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To make a High Voltage Direct Current (HVDC) transmission more energy efficient, the voltage of the system has to be increased. To allow for that the components of the system must be constructed to handle the increases AC and DC stresses that this leads to. One key component in such a transmission is the HVDC converter transformer. The insulation system of the transformer usually consists of oil and oil-impregnated pressboard. Modeling of the electric DC field in the insulation system is currently done with the ion drift diffusion model, which takes into account the transport and generation of charges in the oil and the pressboard. The model is however lacking a description of how charges are being injected from the electrodes and the oil-pressboard interfaces. The task of this thesis work was to develop and implement a model for this which improves the result of the ion drift diffusion model. A theoretical study of ion injection was first carried out and proceeding from this, a model for the ion injection was formulated. By using experimental data from 5 different test geometries, the injection model could be validated and appropriate parameter values of the model could be determined. By using COMSOL Multiphysics®, the ion drift diffusion model with the injection model could be simulated for the different test geometries. The ion injection gave a substantial improvement of the ion drift diffusion model. The positive injection from electrodes into oil was found to be in the range 0.3-0.6 while the negative injection was 0.3 lower. Determination of the parameters for the injection from oil-pressboard interfaces proved to be difficult, but setting the parameters in the range 0.01-1 allowed for a good agreement with the experimental data. Here, a fit could be obtained for multiple assumptions about the set of active injection parameters. Finally it is recommended that the investigation of the ion injection continues in order to further improve the model and more accurately determine the parameters of it. Suggestions on how this work could be carried out are given in the end.

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Une décision perceptive est un processus délibératif consistant à choisir une proposition catégorielle ou un plan d'action parmi plusieurs alternatives sur la base d'information sensorielle. Les modèles de prise décision font l'hypothèse que l'information sensorielle est accumulée au cours du temps jusqu'à un seuil décisionnel. Ces modèles ont récemment reçu un support empirique important grâce à la découverte de neurones accumulateurs dans le cerveau de singes. Toutefois, l'étude neurophysiologique de ces système d'accumulation chez l'homme est rare. Ce travail de thèse vise à mieux comprendre les mécanismes neuronaux de prise de décision chez l'homme dans des contextes de la vie réelle, beaucoup plus complexes que ceux utilisés chez le singe. / A perceptual decision is a deliberative process that aims to choose a categorical proposition or course of action from a set of alternatives on the basis of available sensory information. Models of perceptual decision-making assume that sensory information is accumulated to some threshold level, whence the decision terminates in a choice. The recent discovery of neural correlates of these theoretical predictions in the non-human primate brain has reinforced their validity. However, neurophysiological studies of perceptual decision-making mechanisms in humans are relatively scarce. This work aims at enhancing our understanding of the computations and neurophysiology underpinning such mechanisms in humans, through the study of decision-making contexts more complex than those used in monkey research.

