Biology subjects

Brattico, E.

Publications and source records attributed to Brattico, E..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Temporal pattern recognition in the human brain: a dual simultaneous processing

Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using magnetoencephalography (MEG) and magnetic resonance imaging (MRI), we discovered and mathematically modelled an inedited dual simultaneous processing responsible for pattern recognition in the brain. Indeed, while the objects of the temporal pattern were independently elaborated by a local, rapid brain processing, their combination into a meaningful superordinate pattern depended on a concurrent global, slower processing involving a widespread network of sequentially active brain areas. Expanding the established knowledge of neural information flow from low- to high-order brain areas, we revealed a novel brain mechanism based on simultaneous activity in different frequency bands within the same brain regions, highlighting its crucial role underlying complex cognitive functions.

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bioRxiv2021

Enhanced mismatch negativity responses in harmonic compared to inharmonic sound sequences

Many natural sounds have frequency spectra composed of integer multiples of a fundamental frequency. This property, known as harmonicity, plays an important role in auditory information processing. However, the extent to which harmonicity influences the processing of sound features beyond pitch is still unclear. This is interesting because harmonic sounds have lower information entropy than inharmonic sounds. According to predictive processing accounts of perception, this property could produce more salient neural responses due to the brains weighting of sensory signals according to their uncertainty. In the present study, we used electroencephalography to investigate brain responses to harmonic and inharmonic sounds commonly occurring in music: piano tones and hi-hat cymbal sounds. In a multi-feature oddball paradigm, we measured mismatch negativity (MMN) and P3a responses to timbre, intensity, and location deviants in listeners with and without congenital amusia--an impairment of pitch processing. As hypothesized, we observed larger amplitudes and earlier latencies (for both MMN and P3a) in harmonic compared to inharmonic sounds. These harmonicity effects were modulated by sound feature. Moreover, the difference in P3a latency between harmonic and inharmonic sounds was larger for controls than amusics. We propose an explanation of these results based on predictive coding and discuss the relationship between harmonicity, information entropy, and precision weighting of prediction errors.

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bioRxiv2021

On the brain networks organization of individuals with high versus average fluid intelligence: a combined DTI and MEG study

The neural underpinning of human fluid intelligence (Gf) has gathered a large interest in the scientific community. Nonetheless, previous research did not provide a full understanding of such intriguing topic. Here, we studied the structural (from diffusion tensor imaging, DTI) and functional (from magnetoencephalography (MEG) resting state) connectivity in individuals with high versus average Gf scores. Our findings showed greater values in the brain areas degree distribution and higher proportion of long-range anatomical connections for high versus average Gfs. Further, the two groups presented different community structures, highlighting the structural and functional integration of the cingulate within frontal subnetworks of the brain in high Gfs. These results were consistently observed for structural connectivity and functional connectivity of delta, theta and alpha. Notably, gamma presented an opposite pattern, showing more segregation and lower degree distribution and connectivity in high versus average Gfs. Our study confirmed and expanded previous perspectives and knowledge on the "small-worldness" of the brain. Further, it complemented the widely investigated structural brain network of highly intelligent individuals with analyses on fast-scale functional networks in five frequency bands, highlighting key differences in the integration and segregation of information flow between slow and fast oscillations in groups with different Gf.

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bioRxiv2021

Trait empathy modulates music-related functional connectivity.

It has been well established through behavioural studies that empathy significantly influences music perception. Such individual differences typically manifest as variability in whole brain functional connectivity patterns. To date, nobody has examined the modulatory effect of empathy on functional connectivity patterns during continuous music listening. In the present study, we seek to investigate the global and local connectivity patterns of 36 participants whose fMRI scanning was done by employing the naturalistic paradigm wherein they listened to a continuous piece of music. We used graph-based measures of functional connectivity to identify how cognitive and affective components of empathy modulate functional connectivity. Partial correlation between Eigenvector centrality and measures of empathy showed that cognitive empathy is associated with higher centrality in the sensorimotor regions responsible for motor mimicry while affective empathy showed higher centrality in regions related to auditory affect processing. We furthermore identified a left-hemispheric dominance in the modulatory effect of affective empathy particularly in the Orbitofrontal cortex and the temporal pole. Results are discussed in relation to various theoretical models of empathy and music cognition.

