  We review the reasoning underlying two approaches to combination of sensory
uncertainties. First approach is noncommittal, making no assumptions about
properties of uncertainty or parameters of stimulation. Then we explain the
relationship between this approach and the one commonly used in modeling
"higher level" aspects of sensory systems, such as in visual cue integration,
where assumptions are made about properties of stimulation. The two approaches
follow similar logic, except in one case maximal uncertainty is minimized, and
in the other minimal certainty is maximized. Then we demonstrate how optimal
solutions are found to the problem of resource allocation under uncertainty.
