Jieun
Dont call this a post-hoc analysis. These are called simple slopes in moderation analysis, and have very specific use to explain “how” variables interact
The main issue with the 0 variabiloty is due to the slope of “Meandiff” , you can see in the summary of your model, that slope has SE=0. I would first recommend to look at this variable and this posterior, can help to plot it and see how narrow that posterior is.
Since your moderator is categorical, at the end, you would need 3 hypothesis
ht1 <- hypothesis(twomain_EmoxMask, "Meandiff > 0")
ht2 <- hypothesis(twomain_EmoxMask, "Meandiff + (Facetypecrowdmask:Meandiff)> 0")
ht3 <- hypothesis(twomain_EmoxMask, "Meandiff + (Facetypemask:Meandiff ) > 0")
Where ht1 is the slope of Meandiff when facetype is equal to the baseline category. So, ht2 is the slope of Meandiff when Facetype if equal to crowdmask. And ht3 is the slope of meandiff when facetype is equal to mask
For each of this, the estimate if the mean of the posterior, and the respective Crecibel Interval for each simple slope. Since you have an > sign, the Post.Prob colum indicate what proportion of the posterior is above 0 (in your case because > 0). So, in the case you shared, 100% of the posterior distribution is above 0
Hope this helps