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Humans and other animals base their decisions on noisy sensory input. Muchwork has therefore been devoted to understanding the computations that underlysuch decisions. The problem has been studied in a variety of tasks and withstimuli of differing complexity. However, the impact of correlations in sensorynoise on perceptual judgments is not well understood. Here we examine howstimulus correlations together with correlations in sensory noise impactdecision making. As an example, we consider the task of detecting the presenceof a single or multiple targets amongst distractors. We assume that both thedistractors and the observer's measurements of the stimuli are correlated. Thecomputations of an optimal observer in this task are nontrivial, yet can beanalyzed and understood intuitively. We find that when distractors are stronglycorrelated, measurement correlations can have a strong impact on performance.When distractor correlations are weak, measurement correlations have littleimpact, unless the number of stimuli is large. Correlations in neural responsesto structured stimuli can therefore strongly impact perceptual judgments.

Visual Decisions in the Presence of Measurement and Stimulus Correlations