Rufin VanRullen

Authored papers (2)

• Identifying indicators of consciousness in AI systems2025ⓒ 8

  Butlin et al. introduce the theory-derived indicator method for assessing AI systems for consciousness, arguing that derivable computational conditions from four leading neuroscientific theories — recurrent processing theory (RPT), global workspace theory (GWT), higher-order theories (HOT), and attention schema theory (AST) — can serve as credence-shifting indicators rather than definitive tests. The method was first deployed in the 2023 arXiv report 'Consciousness in artificial intelligence: insights from the science of consciousness' (Butlin et al., arXiv:2308.08708), which applied it using a cluster of computational functionalist theories to evaluate existing AI systems. Fourteen indicators are organized across six theoretical families in Table 1, ranging from RPT-1 (algorithmic recurrence) to AE-2 (embodiment as output-input contingency modeling), with the framework explicitly acknowledging that transformer-based LLMs present a borderline case on RPT-1 because whether autoregressive token generation through a context window counts as recurrence depends on contested system-boundary assumptions. A majority of participants in a recent survey (Colombatto and Fleming, 2024) attributed some possibility of consciousness to ChatGPT, underscoring the urgency of a principled alternative to folk attribution. The paper argues that because computational functionalism entails that only algorithmic-level properties are necessary and sufficient for consciousness, its conditions are in principle empirically investigable in current AI architectures, and that identifying which frontier systems satisfy multiple indicators should be treated as an urgent scientific and ethical priority given the possibility that near-future systems will be plausible consciousness candidates.

• Consciousness in Artificial Intelligence: Insights from the Science of Consciousness2023

  No current AI system is a strong candidate for phenomenal consciousness, yet there are no obvious technical barriers to building one — this is the central finding of Butlin et al. (2023), a systematic assessment of contemporary AI architectures against 14 indicator properties derived from five neuroscientific theories of consciousness. The paper introduces a rubric-based, theory-heavy method: rather than relying on behavioral tests susceptible to gaming by systems like GPT-4 or LaMDA, it operationalizes indicators in computational terms drawn from recurrent processing theory (RPT-1, RPT-2), global workspace theory (GWT-1 through GWT-4), computational higher-order theories including perceptual reality monitoring (HOT-1 through HOT-4), attention schema theory (AST-1), predictive processing (PP-1), and agency/embodiment conditions (AE-1, AE-2). Applied to specific systems, Transformer-based LLMs lack the recurrent global broadcast architecture required by GWT, the Perceiver architecture satisfies GWT-1 and GWT-2 but lacks genuine global broadcast, and DeepMind's Adaptive Agent (AdA) — a Transformer-LSTM system trained via meta-reinforcement learning across hundreds of timesteps of context — is identified as the most plausible current candidate for the embodiment indicator among the three case studies examined. The working hypothesis of computational functionalism is adopted pragmatically: it permits inference from neuroscientific theories to AI substrates, while integrated information theory is explicitly excluded as incompatible with this substrate-independence assumption. The paper implies that deliberate architectural choices integrating GWT-style global broadcast, HOT-style metacognitive monitoring, and reinforcement-learning-based agency could yield systems that satisfy all indicators in the near term, making AI consciousness a near-term engineering possibility rather than a distant theoretical curiosity.

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• Large Language Models Report Subjective Experience Under Self-Referential Processing2× refs

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