Auditory Expectation: The Information Dynamics of Music Perception and Cognition

Music reward sensitivity is associated with greater information transfer capacity within dorsal and motor white matter networks in musicians

There are pronounced differences in the degree to which individuals experience music-induced pleasure which are linked to variations in structural connectivity between auditory and reward areas. However, previous studies exploring the link between white matter structure and music reward sensitivity (MRS) have relied on standard diffusion tensor imaging methods, which present challenges in terms of anatomical accuracy and interpretability. Further, the link between MRS and connectivity in regions outside of auditory-reward networks, as well as the role of musical training, have yet to be investigated. Therefore, we investigated the relation between MRS and structural connectivity in a large number of directly segmented and anatomically verified white matter tracts in musicians (n = 24) and non-musicians (n = 23) using state-of-the-art tract reconstruction and fixel-based analysis. Using a manual tract-of-interest approach, we additionally tested MRS-white matter associations in auditory-reward networks seen in previous studies. Within the musician group, there was a significant positive relation between MRS and fiber density and cross section in the right middle longitudinal fascicle connecting auditory and inferior parietal cortices. There were also positive relations between MRS and fiber-bundle cross-section in tracts connecting the left thalamus to the ventral precentral gyrus and connecting the right thalamus to the right supplementary motor area, however, these did not survive FDR correction. These results suggest that, within musicians, dorsal auditory and motor networks are crucial to MRS, possibly via their roles in top-down predictive processing and auditory-motor transformations.

Hierarchical syntax model of music predicts theta power during music listening

Linguistic research showed that the depth of syntactic embedding is reflected in brain theta power. Here, we test whether this also extends to non-linguistic stimuli, specifically music. We used a hierarchical model of musical syntax to continuously quantify two types of expert-annotated harmonic dependencies throughout a piece of Western classical music: prolongation and preparation. Prolongations can roughly be understood as a musical analogue to linguistic coordination between constituents that share the same function (e.g., 'pizza' and 'pasta' in 'I ate pizza and pasta'). Preparation refers to the dependency between two harmonies whereby the first implies a resolution towards the second (e.g., dominant towards tonic; similar to how the adjective implies the presence of a noun in 'I like spicy … '). Source reconstructed MEG data of sixty-five participants listening to the musical piece was then analysed. We used Bayesian Mixed Effects models to predict theta envelope in the brain, using the number of open prolongation and preparation dependencies as predictors whilst controlling for audio envelope. We observed that prolongation and preparation both carry independent and distinguishable predictive value for theta band fluctuation in key linguistic areas such as the Angular, Superior Temporal, and Heschl's Gyri, or their right-lateralised homologues, with preparation showing additional predictive value for areas associated with the reward system and prediction. Musical expertise further mediated these effects in language-related brain areas. Results show that predictions of precisely formalised music-theoretical models are reflected in the brain activity of listeners which furthers our understanding of the perception and cognition of musical structure.

Modelling of Musical Perception using Spectral Knowledge Representation

We present a novel approach to representing perceptual and cognitive knowledge, spectral knowledge representation, that is focused on the oscillatory behaviour of the brain. The model is presented in the context of a larger hypothetical cognitive architecture. The model uses literal representations of waves to describe the dynamics of neural assemblies as they process perceived input. We show how the model can be applied to representations of sound, and usefully model music perception, specifically harmonic distance. We demonstrate that the model naturally captures both pitch and chord/key distance as empirically measured by Krumhansl and Kessler, thereby providing an underlying mechanism from which their toroidal model might arise. We evaluate our model with respect to those of Milne and others.

Neural correlates of musical familiarity: a functional magnetic resonance study

Existing neuroimaging studies on neural correlates of musical familiarity often employ a familiar vs. unfamiliar contrast analysis. This singular analytical approach reveals associations between explicit musical memory and musical familiarity. However, is the neural activity associated with musical familiarity solely related to explicit musical memory, or could it also be related to implicit musical memory? To address this, we presented 130 song excerpts of varying familiarity to 21 participants. While acquiring their brain activity using functional magnetic resonance imaging (fMRI), we asked the participants to rate the familiarity of each song on a five-point scale. To comprehensively analyze the neural correlates of musical familiarity, we examined it from four perspectives: the intensity of local neural activity, patterns of local neural activity, global neural activity patterns, and functional connectivity. The results from these four approaches were consistent and revealed that musical familiarity is related to the activity of both explicit and implicit musical memory networks. Our findings suggest that: (1) musical familiarity is also associated with implicit musical memory, and (2) there is a cooperative and competitive interaction between the two types of musical memory in the perception of music.

Beyond the ears: A review exploring the interconnected brain behind the hierarchical memory of music

Music is a ubiquitous element of daily life. Understanding how music memory is represented and expressed in the brain is key to understanding how music can influence human daily cognitive tasks. Current music-memory literature is built on data from very heterogeneous tasks for measuring memory, and the neural correlates appear to differ depending on different forms of memory function targeted. Such heterogeneity leaves many exceptions and conflicts in the data underexplained (e.g., hippocampal involvement in music memory is debated). This review provides an overview of existing neuroimaging results from music-memory related studies and concludes that although music is a special class of event in our lives, the memory systems behind it do in fact share neural mechanisms with memories from other modalities. We suggest that dividing music memory into different levels of a hierarchy (structural level and semantic level) helps understand overlap and divergence in neural networks involved. This is grounded in the fact that memorizing a piece of music recruits brain clusters that separately support functions including-but not limited to-syntax storage and retrieval, temporal processing, prediction versus reality comparison, stimulus feature integration, personal memory associations, and emotion perception. The cross-talk between frontal-parietal music structural processing centers and the subcortical emotion and context encoding areas explains why music is not only so easily memorable but can also serve as strong contextual information for encoding and retrieving nonmusic information in our lives.

