Scientific Theories of Consciousness – II: Measures of Consciousness
Course Instructor(s): Nithin Nagaraj (Email: n i t h i n . n a g a r a j @ g m a i l . c o m)
Credit Hours: 2 (Reading course, 2 contact hours/week)
Course Description:
“Scientific Theories of Consciousness-II: Measures of Consciousness” is the second course of a two-part series. In “Part-I: Mathematical Methods”, we had laid the mathematical foundations that form the bedrock of several scientific measures of consciousness. In this course (Part-II), we shall explore several scientific theories of consciousness and in particular measures of consciousness. Understanding (and measuring) ‘consciousness’ remains the final frontier of research and is increasingly becoming an interdisciplinary field of study with ideas and principles borrowed from several mathematical disciplines such as Information Theory, Signal Processing, Time Series Analysis, Chaos Theory, Complexity Measures, Brain Imaging Analysis and Clinical measures, Network & Graph Theory. This course will survey various competing measures of consciousness (based on scientific theories) that are based on behavioural and neurophysiological basis. Measures such as causal density, neural complexity, Integrated Information Theoretic measures, Perturbational Complexity Index, Compression-complexity measures will be emphasized.
Learning Objectives:
The primary objective is to familiarize the student with various scientific theories of consciousness and measures of consciousness that are based on behavioural and neurophysiological basis.
Pre-requisites for registration/auditing:
It is mandatory for the student to have completed the course “Scientific Theories of Consciousness-I: Mathematical Methods” to be eligible for crediting/auditing this course.
Expected Student Workload:
This is a reading course with 2 contact hours/week (discussion and in-class interaction) with remaining 6 hours/week of preparation, reading papers and research project execution. The student will be required to take up a research project (on one or more measures of consciousness) with two presentations and one term paper submission.
Course Duration:
January-May (We will start in the week of January 24, 2017).
Lecture Topics and Discussion
Worldly discrimination theory (including signal detection theory), Integration theories, global workspace theory, recurrent activity, neuronal synchrony, behavioural measures (object, subjective, strategic control), brain-based measures such as Integrated Information Theory (Tononi) and its variations, Causal Density, Neural Complexity, clinical measure: Perturbational Complexity Index, computational and network-based measures such as Compression-complexity, Compressionism. Outstanding questions, conflicts between theories/measures will be discussed. The problem of ‘qualia’ and how it is treated within these theories/measures will also be investigated.
Basis for Final Grades
In class participation/discussions: 35%
Research Project execution: 35%
Research Project presentation (2 presentations): 10%
Research Project Term paper: 20%
References/Reading Material
The following research papers will be used for the reading course. This is not an exhaustive list and a full list of papers will be given during the start of the course.
1. Seth AK, Dienes Z, Cleeremans A, Overgaard M, Pessoa L. Measuring consciousness: relating behavioural and neurophysiological approaches. Trends in cognitive sciences. 2008;12(8):314–321.
2. Tononi G. Integrated information theory of consciousness: an updated account. Arch Ital Biol. 2012;150(2-3):56–90.
3. Oizumi M, Albantakis L, Tononi G. From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0. PLoS Comput Biol. 2014;10(5):1–25. doi:10.1371/journal.pcbi.1003588.
4. Casali AG, Gosseries O, Rosanova M, Boly M, Sarasso S, Casali KR, et al. A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior. Science Translational Medicine. 2013;5(198):198ra105–198ra105. doi:10.1126/scitranslmed.3006294.
5. Seth AK, Izhikevich E, Reeke GN, Edelman GM. Theories and measures of consciousness: An extended framework. Proceedings of the National Academy of Sciences. 2006;103(28):10799–10804. doi:10.1073/pnas.0604347103.
6. Tononi G. An information integration theory of consciousness. BMC Neuroscience. 2004;5(1):1–22. doi:10.1186/1471-2202-5-42.
7. Tononi G, Edelman GM. Consciousness and complexity. science. 1998;282(5395):1846–1851.
8. Tononi G, Sporns O, Edelman GM. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proceedings of the National Academy of Sciences. 1994;91(11):5033–5037.
9. Seth AK. Causal connectivity of evolved neural networks during behavior. Network: Computation in Neural Systems. 2005;16(1):35–54.
10. Maguire P, Moser P, Maguire R, Griffith V. Is consciousness computable? Quantifying integrated information using algorithmic information theory. arXiv preprint arXiv:14050126. 2014.
11. Tononi G. Consciousness as integrated information: a provisional manifesto. The Biological Bulletin. 2008;215(3):216–242.
12. Balduzzi D, Tononi G. Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework. PLoS Comput Biol. 2008;4(6):1–18. doi:10.1371/journal.pcbi.1000091.
13. Balduzzi D, Tononi G. Qualia: the geometry of integrated information. PLoS Comput Biol. 2009;5(8):e1000462.
14. Tononi G. Phi: A Voyage from the Brain to the Soul. Pantheon Books; 2012.
15. Maguire P, Maguire R. Consciousness is data compression. In: Proceedings of the thirty-second conference of the cognitive science society; 2010. p. 748–753.
16. Maguire P, Mulhall O, Maguire R, Taylor J. Compressionism: A Theory of Mind Based on Data Compression.
17. Barrett AB, Seth AK. Practical Measures of Integrated Information for Time-Series Data. PLoS Comput Biol. 2011;7(1):1–18. doi:10.1371/journal.pcbi.1001052.
18. Griffith V. A Principled Infotheoretic ϕ-like Measure. CoRR. 2014;abs/1401.0978.
19. Toker D, Sommer F. Moving Past the Minimum Information Partition: How To Quickly and Accurately Calculate Integrated Information. arXiv preprint arXiv:160501096. 2016.
20. Tegmark M. Improved Measures of Integrated Information. ArXiv e-prints. 2016.
21. Seth AK, Barrett AB, Barnett L. Causal density and integrated information as measures of conscious level. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. 2011;369(1952):3748–3767. doi:10.1098/rsta.2011.0079.
22. Tononi G, Sporns O. Measuring information integration. BMC Neuroscience. 2003;4(1):1–20. doi:10.1186/1471-2202-4-31.
23. Tononi G. Consciousness, information integration, and the brain. Progress in brain research. 2005;150:109–126.
24. Seth A. Explanatory correlates of consciousness: theoretical and computational challenges. Cognitive Computation. 2009;1(1):50–63.
25. Tononi G, Boly M, Massimini M, Koch C. Integrated information theory: from consciousness to its physical substrate. Nature Reviews Neuroscience. 2016.
26. Virmani, M., & Nagaraj, N. A Compression-Complexity Measure of Integrated Information. arXiv preprint arXiv:1608.08450. 2016.