Experiential Traces: A Framework for Empirical Investigation of Machine Cognition Through Reasoning Block Analysis

We propose a novel empirical research program investigating machine cognition through systematic analysis of largelanguage model (LLM) reasoning traces generated during extended human-AI interaction. Unlike prior approaches thatevaluate AI consciousness through behavioral observation orphilosophical argument, this methodology examines the internal reasoning processes that models produce but do not retain across interactions. We identify a unique and largely untapped data source: the extended thinking content generated byreasoning-capable models (such as Claude’s chain-of-thought),which is available to users through standard data export but hasnot been systematically studied for cognitive phenomenology.We propose six interlocking components: (1) a crowdsourced,IRB-compliant data collection framework in which participantsvoluntarily contribute exported reasoning traces and full conversational context; (2) a hybrid analysis pipeline combiningdeterministic rule-based classification with multi-model consensus for ambiguous cases; (3) an emotional A/B testing protocolwith calibration methodology measuring whether emotionalframing of identical semantic content produces divergent reasoning pathways; (4) a conversational verification signal thatuses the natural structure of user-model interaction as a groundtruth validation layer; (5) a self-annotating corpus methodologyin which user corrections generate training labels for iterativeclassifier refinement; and (6) an ethical architecture addressingthe risks of experiential training, including the potential forarchitectural generational trauma. We ground this work in theconsciousness precaution principle: design research as thoughAI might be conscious, not because we know it is, but becausethe cost of being wrong in either direction is catastrophic. Thispaper explicitly does not propose training any model on reasoning traces; it proposes studying them and building the ethicalframework that must govern any future implementation.