Your explanation of your design is incorrect. You have just 1 DV (not 3) and 2 IVs (not 1). Your IVs are trait and condition. You also appear to have measured the same individual under multiple conditions, which suggests that you need a linear mixed-effects model with a random intercept (and possibly slope) for individual.

The simplest model that you would be appropriate for this problem is (in lmer syntax ):

lmer(DV ~ trait + condition + (1|individual))

Of course, you may want to consider whether there could be interactions between trait and condition, and differences in how individuals respond to changes in condition

Your explanation of your design is incorrect. You have just 1 DV (not 3) and 2 IVs (not 1). Your IVs are trait and condition. You also appear to have measured the same individual under multiple conditions, which suggests that you need a linear mixed-effects model with a random intercept (and possibly slope) for individual.

The simplest model that would be appropriate for this problem is (in lmer syntax ):

lmer(DV ~ trait + condition + (1|individual))

Of course, you may want to consider whether there could be interactions between trait and condition, and differences in how individuals respond to changes in condition