We investigate whether Large Language Models (LLMs) exhibit human-like cognitive patterns under four established frameworks frompsychology: Thematic Apperception Test (TAT), Framing Bias, Moral Foundations Theory (MFT), and Cognitive Dissonance. We evaluated several proprietary and open-source models using structured prompts and automated scoring. Our findings reveal that these models often produce coherent narratives, show susceptibility to positive framing, exhibit moral judgments aligned with Liberty/Oppression concerns, and demonstrate self-contradictions tempered by extensive rationalization. Such behaviors mirror human cognitive tendencies yetare shaped by their training data and alignment methods. We discuss the implications for AI transparency, ethical deployment, and futurework that bridges cognitive psychology and AI safety.