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Thesis (M.A.)--Georgia State University, 2005. / Title from title screen. Tricia Z. King, committee chair; Robin Morris, Mary Morris, committee members. Electronic text (102 p. : col. ill.) : digital, PDF file. Description based on contents viewed July 17, 2007. Includes bibliographical references (p. 91-102).

Thesis (Ph.D. in Human Medical Genetics) -- University of Colorado, 2006. / Typescript. Includes bibliographical references (leaves 118-127). Free to UCDHSC affiliates. Online version available via ProQuest Digital Dissertations;

This thesis mainly intends to study and reach some conclusions related to major challenges concerning vocabulary teaching or learning, how vocabulary teaching can be improved, findings obtained from the studies in order to reach that purpose and to what extend the suggested alternative vocabulary techniques are effective. It is also aimed to outline the basic insights of the mind, storage, and retrieval from the literature involving linguistics and language teaching. Based on above mentioned background knowledge, it is also intended to derive some significant conclusions to improve the effectiveness and thus the quality of vocabulary teaching in language instruction. In accordance with the principles of the human memory, how we can alter current vocabulary instruction techniques and activities and what scholars offer language teachers and learners are dealt with in detail. So as to validate and prove the efficiency of suggested techniques and activities, a case study is carried out and findings are discussed at large. Additionally, interviews about vocabulary teaching have been carried out with the involved students and instructors and the obtained data has been evaluated. In the final part of the research, some implications and suggestion related to vocabulary teaching are provided along with the underlying rationale behind them aiming to increase the quality of teaching of lexical items and as a result to increase overall quality of language instruction.

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Humans have an innate ability to deal with numerosity and other aspects of magnitude. This ability is generally honed through education in and experience with mathematics, which necessarily changes the brain structurally and functionally. These changes can be further manipulated through non-invasive electrical brain stimulation. Studying these processes in the case of maths not only constitutes research of great practical impact – given the importance of numerical skills in today's society – but also makes use of maths as a suitable domain in which to study plasticity. In this thesis, I aimed to explore how expertise with numbers shapes brain and behaviour, and also the degree to which processing numbers is similar to other domains in terms of the necessity of healthy brain regions believed to underlie normal processing within and across these domains. In Study 1, behavioural and structural brain differences were found cross-sectionally between mathematicians and non-mathematicians. A double dissociation between those groups was found between grey matter density in the frontal lobe and behavioural performance: their correlation was positive for mathematicians but negative for controls. These effects may have been caused by years of experience, by congenital predispositions, or, plausibly, by both of these factors, whose disambiguation is non-trivial. Study 2 used transcranial random noise stimulation (tRNS) to assist arithmetic learning. A novel montage was used to enhance brain function during the stage when it is believed to be most involved. Real as compared to sham tRNS enhanced reaction times (RTs) and learning rate on a calculation-based task, but not on a retrieval-based task. The effects were only observed in conditions of high task difficulty. Study 3 examined structural MRI measures before and after arithmetic training to determine how either frontal or parietal tRNS applied with the task changes the structure of the brain longitudinally as compared to sham. Previous results (including those of Study 2) of behavioural facilitation in terms of enhanced RTs to calculation problems were replicated, and further interpreted. Both frontal and parietal tRNS modulated the changes that occurred, pre-to-post training, in terms of cortical volume and gyrification of frontal, parietal and temporal areas. Study 4 investigated the shared neural and cognitive resources used for processing numerical magnitude and musical pitch, by probing how stimulus-response compatibility (SRC) effects for each of the two dimensions compare in a group of mainly temporoparietal lesion patients with numerical impairments versus controls. A double dissociation was found in that numerically impaired patients did not show the number-based SRC effect but did show the pitch-based one, while control subjects demonstrated the opposite trend. Overall, the results of these studies leave us with three main messages. First, expertise in numbers and mathematics, whether acquired through years of experience (Study 1) or through a few days of tRNS-assisted training (Study 3), appears to be associated with complex changes in the morphology of several brain structures. Some – but not all – of these structures are maths-relevant, and, in the case of tRNS-assisted training, they are distal to the site of the stimulating electrodes. Second, tRNS can improve performance in arithmetic (Studies 2 and 3), although the mechanisms by which this occurs are not yet fully understood, neither neurally nor behaviourally. Third, I found (Study 4) that brain lesions leading to impairment in the number domain do not necessarily affect processing in other domains – such as pitch – that are otherwise linked to number via a putative common code in the parietal lobes.

Extracting structure from initially incomprehensible streams of events is fundamental to a range of human abilities: from navigating in a new environment to learning a language. These skills rely on our ability to extract spatial and temporal regularities, often with minimal explicit feedback, that is known as statistical learning. Despite the importance of statistical learning for making perceptual decisions, we know surprisingly little about the brain circuits and how they change when learning temporal regularities. In my thesis, I combine behavioural measurements, Diffusion Tensor Imaging (DTI) and resting-state fMRI (rs-fMRI) to investigate the structural and functional circuits that are involved in statistical learning of temporal structures. In particular, I compare structural connectivity as measured by DTI and functional connectivity as measured by rs-fMRI before vs. after training to investigate learning-dependent changes in human brain pathways. Further, I combine the two imaging modalities using graph theory and regression analyses to identify key predictors of individual learning performance. Using a prediction task in the context of sequence learning without explicit feedback, I demonstrate that individuals adapt to the environment’s statistics as they change over time from simple repetition to probabilistic combinations. Importantly, I show that learning of temporal structures relates to decision strategy that varies among individuals between two prototypical distributions: matching the exact sequence statistics or selecting the most probable outcome in a given context (i.e. maximising). Further, combining DTI and rs-fMRI, I show that learning-dependent plasticity in dissociable cortico-striatal circuits relates to decision strategy. In particular, matching relates to connectivity between visual cortex, hippocampus and caudate, while maximisation relates to connectivity between frontal and motor cortices and striatum. These findings have potential translational applications, as alternate brain routes may be re-trained to support learning ability when specific pathways (e.g. memory-related circuits) are compromised by age or disease.

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The guiding question for this essay is: who is the learner? The aim is to examine and criticize one answer to this question, sometimes referred to as the theory of the learning brain, which suggests that the explanation of human learning can be reduced to the transmitting and storing of information in the brain’s formal and representational architecture, i.e., that the brain is the learner. This essay will argue that this answer is misleading, because it cannot account for the way people strive to learn in an attempt to lead a good life as it misrepresents the intentional life of the mind, which results in its counting ourselves out of the picture when it attempts to provide a scientific theory of the learning process. To criticize the theory of the learning brain, this essay will investigate its philosophical foundation, a theory of mind called cognitivism, which is the basis for the cognitive sciences. Cognitivism is itself built on three main tenets: mentalism, the mind-brain identity theory and the computer analogy. Each of these tenets will be criticized in turn, before the essay turns to criticize the theory of the learning brain itself. The focus of this essay is, in other words, mainly negative. The hope is that this criticism will lay the groundwork for an alternative view of mind, one that is better equipped to give meaningful answers to the important questions we have about what it means to learn, i.e., what we learn, how we do it and why. This alternative will emphasize the holistic and intentional character of the human mind, and consider the learning process as an intentional activity performed, not by isolated brains, but by people with minds that are extended, embodied, enacted and embedded in a sociocultural and physical context.