L’objectif de cette étude est de rechercher les facteurs qui sous-tendent les comportements politiques chez les étudiants. L’approche développée est cognitive et conative. À ce titre, les connaissances, les représentations et les mécanismes de raisonnement sont invoqués comme éléments explicatifs des comportements. Ces processus sont appréhendés à l’aide d’outils linguistiques. L’analyse des résultats suggère l’existence de représentations politiques plutôt négatives, au sein de la population. Ces représentions génèrent des raisonnements qui induisent des d’attitudes critiques, méfiantes, à l’égard de la sphère politique, avec en toile de fond l’expression de nombreuses attentes. Pour autant, ces représentations, globalement négatives, ne doivent pas occulter la présence de perceptions positives à l’égard de l’univers politique, développées par les sujets experts qui envisagent la politique dans ses liens avec des domaines connexes. En outre, l’analyse des choix politiques fait ressortir deux formes de raisonnement sous-jacentes : des raisonnements motivés, davantage utilisés par les sujets sans proximité partisane, et des raisonnements plus heuristiques, fondés sur un savoir mémorisé adoptés par les répondants plus politisés. / . The objective of this study to seek the factors which underlie the political behaviors in the students. The developed approach is cognitive. For this reason knowledge, representations, and the mechanisms of reasoning are called upon like explanatory elements of the behaviors. These cognitive processes are apprehended using linguistic tools. The analysis of the results suggests the existence within the population of the political representations rather negative. These represented generate reasoning which induces of critical attitudes, being wary with regard to the political sphere within background the expression of many waiting. For as much, these overall negative representations should not occult the presence of positive perceptions of the political universe developed by the expert subjects which consider the policy in its bonds with related fields. Moreover, the analysis of the political choices emphasizes two subjacent forms of reasoning: the reasoning justified, more used by the subjects without proximity partisan, and the more heuristic reasoning founded on a memorized knowledge adopted by the more politicized guarantors

Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.

Behavioral animation has become popular for creating virtual characters that are autonomous agents and thus self-animating. This is useful for lessening the workload of human animators, populating virtual environments with interactive agents, etc. Unfortunately, current behavioral animation techniques suffer from three key problems: (1) deliberative behavioral models (i.e., cognitive models) are slow to execute; (2) interactive virtual characters cannot adapt online due to interaction with a human user; (3) programming of behavioral models is a difficult and time-intensive process. This dissertation presents a collection of papers that seek to overcome each of these problems. Specifically, these issues are alleviated through novel machine learning schemes. Problem 1 is addressed by using fast regression techniques to quickly approximate a cognitive model. Problem 2 is addressed by a novel multi-level technique composed of custom machine learning methods to gather salient knowledge with which to guide decision making. Finally, Problem 3 is addressed through programming-by-demonstration, allowing a non technical user to quickly and intuitively specify agent behavior.

Cognitive and behavioral models have become popular methods to create autonomous self-animating characters. Creating these models presents the following challenges: (1) Creating a cognitive or behavioral model is a time intensive and complex process that must be done by an expert programmer (2) The models are created to solve a specific problem in a given environment and because of their specific nature cannot be easily reused. Combining existing models together would allow an animator, without the need of a programmer, to create new characters in less time and would be able to leverage each model's strengths to increase the character's performance, and to create new behaviors and animations. This thesis provides a framework that can aggregate together existing behavioral and cognitive models into an ensemble. An animator only has to rate how appropriately a character performed and through machine learning the system is able to determine how the character should act given the current situation. Empirical results from multiple case studies validate the approach taken.