Linear mixed models (LMMs) provide a still underused methodological perspective on combining experimental and individual-differences research. Here we illustrate this approach with two-rectangle cueing in visual attention (Egly et al., 1994). We replicated previous experimental cue-validity effects relating to a spatial shift of attention within an object (spatial effect), to attention switch between objects (object effect), and to the attraction of attention toward the display centroid (attraction effect), also taking into account the design-inherent imbalance of valid and other trials. We simultaneously estimated variance/covariance components of subject-related random effects for these spatial, object, and attraction effects in addition to their mean reaction times (RTs). The spatial effect showed a strong positive correlation with mean RT and a strong negative correlation with the attraction effect. The analysis of individual differences suggests that slow subjects engage attention more strongly at the cued location than fast subjects. We compare this joint LMM analysis of experimental effects and associated subject-related variances and correlations with two frequently used alternative statistical procedures

Visual selective attention operates in space- and object-based frames of reference. Stimulus salience and task demands influence whether a space- or object-based frame of reference guides attention. I conducted two experiments for the present dissertation to evaluate age patterns in the role of inhibition in object-based attention. The biased competition account (Desimone & Duncan, 1995) proposes that one mechanism through which targets are selected is through suppression of irrelevant stimuli. The inhibitory deficit hypothesis (Hasher & Zacks, 1988) predicts that older adults do not appropriately suppress or ignore irrelevant information. The purpose of the first study was to evaluate whether inhibition of return (IOR) patterns, originally found in a laboratory setting, could be replicated with online data collection (prompted by the COVID-19 pandemic). Inhibition of return is a cognitive mechanism to bias attention from returning to previously engaged items. In a lab setting, young and older adults produced location- and object-based IOR. In the current study, both types of IOR were also observed within object boundaries, although location-based IOR from data collected online was smaller than that from the laboratory. In addition, there was no evidence of an age-related reduction in IOR effects. There was some indication that sampling differences or testing circumstances led to increased variability in online data.The purpose of the second study was to evaluate age differences in top-down inhibitory processes during an attention-demanding object tracking task. Data were collected online. I used a dot-probe multiple object tracking (MOT) task to evaluate distractor suppression during target tracking. Both young and older adults showed poorer dot-probe detection accuracies when the probes appeared on distractors compared to when they appeared at empty locations, reflecting inhibition. The findings suggest that top-down inhibition works to suppress distractors during target tracking and that older adults show a relatively preserved ability to inhibit distractor objects. The findings across both experiments support models of selective attention that posit that goal-related biases suppress distractor information and that inhibition can be directed selectively by both young and older adults on locations and objects in the visual field.

A análise de cenas complexas por computadores não é uma tarefa trivial, entretanto, o cérebro humano pode realizar esta função de maneira eficiente. A evolução natural tem desenvolvido formas para otimizar nosso sistema visual de modo que apenas partes importantes da cena sejam analisadas a cada instante. Este mecanismo de seleção é denominado por atenção visual. A atenção visual opera sob dois aspectos: bottom-up e top-down. A atenção bottom-up é dirigida por conspicuidades baseadas na cena, como o contraste de cores, orientação, etc. Por outro lado, a atenção top-down é controlada por tarefas, memórias, etc. A atenção top-down pode ainda modular o mecanismo bottom-up através do enviesamento de determinadas características de acordo com a tarefa. Além do mecanismo de modulação considerado, o que é selecionado a partir da cena também representa uma importante parte para o processo de seleção. Neste cenário, diversas teorias têm sido propostas e podem ser agrupadas em duas linhas principais: atenção baseada no espaço e atenção baseada em objetos. Modelos baseados em objeto, ao invés de apenas direcionar a atenção para locais ou características específicas da cena, requerem que a seleção seja realizada a nível de objeto, significando que os objetos são a unidade básica da percepção. De modo a desenvolver modelos de acordo com a teoria baseada em objetos, deve-se considerar a integração de um módulo de organização perceptual. Este módulo pode segmentar os objetos do fundo da cena baseado em princípios de agrupamento tais como similaridade, proximidade, etc. Esses objetos competirão pela atenção. Diversos modelos de atenção visual baseados em objetos tem sido propostos nos últimos anos. Pesquisas em modelos de atenção visual têm sido desenvolvidas principalmente relacionadas à atenção bottom-up guiadas por características visuais primitivas, desconsiderando qualquer informação sobre os objetos. Por outro lado, trabalhos recentes têm sido realizados em relação ao uso do conhecimento sobre o alvo para influenciar a seleção da região mais saliente. Pesquisas nesta área são relativamente novas e os poucos modelos existentes encontram-se em suas fases iniciais. Aqui, nós propomos um novo modelo para atenção visual com modulações bottom-up e top-down. Comparações qualitativas e quantitativas do modelo proposto são realizadas em relação aos mapas de fixação humana e demais modelos estado da arte propostos / Perceiving a complex scene is a quite demanding task for a computer albeit our brain does it efficiently. Evolution has developed ways to optimize our visual system in such a manner that only important parts of the scene undergo scrutiny at a given time. This selection mechanism is named visual attention. Visual attention operates in two modes: bottom-up and top-down. Bottom-up attention is driven by scene-based conspicuities, such as the contrast of colors, orientation, etc. On the other hand, top-down attention is controlled by task, memory, etc. Top-down attention can even modulate the bottom-up mechanism biasing features according to the task. In additional to modulation mechanism taken into account, what is selected from the scene also represents an important part of the selection process. In this scenario, several theories have been proposed and can be gathered in two main lines: space-based attention and object-based attention. Object-based models, instead of only delivering the attention to locations or specific features of the scene, claim that the selection it be performed on object level, it means that the objects are the basic unit of perception. In order to develop models following object-based theories, one needs to consider the integration of a perceptual organization module. This module might segment the objects from the background of the scene based on grouping principles, such as similarity, closeness, etc. Those objects will compete for attention. Several object-based models of visual attention have been proposed in recent years. Research in models of visual attention has mainly focused on the bottom-up guidance of early visual features, disregarding any information about objects. On the other hand, recently works have been conducted regarding the use of the knowledge of the target to influence the computation of the most salient region. The research in this area is rather new and the few existing models are in their early phases. Here, we propose a new visual attention model with both bottom-up and top-down modulations. We provide both qualitative and quantitative comparisons of the proposed model against an ground truth fixation maps and state-of-the-art proposed methods

