Computational joint action: Dynamical models to understand the development of joint coordination
Fig 2
a. Simulation concept. Each player knows the locations of both rabbit (R) and stag (S). They can see their actions and have imperfect information about their partner’s actions (blurred circles). b. cost matrix used in the simulations, in which the cost can be interpreted as a reduced payoff. c. Costs related to the two equilibria are represented in the action space. Cold colors indicate lower costs, and hot colors indicate higher costs. On the left, whichever action the partner selects it is safe for player i to go for the Rabbit solution. On the right, the two players minimize their costs by selecting both the Stag. d. Simulated Stag Hunt game for different combinations of sensory noise () and internal noise or partner predictability (
). Temporal evolution of the probabilities of selecting the SS (blue), the RR (orange). The curves represent game performance at population level, where probabilities (mean ± SE) have been computed over epochs (1 epoch = 40 trials) and averaged over multiple simulated dyads. The horizontal line indicates the chance level (p = 0.25). Bottom: scatter plots of the joint actions (u1, u2) for one representative dyad for each condition.