Figure 3
From: Introducing a Bayesian model of selective attention based on active inference
Structure of the generative model – colour/shape task (A) This panel shows the graphical representation of the MDP model and the conditional dependencies among the terms in the model. The structure of the environment is expressed in terms of the transition and likelihood matrices. The likelihood matrix (A) is a mapping from the hidden states (st) to the outcomes (ot). The state transitions are mediated by the transition matrix (B) which expresses how likely the current state (st) is given the previous state (st−1). Crucially, the transition matrix is a function of action which can be sampled from the beliefs about the policies. The beliefs about the policies (π) depend on the expected free energy (G) and the precision of policy selection (γ). The expected free energy comprises extrinsic and epistemic values. Extrinsic value is a function of the prior preference matrix (C) which encodes how much one outcome is expected relative to another. Precision of policy selection (γ) is a function of the temperature term (β). The smaller the temperature the more deterministic the policy selection becomes. (B) This panel shows the four sets of hidden states and outcome modalities in the colour/shape task. There are four sets of hidden states, namely Rule, Where, Category: colours and Category: shapes. There are four outcome modalities, namely Rule, Where, What: colours and What: shapes.
