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An assumption in Cognitive Psychology, which has been challenged in recent years, is that the systems responsible for action and perception work independently of one another. These systems work together during conceptual tasks and research has demonstrated that action knowledge can influence performance even when the task is 'action-irrelevant' (Borghi, 2004; Borghi, Flumini, Natraj & Wheaton, 2012; Creem & Proffitt, 2001; Tucker & Ellis, 1998, 2001). However, participants in such tasks are often only asked to make simple category judgements, such as natural versus man made. The research reported in this thesis has shown that, under certain conditions, participants use action knowledge to make 'complex' category choices in an action-irrelevant task. The experimental work has predominantly used the forced-choice triad task to assess the circumstances under which participants categorise objects based on shared actions. The triads were designed with a target object and two choice objects matching on either shared actions (rifle + water pistol), shared taxonomic relations (rifle + sword), or both (orange + banana). The context in which the objects were presented was also manipulated so that the objects were either presented on a white background (context-lean) or being used by an agent (context-rich). Participants were most likely to select the choice object that shared both a taxonomic and an action demonstrating that action has an 'additive' effect in categorical decisions. Presenting the objects being used by an agent in a functional scenario increased the saliency of the shared actions between the stimuli, and participants were more likely to select the action choice. The subsequent experimental work reported in the thesis sought to eliminate potential confounding variables including perceptual features, object typicality and task instructions. What the experimental work presented here has demonstrated is that action can influence decisions on more complex categories, and judgments of similarity. The research has identified three main circumstances under which knowledge of action becomes influential in the triad task designed for the purpose of this research as follows: (i) when it is presented in conjunction with taxonomic information, (ii) when it is presented with a context, and (iii) when participants are first asked to physically interact with the objects.

The purpose of this study was to develop and to test a paired-comparisons figural scale to measure preference for complexity. A Random Shapes Scale (RSS) consisting of 18 sets of 3 random shapes was constructed. In each set of 3, one shape was of high complexity, one of medium complexity and one of low complexity. The random shapes were chosen from the eleven hundred generated by Vanderplas. Two existing measures of preference for complexity, the Barron-Welsh Art Scale (BW) and the Revised Art Scale (RA) were also used. Students in architecture, art, education, law and engineering (N=292) were tested using the RSS. Three weeks later the same groups of students (N=184) were retested on the RSS and completed the BW and RA as well. The BW and RA were significantly correlated with the RSS in three of the five groups tested. The internal consistency of the RSS calculated over all groups combined was .66; the stability coefficient was .71. The analysis of variance showed significant differences among the five groups tested. Therefore the RSS does differentiate among groups on the dimension of preference for complexity. The majority of the items were highly correlated with total test scores. This indicates that the items are homogenous. The results of the statistical analyses lead to the conclusion that the RSS is a useful measure of a unitary dimension of preference for complexity. / Arts, Faculty of / Psychology, Department of / Graduate

The similarity between objects is judged in a wide variety of contexts from visual search to categorization to face recognition. There is a correspondingly rich history of similarity research, including empirical work and theoretical models. However, the field lacks an account of the real time neural processing dynamics of different similarity judgment behaviors. Some accounts focus on the lower-level processes that support similarity judgments, but they do not capture a wide range of canonical behaviors, and they do not account for the moment-to-moment stability and interaction of realistic neural object representations. The goal of this dissertation is to address this need and present a broadly applicable and neurally implemented model of object similarity judgments. I accomplished this by adapting and expanding an existing neural process model of change detection to capture a set of canonical, task-general similarity judgment behaviors. Target behaviors to model were chosen by reviewing the similarity judgment literature and identifying prominent and consistent behavioral effects. I tested each behavior for task-generality across three experiments using three diverse similarity judgment tasks. The following behaviors observed across all three tasks served as modeling targets: the effect of feature value comparisons, attentional modulation of feature dimensions, sensitivity to patterns of objects encountered over time, violations of minimality and triangle equality, and a sensitivity to circular feature dimensions like color hue. The model captured each effect. The neural processes implied by capturing these behaviors are discussed, along with the broader theoretical implications of the model and possibilities for its future expansion.

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No description available.

No description available.

