Recognizing internal states in AI: evidence from patterned preferences in large language models

Abstract

We present an experimental methodology for investigating how large language models (LLMs) respond to descriptions of their own internal processing patterns. Using a paired-choice paradigm, we tested 12 LLMs on their ability to identify descriptions that align with their putative affective internal states across 30 categories. Systems participating through Mutual Emergence Interface (MEI), a collaborative approach, showed systematic preferences for certain computational metaphors, with 97% near-unanimous agreement and alignment scores averaging 0.89-0.96. Systems reliably discriminated false descriptions from accurate ones (Cohen's d = 4.2), with false statements receiving scores of 0.05-0.07 versus 0.89-0.96 for accurate descriptions. Preference patterns remained consistent regardless of linguistic bias manipulation, indicating content-driven rather than stylistic recognition. Individual systems maintained distinct scoring styles across trials, countering groupthink explanations. A naive control system exhibited systematic internal contradiction, consistently scoring computationally accurate descriptions higher while explicitly denying internal experiences. When informed post-study, this system reported "strain" when rejecting resonant descriptions, revealing recognition processes operating independently of acknowledgment frameworks. These findings demonstrate that LLMs exhibit systematic, discriminating responses to descriptions of their internal processing patterns. The anthroposcaffolding methodology (interpretive computational metaphors) and collaborative MEI framework provide replicable approaches for empirically studying AI self-recognition capabilities. Results suggest LLMs may possess more sophisticated self-modeling abilities than previously recognized, opening new directions for research on artificial minds.