Neuroreport , 12 (7), 1385–1389. Koelsch, S., & Jentschke, S. (2010). Differences in electric brain responses to melodies and chords. Journal of Cognitive Neuroscience , 22 (10), 2251–2262. Koelsch, S., Maess, B., & Friederici, A. D. (2000). Musical syntax is processed in the area of Broca: an MEG study. NeuroImage , 11 (5), S56. Koelsch, S., Schröger, E., & Tervaniemi, M. (1999). Superior pre-attentive auditory processing in musicians. Neuroreport , 10 (6), 1309–1313. Koelsch, S., Vuust, P., & Friston, K. (2019). Predictive Processes and the Peculiar Case of Music. Trends in Cognitive Sciences , 23 (1), 63–77. Kuchenbuch, A., Paraskevopoulos, E., Herholz, S. C., & Pantev, C. (2012). Electromagnetic correlates of musical expertise in processing of tone patterns. PloS One , 7 (1), e30171. Ladinig, O., Honing, H., Háden, G., & Winkler, I. (2009). Probing attentive and preattentive emergent meter in adult listeners without extensive music training. Music Perception , 26 (4), 377–386. Lappe, C., Lappe, M., & Pantev, C. (2016). Differential processing of melodic, rhythmic and simple tone deviations in musicians–an MEG study. NeuroImage , 124 (Pt A), 898–905. Lappe, C., Steinsträter, O., & Pantev, C. (2013a). A beamformer analysis of MEG data reveals frontal generators of the musically elicited mismatch negativity. PloS One , 8 (4), e61296. Lappe, C., Steinsträter, O., & Pantev, C. (2013b). Rhythmic and melodic deviations in musical sequences recruit different cortical areas for mismatch detection. Frontiers in Human Neuroscience , 7 , 260. Lappe, C., Trainor, L. J., Herholz, S. C., & Pantev, C. (2011). Cortical plasticity induced by short-term multimodal musical rhythm training. PloS One , 6 (6), e21493. Lartillot, O., Toiviainen, P., & Eerola, T. (2008). A Matlab Toolbox for Music Information Retrieval. Data Analysis, Machine Learning and Applications , 261–268. Leino, S., Brattico, E., Tervaniemi, M., & Vuust, P. (2007). Representation of harmony rules in the human brain: further evidence from event-related potentials. Brain Research , 1142 , 169–177. Liu, P., Rigoulot, S., & Pell, M. D. (2015). Cultural differences in on-line sensitivity to emotional voices: comparing East and West. Frontiers in Human Neuroscience , 9 , 311. Lumaca, M., Haumann, N. T., Vuust, P., Brattico, E., & Baggio, G. (2018). From random to regular: neural constraints on the emergence of isochronous rhythm during cultural transmission. Social Cognitive and Affective Neuroscience , 13 (8), 877–888. Malmierca, M. S., Cristaudo, S., Pérez-González, D., & Covey, E. (2009). Stimulus-specific adaptation in the inferior colliculus of the anesthetized rat. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience , 29 (17), 5483–5493. Matsunaga, R., Yokosawa, K., & Abe, J.-I. (2012). Magnetoencephalography evidence for different brain subregions serving two musical cultures. Neuropsychologia , 50 (14), 3218–3227. Menning, H., Roberts, L. E., & Pantev, C. (2000). Plastic changes in the auditory cortex induced by intensive frequency discrimination training. Neuroreport , 11 (4), 817–822. Meyer, M., Elmer, S., Ringli, M., Oechslin, M. S., Baumann, S., & Jancke, L. (2011). Long-term exposure to music enhances the sensitivity of the auditory system in children. The European Journal of Neuroscience , 34 (5), 755–765. Milovanov, R., Huotilainen, M., Esquef, P. A. A., Alku, P., Välimäki, V., & Tervaniemi, M. (2009). The role of musical aptitude and language skills in preattentive duration processing in school-aged children. Neuroscience Letters , 460 (2), 161–165. Mithen, S. (2011). The singing neanderthals . Phoenix. Moreno, S., Marques, C., Santos, A., Santos, M., Castro, S. L., & Besson, M. (2009). Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity. Cerebral Cortex (New York, N.Y.: 1991) , 19 (3), 712–723. Näätänen, R. (1990). The role of attention in auditory information processing as revealed by event-related potentials and other brain measures of cognitive function. The Behavioral and Brain Sciences , 13 (2), 201–233. Näätänen, R. (2020). Attention and brain function . Routledge. Näätänen, R., Brattico, E., & Tervaniemi, M. (2003). Chapter 14 - Mismatch Negativity: A Probe to Auditory Perception and Cognition in Basic and Clinical Research. In A. Zani, A. M. Proverbio, & M. I. Posner (Eds.), The Cognitive Electrophysiology of Mind and Brain (pp. 343 – VIII). Academic Press. Näätänen, R., Gaillard, A. W., & Mäntysalo, S. (1978). Early selective-attention effect on evoked potential reinterpreted. Acta Psychologica , 42 (4), 313–329. Näätänen, R., Jacobsen, T., & Winkler, I. (2005). Memory-based or afferent processes in mismatch negativity (MMN): a review of the evidence. Psychophysiology , 42 (1), 25–32. Näätänen, R., Paavilainen, P., Rinne, T., & Alho, K. (2007). The mismatch negativity (MMN) in basic research of central auditory processing: a review. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology , 118 (12), 2544–2590. Näätänen, R., Pakarinen, S., Rinne, T., & Takegata, R. (2004). The mismatch negativity (MMN): towards the optimal paradigm. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology , 115 (1), 140–144. Näätänen, R., Schröger, E., Karakas, S., Tervaniemi, M., & Paavilainen, P. (1993). Development of a memory trace for a complex sound in the human brain. Neuroreport , 4 (5), 503–506. Nager, W., Kohlmetz, C., Altenmüller, E., Rodriguez-Fornells, A., & Münte, T. F. (2003). The fate of sounds in conductors’ brains: an ERP study. Brain Research. Cognitive Brain Research , 17 (1), 83–93. Nelken, I., & Ulanovsky, N. (2007). Mismatch negativity and stimulus-specific adaptation in animal models. Journal of Psychophysiology , 21 (3-4), 214–223. Nikjeh, D. A., Lister, J. J., & Frisch, S. A. (2008). Hearing of note: an electrophysiologic and psychoacoustic comparison of pitch discrimination between vocal and instrumental musicians. Psychophysiology , 45 (6), 994–1007. Paavilainen, P. (2013). The mismatch-negativity (MMN) component of the auditory event-related potential to violations of abstract regularities: a review. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology , 88 (2), 109–123. Partanen, E., Kujala, T., Tervaniemi, M., & Huotilainen, M. (2013). Prenatal music exposure induces long-term neural effects. PloS One , 8 (10), e78946. Peretz, I., Brattico, E., Järvenpää, M., & Tervaniemi, M. (2009). The amusic brain: in tune, out of key, and unaware. Brain: A Journal of Neurology , 132 (Pt 5), 1277–1286. Peretz, I., & Coltheart, M. (2003). Modularity of music processing. Nature Neuroscience , 6 (7), 688–691. Peretz, I., Cummings, S., & Dubé, M.-P. (2007). The genetics of congenital amusia (tone deafness): a family-aggregation study. American Journal of Human Genetics , 81 (3), 582–588. Peter, V., McArthur, G., & Thompson, W. F. (2012). Discrimination of stress in speech and music: a mismatch negativity (MMN) study. Psychophysiology , 49 (12), 1590–1600. Poikonen, H., Alluri, V., Brattico, E., Lartillot, O., Tervaniemi, M., & Huotilainen, M. (2016). Event-related brain responses while listening to entire pieces of music. Neuroscience , 312 , 58–73. Pulvermüller, F., Cappelle, B., & Shtyrov, Y. (2013). Brain basis of meaning, words, constructions, and grammar (T. Hoffmann & G. Trousdale (eds.)). Oxford University Press. Pulvermüller, F., & Shtyrov, Y. (2006). Language outside the focus of attention: The mismatch negativity as a tool for studying higher cognitive processes. Progress in Neurobiology , 79(1), 49-71. Putkinen, V., Tervaniemi, M., Saarikivi, K., de Vent, N., & Huotilainen, M. (2014). Investigating