Depth and the Uncertainty of Statistical Knowledge on Musical Creativity Fluctuate Over a Composer's Lifetime

Attention Shifts to More Complex Structures With Experience

Our environments are saturated with learnable information. What determines which of this information is prioritized for limited attentional resources? Although previous studies suggest that learners prefer medium-complexity information, here we argue that what counts as medium should change as someone learns an input's structure. Specifically, we examined the hypothesis that attention is directed toward more complicated structures as learners gain more experience with the environment. College students watched four simultaneous streams of information that varied in complexity. RTs to intermittent search trials (Experiment 1, N = 75) and eye tracking (Experiment 2, N = 45) indexed where participants attended during the experiment. Using two participant- and trial-specific measures of complexity, we demonstrated that participants attended to increasingly complex streams over time. Individual differences in structure learning also predicted attention allocation, with better learners attending to complex structures earlier in learning, suggesting that the ability to prioritize different information over time is related to learning success.

The Embodied-Enactive-Interactive Brain: Bridging Neuroscience and Creative Arts Therapies

The recognition and incorporation of evidence-based neuroscientific concepts into creative arts therapeutic knowledge and practice seem valuable and advantageous for the purpose of integration and professional development. Moreover, exhilarating insights from the field of neuroscience coincide with the nature, conceptualization, goals, and methods of Creative Arts Therapies (CATs), enabling comprehensive understandings of the clinical landscape, from a translational perspective. This paper contextualizes and discusses dynamic brain functions that have been suggested to lie at the heart of intra- and inter-personal processes. Touching upon fundamental aspects of the self and self-other interaction, the state-of-the-art neuroscientific-informed views will shed light on mechanisms of the embodied, predictive and relational brain. The conceptual analysis introduces and interweaves the following contemporary perspectives of brain function: firstly, the grounding of mental activity in the lived, bodily experience will be delineated; secondly, the enactive account of internal models, or generative predictive representations, shaped by experience, will be defined and extensively deliberated; and thirdly, the interpersonal simulation and synchronization mechanisms that support empathy and mentalization will be thoroughly considered. Throughout the paper, the cross-talks between the brain and the body, within the brain through functionally connected neural networks and in the context of agent-environment dynamics, will be addressed. These communicative patterns will be elaborated on to unfold psychophysiological linkage, as well as psychopathological shifts, concluding with the neuroplastic change associated with the formulation of CATs. The manuscript suggests an integrative view of the brain-body-mind in contexts relevant to the therapeutic potential of the expressive creative arts and the main avenues by which neuroscience may ground, enlighten and enrich the clinical psychotherapeutic practice.

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.

Dimensions of Musical Creativity

Current literature on creative cognition has developed rich conceptual landscapes dedicated to the analysis of both individual and collective forms of creativity. This work has favored the emergence of unifying theories on domain-general creative abilities in which the main experiential, behavioral, computational, and neural aspects involved in everyday creativity are examined and discussed. But while such accounts have gained important analytical leverage for describing the overall conditions and mechanisms through which creativity emerges and operates, they necessarily leave contextual forms of creativity less explored. Among the latter, musical practices have recently drawn the attention of scholars interested in its creative properties as well as in the creative potential of those who engage with them. In the present article, we compare previously posed theories of creativity in musical and non-musical domains to lay the basis of a conceptual framework that mitigates the tension between (i) individual and collective and (ii) domain-general and domain-specific perspectives on creativity. In doing so, we draw from a range of scholarship in music and enactive cognitive science, and propose that creative cognition may be best understood as a process of skillful organism-environment adaptation that one cultivates endlessly. With its focus on embodiment, plurality, and adaptiveness, our account points to a structured unity between living systems and their world, disclosing a variety of novel analytical resources for research and theory across different dimensions of (musical) creativity.

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.

Statistical learning and the uncertainty of melody and bass line in music

Statistical learning is the ability to learn based on transitional probability (TP) in sequential information, which has been considered to contribute to creativity in music. The interdisciplinary theory of statistical learning examines statistical learning as a mechanism of human learning. This study investigated how TP distribution and conditional entropy in TP of the melody and bass line in music interact with each other, using the highest and lowest pitches in Beethoven’s piano sonatas and Johann Sebastian Bach’s Well-Tempered Clavier. Results for the two composers were similar. First, the results detected specific statistical characteristics that are unique to each melody and bass line as well as general statistical characteristics that are shared between the melody and bass line. Additionally, a correlation of the conditional entropies sampled from the TP distribution could be detected between the melody and bass line. This suggests that the variability of entropies interacts between the melody and bass line. In summary, this study suggested that TP distributions and the entropies of the melody and bass line interact with but are partly independent of each other.

Music predictability and liking enhance pupil dilation and promote motor learning in non-musicians

Humans can anticipate music and derive pleasure from it. Expectations facilitate the learning of movements associated with anticipated events, and they are also linked with reward, which may further facilitate learning of the anticipated rewarding events. The present study investigates the synergistic effects of predictability and hedonic responses to music on arousal and motor-learning in a naïve population. Novel melodies were manipulated in their overall predictability (predictable/unpredictable) as objectively defined by a model of music expectation, and ranked as high/medium/low liked based on participants' self-reports collected during an initial listening session. During this session, we also recorded ocular pupil size as an implicit measure of listeners' arousal. During the following motor task, participants learned to play target notes of the melodies on a keyboard (notes were of similar motor and musical complexity across melodies). Pupil dilation was greater for liked melodies, particularly when predictable. Motor performance was facilitated in predictable rather than unpredictable melodies, but liked melodies were learned even in the unpredictable condition. Low-liked melodies also showed learning but mostly in participants with higher scores of task perceived competence. Taken together, these results highlight  the effects of stimuli predictability on learning, which can be however overshadowed by the effects of stimulus liking or task-related intrinsic motivation.

Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning

Statistical learning is a learning mechanism based on transition probability in sequences such as music and language. Recent computational and neurophysiological studies suggest that the statistical learning contributes to production, action, and musical creativity as well as prediction and perception. The present study investigated how statistical structure interacts with tonalities in music based on various-order statistical models. To verify this in all 24 major and minor keys, the transition probabilities of the sequences containing the highest pitches in Bach's Well-Tempered Clavier, which is a collection of two series (No. 1 and No. 2) of preludes and fugues in all of the 24 major and minor keys, were calculated based on nth-order Markov models. The transition probabilities of each sequence were compared among tonalities (major and minor), two series (No. 1 and No. 2), and music types (prelude and fugue). The differences in statistical characteristics between major and minor keys were detected in lower- but not higher-order models. The results also showed that statistical knowledge in music might be modulated by tonalities and composition periods. Furthermore, the principal component analysis detected the shared components of related keys, suggesting that the tonalities modulate statistical characteristics in music. The present study may suggest that there are at least two types of statistical knowledge in music that are interdependent on and independent of tonality, respectively.