The interplay between detection and localization in human vision
Detection and localization are fundamental but distinct tasks of sensory systems: detecting a signal requires accumulating evidence for its presence, whereas localizing it requires extracting spatial information. How active sampling should be organized when both tasks must be performed simultaneously remains unclear. Here, using an analytically tractable model of early visual processing and Bayesian ideal-observer inference, we establish a direct relation between the two tasks: localizing a stimulus is equivalent to detecting its spatial gradient. Consequently, detection and localization favor fundamentally different sampling dynamics when the stimulus size exceeds the effective blur scale of the visual system. Applied to fixational eye movements (FEMs), which continually translate stationary visual stimuli across the adapting retina, this distinction yields two competing optimal movement scales. Localization is optimized when the eye moves approximately one retinal blur length during the adaptation time, whereas detection is optimized by motion on the scale of the stimulus size. The scale of physiological FEMs lies near the predicted localization optimum, while stimulus detection remains comparatively robust to variations in eye motion. Our model recovers established laws of temporal and spatial summation while predicting additional scaling regimes arising from FEMs. We propose an experimental paradigm that tests these predictions by measuring localization at matched detectability, eliminating the unknown internal-noise amplitude. Our results reveal a fundamental trade-off between acquiring evidence for the presence and position of spatially extended signals and suggest that human fixational eye movements preferentially support spatial localization.