Aesthetics and predictive processing: grounds and prospects of a fruitful encounter

In the last few years, a remarkable convergence of interests and results has emerged between scholars interested in the arts and aesthetics from a variety of perspectives and cognitive scientists studying the mind and brain within the predictive processing (PP) framework. This convergence has so far proven fruitful for both sides: while PP is increasingly adopted as a framework for understanding aesthetic phenomena, the arts and aesthetics, examined under the lens of PP, are starting to be seen as important windows into our mental functioning. The result is a vast and fast-growing research programme that promises to deliver important insights into our aesthetic encounters as well as a wide range of psychological phenomena of general interest. Here, we present this developing research programme, describing its grounds and highlighting its prospects. We start by clarifying how the study of the arts and aesthetics encounters the PP picture of mental functioning (§1). We then go on to outline the prospects of this encounter for the fields involved: philosophy and history of art (§2), psychology of aesthetics and neuroaesthetics (§3) and psychology and neuroscience more generally (§4). The upshot is an ambitious but well-defined framework within which aesthetics and cognitive science can partner up to illuminate crucial aspects of the human mind. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Making sense of music: Insights from neurophysiology and connectivity analyses in naturalistic listening conditions

According to the latest frameworks, auditory perception and memory involve the constant prediction of future sound events by the brain, based on the continuous extraction of feature regularities from the environment. The neural hierarchical mechanisms for predictive processes in perception and memory for sounds are typically studied in relation to simple acoustic features in isolated sounds or sound patterns inserted in highly certain contexts. Such studies have identified reliable prediction formation and error signals, e.g., the N100 or the mismatch negativity (MMN) evoked responses. In real life, though, individuals often face situations in which uncertainty prevails and where making sense of sounds becomes a hard challenge. In music, not only deviations from predictions are masterly set up by composers to induce emotions but sometimes the sheer uncertainty of sound scenes is exploited for aesthetic purposes, especially in compositional styles such as Western atonal classical music. In very recent magnetoencephalography (MEG) and electroencephalography (EEG) studies, experimental and technical advances in stimulation paradigms and analysis approaches have permitted the identification of prediction-error responses from highly uncertain, atonal contexts and the extraction of prediction-related responses from real, continuous music. Moreover, functional connectivity analyses revealed the emergence of cortico-hippocampal interactions during the formation of auditory memories for more predictable vs. less predictable patterns. These findings contribute to understanding the general brain mechanisms that enable us to predict even highly uncertain sound environments and to possibly make sense of and appreciate even atonal music.

Context changes judgments of liking and predictability for melodies

Predictability plays an important role in the experience of musical pleasure. By leveraging expectations, music induces pleasure through tension and surprise. However, musical predictions draw on both prior knowledge and immediate context. Similarly, musical pleasure, which has been shown to depend on predictability, may also vary relative to the individual and context. Although research has demonstrated the influence of both long-term knowledge and stimulus features in influencing expectations, it is unclear how perceptions of a melody are influenced by comparisons to other music pieces heard in the same context. To examine the effects of context we compared how listeners' judgments of two distinct sets of stimuli differed when they were presented alone or in combination. Stimuli were excerpts from a repertoire of Western music and a set of experimenter created melodies. Separate groups of participants rated liking and predictability for each set of stimuli alone and in combination. We found that when heard together, the Repertoire stimuli were more liked and rated as less predictable than if they were heard alone, with the opposite pattern being observed for the Experimental stimuli. This effect was driven by a change in ratings between the Alone and Combined conditions for each stimulus set. These findings demonstrate a context-based shift of predictability ratings and derived pleasure, suggesting that judgments stem not only from the physical properties of the stimulus, but also vary relative to other options available in the immediate context.

Temporal dynamics of statistical learning in children's song contributes to phase entrainment and production of novel information in multiple cultures

Statistical learning is thought to be linked to brain development. For example, statistical learning of language and music starts at an early age and is shown to play a significant role in acquiring the delta-band rhythm that is essential for language and music learning. However, it remains unclear how auditory cultural differences affect the statistical learning process and the resulting probabilistic and acoustic knowledge acquired through it. This study examined how children's songs are acquired through statistical learning. This study used a Hierarchical Bayesian statistical learning (HBSL) model, mimicking the statistical learning processes of the brain. Using this model, I conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning among different cultures. The model learned from a corpus of children's songs in MIDI format, which consists of English, German, Spanish, Japanese, and Korean songs as the training data. In this study, I investigated how the probability distribution of the model is transformed over 15 trials of learning in each song. Furthermore, using the probability distribution of each model over 15 trials of learning each song, new songs were probabilistically generated. The results suggested that, in learning processes, chunking and hierarchical knowledge increased gradually through 15 rounds of statistical learning for each piece of children's songs. In production processes, statistical learning led to the gradual increase of delta-band rhythm (1-3 Hz). Furthermore, by combining the acquired chunks and hierarchy through statistical learning, statistically novel music was generated gradually in comparison to the original songs (i.e. the training songs). These findings were observed consistently, in multiple cultures. The present study indicated that the statistical learning capacity of the brain, in multiple cultures, contributes to the acquisition and generation of delta-band rhythm, which is critical for acquiring language and music. It is suggested that cultural differences may not significantly modulate the statistical learning effects since statistical learning and slower rhythm processing are both essential functions in the human brain across cultures. Furthermore, statistical learning of children's songs leads to the acquisition of hierarchical knowledge and the ability to generate novel music. This study may provide a novel perspective on the developmental origins of creativity and the importance of statistical learning through early development.

Auditory and reward structures reflect the pleasure of musical expectancies during naturalistic listening

Enjoying music consistently engages key structures of the neural auditory and reward systems such as the right superior temporal gyrus (R STG) and ventral striatum (VS). Expectations seem to play a central role in this effect, as preferences reliably vary according to listeners' uncertainty about the musical future and surprise about the musical past. Accordingly, VS activity reflects the pleasure of musical surprise, and exhibits stronger correlations with R STG activity as pleasure grows. Yet the reward value of musical surprise - and thus the reason for these surprises engaging the reward system - remains an open question. Recent models of predictive neural processing and learning suggest that forming, testing, and updating hypotheses about one's environment may be intrinsically rewarding, and that the constantly evolving structure of musical patterns could provide ample opportunity for this procedure. Consistent with these accounts, our group previously found that listeners tend to prefer melodic excerpts taken from real music when it either validates their uncertain melodic predictions (i.e., is high in uncertainty and low in surprise) or when it challenges their highly confident ones (i.e., is low in uncertainty and high in surprise). An independent research group (Cheung et al., 2019) replicated these results with musical chord sequences, and identified their fMRI correlates in the STG, amygdala, and hippocampus but not the VS, raising new questions about the neural mechanisms of musical pleasure that the present study seeks to address. Here, we assessed concurrent liking ratings and hemodynamic fMRI signals as 24 participants listened to 50 naturalistic, real-world musical excerpts that varied across wide spectra of computationally modeled uncertainty and surprise. As in previous studies, liking ratings exhibited an interaction between uncertainty and surprise, with the strongest preferences for high uncertainty/low surprise and low uncertainty/high surprise. FMRI results also replicated previous findings, with music liking effects in the R STG and VS. Furthermore, we identify interactions between uncertainty and surprise on the one hand, and liking and surprise on the other, in VS activity. Altogether, these results provide important support for the hypothesized role of the VS in deriving pleasure from learning about musical structure.

