Distributed distributional codes

Bayesian models have been successful at accounting for human and animal behavior, yet to what degree they can also explain neural activity is still an open question. While decoding approaches that link neural variability to behavioral uncertainty provide some evidence, stronger tests have tried to link posterior beliefs about specific latent variables in a generative model to neural responses. On one hand, the specificity of the resulting predictions is desirable since it allows us to decide which of the infinitely many parameterizations of the task model (ideal observer) is more closely align

…of rewards, an approach known as distributional RL. Dopamine (DA) neurons projecting from the midbrain to the striatum have long been thought to drive traditional RL in the mammalian brain. Moreover, a recent analysis of the response diversity of these neurons shows they have the appropriate properties to support distributional RL, and thus the learning of complete reward distr