This study analyses the methodology of computational cognitive modeling as one of the ways of conducting research in cognitive science. The aim of the study is to provide an understanding of the place of computational cognitive models in understanding human cognition. Considering the vast number of computational cognitive models which have been just given to account for some cognitive phenomenon by solely simulating some experimental study and fitting to empirical data, a practice-oriented approach is adopted in this study to understand the work of the modeler, and accordingly to discover the potential of computational cognitive models, apart from their being simulation tools. In pursuit of this aim, a framework with a practice-oriented approach from the philosophy of science literature, which is Morgan &amp / Morrison (1999)&rsquo / s account, is employed on a case study. The framework emphasizes four key elements to understand the place of models in science, which are the construction of models, the function of models, the representation they provide, and the ways we learn from models. The case study Q-Soar (Simon, Newell &amp / Klahr, 1991), is a model built with Soar cognitive architecture (Laird, Newell &amp / Rosenbloom, 1987) which is representative of a class of computational cognitive models. Discussions are included for how to make generalizations for computational cognitive models out of this class, i.e. for models that are built with other modeling paradigms.

There has been a recent flurry of activity in consciousness research. Although an operational definition of consciousness has not yet been developed, philosophy has come to identify a set of features and aspects that are thought to be associated with the various elements of consciousness. On the other hand, there have been several recent attempts to develop computational models of consciousness that are claimed to capture or illustrate one or more aspects of consciousness. As a plausible substitute to evaluating how well the current computational models model consciousness, this study examines how the current computational models fare in modeling those aspects and features of consciousness identified by philosophy. Following a detailed and critical review of the literature of philosophy of consciousness, this study constructs a composite and eclectic list of features and aspects that would be expected in any successful model of consciousness. The study then evaluates, from the viewpoint of that list, some of the current self-claimed computational models of consciousness, specifically CLARION, IDA, ACT-R and model proposed in the Cleeremans&#039 / review and study. The computational models studied are evaluated with respect to each identified aspect and feature of consciousness.

Cette thèse en informatique présente un travail de modélisation cognitive computationnelle, à partir de données de mouvements oculaires lors de tâches de recherche d'information dans des textes. Nous nous intéressons à cette situation quotidienne de recherche d'informations dans un journal ou une page web, dans laquelle il faut juger si un texte est sémantiquement relié ou non à un but, exprimé par quelques mots. Parce que le temps est souvent une contrainte, les textes ne sont souvent pas entièrement lus avant qu'intervienne la décision. Plus précisément, nous avons analysé les mouvements des yeux dans deux tâches de recherche d'information consistant à lire un paragraphe et à décider rapidement i) s'il est associé à un but donné et ii) s'il est plus associé à un but donné qu'un autre paragraphe traité auparavant. Un modèle est proposé pour chacune de ces situations. Nos simulations sont réalisées au niveau des fixations et des saccades oculaires. En particulier, nous prédisons le moment auquel les participants décident d'abandonner la lecture du paragraphe parce qu'ils ont suffisamment d'information pour prendre leur décision. Les modèles font ces prédictions par rapport aux mots qui sont susceptibles d'être traités avant que le paragraphe soit abandonné. Les jugements d'association sémantiques humains sont reproduits par le calcul des similarités sémantiques entre mots produits par l'analyse de la sémantique latente (LSA, Landauer et al., 2007). Nous avons suivi une approche statistique paramétrique dans la construction de nos modèles. Ils sont basés sur un classifieur bayésien. Nous proposons un seuil linéaire bi-dimensionnel pour rendre compte de la décision d'arrêter de lire un paragraphe, utilisant le Rang de la fixation et i) la similarité sémantique (Cos) entre le paragraphe et le but ainsi que ii) la différence de similarité sémantique (Gap) entre chaque paragraphe et le but. Pour chacun des modèles, les performances montrent que nous sommes capables de reproduire en moyenne le comportement des participants face aux tâches de recherche d'information étudiées durant cette thèse. Cette thèse comprend deux parties principales : 1) la conception et la passation d'expériences psychophysiques pour acquérir des données de mouvements oculaires et 2) le développement et le test de modèles cognitifs computationnels. / This computer science thesis presents a computational cognitive modeling work using eye movements of people faced to different information search tasks on textual material. We studied situations of everyday life when people are seeking information on a newspaper or a web page. People should judge whether a piece of text is semantically related or not to a goal expressed by a few words. Because quite often time is a constraint, texts may not be entirely processed before the decision occurs. More specifically, we analyzed eye movements during two information search tasks: reading a paragraph with the task of quickly deciding i) if it is related or not to a given goal and ii) whether it is better related to a given goal than another paragraph processed previously. One model is proposed for each of these situations. Our simulations are done at the level of eye fixations and saccades. In particular, we predicted the time at which participants would decide to stop reading a paragraph because they have enough information to make their decision. The models make predictions at the level of words that are likely to be fixated before a paragraph is abandoned. Human semantic judgments are mimicked by computing the semantic similarities between sets of words using Latent Semantic Analysis (LSA) (Landauer et al., 2007). We followed a statistical parametric approach in the construction of our models. The models are based on a Bayesian classifier. We proposed a two-variable linear threshold to account for the decision to stop reading a paragraph, based on the Rank of the fixation and i) the semantic similarity (Cos) between the paragraph and the goal and ii) the difference of semantic similarities (Gap) between each paragraph and the goal. For both models, the performance results showed that we are able to replicate in average people's behavior faced to the information search tasks studied along the thesis. The thesis includes two main parts: 1) designing and carrying out psychophysical experiments in order to acquire eye movement data and 2) developing and testing the computational cognitive models.