So the PCs show a constant difference such that, relative to the congruent condition, the incongruent condition has fewer looks to "character A".The relevant smooth terms are non-significant: this would suggest that neither the congruent or incongruent conditions differ from zero at any point in time. Fittingly, when I remove them ["s(Time):congruent"] from the model, the term does not improve the fit. HOWEVER, my issue is that the visual of the smooths show that their confidence intervals do not overlap with zero (see attached). So that alone should mean they should be significant in the summary stats, right? In the past that has been the case for me, so I have not had to think twice about it. Also, the difference plot showed they were sig different throughout the whole time course. Is it just the case that all the variance of the congruent vs incongruent levels is explained alone by the constant difference (PCs)? Looking at the time course modulation of congruent (curve with time), I'd think that should not be the case?I'll add that the residuals look good via gam.check. I've also run binary predictor model: it was a significant difference - of course, that could be the constant difference OR non-linear difference ... so I did an ordered factor model, which suggest both are driving the difference

Based on that info as a whole: can anyone provide insight into why the smooths of the original model were non-sig, and did not improve the fit of the model, whilst PCs were sig?Thank you! It's a headscratcher for me, and I haven't been able to find published similar examples that would help.