We have previously described a psychophysical paradigm for investigating rapid learning of relevant visual information in detection tasks (M. P. Eckstein, C. K. Abbey, B. T. Pham, & S. S. Shimozaki, 2004). This paradigm uses blocked trials with a set of possible target profiles, and it has demonstrated learning effects after a single trial. When targets are masked by Gaussian luminance noise, there exists a Bayesian ideal observer that also exhibits learning effects over the trials within a block. In this work, we investigate the effect of target contrast and the effect of the information to be learned in the target profile set. Absolute efficiency tracks target contrast closely and ranges from approximately 10% to 25% in these experiments. To disambiguate learning from other effects contributing to absolute efficiency, we define a measure of learning efficiency that measures the observed improvement over a block of trials against the total improvement expected in the ideal observer. We find significant positive trends in learning efficiency both over contrast and the within-block trial number. We find that a two-feature profile set containing orientation and polarity differences leads to a greater within-block gain in performance than a one-feature profile set that contains only orientation differences. However, this apparent difference disappears when efficiency is compared. Lastly, we show that the disparity between task performance and accumulated knowledge of the target profile can be largely explained by a model that only allows learning to occur in trials the observer performs correctly.