Abstract: Music is the crystallization of human culture, characterized by its diversity and complexity specific to human society. As an auditory art form, music essentially consists of a structured sequence of sounds unfolding over time. Every musical composition is composed of varying lengths of sounds and rests, with these sound events always combining in alternating patterns, forming the basis for repetition and development. The concept of metric structure refers to the repetitive organization of strong and weak beats within the sequence of sounds. However, due to the complexity of temporal organization in music, metric structure remains a challenging aspect in the study of music cognition. Metric structure is the temporal framework of music. It is not only the basis for composers to create music, but also the prerequisite for people to process musical aesthetics, musical emotion and musical meaning.
This study integrates predictive coding theory with the processing of metric structure, exploring how musical structural information is integrated and updated over time within a framework of prediction errors. Forming structured internal representations based on existing information is fundamental and prerequisite for perceiving musical metric structure. Once these structural representations are formed, listeners predict upcoming musical events. Upon the occurrence of a musical event, listeners assess whether it aligns with their predictions, integrate it into their existing musical context, and adjust their structural representations based on whether the expectation was met or violated, thereby enhancing future predictions. Therefore, prediction and integration are two indispensable stages in the brain's processing of metric structure. They are interdependent and mutually influential, reflecting an iterative process of autonomous metric structure processing in the brain. Hence, the proposed project explores the cognitive and neural mechanisms underlying the prediction and integration of metric structure, using behavioral experiments and electroencephalogram techniques.
Specifically, it includes the following four studies: (1) track the dynamic neural activity during the representation construction of metric structure and the prediction establishment as rhythmic sequences unfold, (2) explore how listeners use prediction errors to update the prediction of metric structure, (3) examine the neural mechanisms underlying the integration of multiple hierarchical metric structure at the phrase level and (4) investigate how listeners integrate nested metric structure according to long-distance dependency at the period level.
This study reveals the general cognitive mechanism of the processing of musical structure and lay the foundation for the construction of cognitive neural model of music. Firstly, based on predictive coding theory, this paper examines the neural responses preceding the occurrence of musical events by focusing on the anticipatory phase. It provides direct neural evidence for the pre-activation stage of expectation formation. Additionally, it distinguishes between the stages of expectation formation and expectation updating, investigating the role of prediction errors in updating metric structure expectations. The study explores how prediction errors regulate the latter stage, revealing the neural mechanisms underlying the updating of metric structure expectations. Secondly, we investigate how listeners integrate nested metric structure within long-duration musical units based on long-distance dependency relationships. It reveals the processing mechanisms of metric structure within complex musical organizations. Thirdly, through the design of musical materials, not only controls for the interference of psychoacoustic factors but also enhances ecological validity in our study.
In summary, within the framework of predictive coding theory, this paper focuses on the processing of metric structure, with four studies forming an integrated whole that collectively addresses the scientific questions regarding the cognitive mechanisms of musical metric structure. A robust theoretical foundation ensures the feasibility of the research. The experimental materials are adapted from real musical works but undergo rigorous manipulation and control, ensuring internal validity and ecological validity of the research outcomes. EEG allows for precise capture of real-time brain responses to each note change during the unfolding of music, making it a feasible and necessary method for exploring the brain mechanisms and dynamic processes involved in processing musical metric structure. This study not only contributes to revealing the nature of music structure cognition and laying the groundwork for constructing neural models of music cognition, but also provides objective evidence for music appreciation and aesthetics research, with promising potential applications in the field of music.

Key words: music cognition, literary and artistic psychology, neural mechanisms, metric structure, electroencephalogram