Equivalent noise characterization of human lightness constancy

A goal of visual perception is to provide stable representations of task-relevant scene properties (e.g. object reflectance) despite variation in task-irrelevant scene properties (e.g. illumination, reflectance of other nearby objects). To study such representational stability in the context of lightness representations in humans, we introduce a threshold-based psychophysical paradigm. We measure how thresholds for discriminating the achromatic reflectance of a target object (task-relevant property) in rendered naturalistic scenes are impacted by variation in the reflectance functions of background objects (task-irrelevant property). We refer to these thresholds as lightness discrimination thresholds. Our approach has roots in the equivalent noise paradigm. This paradigm relates signals to internal and external sources of noise and has been traditionally used to investigate contrast coding. For low variation in background reflectance, lightness discrimination thresholds were nearly constant, indicating that observers' internal noise determines threshold in this regime. As background object reflectance variation increases, its effects start to dominate performance. We report lightness discrimination thresholds as a function of the amount of variability in the background object reflectance to determine the equivalent noise - the smallest level of task-irrelevant (i.e. background reflectance) variation that substantially corrupts the visual representation (i.e. perceived object lightness) of the task-relevant variable (i.e. achromatic reflectance). A linear receptive field model, which employs a single center-surround receptive field tailored to our stimulus set, captures human behavior in this task. Our approach provides a method for characterizing the effect of task-irrelevant scene variations on the perceptual representation of a task-relevant scene property.