This talk introduces a mean field game for a family of filtering problems related to the classic sequential testing of a Brownian motion’s drift. In our formulation, agents observe a private signal process and want to make a determination about an unknown binary state of nature. The game arises by allowing the drift of the signal process to incorporate information about the other agents’ actions and enforcing that each agent must minimize an associated Bayes risk. In this setting we are able to develop a deep understanding of the solution structure, establish the existence of a mean field equilibrium, and study the equilibria numerically. To the best of our knowledge, this work presents the first treatment in the literature of a tractable mean field game with information filtering, optimal stopping, and a common unobserved noise. This presentation is based on recent joint work with Yuchong Zhang at the University of Toronto.