Again, the only conceptual reason I know to choose between logistic or probit regression is that Probit models can be hard to estimate and unstable when the outcome is rare. This means that in cases when the number of "1's" in your dataset is low the model may have problems, even if you have a huge dataset. Logit models, on the other hand, are way better when handling rare outcomes. I assume that most studies work in understanding the probabilities of something unusual happening, so logit models may be more appropriate for these particular situations. That is why I think logit is far more common.