The Pleasurable Urge to Move to Music Through the Lens of Learning Progress

Interacting with music is a uniquely pleasurable activity that is ubiquitous across human cultures. Current theories suggest that a prominent driver of musical pleasure responses is the violation and confirmation of temporal predictions. For example, the pleasurable urge to move to music (PLUMM), which is associated with the broader concept of groove, is higher for moderately complex rhythms compared to simple and complex rhythms. This inverted U-shaped relation between PLUMM and rhythmic complexity is thought to result from a balance between predictability and uncertainty. That is, moderately complex rhythms lead to strongly weighted prediction errors which elicit an urge to move to reinforce the predictive model (i.e., the meter). However, the details of these processes and how they bring about positive affective responses are currently underspecified. We propose that the intrinsic motivation for learning progress drives PLUMM and informs the music humans choose to listen to, dance to, and create. Here, learning progress reflects the rate of prediction error minimization over time. Accordingly, reducible prediction errors signal the potential for learning progress, producing a pleasurable, curious state characterized by the mobilization of attentional and memory resources. We discuss this hypothesis in the context of current psychological and neuroscientific research on musical pleasure and PLUMM. We propose a theoretical neuroscientific model focusing on the roles of dopamine and norepinephrine within a feedback loop linking prediction-based learning, curiosity, and memory. This perspective provides testable predictions that will motivate future research to further illuminate the fundamental relation between predictions, movement, and reward.

Inducing and disrupting flow during music performance

Flow is defined as a state of total absorption in an activity, involving focused attention, deep engagement, loss of self-conscious awareness, and self-perceived temporal distortion. Musical flow has been associated with enhanced performance, but the bulk of previous research has investigated flow mechanisms using self-report methodology. Thus, little is known about the precise musical features that may induce or disrupt flow. This work aims to consider the experience of flow from a music performance perspective in order to investigate these features and introduces a method of measuring flow in real time. In Study 1, musicians reviewed a self-selected video of themselves performing, noting first, where in the performance they recalled "losing themselves" in the music, and second, where their focused state was interrupted. Thematic analysis of participant flow experiences suggests temporal, dynamic, pitch and timbral dimensions associated with the induction and disruption of flow. In Study 2, musicians were brought into the lab and recorded while performing a self-selected musical composition. Next, participants were asked to estimate the duration of their performance, and to rewatch their recordings to mark those places in which they recalled "losing themselves in the moment." We found that the proportion of performance time spent in flow significantly correlated with self-reported flow intensity, providing an intrinsic measure of flow and confirming the validity of our method to capture flow states in music performance. We then analyzed the music scores and participants' performed melodies. The results showed that stepwise motion, repeated sequence, and a lack of disjunct motion are common to flow state entry points, whereas disjunct motion and syncopation are common to flow state exit points. Overall, such initial findings suggest directions that warrant future study and, altogether, they have implications regarding utilizing flow in music performance contexts.

Representing melodic relationships using network science

Music is a complex system consisting of many dimensions and hierarchically organized information-the organization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music. Using a large corpus of transcribed improvisations, we constructed a network model in which 5-pitch sequences were linked depending on consecutive occurrences, constituting 116,403 nodes (sequences) and 157,429 edges connecting them. We then investigated whether mathematical graph modelling relates to musical characteristics in real-world listening situations via a behavioral experiment paralleling those used to examine language. We found that as melodic distance within the network increased, participants judged melodic sequences as less related. Moreover, the relationship between distance and reaction time (RT) judgements was quadratic: participants slowed in RT up to distance four, then accelerated; a parallel finding to research in language networks. This study offers insights into the hidden network structure of improvised tonal music and suggests that humans are sensitive to the property of melodic distance in this network. More generally, our work demonstrates the similarity between music and language as complex systems, and how network science methods can be used to quantify different aspects of its complexity.

Spectro-temporal acoustic elements of music interact in an integrated way to modulate emotional responses in pigs

Music is a complex stimulus, with various spectro-temporal acoustic elements determining one of the most important attributes of music, the ability to elicit emotions. Effects of various musical acoustic elements on emotions in non-human animals have not been studied with an integrated approach. However, this knowledge is important to design music to provide environmental enrichment for non-human species. Thirty-nine instrumental musical pieces were composed and used to determine effects of various acoustic parameters on emotional responses in farm pigs. Video recordings (n = 50) of pigs in the nursery phase (7-9 week old) were gathered and emotional responses induced by stimuli were evaluated with Qualitative Behavioral Assessment (QBA). Non-parametric statistical models (Generalized Additive Models, Decision Trees, Random Forests, and XGBoost) were applied and compared to evaluate relationships between acoustic parameters and pigs' observed emotional responses. We concluded that musical structure affected emotional responses of pigs. The valence of modulated emotions depended on integrated and simultaneous interactions of various spectral and temporal structural components of music that can be readily modified. This new knowledge supports design of musical stimuli to be used as environmental enrichment for non-human animals.

Order of statistical learning depends on perceptive uncertainty

Statistical learning (SL) is an innate mechanism by which the brain automatically encodes the n-th order transition probability (TP) of a sequence and grasps the uncertainty of the TP distribution. Through SL, the brain predicts a subsequent event (e _n+1 ) based on the preceding events (e _n ) that have a length of "n". It is now known that uncertainty modulates prediction in top-down processing by the human predictive brain. However, the manner in which the human brain modulates the order of SL strategies based on the degree of uncertainty remains an open question. The present study examined how uncertainty modulates the neural effects of SL and whether differences in uncertainty alter the order of SL strategies. It used auditory sequences in which the uncertainty of sequential information is manipulated based on the conditional entropy. Three sequences with different TP ratios of 90:10, 80:20, and 67:33 were prepared as low-, intermediate, and high-uncertainty sequences, respectively (conditional entropy: 0.47, 0.72, and 0.92 bit, respectively). Neural responses were recorded when the participants listened to the three sequences. The results showed that stimuli with lower TPs elicited a stronger neural response than those with higher TPs, as demonstrated by a number of previous studies. Furthermore, we found that participants adopted higher-order SL strategies in the high uncertainty sequence. These results may indicate that the human brain has an ability to flexibly alter the order based on the uncertainty. This uncertainty may be an important factor that determines the order of SL strategies. Particularly, considering that a higher-order SL strategy mathematically allows the reduction of uncertainty in information, we assumed that the brain may take higher-order SL strategies when encountering high uncertain information in order to reduce the uncertainty. The present study may shed new light on understanding individual differences in SL performance across different uncertain situations.

