Individuals who suffer from amyotrophic lateral sclerosis (ALS) have a loss of motor control and possibly the loss of speech. A brain-computer interface (BCI) provides a means for communication through nonmuscular control. Visual BCIs have shown the highest potential when compared to other modalities; nonetheless, visual attention concepts are largely ignored during the development of BCI paradigms. Additionally, individual performance differences and personal preference are not considered in paradigm development. The traditional method to discover the best paradigm for the individual user is trial and error. Visual attention research and personal preference provide the building blocks and guidelines to develop a successful paradigm. This study is an examination of a BCI-based visual attention assessment in an ALS sample. This assessment takes into account the individual’s visual attention characteristics, performance, and personal preference to select a paradigm. The resulting paradigm is optimized to the individual and then tested online against the traditional row-column paradigm. The optimal paradigm had superior performance and preference scores over row-column. These results show that the BCI needs to be calibrated to individual differences in order to obtain the best paradigm for an end user.

An attentional template based on a feature in visual working memory (VWM) can be used to bias attention toward feature-matching objects in the visual field. Attentional guidance based on a single feature is highly efficient and has been well characterized. It is debated, however, whether multiple features can be used to guide attention simultaneously. Some argue that only a single feature in VWM can be elevated to an “active” state and influence perceptual selection. To evaluate whether multiple features can guide attention simultaneously, eye movements were recorded while participants completed both traditional and gaze-contingent visual search tasks. Participants demonstrated guidance by multiple features by switching between relevant colors frequently and without delay. Furthermore, relevant objects of different colors actively competed for saccadic selection. These results provide compelling evidence that multiple attentional templates are able to guide selection simultaneously. Although it was originally proposed that a feature in VWM could also be used to bias attention away from irrelevant items (“template for rejection”), the evidence thus far has been mixed. Some studies report that participants were faster to find a target item after being cued with a distractor feature, suggesting participants were using this feature to avoid matching items, while other studies report a cost and find that participants actually attended to cue-matching items even though they are irrelevant. The current work demonstrates that some evidence in support of feature-guided avoidance can be explained by spatially recoding the cued feature information. Furthermore, when shown a distractor color at the beginning of a trial, participants frequently fixated a matching object early in the trial, but avoided matching objects later in the trial. Other work has suggested that this initial attentional capture by a cue-matching object facilitates later avoidance, but the current data do not support a functional relationship of this nature. In sum, it may not be possible to implement an exclusionary template directly as feature-guided avoidance, but it may be possible to implement indirectly by converting the irrelevant feature information into relevant feature or spatial information.

acase@tulane.edu

<p>Most traffic accidents are caused by human factors. The design of the driver environment has proven essential to facilitate safe driving. With the advent of new devices such as mobile telephones, GPS-navigation and similar systems the workload on the driver has been even more complicated. There is an obvious need for tools supporting objective evaluation of such systems, in order to design more effective and simpler driver environments. </p><p>At the moment video is the most used technique for capturing the drivers visual behaviour. But the analysis of these recordings is very time consuming and only give an estimate of where the visual attention is. An automated tool for analysing visual behaviour would minimize the post processing drastically and leave more time for understanding the data. </p><p>In this thesis the development of a tool for visualising where the driver’s attention is while driving the vehicle. This includes methods for playing back data stored on a hard drive, but also methods for joining data from multiple different sources.</p>