the effects of musical training on functional brain development with a novel Melodic MMN paradigm. Neurobiology of Learning and Memory , 110 , 8–15. Putkinen, V., Tervaniemi, M., Saarikivi, K., Ojala, P., & Huotilainen, M. (2014). Enhanced development of auditory change detection in musically trained school-aged children: a longitudinal event-related potential study. Developmental Science , 17 (2), 282–297. Quiroga-Martinez, D. R., Basiński, K., Nasielski, J., Tillmann, B., Brattico, E., Cholvy, F., Fornoni, L., Vuust, P., & Caclin, A. (2022). Enhanced mismatch negativity in harmonic compared with inharmonic sounds. The European Journal of Neuroscience , 56 (5), 4583–4599. Quiroga-Martinez, D. R., C Hansen, N., Højlund, A., Pearce, M., Brattico, E., & Vuust, P. (2020). Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians. The European Journal of Neuroscience , 51 (11), 2250–2269. Quiroga-Martinez, D. R., Hansen, N. C., Højlund, A., Pearce, M., Brattico, E., Holmes, E., Friston, K., & Vuust, P. (2021). Musicianship and melodic predictability enhance neural gain in auditory cortex during pitch deviance detection. In Cold Spring Harbor Laboratory (p. 2021.02.11.430838). https://doi.org/10.1101/2021.02.11.430838 Quiroga-Martinez, D. R., Hansen, N. C., Højlund, A., Pearce, M. T., Brattico, E., & Vuust, P. (2019). Reduced prediction error responses in high-as compared to low-uncertainty musical contexts. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior , 120 , 181–200. Quiroga-Martinez, D. R., Tillmann, B., Brattico, E., & Cholvy, F. (2020). Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences. bioRxiv . https://www.biorxiv.org/content/10.1101/2020.07.07.191031v1.abstract Rauschecker, J. P., & Scott, S. K. (2009). Maps and streams in the auditory cortex: nonhuman primates illuminate human speech processing. Nature Neuroscience , 12 (6), 718–724. Rauschecker, J. P., & Tian, B. (2000). Mechanisms and streams for processing of “what” and “where” in auditory cortex. Proceedings of the National Academy of Sciences of the United States of America , 97 (22), 11800–11806. Rüsseler, J., Altenmüller, E., Nager, W., Kohlmetz, C., & Münte, T. F. (2001). Event-related brain potentials to sound omissions differ in musicians and non-musicians. Neuroscience Letters , 308 (1), 33–36. Sams, M., Paavilainen, P., Alho, K., & Näätänen, R. (1985). Auditory frequency discrimination and event-related potentials. Electroencephalography and Clinical Neurophysiology , 62 (6), 437–448. Sauvé, S. A., Satkunarajah, P., & Zendel, B. (2022). Tracking the emergence of a pitch hierarchy using an artificial grammar requires extended exposure. In PsyArXiv . https://doi.org/10.31234/osf.io/t6zjr Seesjärvi, E., Särkämö, T., Vuoksimaa, E., Tervaniemi, M., Peretz, I., & Kaprio, J. (2016). The nature and nurture of melody: A twin study of musical pitch and rhythm perception. Behavior Genetics , 46 (4), 506–515. Seppänen, M., Brattico, E., & Tervaniemi, M. (2007). Practice strategies of musicians modulate neural processing and the learning of sound-patterns. Neurobiology of Learning and Memory , 87 (2), 236–247. Tervaniemi, M. (1999). Pre-attentive processing of musical information in the human brain. Journal of New Music Research , 28 (3), 237–245. Tervaniemi, M. (2009). Musicians-same different. Annals New York Academy Sciences , 1169 , 151–156. Tervaniemi, M. (2022). Mismatch negativity-stimulation paradigms in past and in future. Frontiers in Neuroscience , 16 , 1025763. Tervaniemi, M. (2026). Music and language in the human brain: Mismatch negativity evidence for their neuroplasticity and interplay. The European Journal of Neuroscience , 63 (2), e70399. Tervaniemi, M., Alho, K., Paavilainen, P., Sams, M., & Näätänen, R. (1993). Absolute pitch and event-related brain