People increasingly rely on generative artificial intelligence for reasoning, raising questions about the future of human judgment. Tri-System Theory is introduced to extend dual-process accounts of reasoning by adding a third system, System 3.

Artificial General Intelligence (AGI) can accelerate scientific discovery and address significant global challenges. Evaluating progress toward AGI requires diverse methods and approaches, with cognitive science offering valuable tools for assessment.

Current AI models struggle with autonomous learning due to inherent limitations. A new learning architecture is proposed, inspired by human and animal cognition, which incorporates learning from observation and active behavior.

Fluid intelligence peaks around age 20 and declines throughout adulthood, while human achievement in career success typically peaks between ages 55 and 60. This discrepancy suggests that although fluid intelligence declines, other cognitive dimensions such as crystallized intelligence and emotional intelligence improve with age.

The study investigates the cognitive effects of using large language models (LLMs) for essay writing by comparing performance and neural activity among participants using LLMs, search engines, and no tools. A total of 54 participants completed three sessions, with 18 continuing to a fourth session that involved switching between LLM and brain-only conditions.