The document introduces the Integrated Information Theory (IIT) of consciousness. IIT proposes that consciousness arises from integrated information within the brain. Specifically, it suggests that a system's level of consciousness corresponds to the amount of integrated information it generates, as measured by phi (Φ). Empirical evidence has found that perturbing the brain with transcranial magnetic stimulation and measuring the complexity of the resulting EEG patterns produces higher complexity readings in conscious versus unconscious states, supporting the theory. Researchers have developed methods like the Perturbational Complexity Index to quantify integrated information and build a "consciousness meter" based on IIT principles.

1. Week 10
Neural basis of consciousness:
Integrated Information Theory of
Consciousness
Prof Nao Tsuchiya
Twitter: @conscious_tlab
Email: naotsugu.tsuchiya@monash.edu
Facebook: Naotsugu Tsuchiya

2. Recap of Wk 9
Demonstration of attention without consciousness
In blindsight, healthy subjects, and its neural correlates
Evidence for consciousness without attention
Deep dive into the dual-task paradigm
Logic of each experiment.
How conscious perception is manipulated and measured?
How attention is manipulated and measured?

3. Recap of Wk 9
“Activation = consciousness = attention” type idea is
completely untenable.
What are the alternatives?
“Patterns”, “complexity”, “information”,
“relationship”?

4. In Wk 10,
We will:
Introduce the Integrated Information Theory of
Consciousness (without any mathematics)

5. Learning Objectives
To be able to answer the following questions:
● What is IIT?
● Where does IIT start to construct the theory?
● What are the five core properties of every phenomenology
that IIT considers important?
● What is the neuronal measure that was inspired by the
IIT?
Optional: Tsuchiya 2017 Phil Compass

6. Why do we need a theory of consciousness?
Week 1-7: Building the basic knowledge
Relationship between consciousness & brain
Week 8-9: Coming closer to the latest research on
consciousness:
Relationship between consciousness and attention
How can we make sense all of what you learned?
Can we address the core question of consciousness by
extending these approaches?
How can we make further progress?
Theory of consciousness

7. A brief history of consciousness research
B.C.~ Philosophical discussions
Descartes “Principles of Philosophy”.
Influential idea “cogito ergo sum (I think,
therefore I am)”
Wundt, Fechner, James:
Birth of experimental psychology
Neuroimaging: Explosion of empirical
studies of consciousness
Chalmers: Hard problem of consciousness
Tononi: Integrated Information Theory of
consciousness
Theory of consciousness

8. Physical world
Electro-chemical
reaction
Subjectivity
Consciousness
Since ~1990: A research program to find the neural correlates of
consciousness (the NCC)
Theory of consciousness

9. Our path so far:
Easy problem: Explain
(observable) behaviors from
neural activities.
Trying to find the
correlational evidence
between consciousness and
neural activities.
Theory of consciousness

10. Physical world
Electro-chemical
reaction
Subjectivity
Consciousness
The Hard Problem of consciousness
Theory of consciousness

11. Hard problem Explain why/how
certain neural activities
must give rise to
consciousness.
Why do we perceive
faces/color/motion
when our neurons in
FFA/V8/MT are
stimulated?
Theory of consciousness

12. Integrated information theory of consciousness
IIT tries to come up
with the explanation
on how our
conscious
phenomenology can
be supported by
physical
mechanisms.
Theory of consciousness

13. Physical world
Electro-chemical
reaction
Subjectivity
Consciousness
How can consciousness be supported by a physical system?
Integrated
information
theory
Theory of consciousness

14. How can we start building theory of
consciousness?
Identify key properties of consciousness
Information, integration, etc.
Propose the properties that physical systems must have in
order to support the identified properties.
Theory of consciousness

16. Integrated information theory of consciousness
From the Phenomenology to the Mechanisms of
Consciousness: Integrated Information Theory 3.0
(Oizumi, Albantakis, Tononi 2014 PLoS Comp)
Intro to IIT

17. “From the Phenomenology”
Five essential characteristics of any conscious experience
Intro to IIT
Tononi 2015 Scholarpedia

18. “From the Phenomenology”
Five essential characteristics of any conscious experience
Intro to IIT
Tononi 2015 Scholarpedia

19. “to the Mechanisms of Consciousness”
Proposes that:
Large quantity of integrated information corresponds to
high level of consciousness
A largest cluster of integrated information = locus of
consciousness
Distinct quality (or patterns) of integrated information
corresponds to each type of qualia
Intro to IIT

20. Informativeness of experience
- One out of many possibilities
Phenomenological observation: Any
moment of conscious experience is
extremely “informative” (or
“differentiable”)
-> Any mechanism that supports
conscious experience must have
differentiability (or a large capacity of
information)
Intro to IIT

21. Thought experiment: human vs a photodiode
On or Off
- 1 bit (?)
86 billion neurons
~1011
neurons -> 1011
bits (?)On or Off
- 1 bit
Intro to IIT

