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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
"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
• 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.
Masafumi Oizumi
Ralph Adolphs Chris Kovach
Hiroto Kawasaki Matt Howard
Andrew Haun
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?
What it is like to be a bat?
• Thomas Nagel 1974
• Clarify the difficulty of mind-body problem
• Bat - echolocation system
What it is like to be a bat?
• Thomas Nagel 1974
• Clarify the difficulty of mind-body problem
• Bat - echolocation system
Several roads to
consciousness?
• global workspace
• predictive coding
• quantum brain
• higher order theory
• NCC
• gamma oscillation, coherence, p3b, etc
• 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?
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
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
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
Properties of conscious
phenomenology
• Highly informative
• Each experience is highly unique and excludes all other possible
experiences
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?
IIT explains…
• why a thalamo-cortical system generates
consciousness while cerebellum, retina
(afferent), and motor systems (efferent) do
not
IIT predicts …
• waking brains would maximally integrate
information (indirectly supported by TMS-
EEG experiments)
Massimini et al 2005 Science
Casali et al 2013 Science Trans Medicine
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
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
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
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”
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
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
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
(Oizumi et al 2016 PLoS Comp)
Integrated information
=
a distance between the actual and
disconnected model
(Oizumi,Tsuchiya,Amari 2015 Arxiv)
Intuitive explanation 2
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)
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
Stochas(c	interac(on
nforma(on
ed	informa(on
Transfer	entropy
y1
y2
x1
x2
y1
y2
y1
y2
x1
x2
y1
y2
Stochas(c	inte
Mutual	informa(on
Integrated	informa(on
Transfer	entro
x1
x2
y1
y2
x1
x2
x1
x2
y1
y2
x1
x2
A unified framework for causal interactions
based on information geometry
(Oizumi,Tsuchiya,Amari 2015 Arxiv)
• Empirical tests of IIT on contents of
consciousness
• IIT’s prediction
• Patterns of integrated information should
be isomorphic to contents of
consciousness
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
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
−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
Unmasked conditions - 3 of 5 categories shown
Haun et al 2016 bioRxiv
Backward masking - 17 ms face in one of 4 quadrants
Haun et al 2016 bioRxiv
Visibility=4 Visibility=3 Visibility=2 Visibility=1
High
contrast
face
Middle
contrast
face
Low
contrast
face
Exemplar isomorphism across different tasks
Haun et al 2016 bioRxiv
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
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
Quantifying isomorphism across different tasks and
subjects as classification accuracy
Haun et al 2016 bioRxiv
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.
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?
Acknowledgment
• Thanks for your attention & consciousness!
• Support
• ARC Future Fellowship
• ARC Discovery Project
• JST PRESTO grant

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16 apr 27 tucson iit talk

  • 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
  • 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
  • 35.
  • 36.
  • 37.
  • 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