Thesis (Ph.D.)--Indiana University, Dept. of Computer Science and Cognitive Science, 2004. / Title from PDF t.p. (viewed Dec. 1, 2008). Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0369. Chairs: Michael E. Gasser; Linda B. Smith.

<p> This theoretical dissertation utilizes alchemical hermeneutic methodology to identify, compare, and critique core assumptions in modern physics and the Freudian, Jungian, and neuroscience paradigms. Common ideas and parallels in modern physics and analytical psychology are outlined and offered as a way to deepen understanding of contemporary neuroscience research on transference/counter-transference in psychotherapy. From the perspective of Freudian theory, based on Western rationalism, humans are understood as objects among objects operating in a universe of linear causality; the unconscious is understood as an epiphenomenon of biological processes, limited to personal dimensions. Consequently, the analytic process focuses on intrapsychic experience and transference is treated reductively. Neuroscience implicitly shares this worldview.</p><p> Analytical psychology understands consciousness as a temporary state born from and subordinate to the unconscious, which is archetypal in nature. Focus is on ego and archetype operating within the context of a dialectic relationship, each being relatively autonomous, proportionate to its scope. Essentially, archetype is understood as psychophysical in nature, existing in a transcendent unitary reality beyond the apparent duality of psyche and matter, which exhibits <i>acausal orderedness</i> expressible in patterns of archetypal number. Archetypal patterns describing the relationship between ego/matter and archetype/spirit are discerned and shown to reflect a <i>purposive course of psychic energy</i>. These include: spirit/archetype moves towards matter/ego and vice versa, but only reluctantly. Transference, ultimately, could be understood as manifestation of spiritual instinct, consistent with the purposive course of energy, therefore, also expressible in terms of patterns of archetypal number. These same patterns are expressed in terms of analogies to ideas from modern physics.</p><p> Consistent with analytical psychology, neuroscience demonstrates that right-brain (unconscious) develops prior to left-brain (conscious); with brain development dependent upon quality of early attachment relationship, which is <i>re-lived</i> in transference. The <i>double helix approach </i> to therapy is a psychophysical phenomenon in transference/counter-transference that displays dialect between the two brain hemispheres and centers of the psyche (ego and unconscious), thus, demonstrating remarkable parallels to the above described archetypal patterns. These patterns are expressed in terms of archetypal number. This suggests neuroscience might be better served by adopting a worldview consistent with analytical psychology and modern physics. </p>

Thesis (Ph. D.)--University of Chicago, Dept. of Philosophy, 2002. / Includes bibliographical references (p. 122-123). Also available on the Internet.

Thesis (Ph.D.) - University of Queensland, 2005. / Includes bibliography.

Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2004. / Source: Dissertation Abstracts International, Volume: 66-01, Section: A, page: 0012. Chairman: David B. Leake. Title from dissertation home page (viewed Oct. 12, 2006).

