This article proposes a new conceptual framework called collective predictive coding as a model of science (CPC-MS ) to formalize and understand scientific activities. Building on the idea of CPC originally developed to explain symbol emergence, CPC-MS models science as a decentralized Bayesian inference process carried out by a community of agents. The framework describes how individual scientists’ partial observations and internal representations are integrated through communication and peer review to produce shared external scientific knowledge. Key aspects of scientific practice like experimentation, hypothesis formation, theory development and paradigm shifts are mapped onto components of the probabilistic graphical model. This article discusses how CPC-MS provides insights into issues like social objectivity in science, scientific progress and the potential impacts of artificial intelligence on research. The generative view of science offers a unified way to analyse scientific activities and could inform efforts to automate aspects of the scientific process. Overall, CPC-MS aims to provide an intuitive yet formal model of science as a collective cognitive activity.