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bioRxiv2021

Prediction Under Uncertainty: Dissociating Sensory from Cognitive Expectations in Highly Uncertain Musical Contexts

Predictive models in the brain rely on the continuous extraction of regularities from the environment. These models are thought to be updated by novel information, as reflected in prediction error responses such as the mismatch negativity (MMN). However, although in real life individuals often face situations in which uncertainty prevails, it remains unclear whether and how predictive models emerge in high-uncertainty contexts. Recent research suggests that uncertainty affects the magnitude of MMN responses in the context of music listening. However, musical predictions are typically studied with MMN stimulation paradigms based on Western tonal music, which are characterized by relatively high predictability. Hence, we developed an MMN paradigm to investigate how the high uncertainty of atonal music modulates predictive processes as indexed by the MMN and behavior. Using MEG in a group of 20 subjects without musical training, we demonstrate that the magnetic MMN in response to pitch, intensity, timbre, and location deviants is evoked in both tonal and atonal melodies, with no significant differences between conditions. In contrast, in a separate behavioral experiment involving 39 non-musicians, participants detected pitch deviants more accurately and rated confidence higher in the tonal than in the atonal musical context. These results indicate that contextual tonal uncertainty modulates processing stages in which conscious awareness is involved, although deviants robustly elicit low-level pre-attentive responses such as the MMN. The achievement of robust MMN responses, despite high tonal uncertainty, is relevant for future studies comparing groups of listeners MMN responses to increasingly ecological music stimuli.

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bioRxiv2021

Fourier SPoC: A costumised machine-learning analysis pipeline for auditory beat-based entrainment in the MEG

We propose here (the informed use) of a customised, data-driven machine-learning pipeline to analyse magnetoencephalography (MEG) in a theoretical source space, with respect to the processing of a regular beat. This hypothesis- and data-driven analysis pipeline allows us to extract the maximally relevant components in MEG source-space, with respect to the oscillatory power in the frequency band of interest and, most importantly, the beat-related modulation of that power. Our pipeline combines Spatio-Spectral Decomposition as a first step to seek activity in the frequency band of interest (SSD, [1]) with a Source Power Co-modulation analysis (SPoC; [2]), which extracts those components that maximally entrain their activity with the given target function, that is here with the periodicity of the beat in the frequency domain (hence, f-SPoC). MEG data (102 magnetometers) from 28 participants passively listening to a 5-min long regular tone sequence with a 400 ms beat period (the "target function" for SPoC) were segmented into epochs of two beat periods each to guarantee a sufficiently long time window. As a comparison pipeline to SSD and f-SpoC, we carried out a state-of-the-art cluster-based permutation analysis (CBPA, [3]). The time-frequency analysis (TFA) of the extracted activity showed clear regular patterns of periodically occurring peaks and troughs across the alpha and beta band (8-20 Hz) in the f-SPoC but not in the CBPA results, and both the depth and the specificity of modulation to the beat frequency yielded a significant advantage. Future applications of this pipeline will address target the relevance to behaviour and inform analogous analyses in the EEG, in order to finally work toward addressing dysfunctions in beat-based timing and their consequences. Author summaryWhen listening to a regular beat, oscillations in the brain have been shown to synchronise with the frequency of that given beat. This phenomenon is called entrainment and has in previous brain-imaging studies been shown in the form of one peak and trough per beat cycle in a range of frequency bands within 15-25 Hz (beta band). Using machine-learning techniques, we designed an analysis pipeline based on Source-Power Co-Modulation (SPoC) that enables us to extract spatial components in MEG recordings that show these synchronisation effects very clearly especially across 8-20 Hz. This approach requires no anatomical knowledge of the individual or even the average brain, it is purely data driven and can be applied in a hypothesis-driven fashion with respect to the "function" that we expect the brain to entrain with and the frequency band within which we expect to see this entrainment. We here apply our customised pipeline using "f-SPoC" to MEG recordings from 28 participants passively listening to a 5-min long tone sequence with a regular 2.5 Hz beat. In comparison to a cluster-based permutation analysis (CBPA) which finds sensors that show statistically significant power modulations across participants, our individually extracted f-SPoC components find a much stronger and clearer pattern of peaks and troughs within one beat cycle. In future work, this pipeline can be implemented to tackle more complex "target functions" like speech and music, and might pave the way toward rhythm-based rehabilitation strategies.