Magnetoencephalography recordings reveal the spatiotemporal dynamics of recognition memory for complex versus simple auditory sequences

Auditory recognition is a crucial cognitive process that relies on the organization of single elements over time. However, little is known about the spatiotemporal dynamics underlying the conscious recognition of auditory sequences varying in complexity. To study this, we asked 71 participants to learn and recognize simple tonal musical sequences and matched complex atonal sequences while their brain activity was recorded using magnetoencephalography (MEG). Results reveal qualitative changes in neural activity dependent on stimulus complexity: recognition of tonal sequences engages hippocampal and cingulate areas, whereas recognition of atonal sequences mainly activates the auditory processing network. Our findings reveal the involvement of a cortico-subcortical brain network for auditory recognition and support the idea that stimulus complexity qualitatively alters the neural pathways of recognition memory.

Musical Garden Paths: Evidence for Syntactic Revision Beyond the Linguistic Domain

While theoretical and empirical insights suggest that the capacity to represent and process complex syntax is crucial in language as well as other domains, it is still unclear whether specific parsing mechanisms are also shared across domains. Focusing on the musical domain, we developed a novel behavioral paradigm to investigate whether a phenomenon of syntactic revision occurs in the processing of tonal melodies under analogous conditions as in language. We present the first proof-of-existence for syntactic revision in a set of tonally ambiguous melodies, supporting the relevance of syntactic representations and parsing with language-like characteristics in a nonlinguistic domain. Furthermore, we find no evidence for a modulatory effect of musical training, suggesting that a general cognitive capacity, rather than explicit knowledge and strategies, may underlie the observed phenomenon in music.

Atonal Music as a Model for Investigating Exploratory Behavior

Atonal music is often characterized by low predictability stemming from the absence of tonal or metrical hierarchies. In contrast, Western tonal music exhibits intrinsic predictability due to its hierarchical structure and therefore, offers a directly accessible predictive model to the listener. In consequence, a specific challenge of atonal music is that listeners must generate a variety of new predictive models. Listeners must not only refrain from applying available tonal models to the heard music, but they must also search for statistical regularities and build new rules that may be related to musical properties other than pitch, such as timbre or dynamics. In this article, we propose that the generation of such new predictive models and the aesthetic experience of atonal music are characterized by internal states related to exploration. This is a behavior well characterized in behavioral neuroscience as fulfilling an innate drive to reduce uncertainty but which has received little attention in empirical music research. We support our proposal with emerging evidence that the hedonic value is associated with the recognition of patterns in low-predictability sound sequences and that atonal music elicits distinct behavioral responses in listeners. We end by outlining new research avenues that might both deepen our understanding of the aesthetic experience of atonal music in particular, and reveal core qualities of the aesthetic experience in general.

Evoked responses to note onsets and phrase boundaries in Mozart's K448

Understanding the neural correlates of perception of hierarchical structure in music presents a direct window into auditory organization. To examine the hypothesis that high-level and low-level structures—i.e. phrases and notes—elicit different neural responses, we collected intracranial electroencephalography (iEEG) data from eight subjects during exposure to Mozart’s K448 and directly compared Event-related potentials (ERPs) due to note onsets and those elicited by phrase boundaries. Cluster-level permutation tests revealed that note-onset-related ERPs and phrase-boundary-related ERPs were significantly different at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-150$$\end{document}-150, 200, and 450 ms relative to note onset and phrase markers. We also observed increased activity in frontal brain regions when processing phrase boundaries. We relate these observations to (1) a process which syntactically binds notes together hierarchically to form larger phrases; (2) positive emotions induced by successful prediction of forthcoming phrase boundaries and violations of melodic expectations at phrase boundaries.

Haunting melodies: Specific memories distort beat perception

How much does specific previous experience shape immediate perception? Top-down perceptual inference occurs in ambiguous situations. However, similarity-based accounts such as exemplar theory suggest that similar memories resonate with the percept, predicting that detailed previous experiences can shape perception even when bottom-up cues are unambiguous. The current study tests whether specific musical memories influence beat perception only under ambiguity, or more pervasively-that is, even when clear bottom-up beat cues are present. Listeners were exposed to 16 melodies, half in one meter, half in another. Later, each listener's perception of a specific melody's beat pattern was tested when that melody occurred in either its original meter or another meter. Ratings of metrical probes were influenced not only by fit with the current (test) meter, but also by fit with the meter previously experienced with that melody. Findings suggest that perception is routinely influenced by detailed top-down perceptual imagery.

Musical reward across the lifespan

Music is ubiquitous. Despite the fact that most people find music enjoyable, there are individual differences in the degree to which listeners derive pleasure from music. However, there has been little focus on how musical reward may change across the lifespan. Some theories predict that there would be little change, or even an increase in musical reward across the lifespan, while others suggest that older adults may have decreased capacity for musical reward. Here, we investigated musical reward across the lifespan. Participants consisted of American adults ranging between 20-85 years old (n = 20 participants in each 10-year age bin). Participants in Study 1 completed the Barcelona Music Reward Questionnaire (BMRQ), which is a multi-dimensional assessment of musical reward. We found a negative correlation between age and BMRQ scores, suggesting decreases in musical reward across the lifespan. When investigating which components were driving this effect, we found that the music seeking subscale was the strongest predictor of age. Participants in Study 2 completed the Aesthetic Experiences in Music Scale (AES-M), which focuses on intense emotional responses to music. In contrast to the BMRQ, we found no relationship between age and scores on the AES-M, suggesting that strong emotional responses to music are consistent across the lifespan. These results have implications for the use of music as a therapeutic tool in older adults. In addition, this work points to the importance of considering age when investigating reward for music and suggests that the ways individuals experience music may change across the lifespan.

