Yes / Many visual effects are believed to be processed at several functional and anatomical levels of cortical processing. Determining if and how the levels contribute differentially to these effects is a leading problem in visual perception and visual neuroscience. We review and analyze a combination of extant psychophysical findings in the context of neurophysiological and brain-imaging results. Specifically using findings relating to visual illusions, crowding, and masking as exemplary cases, we develop a theoretical rationale for showing how relative levels of cortical processing contributing to these effects can already be deduced from the psychophysically determined functions relating respectively the illusory, crowding and masking strengths to the contrast of the illusion inducers, of the flankers producing the crowding, and of the mask. The wider implications of this rationale show how it can help to settle or clarify theoretical and interpretive inconsistencies and how it can further psychophysical, brain-recording and brain-imaging research geared to explore the relative functional and cortical levels at which conscious and unconscious processing of visual information occur. Our approach also allows us to make some specific predictions for future studies, whose results will provide empirical tests of its validity.

No description available.

We can affirm to apprehend a system in its totality only when we know how it behaves under its natural operating conditions. However, in the face of the complexity of the world, science can only evolve by simplifications, which paradoxically hide a good deal of the very mechanisms we are interested in. On the other hand, scientific enterprise is very tightly related to the advances in technology and the latter inevitably influences the manner in which the scientific experiments are conducted. Due to this factor, experimental conditions which would have been impossible to bring into laboratory not more than 20 years ago, are today within our reach. This thesis investigates neuronal integrative processes by using a variety of theoretical and experimental techniques wherein the approximation of ecologically relevant conditions within the laboratory is the common denominator. The working hypothesis of this thesis is that neurons and neuronal systems, in the sensory and higher cortices, are specifically adapted, as a result of evolutionary processes, to the sensory signals most likely to be received under ecologically relevant conditions. In order to conduct the present study along this line, we first recorded movies with the help of two microcameras carried by cats exploring a natural environment. This resulted in a database of binocular natural movies that was used in our theoretical and experimental studies. In a theoretical study, we aimed to understand the principles of binocular disparity encoding in terms of spatio-temporal statistical properties of natural movies in conjunction with simple mathematical expressions governing the activity levels of simulated neurons. In an unsupervised learning scheme, we used the binocular movies as input to a neuronal network and obtained receptive fields that represent these movies optimally with respect to the temporal stability criterion. Many distinctive aspects of the binocular coding in complex cells, such as the phase and position encoding of disparity and the existence of unbalanced ocular contributions, were seen to emerge as the result of this optimization process. Therefore we conclude that the encoding of binocular disparity by complex cells can be understood in terms of an optimization process that regulates activities of neurons receiving ecologically relevant information. Next we aimed to physiologically characterize the responses of the visual cortex to ecologically relevant stimuli in its full complexity and compare these to the responses evoked by artificial, conventional laboratory stimuli. To achieve this, a state-of-the-art recording method, voltage-sensitive dye imaging was used. This method captures the spatio-temporal activity patterns within the millisecond range across large cortical portions spanning over many pinwheels and orientation columns. It is therefore very well suited to provide a faithful picture of the cortical state in its full complexity. Drifting bar stimuli evoked two major sets of components, one coding for the position and the other for the orientation of the grating. Responses to natural stimuli involved more complex dynamics, which were locked to the motion present in the natural movies. In response to drifting gratings, the cortical state was initially dominated by a strong excitatory wave. This initial spatially widespread hyper-excitatory state had a detrimental effect on feature selectivity. In contrast, natural movies only rarely induced such high activity levels and the onset of inhibition cut short a further increase in activation level. An increase of 30% of the movie contrast was estimated to be necessary in order to produce activity levels comparable to gratings. These results show that the operating regime within which the natural movies are processed differs remarkably. Moreover, it remains to be established to what extent the cortical state under artificial conditions represents a valid state to make inferences concerning operationally more relevant input. The primary visual cortex contains a dense web of neuronal connections linking distant neurons. However the flow of information within this local network is to a large extent unknown under natural stimulation conditions. To functionally characterize these long-range intra-areal interactions, we presented natural movies also locally through either one or two apertures and analyzed the effects of the distant visual stimulation on the local activity levels. The distant patch had a net facilitatory effect on the local activity levels. Furthermore, the degree of the facilitation was dependent on the congruency between the two simultaneously presented movie patches. Taken together, our results indicate that the ecologically relevant stimuli are processed within a distinct operating regime characterized by moderate levels of excitation and/or high levels of inhibition, where facilitatory cooperative interactions form the basis of integrative processes. To gather better insights into the motion locking phenomenon and test the generalizability of the local cooperative processes toward larger scale interactions, we resorted to the unequalized temporal resolution of EEG and conducted a multimodal study. Inspired from the temporal properties of our natural movies, we designed a dynamic multimodal stimulus that was either congruent or incongruent across visual and auditory modalities. In the visual areas, the dynamic stimulation unfolded neuronal oscillations with frequencies well above the frequency spectrum content of the stimuli and the strength of these oscillations was coupled to the stimuli's motion profile. Furthermore, the coupling was found to be stronger in the case where the auditory and visual streams were congruent. These results show that the motion locking, which was so far observed in cats, is a phenomenon that also exists in humans. Moreover, the presence of long-range multimodal interactions indicates that, in addition to local intra-areal mechanisms ensuring the integration of local information, the central nervous system embodies an architecture that enables also the integration of information on much larger scales spread across different modalities. Any characterization of integrative phenomena at the neuronal level needs to be supplemented by its effects at the behavioral level. We therefore tested whether we could find any evidence of integration of different sources of information at the behavioral level using natural stimuli. To this end, we presented to human subjects images of natural scenes and evaluated the effect of simultaneously played localized natural sounds on their eye movements. The behavior during multimodal conditions was well approximated by a linear combination of the behavior under unimodal conditions. This is a strong indication that both streams of information are integrated in a joint multimodal saliency map before the final motor command is produced. The results presented here validate the possibility and the utility of using natural stimuli in experimental settings. It is clear that the ecological relevance of the experimental conditions are crucial in order to elucidate complex neuronal mechanisms resulting from evolutionary processes. In the future, having better insights on the nervous system can only be possible when the complexity of our experiments will match to the complexity of the mechanisms we are interested in.

