Ce travail doctoral, centré sur l'étude des processus de contrôle d'un périphérique d'interaction de type joystick, poursuivait un double objectif. D'une part, ce travail visait à comprendre comment l'utilisateur d'un périphérique adapte son comportement pour faire face à l'ensemble des contraintes qu'il rencontre. En particulier, nous avons manipulé les contraintes relatives à la tâche à réaliser (l'indice de difficulté et le comportement de l'objet déplacé) et les propriétés physiques du périphérique. D'autre part, d'un point de vue appliqué, comprendre comment le comportement du sujet émerge d'un ensemble de contraintes nous renseigne sur la façon dont un contrôle plus intuitif des périphériques, et par la même une meilleure performance, peuvent être favorisés. Pour cela, un cadre d'analyse qui permet de manipuler l'ensemble des contraintes dans des conditions de contrôle qui se rapproche de situation réelle d'interaction (chirurgie vidéoassistée, aéronautique ou contrôle industriel) a été proposé. L'analyse comportementale à deux niveaux (niveau local et niveau global) qui est utilisée nécessitait l'intégration des concepts, moyens et méthodes des sciences comportementales (contrôle moteur) d'une part, et de la biomécanique d'autre part. Au regard des résultats obtenus, l'étude des processus de contrôle d'un périphérique d'interaction constitue une voie d'entrée sur une compréhension plus générale du comportement perceptivo-moteur. / This doctoral work focused on the study of the process involved in the control of a joystick-type device of interaction and had a double objective. First, this work was aimed to understand how the user of a device adapts its behavior to face a set of encountered constraints. In particular, the constraints inherent to the task (the index of difficulty and behavior of the object displaced) and the physical properties of the device (joystick or rotorcraft stick) have been manipulated. Second, understanding how the subject's behavior emerges from the influence of this set of constraints informed us about how we can access to a more intuitive control of the device and a better performance level. To achieve this goal, an analysis framework allowing the manipulation of all the constraints in conditions that are close to real situations of control (e.g. video-assisted surgery, aeronautics and industrial control) has been proposed. The behavioral analysis at two levels (local level and global level) that is used required the integration of concepts, means and methods from behavioral sciences (motor control) and from biomechanics. Taking all the results together, the study of the control process of a device of interaction allows a more general understanding of the perceptual-motor behavior.

Neste estudo foi analisado o efeito de distância, velocidade, tamanhos do disco e do alvo, e massas do disco e da manopla, sobre o desempenho motor em movimentos requisitando rapidez e precisão. Para tanto, foram analisadas as características cinemáticas da tarefa de projetar um disco a um alvo com movimento de contato balístico, empunhando uma manopla. Os movimentos foram desempenhados sobre uma base plana e filmados com câmera optoeletrônica de alta freqüência. O estudo foi conduzido em seis experimentos com um grupo único de participantes. Os resultados indicaram que os modelos de controle motor que têm sido empregados para analisar a relação velocidade-precisão em tarefas mais simples não foram apropriados para explicar o comportamento da tarefa utilizada. O controle motor na tarefa demonstrou ser dinâmico e flexível, frente às diferentes restrições de movimento. As seguintes estratégias de controle foram sugeridas na explicação dos resultados: sincronização da maior velocidade e do instante de contato manopla-disco, manutenção na proporção entre as fases aceleração-desaceleração, maior inércia e menor impacto para aumentar a estabilidade de movimento, e o controle da velocidade e da precisão em dimensões independentes. A análise das ações articulares demonstrou as particularidades das estratégias utilizadas pelo sistema no movimento em função das variáveis manipuladas. Em conjunto, esta seqüência de experimentos permitiu uma compreensão mais ampla das estratégias de controle motor empregadas em movimentos com alta demanda de velocidade e de precisão / In this study it was analyzed the effect of distance, velocity, disc and target width, disc and manipulandum mass, over motor control of a rapid and accurate movement. For such, it was analyzed the kinematic characteristics of the task of launching a disc to a target using a ballistic movement, performed with a manipulandum. Movements were performed on a flat surface and filmed with a high frequency optoelectronic camera. The study was conducted through six experiments with a single group of participants. Analysis of results indicated that models of motor control that has been used to analyze speed-accuracy tradeoff in simpler tasks were not appropriate to explain the observed behavior in the task used. Motor control in the task showed to be dynamic and flexible, regarding the several constraints manipulated. The follow strategies of control were suggested to explain the results: synchronization of the peak velocity and the instant of disc-manipulandum contact, maintenance of the proportion between the acceleration-deceleration phases, greater inertia and minor impact to increase movement stability, and control of velocity and accuracy of independent dimensions. The prediction of Analysis of joint actions showed the particularities of the strategies used by the system on movement as a function of the manipulated variables. Together, this study sequence of experiments allowed for a deeper comprehension of the control strategies used in the control of rapid and accurate movements

