Large Language Models (LLMs) are trained on extensive human-generated corpora, exposing them to population-level statistical regularities in cognition, behavior, and psychological dynamics. This raises a critical empirical question: can LLMs generate valid cognitive insights about individual humans—insights that are accurate against external behavioral criteria, genuinely novel relative to the recipient’s prior predictive models, and capable of producing measurable, directionally interpretable b…
Read moreLarge Language Models (LLMs) are trained on extensive human-generated corpora, exposing them to population-level statistical regularities in cognition, behavior, and psychological dynamics. This raises a critical empirical question: can LLMs generate valid cognitive insights about individual humans—insights that are accurate against external behavioral criteria, genuinely novel relative to the recipient’s prior predictive models, and capable of producing measurable, directionally interpretable behavioral change? This revised proposal presents a four-condition controlled study to evaluate LLM-derived insight against human expert insight, a generic (nonpersonalized) insight control, and a no-insight control. We operationalize insight quality across three dimensions (accuracy, novelty, impact) using a multi-method validation framework that combines external behavioral panel ratings, behavioral prediction tasks, and directional behavioral tracking over 12 weeks. If confirmed, these findings will inform AI-assisted self-knowledge, therapeutic augmentation, and the epistemology of machine-generated psychological content.
