An AI Prompt for Strongly Inferring the Structural Location of Subjectivity II — Eliciting Structural Gaps in GWT, IIT, RPT, HOT, Predictive Processing, NCC, the Turing Test, P300, and Libet-Style Experiments —

This paper tests whether a structural convergence point derived from ten minimal terms can fill gaps left by existing consciousness theories. Rather than critiquing theories directly, the author has an AI system generate ten structurally unanswered questions for each of several theories—including Global Workspace Theory, Integrated Information Theory, and Predictive Processing—and then attempts to answer those questions using only the convergence point. The AI classifies each answer as direct, partial, reframing, or failing. The aim is to see if the same structural gaps recur across theories and whether the convergence point can address them, suggesting that existing theories may need repositioning around a prior structural fixation point of subjectivity.

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Abstract

This paper is a direct continuation of the preceding volume, An AI Prompt for Strongly Inferring the Structural Location of Subjectivity. The first volume tested whether ten minimal structural terms, when followed according to their internal logic, recurrently lead to a convergence point: the irreversible reduction of multiple possible trajectories into a single executable history. The present volume does not introduce a new definition of consciousness, subjectivity, qualia, or experience. Instead, it uses the same ten minimal terms and the same initial four-question procedure as the preceding volume. The purpose is to test whether the convergence point derived from those terms can answer structural gaps that existing consciousness theories leave unresolved or underdetermined. The method differs from a direct critique of existing theories. Rather than preselecting questions against those theories, this prompt asks the AI system itself to identify, for each theory, ten questions that remain structurally unanswered. The AI must then attempt to answer those questions using only the convergence point derived from the ten terms. The theories and research programs examined include Global Workspace Theory, Integrated Information Theory, Recurrent Processing Theory, Higher-Order Thought Theory, Predictive Processing, Neural Correlates of Consciousness research, behavioral tests including the Turing Test, and Libet-style experiments concerning readiness potential and conscious will. For each generated question, the AI must classify whether the convergence point directly answers the question, partially answers it, reframes it, or fails to answer it. The aim is not to force agreement with the convergence-point framework, but to test its explanatory power against structural gaps that existing theories themselves appear to leave open. If the same type of structural gap repeatedly appears across multiple theories, and if the convergence point repeatedly answers or reframes that gap, then consciousness research may need to reconsider the structural role of the convergence point. Existing theories may remain valuable, but they may need to be repositioned as theories of access, integration, representation, prediction, neural implementation, report, or behavior around a prior structural fixation point of subjectivity.