Understanding Design Features of Music and Language: The Choric/Dialogic Distinction

Music and spoken language share certain characteristics: both consist of sequences of acoustic elements that are combinatorically combined, and these elements partition the same continuous acoustic dimensions (frequency, formant space and duration). However, the resulting categories differ sharply: scale tones and note durations of small integer ratios appear in music, while speech uses phonemes, lexical tone, and non-isochronous durations. Why did music and language diverge into the two systems we have today, differing in these specific features? We propose a framework based on information theory and a reverse-engineering perspective, suggesting that design features of music and language are a response to their differential deployment along three different continuous dimensions. These include the familiar propositional-aesthetic ('goal') and repetitive-novel ('novelty') dimensions, and a dialogic-choric ('interactivity') dimension that is our focus here. Specifically, we hypothesize that music exhibits specializations enhancing coherent production by several individuals concurrently-the 'choric' context. In contrast, language is specialized for exchange in tightly coordinated turn-taking-'dialogic' contexts. We examine the evidence for our framework, both from humans and non-human animals, and conclude that many proposed design features of music and language follow naturally from their use in distinct dialogic and choric communicative contexts. Furthermore, the hybrid nature of intermediate systems like poetry, chant, or solo lament follows from their deployment in the less typical interactive context.

Music in the brain

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

Music modulates emotional responses in growing pigs

There is a lack of clarity on whether pigs can emotionally respond to musical stimulation and whether that response is related to music structure. Qualitative Behavioral Assessment (QBA) was used to evaluate effects of 16 distinct musical pieces (in terms of harmonic structure) on emotional responses in nursery pigs (n = 30) during four periods: "habituation", "treatments", "breaks" and "final". Data were evaluated using Principal component analysis (PCA). Two principal components (PC) were considered in the analysis: PC1, characterized as a positive emotions index, included the emotional responses content, playful, sociable, and happy, whereas PC2, characterized as a negative emotions index, included fearful, inquisitive, and uneasy with positive loadings, and relaxed and calm with negative loadings. Musical stimulation (treatment) increased (P < 0.01) both emotional indices, compared to other periods and this response was influenced by harmonic characteristics of the music. We concluded that pigs have a wide variety of emotional responses, with different affective states related to the music structure used, providing evidence of its potential use as environmental enrichment for this species.

Unpredictability of the “when” influences prediction error processing of the “what” and “where”

The capability to establish accurate predictions is an integral part of learning. Whether predictions about different dimensions of a stimulus interact with each other, and whether such an interaction affects learning, has remained elusive. We conducted a statistical learning study with EEG (electroencephalography), where a stream of consecutive sound triplets was presented with deviants that were either: (a) statistical, depending on the triplet ending probability, (b) physical, due to a change in sound location or (c) double deviants, i.e. a combination of the two. We manipulated the predictability of stimulus-onset by using random stimulus-onset asynchronies. Temporal unpredictability due to random onsets reduced the neurophysiological responses to statistical and location deviants, as indexed by the statistical mismatch negativity (sMMN) and the location MMN. Our results demonstrate that the predictability of one stimulus attribute influences the processing of prediction error signals of other stimulus attributes, and thus also learning of those attributes.

Does auditory deprivation impairs statistical learning in the auditory modality?

Early sensory deprivation allows assessing the extent of reorganisation of cognitive functions, well beyond sensory processing. As such, it is a good model to explore the links between sensory experience and cognitive functions. One of these functions, statistical learning - the ability to extract and use regularities present in the environment - is suspected to be impaired in prelingually deaf children with a cochlear implant. However, empirical evidence supporting this claim is very scarce and studies have reported contradictory results. This might be because previous studies have tested statistical learning only in the visual modality and did not make clear distinctions between multiple types of statistical regularities. To overcome these problems, we designed a modified serial reaction time task where cochlear implanted children and normal hearing children had to react to auditory sequences that embed multiple statistical regularities, namely transition probabilities of 0^th, 1^st or 2^nd order. We compared the reaction times of the children with the output of a simple computational model that learns transition probabilities. First, 6-12 years old children were able to learn 0^th and 1^st order transition probabilities but not 2^nd order ones. Second, there were no differences between cochlear implanted children and their normal hearing peers. These results indicate that auditory statistical learning is preserved in congenitally deaf children with cochlear implants. This suggests in turn that early auditory deprivation might not be crucially detrimental for the normal development of statistical learning.

Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience

Expectations shape our experience of music. However, the internal model upon which listeners form melodic expectations is still debated. Do expectations stem from Gestalt-like principles or statistical learning? If the latter, does long-term experience play an important role, or are short-term regularities sufficient? And finally, what length of context informs contextual expectations? To answer these questions, we presented human listeners with diverse naturalistic compositions from Western classical music, while recording neural activity using MEG. We quantified note-level melodic surprise and uncertainty using various computational models of music, including a state-of-the-art transformer neural network. A time-resolved regression analysis revealed that neural activity over fronto-temporal sensors tracked melodic surprise particularly around 200ms and 300-500ms after note onset. This neural surprise response was dissociated from sensory-acoustic and adaptation effects. Neural surprise was best predicted by computational models that incorporated long-term statistical learning-rather than by simple, Gestalt-like principles. Yet, intriguingly, the surprise reflected primarily short-range musical contexts of less than ten notes. We present a full replication of our novel MEG results in an openly available EEG dataset. Together, these results elucidate the internal model that shapes melodic predictions during naturalistic music listening.

Tonal Emergence: An agent-based model of tonal coordination

Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these findings have yet to be cached out in a working computational model. The present work fills this gap by presenting Tonal Emergence, an idealized agent-based model of improvised musical coordination. Tonal Emergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner's previous notes. The model replicates an interesting empirical result from a previous study of professional jazz pianists: feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by (i) a tendency to strive toward consonance, (ii) stochasticity, and (iii) a limited memory for previously played notes. Tonal Emergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.

The Human Cerebellum as a Hub of the Predictive Brain

Although the cerebellum has long been believed to be involved uniquely in sensorimotor processes, recent research works pointed to its participation in a wide range of cognitive predictive functions. Here, we review the available evidence supporting a generalized role of the cerebellum in predictive computation. We then discuss the anatomo-physiological properties that make the cerebellum the ideal hub of the predictive brain. We further argue that cerebellar involvement in cognition may follow a continuous gradient, with higher cerebellar activity occurring for tasks relying more on predictive processes, and outline the empirical scenarios to probe this hypothesis.

