The issue of consistency applies also to the index of discounting. In the case of curve fitting, nonlinear regression is the most typical procedure but multiple models can be applied and the solutions almost always includes some level of error (i.e., R2 <1.0). For this reason, metrics like assumption-free area under the curve (Myerson et al., 2001; Odum, 2011b) or impulsive choice ratio (Mitchell et al., 2005; Odum, 2011b) may be more appropriate to avoid incorporating that error. Alternatively, differences in the sensitivity of different methods of deriving discounting functions to genetic influences may be informative with regard to the nature of discounting and the optimal derivation methods. For example, if reliable associations with specific loci are consistently of larger magnitude using one model compared to the others, it would provide further support for that analytic strategy. In any case, it will be important for these methodological issues to be thoroughly considered and addressed as findings in this area accumulate.
