Neural entrainment is associated with subjective groove and complexity for performed but not mechanical musical rhythms.

Both movement and neural activity in humans can be entrained by the regularities of an external stimulus, such as the beat of musical rhythms. Neural entrainment to auditory rhythms supports temporal perception, and is enhanced by selective attention and by hierarchical temporal structure imposed on rhythms. However, it is not known how neural entrainment to rhythms is related to the subjective experience of groove (the desire to move along with music or rhythm), the perception of a regular beat, the perception of complexity, and the experience of pleasure. In two experiments, we used musical rhythms (from Steve Reich's Clapping Music) to investigate whether rhythms that are performed by humans (with naturally variable timing) and rhythms that are mechanical (with precise timing), elicit differences in (1) neural entrainment, as measured by inter-trial phase coherence, and (2) subjective ratings of the complexity, preference, groove, and beat strength of rhythms. We also combined results from the two experiments to investigate relationships between neural entrainment and subjective perception of musical rhythms. We found that mechanical rhythms elicited a greater degree of neural entrainment than performed rhythms, likely due to the greater temporal precision in the stimulus, and the two types only elicited different ratings for some individual rhythms. Neural entrainment to performed rhythms, but not to mechanical ones, correlated with subjective desire to move and subjective complexity. These data, therefore, suggest multiple interacting influences on neural entrainment to rhythms, from low-level stimulus properties to high-level cognition and perception.

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.

Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music.

We present the results of a study testing the often-theorized role of musical expectations in inducing listeners' emotions in a live flute concert experiment with 50 participants. Using an audience response system developed for this purpose, we measured subjective experience and peripheral psychophysiological changes continuously. To confirm the existence of the link between expectation and emotion, we used a threefold approach. (1) On the basis of an information-theoretic cognitive model, melodic pitch expectations were predicted by analyzing the musical stimuli used (six pieces of solo flute music). (2) A continuous rating scale was used by half of the audience to measure their experience of unexpectedness toward the music heard. (3) Emotional reactions were measured using a multicomponent approach: subjective feeling (valence and arousal rated continuously by the other half of the audience members), expressive behavior (facial EMG), and peripheral arousal (the latter two being measured in all 50 participants). Results confirmed the predicted relationship between high-information-content musical events, the violation of musical expectations (in corresponding ratings), and emotional reactions (psychologically and physiologically). Musical structures leading to expectation reactions were manifested in emotional reactions at different emotion component levels (increases in subjective arousal and autonomic nervous system activations). These results emphasize the role of musical structure in emotion induction, leading to a further understanding of the frequently experienced emotional effects of music.

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.

Multilevel rhythms in multimodal communication.

It is now widely accepted that the brunt of animal communication is conducted via several modalities, e.g. acoustic and visual, either simultaneously or sequentially. This is a laudable multimodal turn relative to traditional accounts of temporal aspects of animal communication which have focused on a single modality at a time. However, the fields that are currently contributing to the study of multimodal communication are highly varied, and still largely disconnected given their sole focus on a particular level of description or their particular concern with human or non-human animals. Here, we provide an integrative overview of converging findings that show how multimodal processes occurring at neural, bodily, as well as social interactional levels each contribute uniquely to the complex rhythms that characterize communication in human and non-human animals. Though we address findings for each of these levels independently, we conclude that the most important challenge in this field is to identify how processes at these different levels connect. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.

Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation.

