Breaking down an interaction using Simple Effects analysis

Ok, so I used Simple Effects analysis to understand an interaction effect. But the analysis did not yield any difference between the levels. I am not sure if I did it wrong. So I will explain what I did below, and if anyone would let me know, I would be very grateful.

So, the study was a 2 by 2 by 2 by 2 design. The first 2 factors were Repeated Measures. Let's call them Mapping and Expression respectively. N of participants = 227. There was a significant interaction of the 2 Repeated Measures factors (Mapping and Expression). This means that the effect of Expression was different for the 2 levels of Mapping (see image 1).

Therefore, I used the SPSS Syntax for Repeated Measures (beginning with MANOVA) to understand the interaction. The expectation was that the distance between the solid and dotted lines is significant at level 1 of Mapping (Transparent) but not at level 2 of Mapping (Non-Transparent). Was this the correct expectation?

However, after running the syntax codes, it was shown that the effect of Expression was significant at both levels of Expression (see output at the 2nd image attached).

So what does this mean, and how should one report it?

Do I merely say that, based on the 2-way interaction patterns, the effect of Expression is stronger for level 1 of Mapping than for level 2 of Mapping, but that simple effects analyses nonetheless showed that this effect was significant at both levels?