Les individus diffèrent les uns des autres dans leur manière générale de se comporter au fil du temps et entre les contextes, ainsi que dans leur habileté à ajuster leurs comportements lorsqu’un changement environnemental survient. Encore aujourd’hui, les causes proximales et ultimes de ces différences ne sont pas bien comprises. Certains défendent que différents compromis de risque liant les types comportementaux rapide/proactif-lent/réactif aux styles cognitifs rapide/inflexible-exact/flexible pourraient en partie expliquer le maintien de ces différences. Toutefois, il semble y avoir des écarts dans la littérature quant à l’existence et la nature d’un tel compromis vitesse-exactitude. Une explication serait que ce genre de relations entre la personnalité et la cognition peuvent être modérées par différents facteurs. Ici, nous explorons la possibilité que l’impulsivité d’action, l’impulsivité de choix et l’incertitude attendue interagissent sur les performances d’apprentissage et de renversement de l’apprentissage chez l’humain. Pour évaluer leur impulsivité, les participants devaient remplir le questionnaire d’impulsivité de Barratt et effectuer une tâche de signal de stop ainsi qu’une tâche de dévaluation temporelle expérientielle. Puis, leurs performances d’apprentissage et de renversement de l’apprentissage ont été mesurées avec une nouvelle tâche de renversement de l’apprentissage, sous différents niveaux d’incertitude attendue. Les résultats démontrent que les patrons de performances d’apprentissage et de renversement de l’apprentissage associés à l’impulsivité d’action dépendent de l’incertitude attendue et de l’impulsivité de choix. Ils appuient également l’idée que l’impulsivité ne serait pas inadaptée per se, ce qui pourrait en partie expliquer le maintien des différences interindividuelles d’impulsivité et de flexibilité. / Individuals differ in how they each generally behave across time and contexts, as well as in their ability to acquire new information, and flexibly adjust their behavioral responses when a change in contingencies occurs. Still today, the proximal and ultimate causes of these differences are not well understood. In recent years, some advocate that divergent risk-reward trade-offs linking fast/proactive-slow/reactive behavioral types to fast/inflexible-accurate/flexible cognitive styles could partly explain the maintenance of these differences. However, it seems that there is a discrepancy in the literature about the existence and nature of such a speed-accuracy trade-off. One explanation could be that the link between personality and cognition is moderated by different factors. Thus, we propose here an exploratory study on how impulsive actions, impulsive choices, and expected uncertainty may interact altogether on learning and reversal learning performances in humans. To assess their impulsivity, participants had to fill out the Barratt impulsiveness scale questionnaire and to complete both a stop signal task and an experiential discounting task. Then, their learning and reversal learning performances were measured in a new reversal learning task, under different levels of reinforcer uncertainty. Results show that learning and reversal learning performances patterns linked to action impulsivity depend on expected uncertainty and choice impulsivity. In addition, they also support the idea that these dimensions of impulsivity are not maladaptive per se, which may provide another line of explanation for the maintenance of variation in impulsivities and flexibility.

La prise de décision est un processus computationnel fondamental dans de nombreux aspects du comportement animal. Le modèle le plus souvent rencontré dans les études portant sur la prise de décision est appelé modèle de diffusion. Depuis longtemps, il explique une grande variété de données comportementales et neurophysiologiques dans ce domaine. Cependant, un autre modèle, le modèle d’urgence, explique tout aussi bien ces mêmes données et ce de façon parcimonieuse et davantage encrée sur la théorie. Dans ce travail, nous aborderons tout d’abord les origines et le développement du modèle de diffusion et nous verrons comment il a été établi en tant que cadre de travail pour l’interprétation de la plupart des données expérimentales liées à la prise de décision. Ce faisant, nous relèveront ses points forts afin de le comparer ensuite de manière objective et rigoureuse à des modèles alternatifs. Nous réexaminerons un nombre d’assomptions implicites et explicites faites par ce modèle et nous mettrons alors l’accent sur certains de ses défauts. Cette analyse servira de cadre à notre introduction et notre discussion du modèle d’urgence. Enfin, nous présenterons une expérience dont la méthodologie permet de dissocier les deux modèles, et dont les résultats illustrent les limites empiriques et théoriques du modèle de diffusion et démontrent en revanche clairement la validité du modèle d'urgence. Nous terminerons en discutant l'apport potentiel du modèle d'urgence pour l'étude de certaines pathologies cérébrales, en mettant l'accent sur de nouvelles perspectives de recherche. / Decision‐making is a computational process of fundamental importance to many aspects of animal behavior. The prevailing model in the experimental study of decision‐making is the drift‐diffusion model, which has a long history and accounts for a broad range of behavioral and neurophysiological data. However, an alternative model – called the urgency‐gating model – has been offered which can account equally well for much of the same data in a more parsimonious and theoretically‐sound manner. In what follows, we will first trace the origins and development of the DDM, as well as give a brief overview of the manner in which it has supplied an explanatory framework for a large number of behavioral and physiological studies in the domain of decision‐making. In so doing, we will attempt to build a strong and clear case for its strengths so that it can be fairly and rigorously compared to potential alternative models. We will then re‐examine a number of the implicit and explicit theoretical assumptions made by the drift‐diffusion model, as well as highlight some of its empirical shortcomings. This analysis will serve as the contextual backdrop for our introduction and discussion of the urgency‐gating model. Finally, we present a novel experiment, the methodological design of which uniquely affords a decisive empirical dissociation of the models, the results of which illustrate the empirical and theoretical shortcomings of the drift‐diffusion model and instead offer clear support for the urgency‐gating model. We finish by discussing the potential for the urgency gating model to shed light on a number of clinical disorders, highlighting a number of future directions for research.

