Repeated perceptual decision-making is typically investigated under the tacit assumption that each decision is an independent process or, at most, it is influenced by a few decisions made prior to it. We investigated human sequential 2-AFC decision-making under the condition, when more than one aspect of the context could vary during the experiment: both the level of noise added to the stimulus and the cumulative base rate of appearance (how often A vs. B appeared) followed various predefined patterns. In seven experiments, we established that long-term patterns in the context had very significant effects on human decisions. Despite being asked about only the identity of the present stimulus, participants’ decisions strongly reflected summary statistics of noise and base rates collected dozens to hundreds of trials before. In addition, these effects could not be described simply as cumulative statistics of earlier trials: for example, a significant step change in base rate (a change point) could induce the same effect as a prolonged shift, while a gradual change did not induce any effect. As standard decision making models cannot explain these results, we developed a hierarchical Bayesian model that simultaneously represented the priors over the base rates and a potentially non-uniform noise model over the different stimulus identities. Based on simulations with the model, we conducted additional experiments and found that when a change occurred in the context that could be captured equally well by adjusting one or another aspects of the model, humans chose adjusting the variable that was less reliable as defined by variability in the preceding extended set of trials. In general, regardless of the simplicity of a perceptual decision-making task, humans automatically develop a complex internal model, and in the light of a detected change, they adaptively alter the component of this model that is implicitly judged to be the least reliable one.