This presentation introduces the Process Turing Test, a new framework for distinguishing humans from AI that goes beyond linguistic output to analyze behavioral dynamics. As LLMs routinely pass text-based Turing Tests, we need methods that probe the underlying architecture of intelligence through perception, control, and decision-making patterns. Using process tracing data from canonical cognitive tasks (including Stroop, CAPTCHA, and moral dilemmas) we demonstrate how behavioral embeddings capture computational signatures unique to human cognition, providing robust proof-of-personhood that resists adversarial attacks while revealing fundamental principles about how humans and machines process information differently.
