Temporal Selection in Dynamic Displays: Sensory Information Persists Despite Masking

The visual system receives a dynamic stream of information, but it has a limited capacity and must deploy its resources to behaviourally relevant stimuli - a process referred to as “attention”. Rapid serial visual presentation (RSVP) is an experimental method for investigating attention’s time course by presenting a rapid sequence of stimuli at a single location. Attentional selection in both naturalistic viewing and RSVP is limited by masking, and many models of selection in RSVP assume that masking terminates sensory memory for stimuli that are no longer present. However, there is indirect evidence that information about unselected RSVP stimuli may persist in a buffer despite masking. In this thesis we directly investigate buffering and selection of a cued item from one of multiple simultaneous RSVP streams. We use mixture modelling to analyse reports from only those trials in which participants identified a letter in response to the cue, and outline a novel quantitative test for buffering (Chapter 2). This provides new insights into the temporal variability of selection with exogenous and endogenous cues (Chapter 3). A series of experiments show that participants can select buffered representations, despite masking, and this appears to be related to the number of simultaneous RSVP streams (Chapter 4). We also investigate possible contributions of crowding and eccentricity to selection (Chapter 5). RSVP provides a measure of attention’s timing that replicates classic attentional effects. However, participants appear to dedicate attention to the streams prior to the cue’s appearance. When there are few streams, this leads to attentional speeds fast enough to select a stimulus representation that persists briefly, despite the masking inherent in RSVP. This falsifies theoretical claims about masking in RSVP, and demonstrates that the dynamic nature of naturalistic viewing does not prevent selection from sensory memory

Optimization of MLS receivers for multipath environments

Optimal design studies of MLS angle-receivers and a theoretical design-study of MLS DME-receivers are reported. The angle-receiver results include an integration of the scan data processor and tracking filter components of the optimal receiver into a unified structure. An extensive simulation study comparing the performance of the optimal and threshold receivers in a wide variety of representative dynamical interference environments was made. The optimal receiver was generally superior. A simulation of the performance of the threshold and delay-and-compare receivers in various signal environments was performed. An analysis of combined errors due to lateral reflections from vertical structures with small differential path delays, specular ground reflections with neglible differential path delays, and thermal noise in the receivers is provided

Deep probabilistic methods for improved radar sensor modelling and pose estimation

Radar’s ability to sense under adverse conditions and at far-range makes it a valuable alternative to vision and lidar for mobile robotic applications. However, its complex, scene-dependent sensing process and significant noise artefacts makes working with radar challenging. Moving past classical rule-based approaches, which have dominated the literature to date, this thesis investigates deep and data-driven solutions across a range of tasks in robotics. Firstly, a deep approach is developed for mapping raw sensor measurements to a grid-map of occupancy probabilities, outperforming classical filtering approaches by a significant margin. A distribution over the occupancy state is captured, additionally allowing uncertainty in predictions to be identified and managed. The approach is trained entirely using partial labels generated automatically from lidar, without requiring manual labelling. Next, a deep model is proposed for generating stochastic radar measurements from simulated elevation maps. The model is trained by learning the forward and backward processes side-by-side, using a combination of adversarial and cyclical consistency constraints in combination with a partial alignment loss, using labels generated in lidar. By faithfully replicating the radar sensing process, new models can be trained for down-stream tasks, using labels that are readily available in simulation. In this case, segmentation models trained on simulated radar measurements, when deployed in the real world, are shown to approach the performance of a model trained entirely on real-world measurements. Finally, the potential of deep approaches applied to the radar odometry task are explored. A learnt feature space is combined with a classical correlative scan matching procedure and optimised for pose prediction, allowing the proposed method to outperform the previous state-of-the-art by a significant margin. Through a probabilistic consideration the uncertainty in the pose is also successfully characterised. Building upon this success, properties of the Fourier Transform are then utilised to separate the search for translation and angle. It is shown that this decoupled search results in a significant boost to run-time performance, allowing the approach to run in real-time on CPUs and embedded devices, whilst remaining competitive with other radar odometry methods proposed in the literature