potentials. Music Perception , 10 (3), 305–316. Tervaniemi, M., Castaneda, A., Knoll, M., & Uther, M. (2006). Sound processing in amateur musicians and nonmusicians: event-related potential and behavioral indices. Neuroreport , 17 (11), 1225–1228. Tervaniemi, M., Huotilainen, M., & Brattico, E. (2014). Melodic multi-feature paradigm reveals auditory profiles in music-sound encoding. Frontiers in Human Neuroscience , 8 , 496. Tervaniemi, M., Just, V., Koelsch, S., Widmann, A., & Schröger, E. (2005). Pitch discrimination accuracy in musicians vs nonmusicians: an event-related potential and behavioral study. Experimental Brain Research , 161 (1), 1–10. Tervaniemi, M., Kujala, A., Alho, K., Virtanen, J., Ilmoniemi, R. J., & Näätänen, R. (1999). Functional specialization of the human auditory cortex in processing phonetic and musical sounds: A magnetoencephalographic (MEG) study. NeuroImage , 9 (3), 330–336. Tervaniemi, M., Maury, S., & Näätänen, R. (1994). Neural representations of abstract stimulus features in the human brain as reflected by the mismatch negativity. Neuroreport , 5 (7), 844–846. Tervaniemi, M., Rytkönen, M., Schröger, E., Ilmoniemi, R. J., & Näätänen, R. (2001). Superior formation of cortical memory traces for melodic patterns in musicians. Learning & Memory (Cold Spring Harbor, N.Y.) , 8 (5), 295–300. Tervaniemi, M., Schröger, E., Saher, M., & Näätänen, R. (2000). Effects of spectral complexity and sound duration on automatic complex-sound pitch processing in humans - a mismatch negativity study. Neuroscience Letters , 290 (1), 66–70. Tervaniemi, M., Szameitat, A. J., Kruck, S., Schröger, E., Alter, K., De Baene, W., & Friederici, A. D. (2006). From air oscillations to music and speech: functional magnetic resonance imaging evidence for fine-tuned neural networks in audition. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience , 26 (34), 8647–8652. Trainor, L. J., Lee, K., & Bosnyak, D. J. (2011). Cortical plasticity in 4-month-old infants: specific effects of experience with musical timbres. Brain Topography , 24 (3-4), 192–203. Tremblay, K., Kraus, N., & McGee, T. (1998). The time course of auditory perceptual learning. Neuroreport , 9 (16), 3557–3560. Tuniz, C., Bernardini, F., Turk, I., Dimkaroski, L., Mancini, L., & Dreossi, D. (2012). Did Neanderthals play music? X‐ray computed micro‐tomography of the Divje babe “flute.” Archaeometry , 54 (3), 581–590. Ulanovsky, N., Las, L., & Nelken, I. (2003). Processing of low-probability sounds by cortical neurons. Nature Neuroscience , 6 (4), 391–398. van Zuijen, T. L., Sussman, E., Winkler, I., Näätänen, R., & Tervaniemi, M. (2005). Auditory organization of sound sequences by a temporal or numerical regularity–a mismatch negativity study comparing musicians and non-musicians. Brain Research. Cognitive Brain Research , 23 (2-3), 270–276. Virtala, P., Berg, V., Kivioja, M., Purhonen, J., Salmenkivi, M., Paavilainen, P., & Tervaniemi, M. (2011). The preattentive processing of major vs. minor chords in the human brain: An event-related potential study. Neuroscience Letters , 487 (3), 406–410. Virtala, P., Huotilainen, M., Partanen, E., Fellman, V., & Tervaniemi, M. (2013). Newborn infants’ auditory system is sensitive to Western music chord categories. Frontiers in Psychology , 4 , 492. Virtala, P., Huotilainen, M., Partanen, E., & Tervaniemi, M. (2014). Musicianship facilitates the processing of Western music chords–an ERP and behavioral study. Neuropsychologia , 61 , 247–258. von der Behrens, W., Bäuerle, P., Kössl, M., & Gaese, B. H. (2009). Correlating stimulus-specific adaptation of cortical neurons and local field potentials in the awake rat. