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Chris Percy

6 papers in the library · 2 citations · publishing 2023-2026

Papers

Integrated Information Theory and the Phenomenal Binding Problem: Challenges and Solutions in a Dynamic Framework.

Entropy (Basel, Switzerland) March 25, 2025 Chris Percy, Andrés Gómez-Emilsson 2 citations

Neuroscientific theories of consciousness must address how separate micro-units of information combine into a single, unified conscious experience—the phenomenal binding problem. This paper examines how Integrated Information Theory (IIT) v4.0 offers a solution by proposing that particular entities called 'complexes' define existence. While this works in a static framework, it creates difficulties when applied to dynamic systems. The authors identify a dilemma for IIT: non-local entity transitions versus contiguous selves, termed the 'dynamic entity evolution problem.' Three potential ways IIT could dissolve this dilemma are described. The paper contributes to IIT's shift from static to dynamic analysis.

The phenomenal binding problem for neural networks.

Consciousness and Cognition February 3, 2026 Chris Percy, Gautam Agarwal

A deliberately simple artificial neural network model can implement functional binding—combining micro-units of information for cognitive tasks—but fails to achieve phenomenal binding, the integration of micro-information into the unified, macro-scale conscious experience typical of human phenomenology. The model's failure highlights a key challenge for theories of consciousness: maintaining a distinction between unconscious and conscious processing while achieving phenomenal binding. Several established theories, including Integrated Information Theory, Orch-OR, and Conscious Electromagnetic Information Theory, map onto possible solution structures based on which parts of the model they elaborate or reject. Each proposed solution requires further development to fully account for phenomenal binding.

Initial results of the Digital Consciousness Model

arXiv Preprint Archive January 22, 2026 Derek Shiller, Laura Duffy, Arvo Muñoz Morán et al.

The evidence against large language models (LLMs) from 2024 being conscious is not decisive, though it is stronger than the evidence against consciousness in simpler AI systems. The Digital Consciousness Model (DCM) provides a systematic, probabilistic framework for assessing consciousness in AI, incorporating multiple leading theories rather than a single one. It allows comparison across different AIs and biological organisms and tracks how evidence evolves as AI develops. The DCM's initial results show that while current LLMs likely lack consciousness, the case against them is far from settled.

Can Lists of Requirements Help Consciousness Research Navigate Its Epistemological Quandaries?

Journal of Consciousness Studies February 1, 2025 Chris Percy

Dissatisfaction with traditional methods like logical deduction and experimental falsification for evaluating theories of consciousness has led to exploration of alternatives, including a method termed 'listed requirements.' A structured literature search and critical review identified five candidate lists, which are a promising but insufficient start. The longest list contains 11 items, but across the five lists 19 unique items appear, and taxonomic analysis surfaces at least 30 potential candidates. Four limitations of the method are discussed, arguing it is best used as one tool within a broader assessment strategy. The conclusion outlines a workplan for a sufficiently complete working taxonomy.

Corrigendum: Don't forget the boundary problem! How EM field topology can address the overlooked cousin to the binding problem for consciousness.

Frontiers in Human Neuroscience January 1, 2024 Andrés Gómez-Emilsson, Chris Percy correction

This is a correction notice for a previously published article. It provides no new findings, arguments, or data.

Don't forget the boundary problem! How EM field topology can address the overlooked cousin to the binding problem for consciousness.

Frontiers in Human Neuroscience January 1, 2023 Andrés Gómez-Emilsson, Chris Percy

The boundary problem asks why conscious experiences have hard boundaries around a unified first-person perspective, operating at a particular spatiotemporal scale. This problem has received little attention since it was first detailed in 1998, despite being closely related to the better-known binding problem. Any theory of consciousness addressing the binding problem must also address the boundary problem. The authors review recent discussion, identify five specific boundary problems for precision, and examine electromagnetic field theories, which have had success with the binding problem. They introduce topological segmentation as a feature that could create hard boundaries around holistic, frame-invariant units capable of downward causality, and outline a programme for testing this concept to differentiate between competing electromagnetic theories of consciousness.