Affective priming - is it only affect that primes?

A target word is classified faster as pleasant or unpleasant if preceded by a prime that matches the target word’s valence. This affective priming phenomenon is currently popular as an implicit measure of stimulus valence. The present set of experiments investigated whether rated stimulus arousal will affect target classification as well. In three experiments, word targets were preceded by prime stimuli that differed in rated arousal and valence. The basic priming effect was replicated in all experiments, however, priming was largest after high arousal unpleasant and low arousal pleasant primes, and reduced after low arousal unpleasant and high arousal pleasant primes. This finding emerged for picture and word primes and does not reflect the effect of differences in stimulus complexity. The difference in the effectiveness of the primes was not affected by SOA and seemed to hold across a wide range (50-200 ms for words and 200-500 ms for pictures). The present results suggest that some failures to find affective priming may not reflect on prime valence, but on prime arousal. Moreover, it suggests that increases in stimulus arousal have differential effects for the processing of pleasant and unpleasant stimuli.

A Correlation Analysis on LSA and HAL Semantic Space Models

In this paper, we compare a well-known semantic spacemodel, Latent Semantic Analysis (LSA) with another model, Hyperspace Analogue to Language (HAL) which is widely used in different area, especially in automatic query refinement. We conduct this comparative analysis to prove our hypothesis that with respect to ability of extracting the lexical information from a corpus of text, LSA is quite similar to HAL. We regard HAL and LSA as black boxes. Through a Pearsonrsquos correlation analysis to the outputs of these two black boxes, we conclude that LSA highly co-relates with HAL and thus there is a justification that LSA and HAL can potentially play a similar role in the area of facilitating automatic query refinement. This paper evaluates LSA in a new application area and contributes an effective way to compare different semantic space models.

Semantic caching for multiresolution spatial query processing in mobile environments

Spatial data are particularly useful in mobile environments. However, due to the low bandwidth of most wireless networks, developing large spatial database applications becomes a challenging process. In this paper, we provide the first attempt to combine two important techniques, multiresolution spatial data structure and semantic caching, towards efficient spatial query processing in mobile environments. Based on the study of the characteristics of multiresolution spatial data (MSD) and multiresolution spatial query, we propose a new semantic caching model called Multiresolution Semantic Caching (MSC) for caching MSD in mobile environments. MSC enriches the traditional three-category query processing in semantic cache to five categories, thus improving the performance in three ways: 1) a reduction in the amount and complexity of the remainder queries; 2) the redundant transmission of spatial data already residing in a cache is avoided; 3) a provision for satisfactory answers before 100% query results have been transmitted to the client side. Our extensive experiments on a very large and complex real spatial database show that MSC outperforms the traditional semantic caching models significantly

Semantic caching for web-based spatial applications

Client-side caching of spatial data is an important yet very much under investigated issue. Effective caching of vector spatial data has the potential to greatly improve the performance of spatial applications in the Web and wireless environments. In this paper, we study the problem of semantic spatial caching, focusing on effective organization of spatial data and spatial query trimming to take advantage of cached data. Semantic caching for spatial data is a much more complex problem than semantic caching for aspatial data. Several novel ideas are proposed in this paper for spatial applications. A number of typical spatial application scenarios are used to generate spatial query sequences. An extensive experimental performance study is conducted based on these scenarios using real spatial data. We demonstrate a significant performance improvement using our ideas.

A web recommendation technique based on probabilistic latent semantic analysis

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

Semantic properties of μ-charts

μ-Charts are a Statechart-like language which is designed for specifying reactive systems. This paper extends the language of μ-charts with a new parallel operator; it defines a formal semantics for the language, and then it explores the semantic properties of the extended language. The paper concludes with a simple case study to illustrate how the language may be used to specify and reason about reactive systems.

CONSCIÊNCIA E EMOÇÃO: O EFEITO DO PRIMING AFETIVO SUBLIMINAR EM TAREFAS DE ATENÇÃO

Esta tese revisou duas linhas de pesquisa, desenvolvidas nas últimas décadas: o estudo de efeitos de estimulação subliminar priming , e de desencadeamento de reações emocionais por estímulos controlados. Este estudo tem o objetivo de combinar tais linhas para o estudo da consciência com pré-preparo afetivo: efeito de estímulos de conteúdo aversivo, subliminares e supraliminares, sobre a cognição, pela análise do desempenho em tarefa de atenção. Três tarefas experimentais foram realizadas por 35 indivíduos em laboratório de neuropsicologia: a tarefa base onde testamos à detecção de alvo visual simples, e a mesma tarefa de base, porém com estímulos distratores aversivos intercalados, de forma supraliminar ou subliminar (500 ms ou 50 ms de duração), em blocos aleatorizados entre os indivíduos. Calcularam-se índices de detectabilidade e critério de resposta, que serviram para a comparação estatística entre condições (medidas repetidas). Os resultados mostram uma mudança significativa do índice critério , indicando mudança de estratégia na presença de distratores subliminares aversivos. Concluiu-se que a tarefa subliminar fez um efeito destruidor ou devastador na tarefa supraliminar, cometendo menos falso-alarmes protegendo a tarefa supraliminar, tendo um efeito protetor . Os resultados são discutidos no contexto da relevância de influências emocionais sobre o comportamento para a Psicologia da Saúde.