Dynamics of perceptual learning in visual search

The present work is concerned with a phenomenon referred to as contextual cueing. In visual search, if a searched-for target object is consistently encountered within a stable spatial arrangement of distractor objects, detecting the target becomes more efficient over time, relative to non-repeated, random arrangements. This effect is attributed to learned target-distractor spatial associations stored in long-term memory, which expedite visual search. This Thesis investigates four aspects of contextual cueing: Study 1 tackled the implicit-explicit debate of contextual cueing from a new perspective. Previous studies tested explicit access to learned displays by applying a recognition test, asking observers whether they have seen a given display in the previous search task. These tests, however, typically yield mixed findings and there is an on-going controversy whether contextual cueing can be described as an implicit or an explicit effect. The current study applied the new perspective of metacognition to contextual cueing and combined a contextual cueing task with metacognitive ratings about the clarity of the visual experience, either of the display configuration or the target stimulus. Bayesian analysis revealed that there was an effect of repeated context on metacognitive sensitivity for configuration, but not target, ratings. It was concluded that effects of contextual memory on metacognition are content-specific and lead to increased metacognitive access to the display configuration, but not to the target stimulus. The more general implication is that from the perspective of metacognition, contextual cueing can be considered as an explicit effect. Study 2 aimed at testing how explicit knowledge affects memory-guided visual search. Two sets of search displays were shown to participants: explicit and implicit displays. Explicit displays were introduced prior to the search experiment, in a dedicated learning session, and observers should deliberately learn these displays. Implicit displays, on the other hand, were first shown in the search experiment and learning was incidental through repeated exposure to these displays. Contextual cueing arising from explicit and implicit displays was assessed relative to a baseline condition of non-repeated displays. The results showed a standard contextual cueing effect for explicit displays and, interestingly, a negative cueing effect for implicit displays. Recognition performance was above chance for both types of repeated displays; however, it was higher for explicit displays. This pattern of results confirmed – in part – the predictions of a single memory model of attention-moderated associative learning, in which different display types compete for behavior and explicit representations block the retrieval of implicit representations. Study 3 investigates interactions between long-term contextual memory with short-term perceptual hypotheses. Both types of perceptual memory share high similarities with respect to their content, therefore the hypothesis was formulated that they share a common memory resource. In three experiments of interrupted search with repeated and non-repeated displays, it was shown that contextual cueing expedites performance in interrupted search; however, there was no interaction of contextual cueing with the generation or the confirmation of perceptual hypotheses. Rather, the analysis of fixational eye movements showed that long-term memory exerts its influence on search performance upon the first glance of a given display, essentially affecting the starting point of the search process. The behavior of approaching the target stimulus is then a product of generating and confirming perceptual hypotheses with these processes being unaffected by long-term contextual memory. It was concluded that long-term and short-term memory representations of the same search display are independent and exhibit additive effects on search performance. Study 4 is concerned with the effects of reward on perceptual learning. It was argued that rewarding repeated displays in a contextual cueing paradigm leads to an acceleration of the learning effect; however, it was not considered whether reward also has an effect in non-repeated displays. In these displays, at least the target position is kept constant while distractor configurations are random across repetitions. Usually this is done in order to account for target position-specific probability learning in contextual cueing. However, it is possible that probability learning itself is modulated by reward. The current experiment introduced high or low reward to repeated and importantly, also non-repeated displays. It was shown that reward had a huge effect on non-repeated displays, indicating that rewarding certain target positions, irrespective of the distractor layout, facilitates RT performance. Interestingly, reward effects were even larger for non-repeated compared to repeated displays. It was concluded that reward has a strong effect on probability-, and not context learning

Somatic salience and sensory precision in persistent depression

Persistent depression is a debilitating health condition with a poor prognosis even in the context of current gold-standard pharmacological and psychological interventions. A better understanding of the mechanisms contributing to its maintenance is needed to facilitate the development of more targeted psychological interventions. Bayesian predictive processing models of depression propose that negative emotional and physiological outcomes arise in depressive illness as a result of disturbed interoceptive precision estimation in depressed individuals; however, evidence from the clinical and cognitive neuroscience literatures suggests the hypothesis that sensory precision is attenuated in persistent depression across sensory modalities in general. A series of studies was designed to index sensory precision across somatic and auditory modalities and to identify the level at which any disruption manifested in persistently-depressed participants relative to controls. Study 1 (Chapter 3) measured baseline signal discriminability under conditions of focused attention. Study 2 (Chapter 4) measured the impact of failures of voluntary attention on signal discriminability. Study 3 (Chapter 5) used a simulation approach to model sensory precision in the first two studies and to identify mechanisms which could successfully predict the data. Studies 4 (Chapter 6) and 5 (Chapter 7) measured attentional capture by task-irrelevant and predictive sensory cues respectively. Study 6 (Chapter 8) partially replicated Studies 4 and 5 and used the resulting data to estimate the group-level sensory precision and salience parameters of a predictive processing model of precision optimization. The results suggest that sensory precision is attenuated in persistent depression across sensory modalities, and that this attenuation results from disturbances of voluntary and involuntary attention rather than baseline perceptual sensitivity. Under conditions of voluntary attention, reduced sensory precision may result from efforts at resource conservation; and under conditions of involuntary attentional capture, it may be related to a loss of target discriminability and salience. Conversely, the bottom-up salience of somatic stimuli was uniquely enhanced among depressed participants and was predicted by high anxiety and by low interoceptive sensibility. These findings open up new avenues for investigation of the mechanisms underlying persistent forms of depression, and have direct implications for clinical practice with respect to psychological intervention.Medical Research Council Studentshi

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Metric Gaussian variational inference

One main result of this dissertation is the development of Metric Gaussian Variational Inference (MGVI), a method to perform approximate inference in extremely high dimensions and for complex probabilistic models. The problem with high-dimensional and complex models is twofold. Fist, to capture the true posterior distribution accurately, a sufficiently rich approximation for it is required. Second, the number of parameters to express this richness scales dramatically with the number of model parameters. For example, explicitly expressing the correlation between all model parameters requires their squared number of correlation coefficients. In settings with millions of model parameter, this is unfeasible. MGVI overcomes this limitation by replacing the explicit covariance with an implicit approximation, which does not have to be stored and is accessed via samples. This procedure scales linearly with the problem size and allows to account for the full correlations in even extremely large problems. This makes it also applicable to significantly more complex setups. MGVI enabled a series of ambitious signal reconstructions by me and others, which will be showcased. This involves a time- and frequency-resolved reconstruction of the shadow around the black hole M87* using data provided by the Event Horizon Telescope Collaboration, a three-dimensional tomographic reconstruction of interstellar dust within 300pc around the sun from Gaia starlight-absorption and parallax data, novel medical imaging methods for computed tomography, an all-sky Faraday rotation map, combining distinct data sources, and simultaneous calibration and imaging with a radio-interferometer. The second main result is an an approach to use several, independently trained and deep neural networks to reason on complex tasks. Deep learning allows to capture abstract concepts by extracting them from large amounts of training data, which alleviates the necessity of an explicit mathematical formulation. Here a generative neural network is used as a prior distribution and certain properties are imposed via classification and regression networks. The inference is then performed in terms of the latent variables of the generator, which is done using MGVI and other methods. This allows to flexibly answer novel questions without having to re-train any neural network and to come up with novel answers through Bayesian reasoning. This novel approach of Bayesian reasoning with neural networks can also be combined with conventional measurement data