I run a two-way repeated measures anova in R:
a<-aov(acc~sound*cnd + Error(sbj/(sound + cnd)), data=d)
I regressed accuracy (of participants, sbj) on cnd (condition, having two levels: abstract vs. environmental), and sound (having four levels: 1st,2nd,3rd,4th).
There is an interaction, so I analysed data from each condition separately.
Results were similar in both conditions: There was an effect of sound, and post-hoc comparisons (following this post) suggested these differences : (1st)>(4th)> (2nd or 3rd), for both conditions, as can also be seen on the graph below:

But the interesting part in my results is the interaction.
My question is how can I compare the differences between conditions at each level of sound.
I have already used package emmeans, so I managed to see that the difference is significant for each level of sound.
m<-emmeans(a, ~cnd|sound)
pairs(m)
But this not what I am looking for. I need to conduct multiple comparisons on the differences between means. To be more clear(please see the image), I need to check if a and d are greater to b and c.
Any ideas?
Any suggestion would be greatly appreciated.
Fotis
con <- pairs(m); contrast(con, list(ab.vs.cd = c(1,1,-1,-1)/2), by = NULL)– Russ Lenth Jul 28 '20 at 23:24pairs(con, by = NULL). Orcontrast(m, interaction ="pairwise", by =NULL). This stuff is all pretty well documented. Please read the vignetter on interactions. – Russ Lenth Jul 29 '20 at 12:30m2<-emmeans(a, ~sound|cnd), and then consecutive contrasts,con2<-contrast(m2, "consec"). Now, what I finally need are these comparisons:pairs(con2, by="contrast"). But the output suggests that these are 3 independent tests. Right? Any way that the 3 comparisons are adjusted for multiplicity? Thank you again – fotisfotiadis Aug 15 '20 at 09:49summary(pairs(con2, by = "contrast"), by = NULL, adjust = "mvt")– Russ Lenth Aug 15 '20 at 12:24