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. 2020 Oct 28;3:509354. doi: 10.3389/frai.2020.509354

Figure 9.

Figure 9

Simulated trial of scene construction under high sensory precision. (A) The evolution of posterior beliefs about scene identity—the first factor of hidden states at Level 2—as a deep active inference agent explores the visual array. In this case, sensory precision at Level 1 is high, meaning that posterior beliefs about the motion direction of each RDM-containing quadrant are resolved easily, resulting in fast and accurate scene categorization. Cells are gray-scale colored according to the probability of the belief for that hidden state and time index (darker colors correspond to higher probabilities). Cyan dots indicates the true hidden state at each time step. The top row of (A) shows evolving beliefs about the fully-enumerated scene identity (48 possibilities), with every 12 configurations highlighted with a differently-colored bounding box, correspond to beliefs about each type of scene (i.e., UP-RIGHT, RIGHT-DOWN, DOWN-LEFT, LEFT-UP). The bottom panel shows the collapsed beliefs over the four scenes, computed by summing the hidden state beliefs across the 12 spatial configurations. (B) Evolution of posterior beliefs about actions (fixation starting location not shown), culminating in the categorization decision (here, the scene was categorized as UP-RIGHT, corresponding to a saccade to location 6. (C) Visual representation of the agent's behavior for this trial. Saccades are depicted as curved gray lines connecting one saccade endpoint to the next. Fixation locations (corresponding to 2nd factor hidden state indices) are shown as red numbers. The Level 1 active inference process occurring within a single fixation is schematically represented on the right side, with individual motion samples shown as issued from the true motion direction via the low level likelihood A(1),1. The agent observes the true RDM at Level 1 and updates its posterior beliefs about this hidden state. As uncertainty about the RDM direction is resolved, the “Break-sampling” action becomes more attractive (since epistemic value contributes increasingly less to the expected free energy of policies). In this case, the sampling process at Level 1 is terminated after only three timesteps, since the precision of the likelihood mapping is high (p = 5.0) which relates to the speed at which uncertainty is resolved about the RDM motion direction—see the text for more details.