Published April 28, 2026 | Version Version 27
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Awareness as Relevance Selection: A Causal Framework for Attention, Internal Feedback, and Artificial Intelligence

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  • 1. Robotech Frontier Hub

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Awareness as Relevance Selection develops a causal framework for studying awareness as criterion-sensitive relevance feedback in biological and artificial systems. The manuscript distinguishes attention, relevance, internal feedback, metacognition, and awareness as separable operational constructs. It defines awareness as a recurrent control regime in which selected information is compressed into a persistent relevance variable, re-injected into latent dynamics, and tested through downstream effects on switching cost, calibration error, distractor resistance, and policy stability. The central empirical proposal is a four-arm perturbation design comparing interventions on attention, relevance, metacognition, and generic latent state. The framework is explicitly falsifiable: if a decoded relevance subspace fails to predict held-out control outcomes across independent task paradigms, or if relevance perturbation fails to produce disproportionate impairment relative to matched control interventions, the strong form of the theory fails. The manuscript develops formal definitions, falsifiable hypotheses, biological and artificial experimental protocols, cross-domain comparison logic, rival-theory contrasts, and limitations. Its aim is to establish a comparative science of awareness grounded in measurable latent variables and causal intervention rather than metaphor.

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References

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