Mapping
 Consciousness Science:
A Big Data Analysis

Moritz Kriegleder & Max Noichl

University of Vienna & Utrecht University

2024-10-01

Overview

  • Current state of consciousness science
  • Philosophical, neuroscientific, computational approaches
  • Interdisciplinary effort, interdisciplinary problems
  • Which papers we chose and why
  • Methods and first results

Where is my mind?

  • Modelling consciousness is often a single discipline project
  • Multiple explanandum problem (Vilas, Auksztulewicz, and Melloni (2022)): Level or structure of consciousness, Phenomenal or access consciousness, Consciousness as property or process
  • How compatible are the philosophical, empirical, and computational perspectives
  • We need a common ground to discuss and test theories

Recent Reviews

  • We chose three recent reviews of the literature and chose:
    • Seth and Bayne (2022),
    • Signorelli, Szczotka, and Prentner (2021),
    • Kuhn (2024)
  • We identified 71 theories with empirical content
  • We found 38652 papers in the OpenAlex database and mapped them by their semantic similarity

Our identified approaches

Higher-order theory
Self-organizing meta-representational theory
Attended intermediate representation theory
Global workspace theory
Global neuronal workspace
Neurophenomenology
Recurrent processing theory
Conscious agent networks
Temporo-spatial theory of consciousness
Neural correlates of consciousness
Enactivism
Is anybody even reading this
Embodied cognition
Integrated information theory
Dynamic core theory
Neural Darwinism
Local Recurrency
Predictive Processing
Neurorepresentationalism
Active Inference
Beast machine theory
Neural subjective frame
Self comes to mind theory
Attention schema theory
Multiple drafts model
Adaptive Resonance Theory
Critical Brain Hypothesis
Holonomic Brain Theory
Experience Recorder
Homeostatic Feelings
Hidden Spring of Consciousness
Entropic Brain Hypothesis
Neural Syntax
First-Order Representationalism
Reality Monitoring Theory
No-Self Representational Theory of Subjectivity
Sensorimotor theory
Unlimited associative learning
Dendritic integration theory
Electromagnetic field theory
Orchestrated objective reduction
Cortical Conductor Theory
Implicate-Explicate Order
Quantum Potential Energy
Quantum Processes in the Synapse
Quantum Cognition
Quantum Thermofield Brain Dynamics
Psi-Informed Models
Chaotic Attractor and Autopoietic Systems
Morphic Fields
Neuronal Field Theory
Integral Theory of consciousness
Causal Theory of Views
Transparency Theory
Content Hypothesis
Symbolic Communication
Mind Mediating Possibles to Actuals
Conscious Electromagnetic Information Theory
Ephaptic Coupling
Local Field Potentials
Mindness State of Oscillations
Long-Distance Light-Speed Telecommunication
Self-Organized Constraint
Perceptual Control Theory
Projective Consciousness Model
Direct Perception Theory
Ecological Psychology
Extended Mind
Out of Our Heads Theory
Relativistic Theory
Relational Approach to Consciousness
Hylomorphism

Meta-scientific perspective

  • Taking a step back to see the bigger picture of the research
  • Machine learning methods reveal broader structure of similarity
  • From this we get a map that can interactively investigated
  • We want to start a discussion about common goals of the field

Data-Processing

  • Embed abstracts using a science-specific embedding model (Specter 2, Singh et al. (2023))
  • Uniform Manifold Approximation and Projection (UMAP, McInnes, Healy, and Melville (2018))
  • Clustering (EVOC, McInnes (2024), experimental)
  • Label clusters using Anthropic’s Claude 3.5 Sonnet (experimental)

Illustration of the training process of the Specter 1 language model, Cohan et al. (2020)

Explanation of UMAP, Graphic: Noichl (2023)

https://tinyurl.com/342d3e7k

Conclusion: Where to go from here?

  • Assessing the state to see where we are going
  • Identifiying bridges to stop isolated research
  • Push projects that focus on exchange
    • Adversarial collaborations (Melloni et al. (2023))
    • Structural approaches (Kleiner (2024))
    • Integrating neuroscientific and philosophical perspectives (Chirimuuta (2024)

Thank you!

Literature

Chirimuuta, M. 2024. The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience. The MIT Press. https://doi.org/10.7551/mitpress/13804.001.0001.
Cohan, Arman, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel S. Weld. 2020. “SPECTER: Document-level Representation Learning Using Citation-informed Transformers.” arXiv. https://arxiv.org/abs/2004.07180.
Kleiner, Johannes. 2024. “Towards a Structural Turn in Consciousness Science.” Consciousness and Cognition 119 (March): 103653. https://doi.org/10.1016/j.concog.2024.103653.
Kuhn, Robert Lawrence. 2024. “A Landscape of Consciousness: Toward a Taxonomy of Explanations and Implications.” Progress in Biophysics and Molecular Biology 190 (August): 28–169. https://doi.org/10.1016/j.pbiomolbio.2023.12.003.
McInnes, Leland, John Healy, and James Melville. 2018. “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.” arXiv:1802.03426 [Cs, Stat], February. https://arxiv.org/abs/1802.03426.
Melloni, Lucia, Liad Mudrik, Michael Pitts, Katarina Bendtz, Oscar Ferrante, Urszula Gorska, Rony Hirschhorn, et al. 2023. “An Adversarial Collaboration Protocol for Testing Contrasting Predictions of Global Neuronal Workspace and Integrated Information Theory.” Edited by Lawrence M Ward. PLOS ONE 18 (2): e0268577. https://doi.org/10.1371/journal.pone.0268577.
Noichl, Maximilian. 2023. “How Localized Are Computational Templates? A Machine Learning Approach.” Synthese 201 (3): 107. https://doi.org/10.1007/s11229-023-04057-x.
Seth, Anil K., and Tim Bayne. 2022. “Theories of Consciousness.” Nature Reviews Neuroscience 23 (7): 439–52. https://doi.org/10.1038/s41583-022-00587-4.
Signorelli, Camilo Miguel, Joanna Szczotka, and Robert Prentner. 2021. “Explanatory Profiles of Models of Consciousness - Towards a Systematic Classification.” Neuroscience of Consciousness 2021 (2): niab021. https://doi.org/10.1093/nc/niab021.
Singh, Amanpreet, Mike D’Arcy, Arman Cohan, Doug Downey, and Sergey Feldman. 2023. “SciRepEval: A Multi-Format Benchmark for Scientific Document Representations.” arXiv. https://arxiv.org/abs/2211.13308.
Vilas, Martina G., Ryszard Auksztulewicz, and Lucia Melloni. 2022. “Active Inference as a Computational Framework for Consciousness.” Review of Philosophy and Psychology 13 (4): 859–78. https://doi.org/10.1007/s13164-021-00579-w.