Uma das tarefas básicas dos mecanismos atencionais é decidir qual a localização dentro do campo visual, em que devemos prestar atenção primeiro. Um objeto que contenha características distintas, tais como orientação, forma, cor, tamanho, brilho, textura, etc. diferentes, pode atrair a atenção de uma maneira \"bottom-up\". A informação \"top-down\" baseia-se no conhecimento prévio e tem uma grande influência nas localizações atendidas. Inspirado nos mecanismos da Atenção Visual Humana, embora sem a pretensão de simulá-la, este trabalho prevê o desenvolvimento de uma nova metodologia que integra os dois tipos de informações: \"bottom-up\" e \"top-down\". Características \"bottom-up\" são geradas a partir de Momentos e essas informações são utilizadas em mapas de saliência, enquanto que um conhecimento prévio é utilizado para gerar pistas \"top-down\". Neste trabalho, desenvolveu-se uma metodologia específica para a busca e o reconhecimento visual em cenas com múltiplos objetos, utilizando para isso uma rede \"fuzzy\" contendo três subsistemas \"fuzzy\". Dada uma imagem de entrada, o objetivo consiste em se detectar regiões que possam conter informações mais significativas, a fim de que se possa guiar e restringir processamentos mais complexos. A inclusão de mecanismos de atenção (seleção de uma região de interesse dentro da imagem) é de fundamental importância pois os resultados obtidos pelo método podem ser usados para controlar a aquisição da imagem de uma maneira dinâmica. O modelo proposto está estruturado em três estágios principais: O primeiro estágio consiste em se segmentar os objetos e extrair características globais dos mesmos baseadas principalmente na teoria dos momentos, tais como tamanho, orientação, formato e distância e também média de nível de cinza. Por intermédio da comparação de um objeto com os outros presentes na cena, características \"bottom-up\" de conspicuidade são usadas para guiar a atenção ao objeto mais diferente. Por intermédio do uso da lógica \"fuzzy\" é possível inferir com grande flexibilidade algumas regras de decisão baseadas nos princípios de percepção visual tais como as leis Gestalt. O segundo estágio consiste de um subsistema \"fuzzy top-down\" que combina diferentes características de acordo com a relevância das mesmas em diferentes tarefas. Finalmente, o terceiro estágio consiste de um subsistema \"fuzzy\" que integra as informações obtidas dos subsistemas anteriores e fornece um índice geral de saliência, e indica a provável localização do objeto a ser reconhecido. A nova abordagem foi testada com objetos geométricos levando-se em consideração as características que atraem a atenção dos serem humanos / One of the basic tasks assigned to the human attentional mechanisms is to decide which location in the visual field we must pay attention first. An object containing distinctive features (such as different orientation, shape, color, size, shine, texture, etc.) can attract attention in a bottom-up way. Top-down information is based on the previous knowledge and has a large influence on the attended locations. Inspired on human visual attention mechanisms, although it doesn\'t want simulate it, this work presents a new methodology to integrate two different kind of information: bottom-up and top-down. Bottom-up features are obtained from Moment Theory and this information is used in salience maps, while a previous knowledge is used to create top-down hints. In this work, an specific methodology to visual search and recognition was developed to be applied to scenes containing multiple objects by a fuzzy net with three fuzzy subsystems. The aim of this methodology is to detect regions that may contain the most significant information, in order to guide and to restrict most complex processing. The inclusion of attentional mechanisms (the selection of a region of interest in the image) is fundamental and can be used to control the image acquisition in a dynamic way. The proposed model is structured in three main stages. The first stage segments the objects and extracts global features of them, based on the Moment Theory such as size, orientation, shape and distance and gray level average. By comparing one object with the other ones present in the scene, bottom-up features of conspicuity are used to guide the attention to the most different object. The Fuzzy Logic allows us to infer with great flexibility some of decision rules based on the visual perception principles such as the Gestalt Laws. The second stage is a top-down fuzzy subsystem that combines different features according to the relevance of them in different tasks. Finally, the third stage is a fuzzy subsystem that integrates the information obtained from the previous sub-systems and gives us a general salience index. The new methodology was tested in geometrical objects considering the feature that attracts attention to human beings

