To understand your dataset, I still believe that descriptive statics can give you the broad general picture. In terms of statistical analysis, the hypothesis always comes first and then models, not the other way around. So get back to your question, I don't really recommend you do the linear regressions, whatever types, just blindly before you have any hypotheses in mind. And for sure that the software will throw out results whatsoever, even you design has problems. The results will then make no sense.
It might be okay to explore around. But the four-way interaction is way too complicated to be interpretable! It seems that in your question, the same pilot may have completed multiple sessions. That will be where random effects are needed.
To showcase a simpler scenario, for example you'd like to test whether frequent and infrequent pilots (group factor) perform differently in different places, and you'd like to control for demographic varations, you may want to apply this lme model: outcome ~ group * place + age + sex + background + (1|pilot)
the (1|pilot) part in the formula is to take the within-pilot correlation into consideration (random effect).