The synthetic self-hypothesis: dopaminergic redirection through self-face recognition in stuttering therapy

Area of Science:

• Neuroscience
• Speech-Language Pathology
• Artificial Intelligence

Background:

• Stuttering affects ~1% of adults, with imitation therapies yielding limited success.
• Neuroimaging suggests self-face recognition uniquely activates reward circuits (VTA) in some individuals who stutter.
• Positron emission tomography (PET) indicates elevated dopamine synthesis in some people who stutter.

Purpose of the Study:

• To propose the Synthetic Self Hypothesis (SSH) explaining stuttering neurobiology.
• To posit AI-driven interventions for rerouting dopaminergic signals.
• To outline testable neural and behavioral predictions for stuttering treatment.

Main Methods:

• Conceptual framework integrating neurobiology, AI, and reinforcement learning.
• Hypothesizing mechanisms: self-referential reward tagging and motor chunk consolidation.
• Predicting neural connectivity (VTA-striatum) and behavioral outcomes.

Main Results:

• SSH suggests dopamine misdirection, not excess, underlies stuttering.
• AI self-models may enhance VTA activation and motor learning.
• Predicted outcomes include reduced stuttering frequency and increased communication self-efficacy.

Conclusions:

• Stuttering may be a dopamine targeting failure, treatable by redirection.
• AI offers personalized interventions by optimizing dopaminergic reinforcement.
• Individual dopamine synthesis capacity may predict treatment response.