Uma das tarefas básicas dos mecanismos atencionais é decidir qual a localização dentro do campo visual, em que devemos prestar atenção primeiro. Um objeto que contenha características distintas, tais como orientação, forma, cor, tamanho, brilho, textura, etc. diferentes, pode atrair a atenção de uma maneira \"bottom-up\". A informação \"top-down\" baseia-se no conhecimento prévio e tem uma grande influência nas localizações atendidas. Inspirado nos mecanismos da Atenção Visual Humana, embora sem a pretensão de simulá-la, este trabalho prevê o desenvolvimento de uma nova metodologia que integra os dois tipos de informações: \"bottom-up\" e \"top-down\". Características \"bottom-up\" são geradas a partir de Momentos e essas informações são utilizadas em mapas de saliência, enquanto que um conhecimento prévio é utilizado para gerar pistas \"top-down\". Neste trabalho, desenvolveu-se uma metodologia específica para a busca e o reconhecimento visual em cenas com múltiplos objetos, utilizando para isso uma rede \"fuzzy\" contendo três subsistemas \"fuzzy\". Dada uma imagem de entrada, o objetivo consiste em se detectar regiões que possam conter informações mais significativas, a fim de que se possa guiar e restringir processamentos mais complexos. A inclusão de mecanismos de atenção (seleção de uma região de interesse dentro da imagem) é de fundamental importância pois os resultados obtidos pelo método podem ser usados para controlar a aquisição da imagem de uma maneira dinâmica. O modelo proposto está estruturado em três estágios principais: O primeiro estágio consiste em se segmentar os objetos e extrair características globais dos mesmos baseadas principalmente na teoria dos momentos, tais como tamanho, orientação, formato e distância e também média de nível de cinza. Por intermédio da comparação de um objeto com os outros presentes na cena, características \"bottom-up\" de conspicuidade são usadas para guiar a atenção ao objeto mais diferente. Por intermédio do uso da lógica \"fuzzy\" é possível inferir com grande flexibilidade algumas regras de decisão baseadas nos princípios de percepção visual tais como as leis Gestalt. O segundo estágio consiste de um subsistema \"fuzzy top-down\" que combina diferentes características de acordo com a relevância das mesmas em diferentes tarefas. Finalmente, o terceiro estágio consiste de um subsistema \"fuzzy\" que integra as informações obtidas dos subsistemas anteriores e fornece um índice geral de saliência, e indica a provável localização do objeto a ser reconhecido. A nova abordagem foi testada com objetos geométricos levando-se em consideração as características que atraem a atenção dos serem humanos / One of the basic tasks assigned to the human attentional mechanisms is to decide which location in the visual field we must pay attention first. An object containing distinctive features (such as different orientation, shape, color, size, shine, texture, etc.) can attract attention in a bottom-up way. Top-down information is based on the previous knowledge and has a large influence on the attended locations. Inspired on human visual attention mechanisms, although it doesn\'t want simulate it, this work presents a new methodology to integrate two different kind of information: bottom-up and top-down. Bottom-up features are obtained from Moment Theory and this information is used in salience maps, while a previous knowledge is used to create top-down hints. In this work, an specific methodology to visual search and recognition was developed to be applied to scenes containing multiple objects by a fuzzy net with three fuzzy subsystems. The aim of this methodology is to detect regions that may contain the most significant information, in order to guide and to restrict most complex processing. The inclusion of attentional mechanisms (the selection of a region of interest in the image) is fundamental and can be used to control the image acquisition in a dynamic way. The proposed model is structured in three main stages. The first stage segments the objects and extracts global features of them, based on the Moment Theory such as size, orientation, shape and distance and gray level average. By comparing one object with the other ones present in the scene, bottom-up features of conspicuity are used to guide the attention to the most different object. The Fuzzy Logic allows us to infer with great flexibility some of decision rules based on the visual perception principles such as the Gestalt Laws. The second stage is a top-down fuzzy subsystem that combines different features according to the relevance of them in different tasks. Finally, the third stage is a fuzzy subsystem that integrates the information obtained from the previous sub-systems and gives us a general salience index. The new methodology was tested in geometrical objects considering the feature that attracts attention to human beings