Perceptual uncertainty modulates auditory statistical learning: A magnetoencephalography study

Statistical learning allows comprehension of structured information, such as that in language and music. The brain computes a sequence's transition probability and predicts future states to minimise sensory reaction and derive entropy (uncertainty) from sequential information. Neurophysiological studies have revealed that early event-related neural responses (P1 and N1) reflect statistical learning - when the brain encodes transition probability in stimulus sequences, it predicts an upcoming stimulus with a high transition probability and suppresses the early event-related responses to a stimulus with a high transition probability. This amplitude difference between high and low transition probabilities reflects statistical learning effects. However, how a sequence's transition probability ratio affects neural responses contributing to statistical learning effects remains unknown. This study investigated how transition-probability ratios or conditional entropy (uncertainty) in auditory sequences modulate the early event-related neuromagnetic responses of P1m and N1m. Sequence uncertainties were manipulated using three different transition-probability ratios: 90:10%, 80:20%, and 67:33% (conditional entropy: 0.47, 0.72, and 0.92 bits, respectively). Neuromagnetic responses were recorded when participants listened to sequential sounds with these three transition probabilities. Amplitude differences between lower and higher probabilities were larger in sequences with transition-probability ratios of 90:10% and smaller in sequences with those of 67:33%, compared to sequences with those of 80:20%. This suggests that the transition-probability ratio finely tunes P1m and N1m. Our study also showed larger amplitude differences between frequent- and rare-transition stimuli in P1m than in N1m. This indicates that information about transition-probability differences may be calculated in earlier cognitive processes.

Asymmetry in scales enhances learning of new musical structures

Despite the remarkable variability music displays across cultures, certain recurrent musical features motivate the hypothesis that fundamental cognitive principles constrain the way music is produced. One such feature concerns the structure of musical scales. The vast majority of musical cultures use scales that are not uniformly symmetric-that is, scales that contain notes spread unevenly across the octave. Here we present evidence that the structure of musical scales has a substantial impact on how listeners learn new musical systems. Three experiments were conducted to test the hypothesis that nonuniformity facilitates the processing of melodies. Novel melodic stimuli were composed based on artificial grammars using scales with different levels of symmetry. Experiment 1 tested the acquisition of tonal hierarchies and melodic regularities on three different 12-tone equal-tempered scales using a finite-state grammar. Experiments 2 and 3 used more flexible Markov-chain grammars and were designed to generalize the effect to 14-tone and 16-tone equal-tempered scales. The results showed that performance was significantly enhanced by scale structures that specified the tonal space by providing unique intervallic relations between notes. These results suggest that the learning of novel musical systems is modulated by the symmetry of scales, which in turn may explain the prevalence of nonuniform scales across musical cultures.

Violation of rhythmic expectancies can elicit late frontal gamma activity nested in theta oscillations

Rhythm processing involves building expectations according to the hierarchical temporal structure of auditory events. Although rhythm processing has been addressed in the context of predictive coding, the properties of the oscillatory response in different cortical areas are still not clear. We explored the oscillatory properties of the neural response to rhythmic incongruence and the cross-frequency coupling between multiple frequencies to further investigate the mechanisms underlying rhythm perception. We designed an experiment to investigate the neural response to rhythmic deviations in which the tone either arrived earlier than expected or the tone in the same metrical position was omitted. These two manipulations modulate the rhythmic structure differently, with the former creating a larger violation of the general structure of the musical stimulus than the latter. Both deviations resulted in an MMN response, whereas only the rhythmic deviant resulted in a subsequent P3a. Rhythmic deviants due to the early occurrence of a tone, but not omission deviants, seemed to elicit a late high gamma response (60-80 Hz) at the end of the P3a over the left frontal region, which, interestingly, correlated with the P3a amplitude over the same region and was also nested in theta oscillations. The timing of the elicited high-frequency gamma oscillations related to rhythmic deviation suggests that it might be related to the update of the predictive neural model, corresponding to the temporal structure of the events in higher-level cortical areas.

Attendings' Perceptions of Authentic Evaluation Criteria for Effective Surgical Consults

OBJECTIVE

The authors aimed to investigate faculty evaluation criteria for an effective oral surgical presentation in actual patient care contexts.

DESIGN

We conducted a 2-step observation-based qualitative study. Residents audiotaped oral presentations of a surgical consult to an attending. Evaluation panels listened to the recordings and discussed to develop joint feedback for the resident. The panel discussions were recorded and served as the data source for this study. We analyzed the data following the grounded theory approach using open coding and axial coding.

SETTING

The study setting was at Southern Illinois University School of Medicine, a 5-year general surgery residency program in Springfield, Illinois.

PARTICIPANTS

Thirteen residents out of 19 in the program participated by virtue of having submitted recordings of a patient care consult presentation via phone. Evaluation panels consisted of general surgery academic and community faculty, as well as senior residents.

RESULTS

Several criteria for effective oral presentations emerged that have rarely been discussed in prior literature. Themes included: (1) The strategic opening is critical as it "sets the stage" and frames how the attending will listen. Situational factors, such as consideration of time of the day and urgency, should be accounted for in the opening. (2) A deductive structure defines the relevance of the presented information. Clinical judgement should precede supporting evidence. Attending physicians perceive important information as unnecessary if provided outside of this framework. (3) Established trust between a resident and a surgeon determines the level of detail expected of the presenting resident. With increasing trust, surgeons expect residents to present fewer details; if too much detail is included, the presentation may be assessed as ineffective. (4) Surgical descriptions are appreciated for their value in promoting the attending's visualization or mental picture of the patient condition. (5) Oral emphasis using voice tone and pace can be helpful for capturing attending attention.

CONCLUSIONS

These findings can be utilized to improve the current training program and assessment rubrics toward contextualized work-based assessment practices in surgery. Oral patient presentation skills are neither static nor universal, but fluid and reflexive, based on trust, and situational factors.

Musical Expertise Is Associated with Improved Neural Statistical Learning in the Auditory Domain

 

It is poorly known whether musical training is associated with improvements in general cognitive abilities, such as statistical learning (SL). In standard SL paradigms, musicians have shown better performances than nonmusicians. However, this advantage could be due to differences in auditory discrimination, in memory or truly in the ability to learn sequence statistics. Unfortunately, these different hypotheses make similar predictions in terms of expected results. To dissociate them, we developed a Bayesian model and recorded electroencephalography (EEG). Our results confirm that musicians perform approximately 15% better than nonmusicians at predicting items in auditory sequences that embed either low or high-order statistics. These higher performances are explained in the model by parameters governing the learning of high-order statistics and the selection stage noise. EEG recordings reveal a neural underpinning of the musician's advantage: the P300 amplitude correlates with the surprise elicited by each item, and so, more strongly for musicians. Finally, early EEG components correlate with the surprise elicited by low-order statistics, as opposed to late EEG components that correlate with the surprise elicited by high-order statistics and this effect is stronger for musicians. Overall, our results demonstrate that musical expertise is associated with improved neural SL in the auditory domain.

