No description available.

No description available.

No description available.

This study was designed to assess follower prototypes as dynamic structures. Connectionist theory is a good framework to understand the process by which followership perceptions are altered by contextual factors. Organizational culture, change in immediate leader and follower prototypes were measured in an applied setting across time to assess the dynamism of the cognitive networks of implicit followership theories. Change in culture and immediate leader was measured at three time points, across six months, during the acquisition of one organization by a second. Change scores were created by computing difference scores from surveys completed at the first time point to the second time point, three months later, to the third and final time point, three months later. There were no significant effects of change in culture on reported follower networks. There was, however, a significant effect of leader change at time points two and three when regressed on individual's follower networks. The overall findings of this study suggest that IFT's like leadership prototypes remain fairly stable across time (Epitropaki, 2004), but are subject to organizational change. / Master of Science

1. Intuitions are often treated in philosophy as a basic evidential source to confirm/discredit a proposed definition or theory; e.g. intuitions about Gettier cases are taken to deny a justified-true-belief analysis of ‘knowledge’. Recently, Weinberg, Nichols & Stitch (WN&S) provided evidence that epistemic intuitions vary across persons and cultures. In-so-far as philosophy of this type (Standard Philosophical Methodology – SPM) is committed to provide conceptual analyses, the use of intuition is suspect – it does not exhibit the requisite normativity. I provide an analysis of intuition, with an emphasis on its neural – or connectionist – cognitive backbone; the analysis provides insight into its epistemic status and proper role within SPM. Intuition is initially characterized as the recognition of a pattern. 2. The metaphysics of ‘pattern’ is analyzed for the purpose of denying that traditional symbolic computation is capable of differentiating the patterns of interest. 3. The epistemology of ‘recognition’ is analyzed, again, to deny that traditional computation is capable of capturing human acts of recognition. 4. Fodor’s informational semantics, his Language of Thought and his Representational Theory of Mind are analyzed and his arguments denied. Again, the purpose is to deny traditional computational theories of mind. 5. Both intuition and a theory of concepts – pragmatic conceptualism - are developed within the connectionist computational paradigm. Intuition is a particular sort of occurrent signal, and a concept is a counterfactually defined set of signals. Standard connectionist theory is significantly extended to develop my position, and consciousness plays a key functional role. This extension – taking connectionism seriously – is argued to be justified on the basis of the failure of the traditional computing paradigm to account for human cognition. 6. Repercussions for the use of intuition in SPM are developed. Variance in intuition is characterized – and expected - as a kind of bias in the network, either inherent or externally-provoked. The WN&S data is explained in the context of this bias. If SPM remains committed to the use of intuition, then intuition must be taken as a part of a larger body of evidence, and it is from experts – not the folk – that intuitions should be solicited.

1. Intuitions are often treated in philosophy as a basic evidential source to confirm/discredit a proposed definition or theory; e.g. intuitions about Gettier cases are taken to deny a justified-true-belief analysis of ‘knowledge’. Recently, Weinberg, Nichols & Stitch (WN&S) provided evidence that epistemic intuitions vary across persons and cultures. In-so-far as philosophy of this type (Standard Philosophical Methodology – SPM) is committed to provide conceptual analyses, the use of intuition is suspect – it does not exhibit the requisite normativity. I provide an analysis of intuition, with an emphasis on its neural – or connectionist – cognitive backbone; the analysis provides insight into its epistemic status and proper role within SPM. Intuition is initially characterized as the recognition of a pattern. 2. The metaphysics of ‘pattern’ is analyzed for the purpose of denying that traditional symbolic computation is capable of differentiating the patterns of interest. 3. The epistemology of ‘recognition’ is analyzed, again, to deny that traditional computation is capable of capturing human acts of recognition. 4. Fodor’s informational semantics, his Language of Thought and his Representational Theory of Mind are analyzed and his arguments denied. Again, the purpose is to deny traditional computational theories of mind. 5. Both intuition and a theory of concepts – pragmatic conceptualism - are developed within the connectionist computational paradigm. Intuition is a particular sort of occurrent signal, and a concept is a counterfactually defined set of signals. Standard connectionist theory is significantly extended to develop my position, and consciousness plays a key functional role. This extension – taking connectionism seriously – is argued to be justified on the basis of the failure of the traditional computing paradigm to account for human cognition. 6. Repercussions for the use of intuition in SPM are developed. Variance in intuition is characterized – and expected - as a kind of bias in the network, either inherent or externally-provoked. The WN&S data is explained in the context of this bias. If SPM remains committed to the use of intuition, then intuition must be taken as a part of a larger body of evidence, and it is from experts – not the folk – that intuitions should be solicited.