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Décider, c’est sélectionner une alternative parmi l’ensemble des options possibles en accord avec nos buts. Les décisions perceptuelles, correspondant à la sélection d’une action sur la base d’une perception, résultent de l’accumulation progressive d’information sensorielle jusqu’à un seuil de décision. Aux niveaux comportemental et cérébral, ce processus est bien capturé par les modèles de décision par échantillonnage séquentiel. L’étude neurobiologique des processus de décision, guidée par l’usage de modèles computationnels, a permis d’établir un lien clair entre cette accumulation d’information sensorielle et un réseau cortical incluant le sillon intra-pariétal et le cortex dorso-latéral préfrontal. L’architecture des réseaux biologiques impliqués dans la prise de décision, la nature des algorithmes qu’ils implémentent et surtout, l’étude des relations entre structure biologique et computation est au cœur des questionnements actuels en neurosciences cognitives et constitue le fil conducteur de cette thèse. Dans un premier temps, nous nous sommes intéressés aux mécanismes neuraux et computationnels permettant l’ajustement du processus de décision perceptuelle à son contexte. Nous avons montré que l’information a priori disponible pour prédire nos choix diminue la distance au seuil de décision, régulant ainsi dynamiquement la quantité d’information sensorielle nécessaire pour sélectionner une action. Pendant la prise de décision perceptuelle, le cortex cingulaire antérieur ajuste le seuil de décision proportionnellement à la quantité d’information prédictive disponible et le cortex dorso-latéral préfrontal implémente l’accumulation progressive d’information sensorielle. Dans un deuxième temps, nous avons abordé la question de l’unicité, au travers des domaines cognitifs, des mécanismes neuro-computationnels implémentant la prise de décision. Nous avons montré qu’un modèle de décision par échantillonnage séquentiel utilisant la valeur subjective espérée de chaque option prédisait avec précision le comportement de sujets lors de choix économiques risqués. Pendant la décision, la portion médiale du cortex orbito-frontal code la différence entre les valeurs subjectives des options considérées, exprimées sur une échelle de valeur commune. Ce signal orbito-frontal médian sert d’entrée à un processus de décision par échantillonnage séquentiel implémenté dans le cortex dorso-latéral préfrontal. Pris ensemble, nos travaux précisent les contours d’une architecture fonctionnelle de la prise de décision dans le cortex préfrontal humain en établissant une cartographie des modules computationnels qu’il implémente, mais aussi en caractérisant la façon dont l’intégration fonctionnelle de ces régions cérébrales permet l’émergence de la capacité à prendre des décisions / Decision-making is the selection of an alternative according to our inner goals. Perceptual decisions, the selection of an action based on our perceptions, are made when sensory evidence accumulated over time reaches a decision threshold. This cognitive process is well accounted for by sequential sampling models of decision-making. Moreover, the model-driven neurobiological study of the decision-making process has linked the accumulation of sensory information with a parieto-prefrontal cortical network. The architecture of these cortical networks, the algorithms implemented and the mapping of elementary computations onto biological structures are the questions at the core of this thesis. First, we investigated the neural mechanisms underlying the contextual modulation of the decision-making process. We showed that predictive information on the forthcoming stimuli decreased the distance to the decision threshold, adjusting dynamically the amount of sensory information required to commit to a choice. In our study, the anterior cingulate cortex modulated the decision threshold in proportion to the amount of predictive information and the dorso-lateral prefrontal cortex accumulated sensory information. Then, we addressed the question of the unicity across cognitive domains of the neuro-computational mechanisms of decision-making. We showed that a sequential sampling model of decision-making using subjective values as its inputs precisely predicted Human economic decision-making behavior. Moreover, we showed that the medial part of the orbito-frontal cortex coded the difference between the subjective values of the options under scrutiny on a common scale. This orbito-frontal decision-related value signal drove the sequential sampling decision-making process implemented in the dorso-lateral prefrontal cortex. Taken together, our work delineates a functional architecture of Human decision-making by mapping elementary computations onto the human prefrontal cortex and by characterizing how the functional integration between these brain regions subserves the ability to make choices

The study of neural and behavioural mechanisms of perceptual decision making is often done by experimental tasks involving the categorization of sensory stimuli. Among the key perceptual tasks that decision neuroscience researchers use are motion discrimination paradigms that include tracking and specifying the net direction of a single dot or a group of moving dots. These motion discrimination paradigms, such as the random-dot motion task, allow the measurement of the participant's perceptual decision making abilities in multiple task difficulty levels by varying the amount of noise in the sensory stimuli. Computational models of perceptual decision making, such as the drift-diffusion model, are widely used to analyze the behavioural measurements from these motion discrimination experiments. However, the standard drift-diffusion model can only analyze the average measures like reaction times or the proportion of correct decisions to explain the behavioural data. In the past decade, an emerging computational modeling approach was introduced to analyze the choice behaviour based on precise noise patterns in the sensory stimuli. These computational models that use spatiotemporal stimulus details have shown promise in the single-trial analysis of motion discrimination behaviour. In this thesis, I further develop the advanced computational models of perceptual decision making that use spatiotemporal dynamics of motion stimuli to provide detailed explanations of perceptual choice behaviour. First, I demonstrate the usefulness of equipping an extended Bayesian Model, equivalent to the extended drift-diffusion model, with trial-wise stimulus information leading to a significantly better explanation of behavioural data from a single-dot tracking experiment. Second, I show that the extended drift-diffusion model constrained by spatiotemporal stimulus details can explain the consistent biased choice behaviour in response to stochastic motion stimuli. Based on this model-based analysis, I provide evidence that the source of the observed biased choice behaviour is the presence of subtle motion information in the sensory stimuli. These results further emphasize the effectiveness of using spatiotemporal details of stochastic stimuli in detailed model-based analyses of the experimental data and provide computational interpretations of the data related to underlying mechanisms of perceptual decision making.

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