AIM Framework glossary
Mimetic-Contagion-Free Comparator
Canonical definition
The Mimetic-Contagion-Free Comparator is the diagnostic method that exploits AI's architectural absence of Preconscious Transmission to estimate M-contributions to human cognition, by comparing human and AI outputs on identical decision tasks while holding informational content constant.
Explanation
The comparator logic is analogous to a placebo control: AI processes the same informational content without the M-channel, and the human-AI divergence estimates the M-contribution. The divergence should correlate with the coupling-strength gradient (maximal in high-coupling-strength domains, minimal in low-coupling-strength domains) and should be modulated by audience manipulation (decreasing under audience-removal, since M is observability-dependent). The comparator extends the AI Assay architecture to four layers: Clean Room (detecting human patterns in AI), Structural Fidelity Check (assessing detection reliability), Historical Separator (quantifying M in past data), and Mimetic-Contagion-Free Comparator (estimating M in real-time decisions). The architectural difference exploited by the comparator is permanent: AI will not develop mirror-neuron systems regardless of capability advancement. (Framework-Specific Principle)
Epistemic status
Framework-Specific Principle.