Neurons are connected to form functional networks in the brain. When neurons are combined in sequence, nontrivial effects arise. One example is disinhibition; that is, inhibition to another inhibitory factor. Disinhibition may be serving an important purpose because a large number of local circuits in the brain contain disinhibitory connections. However, their exact functional role is not well understood. The objective of this dissertation is to analyze the computational role of disinhibition in brain function, especially in visual perception and attentional control. My approach is to propose computational models of disinhibition and then map the model to the local circuits in the brain to explain psychological phenomena. Several computational models are proposed in this dissertation to account for disinhibition. (1) A static inverse difference of Gaussian filter (IDoG) is derived to account explicitly for the spatial effects of disinhibition. IDoG can explain a number of complex brightness-contrast illusions, such as the periphery problem in the Hermann grid and the White's effect. The IDoG model can also be used to explain orientation perception of multiple lines as in the modified version of Poggendorff illusion. (2) A spatio-temporal model (IDoGS) in early vision is derived and it successfully explains the scintillating grid illusion, which is a stationary display giving rise to a striking, dynamic, scintillating effect. (3) An interconnected Cohen-Grossberg neural network model (iCGNN) is proposed to address the dynamics of disinhibitory neural networks with a layered structure. I derive a set of sufficient conditions for such an interconnected system to reach asymptotic stability. (4) A computational model combining recurrent and feed-forward disinhibition is designed to account for input-modulation in temporal selective attention. The main contribution of this research is that it developed a unified framework of disinhibition to model several different kinds of neural circuits to account for various perceptual and attentional phenomena. Investigating the role of disinhibition in the brain can provide us with a deeper understanding of how the brain can give rise to intelligent and complex functions.

This study analyses the methodology of computational cognitive modeling as one of the ways of conducting research in cognitive science. The aim of the study is to provide an understanding of the place of computational cognitive models in understanding human cognition. Considering the vast number of computational cognitive models which have been just given to account for some cognitive phenomenon by solely simulating some experimental study and fitting to empirical data, a practice-oriented approach is adopted in this study to understand the work of the modeler, and accordingly to discover the potential of computational cognitive models, apart from their being simulation tools. In pursuit of this aim, a framework with a practice-oriented approach from the philosophy of science literature, which is Morgan &amp / Morrison (1999)&rsquo / s account, is employed on a case study. The framework emphasizes four key elements to understand the place of models in science, which are the construction of models, the function of models, the representation they provide, and the ways we learn from models. The case study Q-Soar (Simon, Newell &amp / Klahr, 1991), is a model built with Soar cognitive architecture (Laird, Newell &amp / Rosenbloom, 1987) which is representative of a class of computational cognitive models. Discussions are included for how to make generalizations for computational cognitive models out of this class, i.e. for models that are built with other modeling paradigms.

There has been a recent flurry of activity in consciousness research. Although an operational definition of consciousness has not yet been developed, philosophy has come to identify a set of features and aspects that are thought to be associated with the various elements of consciousness. On the other hand, there have been several recent attempts to develop computational models of consciousness that are claimed to capture or illustrate one or more aspects of consciousness. As a plausible substitute to evaluating how well the current computational models model consciousness, this study examines how the current computational models fare in modeling those aspects and features of consciousness identified by philosophy. Following a detailed and critical review of the literature of philosophy of consciousness, this study constructs a composite and eclectic list of features and aspects that would be expected in any successful model of consciousness. The study then evaluates, from the viewpoint of that list, some of the current self-claimed computational models of consciousness, specifically CLARION, IDA, ACT-R and model proposed in the Cleeremans&#039 / review and study. The computational models studied are evaluated with respect to each identified aspect and feature of consciousness.

