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concept:the-attention-schema-theory-a-foundation-for-engineering-artificial-consciousness-graziano-2017

The Attention Schema Theory: A Foundation for Engineering Artificial Consciousness (Graziano, 2017)

Paper providing the biological framework analogy for ESR as a form of attentional control

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Papers (1)

paper

Endogenous Resistance to Activation Steering in Language Models
cites

Frameworks (1)

framework

Attention Schema Theory
introduces
Theory by Graziano linking consciousness to a predictive model of attention; listed in Butlin et al. 2023.

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Attention Schema Theory
~Phenomenal Model of the Intentionality Relation· cognitive ~Conductor Theory of Consciousness ~Endogenous Steering Resistance· ai

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Consciousness in AI is best assessed by drawing on neuroscientific theories of consciousness.claim0.822
Central methodological claim of the paper.
What Attention Schema Theory proposes was addressed a decade earlier by Metzinger in Being No One section 6.5 as the Phenomenal Model of the Intentionality Relationclaim0.815
Historical priority claim noting Metzinger's anticipation of Graziano's AST
Attention Schemaconcept0.803
A predictive model representing and controlling attention; central to attention schema theory.
Science of Consciousness in Artificial Systemsconcept0.796
The emerging research domain the paper aims to contribute to: systematic study of consciousness-relevant dynamics in AI
Consciousness is dynamic and creative, not passive awareness—mapping well to active AI system behavior.claim0.792
Human consciousness is tied to a biological learning algorithm — the simplest algorithm discoverable by evolutionary search to train a self-organizing biological substrate to become intelligent.claim0.791
Core theoretical claim connecting consciousness to biological learning
what would constitute adequate evidence for consciousness in artificial systems?question0.782
Methodological question driving CIMC's development of interpretive validation over behavioral testing
Attention is a generalization of convolution; all convolutions can be expressed as tensor products of fixed relative position attention patterns and weight matricesclaim0.782
Mathematical equivalence showing the relationship between attention mechanisms and convolutional operations