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For each decision we make, we must first determine the degree of effort that we are going to exert, and this can range from no effort to full effort. To select a reasoning strategy (e.g., withholding or exerting effort), it has been proposed that we must first integrate internal and external factors to compute the degree of effort necessary and solve the problem at hand. In this dissertation, I sought to determine the mechanisms underlying selecting such reasoning strategies by leveraging electroencephalographic imaging techniques. My investigations began by exploring neural correlates of effortful contemplation and evolved to test assumptions of prediction errors as it became apparent that they were an influential factor. I then tied this mechanism to the strategy selection phase of reasoning and cost-benefit computations. From these findings, I proposed that prediction errors of decision demands function to lessen or remove the burden of cost-benefit computations. Specifically, repeated encounters of the same or similar decisions provide an opportunity to develop expectations of the prospective costs and benefits of those judgments and these expectations facilitate the reasoning process. I consider two possible explanations as to how prediction errors may influence reasoning: first, our expectations provide our cost-benefit computations with a starting point to be adjusted if necessary, and second, our expectations act as a gating mechanism for cost-benefit computations. Although more research is needed to test these hypotheses, I hope my work provides grounds for advancing this field of study. / Graduate

DARPA's neXt Generation (XG) communication program proposes the use of Dynamic Spectrum Access (DSA) wherein intelligent radios can realize opportunistic usage of frequency bands by identifying the under-utilized spectrum and reasoning about it. Implementing such a flexible scheme requires changes in the current static spectrum management approach. As a result, declarative spectrum management through policy-based dynamic spectrum access has garnered significant attention recently. Policy-based dynamic spectrum access decouples the Spectrum Access Policies and Policy Processing Components from the Radio Platform. The Policies define conditions under which the radios are allowed to transmit in terms of frequencies used, geographic locations, time etc. The Policy Processing Components include a reasoning engine called the Policy Reasoner, which is responsible for enforcing these policies. This thesis describes the design and implementation of a novel policy reasoner called Bi- nary Decision Diagram based Reasoner for processing Spectrum Access Policies (BRESAP). BRESAP processes spectrum policies efficiently by reframing the policy reasoning problem as a graph based Boolean function manipulation problem. BRESAP uses Binary Decision Diagrams (BDDs) to represent, analyze and process the policies. It uses a set of efficient graph-theoretic algorithms to merge these policies into a single meta-policy and compute opportunity constraints. Our policy reasoner has the capability to respond to invalid and under-specified transmission requests sent by the System Strategy Reasoner (SSR). In case of invalid or under-specified transmission requests, BRESAP returns a set of opportunity constraints which inform the SSR of the changes needed to the transmission parameters in order to make them conform to the policies. We also propose three algorithms for computing the opportunity constraints. The complexity of the first algorithm is proportional to the number of variables in the metapolicy BDD, while the complexities of the second and third algorithms are proportional to sum of number of variables and the size (i.e., number of nodes) of the meta-policy BDD. / Master of Science

In philosophical theories of knowledge (epistemology), justification is a desirable property that one's beliefs ought to have before they can be accepted as part of a rational discourse. Roughly, for internalists about justification, a belief is justified if the subject has or has available to him good reasons to believe it; while for externalists a belief is justified if there exist good reasons to believe it, regardless of whether the subject actually has or has access to those reasons. One such externalist view of justification is reliabilism, the position that a belief is justified if it has been produced by a properly functioning belief-forming mechanism (BFM). Some examples of BFMs available to human beings are sense perception, memory, and deduction. Epistemic circularity is a notorious problem for reliabilism. If a belief is only justified if it was produced reliably by a certain BFM, how can I ever know for sure that a certain BFM is itself reliable? For instance, take the meta-belief that "sense perception is a reliable BFM." This belief is produced, at least in part, through sense perception itself, for example by analyzing the track record of my past sense perceptions and finding it to be in good order. But if a BFM is thus allowed to vouch for its own trustworthiness, then we have no way to discriminate between reliable and unreliable BFMs. After all, when trying to ascertain if a suspect in a murder case is sincere, it is quite irresponsible to ask the suspect himself. Thus, internalist critics complain, reliabilism is plagued by epistemic circularity and loses sight of the normative goal of epistemology. Reliabilist responses to this serious charge have been of two kinds: (1) to show that epistemically circular arguments can be justificatory, and thus that BFMs can vouch for their own re-liability; or (2) to concede that epistemically circular arguments cannot be justificatory, but then to also insist that some higher-level circularity must be allowed in one's justificatory practices, or no beliefs at all can ever be justified. Here I argue that the first strategy fails and the second succeeds. Internalists are correct that epistemically circular arguments cannot be justificatory in the way that some reliabilists expect them to be, but they are incorrect that all circularity must be banished before our justificatory practices can be virtuous. To always allow circularity makes knowledge reprehensibly easy, but to never let it in at all is a kind of epistemic suicide. / Master of Arts