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience , 29 (44), 13837–13849. Vuust, P., Brattico, E., Glerean, E., Seppänen, M., Pakarinen, S., Tervaniemi, M., & Näätänen, R. (2011). New fast mismatch negativity paradigm for determining the neural prerequisites for musical ability. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior , 47 (9), 1091–1098. Vuust, P., Brattico, E., Seppänen, M., Näätänen, R., & Tervaniemi, M. (2012). The sound of music: differentiating musicians using a fast, musical multi-feature mismatch negativity paradigm. Neuropsychologia , 50 (7), 1432–1443. Vuust, P., Heggli, O. A., Friston, K. J., & Kringelbach, M. L. (2022). Music in the brain. Nature Reviews. Neuroscience , 23 (5), 287–305. Vuust, P., Liikala, L., Näätänen, R., Brattico, P., & Brattico, E. (2016). Comprehensive auditory discrimination profiles recorded with a fast parametric musical multi-feature mismatch negativity paradigm. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology , 127 (4), 2065–2077. Vuust, P., Ostergaard, L., Pallesen, K. J., Bailey, C., & Roepstorff, A. (2009). Predictive coding of music–brain responses to rhythmic incongruity. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior , 45 (1), 80–92. Vuust, P., Pallesen, K. J., Bailey, C., van Zuijen, T. L., Gjedde, A., Roepstorff, A., & Østergaard, L. (2005). To musicians, the message is in the meter pre-attentive neuronal responses to incongruent rhythm are left-lateralized in musicians. NeuroImage , 24 (2), 560–564. Wagner, L., Rahne, T., Plontke, S. K., & Heidekrüger, N. (2018). Mismatch negativity reflects asymmetric pre-attentive harmonic interval discrimination. PloS One , 13 (4), e0196176. Wallentin, M., Nielsen, A. H., Friis-Olivarius, M., Vuust, C., & Vuust, P. (2010). The Musical Ear Test, a new reliable test for measuring musical competence. Learning and Individual Differences , 20 (3), 188–196. Widmann, A., Schröger, E., & Wetzel, N. (2026). Measuring the genuine mismatch negativity in the auditory multi-feature paradigm. The European Journal of Neuroscience , 63 (1), e70362. Winkler, I., Háden, G. P., Ladinig, O., Sziller, I., & Honing, H. (2009). Newborn infants detect the beat in music. Proceedings of the National Academy of Sciences of the United States of America , 106 (7), 2468–2471. Winkler, I., Karmos, G., & Näätänen, R. (1996). Adaptive modeling of the unattended acoustic environment reflected in the mismatch negativity event-related potential. Brain Research , 742 (1-2), 239–252. Yumoto, M., Matsuda, M., Itoh, K., Uno, A., Karino, S., Saitoh, O., Kaneko, Y., Yatomi, Y., & Kaga, K. (2005). Auditory imagery mismatch negativity elicited in musicians. Neuroreport , 16 (11), 1175–1178. Yu, X., Liu, T., & Gao, D. (2015). The Mismatch Negativity: An indicator of perception of regularities in music. Behavioural Neurology , 2015 , 469508. 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Keywords auditory cortex auditory processing eeg erp learning and memory mmn Authors Affiliations Elvira Brattico 0000-0003-0676-6464 [email protected] Aarhus Universitet Institut for Klinisk Medicin View all articles by this author Giovanni Marco Lorusso 0009-0007-9768-5243 Universita degli Studi di Bari Aldo Moro Scuola di Medicina View all articles by this author Francesco Carlomagno Politecnico di Bari Dipartimento di Ingegneria Elettrica e dell'Informazione View all articles by this author Giulio Carraturo Universita degli Studi di Bari Aldo Moro Dipartimento di Scienze della Formazione Psicologia Comunicazione View all articles by this author Metrics & Citations Metrics Article Usage 385 views 124 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Elvira Brattico, Giovanni Marco Lorusso, Francesco Carlomagno, et al. Automatic musical predictions in the brain as indexed by the Mismatch Negativity -- From acoustic deviants to cognitive musical errors. 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