Attentional modulation of neural responses to action observation: Implications for models of the human 'mirror' system

In primates, the observation of meaningful, goaldirected actions engages a network of cortical areas located within the premotor and inferior parietal lobules. Current models suggest that activity within these regions arises relatively automatically during passive action observation without the need for topdown control. Here we used functional magnetic resonance imaging to determine whether cortical activit)' associated with action observation is modulated by the strategic allocation of selective attention. Normal observers viewed movie clips of reach-to-grasp actions while performing an easy or difficult visual discrimination at the fovea. A wholebrain analysis was performed to determine the effects of attentional load on neural responses to observed hand actions. Our results suggest that cortical areas involved in action observation are significantiy modulated by attentional load. These findings have important implications for recent attempts to link the human action-observation system to response properties of "mirror neurons" in monkeys.

O sentido espacial das crianças de 3 e 4 anos

Relatório de estágio de mestrado em Educação Pré-Escolar e Ensino do 1ºCiclo do Ensino Básico

Strategy selection during exploratory behavior: sex differences

This study was designed to assess sex-related differences in the selection of an appropriate strategy when facing novelty. A simple visuo-spatial task was used to investigate exploratory behavior as a specific response to novelty. The exploration task was followed by a visual discrimination task, and the responses were analyzed using signal detection theory. During exploration women selected a local searching strategy in which the metric distance between what is already known and what is unknown was reduced, whereas men adopted a global strategy based on an approximately uniform distribution of choices. Women's exploratory behavior gives rise to a notion of a secure base warranting a sense of safety while men's behavior does not appear to be influenced by risk. This sex-related difference was interpreted as a difference in beliefs concerning the likelihood of uncertain events influencing risk evaluation. Keywords: exploration, spontaneous strategies, sex differences, decision-making.

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Use of decision trees in colour feature selection: application to object recognition in outdoor scenes

A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach

Systematic distortions of perceptual stability investigated using immersive virtual reality

Using an immersive virtual reality system, we measured the ability of observers to detect the rotation of an object when its movement was yoked to the observer's own translation. Most subjects had a large bias such that a static object appeared to rotate away from them as they moved. Thresholds for detecting target rotation were similar to those for an equivalent speed discrimination task carried out by static observers, suggesting that visual discrimination is the predominant limiting factor in detecting target rotation. Adding a stable visual reference frame almost eliminated the bias. Varying the viewing distance of the target had little effect, consistent with observers underestimating distance walked. However, accuracy of walking to a briefly presented visual target was high and not consistent with an underestimation of distance walked. We discuss implications for theories of a task-independent representation of visual space. © 2005 Elsevier Ltd. All rights reserved.

Desempenho de escolares com e sem dificuldades de aprendizagem de ensino particular em habilidade fonológica, nomeação rápida, leitura e escrita

OBJETIVOS: caracterizar e comparar o desempenho de escolares com e sem dificuldades de aprendizagem no ensino particular em habilidades fonológicas, nomeação rápida, leitura e escrita. MÉTODOS: participaram desse estudo 60 escolares de 2ª a 4ª séries de escola de ensino particular, distribuídos em 6 grupos, sendo cada grupo composto por 10 escolares, sendo 3 grupos de escolares com dificuldades de aprendizagem e 3 grupos de escolares sem dificuldades de aprendizagem. Como procedimentos, foram realizadas a prova de nomeação automática rápida, a de consciência fonológica e a prova de leitura oral e escrita sob ditado. RESULTADOS: os resultados desse estudo evidenciaram desempenho superior dos escolares sem dificuldades de aprendizagem em relação àqueles com dificuldades. Os escolares com dificuldades de aprendizagem apresentaram maior relação velocidade/tempo em tarefas de nomeação e, conseqüentemente, desempenho inferior em tarefas de consciência fonológica e leitura e escrita de palavras isoladas quando comparados aos sem dificuldades de aprendizagem. CONCLUSÃO: os escolares com dificuldades de aprendizagem apresentaram comprometimento na relação entre as capacidades de nomeação e automatização dos estímulos apresentados com a capacidade de acesso lexical, discriminação visual, freqüência de uso dos estímulos e competição para a apresentação do menor tempo possível na nomeação dos códigos necessários para o estabelecimento do mecanismo de conversão fonema-grafema, exigido para a realização da leitura e escrita em um sistema alfabético como o português.

Desempenho de escolares em consciência fonológica, nomeação rápida, leitura e escrita

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A place preference test in the fish Nile tilapia

The Nile tilapia fish (Oreochromis niloticus) has a high potential to be used as a model in neuroscience studies. In the present study, the preference of the Nile tilapia between a gravel-enriched (GEE), a shelter-enriched (SEE) or a non-enriched (NEE) environment was determined, for developing a place preference model. Nile tilapia had an initial preference for GEE, but after 1 day of observation, the fish stabilized their frequency of visits among compartments. Hence, any stimulus motivating tilapia increase in compartment visiting indicates a positively reinforcing effect. This feature is very useful for the development of new behavioural paradigms for fish in tests using environmental discrimination, such as the conditioning place preference test. © 2006 Elsevier GmbH. All rights reserved.

Elaboração de programa de intervenção com as habilidades percepto-viso-motoras em escolares com dislexia do desenvolvimento

Pós-graduação em Fonoaudiologia - FFC