Is there any study or theory behind combining binary search and interpolation search?
Interleaving two algorithms to get the best of both worlds is a known technique, though it is usually stated as running them in "parallel" and returning an answer as soon as either terminates.

Though theoretically faster, interpolation search has two disadvantages compared to binary search:


It has terrible (linear) worst case performance 
The overhead of computing the midpoint is rather large; a binary search iteration is hundreds of times faster than an interpolation search one


I would expect that an approach where you do interpolation search while the range is large and switch to binary search when the range becomes small is the most efficient. It would be nice if you could try this experiment.

As your dataset becomes small, the difference between $\log n$ and $\log \log n$ becomes insignificant; $\log n$ is already really small, and $\log \log n$ couldn't possibly be much smaller. At this point, the overhead of doing interpolation search is not worth it compared to the iterations you might save.

I think that your results can be explained by two phenomena:


Combining with binary search allows you to avoid the worst-case behavior
The positive effect of switching to binary search on a small dataset