Noise reveals visual mechanisms of detection and discrimination.

When performance is limited by stochastically defined masks, (psychophysical) reverse correlation has proven to be an especially efficient tool for estimating the templates used by detection and discrimination mechanisms. Here I describe a maximum-likelihood approach to quantifying the significance of differences between estimates of template. Four methodologically related experiments illustrate the versatility of reverse correlation. Experiment 1 shows significant differences between the templates used by different observers when detecting a bright Gaussian blob. The results of Experiment 2 are consistent with observers not using information about the phase of a parafoveal wavelet when detecting it. Experiments 3 and 4 reveal not only the templates used by detection mechanisms but also aspects of their response functions. Both results are consistent with a sensory threshold. Experiment 3 shows that 2-alternative forced-choice detection errors are caused when the target's effective contrast is reduced, not when the mask looks more like the expected target+mask than the actual target+mask. Experiment 4 suggests that observers use optimally tuned detection templates for orientation discrimination.