This document discusses various approaches to measuring integrated information and consciousness. It covers two main approaches: 1) IIT-inspired psychophysics experiments to better quantify the "bandwidth of consciousness" and 2) Applying the PyPhi toolbox to analyze fly brain recordings to measure intrinsic information. The document also discusses ideas for future experiments such as relating conscious and non-conscious percepts to informational structures, examining how attention impacts integrated information, and exploring empirical findings to inform refinements to integrated information theory.

1. Integrated information:
a practical approach
Part 3.
Various approaches
18 June 26 @ASSC
Nao Tsuchiya

2. 1. IIT-inspired psychophysics experiments
• Bandwidth of consciousness
• Similarity-based psychophysics
2. Alternative to phi-toolbox
• pyphi toolbox applied to empirical data
• Fly LFP recording under awake and anesthesia

3. 1. IIT-inspired psychophysics
• Phenomenological Axiom: “Information”
• Each moment of conscious experience should be
enormously “informative”
• Previous experiments tended to show <40bits/s
information (Norretanders 1991, Cohen et al 2016 TICS)
• Why?
• Experimenters’ bias and artifacts in experimental designs
(Haun, Koch, Tononi, Tsuchiya 2017 Neuroscience of
Consciousness)?

4. Re-considering
Sperling’s paradigms
Not seeing natural scene
Not seeing movies
Not talking to people
…
…
…
• Haun, Koch, Tononi, Tsuchiya 2017 Neuroscience of Consciousness
Alternatives:
Is ‘A’ there?
Is ‘B’ there?
…
…
..
Actual experience:
Bunch of letters
Letters in 4 x 3
Regular spacing
In the center of screen
Screen in front of me
….

5. Consideration on
2AFC paradigms
Actual experience
Alternative 1
Up
Alternative 2
Down
Artificially forced decision ~= experience
Neither

6. Another case
Actual experience
Alternative 1
Front-to-right
decision = (part of) experience
Alternative 2
Front-to-left

7. better measures to
characterize experience?
Experience-Unmatched 2AFC
vs Experience-Matched 2AFC
2-stage Confidence rating
vs 1-stage Visibility Rating (or PAS)
Attention-dependency
Similarity rating

8. Mathews, Kauinitz, van Boxtel, Tsuchiya (2017) Phil Trans B

9. Mathews, Kauinitz, van Boxtel, Tsuchiya (2017) Phil Trans B

10. 2. Alternative to phi-toolbox: PyPhi toolbox
PyPhi toolbox
Computes phi based on Oizumi, Albantakis, Tononi 2014
PLoS Comp: IIT 3.0
Various differences from Phi*, PhiG (based on IIT 2.0)
Major theoretical difference
Difference in distance measure, big vs small phi,
min(phi_past, phi_future), cause, effects
Practically, requires conditional independence, TPM-based
(requires some discretization), not computable for > 10
neurons

11. Laminar electrode recordings in Drosophila
in wakeful and under isoflurane anesthesia
Cohen, van Swinderen, Tsuchiya (2016) eNeuro
Cohen, van Swinderen, Tsuchiya (2018) eNeuro

12. Cohen, van Swinderen, Tsuchiya (2018) eNeuro

13. Leung, Cohen, van Swinderen, Tsuchiya (in prep)

14. Future experimental ideas
• Relationship between conscious vs non-
conscious percepts and its associated with
informational structures
• Complex analysis?
• Just collapse of phi-structure?
• Attention-dependent percept: can their
change be captured by the state change? Or
changes in structure? (TPM)

15. Discussion:
Empirical findings informing IIT?
Or newer-version IIT or different theory?
• Conjectures based on empirical approaches
• Why PCI works?
• Why ECoG works?
• What can work? Why?
• Alternative to Masafumi’s approach - PyPhi application to fly data
• Bottom-up approach with presumption that the system is a part of complex
• Spontaneous, event-related vs properly perturbed?
• What is the appropriate scales for space, time, measure,…?

16. • Inspired from IIT
• Comparative measures, relations among measures
• Structure of conscious experience
• How the shapes of integrated information structure differ
among
• modalities, sub-modalities, dependence on attention
• species, individual-difference (e.g., synesthesia)
• Difference in the effects of learning - motor-skill vs object
• PFC vs back-of-cortex (Odegaard et al vs Boly et al 2017 JNS)
• Emergence of macro vs. micro (Hoel et al 2013 PNAS)

17. Information processing vs.
intrinsic information

20. Information processing Intrinsic information
structure