The ability to anticipate forthcoming events has clear evolutionary advantages, and predictive successes or failures often entail significant psychological and physiological consequences. In music perception, the confirmation and violation of expectations are critical to the communication of emotion and aesthetic effects of a composition. Neuroscientific research on musical expectations has focused on harmony. Although harmony is important in Western tonal styles, other musical traditions, emphasizing pitch and melody, have been rather neglected. In this study, we investigated melodic pitch expectations elicited by ecologically valid musical stimuli by drawing together computational, behavioural, and electrophysiological evidence. Unlike rule-based models, our computational model acquires knowledge through unsupervised statistical learning of sequential structure in music and uses this knowledge to estimate the conditional probability (and information content) of musical notes. Unlike previous behavioural paradigms that interrupt a stimulus, we devised a new paradigm for studying auditory expectation without compromising ecological validity. A strong negative correlation was found between the probability of notes predicted by our model and the subjectively perceived degree of expectedness. Our electrophysiological results showed that low-probability notes, as compared to high-probability notes, elicited a larger (i) negative ERP component at a late time period (400-450 ms), (ii) beta band (14-30 Hz) oscillation over the parietal lobe, and (iii) long-range phase synchronization between multiple brain regions. Altogether, the study demonstrated that statistical learning produces information-theoretic descriptions of musical notes that are proportional to their perceived expectedness and are associated with characteristic patterns of neural activity.

Creativity, information, and consciousness: The information dynamics of thinking.

This paper presents a theory of the basic operation of mind, Information Dynamics of Thinking, which is intended for computational implementation and thence empirical testing. It is based on the information theory of Shannon, and treats the mind/brain as an information processing organ that aims to be information-efficient, in that it predicts its world, so as to use information efficiently, and regularly re-represents it, so as to store information efficiently. The theory is presented in context of a background review of various research areas that impinge upon its development. Consequences of the theory and testable hypotheses arising from it are discussed.

Learning and Consolidation as Re-representation: Revising the Meaning of Memory.

In this Hypothesis and Theory paper, we consider the problem of learning deeply structured knowledge representations in the absence of predefined ontologies, and in the context of long-term learning. In particular, we consider this process as a sequence of re-representation steps, of various kinds. The Information Dynamics of Thinking theory (IDyOT) admits such learning, and provides a hypothetical mechanism for the human-like construction of hierarchical memory, with the provision of symbols constructed by the system that embodies the theory. The combination of long-term learning and meaning construction in terms of symbols grounded in perceptual experience entails that the system, like a human, be capable of memory consolidation, to manage the complex and inconsistent structures that can result from learning of information that becomes more complete over time. Such consolidation changes memory structures, and thus changes their meaning. Therefore, memory consolidation entails re-representation, while re-representation entails changes of meaning. Ultimately, the theory proposes that the processes of learning and consolidation should be considered as repeated re-representation of what is learned.

Linking Neural and Symbolic Representation and Processing of Conceptual Structures.

We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking), which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.

Entraining IDyOT: Timing in the Information Dynamics of Thinking.

We present a novel hypothetical account of entrainment in music and language, in context of the Information Dynamics of Thinking model, IDyOT. The extended model affords an alternative view of entrainment, and its companion term, pulse, from earlier accounts. The model is based on hierarchical, statistical prediction, modeling expectations of both what an event will be and when it will happen. As such, it constitutes a kind of predictive coding, with a particular novel hypothetical implementation. Here, we focus on the model's mechanism for predicting when a perceptual event will happen, given an existing sequence of past events, which may be musical or linguistic. We propose a range of tests to validate or falsify the model, at various different levels of abstraction, and argue that computational modeling in general, and this model in particular, can offer a means of providing limited but useful evidence for evolutionary hypotheses.

Principles of structure building in music, language and animal song.

Human language, music and a variety of animal vocalizations constitute ways of sonic communication that exhibit remarkable structural complexity. While the complexities of language and possible parallels in animal communication have been discussed intensively, reflections on the complexity of music and animal song, and their comparisons, are underrepresented. In some ways, music and animal songs are more comparable to each other than to language as propositional semantics cannot be used as indicator of communicative success or wellformedness, and notions of grammaticality are less easily defined. This review brings together accounts of the principles of structure building in music and animal song. It relates them to corresponding models in formal language theory, the extended Chomsky hierarchy (CH), and their probabilistic counterparts. We further discuss common misunderstandings and shortcomings concerning the CH and suggest ways to move beyond. We discuss language, music and animal song in the context of their function and motivation and further integrate problems and issues that are less commonly addressed in the context of language, including continuous event spaces, features of sound and timbre, representation of temporality and interactions of multiple parallel feature streams. We discuss these aspects in the light of recent theoretical, cognitive, neuroscientific and modelling research in the domains of music, language and animal song.

