Recent work in theoretical neuroscience has shown that efficient neural codes, which allocate neural resources to maximize the mutual information between stimuli and neural responses, give rise to a lawful relationship between perceptual bias and discriminability in psychophysical measurements (Wei & Stocker 2017, [1]). Here we generalize these results to show that the same law arises under a much larger family of optimal neural codes, which we call power-law efficient codes. These codes provide a unifying framework for understanding the relationship between perceptual bias and discriminability, and how it depends on the allocation of neural resources. Specifically, we show that the same lawful relationship between bias and discriminability arises whenever Fisher information is allocated proportional to any power of the prior distribution.

Recent work in theoretical neuroscience has shown that efficient neural codes, which allocate neural resources to maximize the mutual information between stimuli and neural responses, give rise to a lawful relationship between perceptual bias and discriminability in psychophysical measurements (Wei & Stocker 2017, [1]). Here we generalize these results to show that the same law arises under a much larger family of optimal neural codes, which we call power-law efficient codes. These codes provide a unifying framework for understanding the relationship between perceptual bias and discriminability, and how it depends on the allocation of neural resources. Specifically, we show that the same lawful relationship between bias and discriminability arises whenever Fisher information is allocated proportional to any power of the prior distribution.