Why does logistic/poisson regression in R give z-values while linear regression gives t-values in the summary output? In general, z-test is used when the population variance is known, but I am finding it difficult to translate this to the (generalized) linear model context. 

I wonder how to better understand their difference (of using z-value vs t-value) more mathematically?