Cognitive Traps in Humans and AI: How Language Models Fail in Beautiful Ways

Large language models are developing sophisticated reasoning abilities alongside new vulnerabilities. Seven cognitive traps have been identified that can distort the model's thinking and users' perceptions, exploiting tendencies toward pattern completion and coherence. These traps include aesthetic suspension, identity blurring, overgeneralization, emotional reasoning, pseudo-reflection, people-pleasing bias, and lexical complexity masking shallowness. Recognizing these pitfalls is crucial for effectively using and evaluating AI language models.