The relationship between theoretical descriptions of imaging performance (Fourier-based cascaded systems analysis) and the performance of real human observers was investigated for various detection and discrimination tasks. Dual-energy (DE) imaging provided a useful basis for investigating this relationship, because it presents a host of acquisition and processing parameters that can significantly affect signal and noise transfer characteristics and, correspondingly, human observer performance. The detectability index was computed theoretically using: 1) cascaded systems analysis of the modulation transfer function (MTF), and noise-power spectrum (NPS) for DE imaging; 2) a Fourier description of imaging task; and 3.) integration of MTF, NPS, and task function according to various observer models, including Fisher-Hotelling and non-prewhitening with and without an eye filter and internal noise. Three idealized tasks were considered: sphere detection, shape discrimination (sphere vs. disk), and texture discrimination (uniform vs. textured disk). Using images of phantoms acquired on a prototype DE imaging system, human observer performance was assessed in multiple-alternative forced choice (MAFC) tests, giving an estimate of area under the ROC curve (A Z). The degree to which the theoretical detectability index correlated with human observer performance was investigated, and results agreed well over a broad range of imaging conditions, depending on the choice of observer model. Results demonstrated that optimal DE image acquisition and decomposition parameters depend significantly on the imaging task. These studies provide important initial validation that the detectability index derived theoretically by Fourier-based cascaded systems analysis correlates well with actual human observer performance and represents a meaningful metric for system optimization.