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bioRxiv2021

BDNF Val66Met polymorphism as putative genetic substrate of music-induced plasticity in auditory prediction

Predictive processing of sounds depends on the constant updating of priors based on exposure to posteriors, which through repeated exposure mediates learning. The result of such corrections to the model is seen in musicians, whose lifelong training results in measurable plasticity of audio-motor brain anatomy and functionality. It has been suggested that the plasticity of auditory predictive processes depends on the interaction between the environment and the individuals genetic substrate. However, empirical evidence to this is still missing. BDNF is a critical genetic factor affecting learning and plasticity, and its widely studied functional variant Val66Met single-nucleotide polymorphism offers a unique opportunity to investigate neuroplastic functional changes occurring upon a years-long training. We hypothesised that BDNF gene variations would be driving neuroplasticity of the auditory cortex in musically trained human participants. To this goal, musicians and non-musicians were recruited and divided in Val/Val and Met carriers and their brain activity measured with magnetoencephalography (MEG) while they listened to a regular auditory sequence containing different types of prediction errors. The auditory cortex responses to prediction errors was enhanced in Val/Val carriers who underwent intensive musical training, compared to Met and non-musicians. Our results point at a role of gene-regulated neurotrophic factors in the neural adaptations of auditory processing after long-term training.

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bioRxiv2021

Rediscovering the musician's brain: a systematic review and meta-analysis

Through long-term training, music experts acquire complex and specialized sensorimotor skills, which are paralleled by continuous neuro-anatomical and -functional adaptations. The underlying neuroplasticity mechanisms have been extensively explored in decades of research in music, cognitive, and translational neuroscience. However, the absence of a comprehensive review and quantitative meta-analysis prevents the plethora of variegated findings to ultimately converge into a unified picture of the neuroanatomy of musical expertise. Here, we performed a comprehensive neuroimaging meta-analysis of publications investigating neuro-anatomical and -functional differences between musicians (M) and non-musicians (NM). Eighty-four studies were included in the qualitative synthesis. From these, 58 publications were included in coordinate-based meta-analyses using the anatomic/activation likelihood estimation (ALE) method. This comprehensive approach delivers a coherent cortico-subcortical network encompassing sensorimotor and limbic regions bilaterally. Particularly, M exhibited higher volume/activity in auditory, sensorimotor, interoceptive, and limbic brain areas and lower volume/activity in parietal areas as opposed to NM. Notably, we reveal topographical (dis-)similarities between the identified functional and anatomical networks and characterize their link to various cognitive functions by means of meta-analytic connectivity modelling. Overall, we effectively synthesized decades of research in the field and provide a consistent and controversies-free picture of the neuroanatomy of musical expertise.

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bioRxiv2021

Musicianship and melodic predictability enhance neural gain in auditory cortex during pitch deviance detection

When listening to music, pitch deviations are more salient and elicit stronger prediction error responses when the melodic context is predictable and when the listener is a musician. Yet, the neuronal dynamics and changes in synaptic efficacy underlying such effects remain unclear. Here, we employed dynamic causal modeling (DCM) to investigate whether the magnetic mismatch negativity response (MMNm)--and its modulation by context predictability and musical expertise--are associated with enhanced neural gain of auditory areas, as a plausible mechanism for encoding precision-weighted prediction errors. Using Bayesian model comparison, we asked whether models with intrinsic connections within primary auditory cortex (A1) and superior temporal gyrus (STG)--typically related to gain control--or extrinsic connections between A1 and STG--typically related to propagation of prediction and error signals--better explained magnetoencephalography (MEG) responses. We found that, compared to regular sounds, out-of-tune pitch deviations were associated with lower intrinsic (inhibitory) connectivity in A1 and STG, and lower backward (inhibitory) connectivity from STG to A1, consistent with disinhibition and enhanced neural gain in these auditory areas. More predictable melodies were associated with disinhibition in right A1, while musicianship was associated with disinhibition in left A1 and reduced connectivity from STG to left A1. These results indicate that musicianship and melodic predictability, as well as pitch deviations themselves, enhance neural gain in auditory cortex during deviance detection. Our findings are consistent with predictive processing theories suggesting that precise and informative error signals are selected by the brain for subsequent hierarchical processing. Significance statementIn complex auditory contexts, being able to identify informative signals is of paramount importance. Such is the case of music listening, where surprising sounds play a fundamental role in its perceptual, aesthetical, and emotional experience. Crucially, surprising sounds in the pitch dimension are more easily detected and generate stronger cortical responses when melodies are predictable and when the listener is a musician. Using Dynamic Causal Modelling, here we show that such effects arise from a local increase in neural gain within auditory areas, rather than from passing of prediction and error signals between brain regions. Consistent with predictive processing theories, this suggests that the enhanced precision of auditory predictive models--through melodic predictability and musical training--up-regulates the processing of informative error signals in the brain.

