Awareness as Relevance Selection: A Causal Framework for Attention, Internal Feedback, and Artificial Intelligence
Awareness as Relevance Selection: A Causal Framework for Attention, Internal Feedback, and Artificial Intelligence
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.
Keywords: awareness; attention; relevance selection; causal inference; metacognition; internal feedback; artificial intelligence; cognitive neuroscience; computational neuroscience; neural decoding; latent variables; causal perturbation; predictive processing; reward prediction error; global neuronal workspace; reinforcement learning; representation learning; active inference; consciousness studies; NeuroAI Subjects: Cognitive Neuroscience; Computational Neuroscience; Artificial Intelligence; Machine Learning; Cognitive Science; Consciousness Studies; Causal Inference; NeuroAI; Reinforcement Learning; Systems Neuroscience; Philosophy of Mind; Representation Learning. The manuscript itself frames the work around awareness, attention, relevance selection, causal inference, metacognition, predictive processing, reward prediction error, global neuronal workspace, artificial intelligence, and cognitive neuroscience., Keywords: awareness; attention; relevance selection; causal inference; metacognition; internal feedback; artificial intelligence; cognitive neuroscience; computational neuroscience; neural decoding; latent variables; causal perturbation; predictive processing; reward prediction error; global neuronal workspace; reinforcement learning; representation learning; active inference; consciousness studies; NeuroAI Subjects: Cognitive Neuroscience; Computational Neuroscience; Artificial Intelligence; Machine Learning; Cognitive Science; Consciousness Studies; Causal Inference; NeuroAI; Reinforcement Learning; Systems Neuroscience; Philosophy of Mind; Representation Learning. The manuscript itself frames the work around awareness, attention, relevance selection, causal inference, metacognition, predictive processing, reward prediction error, global neuronal workspace, artificial intelligence, and cognitive neuroscience.
Keywords: awareness; attention; relevance selection; causal inference; metacognition; internal feedback; artificial intelligence; cognitive neuroscience; computational neuroscience; neural decoding; latent variables; causal perturbation; predictive processing; reward prediction error; global neuronal workspace; reinforcement learning; representation learning; active inference; consciousness studies; NeuroAI Subjects: Cognitive Neuroscience; Computational Neuroscience; Artificial Intelligence; Machine Learning; Cognitive Science; Consciousness Studies; Causal Inference; NeuroAI; Reinforcement Learning; Systems Neuroscience; Philosophy of Mind; Representation Learning. The manuscript itself frames the work around awareness, attention, relevance selection, causal inference, metacognition, predictive processing, reward prediction error, global neuronal workspace, artificial intelligence, and cognitive neuroscience., Keywords: awareness; attention; relevance selection; causal inference; metacognition; internal feedback; artificial intelligence; cognitive neuroscience; computational neuroscience; neural decoding; latent variables; causal perturbation; predictive processing; reward prediction error; global neuronal workspace; reinforcement learning; representation learning; active inference; consciousness studies; NeuroAI Subjects: Cognitive Neuroscience; Computational Neuroscience; Artificial Intelligence; Machine Learning; Cognitive Science; Consciousness Studies; Causal Inference; NeuroAI; Reinforcement Learning; Systems Neuroscience; Philosophy of Mind; Representation Learning. The manuscript itself frames the work around awareness, attention, relevance selection, causal inference, metacognition, predictive processing, reward prediction error, global neuronal workspace, artificial intelligence, and cognitive neuroscience.
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!