 Cell decision-making involves gathering information from the environment and responding accordingly. This process is facilitated by sensors within the cell, which detect changes in the environment and relay them to the cell's internal state. The Bayesian learning hypothesis suggests that cells learn from their environment and adjust their internal states based on the information they receive. We have developed a mathematical model that combines this hypothesis with a timescale separation between internal and external variables. This allows us to understand how cell-sensing affects cell decision-making. This article was authored by Arna Barua and Harold Ampo's Hatsukiro.