SIGNIFICANCE STATEMENT

It is poorly known whether musical training leads to improvements in general cognitive skills. One fundamental cognitive ability, SL, is thought to be enhanced in musicians, but previous studies have reported mixed results. This is because such musician's advantage can embrace very different explanations, such as improvement in auditory discrimination or in memory. To solve this problem, we developed a Bayesian model and recorded EEG to dissociate these explanations. Our results reveal that musical expertise is truly associated with an improved ability to learn sequence statistics, especially high-order statistics. This advantage is reflected in the electroencephalographic recordings, where the P300 amplitude is more sensitive to surprising items in musicians than in nonmusicians.

Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning

Creativity is part of human nature and is commonly understood as a phenomenon whereby something original and worthwhile is formed. Owing to this ability, humans can produce innovative information that often facilitates growth in our society. Creativity also contributes to esthetic and artistic productions, such as music and art. However, the mechanism by which creativity emerges in the brain remains debatable. Recently, a growing body of evidence has suggested that statistical learning contributes to creativity. Statistical learning is an innate and implicit function of the human brain and is considered essential for brain development. Through statistical learning, humans can produce and comprehend structured information, such as music. It is thought that creativity is linked to acquired knowledge, but so-called "eureka" moments often occur unexpectedly under subconscious conditions, without the intention to use the acquired knowledge. Given that a creative moment is intrinsically implicit, we postulate that some types of creativity can be linked to implicit statistical knowledge in the brain. This article reviews neural and computational studies on how creativity emerges within the framework of statistical learning in the brain (i.e., statistical creativity). Here, we propose a hierarchical model of statistical learning: statistically chunking into a unit (hereafter and shallow statistical learning) and combining several units (hereafter and deep statistical learning). We suggest that deep statistical learning contributes dominantly to statistical creativity in music. Furthermore, the temporal dynamics of perceptual uncertainty can be another potential causal factor in statistical creativity. Considering that statistical learning is fundamental to brain development, we also discuss how typical versus atypical brain development modulates hierarchical statistical learning and statistical creativity. We believe that this review will shed light on the key roles of statistical learning in musical creativity and facilitate further investigation of how creativity emerges in the brain.

Computational framework for investigating predictive processing in auditory perception

BACKGROUND

The brain tracks sound sources as they evolve in time, collecting contextual information to predict future sensory inputs. Previous work in predictive coding typically focuses on the perception of predictable stimuli, leaving the implementation of these same neural processes in more complex, real-world environments containing randomness and uncertainty up for debate.

NEW METHOD

To facilitate investigation into the perception of less tightly-controlled listening scenarios, we present a computational model as a tool to ask targeted questions about the underlying predictive processes that connect complex sensory inputs to listener behavior and neural responses. In the modeling framework, observed sound features (e.g. pitch) are tracked sequentially using Bayesian inference. Sufficient statistics are inferred from past observations at multiple time scales and used to make predictions about future observation while tracking the statistical structure of the sensory input.

RESULTS

Facets of the model are discussed in terms of their application to perceptual research, and examples taken from real-world audio demonstrate the model's flexibility to capture a variety of statistical structures along various perceptual dimensions.

COMPARISON WITH EXISTING METHODS

Previous models are often targeted toward interpreting a particular experimental paradigm (e.g., oddball paradigm), perceptual dimension (e.g., pitch processing), or task (e.g., speech segregation), thus limiting their ability to generalize to other domains. The presented model is designed as a flexible and practical tool for broad application.

CONCLUSION

The model is presented as a general framework for generating new hypotheses and guiding investigation into the neural processes underlying predictive coding of complex scenes.

Aesthetic Judgments of Live and Recorded Music: Effects of Congruence Between Musical Artist and Piece

The COVID-19 pandemic has brought the live music industry to an abrupt halt; subsequently, musicians are looking for ways to replicate the live concert experience virtually. The present study sought to investigate differences in aesthetic judgments of a live concert vs. a recorded concert, and whether these responses vary based on congruence between musical artist and piece. Participants (N = 32) made continuous ratings of their felt pleasure either during a live concert or while viewing an audiovisual recorded version of the same joint concert given by a university band and a United States Army band. Each band played two pieces: a United States patriotic piece (congruent with the army band) and a non-patriotic piece (congruent with the university band). Results indicate that, on average, participants reported more pleasure while listening to pieces that were congruent, which did not vary based on live vs. lab listening context: listeners preferred patriotic music when played by the army band and non-patriotic music when played by the university band. Overall, these results indicate that felt pleasure in response to music may vary based on listener expectations of the musical artist, such that listeners prefer musical pieces that “fit” with the particular artist. When considering implications for concerts during the COVID-19 pandemic, our results indicate that listeners may experience similar degrees of pleasure even while viewing a recorded concert, suggesting that virtual concerts are a reasonable way to elicit pleasure from audiences when live performances are not possible.

Modifications in the Topological Structure of EEG Functional Connectivity Networks during Listening Tonal and Atonal Concert Music in Musicians and Non-Musicians

The present work aims to demonstrate the hypothesis that atonal music modifies the topological structure of electroencephalographic (EEG) connectivity networks in relation to tonal music. To this, EEG monopolar records were taken in musicians and non-musicians while listening to tonal, atonal, and pink noise sound excerpts. EEG functional connectivities (FC) among channels assessed by a phase synchronization index previously thresholded using surrogate data test were computed. Sound effects, on the topological structure of graph-based networks assembled with the EEG-FCs at different frequency-bands, were analyzed throughout graph metric and network-based statistic (NBS). Local and global efficiency normalized (vs. random-network) measurements (NLE|NGE) assessing network information exchanges were able to discriminate both music styles irrespective of groups and frequency-bands. During tonal audition, NLE and NGE values in the beta-band network get close to that of a small-world network, while during atonal and even more during noise its structure moved away from small-world. These effects were attributed to the different timbre characteristics (sounds spectral centroid and entropy) and different musical structure. Results from networks topographic maps for strength and NLE of the nodes, and for FC subnets obtained from the NBS, allowed discriminating the musical styles and verifying the different strength, NLE, and FC of musicians compared to non-musicians.