No current laboratory test can reliably identify patients with schizophrenia. Instead, key symptoms are observed via language, including derailment, where patients cannot follow a coherent storyline, and delusions, where false beliefs are repeated as fact. Brain processes underlying these and other symptoms remain unclear, and characterizing them would greatly enhance our understanding of schizophrenia. In this situation, computational models can be valuable tools to formulate testable hypotheses and to complement clinical research. This dissertation aims to capture the link between biology and schizophrenic symptoms using DISCERN, a connectionist model of human story processing. Competing illness mechanisms proposed to underlie schizophrenia are simulated in DISCERN, and are evaluated at the level of narrative language, the same level used to diagnose patients. The result is the first simulation of a speaker with schizophrenia. Of all illness models, hyperlearning, a model of overly intense memory consolidation, produced the best fit to patient data, as well as compelling models of delusions and derailments. If validated experimentally, the hyperlearning hypothesis could advance the current understanding of schizophrenia, and provide a platform for simulating the effects of future treatments. / text

Although research in leadership perception tends to show males have an advantage over females as a result of gender stereotypes, researchers have theorized recently some of this gender-related cognitive bias may be offset by perceiver affect (Medvedeff & Lord, 2007). In this experiment, a between-participants factorial design was used to examine the impact of gender stereotypes (male or female) and perceiver affect (positive or negative) on participants\' leader networks and dynamic perceptions of leadership. Participants were randomly assigned to a affect and leader gender condition with roughly 33 undergraduate students in each group. Leadership perceptions were assessed by examining connections between concepts in cognitive networks and repeated measurements of dynamic ratings. Data were analyzed using the Pathfinder and GEMCAT II (General Multivariate Methodology for Estimating Catastrophe Models) programs. Results suggested gender stereotypes and perceiver affect yield differential effects on leader networks. There was more stability in leader networks for a male leader than for a female, whereas there was more accuracy for perceivers in a neutral mood when compared to those in a negative mood condition. Furthermore, dynamic ratings showed the perceptual process in leadership emergence recognition was non-linear for both the male and female leader. Additionally, those in the negative mood condition were less resistant to changing their leadership perceptions when compared to those in the neutral mood condition. Potential interpretations for these findings are discussed and recommendations for future work in this area are provided. / Ph. D.

