A powerful tool to assess human performance in visual search tasks involving complex displays is to incorporate hazard functions as a means of quantifying performance in terms of efficiency or capacity. Another advantage of this analysis procedure is to help discover the underlying architecture of the processing of information. Usually more parallel processing of multiple sensory channels has benefits of reduced search time in such tasks. A description of the theoretical basis of hazard functions as they apply to visual search tasks is outlined. A particular case where the human-machine performance is optimized occurs when certain types of displays generate the situation of super capacity which means the capacity coefficient is greater than 1.0. A special signature (survivor interaction contrast function or SIC) of these super capacity displays is decomposed into individual factor’s probability density functions in an effort to discern properties of key factors that may be combined in a manner to improve the efficiency in a visual search task. Using Weibull densities to discern linear combinations of the probability densities for signatures of the super capacity displays provides some insight into how the individual factors in complex displays need to be combined to optimize human performance in visual search tasks.