AI & Neurobiology of Deception: How AI Helps Us Understand Ourselves
summary
AI-driven analysis combines eye-tracking and HRV with skin conductance, breathing and 200 derived features, processed with NeuroKit and simple k‑means clustering; results show two response clusters: one cluster (more smokers, higher daytime sleepiness) shows blunted physiological reactions to covert false feedback, the other (fewer smokers, less sleepy) shows pronounced pupil and heart responses. Pipeline works on webcam-grade cameras by extracting ocular pulse and promises noninvasive deception/mismatch detection, raising clear data‑privacy and interpretation risks.
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chapters (8)
- 0:00AI and neurobiology of deception
- 1:52Brain and deception reactions
- 5:06Neurobiology of lying and truth
- 9:25Limitations of brain imaging
- 10:53Subconscious deception detection
- 17:02Deception detection applications
- 19:10Ethics and privacy in detection
- 20:35Q&A
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