(Non)Issues of infinite regress in modeling motor behavior

Area of Science:

• Motor behavior research
• Theoretical modeling
• Philosophy of science

Background:

• Infinite regress criticisms have historically targeted executive models of motor behavior.
• Gödel's proof demonstrates algorithms cannot self-validate, suggesting broader implications.
• Previous critiques focused on specific model types, potentially overlooking systemic issues.

Purpose of the Study:

• To extend infinite regress criticisms to self-organized models of motor behavior.
• To argue against using infinite regress as the primary criterion for evaluating theoretical importance.
• To propose alternative metrics, such as falsifiability, for assessing model significance.

Main Methods:

• Applying Gödel's incompleteness theorems to computational models of motor control.
• Theoretical analysis of executive and self-organized models concerning infinite regress.
• Literature review of established criteria for scientific model evaluation.

Main Results:

• Both executive and self-organized models of motor behavior face infinite regress difficulties.
• Infinite regress is not a sufficient reason to dismiss a model's theoretical importance.
• Falsifiability emerges as a more relevant criterion for assessing model utility.

Conclusions:

• Infinite regress issues are inherent to many explanatory models, not just executive types.
• Model evaluation should prioritize empirical testability and falsifiability over abstract logical concerns.
• Shifting focus to falsifiability enhances the scientific rigor of motor behavior research.