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Dopamine, Affordance and Active Inference

Figure 2

This figure provides a schematic overview of winnerless competition.

These itinerant (wandering) dynamics are used to model sequential neuronal dynamics that, in this paper, encode prior beliefs about sequential changes in hidden states (e.g., affordance). Technically, these dynamics comprise stable heteroclinic channels or cycles that connect unstable fixed points. The fixed points are the colored dots in the upper left diagram. Each unstable fixed point is attractive in one dimension and repelling in another, expelling the state so that it is captured by the next unstable fixed point and so. A common example of these dynamics is provided by predator-prey relationships modeled with Lotka-Volterra equations of motion, denoted by in the lower panel. The speed with which the fixed points are visited is controlled by a variable that scales the elements in a transition matrix , which couples the attractor states. In this paper, the attractor states are mapped to fixed locations in an extrinsic (physical) frame of reference to encode their affordance, using a softmax function of the attractor states and a matrix , encoding their locations. This means that the orbit or trajectory in the four dimensional attractor space maps to a two-dimensional trajectory, which cycles through the four locations in a fixed order. We use this trajectory to generate forces that elicit pointing movements: See [87] and [23] for details.

doi: https://doi.org/10.1371/journal.pcbi.1002327.g002