We study changes in expectations by incorporating tail risk in a Bayesian learning framework with information frictions. Using a signal extraction problem, we find that economic agents behave differently in the face of tail risk. First, under tail risk, uncertainty shocks lead to a decrease in expectations, which implies more pessimistic forecasts. In comparison, in the absence of tail risks, uncertainty shocks do not influence expectations. Second, we show that individuals overreact under tail risk, that is, individuals are excessively optimistic and pessimistic as compared to a Bayesian learning framework without tail risk. Third, the magnitude of overreaction under tail risk depends on the level of uncertainty in the economy. To validate these theoretical findings, we use quantile regressions to estimate the GDP growth distribution and its measure of tail risk. By comparing the state of the economy in tail risk and non-tail risk episodes, we find that uncertainty shocks lead to a larger fall in sentiments when the economy exhibits tail risk. In addition, using an event study approach, we show that there is more overreaction under tail risk episodes. Also, consistent with the theoretical findings, overreaction is larger in periods of higher uncertainty. Our findings seek to shed light on factors driving overreaction in expectations, and to highlight the importance of uncertainty shocks in propagating macroeconomic stability.