For the medically relevant task of joint detection and localization of a signal (lesion) in an emission computed tomographic (ECT) image, it is of interest to measure the efficiency, defined as the relative task performance of a human observer versus that of an ideal observer. Efficiency studies can be used in system optimization, improving postprocessing (e.g., reconstruction) algorithms, deriving human-emulating model observers and computer-aided detection methods. Calculation of ideal observer performance for ECT is highly computationally complex. We can, however, compute ideal observer performance exactly using a simplified 'filtered-noise' model of ECT. This model results in images whose correlation structure, due to quantum noise, background variability and regularization, is similar to that of real ECT reconstructed images. A two-alternative forced choice test is used to obtain the performance of the human observers. We compare the efficiency of our joint detection-localization task with that of a corresponding signal-known-exactly (SKE) detection task. For the joint task, efficiency is low when the search tolerance is stringent. Efficiency for the joint task rises with signal intensity but is flat for the SKE task. For both tasks, efficiency peaks at a mid-range level of regularization corresponding to a particular noise-resolution tradeoff.