Too Good to be True? Ideomotor Theory from a Computational Perspective

Area of Science:

• Cognitive Science
• Computational Neuroscience
• Psychology

Background:

• Ideomotor Theory has resurged, influencing theories of goal-directed behavior and learning.
• Previous research often simplifies Ideomotor Theory's application and struggles to empirically test its core claims.
• Key claims include direct action triggering by effect anticipation and direct action-effect associations for learning.

Purpose of the Study:

• To computationally model Ideomotor Theory in complex tasks.
• To address empirical limitations in testing Ideomotor Theory's direct action-effect association claims.
• To evaluate the feasibility of Ideomotor Theory for efficient action-effect learning.

Main Methods:

• Development of a simple computational model based on Ideomotor Theory.
• Testing the model across tasks of varying complexity.
• Analysis of model performance under specific preconditions.

Main Results:

• Ideomotor Theory is computationally feasible for efficient action-effect learning under restricted action/effect ranges and short action-effect time windows.
• These preconditions align with observed human performance limitations.
• Complex action sequencing can be accommodated by extending the model with indirect action generation.

Conclusions:

• Ideomotor Theory provides a robust framework for understanding action-effect learning.
• Additional mediating processes may be necessary for translating effect anticipations into actions in many real-world scenarios.
• Computational modeling offers a viable approach to empirically investigate Ideomotor Theory.