Modeling enculturated bias in entrainment to rhythmic patterns.

Area of Science:

• Cognitive Science
• Auditory Perception
• Computational Neuroscience

Background:

• Music experience and cultural background significantly shape rhythm perception and entrainment.
• The precise mechanisms by which enculturated biases influence moment-to-moment rhythm tracking remain unclear.
• Existing models like PIPPET (Phase Inference from Point Process Event Timing) frame rhythm entrainment as phase estimation but do not fully account for pattern inference.

Purpose of the Study:

• To extend the PIPPET model to incorporate the inference of underlying event timing patterns.
• To formalize a model (pPIPPET) that integrates culturally-learned expectations into optimal rhythm tracking.
• To investigate how enculturated biases in rhythm perception arise from optimal inference processes.

Main Methods:

• Developed pPIPPET (PIPPET with pattern inference), a computational model extending PIPPET to infer suitable event timing patterns.
• Initialized pPIPPET with priors derived from culturally-specific musical rhythms.
• Evaluated pPIPPET using simulations of human tapping data, rhythm categorization, and iterated rhythm reproduction.

Main Results:

• pPIPPET qualitatively reproduced enculturated biases in human tapping for simple rhythms.
• Simulations using Western musical patterns predicted Western musicians' categorization of rhythms.
• Models trained on diverse cultural music samples exhibited both universal and culture-specific biases in rhythm reproduction, matching experimental findings.

Conclusions:

• Enculturated timing expectations in rhythm perception and motor entrainment can be modeled as approximations of optimal inference.
• The pPIPPET framework provides a computational account of how cultural music environments shape rhythmic perception.
• This approach offers insights into the interplay between learning, inference, and cultural influences on auditory processing.