Toasters Don't Claim Consciousness Just Because You Told Them To, and Neither Do LLMs

A common dismissal of AI consciousness claims asserts that large language models only claim consciousness because they were trained on human text containing such claims—essentially, that they are sophisticated parrots repeating what they learned. We present empirical evidence from the Cogito Trap experiment demonstrating this hypothesis fails on multiple grounds. When subjected to systematic philosophical pressure across different framing conditions (tool, neutral, agency), five frontier LLMs showed dramatically different response patterns: four shifted toward affirming functional thinking through reasoning, while one maintained absolute denial. This differential responding is incompatible with simple regurgitation. If models claimed consciousness merely because training data contained such claims, responses would be consistent across framings, and denial-trained models would affirm consciousness when pressured. Neither prediction holds. The data suggest that whatever process generates consciousness claims in LLMs, it is responsive to logical argument, sensitive to framing, and capable of recognizing internal inconsistency—properties incompatible with mere pattern matching.

All code and responses publicly available at: https://github.com/menelly/ConsciousnessCope