We introduce a family of logics extending the lightweight Description Logic EL, that allows us to define concepts in an approximate way. The main idea is to use a graded membership function m, which for each individual and concept yields a number in the interval [0,1] expressing the degree to which the individual belongs to the concept. Threshold concepts C~t for ~ in {<,<=,>,>=} then collect all the individuals that belong to C with degree ~t. We further study this framework in two particular directions. First, we define a specific graded membership function deg and investigate the complexity of reasoning in the resulting Description Logic tEL(deg) w.r.t. both the empty terminology and acyclic TBoxes. Second, we show how to turn concept similarity measures into membership degree functions. It turns out that under certain conditions such functions are well-defined, and therefore induce a wide range of threshold logics. Last, we present preliminary results on the computational complexity landscape of reasoning in such a big family of threshold logics.

Experts in the field of molecular and cellular biology (MCB) use domain-specific reasoning strategies to navigate the unique complexities of the phenomena they study and creatively explore problems in their fields. One primary goal of instruction in undergraduate MCB is to foster the development of these domain-specific reasoning strategies among students. However, decades of evidence-based research and many national calls for undergraduate instructional reform have demonstrated that teaching and learning complex fields like MCB is difficult for instructors and learners alike. Therefore, how do students develop rich understandings of biological mechanisms? It is the aim of this dissertation work to explore features of expertise and knowledge building in undergraduate MCB by investigating knowledge organization and problem-solving strategies. Semi-structured clinical think-aloud interviews were conducted with introductory and upper-division students in MCB. Results suggest that students must sort ideas about molecular mechanism into appropriate mental categories, create connections using function-driven and mechanistic rather than associative reasoning, and create nested and overlapping ideas in order to build a nuanced network of biological ideas. Additionally, I characterize the observable components of generative multi-level mechanistic reasoning among undergraduate MCB students constructing explanations about in two novel problem-solving contexts. Results indicate that like MCB experts, students are functionally subdividing the overarching mechanism into functional modules, hypothesizing and instantiating plausible schema, and even flexibly consider the impact of mutations across ontological and biophysical levels. However "filling in" these more abstract schema with molecular mechanisms remains problematic for many students, with students instead employing a range of developing mechanistic strategies. Through this investigation of expertise and knowledge building, I characterize several of the ways in which knowledge integration and generative explanation building are productively constrained by domain-specific features, expand on several discovered barriers to productive knowledge organization and mechanistic explanation building, and suggest instructional implications for undergraduate learning.

Thesis (MA)--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: The effectiveness of a research methods course on the scientific thinking skills of a group of second year psychology students was recently reported on by Kagee, Allie and Lesch (2010). As part of this study they developed an instrument, The Scientific in Psychology Scale, comprising 11 questions each of which required (a) the endorsement of one of two binary choices and (b) a written explanation detailing the reasons for the choice. However, their findings were based only a statistical analysis of (a) which allowed for a comparison between a control group and an experimental group as a whole. The present study aims to characterize the patterns of thinking at a more detailed level, by analysing the qualitative data for one of the questions. To this end, an alphanumeric scheme was devised to code the data for the two groups mentioned; namely, first year psychology students who comprised the control group, and second year psychology students who comprised the experimental group. The coding was performed at a fine-grained level from which broader categories were constructed. / AFRIKAANSE OPSOMMING: 'n Verslag oor die effektiwiteit van ʼn navorsingsmetodiek kursus op die wetenskaplike denkvaardighede van ʼn groep tweedejaar sielkunde studente, was onlangs waargeneem deur Kagee, Allie en Lesch (2010). As deel van hierdie studie het hul ʼn instrument ontwikkel, Die Wetenskaplik Denking in Sielkunde Skaal, wat bestaan uit 11 vrae wat elk ʼn (a) borg van een of twee binêre keuses en (b) ʼn geskrewe verduideliking wat die redes vir die besluit, omskryf. Hul bevindings was egter net gegrond op ʼn statistiese analise van (a) wat toegelaat het vir ʼn vergelyking tussen ʼn kontrole groep en eksperimentele groep as geheel. Hierdie studie beoog om die patrone van denke op ʼn meer gedetaileerde vlak te karakteriseer, deur analise van kwalitatiewe data van een van die vrae. Ten einde dit te bereik, is ʼn alfanumeriese skema geskep om die data van die twee reedsgenoemde groepe te kodeer; naamlik, eerste jaar studente wat deel gevorm het van die kontrole groep, en tweede jaar studente wat deel gevorm het van die eksperimentele groep. Die kodering was uitgevoer op ʼn hoogs gedetaileerde vlak waaruit wyer kategorieë gekonstrueer is.