Probing an Interaction b/w 3-level categ. predictor and continuous predictor


I'm a bit stuck and have scoured the internet for help but to no avail. Any guidance would be much appreciated ...

From my omnibus model, I get a significant interaction between my experimental condition assignment (3 level categorical predictor) and Ps' measure of tolerance to ambiguity (continuous predictor). In accordance with my hypotheses, of course, condition assignment acts as my IV, and its effect on my DV (an intergroup bias score) is moderated by level of tolerance to ambiguity (my *centered* MV).

Where I'm stuck is in probing the interaction and running my simple slopes analyses. Following Aiken & West (1991) I've created two dummy coded variables for each condition separately acting as the reference category (i.e., 6 dummy coded variables); in addition, I've created conditional-value variables of the MV, one high (- 1 SD) and one low (+1 SD) for Tol. to Ambig.

My question, then, is how many significance tests do I need to run? Am I conducting 6 separate regression equations, two (high and low MV) for each level of the three conditions? As for my pstat in these output, which lower order main effects will I be looking to? If this is the case, how do I intuitively make sense of so many simple slopes tests? Last, what is the most appropriate way to plot the simple slopes -- with my conditions as the separate lines at the levels of the moderator OR with my 'high'/'low' moderator as the separate lines at the three levels of condition? I've attached an excel file with both plots.

I apologize for the flurry of questions. I've consulted peers, lectures, books, and I'm hoping to figure it out with the help of any one out there in stats land!

Much appreciated.