Domínios imagísticos são os domínios nos quais a resolução de problemas inicia com um processo de reconhecimento dos objetos de domínio a partir da informação visual capturada, suportando interpretações mais abstratas em eventuais etapas subsequentes. A resolução de problemas, em domínio imagísticos, demanda dos especialistas a aplicação intensiva de conhecimento visual, que corresponde ao conjunto de modelos mentais que suportam o processo de raciocínio sobre a informação associada ao arranjo espacial e outros aspectos visuais das entidades do domínio. O conhecimento visual geralmente corresponde à porção tácita do conhecimento dos praticantes do domínio, de modo que ele geralmente é utilizado de modo inconsciente, resistindo à verbalização explícita. Estas características do conhecimento visual são desafiadoras do ponto de vista de Engenharia do Conhecimento. O objetivo geral deste trabalho é delinear uma abordagem integrada para aquisição, modelagem, representação e raciocínio sobre conhecimento visual, do ponto de vista da Engenharia do Conhecimento. A interpretação visual é uma tarefa comum em domínios imagísticos, cuja resolução demanda dos especialistas um raciocínio que envolve a realização de um processo cognitivo que inicia com a percepção visual direta de características dos objetos já conhecidos no domínio, e que resulta em compreensões mais abstratas da cena observada, tais como: comportamentos dinâmicos dos objetos da cena, significado do contexto da cena, causas ou efeitos do estado de coisas capturado pela cena, etc. Desta forma, para realizar o objetivo geral, este trabalho assume como objetivo específico estudar e modelar o processo de raciocínio utilizado pelos especialistas para resolver tarefas de interpretação visual, bem como as próprias estruturas para representação de conhecimento inferencial utilizadas pelos especialistas em domínios imagísticos durante a realização deste tipo de tarefa. Os principais resultados deste trabalho são um modelo de raciocínio para resolução de tarefas de interpretação visual; um modelo de estrutura para representação de conhecimento inferencial, cognitiva e filosoficamente fundamentada, chamada pacote visual; e uma abordagem para aquisição de conhecimento visual e refinamento de ontologias. O estudo foi conduzido no domínio da Estratigrafia Sedimentar, com foco na tarefa de interpretação visual de processos deposicionais geradores de fácies sedimentares. Os modelos desenvolvidos no trabalho foram testados no domínio, alcançando resultados satisfatórios. Para isto, realizou-se também o refinamento de uma ontologia de domínio. Este processo foi realizado através da aplicação de diversas técnicas de aquisição de conhecimento em sessões com o especialista. / Imagistic domains are those in which problem-solving process begins with the recognition of domain objects trough the visual information captured, supporting more abstract interpretations in subsequent steps. The problem-solving process, in imagistic domains, is performed trough intensive application of visual knowledge, which corresponds to the set of mental models that support the process of reasoning about the information of the spatial arrangement and other visual aspects of the domain entities. The visual knowledge corresponds to a tacit kind of knowledge of practitioners in the field, so it is often used unconsciously, resisting to explicit verbalization. These characteristics of visual knowledge are challenging from the standpoint of Knowledge Engineering. The general aim of this work is to outline an integrated approach to acquisition, modeling, representation and reasoning, to handle visual knowledge, from the standpoint of Knowledge Engineering. The visual interpretation is a common task in imagistic domains, whose resolution demands a reasoning that involves a cognitive process that starts with the direct perception of visual features of objects, and results in abstract understandings of observed scene, such as dynamic behavior of objects in the scene, the meaning of the context of the scene, causes or effects of the state of affairs captured in the scene, etc. Thus, to achieve the overall goal, this work takes as a specific aim to study and model the reasoning process used by experts to solve tasks of visual interpretation, as well as the inferential knowledge structures applied by experts in imagistic domains to accomplish this type of task. The main results of this work are a model of reasoning for solving tasks of visual interpretation; a cognitive grounded model of structure for inferential knowledge representation, called visual chunk; and an approach to visual knowledge acquisition and refinement of ontologies, which explores the reasoning used by the expert as a tool to reveal the lack of important terms in the domain ontology. The study was conducted in the field of sedimentary stratigraphy, focusing on the task of visual interpretation of depositional processes responsible by the generation of sedimentary facies. The models developed in this work was tested in the domain, achieving satisfactory results. In order to apply our approach, a refinement of domain ontology was performed. This process was carried out by applying several techniques of knowledge acquisition in sessions with the expert.

xx, 191 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / Visual search is an important part of human-computer interaction (HCI). The visual search processes that people use have a substantial effect on the time expended and likelihood of finding the information they seek. This dissertation investigates visual search through experiments and computational cognitive modeling. Computational cognitive modeling is a powerful methodology that uses computer simulation to capture, assert, record, and replay plausible sets of interactions among the many human processes at work during visual search. This dissertation aims to provide a cognitive model of visual search that can be utilized by predictive interface analysis tools and to do so in a manner consistent with a comprehensive theory of human visual processing, namely active vision. The model accounts for the four questions of active vision, the answers to which are important to both practitioners and researchers in HCI: What can be perceived in a fixation? When do the eyes move? Where do the eyes move? What information is integrated between eye movements? This dissertation presents a principled progression of the development of a computational model of active vision. Three experiments were conducted that investigate the effects of visual layout properties: density, color, and word meaning. The experimental results provide a better understanding of how these factors affect human- computer visual interaction. Three sets of data, two from the experiments reported here, were accurately modeled in the EPIC (Executive Process-Interactive Control) cognitive architecture. This work extends the practice of computational cognitive modeling by (a) informing the process of developing computational models through the use of eye movement data and (b) providing the first detailed instantiation of the theory of active vision in a computational framework. This instantiation allows us to better understand (a) the effects and interactions of visual search processes and (b) how these visual search processes can be used computationally to predict people's visual search behavior. This research ultimately benefits HCI by giving researchers and practitioners a better understanding of how users visually interact with computers and provides a foundation for tools to predict that interaction. This dissertation includes-both previously published and co-authored material. / Adviser: Anthony J. Hornof

