Learning the latent state-space, the state-transition and state-value models.

Given (1) the last input xt−1 and latent state st−1, (2) performed action at−1, (3) observed new input xt, reward rt, and inferred latent state st, learning consists of (5) adjusting the categorization model to make it more congruent with the state-transition model and updating the conjugate priors and of the state-transition and state-value models to accommodate the internal perception of the experienced behavioral evidence (see the text for details). Notably, the update of the state-value conjugate is a Bayesian analog of TD-learning using predicted discounted future value accumulated in (4) a forward sweep.