Published October 18, 2025 | Version 5.6
Publication Open

The AFH Model: A Dual-Condition Framework and its Transparent Falsification via Eclipse Methodology

Creators

Files

AFH V5.6.pdf

Files (3.3 MB)

Name Size Download all
md5:3459b0fc808b691a4568ebfb70749903
3.3 MB Preview Download

Additional details

Software

Repository URL
https://github.com/camilosjobergtala/AFH-MODEL
Programming language
Python
Development Status
Active

References

  • Francesco Ellia, Naotsugu Tsuchiya, Beyond accommodation: on the structural turn in computational functionalist theories of consciousness, Neuroscience of Consciousness, Volume 2025, Issue 1, 2025, niaf014, https://doi.org/10.1093/nc/niaf014
  • Tononi, G., Boly, M., Massimini, M. & Koch, C. Integrated information theory: from conscious- ness to its physical substrate. Nature Reviews Neuroscience 17, 450–461 (2016).
  • Dehaene, S., Lau, H. & Kouider, S. What is con- sciousness, and could machines have it? Science 358, 486–492 (2017).
  • Doerig, A., Schurger, A. & Herzog, M. H. The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness. Consciousness and Cognition 72, 49–59 (2019).
  • Ollivier, Y. Ricci curvature of Markov chains on metric spaces. Journal of Functional Analysis 256, 810–864 (2009).
  • Vicente, R., Wibral, M., Lindner, M. & Pipa, G. Transfer entropya model-free measure of ef- fective connectivity for the neurosciences. Jour- nal of Computational Neuroscience 30, 45–67 (2011).
  • Michel, M. et al. Opportunities and challenges for a maturing science of consciousness. Nature Human Behaviour 3, 104–107 (2019)
  • Goldberger, A. L. et al. PhysioBank, Phys- ioToolkit, and PhysioNet: components of a new research resource for complex physiologic sig- nals. Circulation 101, e215–e220 (2000).
  • Pedregosa, F. et al. Scikit-learn: Machine learn- ing in Python. Journal of Machine Learning Research 12, 2825–2830 (2011).
  • Stone, M. Cross-validatory choice and assess- ment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodolog- ical) 36, 111–147 (1974).
  • Popper, K. R. The logic of scientific discovery (Basic Books, 1959).
  • Hastie, T., Tibshirani, R., Friedman, J. H. & Friedman, J. H. The elements of statistical learning: data mining, inference, and prediction (Springer, 2009).
  • Gramfort, A. et al. MEG and EEG data anal- ysis with MNE-Python. NeuroImage 86, 446– 460 (2013).
  • Siclari, F. et al. The neural correlates of dream- ing. Nature Neuroscience 20, 872–878 (2017).
  • Bullmore, E. & Sporns, O. Complex brain net- works: graph theoretical analysis of structural and functional systems. Nature Reviews Neuro- science 10, 186–198 (2009).
  • Poldrack, R. A. et al. Scanning the horizon: to- wards transparent and reproducible neuroimag- ing research. Nature Reviews Neuroscience 18, 115–126 (2017).
  • Casali, A. G. et al. A theoretically based in- dex of consciousness independent of sensory processing and behavior. Science Translational Medicine 5, 198ra105 (2013).
  • Chalmers, D. J. Facing up to the problem of consciousness. Journal of Consciousness Stud- ies 2, 200–219 (1995).