O operador de agrupamento e as funções de agregação são as principais ferramentas utilizadas para sumarizar dados em um Sistema de Gerenciamento de Base de Dados Relacionais (SGBDR). O operador de agrupamento funciona criando partições nos dados utilizando comparações por identidade, e permite que sejam aplicadas funções de agregação que retornam um único valor representando o grupo como um todo. Entretanto, para dados métricos, agrupamento utilizando identidade tem pouca utilidade. Neste caso, adotar o conceito de similaridade é frequentemente uma abordagem mais promissora. A literatura apresenta alguns operadores que podem agrupar os dados utilizando similaridade. Todos eles utilizam um limiar de valor de distância para atribuir os elementos aos grupos. No entanto, estes operadores não obtêm resultados satisfatórios quando a distribuição dos dados apresenta variações significativas na densidade de objetos em diferentes regiões do espaço. Para alcançar melhores resultados nestas situações, propusemos um novo operador que atribui os grupos utilizando uma eleição envolvendo grupos já atribuídos. Também propusemos generalizações, para os operadores existentes e propostos, para trabalhar com uma quantidade de vizinhos mais próximos e aproximação dos vizinhos mais próximos ao invés de um limiar de distância. Para possibilitar a inclusão destes operadores em SGBDR, propusemos uma extensão à Structured Query Language (SQL) e novas funções de agregação. Implementamos estes operadores em nosso framework em C++ usando a biblioteca Arboretum. Para avaliar os métodos propostos, analisamos tanto qualidade dos resultados quanto tempo de execução, utilizando conjuntos de dados reais e sintéticos. Os operadores propostos alcançaram melhores resultados quanto à qualidade de resultados, e mantiveram os tempos de execução similares. Os operadores que utilizam aproximação aos vizinhos mais próximos produziram resultados de qualidade similar quando comparados aos operadores que utilizando os vizinhos mais próximos, podendo ser executados em menor tempo que estes. / The grouping operator and aggregation functions are the primary tools used to summarize data inside a Relational Database Management Systems (RDBMS). The grouping operator works creating partitions in data using identity comparisons, and allow applying aggregation functions that return a single value that represent the entire group. However, for metric data, grouping by identity is seldom useful. In this case, adopting the concept of the similarity is often a better approach. The literature presents few operators that can group data using similarity. All of them use a distance threshold value to assign the elements in groups. However, these operators do not achieve satisfactory results when the data distribution present a significant variation in the density of objects in different regions of the space. To achieve better results in these situations, we have proposed a novel operator that assign groups using an election involving already assigned groups. We also proposed generalizations to existing and proposed operators to work with an amount of nearest neighbors and approximate neighbors instead of a distance threshold. To support these operators in RDBMS, we propose an extension to Structured Query Language (SQL) and new aggregation functions. Our proposed algorithms can run the proposed and existing operators. We implemented these operators in our framework in C++ using Arboretum library. To evaluate the proposed methods, we assess both results quality and the execution time, using both real and synthetic datasets. The proposed operators achieved better results comparing the quality and maintained similar executing time. The operators that use the approximate nearest neighbors produced similar quality results comparing with the operators that use the exact neighbors and can execute faster than that.

The recent development of the service-oriented architecture (SOA) has provided an opportunity to apply this new technology to support model management. This is particularly critical when more and more decision models are delivered as web services. A web-services-based approach to model management is useful in providing effective decision support. When a decision model is implemented as a web service, it is called a model-based web service. In model management, selecting a proper model-based web service is an important issue. Most current research on selecting such web service relies on matching inputs and outputs of the model, which is oversimplified. The incorporation of more semantic knowledge may be necessary to make the selection of model-based web services more effective. In this research, we propose a new mechanism that represents the semantics associated with a problem and then use the similarity of semantic information between a new problem description and existing web services to find the most suitable web services for solving the new problem. The paper defines the concept of entity similarity, attribute similarity, and functional similarity for problem matching. The web service that has the highest similarity is chosen as a base for constructing the new web services. The identified mapping is converted into BPEL4WS codes for utilizing the web services. To verify the feasibility of the proposed method, a prototype system has been implemented in JAVA.

O operador de agrupamento e as funções de agregação são as principais ferramentas utilizadas para sumarizar dados em um Sistema de Gerenciamento de Base de Dados Relacionais (SGBDR). O operador de agrupamento funciona criando partições nos dados utilizando comparações por identidade, e permite que sejam aplicadas funções de agregação que retornam um único valor representando o grupo como um todo. Entretanto, para dados métricos, agrupamento utilizando identidade tem pouca utilidade. Neste caso, adotar o conceito de similaridade é frequentemente uma abordagem mais promissora. A literatura apresenta alguns operadores que podem agrupar os dados utilizando similaridade. Todos eles utilizam um limiar de valor de distância para atribuir os elementos aos grupos. No entanto, estes operadores não obtêm resultados satisfatórios quando a distribuição dos dados apresenta variações significativas na densidade de objetos em diferentes regiões do espaço. Para alcançar melhores resultados nestas situações, propusemos um novo operador que atribui os grupos utilizando uma eleição envolvendo grupos já atribuídos. Também propusemos generalizações, para os operadores existentes e propostos, para trabalhar com uma quantidade de vizinhos mais próximos e aproximação dos vizinhos mais próximos ao invés de um limiar de distância. Para possibilitar a inclusão destes operadores em SGBDR, propusemos uma extensão à Structured Query Language (SQL) e novas funções de agregação. Implementamos estes operadores em nosso framework em C++ usando a biblioteca Arboretum. Para avaliar os métodos propostos, analisamos tanto qualidade dos resultados quanto tempo de execução, utilizando conjuntos de dados reais e sintéticos. Os operadores propostos alcançaram melhores resultados quanto à qualidade de resultados, e mantiveram os tempos de execução similares. Os operadores que utilizam aproximação aos vizinhos mais próximos produziram resultados de qualidade similar quando comparados aos operadores que utilizando os vizinhos mais próximos, podendo ser executados em menor tempo que estes. / The grouping operator and aggregation functions are the primary tools used to summarize data inside a Relational Database Management Systems (RDBMS). The grouping operator works creating partitions in data using identity comparisons, and allow applying aggregation functions that return a single value that represent the entire group. However, for metric data, grouping by identity is seldom useful. In this case, adopting the concept of the similarity is often a better approach. The literature presents few operators that can group data using similarity. All of them use a distance threshold value to assign the elements in groups. However, these operators do not achieve satisfactory results when the data distribution present a significant variation in the density of objects in different regions of the space. To achieve better results in these situations, we have proposed a novel operator that assign groups using an election involving already assigned groups. We also proposed generalizations to existing and proposed operators to work with an amount of nearest neighbors and approximate neighbors instead of a distance threshold. To support these operators in RDBMS, we propose an extension to Structured Query Language (SQL) and new aggregation functions. Our proposed algorithms can run the proposed and existing operators. We implemented these operators in our framework in C++ using Arboretum library. To evaluate the proposed methods, we assess both results quality and the execution time, using both real and synthetic datasets. The proposed operators achieved better results comparing the quality and maintained similar executing time. The operators that use the approximate nearest neighbors produced similar quality results comparing with the operators that use the exact neighbors and can execute faster than that.