A computational agent model incorporating prior and retrospective ownership states for actions

The computational agent model presented in this paper generates prior and retrospective ownership states for an action based on principles from recent neurological theories. A prior ownership state is affected by prediction of the effects of a prepared action, and exerts control by strengthening or suppressing actual execution of the action. A retrospective ownership state depends on whether the sensed consequences of an executed action co-occur with the predicted consequences, and is the basis for acknowledging authorship of actions, for example, in social context. It is shown how a number of known phenomena can occur. For example, scenarios were shown for vetoing a prepared action due to unsatisfactory predicted effects, and for mirroring an observed action performed by another agent, without imitating the action. Moreover, it is shown how poor action effect prediction capabilities can lead to reduced retrospective ownership states, as in persons suffering from schizophrenia. The obtained computational model can be used as a basis for simulation-based training, for example, to develop a virtual patient based on the model so that a psychiatrist or psycho-therapist (e.g., during his or her education) can gain insight in the processes in certain types of patients, or to analyse how effective a certain form of therapy can be. A second type of application is in the area of gaming or virtual stories in which, for example, persons with deviations in ownership states play a role and based on that show unexpected behaviour.