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

Several decades of physiology, imaging and psychophysics research on attention has generated an enormous amount of data describing myriad forms of attentional effects. A similar breadth of theoretical models have been proposed that attempt to explain these effects in varying amounts of detail. However, there remains a need for neurally detailed mechanistic models of attention that connect more directly with various kinds of experimental data -- behavioural, psychophysical, neurophysiological, and neuroanatomical -- and that provide experimentally testable predictions. Research has been conducted that aims to identify neurally consistent principles that underlie selective attentional processing in cortex. The research specifically focuses on describing the functional mechanisms of attentional routing in a large-scale hierarchical model, and demonstrating the biological plausibility of the model by presenting a spiking neuron implementation that can account for a variety of attentional effects. The thesis begins by discussing several significant physiological effects of attention, and prominent brain areas involved in selective attention, which provide strong constraints for developing a model of attentional processing in cortex. Several prominent models of attention are then discussed, from which a set of common limitations in existing models is assembled that need to be addressed by the proposed model. One central limitation is that, for many existing models, it remains to be demonstrated that their computations can be plausibly performed in spiking neurons. Further, few models address attentional effects for more than a single neuron or single cortical area. And finally, few are able to account for different forms of attentional modulation in a single detailed model. These and other limitations are addressed by the Attentional Routing Circuit (ARC) proposed in this thesis. The presentation of the ARC begins with the proposal of a high-level mathematical model for selective routing in the visual hierarchy. The mathematical model is used to demonstrate that the suggested mechanisms allow for scale- and position-invariant representations of attended stimuli to be formed, and provides a functional context for interpreting detailed physiological effects. To evaluate the model's biological plausibility, the Neural Engineering Framework (NEF) is used to implement the ARC as a detailed spiking neuron model. Simulation results are then presented which demonstrate that selective routing can be performed efficiently in spiking neurons in a way that is consistent with the mathematical model. The neural circuitry for computing and applying attentional control signals in the ARC is then mapped on to neural populations in specific cortical laminae using known anatomical interlaminar and interareal connections to support the plausibility of its cortical implementation. The model is then tested for its ability to account for several forms of attentional modulation that have been reported in neurophysiological experiments. Three experiments of attention in macaque are simulated using the ARC, and for each of these experiments, the model is shown to be quantitatively consistent with measured data. Specifically, a study by Womelsdorf et al. (2008) demonstrates that spatial shifts of attention result in a shifting and shrinking of receptive fields depending on the target's position. An experiment by Treue and Martinez-Trujllo (1999) reports that attentional shifts between receptive field stimuli produce a multiplicative scaling of responses, but do not affect the neural tuning sensitivity. Finally, a study by Lee and Maunsell (2010) demonstrates that attentional shifts result in a multiplicative scaling of neural contrast-response functions that is consistent with a response-gain effect. The model accounts for each of these experimentally observed attentional effects using a single mechanism for selectively processing attended stimuli. In conclusion, it is suggested that the ARC is distinguished from previous models by providing a unifying interpretation of attentional effects at the level of single cells, neural populations, cortical areas, and over the bulk of the visual hierarchy. As well, there are several advantages of the ARC over previous models, including: (1) scalability to larger implementations without affecting the model's principles; (2) a significant increase in biological plausibility; (3) the ability to account for experimental results at multiple levels of analysis; (4) a detailed description of the model's anatomical substrate; (5) the ability to perform selective routing while preserving biological detail; and (6) generating a variety of experimentally testable predictions.

The current study examined two means of reducing uncertainty in visual search: 1) visual relatedness of a prime to the target (a data-driven, bottom-up processing) and 2) expectancy (a top-down process based on the proportion of validly primed trials). The two processes were decoupled using a short and a long inter-stimulus interval (ISI) to examine their time course in visual search. Competing hypotheses were contrasted in order to determine whether relatedness is associated with iconic memory (Neely, 1977) or a longer lasting visual-structural implicit memory (Schacter and Cooper, 1995) and what role participant expectancy plays in visual search performance. Twelve participants engaged in a discrimination task and a visual search task. The obtained results suggest that visual relatedness is a bottom-up process, probably mediated by a short-term iconic store that affects search performance early, but whose effects rapidly decay. They also suggest that expectancy is a top-down process that requires time to build up before it can affect visual search performance, but whose effects are more long lasting than visual relatedness.

