Mice infer probabilistic models for timing

Reinforcement learning (RL) suffers when tasks change. For example, RL does not have a built-in mechanism to switch between modes of exploitation and exploration, which is critical to mapping optimal rewards in a variable environment. Here, Dudman and Li show that mice—like humans—model rewards on highly flexible probability distributions.

Optimality and heuristics in perceptual neuroscience

Why are there errors in our decisions? Is it that noise gets into a perfect signal, or that decision machinery is inherently probabilistic? This paper argues the latter. From the perspective of optimal coding—where optimal means metabolically efficient—it offers a useful lens through which we can view modern findings in neuroscience and behavior research.