Tutorial I gave at ASSC 2018 Krakau part 1. Same material as I gave at HBP "Understanding Consciousness" Barcelona

1. 18 June 26 @ Krakaw
Nao Tsuchiya 土谷 尚嗣
School of Psychological Sciences & Monash Institute of Cognitive & Clinical Neuroscience (MICCN)
Monash University, Australia
https://goo.gl/78vjXS
Towards empirical testing of
theories of consciousness
Postdoc position available
contact: naotsugu.tsuchiya@monash.edu

2. • Survey:
1. How familiar are you with programming
(Matlab, Python, etc)?
2. Do you plan to compute Phi using your own
data? (Or have you tried?)
3. Are you interested in empirical approach to
IIT, other than computational approaches?
4. Your background is a) philosophy, b)
psychology, c) neuroscience, d) medicine, e)
other
5. Did you attend to the morning IIT tutorial?

3. Any moment of conscious experience
(=qualia) is structured

4. Brain is structured
to form a hierarchy of causal interactions
among neurons
= Informational structure

5. Need to understand the relationship between
structure of consciousness and structure of information
Integrated Information Theory (IIT) of consciousness
— provides a promising candidate for structure of
information
Tsuchiya, Taguchi, Saigo 2016 Neuroscience Research
Northoff, Tsuchiya, Saigo (in preparation)
Category Theoretic approach (?)
NN —> C

6. 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

7. H=log2(4)=2 I=H-H*=2 Φ=I-I*=2
ΦAB, ΦAC,
ΦBC, ΦABC
Integrated information theory in a nut shell
Tsuchiya 2017 Philosophy Compass
Copy
Copy

8. 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

9. 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
Oizumi,Tsuchiya,Amari 2016 PNAS
Amari,Tsuchiya, Oizumi, 2017 arXiv

10. 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

11. (Oizumi et al 2016 PLoS Comp)

12. Integrated information
=
a distance between the actual and
disconnected model
Oizumi,Tsuchiya,Amari 2016 PNAS
Intuitive explanation 2

13. 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

14. 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

15. A unified framework for causal interactions
based on information geometry
Oizumi,Tsuchiya,Amari 2016 PNAS

16. Is the structure of integrated
information correlated with
contents of consciousness?
Haun et al 2017 eNeuro

17. Baroni et al 2017 Neuroimage
Haun et al 2017 eNeuro

18. 11 possible subsets
for 4 channels:
6 x 2 channels (AB,AC,..)
4 x 3 channels (ABC, …)
1 x 4 channels (ABCD)
Haun et al 2017 eNeuro

19. Gaussian assumption
Randomness of
the system
- entropy: H(Xt)
Haun et al 2017 eNeuro

20. Mutual information
between
present and past
(tau=3 ms)
Haun et al 2017 eNeuro

21. Haun et al 2017 eNeuro

24. Comparing contents of consciousness across three tasks

25. Unmasked conditions - 3 of 5 categories shown
Haun et al 2017 eNeuro

26. Backward masking - 17 ms face in one of 4 quadrants
Haun et al 2017 eNeuro

27. Visibility=4 Visibility=3 Visibility=2 Visibility=1
High
contrast
face
Middle
contrast
face
Low
contrast
face

28. Exemplar isomorphism across different tasks
Haun et al 2017 eNeuro

29. 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
Phi, but not I and H, are correlated with contents of consciousness
on a trial-by-trial basis
Haun et al 2017 eNeuro

30. Quantifying isomorphism across different tasks and
subjects as classification accuracy
Haun et al 2017 eNeuro

31. Haun et al 2017 eNeuro

32. Summary of this talk
• Conscious experience is essentially structural
• Causal interactions among neurons are structural
• IIT proposes the link between particular type of
informational structure with structure of consciousness
• Integrated information structure can be empirically
computed
• Structure of integrated information better correlates
with conscious perception than those of entropy or
mutual information.

33. Acknowledgment
• Thanks for your attention & consciousness!
• Support
• ARC Future Fellowship
• ARC Discovery Project
• JST PRESTO grant
• Templeton World
Charity Foundation
Postdoc position available
contact: naotsugu.tsuchiya@monash.edu