Decision making under uncertainty is challenging for any autonomous agent. The challenge increases when the environment's stochastic properties change over time, i.e., when the environment is volatile. In order to efficiently adapt to volatile environments, agents must primarily rely on recent outcomes to quickly change their decision strategies; in other words, they need to increase their knowledge plasticity. On the contrary, in stable environments, knowledge stability must be preferred to preserve useful information against noise. Here we propose that in mammalian brain, the locus coeruleus (LC) is one of the nuclei involved in volatility estimation and in the subsequent control of neural plasticity. During a reinforcement learning task, LC activation, measured by means of pupil diameter, coded both for environmental volatility and learning rate. We hypothesize that LC could be responsible, through norepinephrinic modulation, for adaptations to optimize decision making in volatile environments. We also suggest a computational model on the interaction between the anterior cingulate cortex (ACC) and LC for volatility estimation.
Keywords: ACC; learning rate; locus coeruleus; norepinephrine; plasticity; prediction error; reinforcement learning; volatility.