Thesis (Ph.D.)--University of California, Irvine, 2007. / (UMI)AAI3271310. Source: Dissertation Abstracts International, Volume: 68-07, Section: A, page: 2965. Adviser: Ellen Burt.

Includes bibliographic references (p. 260-264).

Thesis (MA)--University of Stellenbosch, 2005. / Bibliography. Also available via the Internet.

<p> This qualitative study uses children&rsquo;s writing to explore the divide between a conception of Science as a humanistic discipline reliant on creativity, ingenuity and out of the box thinking and a persistent public perception of science and scientists as rigid and methodical. Artifacts reviewed were 506 scripts written during 2014 and 2016 by 5th graders participating in an out-of classroom, mentor supported, free-choice 10-week arts and literacy initiative. 47% (237) of these scripts were found to contain content relating to Science, Scientists, Science Education and the Nature of Science. These 237 scripts were coded for themes; characteristics of named scientist characters were tracked and analyzed. Findings included NOS understandings being expressed by representation of Science and Engineering Practices; Ingenuity being primarily linked to Engineering tasks; common portrayals of science as magical or scientists as villains; and a persistence in negative stereotypes of scientists, including a lack of gender equity amongst the named scientist characters. Findings suggest that representations of scientists in popular culture highly influence the portrayals of scientists constructed by the students. Recommendations to teachers include encouraging explicit consideration of big-picture NOS concepts such as ethics during elementary school and encouraging the replacement of documentary or educational shows with more engaging fictional media.</p><p>

<p> According to Craig Keener there are hundreds of millions of people around the world who believe that they have experienced or witnessed a healing miracle. Unfortunately, the vast majority of these miracles (Special Divine Action) occur in the third world and lack medical and scientific documentation. However, in Craig Keener&rsquo;s text <i>Miracles: The Credibility of the New Testament Accounts</i> (2011), he details over one hundred modern medical miracles which have documentation from American doctors and scientists. In addition to Keener, the Vatican&rsquo;s <i>Medica Consulta</i> has also catalogued seventy cases of modern medical miracles originating from the shrine at Lourdes France which meet the Vatican&rsquo;s rigid documentation criteria. Finally, the Renewal linked <i>Global Medical Research Institute </i> (GMRI) has begun an extensive study to verify and document claims of personal medical miracles. Thus, there is strong scientific evidence for the occurrence of modern miracles. </p><p> While proving the occurrence of modern miracles is foundational in the development of a Renewal apologetic, the theological implications and meanings of the miraculous are the larger questions according to Polkinghorne, Richard Swinburne and others. What is ultimately needed in the development of a Renewal apologetic, is a comprehensive theology of the miraculous, which places modern miracles within the larger history of God&rsquo;s interaction with his creation, as a means for the expansion of his kingdom. This will be the focus of the second part of this paper, as modern miracles are shown to be an integral part of the Renewal and expansion of the Kingdom of God; and ultimately the development of a Renewal Apologetic.</p><p>

<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>

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Scientific realism and antirealism are two main views in the philosophy of science regarding the status of unobservable entities in science and whether we have good epistemic reasons to believe that our current successful scientific theories are (approximately) true. Briefly, the former claims that our scientific theories are (approximately) true and unobservable entities these scientific theories postulate exist. On the other hand, the latter claims that we do not have good epistemic reasons to believe that our scientific theories are (approximately) true and that unobservable entities our scientific theories postulate exist. The scientific realism has two primary tenets, one axiological (i.e., science should seek truth) and the other epistemological (namely, our current successful theories are (approximately) true). In this thesis, the issue has been examined from standpoint of the account of Peirce’s philosophy of science, more accurately based on his understanding of reality, truth and basic idealism. In the first chapter, I outline the main points of the debate from the perspectives of both sides. In the second chapter, I give reasons why the scientific realists’ argument is not convincing. In the third chapter, I attempt to draw an accurate picture of the account of Peirce’s views on the nature of scientific theories. In the last chapter, I make a case for scientific realism from the Peircean account of philosophy of science. I have claimed why the current debate cannot be settled without accepting a kind of Peirce's basic idealism and his understanding of reality. I think both scientific realists and antirealists accept a kind of naïve realism. This is the main reason why it is not possible to settle the debate from their standpoints. In order to overcome this issue, I attempt to develop a more sophisticated realism based on Peirce’s understanding of reality, truth and basic idealism.

No description available.

The current approach to science for mainstream psychology relies on the philosophical foundation of positivism that cannot account for meaning as humans experience it. Phenomenology provides an alternative scientific approach in which meaning is constituted by acting toward objects in the world that is more consistent with how humans experience meaning. Immanuel Levinas argues that the phenomenological approach, while more consistent with human experience, does not provide a grounding for meaning. Rather, Levinas argues that meaning is grounded in the ethical encounter with the Other, or other person, such that meaning is given by the Other in rupture. For Levinas, the physical world, or elemental, and the I provide constraints for the meaning given by the Other but the Other is logically prior to all other experience. This alternative to the mainstream scientific approach in psychology of positivism has implications for the epistemology, methodology, and scientific community of psychology. The Levinasian perspective advocates an epistemology that is open to the rupture of the Other as a way to provide new knowledge. This emphasis on openness to rupture produces a methodology in which the scientist must allow object of study to influence the method used in research. Finally, the Levinasian perspective implies a scientific community that is sensitive to the rupture occasioned by the encounter with the Other.