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bioRxiv2020

Rapid encoding of temporal sequences discovered in brain dynamics

Information encoding has received a wide neuroscientific attention, but the underlying rapid spatiotemporal brain dynamics remain largely unknown. Here, we investigated the rapid brain mechanisms for encoding and prediction of sounds forming a complex temporal sequence. Specifically, we used magnetoencephalography (MEG) to record the brain activity of 68 participants while they listened to a highly structured musical prelude. Advanced analysis of the phase synchronisation and graph theoretical measures showed the rapid transition of brain activity from primary auditory cortex to higher order association areas including insula and superior temporal pole within a whole-brain network, occurring during the first 220 ms of the encoding process. We discovered individual differences, revealing the rapid unfolding of brain network dynamics responsible for the processing of the current sounds and the prediction of the forthcoming events of the sequence. This provides a first glimpse of the general mechanisms underlying pattern encoding in the human brain.

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bioRxiv2020

Beauty and the brain: Investigating the neural and musical attributes of beauty during a naturalistic music listening experience

Evaluative beauty judgments are very common, but in spite of this commonality, are rarely studied in cognitive neuroscience. Here we investigated the neural and musical attributes of musical beauty using a naturalistic free-listening paradigm applied to behavioral and neuroimaging recordings and validated by experts judgments. In Study 1, 30 Western healthy adult participants rated continuously the perceived beauty of three musical pieces using a motion sensor. This allowed us to identify the passages in the three musical pieces that were inter-subjectively judged as beautiful or ugly. This informed the analysis for Study 2, where additional 36 participants were recorded with functional magnetic resonance imaging (fMRI) while they listened attentively to the same musical pieces as in Study 1. In Study 3, in order to identify the musicological features characterizing the passages that were consistently rated as beautiful or ugly in Study 1, we collected post-hoc questionnaires from 12 music-composition experts. Results from Study 2 evidenced focal regional activity in the orbitofrontal brain structure when listening to beautiful passages of music, irrespectively of the subjective reactions and individual listening biographies. In turn, the moments in the music that were consistently rated as ugly were associated with bilateral supratemporal activity. Effective connectivity analysis also discovered inhibition of auditory activation and neural communication with orbitofrontal cortex, especially in the right hemisphere, during listening to beautiful musical passages as opposed to intrinsic activation of auditory cortices and decreased coupling to orbitofrontal cortex during listening to ugly musical passages. Experts questionnaires indicated that the beautiful passages were more melodic, calm, sad, slow, tonal, traditional and simple than the ones negatively valenced. In sum, we identified a neural mechanism for inter-subjective beauty judgments of music in the supratemporal-orbitofrontal circuit, irrespectively of individual taste and listening biography. Furthermore, some invariance in objective musical attributes of beautiful and ugly passages was evidenced. Future studies might address the generalizability of the findings to non-Western listeners.

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bioRxiv2020

Applying SCA for high-accuracy cortical auditory ERPs in children

Overlapping neurophysiological signals are the main obstacle preventing from using cortical event-related potentials (ERPs) in clinical settings. Children ERPs are particularly affected by this problem, as their cerebral cortex is still maturing. To overcome this problem, we applied a new version of Spike-density Component Analysis (SCA), an analysis method recently introduced, to isolate with high accuracy the neural components of auditory ERP responses (AEPs) in 8-year-old children. Electroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard ERP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC). SCA-HSAC most successfully allowed the extraction of AEPs for each child, revealing that the most consistent components were P1 and N2. An immature N1 component was also detected. Superior accuracy in isolating neural components at the individual level even in children was demonstrated for SCA-HSAC over other SCA approaches. Reliable methods of extraction of neurophysiological signals at the individual level are crucial for the application of cortical AEPs for routine diagnostic exams in clinical settings both in children and adults. HighlightsO_LISpike-density component analysis (SCA) was validated on children ERPs C_LIO_LISCA extracted overlapping neural components from auditory ERPs (AEPs) C_LIO_LIChild AEPs were modelled at the individual level C_LI