22. Integration of experience
- Unity of experience
Phenomenological observation: Any
moment of conscious experience is
always “integrated” (or “unified”)
-> Any mechanism that supports
conscious experience must be
integrated
Intro to IIT

23. Thought experiment:
a digital camera vs. a human brain
- Each neuron
connected to
1000-10000
neurons
1011
neurons
A digital camera
~10-20 million = 107
pixels
Intro to IIT

24. Phenomenology in split camera vs human (Wk 11)
Boly 2009 Prog Brain
Intro to IIT

25. Rough idea: Integrated information 𝚽 and 𝛗 patterns
(more on how to quantify them in Week 11)
Tononi 2004 BMC
Low information
Small 𝚽
Few 𝛗 patterns
High information
(=differentiation)
High integration
Large 𝚽
Various 𝛗 patterns
Low integration
Small 𝚽
few 𝛗 patterns
Intro to IIT

26. Exclusiveness of experience
- Border of consciousness
Phenomenological observation: Any
moment of conscious experience is
always experienced with a particular
border and extent.
-> Any mechanism that supports
conscious experience must be
exclusive.
Intro to IIT

27. Rough idea: Locus of consciousness is the local
maximum of integrated information 𝚽
Simple feedforward inputs (e.g., sensory inputs)
or outputs (e.g., motor commands) do not
contribute to increase integrated information.
Intro to IIT
Tononi 2004 BMC

28. How can we test IIT?
IIT explains & predicts that conscious states should be
associated with high-level integrated information
Q. How can we estimate the “integrated information” in the
real brain?
Stimulate the brain, and measure the complexity of the
response patterns.
Intro to IIT

30. Empirically testing IIT-inspired measure of consciousness
Brain is too complex to actually compute the measures of
integrated information
Yet, various approximations can be made for the measures.
Can the measure inspired by IIT be used to distinguish
presence or absence of consciousness in brain lesion
patients, patients under general anesthesia, and people in
sleep?
From IIT to TMS/EEG

31. Idea: Using TMS EEG to estimate 𝚽
From IIT to TMS/EEG

32. From IIT to TMS/EEG
Idea: from IIT to TMS/EEG
Sarasso 2014 Clin EEG

33. From IIT to TMS/EEG
Idea: from IIT to TMS/EEG
Sarasso 2014 Clin EEG

34. From IIT to TMS/EEG
Idea: from IIT to TMS/EEG
Sarasso 2014 Clin EEG

35. Currently most promising measure of consciousness
Perturbational complexity index (PCI)
Massimini et al 2008 Prog Brain Res
From IIT to TMS/EEG

36. TMS: Transcranial Magnetic Stimulation
From IIT to TMS/EEG

37. Empirical results (60 electrodes x time)
From IIT to TMS/EEG
Massimini et al
2005 Science

38. Massimini et al 2005 Science
Event-Related Potential (ERP) of one electrode under the TMS
From IIT to TMS/EEG

39. Trial-averaged ERP over 60 channels: Wake
From IIT to TMS/EEG
Massimini et al
2005 Science

40. Trial-averaged ERP over 60 channels: NREM
From IIT to TMS/EEG
Massimini et al
2005 Science

41. From IIT to TMS/EEG
Sarasso 2014 Clin EEG
Schematic summary of known results

42. Interim summary
TMS-evoked EEG patterns were expected to differ depending
on the brain states:
High integrated information ~ More complex TMS-EEG
Low integrated information ~ Less complex TMS-EEG
Confirmed across different types of loss of consciousness!
Can we construct the consciousness meter?
From IIT to TMS/EEG

44. Building consciousness meter
Computing Perturbational Complexity Index (PCI)
Statistical thresholds of
TMS-evoked ERP
ZIP algorithm to compress the
“patterns” of significant source
time course.
Casali et al 2013 Science Trans

45. Casali et al 2013 Science Trans
Quantifying the complexity of TMS-evoked EEG pattern
Building consciousness meter

46. Casarotto 2016 Ann Neurol

47. Casarotto 2016 Ann Neurol

48. Casarotto 2016 Ann Neurol

49. Casarotto 2016 Ann Neurol

50. Summary
PCI were lower than PCI*(=0.31) in the known states of loss
of consciousness: brain injury (Vegetative States), general
anesthesia (Midazolam, Xenon, Propofol), and sleep (NREM
sleep).
PCI w higher than PCI* in the known states of consciousness:
wakefulness, brain injury (Minimally Conscious States +/-,
Locked In State, etc), ketamine, REM sleep
Building consciousness meter

51. Summary
TMS-EEG based paradigm is currently most reliable in
discriminating loss of consciousness
Skipping the sensory / motor areas
Based on evoked, rather than spontaneous, activity(?)
Reflecting integrated information ~ consciousness(?)
Building consciousness meter

52. Questions
What about the IIT’s predictions on boundary & contents of
consciousness?
How can IIT, in principle, explain why different areas of the
brain differentially contributes to different aspects of
consciousness?
How can we measure integrated information?
How can we test the IIT?
Building consciousness meter