<p> Humans build theories out of the data we observe, and out of those theories arise wonders. The most powerful theories are causal theories, which organise data into actionable structures. Causal theories make explicit claims about the structure of the world: what entities and processes exist in it, which of these relate to one another and in what form those relations consist. We can use causal theories to induce new generalisations about the world (in the form of particular models or other causal theories) and to explain particular occurrences. This allows rapidly disseminating causal information throughout our cognitive communities. Causal theories and the explanations derived from them guide decisions we make, including where and when to look for more data, completing the cycle.</p><p> Causal theories play a ubiquitous and potent role in everyday life, in formal pursuit of them in the sciences, and through their applications in medicine, technology and industry. Given this, the rarity of analyses that attempt to characterise causal theories and their uses in general, computational terms is surprising. Only in recent years has there been a substantial refinement of our models of causal induction due to work by computational cognitive scientists &mdash; the interdisciplinary tradition out of which which this dissertation originates. And even so, many issues related to causal theories have been left unattended; three features in particular merit much greater attention from a computational perspective: generating and evaluating explanation, the role of simplicity in explanation choice, and continuous-time causal induction. I aim to redress this situation with this dissertation. </p><p> In Chapter 0, I introduce the primary paradigms from computational cognitive science &ndash; computational level analysis and rational analysis &ndash; that govern my research. In Chapter 1, I study formal theories of causal explanation in Bayesian networks by comparing the explanations the generate and evaluate to human judgements about the same systems. No one model of causal explanation captures the pattern of human judgements, though the intuitive hypothesis, that the most probable a posteriori explanation is the best performs worst of the models evaluated. I conclude that the premise of finding model for all of human causal explanation (even in this limited domain) is flawed; the research programme should be refined to consider the features of formal models and how well they capture our explanatory practices as they vary between individuals and circumstances. One feature not expressed in these models explicitly but that has been shown to matter for human explanation is simplicity. Chapter 2 considers the problem of simplicity in human causal explanation choice in a series of four experiments. I study <i>what</i> makes an explanation simple (whether it is the number of causes invoked in or the number of assumptions made by an explanation), <i>how</i> simplicity concerns are traded off against data-fit, <i>which</i> cognitive consequences arise from choosing simpler explanations when the data does not fit, and <i> why</i> people prefer simpler explanations. </p><p> In Chapter 3, I change the focus from studying causal explanation to causal induction &mdash; in particular, I develop a framework for continuous time causal theories (<p style="font-variant: small-caps">CTCTS</p>). A <p style="font-variant: small-caps">CTCT</p> defines a generative probabilistic framework for other generative probabilistic models of causal systems, where the data in those systems expressed in terms of continuous time. Chapter 3 is the most interdisciplinary piece of my dissertation, accordingly it begins by reviewing a number of topics: the history of theories of causal induction within philosophy, statistics and medicine; empirical work on causal induction in cognitive science, focusing on issues related to causal induction with temporal data; conceptual issues surrounding the formal definition of time, data, and causal models; and probabilistic graphical models, causal theories, and stochastic processes. I then introduce the desiderata for the <p style="font-variant: small-caps"> CTCT</p> framework and how those criteria are met. I then demonstrate the power of <p style="font-variant: small-caps">CTCTS</p> by using them to analyse five sets of experiments (some new and some derived from the literature) on human causal induction with temporal data. Bookending each experiment and the model applied to it is are case from medical history that illustrate a real-world instance of the variety of problem being solved in the section; the opening discussion describes the case and why it fits the problem structure of the model used to analyse the experimental results and the closing discussion illustrates aspects of the case omitted from the initial discussion that complicate the model and fit better with the model introduced in the next section. Then, I discuss ways to incorporate other advances in probabilistic programming, generative theories and stochastic processes into the <p style="font-variant: small-caps">CTCT</p> framework, identify potential applications with specific focus on mechanisms and feedback loops, and conclude by analysing the centrality of temporal information in the study of the mind more generally.</p><p> Excepting the supporting appendices and bibliography that end the dissertation, I conclude in two parts. First, in Chapter 4, I analyse issues at the intersection of three of the main themes of my work: namely, (causal) explanation, (causal) induction and time. This proceeds by examining these topics first in pairs and then as a whole. Following that, is Chapter 5, an epilogue that clarifies the interpretations and intended meanings of the &ldquo;Mind as Theory Engine&rdquo; metaphor as it applies to human cognition.</p>

L'objectif de ce mémoire est d'évaluer l'approche énactive en sciences cognitives. Pour ce faire, il sera question de soulever les enjeux clés entourant la recherche sur la conscience, à la lumière des récents travaux sur la danse du philosophe américain Alva Noë. En se basant sur l'approche énactive de la conscience, celui-ci élabore une théorie sensori-motrice de l'appréciation esthétique. C'est donc en participant aux ateliers de danse Tuning score, conçue par la chorégraphe canadienne Lisa Nelson, que Noë élabore son approche. Dans le premier chapitre, nous situerons l'approche énactive et la recherche sur la conscience dans les sciences cognitives en général. En discutant celle-ci par rapport à trois autres approches, nous soulèverons le débat entre l'internalisme et l'externalisme de la conscience, tout en décrivant le passage d'une conception internaliste à une conception externaliste en sciences cognitives. Ce chapitre a donc pour objectif de définir en quoi consiste l'approche énactive afin d'introduire les éléments fondamentaux à l'approche sensori-motrice. Dans le deuxième chapitre, nous poursuivrons le travail enclenché dans le premier chapitre en faisant une analyse approfondie de l'approche énactive de la perception. Développée principalement par Kevin O'Regan, Susan Hurley et Alva Noë, cette approche défend une conception externaliste de la conscience en s'opposant à l'orthodoxie dans la recherche sur la perception en sciences cognitives. L'objectif de ce chapitre sera donc de présenter les différents arguments de l'approche sensori-motrice contre l'orthodoxie, principalement présenté par Noë dans son ouvrage Action in Perception (2004). Dans le troisième et dernier chapitre, nous ferons l'analyse et la critique des récents travaux de Noë concernant la danse. Une présentation de ses recherches sera suivie d'un argument allant contre la méthodologie employée dans ses travaux. Le tout sera suivi d'une conclusion où une évaluation globale de l'approche énactive sera faite. ______________________________________________________________________________

Thesis (Ph. D.)--Oregon State University, 1989. / Typescript (photocopy). Includes bibliographical references (leaves 111-141). Also available on the World Wide Web.

Thesis (M.A.)--Carleton University, 2004. / Includes bibliographical references (p. 125-126). Also available in electronic format on the Internet.

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 93-103).