The evolutionary roots of creativity: mechanisms and motivations.

We consider the evolution of cognition and the emergence of creative behaviour, in relation to vocal communication. We address two key questions: (i) what cognitive and/or social mechanisms have evolved that afford aspects of creativity?; (ii) has natural and/or sexual selection favoured human behaviours considered 'creative'? This entails analysis of 'creativity', an imprecise construct: comparable properties in non-humans differ in magnitude and teleology from generally agreed human creativity. We then address two apparent problems: (i) the difference between merely novel productions and 'creative' ones; (ii) the emergence of creative behaviour in spite of high cost: does it fit the idea that females choose a male who succeeds in spite of a handicap (costly ornament); or that creative males capable of producing a large and complex song repertoire grew up under favourable conditions; or a demonstration of generally beneficial heightened reasoning capacity; or an opportunity to continually reinforce social bonding through changing communication tropes; or something else? We illustrate and support our argument by reference to whale and bird song; these independently evolved biological signal mechanisms objectively share surface properties with human behaviours generally called 'creative'. Studying them may elucidate mechanisms underlying human creativity; we outline a research programme to do so.

Mind the gap: an attempt to bridge computational and neuroscientific approaches to study creativity.

Creativity is the hallmark of human cognition and is behind every innovation, scientific discovery, piece of music, artwork, and idea that have shaped our lives, from ancient times till today. Yet scientific understanding of creative processes is quite limited, mostly due to the traditional belief that considers creativity as a mysterious puzzle, a paradox, defying empirical enquiry. Recently, there has been an increasing interest in revealing the neural correlates of human creativity. Though many of these studies, pioneering in nature, help demystification of creativity, but the field is still dominated by popular beliefs in associating creativity with "right brain thinking", "divergent thinking", "altered states" and so on (Dietrich and Kanso, 2010). In this article, we discuss a computational framework for creativity based on Baars' Global Workspace Theory (GWT; Baars, 1988) enhanced with mechanisms based on information theory. Next we propose a neurocognitive architecture of creativity with a strong focus on various facets (i.e., unconscious thought theory, mind wandering, spontaneous brain states) of un/pre-conscious brain responses. Our principal argument is that pre-conscious creativity happens prior to conscious creativity and the proposed computational model may provide a mechanism by which this transition is managed. This integrative approach, albeit unconventional, will hopefully stimulate future neuroscientific studies of the inscrutable phenomenon of creativity.

Auditory expectation: the information dynamics of music perception and cognition.

Following in a psychological and musicological tradition beginning with Leonard Meyer, and continuing through David Huron, we present a functional, cognitive account of the phenomenon of expectation in music, grounded in computational, probabilistic modeling. We summarize a range of evidence for this approach, from psychology, neuroscience, musicology, linguistics, and creativity studies, and argue that simulating expectation is an important part of understanding a broad range of human faculties, in music and beyond.

The role of expectation and probabilistic learning in auditory boundary perception: a model comparison.

Grouping and boundary perception are central to many aspects of sensory processing in cognition. We present a comparative study of recently published computational models of boundary perception in music. In doing so, we make three contributions. First, we hypothesise a relationship between expectation and grouping in auditory perception, and introduce a novel information-theoretic model of perceptual segmentation to test the hypothesis. Although we apply the model to musical melody, it is applicable in principle to sequential grouping in other areas of cognition. Second, we address a methodological consideration in the analysis of ambiguous stimuli that produce different percepts between individuals. We propose and demonstrate a solution to this problem, based on clustering of participants prior to analysis. Third, we conduct the first comparative analysis of probabilistic-learning and rule-based models of perceptual grouping in music. In spite of having only unsupervised exposure to music, the model performs comparably to rule-based models based on expert musical knowledge, supporting a role for probabilistic learning in perceptual segmentation of music.