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bioRxiv2020

Association between COMT gene Val158Met heterozygote polymorphism and enhanced brain predicting processes

Predicting events in the ever-changing environment is a fundamental survival function intrinsic to the physiology of sensory systems, whose efficiency varies among the population. Even though it is established that a major source of such variations is genetic heritage, there are no studies tracking down auditory predicting processes to genetic mutations. Thus, we examined the neurophysiological responses to deviant stimuli recorded with magnetoencephalography (MEG) in 108 healthy participants carrying different variants of the Val158Met single-nucleotide polymorphism (SNP) within the catechol-O-methyltransferase (COMT) gene, which is responsible for the majority of catecholamines degradation in the prefrontal cortex. Our results showed significant amplitude enhancement of neural responses localized within inferior frontal gyrus, superior and middle temporal cortices to deviant auditory stimuli in heterozygote genotype carriers (Val/Met) vs homozygote (Val/Val and Met/Met) carriers. Integrating neurophysiology and genetics, this study provided new and broader insights into the brain mechanisms underlying optimal deviant detection.

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bioRxiv2020

Musical skills can be decoded from magnetic resonance images

Learning induces structural changes in the brain. Especially repeated, long-term behaviors, such as extensive training of playing a musical instrument, are likely to produce characteristic features to brain structure. However, it is not clear to what extent such structural features can be extracted from magnetic resonance images of the brain. Here we show that it is possible to predict whether a person is a musician or a non-musician based on the thickness of the cerebral cortex measured at 148 brain regions en-compassing the whole cortex. Using a supervised machine-learning technique, we achieved a significant ({kappa} = 0.321, p < 0.001) agreement between the actual and predicted participant groups of 30 musicians and 85 non-musicians. The areas contributing to the prediction were mostly in the frontal, parietal, and occipital lobes of the left hemisphere. Our results suggest that decoding musicianship from magnetic resonance images of brain structure is feasible. Further, the distribution of the areas that were informative in the classification, which mostly, but not entirely, overlapped with earlier findings on areas relevant for musical skills, implies that decoding-based analyses of structural properties of the brain can reveal novel aspects of musical aptitude. In particular, our results highlight differences in visual areas in addition to the already more established differences located in motor networks and networks of higher-order cognitive function.

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bioRxiv2020

Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences

In typical listeners, the perceptual salience of a surprising auditory event depends on the uncertainty of its context. For example, in melodies, pitch deviants are more easily detected and generate larger neural responses when the context is highly predictable than when it is less so. However, it is not known whether amusic listeners with abnormal pitch processing are sensitive to the degree of uncertainty of pitch sequences and, if so, whether they are to a different extent than typical non-musician listeners. To answer this question, we manipulated the uncertainty of short melodies while participants with and without congenital amusia underwent EEG recordings in a passive listening task. Uncertainty was manipulated by presenting melodies with different levels of complexity and familiarity, under the assumption that simpler and more familiar patterns would enhance pitch predictability. We recorded mismatch negativity (MMN) responses to pitch, intensity, timbre, location, and rhythm deviants as a measure of auditory surprise. In both participant groups, we observed reduced MMN amplitudes and longer peak latencies for all sound features with increasing levels of complexity, and putative familiarity effects only for intensity deviants. No significant group-by-complexity or group-by-familiarity interactions were detected. However, in contrast to previous studies, pitch MMN responses in amusics were disrupted in high complexity and unfamiliar melodies. The present results thus indicate that amusics are sensitive to the uncertainty of melodic sequences and that preattentive auditory change detection is greatly spared in this population across sound features and levels of predictability. However, our findings also hint at pitch-specific impairments in this population when uncertainty is high, thus suggesting that pitch processing under high uncertainty conditions requires an intact frontotemporal loop.

