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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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Ideonamic：意念运动学习和基于效果的动作控制的综合计算动态模型。


根据观念运动理论，行为是根据这些行为在经验上产生的知觉效果来表示、控制和检索的。当主体执行运动动作时，他们会观察其随后的感知效果并建立动作-效果关联。当他们想要稍后实现此效果时，他们使用动作-效果关联通过内部激活效果表示来预激活动作。近年来，意念运动理论得到了广泛的支持。为了捕捉这种基于效果的行动控制和目标导向行为的特定观点，我们开发了 IDEONAMIC，这是一种基于动态场理论的综合计算模型，它将行动控制过程的特定组成部分表示为动态神经场。我们证明 IDEONAMIC 可以方便地应用于不同类型的实验意念运动设置，模拟关键发现，从数据动态中生成新颖的预测，并允许从计算的角度重新探讨潜在的认知机制。我们鼓励将 IDEONAMIC 应用到更多类型的意念运动设置中，以深入了解基于效果的行动控制。该模型可在 https://osf.io/hbc6n 上获取。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。