Este trabalho apresenta a formulação de um sistema híbrido de apoio à decisão que, através de técnicas adaptativas, permite que múltiplos dispositivos sejam utilizados de forma colaborativa para encontrar uma solução para um problema de tomada de decisão. É proposta uma estratégia particular para o trabalho colaborativo que restringe o acesso aos dispositivos mais lentos com base na dificuldade encontrada pelos dispositivos mais rápidos para solucionar um problema específico. As soluções encontradas por cada dispositivo são propagadas aos demais, permitindo que cada um deles agregue estas novas soluções com o auxílio de técnicas adaptativas. É feito um estudo sobre aprendizagem de máquina mediante incertezas para verificar e minimizar os impactos negativos que uma nova solução, possivelmente errônea, possa ter. O sistema híbrido proposto é apresentado numa aplicação particular, utilizando testes padronizados para compará-lo com os dispositivos individuais que o compõem e com sistemas híbridos de mesma finalidade. Através destes testes, é mostrado que dispositivos consolidados, mesmo que de naturezas distintas, podem ser utilizados de maneira colaborativa, permitindo não só calibrar um compromisso entre o tempo de resposta e a taxa de acerto, mas também evoluir de acordo com o histórico de problemas processados. / This work presents a formulation of a hybrid decision-making system that employs adaptive techniques as a way to coordinate multiple devices in order to make a collaborative decision. The strategy proposed here is to restrict the use of slower devices, based on how difficult the specific problem is - easier problems may be solved on faster devices. Each device is able to learn through solutions given by the others, aggregating new knowledge with the aid of adaptive techniques. In order to evaluate and minimize the negative impact those new solutions may have, a study concerning machine learning under uncertainty is carried out. A particular application of this system has been tested and compared, not only to each individual device that is part of the system itself, but to similar hybrid systems as well. It is shown that even devices of distinct natures may be reused in a collaborative manner, making it possible to calibrate the trade-off between hit rate and response time, and to evolve according to the input stimuli received as well.

Este trabalho apresenta a formulação de um sistema híbrido de apoio à decisão que, através de técnicas adaptativas, permite que múltiplos dispositivos sejam utilizados de forma colaborativa para encontrar uma solução para um problema de tomada de decisão. É proposta uma estratégia particular para o trabalho colaborativo que restringe o acesso aos dispositivos mais lentos com base na dificuldade encontrada pelos dispositivos mais rápidos para solucionar um problema específico. As soluções encontradas por cada dispositivo são propagadas aos demais, permitindo que cada um deles agregue estas novas soluções com o auxílio de técnicas adaptativas. É feito um estudo sobre aprendizagem de máquina mediante incertezas para verificar e minimizar os impactos negativos que uma nova solução, possivelmente errônea, possa ter. O sistema híbrido proposto é apresentado numa aplicação particular, utilizando testes padronizados para compará-lo com os dispositivos individuais que o compõem e com sistemas híbridos de mesma finalidade. Através destes testes, é mostrado que dispositivos consolidados, mesmo que de naturezas distintas, podem ser utilizados de maneira colaborativa, permitindo não só calibrar um compromisso entre o tempo de resposta e a taxa de acerto, mas também evoluir de acordo com o histórico de problemas processados. / This work presents a formulation of a hybrid decision-making system that employs adaptive techniques as a way to coordinate multiple devices in order to make a collaborative decision. The strategy proposed here is to restrict the use of slower devices, based on how difficult the specific problem is - easier problems may be solved on faster devices. Each device is able to learn through solutions given by the others, aggregating new knowledge with the aid of adaptive techniques. In order to evaluate and minimize the negative impact those new solutions may have, a study concerning machine learning under uncertainty is carried out. A particular application of this system has been tested and compared, not only to each individual device that is part of the system itself, but to similar hybrid systems as well. It is shown that even devices of distinct natures may be reused in a collaborative manner, making it possible to calibrate the trade-off between hit rate and response time, and to evolve according to the input stimuli received as well.