Decisions from Experience (DFE) involve situations where decision makers sample information before making a final choice. Trying clothes before choosing a garment and enquiring about jobs before opting for one are some examples involving such situations. In DFE research, conventionally, the final choice that is made after sampling information is aggregated over all participants and problems in a given dataset. However, this aggregation does not explain the individual choices made by participants. In this paper, we test the ability of computational models of aggregate choice to explain choices at the individual level. Top three DFE models of aggregate choices are evaluated on how these models account for individual choices using the maximization criterion. A Primed-Sampler (PS) model, a Natural-Mean Heuristic (NMH) model, and an Instance-Based Learning (IBL) model are calibrated to explain individual choices (maximizing or non-maximizing) in the Technion Prediction Tournament (the largest publically available DFE dataset) and the generalization Hertwig2004 dataset. Our results reveal that all the three DFE models of aggregate choices perform average to explain individual choices. Although the IBL model performs slightly better than PS and NMH models; all the three models are able to account for all individuals in both the calibration and generalization datasets. We conclude by drawing implications for computational cognitive models in explaining individual choices in DFE research.