A key uncertainty in projecting future climate change is the magnitude of Equilibrium Climate Sensitivity (ECS), i.e., the eventual increase in global annual average surface temperature in response to a doubling of atmospheric CO2 concentration. The lower bound of the likely range for ECS given in The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) was revised downwards from 2°C in its previous reportto 1.5°C, mainly as an effect of considering observations over the warming hiatus – the period of slowdown of global average temperature increase since the early 2000s. In this study, conducted by exploring the parameter uncertainty in a reduced complexity energy balance model, we analyze how estimates of ECS change as observations accumulate over time by modeling this learning process through a formal Bayesian approach, and estimate the contribution of potential causes to the hiatus. We find that including observations over the hiatus reduces the most likely value for ECS from 2.8°C to 2.5°C, but that the lower bound of the 90% range remains stable around 2°C. We also find that the hiatus is primarily attributable to El-Niño/Southern Oscillation (ENSO) related variability and reduced solar forcing. This learning exercise quantifies the value of information accumulating over time in shaping our uncertainty over ECS. We find that the inclusion of observations over the hiatus period contributes to a more constrained estimate of ECS. However, the degree to which this was due to the hiatus per se, as opposed to the accumulation of more data in general, is unclear. Our quantification of the role that unforced internal variability plays in the updating of the PDF leads us to conclude that it is still too early to stipulate that the hiatus has had any particular impact on estimates of ECS.