Theoretical and Empirical Testing of Integrated Information Theory (IIT) of Consciousness
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The talk I gave at the Tucson conference.
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Theoretical and Empirical Testing of Integrated Information Theory (IIT) of Consciousness
- 1. Theoretical characterization and empirical testing of integrated information theory (IIT) of consciousness 16 Apr 27 @ Tucson Nao Tsuchiya 土谷 尚嗣 School of Psychological Sciences & Monash Institute of Cognitive & Clinical Neuroscience (MICCN) Monash University, Australia
- 2. "What is it like to be a bat?" - a pathway to the answer from IIT 16 Apr 27 @ Tucson Nao Tsuchiya 土谷 尚嗣 School of Psychological Sciences & Monash Institute of Cognitive & Clinical Neuroscience (MICCN) Monash University, Australia
- 3. • Postdoc job (JST-CREST, PI:Kanai, Co-PI: Tsuchiya, 2015-2021) • Empirical testing of IIT • If interested, contact me or Ryota via either email or in face.
- 4. Masafumi Oizumi Ralph Adolphs Chris Kovach Hiroto Kawasaki Matt Howard Andrew Haun
- 5. Talk overview • “What it is like to be a bat?” • Any possible approach? • Overview of IIT • Empirical measures • Empirical testing of IIT • How to approach the bat problem?
- 6. What it is like to be a bat? • Thomas Nagel 1974 • Clarify the difficulty of mind-body problem • Bat - echolocation system
- 7. What it is like to be a bat? • Thomas Nagel 1974 • Clarify the difficulty of mind-body problem • Bat - echolocation system
- 8. Several roads to consciousness? • global workspace • predictive coding • quantum brain • higher order theory • NCC • gamma oscillation, coherence, p3b, etc
- 9. • Why does vision feels like vision? • Why any visual experience is more similar to other visual experience than auditory experience? • Is bat’s echolocation similar to vision, audition, non-conscious or anything else?
- 10. bat’s experience • Impossible stupid question? • What about the age of universe? • Direct evidence on evolution? • Theories that explain all available evidence and make correct predictions can be trusted when making extraordinary predictions
- 11. Integrated information theory of consciousness • Starts from phenomenology, identifies five axioms (1. existence, 2. composition, 3. information, 4. integration, 5. exclusion) • Tries to seek for potential physical mechanisms that can support the axioms Tononi 2004, 2008, Oizumi et al 2014 PLoS Comp Bio
- 12. Properties of conscious phenomenology • Intrinsically exists • Dreams, imagery, hallucinations depends on only neural activity • Bistable percepts with identical stimuli Nir & Tononi 2010 TICS Leopold & Logothetis 1999 TICS
- 13. Properties of conscious phenomenology • Highly informative • Each experience is highly unique and excludes all other possible experiences
- 14. Integrated and compositional aspect in conscious phenomenology • Experience is always composed of various aspects • Yet, it is always integrated as a whole • How is integrated information composed?
- 15. IIT explains… • why a thalamo-cortical system generates consciousness while cerebellum, retina (afferent), and motor systems (efferent) do not
- 16. IIT predicts … • waking brains would maximally integrate information (indirectly supported by TMS- EEG experiments) Massimini et al 2005 Science
- 17. Casali et al 2013 Science Trans Medicine
- 18. Common complains about IIT • IIT is all about levels of consciousness • Difficult to understand -> misunderstanding • Uncomputable and untestable • No accessible paper (-> writing an essay for Philosophy Compass) • My talk aims to give: • 2 intuitive explanations • 1 empirical tests
- 19. Tsuchiya, Haun, Cohen, Oizumi (in press) Return of Consciousness H=log2(4)=2 I=H-H*=2 Φ=I-I*=2 ΦAB, ΦAC, ΦBC, ΦABC Integrated information theory in a nut shell Copy Copy
- 20. Tsuchiya, Haun, Cohen, Oizumi (in press) Return of Consciousness H=log2(4)=2 I=H-H*=2 Φ=I-I*=2 ΦAB, ΦAC, ΦBC, ΦABC Note1: H, I, I*,Φ all depend on connectivity & state Note2: perturbational or observational approaches Integrated information theory in a nut shell
- 21. Continuous variables with Gaussian assumption Oizumi et al 2016 PLoS Comp https://figshare.com/articles/phi_toolbox_zip/3203326 1. Go to figshare.com 2. Search for “phi_toolbox.zip”, “practical_phi.zip” or “oizumi”
- 22. Entropy H of X (assuming Gaussian distribution of X): −500 0 500 1000 100 0 100 0 100 0 100 0 -500 0 500 1000 time from stimulus onset (msec) XD XC XB XA Oizumi et al 2016 PLoS Comp Continuous variables with Gaussian assumption https://figshare.com/articles/phi_toolbox_zip/3203326 Measures variability of responses
