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

The Domain-Specific Neural Basis of Auditory Statistical Learning in 5-7-Year-Old Children

Statistical learning (SL) is the ability to rapidly track statistical regularities and learn patterns in the environment. Recent studies show that SL is constrained by domain-specific features, rather than being a uniform learning mechanism across domains and modalities. This domain-specificity has been reflected at the neural level, as SL occurs in regions primarily involved in processing of specific modalities or domains of input. However, our understanding of how SL is constrained by domain-specific features in the developing brain is severely lacking. The present study aims to identify the functional neural profiles of auditory SL of linguistic and nonlinguistic regularities among children. Thirty children between 5 and 7 years old completed an auditory fMRI SL task containing interwoven sequences of structured and random syllable/tone sequences. Using traditional group univariate analyses and a group-constrained subject-specific analysis, frontal and temporal cortices showed significant activation when processing structured versus random sequences across both linguistic and nonlinguistic domains. However, conjunction analyses failed to identify overlapping neural indices across domains. These findings are the first to compare brain regions supporting SL of linguistic and nonlinguistic regularities in the developing brain and indicate that auditory SL among developing children may be constrained by domain-specific features.

Temporal dynamics of uncertainty and prediction error in musical improvisation across different periods

Human improvisational acts contain an innate individuality, derived from one's experiences based on epochal and cultural backgrounds. Musical improvisation, much like spontaneous speech, reveals intricate facets of the improviser's state of mind and emotional character. However, the specific musical components that reveal such individuality remain largely unexplored. Within the framework of human statistical learning and predictive processing, this study examined the temporal dynamics of uncertainty and surprise (prediction error) in a piece of musical improvisation. This cognitive process reconciles the raw auditory cues, such as melody and rhythm, with the musical predictive models shaped by its prior experiences. This study employed the Hierarchical Bayesian Statistical Learning (HBSL) model to analyze a corpus of 456 Jazz improvisations, spanning 1905 to 2009, from 78 distinct Jazz musicians. The results indicated distinctive temporal patterns of surprise and uncertainty, especially in pitch and pitch-rhythm sequences, revealing era-specific features from the early 20th to the 21st centuries. Conversely, rhythm sequences exhibited a consistent degree of uncertainty across eras. Further, the acoustic properties remain unchanged across different periods. These findings highlight the importance of how temporal dynamics of surprise and uncertainty in improvisational music change over periods, profoundly influencing the distinctive methodologies artists adopt for improvisation in each era. Further, it is suggested that the development of improvisational music can be attributed to the adaptive statistical learning mechanisms. This study explores the period-specific characteristics in the temporal dynamics of improvisational music, emphasizing how artists adapt their methods to resonate with the cultural and emotional contexts of their times. Such shifts in improvisational ways offer a window into understanding how artists intuitively respond and adapt their craft to resonate with the cultural zeitgeist and the emotional landscapes of their respective times.

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.

Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment

Statistical learning starts at an early age and is intimately linked to brain development and the emergence of individuality. Through such a long period of statistical learning, the brain updates and constructs statistical models, with the model's individuality changing based on the type and degree of stimulation received. However, the detailed mechanisms underlying this process are unknown. This paper argues three main points of statistical learning, including 1) cognitive individuality based on "reliability" of prediction, 2) the construction of information "hierarchy" through chunking, and 3) the acquisition of "1-3Hz rhythm" that is essential for early language and music learning. We developed a Hierarchical Bayesian Statistical Learning (HBSL) model that takes into account both reliability and hierarchy, mimicking the statistical learning processes of the brain. Using this model, we conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning. By modulating the sensitivity to sound stimuli, we simulated three cognitive models with different reliability on bottom-up sensory stimuli relative to top-down prior prediction: hypo-sensitive, normal-sensitive, and hyper-sensitive models. We suggested that statistical learning plays a crucial role in the acquisition of 1-3 Hz rhythm. Moreover, a hyper-sensitive model quickly learned the sensory statistics but became fixated on their internal model, making it difficult to generate new information, whereas a hypo-sensitive model has lower learning efficiency but may be more likely to generate new information. Various individual characteristics may not necessarily confer an overall advantage over others, as there may be a trade-off between learning efficiency and the ease of generating new information. This study has the potential to shed light on the heterogeneous nature of statistical learning, as well as the paradoxical phenomenon in which individuals with certain cognitive traits that impede specific types of perceptual abilities exhibit superior performance in creative contexts.

Creativity in Generative Musical Networks: Evidence From Two Case Studies

Deep learning, one of the fastest-growing branches of artificial intelligence, has become one of the most relevant research and development areas of the last years, especially since 2012, when a neural network surpassed the most advanced image classification techniques of the time. This spectacular development has not been alien to the world of the arts, as recent advances in generative networks have made possible the artificial creation of high-quality content such as images, movies or music. We believe that these novel generative models propose a great challenge to our current understanding of computational creativity. If a robot can now create music that an expert cannot distinguish from music composed by a human, or create novel musical entities that were not known at training time, or exhibit conceptual leaps, does it mean that the machine is then creative? We believe that the emergence of these generative models clearly signals that much more research needs to be done in this area. We would like to contribute to this debate with two case studies of our own: TimbreNet, a variational auto-encoder network trained to generate audio-based musical chords, and StyleGAN Pianorolls, a generative adversarial network capable of creating short musical excerpts, despite the fact that it was trained with images and not musical data. We discuss and assess these generative models in terms of their creativity and we show that they are in practice capable of learning musical concepts that are not obvious based on the training data, and we hypothesize that these deep models, based on our current understanding of creativity in robots and machines, can be considered, in fact, creative.