Domínios imagísticos são os domínios nos quais a resolução de problemas inicia com um processo de reconhecimento dos objetos de domínio a partir da informação visual capturada, suportando interpretações mais abstratas em eventuais etapas subsequentes. A resolução de problemas, em domínio imagísticos, demanda dos especialistas a aplicação intensiva de conhecimento visual, que corresponde ao conjunto de modelos mentais que suportam o processo de raciocínio sobre a informação associada ao arranjo espacial e outros aspectos visuais das entidades do domínio. O conhecimento visual geralmente corresponde à porção tácita do conhecimento dos praticantes do domínio, de modo que ele geralmente é utilizado de modo inconsciente, resistindo à verbalização explícita. Estas características do conhecimento visual são desafiadoras do ponto de vista de Engenharia do Conhecimento. O objetivo geral deste trabalho é delinear uma abordagem integrada para aquisição, modelagem, representação e raciocínio sobre conhecimento visual, do ponto de vista da Engenharia do Conhecimento. A interpretação visual é uma tarefa comum em domínios imagísticos, cuja resolução demanda dos especialistas um raciocínio que envolve a realização de um processo cognitivo que inicia com a percepção visual direta de características dos objetos já conhecidos no domínio, e que resulta em compreensões mais abstratas da cena observada, tais como: comportamentos dinâmicos dos objetos da cena, significado do contexto da cena, causas ou efeitos do estado de coisas capturado pela cena, etc. Desta forma, para realizar o objetivo geral, este trabalho assume como objetivo específico estudar e modelar o processo de raciocínio utilizado pelos especialistas para resolver tarefas de interpretação visual, bem como as próprias estruturas para representação de conhecimento inferencial utilizadas pelos especialistas em domínios imagísticos durante a realização deste tipo de tarefa. Os principais resultados deste trabalho são um modelo de raciocínio para resolução de tarefas de interpretação visual; um modelo de estrutura para representação de conhecimento inferencial, cognitiva e filosoficamente fundamentada, chamada pacote visual; e uma abordagem para aquisição de conhecimento visual e refinamento de ontologias. O estudo foi conduzido no domínio da Estratigrafia Sedimentar, com foco na tarefa de interpretação visual de processos deposicionais geradores de fácies sedimentares. Os modelos desenvolvidos no trabalho foram testados no domínio, alcançando resultados satisfatórios. Para isto, realizou-se também o refinamento de uma ontologia de domínio. Este processo foi realizado através da aplicação de diversas técnicas de aquisição de conhecimento em sessões com o especialista. / Imagistic domains are those in which problem-solving process begins with the recognition of domain objects trough the visual information captured, supporting more abstract interpretations in subsequent steps. The problem-solving process, in imagistic domains, is performed trough intensive application of visual knowledge, which corresponds to the set of mental models that support the process of reasoning about the information of the spatial arrangement and other visual aspects of the domain entities. The visual knowledge corresponds to a tacit kind of knowledge of practitioners in the field, so it is often used unconsciously, resisting to explicit verbalization. These characteristics of visual knowledge are challenging from the standpoint of Knowledge Engineering. The general aim of this work is to outline an integrated approach to acquisition, modeling, representation and reasoning, to handle visual knowledge, from the standpoint of Knowledge Engineering. The visual interpretation is a common task in imagistic domains, whose resolution demands a reasoning that involves a cognitive process that starts with the direct perception of visual features of objects, and results in abstract understandings of observed scene, such as dynamic behavior of objects in the scene, the meaning of the context of the scene, causes or effects of the state of affairs captured in the scene, etc. Thus, to achieve the overall goal, this work takes as a specific aim to study and model the reasoning process used by experts to solve tasks of visual interpretation, as well as the inferential knowledge structures applied by experts in imagistic domains to accomplish this type of task. The main results of this work are a model of reasoning for solving tasks of visual interpretation; a cognitive grounded model of structure for inferential knowledge representation, called visual chunk; and an approach to visual knowledge acquisition and refinement of ontologies, which explores the reasoning used by the expert as a tool to reveal the lack of important terms in the domain ontology. The study was conducted in the field of sedimentary stratigraphy, focusing on the task of visual interpretation of depositional processes responsible by the generation of sedimentary facies. The models developed in this work was tested in the domain, achieving satisfactory results. In order to apply our approach, a refinement of domain ontology was performed. This process was carried out by applying several techniques of knowledge acquisition in sessions with the expert.