Visual Attention is an interesting concept that constantly widens its application areas in the field of image processing and computer vision. The main idea of visual attention is to find the locations on the image that are visually attractive. In this thesis, the visually attractive regions are extracted and tracked in video sequences coming from the vision systems of mobile platforms. First, the salient regions are extracted in each frame and a feature vector is constructed for each one. Then Scale Invariant Feature Transform (SIFT) is applied only to the salient regions to extract more stable features. The tracking is achieved by matching the salient regions of consecutive frames by comparing their feature vectors. Then the SIFT points of salient regions are matched to calculate the shift values for the matched pairs. Limiting the SIFT application to only the salient regions results in significantly reduced computational cost. Moreover, the salient region detection procedure is also limited to the predetermined regions throughout the video sequence in order to increase the efficiency. In addition, the visual attention channels are limited to the most dominant features of the regions. Experimental results that compare the algorithm outputs with ground-truth data reveal that, the proposed algorithm has fine tracking performance together with acceptable computational cost. Promising results are obtained even with blurred video sequences typical of ground vehicles and robots and in an uncontrolled environment.

When multiple objects are present in a visual scene, salient and behaviorally relevant objects are selectively processed at the expense of less salient or irrelevant objects. Here I used three lateralized components of the event-related potential â " the N2pc, Ptc, and SPCN â " to examine how objects compete for representation in our limited capacity visual system, and how task-relevant objects are selectively processed. Participants responded to the orientation of a color singleton target while ignoring a color singleton distractor. Competition between the objects was manipulated by presenting visual search arrays that contained only a target, only a distractor, or both objects together. In Experiment 1, observers did not know the color of the target in advance, whereas in Experiment 2 this information was provided. Experiment 3 was a control experiment to rule out low-level sensory explanations of the effects. The results suggest that the N2pc component indexes capture of attention by salient objects which is modulated both by competition between the objects and top-down knowledge. The Ptc component may index inhibition of return so that once an object is processed it is not selected again. The SPCN component may index enhancement of goal-relevant objects once task-irrelevant objects have been suppressed. Together these lateralized event-related potentials reveal the temporal dynamics of competition and selectivity in the human visual system.

The main focus of this thesis is to develop biologically-based computational models for object recognition. A series of models for attention and object recognition were developed in the order of increasing functionality and complexity. These models are based on information processing in the primate brain, and specially inspired from the theory of visual information processing along the two parallel processing pathways of the primate visual cortex. To capture the true essence of incremental, constraint satisfaction style processing in the visual system, interactive neural networks were used for implementing our models. Results from eye-tracking studies on the relevant visual tasks, as well as our hypothesis regarding the information processing in the primate visual system, were implemented in the models and tested with simulations. As a first step, a model based on the ventral pathway was developed to recognize single objects. Through systematic testing, structural and algorithmic parameters of these models were fine tuned for performing their task optimally. In the second step, the model was extended by considering the dorsal pathway, which enables simulation of visual attention as an emergent phenomenon. The extended model was then investigated for visual search tasks. In the last step, we focussed on occluded and overlapped object recognition. A couple of eye-tracking studies were conducted in this regard and on the basis of the results we made some hypotheses regarding information processing in the primate visual system. The models were further advanced on the lines of the presented hypothesis, and simulated on the tasks of occluded and overlapped object recognition. On the basis of the results and analysis of our simulations we have further found that the generalization performance of interactive hierarchical networks improves with the addition of a small amount of Hebbian learning to an otherwise pure error-driven learning. We also concluded that the size of the receptive fields in our networks is an important parameter for the generalization task and depends on the object of interest in the image. Our results show that networks using hard coded feature extraction perform better than the networks that use Hebbian learning for developing feature detectors. We have successfully demonstrated the emergence of visual attention within an interactive network and also the role of context in the search task. Simulation results with occluded and overlapped objects support our extended interactive processing approach, which is a combination of the interactive and top-down approach, to the segmentation-recognition issue. Furthermore, the simulation behavior of our models is in line with known human behavior for similar tasks. In general, the work in this thesis will improve the understanding and performance of biologically-based interactive networks for object recognition and provide a biologically-plausible solution to recognition of occluded and overlapped objects. Moreover, our models provide some suggestions for the underlying neural mechanism and strategies behind biological object recognition.