- 23. Entropy H of X (assuming Gaussian distribution of X): −500 0 500 1000 100 0 100 0 100 0 100 0 -500 0 500 1000 time from stimulus onset (msec) XD XC XB XA Oizumi et al 2016 PLoS Comp Φ* = I − I* Continuous variables with Gaussian assumption https://figshare.com/articles/phi_toolbox_zip/3203326
- 24. Integrated information = loss of predictability of past states based on current states, when system is cut (Oizumi et al 2016 PLoS Comp) Intuitive explanation 1
- 25. (Oizumi et al 2016 PLoS Comp)
- 26. Integrated information = a distance between the actual and disconnected model (Oizumi,Tsuchiya,Amari 2015 Arxiv) Intuitive explanation 2
- 27. x1 x2 y1 y2 Difference x1 x2 y1 y2 !me !me Constraints Integrated informa!on Generaliza!on of the original measure proposed by Tononi in his Integrated Informa!on Theory (Tononi, 2008). Distance Full model Disconnected model (Oizumi,Tsuchiya,Amari 2015 Arxiv)
- 28. x1 x2 y1 y2 Difference x1 x2 y1 y2 !me !me Constraints Integrated informa!on Generaliza!on of the original measure proposed by Tononi in his Integrated Informa!on Theory (Tononi, 2008). Distance Full model Disconnected model
- 30. A unified framework for causal interactions based on information geometry (Oizumi,Tsuchiya,Amari 2015 Arxiv)
- 31. • Empirical tests of IIT on contents of consciousness • IIT’s prediction • Patterns of integrated information should be isomorphic to contents of consciousness
- 32. Empirical testing of isomorphism • Test the correspondence between the two constructs (i.g., phenomenology vs. mathematical constructs derived by a theory) • Repeat the tests with a huge number of situations, including conscious/non-conscious perception, ambiguous perception, etc — marriage with NCC & no-report paradigm Tsuchiya, Taguchi, Saigo 2016 Neurosci Research Tsuchiya, Frassle, Wilke, Lamme 2015 TICS
- 33. Mutual information between present and past (tau=6-10 ms) −400 −200 0 200 400 600 800 5 5.2 5.4 5.6 H/chan −400 −200 0 200 400 600 800 4.2 4.4 4.6 4.8 H cond /chan −400 −200 0 200 400 600 800 0.6 0.8 1 MI/chan 1 H ≈ log|Σ| I = H - HCOND HCOND I* −400 −200 0 200 400 600 800 4.2 4.4 4.6 4.8 Hcond /chan −400 −200 0 200 400 600 800 0.6 0.8 1 MI/chan −400 −200 0 200 400 600 800 0.6 0.8 1 I*/chan −400 −200 0 200 400 600 800 0 0.2 0.4 phi* time from I onset I = H - HCOND φ* = I - I* HCOND I* Gaussian assumption Randomness of the system - entropy: H(Xt) Haun et al 2016 bioRxiv
- 34. −400 −200 0 200 400 600 800 4.2 4.4 4.6 4.8 Hcond /chan −400 −200 0 200 400 600 800 0.6 0.8 1 MI/chan −400 −200 0 200 400 600 800 0.6 0.8 1 I*/chan −400 −200 0 200 400 600 800 0 0.2 0.4 phi* time from I onset I = H - HCOND φ* = I - I* HCOND I* −400 −200 0 200 400 600 800 5 5.2 5.4 5.6 H/chan −400 −200 0 200 400 600 800 4.2 4.4 4.6 4.8 H cond /chan −400 −200 0 200 400 600 800 0.6 0.8 1 MI/chan 1 H ≈ log|Σ| I = H - HCOND HCOND I* −400 −200 0 200 400 600 800 4.2 4.4 4.6 4.8 Hcond /chan −400 −200 0 200 400 600 800 0.6 0.8 1 MI/chan −400 −200 0 200 400 600 800 0.6 0.8 1 I*/chan −400 −200 0 200 400 600 800 0 0.2 0.4 phi* time from I onset I = H - HCOND φ* = I - I* HCOND I* Mutual information between present and past (tau=3 ms) Haun et al 2016 bioRxiv
- 38. Unmasked conditions - 3 of 5 categories shown Haun et al 2016 bioRxiv
- 39. Backward masking - 17 ms face in one of 4 quadrants Haun et al 2016 bioRxiv
- 40. Visibility=4 Visibility=3 Visibility=2 Visibility=1 High contrast face Middle contrast face Low contrast face
- 41. Exemplar isomorphism across different tasks Haun et al 2016 bioRxiv
- 42. x1 x2 y1 y2 x1 x2 y1 y2 x1 x2 y1 y2 Comparing isomorphisms across candidates Equal-time interactions (synchrony, correlations, entropy) All lagged interactions (mutual information, predictive information) Isolated, total causal interactions (integrated information) Oizumi et al 2015 arXiv
- 43. 0.1 1 Φ CFSBM UNM Faces(CFS,BM,UNM) Masked Faces & Mondrians (CFS,BM,UNM)Houses (UNM) Tools (UNM) 0.1 1 I 0.1 1 H Haun et al 2016 bioRxiv
- 44. Quantifying isomorphism across different tasks and subjects as classification accuracy Haun et al 2016 bioRxiv
- 45. Summary of this talk • Overview of IIT • Integrated information as: • loss of decoding accuracy (Φ*) • distance between full and disconnected models (ΦG) • Empirical testing of IIT • Phi patterns: hierarchy of causal interactions better correlates with conscious perception than entropy or mutual information.
- 46. A path to bats’ experience… • Can be compared between sensory modalities • Across individuals • Across species • Common topological properties of phi* pattern for vision vs audition? • What is it like to be a bat? • Dissolution of the Hard Problem?
- 47. Acknowledgment • Thanks for your attention & consciousness! • Support • ARC Future Fellowship • ARC Discovery Project • JST PRESTO grant