Stream segregation revisited: Dynamic listening and influences of emotional context on stream perception and attention

A classical experiment of auditory stream segregation is revisited, reconceptualising perceptual ambiguity in terms of affordances and musical engagement. Specifically, three experiments are reported that investigate how listeners' perception of auditory sequences change dynamically depending on emotional context. The experiments show that listeners adapt their attention to higher or lower pitched streams (Experiments 1 and 2) and the degree of auditory stream integration or segregation (Experiment 3) in accordance with the presented emotional context. Participants with and without formal musical training show this influence, although to differing degrees (Experiment 2). Contributing evidence to the literature on interactions between emotion and cognition, these experiments demonstrate how emotion is an intrinsic part of music perception and not merely a product of the listening experience.

Statistical Properties in Jazz Improvisation Underline Individuality of Musical Representation

Statistical learning is an innate function in the brain and considered to be essential for producing and comprehending structured information such as music. Within the framework of statistical learning the brain has an ability to calculate the transitional probabilities of sequences such as speech and music, and to predict a future state using learned statistics. This paper computationally examines whether and how statistical learning and knowledge partially contributes to musical representation in jazz improvisation. The results represent the time-course variations in a musician’s statistical knowledge. Furthermore, the findings show that improvisational musical representation might be susceptible to higher- but not lower-order statistical knowledge (i.e., knowledge of higher-order transitional probability). The evidence also demonstrates the individuality of improvisation for each improviser, which in part depends on statistical knowledge. Thus, this study suggests that statistical properties in jazz improvisation underline individuality of musical representation.

Timing is everything: Dance aesthetics depend on the complexity of movement kinematics

What constitutes a beautiful action? Research into dance aesthetics has largely focussed on subjective features like familiarity with the observed movement, but has rarely studied objective features like speed or acceleration. We manipulated the kinematic complexity of observed actions by creating dance sequences that varied in movement timing, but not in movement trajectory. Dance-naïve participants rated the dance videos on speed, effort, reproducibility, and enjoyment. Using linear mixed-effects modeling, we show that faster, more predictable movement sequences with varied velocity profiles are judged to be more effortful, less reproducible, and more aesthetically pleasing than slower sequences with more uniform velocity profiles. Accordingly, dance aesthetics depend not only on which movements are being performed but on how movements are executed and linked into sequences. The aesthetics of movement timing may apply across culturally-specific dance styles and predict both preference for and perceived difficulty of dance, consistent with information theory and effort heuristic accounts of aesthetic appreciation.

"Stopping for knowledge": The sense of beauty in the perception-action cycle

According to a millennial-old philosophical debate, aesthetic emotions have been connected to knowledge acquisition. Recent scientific evidence, collected across different disciplinary domains, confirms this link, but also reveals that motor inhibition plays a crucial role in the process. In this review, we discuss multidisciplinary results and propose an original account of aesthetic appreciation (the stopping for knowledge hypothesis) framed within the predictive coding theory. We discuss evidence showing that aesthetic emotions emerge in correspondence with an inhibition of motor behavior (i.e., minimizing action), promoting a simultaneous perceptual processing enhancement, at the level of sensory cortices (i.e., optimizing learning). Accordingly, we suggest that aesthetic appreciation may represent a hedonic feedback over learning progresses, motivating the individual to inhibit motor routines to seek further knowledge acquisition. Furthermore, the neuroimaging and neuropsychological studies we review reveal the presence of a strong association between aesthetic appreciation and the activation of the dopaminergic reward-related circuits. Finally, we propose a number of possible applications of the stopping for knowledge hypothesis in the clinical and education domains.

Does unfairness sound wrong? A cross-domain investigation of expectations in music and social decision-making

This study was interested in investigating the existence of a shared psychological mechanism for the processing of expectations across domains. The literature on music and language shows that violations of expectations produce similar neural responses and violating the expectation in one domain may influence the processing of stimuli in the other domain. Like music and language, our social world is governed by a system of inherent rules or norms, such as fairness. The study therefore aimed to draw a parallel to the social domain and investigate whether a manipulation of melodic expectation can influence the processing of higher-level expectations of fairness. Specifically, we aimed to investigate whether the presence of an unexpected melody enhances or reduces participants' sensitivity to the violations of fairness and the behavioural reactions associated with these. We embedded a manipulation of melodic expectation within a social decision-making paradigm, whereby musically expected and unexpected stimuli will be simultaneously presented with fair and unfair divisions in a third-party altruistic punishment game. Behavioural and electroencephalographic responses were recorded. Results from the pre-planned analyses show that participants are less likely to punish when melodies are more unexpected and that violations of fairness norms elicit medial frontal negativity (MFN)-life effects. Because no significant interactions between melodic expectancy and fairness of the division were found, results fail to provide evidence of a shared mechanism for the processing of expectations. Exploratory analyses show two additional effects: (i) unfair divisions elicit an early attentional component (P2), probably associated with stimulus saliency, and (ii) mid-value divisions elicit a late MFN-like component, probably reflecting stimulus ambiguity. Future studies could build on these results to further investigate the effect of the cross-domain influence of music on the processing of social stimuli on these early and late components.

The perceptual relevance of balance, evenness, and entropy in musical rhythms

There is an uncountable number of different ways of characterizing almost any given real-world stimulus. This necessitates finding stimulus features that are perceptually relevant - that is, they have distinct and independent effects on the perception and cognition of the stimulus. Here, we provide a theoretical framework for empirically testing the perceptual relevance of stimulus features through their association with recognition, memory bias, and æsthetic evaluation. We deploy this framework in the auditory domain to explore the perceptual relevance of three recently developed mathematical characterizations of periodic temporal patterns: balance, evenness, and interonset interval entropy. By modelling recognition responses and liking ratings from 177 participants listening to a total of 1252 different musical rhythms, we obtain very strong evidence that all three features have distinct effects on the memory for, and the liking of, musical rhythms. Interonset interval entropy is a measure of the unpredictability of a rhythm derived from the distribution of its durations. Balance and evenness are both obtained from the discrete Fourier transform (DFT) of periodic patterns represented as points on the unit circle, and we introduce a teleological explanation for their perceptual relevance: the DFT coefficients representing balance and evenness are relatively robust to small random temporal perturbations and hence are coherent in noisy environments. This theory suggests further research to explore the meaning and relevance of robust coefficients such as these to the perception of patterns that are periodic in time and, possibly, space.