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Not by Data Alone: The Promises and Pitfalls of Data Analysis in Understanding Consciousness

Published online by Cambridge University Press:  21 June 2019

Paula Droege
Paula Droege*
Affiliation:
Philosophy Department, Pennsylvania State University, 244 Sparks Building, University Park, PA 16802, USA. Email: pdroege@psu.edu
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Abstract

Since the introduction of new technologies, the deluge of neuroscientific data has been overwhelming. On one hand this new information has produced remarkable breakthroughs in our understanding of brain function and development as well as lifesaving treatments for trauma and disease. On the other hand, the lure and reward for explanations of mental phenomena in terms of simple, manipulable brain processes has led to questionable research methodologies and unsubstantiated claims. A more fundamental issue is raised by the attempt to explain consciousness by means of information, as proposed by the Information Integration Theory (IIT). While the models produced by this massive computation of data will no doubt improve our understanding of brain function and capacity, a strict information processing approach cannot address the problem of meaning. A solution to this problem demands an evolutionary, developmental, and dynamic account of an organism in its environment. Data analysis will play a role in this inclusive explanatory program, but explanation is insufficient by data alone.

Type
Articles
Information
European Review , Volume 27 , Issue 3 , July 2019 , pp. 341 - 356
Copyright
© Academia Europaea 2019 

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Further Reading

UCSD Center for Functional MRI (http://fmri.ucsd.edu/Research/whatisfmri.html)Google Scholar
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Young, B.D., Keller, A. and Rosenthal, D. (2014) Quality-space theory in olfaction. Frontiers in Psychology, 5, pp. 1–15.CrossRefGoogle ScholarPubMed
Oizumi, M., Albantakis, L., Tononi, G. (2014) From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0.. PLoS Computational Biology, 10, pp. e1003588.CrossRefGoogle ScholarPubMed
Tononi, G., Boly, M., Massimini, M. and Koch, C. (2016) Integrated information theory: from consciousness to its physical substrate. Nature Reviews Neuroscience, 17, pp. 450–461. http://integratedinformationtheory.org/ CrossRefGoogle ScholarPubMed
Millikan, R.G. (2004) Varieties of Meaning (Cambridge, MA: MIT Press).CrossRefGoogle Scholar
UCSD Center for Functional MRI (http://fmri.ucsd.edu/Research/whatisfmri.html)Google Scholar
Preuschoff, K.. Website (http://www.fil.ion.ucl.ac.uk/spm/course/slides10-zurich/Kerstin_BOLD.pdf)Google Scholar
Balduzzi, D. and Tononi, G. (2009) Qualia: the geometry of integrated information. PLOS Computational Biology, 5, e1000462.CrossRefGoogle ScholarPubMed
Clark, A. (2000) A Theory of Sentience (Oxford: Oxford University Press).CrossRefGoogle Scholar
Gouras, P. (2017) Color vision. Retrieved 2 January 2017, from Webvision: The Organization of the Retina and Visual System, http://webvision.med.utah.edu/book/part-vii-color-vision/color-vision/ Google Scholar
Rosenthal, D. (2010) How to think about mental qualities. Philosophical Issues, 20, pp. 368–393.CrossRefGoogle Scholar
Tye, M. (2000) Consciousness, Color, and Content (Cambridge, MA: A Bradford Book).CrossRefGoogle Scholar
Young, B.D., Keller, A. and Rosenthal, D. (2014) Quality-space theory in olfaction. Frontiers in Psychology, 5, pp. 1–15.CrossRefGoogle ScholarPubMed
Oizumi, M., Albantakis, L., Tononi, G. (2014) From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0.. PLoS Computational Biology, 10, pp. e1003588.CrossRefGoogle ScholarPubMed
Tononi, G., Boly, M., Massimini, M. and Koch, C. (2016) Integrated information theory: from consciousness to its physical substrate. Nature Reviews Neuroscience, 17, pp. 450–461. http://integratedinformationtheory.org/ CrossRefGoogle ScholarPubMed
Millikan, R.G. (2004) Varieties of Meaning (Cambridge, MA: MIT Press).CrossRefGoogle Scholar