 In recent perceptual neuroscience, an alternative picture of neural representational content often tied to Bayesian approaches to perceptual computation has gained traction (for accessible discussions, see Colombo &amp; Seriès 2012; Rescorla 2015). On Bayesian models, extracting information from populations of (say) MT neurons does not yield a specific value of motion direction but rather a probability density function, across the space of possible motion directions (for a philosophical discussion of neural probabilistic codes, see Shea 2014). A key idea is not the generation of specific values as what neurons represent, say 0 degree motion as proposed earlier, but rather the conceptualization of the population response as reflecting uncertainty inherent in neural activity given noise.