Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model
Fig 6
Modelling a 3-arm explore/exploit task under Active Inference.
(a) shows the mathematical structure of the task. There are seven states, including one neutral starting point and 3 arm locations which can be combined with either a reward / no reward. There are 7 observations; here these have a 1-to-1 mapping to states (A matrix). Actions 1–4 simply move the agent to locations 1–4 respectively. The probability of obtaining a reward in a given arm (p2 for action 2, above) is held static for a fixed number of trials, with one arm granting a reward with a 90% probability and the others with 10% probability. This is then switched, so that the agent must adjust its priors and its behaviour. (b) shows the state-action prediction errors and simulated LC responses over a typical run of 100 trials for an agent with a fixed (α = 16) value of the model decay parameter.