IDEONAMIC: An Integrative Computational Dynamic Model of Ideomotor Learning and Effect-Based Action Control

According to ideomotor theory, actions are represented, controlled, and retrieved in terms of the perceptual effects that these actions experientially engender. When agents perform a motor action, they observe its subsequent perceptual effects and establish action-effect associations. When they want to achieve this effect at a later time, they use the action-effect associations to preactivate the action by internally activating the effect representation. Ideomotor theory has received extensive support in recent years. To capture this particular effect-based view on action control and goal-directed behavior, we developed IDEONAMIC, an integrative computational model based on dynamic field theory that represents the specific components of the action control process as dynamic neural fields. We show that IDEONAMIC applies conveniently to different types of experimental ideomotor settings, simulates key findings, generates novel predictions from the dynamics of data, and allows reapproaching the underlying cognitive mechanisms from a computational point of view. We encourage the application of IDEONAMIC to more types of ideomotor settings to gain insights into effect-based action control. The model is available at https://osf.io/hbc6n.