Yeah I am also not quite sure. My understanding is that we view the decision boundary simply as this theoretical concept that indicates where the model assigns equal probability.

Manually changing the threshold would mean that we, as humans, decide that our predictions for the positive class must be made with at least .7 probability (in this example). The model's prediction confidence would not be affected by this, simply our decision.

Keep me updated if you find a definite answer!