Domínios imagísticos são os domínios nos quais a resolução de problemas inicia com um processo de reconhecimento dos objetos de domínio a partir da informação visual capturada, suportando interpretações mais abstratas em eventuais etapas subsequentes. A resolução de problemas, em domínio imagísticos, demanda dos especialistas a aplicação intensiva de conhecimento visual, que corresponde ao conjunto de modelos mentais que suportam o processo de raciocínio sobre a informação associada ao arranjo espacial e outros aspectos visuais das entidades do domínio. O conhecimento visual geralmente corresponde à porção tácita do conhecimento dos praticantes do domínio, de modo que ele geralmente é utilizado de modo inconsciente, resistindo à verbalização explícita. Estas características do conhecimento visual são desafiadoras do ponto de vista de Engenharia do Conhecimento. O objetivo geral deste trabalho é delinear uma abordagem integrada para aquisição, modelagem, representação e raciocínio sobre conhecimento visual, do ponto de vista da Engenharia do Conhecimento. A interpretação visual é uma tarefa comum em domínios imagísticos, cuja resolução demanda dos especialistas um raciocínio que envolve a realização de um processo cognitivo que inicia com a percepção visual direta de características dos objetos já conhecidos no domínio, e que resulta em compreensões mais abstratas da cena observada, tais como: comportamentos dinâmicos dos objetos da cena, significado do contexto da cena, causas ou efeitos do estado de coisas capturado pela cena, etc. Desta forma, para realizar o objetivo geral, este trabalho assume como objetivo específico estudar e modelar o processo de raciocínio utilizado pelos especialistas para resolver tarefas de interpretação visual, bem como as próprias estruturas para representação de conhecimento inferencial utilizadas pelos especialistas em domínios imagísticos durante a realização deste tipo de tarefa. Os principais resultados deste trabalho são um modelo de raciocínio para resolução de tarefas de interpretação visual; um modelo de estrutura para representação de conhecimento inferencial, cognitiva e filosoficamente fundamentada, chamada pacote visual; e uma abordagem para aquisição de conhecimento visual e refinamento de ontologias. O estudo foi conduzido no domínio da Estratigrafia Sedimentar, com foco na tarefa de interpretação visual de processos deposicionais geradores de fácies sedimentares. Os modelos desenvolvidos no trabalho foram testados no domínio, alcançando resultados satisfatórios. Para isto, realizou-se também o refinamento de uma ontologia de domínio. Este processo foi realizado através da aplicação de diversas técnicas de aquisição de conhecimento em sessões com o especialista. / Imagistic domains are those in which problem-solving process begins with the recognition of domain objects trough the visual information captured, supporting more abstract interpretations in subsequent steps. The problem-solving process, in imagistic domains, is performed trough intensive application of visual knowledge, which corresponds to the set of mental models that support the process of reasoning about the information of the spatial arrangement and other visual aspects of the domain entities. The visual knowledge corresponds to a tacit kind of knowledge of practitioners in the field, so it is often used unconsciously, resisting to explicit verbalization. These characteristics of visual knowledge are challenging from the standpoint of Knowledge Engineering. The general aim of this work is to outline an integrated approach to acquisition, modeling, representation and reasoning, to handle visual knowledge, from the standpoint of Knowledge Engineering. The visual interpretation is a common task in imagistic domains, whose resolution demands a reasoning that involves a cognitive process that starts with the direct perception of visual features of objects, and results in abstract understandings of observed scene, such as dynamic behavior of objects in the scene, the meaning of the context of the scene, causes or effects of the state of affairs captured in the scene, etc. Thus, to achieve the overall goal, this work takes as a specific aim to study and model the reasoning process used by experts to solve tasks of visual interpretation, as well as the inferential knowledge structures applied by experts in imagistic domains to accomplish this type of task. The main results of this work are a model of reasoning for solving tasks of visual interpretation; a cognitive grounded model of structure for inferential knowledge representation, called visual chunk; and an approach to visual knowledge acquisition and refinement of ontologies, which explores the reasoning used by the expert as a tool to reveal the lack of important terms in the domain ontology. The study was conducted in the field of sedimentary stratigraphy, focusing on the task of visual interpretation of depositional processes responsible by the generation of sedimentary facies. The models developed in this work was tested in the domain, achieving satisfactory results. In order to apply our approach, a refinement of domain ontology was performed. This process was carried out by applying several techniques of knowledge acquisition in sessions with the expert.

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