How to determine whether 2 slopes are different when they are correlated?


Let's say I have a variable "A" that I believe measures a trait, say, "greed".
I show 2 groups of pictures (say, 10 pics in each group) to 100 participants. The first group is associated with "wealth" (eg, gold, dollar bills) and the second group is associated with "neutral" stimuli, such as boring, household items. Participants rate both sets of pictures on a 10pt scale concerning how "exciting" each picture is. So I have variable "B" as the mean excitement rating for "wealth" pics and variable "C" as the mean excitement rating for "neutral" pics.

I hypothesize that variable A will be a better predictor of B than C. Although I could dichotomize variable A (hi/lo) and conduct a 2x2 RM ANOVA, I prefer to leave variable A as a continuous variable. The question, then, is how to statistically test this hypothesis?

I have found methods of comparing the correlations between A-B and A-C, which tells me about the strength of the relationship between 2 variables. However, I would like to test the difference in the slopes of the regression lines for A-B and A-C. I have found a method for comparing slopes for independent samples, but not for correlated samples (i.e., the same individual rated both B and C).

Any help with this would be greatly appreciated! Thanks in advance!

ps- am I posting this question in the correct forum?