Classification images for detection, contrast discrimination, and identification tasks with a common ideal observer

We consider three simple forced-choice visual tasks--detection, contrast discrimination, and identification--in Gaussian white noise. The three tasks are designed so that the difference signal in all three cases is the same difference-of-Gaussians (DOG) profile. The distribution of the image noise implies that the ideal observer uses the same DOG filter to perform all three tasks. But do human observers also use the same visual strategy to perform these tasks? We use classification image analysis to evaluate the visual strategies of human observers. We find significantly different subject classification images across the three tasks. The domain of greatest variability appears to be low spatial frequencies [<5 cycles per degree (cpd)]. In this range, we find frequency enhancement in the detection task, and frequency suppression and reversal in the contrast discrimination task. In the identification task, subject classification images agree reasonably well with the ideal observer filter. We evaluate the effect of nonlinear transducers and intrinsic spatial uncertainty to explain divergence from the ideal observer found in detection and contrast discrimination tasks.