[Obligatory disclaimer: This is not a challenge.]

I think this is a mistake, and a missed chance to practice the virtue of scholarship.

I honestly don't see how or why.

I already have a rather huge list of things I want to do scholarship with, and I don't see any use I could have for knowledge about the persons behind these things I want to study. Knowing a name for the purposes of searching for more articles written under this name is useful, knowing a name to know the rate of accuracy of predictions made by this name is useful, and often the "central early figures" in a field will coincide with at least one of these or some other criteria for scholarly interest.

I hear Galileo is also a central early figure for something related to stars or stellar motion or heliocentrism or something. Something about stellar bodies, probably. This seems entirely screened off (so as to make knowledge about Galileo useless to me) by other knowledge I have from other sources about other things, like newtonian physics and relativity and other cool things.

Studying history is interesting, studying the history of some things is also interesting, but the central early figures of some field are only nodes in a history, and relevant to me proportionally to their relevance to the parts of said history that carry information useful for me to remember after having already propagated the effects of this through my belief network.

Once I've done updates on my model based on what happened historically, I usually prefer forgetting the specifics of the history, as I tend to remember that I already learned about this history anyway (which means I won't learn it again, count it again, and break my mind even more later on).

So... I don't see where knowledge about the people comes in, or why it's a good opportunity to learn more. Am I cheating by already having a list of things to study and a large collection of papers to read?

To rephrase, if the information gained by knowing the history of something can be screened off by a more compact or abstract model, I prefer the latter.