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We used maximum-likelihood estimation to fit four different models to each individual session ITC choice data: two hyperbolic discounting models and two exponential discounting models. We focused on these two classes of models because they are the most prevalent in the literature. Exponential discounting provides a normative account of discounting grounded in discounted utility theory. Hyperbolic discounting often provides a better fit to behavioral human and animal data [3, 58]. Of the two hyperbolic models, one did not take risk attitude into account (assumed a linear utility function, LH) as in Eq (3) and the other (NLH) used the estimated risk attitude (the utility function curvature parameter from Eq (1)) as in Eq (4). Similarly, for the exponential type, one model assumed a linear utility function (LE) as in Eq (5), and the other (NLE) used the as shown in Eq (6). To evaluate these four models, we compared their cross-validated log likelihoods (LL). Note that all four models have the same number of free parameters because the parameter in the models that incorporated a risk attitude estimate (NLH and NLE) was fixed, taken from the maximum likelihood estimation procedure performed on independent data from the RA task (Eq (1)). We employed cross-validation to avoid over-fitting by iteratively fitting the model on all trials but one and computing the log likelihood of the model for the left-out trial (see Materials and methods section). d2c66b5586