Recent work from Wei & Stocker (2017) proposed a new "perceptual law" relating perceptual bias and discrimination threshold. This law was shown to arise under an information-theoretically optimal allocation of Fisher Information in a neural population. In this talk, I will discuss recent work with Mike Morais that generalizes and extends these results. Specifically, we show that the same law arises under a much larger family of optimal neural codes, which we call "power-law efficient codes". This family includes neural codes that are optimal for minimizing L_p error for any p, indicating that the lawful relationship observed in human psychophysical data does not require information-theoretically optimal neural codes. Moreover, our framework provides new insights into “anti-Bayesian” perceptual biases, in which percepts are biased away from the center of mass of the prior. Power-law efficient codes provide a unifying framework for understanding the relationship between perceptual bias, discriminability, and the allocation of neural resources.
