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Understanding Cognition via Complexity Science

Favela, Luis H, Jr.
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427981743

Abstract Details

2015, PhD, University of Cincinnati, Arts and Sciences: Philosophy.
Mechanistic frameworks of investigation and explanation dominate the cognitive, neural, and psychological sciences. In this dissertation, I argue that mechanistic frameworks cannot, in principle, explain some kinds of cognition. In its place, I argue that complexity science has methods and theories more appropriate for investigating and explaining some cognitive phenomena. I begin with an examination of the term `cognition.’ I defend the idea that “cognition” has been a moving target of investigation in the relevant sciences. As such it is not historically true that there has been a thoroughly entrenched and agreed upon conception of “cognition.” Next, I take up mechanistic frameworks. Although `mechanism’ is an umbrella term for a set of loosely related characteristics, there are common features: linearity, localization, and component dominance. I then describe complexity science, with emphasis on its utilization of dynamical systems modeling. Next, I discuss two phenomena that typically fall under the purview of complexity science: nonlinearity and interaction dominance. A complexity science framework guided by the theory of self-organized criticality and utilizing the methods of dynamical systems modeling can surmount a number of challenges that face mechanistic frameworks when investigating some kinds of cognition. The first challenge is epistemic and concerns the inadequacy of mechanistic frameworks to facilitate the comprehensibility of massive amounts of data across various scales and areas of inquiry. I argue that complexity science is more appropriate for making big data comprehensible when investigating cognition, particularly across disciplines. I demonstrate this via an approach called nested dynamical modeling (NDM). NDM can facilitate comprehensibility of large amounts of data obtained from various scales of investigation by eliminating irrelevant degrees of freedom of that system as relates to the target of investigation. The second shortcoming concerns ontological blind spots within mechanistic frameworks. Cognitive phenomena like extended cognition often fail to meet most, if not all, of the criteria assumed by many mechanistic approaches, especially component dominance. I argue that research guided by the notion of interaction dominance allows for extended cognition to be a real, empirically supported phenomenon within complex systems frameworks. In this chapter I discuss some of my experimental work on extended cognitive systems. The search for mechanisms can be a reasonable starting position when attempting to explain natural phenomena in the life sciences. However, too strict of an adherence to theoretical and methodological commitments such as linearity, localization, and component dominance can result in intractable epistemic challenges and ontological blind spots. Complexity science has theories and methods to overcome such challenges in the investigation of cognition.
Anthony Chemero, Ph.D. (Committee Chair)
Rick Dale, Ph.D. (Committee Member)
Valerie Hardcastle, Ph.D. (Committee Member)
Robert Richardson, Ph.D. (Committee Member)
172 p.
Philosophy
Cognition; Complexity Science; Component Dominance; Dynamical Systems Modeling; Interaction Dominance; Mechanistic Explanation

Recommended Citations

Citations

  • Favela, Jr., L. H. (2015). Understanding Cognition via Complexity Science [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427981743

    APA Style (7th edition)

  • Favela, Jr., Luis. Understanding Cognition via Complexity Science. 2015. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427981743.

    MLA Style (8th edition)

  • Favela, Jr., Luis. "Understanding Cognition via Complexity Science." Doctoral dissertation, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427981743

    Chicago Manual of Style (17th edition)

ucin1427981743
993
© 2015, some rights reserved.Creative Commons License Understanding Cognition via Complexity Science by Luis H Favela Jr. is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.