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bioRxiv2020

Spatiotemporal brain dynamics during recognition of the music of Johann Sebastian Bach

Music is a non-verbal human language, built on logical structures and articulated in balanced hierarchies between sounds, offering excellent opportunities to explore how the brain creates meaning for complex spatiotemporal auditory patterns. Using the high temporal resolution of magnetoencephalography in 70 participants, we investigated their unfolding brain dynamics during the recognition of previously memorized J.S. Bachs musical patterns from prelude in C minor BWV 847 compared to novel patterns matched in terms of entropy and information content. Remarkably, the recognition of the memorized music ignited a widespread brain network comprising primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus and hippocampus. Furthermore, measures of both brain activity and functional connectivity presented an overall increase over time, following the evolution and unfolding of the memorized musical patterns. Specifically, while the auditory cortex responded mainly to the first tones of the patterns, the activity and synchronization of higher-order brain areas such as cingulate, frontal operculum, hippocampus and orbitofrontal cortex largely increased over time, arguably representing the key whole-brain mechanisms for conscious recognition of auditory patterns as predicted by the global neuronal workspace hypothesis. In conclusion, our study described the fine-grained whole-brain activity and functional connectivity dynamics responsible for processing and recognition of previously memorized music. Further, the study highlights how the use of musical patterns in combination with a wide array of analytical tools and neuroscientific measures spanning from decoding to fast neural phase synchronization can shed new light on meaningful, complex cognitive processes.

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bioRxiv2020

The early adolescent brain on music: analysis of functional dynamics reveals engagement of orbitofrontal cortex reward system

Music listening plays a pivotal role for children and adolescents, yet surprisingly few neuroimaging studies have studied the underlying functional dynamics. We used functional magnetic resonance imaging to scan 17 preadolescents aged 10-11 years old while listening to music. We subsequently tracked the occurrence of functional brain networks over time by using a recent method that detects recurrent BOLD phase-locking states: the Leading Eigenvector Dynamics Analysis (LEiDA). In particular, we compared the probabilities of occurrence and switching profiles of different patterns of BOLD phase-locking between music and no music. Moreover, we used an adapted version of the Barcelona Music Reward Questionnaire (BMRQ) to measure the music reward sensitivity of the participants. Our results showed significantly increased occurrence of a BOLD phase-locking pattern during music listening compared to no music, characterized by a phase-shift in the BOLD signals of the medial orbitofrontal and ventromedial prefrontal cortices - a brain subsystem associated to reward processing - from the rest of the brain. Moreover, we observed a significantly higher probability of switching to this pattern while listening to music. We also found a positive correlation between the individual musical reward sensitivity and the tendency to switch to this reward state during music. Our findings highlight the involvement of a brain subsystem involved in hedonic processing during music listening in the early adolescent brain. These results offer novel insight into the neural underpinnings of musical reward in early adolescence and may help us to understand the importance of music at this delicate age.

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bioRxiv2019

Decomposing neural responses to melodic surprise in musicians and non-musicians: evidence for a hierarchy of predictions in the auditory system

Neural responses to auditory surprise are typically studied with highly unexpected, disruptive sounds. Consequently, little is known about auditory prediction in everyday contexts that are characterized by fine-grained, non-disruptive fluctuations of auditory surprise. To address this issue, we used IDyOM, a computational model of auditory expectation, to obtain continuous surprise estimates for a set of newly composed melodies. Our main goal was to assess whether the neural correlates of non-disruptive surprising sounds in a musical context are affected by musical expertise. Using magnetoencephalography (MEG), auditory responses were recorded from musicians and non-musicians while they listened to the melodies. Consistent with a previous study, the amplitude of the N1m component increased with higher levels of computationally estimated surprise. This effect, however, was not different between the two groups. Further analyses offered an explanation for this finding: Pitch interval size itself, rather than probabilistic prediction, was responsible for the modulation of the N1m, thus pointing to low-level sensory adaptation as the underlying mechanism. In turn, the formation of auditory regularities and proper probabilistic prediction were reflected in later components: the mismatch negativity (MMNm) and the P3am, respectively. Overall, our findings reveal a hierarchy of expectations in the auditory system and highlight the need to properly account for sensory adaptation in research addressing statistical learning. Highlights- In melodies, sound expectedness (modeled with IDyOM) is associated with the amplitude of the N1m. - This effect is not different between musicians and non-musicians. - Sensory adaptation related to melodic pitch intervals explains better the N1m effect. - Auditory regularities and the expectations captured by IDyOM are reflected in the MMNm and P3am. - Evidence for a hierarchy of auditory predictions during melodic listening.

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