Porella : features, morphology, anatomy, reproduction etc.
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.
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
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
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
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
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