A análise de cenas complexas por computadores não é uma tarefa trivial, entretanto, o cérebro humano pode realizar esta função de maneira eficiente. A evolução natural tem desenvolvido formas para otimizar nosso sistema visual de modo que apenas partes importantes da cena sejam analisadas a cada instante. Este mecanismo de seleção é denominado por atenção visual. A atenção visual opera sob dois aspectos: bottom-up e top-down. A atenção bottom-up é dirigida por conspicuidades baseadas na cena, como o contraste de cores, orientação, etc. Por outro lado, a atenção top-down é controlada por tarefas, memórias, etc. A atenção top-down pode ainda modular o mecanismo bottom-up através do enviesamento de determinadas características de acordo com a tarefa. Além do mecanismo de modulação considerado, o que é selecionado a partir da cena também representa uma importante parte para o processo de seleção. Neste cenário, diversas teorias têm sido propostas e podem ser agrupadas em duas linhas principais: atenção baseada no espaço e atenção baseada em objetos. Modelos baseados em objeto, ao invés de apenas direcionar a atenção para locais ou características específicas da cena, requerem que a seleção seja realizada a nível de objeto, significando que os objetos são a unidade básica da percepção. De modo a desenvolver modelos de acordo com a teoria baseada em objetos, deve-se considerar a integração de um módulo de organização perceptual. Este módulo pode segmentar os objetos do fundo da cena baseado em princípios de agrupamento tais como similaridade, proximidade, etc. Esses objetos competirão pela atenção. Diversos modelos de atenção visual baseados em objetos tem sido propostos nos últimos anos. Pesquisas em modelos de atenção visual têm sido desenvolvidas principalmente relacionadas à atenção bottom-up guiadas por características visuais primitivas, desconsiderando qualquer informação sobre os objetos. Por outro lado, trabalhos recentes têm sido realizados em relação ao uso do conhecimento sobre o alvo para influenciar a seleção da região mais saliente. Pesquisas nesta área são relativamente novas e os poucos modelos existentes encontram-se em suas fases iniciais. Aqui, nós propomos um novo modelo para atenção visual com modulações bottom-up e top-down. Comparações qualitativas e quantitativas do modelo proposto são realizadas em relação aos mapas de fixação humana e demais modelos estado da arte propostos / Perceiving a complex scene is a quite demanding task for a computer albeit our brain does it efficiently. Evolution has developed ways to optimize our visual system in such a manner that only important parts of the scene undergo scrutiny at a given time. This selection mechanism is named visual attention. Visual attention operates in two modes: bottom-up and top-down. Bottom-up attention is driven by scene-based conspicuities, such as the contrast of colors, orientation, etc. On the other hand, top-down attention is controlled by task, memory, etc. Top-down attention can even modulate the bottom-up mechanism biasing features according to the task. In additional to modulation mechanism taken into account, what is selected from the scene also represents an important part of the selection process. In this scenario, several theories have been proposed and can be gathered in two main lines: space-based attention and object-based attention. Object-based models, instead of only delivering the attention to locations or specific features of the scene, claim that the selection it be performed on object level, it means that the objects are the basic unit of perception. In order to develop models following object-based theories, one needs to consider the integration of a perceptual organization module. This module might segment the objects from the background of the scene based on grouping principles, such as similarity, closeness, etc. Those objects will compete for attention. Several object-based models of visual attention have been proposed in recent years. Research in models of visual attention has mainly focused on the bottom-up guidance of early visual features, disregarding any information about objects. On the other hand, recently works have been conducted regarding the use of the knowledge of the target to influence the computation of the most salient region. The research in this area is rather new and the few existing models are in their early phases. Here, we propose a new visual attention model with both bottom-up and top-down modulations. We provide both qualitative and quantitative comparisons of the proposed model against an ground truth fixation maps and state-of-the-art proposed methods