Perception-memory interactions reveal a computational strategy for perceptual constancy.

A key challenge for the visual system is to extract constant object properties from incoming sensory information. This information is ambiguous because the same sensory signal can arise from many combinations of object properties and viewing conditions and noisy because of the variability in sensory encoding. The competing accounts for perceptual constancy of surface lightness fall into two classes of model: One derives lightness estimates from border contrasts, and another explicitly infers surface reflectance. To test these accounts, we combined a novel psychophysical task with probabilistic implementations of both models. Observers compared the lightness of two stimuli under a memory demand (a delay between the stimuli), a context change (different surround luminance), or both. Memory biased perceived lightness toward the mean of the whole stimulus ensemble. Context change caused the classical simultaneous lightness contrast effect, in which a target appears lighter against a dark surround and darker against a light surround. These effects were not independent: Combined memory load and context change elicited a bias smaller than predicted assuming an independent combination of biases. Both models explain the memory bias as an effect of prior expectations on perception. Both models also produce a context effect, but only the reflectance model correctly describes the magnitude. The reflectance model, finally, captures the memory-context interaction better than the contrast model, both qualitatively and quantitatively. We conclude that (a) lightness perception is more consistent with reflectance inference than contrast coding and (b) adding a memory demand to a perceptual task both renders it more ecologically valid and helps adjudicate between competing models.