In Chinese orthography, phonetic compounds comprise about 80% of the most frequent characters. They contain separate phonological and semantic elements, referred to as phonetic and semantic radicals respectively. A dominant type exists in which the se-mantic radical appears on the left and the phonetic radical on the right (SP characters); an opposite, minority structure also exists in which the semantic radical appears on the right and the phonetic radical on the left (PS characters). Through statistical analyses, connectionist modelling, behavioural experiments, and neuroimaging studies, this dis-sertation demonstrates that the distinct structures of these two types of characters allow us crucial insights into the relationship between brain structure and reading processes. The statistical analyses of a Chinese lexical database show that, because of the different information profiles of SP and PS characters and the imbalanced distribution between them in the lexicon, the overall information is skewed to the right. This information skew provides important opportunities to examine the interaction between foveal split-ting and the information structure of the characters. The foveal splitting hypothesis as-sumes a vertical meridian split in the foveal representation and the consequent contra-lateral projection to the two cerebral hemispheres; it has been shown to have important implications for visual word recognition. The square shape and the condensed structure of Chinese characters make them a severe test case for the split fovea claim. Through a lateralized cueing examination and a TMS study of the semantic radical combinability effect with foveally presented characters in character semantic judgements, a flexible division of labour between the hemispheres in character recognition is demonstrated, with each hemisphere responding optimally to the information in the contralateral visual hemifield. The interaction between stimulation site and radical combinability in the TMS study also provides further support for the split fovea claim, suggesting functional foveal splitting as a universal processing constraint in reading. Even if foveal splitting is true, it is still unclear about how far the effects of foveal split-ting can extend from the retina into the process of character recognition. We show that, in naming isolated, foveally presented SP and PS characters, adult male and female readers process them differently, with opposite patterns of ease and difficulty: males responded significantly faster to SP than PS characters; females showed a non-significant tendency in the opposite direction. This result is also supported by a corre-sponding ERP study showing larger N350 amplitude elicited by PS character than SP characters in the male brain, and an opposite pattern in the female brain. The split fovea claim suggests that the two halves of a centrally fixated character are initially processed in different hemispheres. The male brain typically relies more on the left hemisphere for phonological processing compared with the female brain, causing this gender difference to emerge. This interaction is also predicted by an implemented computational model, contrasting a split cognitive architecture, in which the mapping between orthography to phonology is mediated by two partially encapsulated, interconnected processing do-mains, and a non-split cognitive architecture, in which the mapping is mediated by a single, undifferentiated processing domain. Thus, the effects of foveal splitting in read-ing extend far enough to interact with the gender of the reader in a naturalistic reading task. In short, this dissertation demonstrates that foveal splitting is a universal language proc-essing phenomenon, precise enough to project the two radicals of a centrally-fixated Chinese character to different hemispheres to allow a flexible division of labour be-tween the two hemispheres to emerge, and its effects in reading extend far enough into word recognition to interact with the gender of the reader in a naturalistic reading task. The results can also be extrapolated to Chinese word and sentence processing as well as to other languages. This dissertation thus has contributed to a better understanding of the relationship between brain structure and language processes.

Ecological modelling problems have characteristics both featured in other modelling fields and specific ones, hence, methods developed and tested in other research areas may not be suitable for modelling ecological problems or may perform poorly when used on ecological data. This thesis identifies issues associated with the techniques typically used for solving ecological problems and develops new generic methods for decision support, especially suitable for ecological data modelling, which are characterised by: (1) adaptive learning, (2) knowledge discovery and (3) accurate prediction. These new methods have been successfully applied to challenging real world ecological problems. Despite the fact that the number of possible applications of computational intelligence methods in ecology is vast, this thesis primarily concentrates on two problems: (1) species establishment prediction and (2) environmental monitoring. Our review of recent papers suggests that multi-layer perceptron networks trained using the backpropagation algorithm are most widely used of all artificial neural networks for forecasting pest insect invasions. While the multi-layer perceptron networks are appropriate for modelling complex nonlinear relationships, they have rather limited exploratory capabilities and are difficult to adapt to dynamically changing data. In this thesis an approach that addresses these limitations is proposed. We found that environmental monitoring applications could benefit from having an intelligent taste recognition system possibly embedded in an autonomous robot. Hence, this thesis reviews the current knowledge on taste recognition and proposes a biologically inspired artificial model of taste recognition based on biologically plausible spiking neurons. The model is dynamic and is capable of learning new tastants as they become available. Furthermore, the model builds a knowledge base that can be extracted during or after the learning process in form of IF-THEN fuzzy rules. It also comprises a layer that simulates the influence of taste receptor cells on the activity of their adjacent cells. These features increase the biological relevance of the model compared to other current taste recognition models. The proposed model was implemented in software on a single personal computer and in hardware on an Altera FPGA chip. Both implementations were applied to two real-world taste datasets.In addition, for the first time the applicability of transductive reasoning for forecasting the establishment potential of pest insects into new locations was investigated. For this purpose four types of predictive models, built using inductive and transductive reasoning, were used for predicting the distributions of three pest insects. The models were evaluated in terms of their predictive accuracy and their ability to discover patterns in the modelling data. The results obtained indicate that evolving connectionist systems can be successfully used for building predictive distribution models and environmental monitoring systems. The features available in the proposed dynamic systems, such as on-line learning and knowledge discovery, are needed to improve our knowledge of the species distributions. This work laid down the foundation for a number of interesting future projects in the field of ecological modelling, robotics, pervasive computing and pattern recognition that can be undertaken separately or in sequence.