Analyzing behavioral data for refining cognitive models of operator

International audienceWe present a methodology and a tool for analyzing the activity of an operator interacting with a complex technical device. The goal is of refining cognitive models of the operator by relating them to patterns of behavior in real situations. The activity is observed to be modeled as a trace having a graph structure. The trace is transformed according to a "use model" in order to become meaningful in the context of modeling theories. Our "Trace Based System" thus gathers both a representation of the activity and of the analyst's expertise for facilitating the discovery of knowledge in the field of cognitive psychology. The approach is illustrated by its application for car driver cognitive modeling

Driver behaviour modelling and cognitive engineering tools development in order to assess driver sitation awareness

International audienceOur global objective is to define a framework for car driving behaviour analysis in order to assess driver's situation awareness. We present models, methods and software tools inspired from the "Experience Based Reasoning" theory coming from the field of artificial intelligence. It allows a construction of a representation of the driving activity including data collected in real driving situations as well as interpretations made on the driver's mental model of the situation, and permits a refinement of psychological theories

A Computational Cognitive Model Integrating Different Emotion Regulation Strategies

AbstractIn this paper a cognitive model is introduced which integrates a model for emotion generation with models for three different emotion regulation strategies. Given a stressful situation, humans often apply multiple emotion regulation strategies. The presented computational model has been designed based on principles from recent neurological theories based on brain imaging, and psychological and emotion regulation theories. More specifically, the model involves emotion generation and integrates models for the emotion regulation strategies reappraisal, expressive suppression, and situation modification. The model was designed as a dynamical system. Simulation experiments are reported showing the role of the emotion regulation strategies. The simulation results show how a potential stressful situation in principle could lead to emotional strain and how this can be avoided by applying the emotion regulation strategies decreasing the stressful effects

Abstract: un outil et une méthodologie pour analyser une activité humaine médiée par un artefact technique complexe

National audienceNous présentons une méthodologie et un outil pour analyser l'activité d'un opérateur humain en interaction avec un dispositif technique complexe. L'activité est observée pour être modélisée sous la forme d'une trace ayant une structure de graphe. La trace collectée est constituée initialement d'une succession de descripteurs d'événements, liés par une relation de séquentialité. Elle est ensuite enri-chie selon un modèle d'utilisation pour construire une représentation de l'activité à différents ni-veaux d'abstraction. Cela permet de retrouver des signatures de schémas mentaux mis en ½uvre par l'opérateur. Cette approche est utilisée pour la modélisation cognitive du conducteur automobile

Analyzing traces of activity for modeling cognitive schemes of operators

International audienceModern design of human/machine interfaces requires a better understanding of how operators control their interaction with machines. To understand these interactions, cognitive ergonomists seek to construct cognitive models of operators. These models generally depict operator activity as a process of information- collecting, computing, decision-making, and action. While this symbolic approach effectively describes formal reasoning, it becomes ambiguous when con- sidering an activity in which operators are physically involved, such as driving a car. Here, operators’ cognitive process accompanies their actions and can be equally viewed as a cause or as a consequence of their activity. Perception, cognition, and action can hardly be separated, because expectations drive perception, and the feeling of comprehension relies on possibilities of action.Where interaction and perception are so tightly coupled, we take inspiration from psychologists like Piaget, who have proposed to keep perception and action embedded into schemes. We consider schemes and cognitive schemas as the basic elements of our cognitive modelling, and we seek to highlight and model them from “traces of activity” (Georgeon, 2008). To do this, we have implemented knowledge engineering software and a method of cognitive modeling, which derives from “traces of activity”. This software includes graph processing and visualization, symbolic inference, as well as ontology manipulatio

Analyse de traces d'activité pour la modélisation cognitive: Application à la conduite automobile

How to describe the mental processes and states carried out by a human subject when he performs an activity? I.e. what is a mental process or state? How to infer them from the subject’s behavior and statements? How to describe them? Our goal is to provide answers to these questions, in the context of car driving. To this end, we have collected "traces of activity" during an experiment made with an instrumented vehicle. They are sequences of events that describe the interaction of the driver with his environment. They contain data from sensors or from assessments made by the ergonomist or by the driver. We have developed a progressive process for modeling these traces. It is based on a knowledge engineering tool, which we have specifically designed and implemented. This process allows us to produce models of the activity at different levels of abstraction. From these models, we show how an ergonomist can explain the activity, in reference to his own theoretical explicative framework. The framework that we have chosen comes from cognitive psychology. It proposes to describe the mental processes and states of the subject as "cognitive schemas" carried out at different "levels of control". Our approach allows us to search and find the implementation of cognitive schemas in the activity and to propose models of these schemas. From an epistemological viewpoint, this approach is based on a "constructivist" positioning, i.e. evolutionist, pragmatic and driven by the cesses and states of the subject as "cognitive schemas" carried out at different "levels of control". Our results consist of the methodology that we have developed, the software tool that we have built, the models of the activity that we have produced, and the cognitive schemas that we have modeled.Comment décrire les processus et états "mentaux" qu’un sujet humain met en œuvre lorsqu’il réalise une activité ? C'est-à-dire : qu’est-ce que des processus et états "mentaux" ? Comment les inférer à partir des comportements et des déclarations de ce sujet ? Quelle description obtient-on ? Notre objectif est d’apporter des réponses à ces questions dans le contexte de la conduite automobile. Pour cela nous avons collecté des "traces d’activité" pendant une expérimentation de conduite avec un véhicule instrumenté. Ce sont des séquences d’événements qui décrivent l’interaction du sujet avec son environnement. Nous avons mis au point un processus progressif de modélisation de ces traces. Il est basé sur un outil d’ingénierie des connaissances, que nous avons spécifiquement développé. Ce processus nous permet de produire des modèles de l’activité à différents niveaux d’abstraction. A partir de ces modèles, nous montrons comment un ergonome peut expliquer l’activité, en référence à son propre cadre théorique explicatif, notamment avec les notions de "schémas cognitifs", mis en œuvre à différents "niveaux de contrôle".Notre approche nous permet de repérer la mise en œuvre de schémas cognitifs dans l’activité et d’en proposer une modélisation. Sur le plan épistémologique, cette démarche se fonde sur un positionnement "constructiviste", c'est-à-dire évolutionniste, pragmatique, et dirigé par l’ergonome. Les résultats obtenus sont constitués de la méthodologie que nous avons mise au point, de l’outil informatique que nous avons conçu et réalisé, des modèles de l’activité que nous avons produits, et des schémas cognitifs de conduite automobile que nous avons modélisés

Analyse de traces d'activité pour la modélisation cognitive: Application à la conduite automobile

How to describe the mental processes and states carried out by a human subject when he performs an activity? I.e. what is a mental process or state? How to infer them from the subject’s behavior and statements? How to describe them? Our goal is to provide answers to these questions, in the context of car driving. To this end, we have collected "traces of activity" during an experiment made with an instrumented vehicle. They are sequences of events that describe the interaction of the driver with his environment. They contain data from sensors or from assessments made by the ergonomist or by the driver. We have developed a progressive process for modeling these traces. It is based on a knowledge engineering tool, which we have specifically designed and implemented. This process allows us to produce models of the activity at different levels of abstraction. From these models, we show how an ergonomist can explain the activity, in reference to his own theoretical explicative framework. The framework that we have chosen comes from cognitive psychology. It proposes to describe the mental processes and states of the subject as "cognitive schemas" carried out at different "levels of control". Our approach allows us to search and find the implementation of cognitive schemas in the activity and to propose models of these schemas. From an epistemological viewpoint, this approach is based on a "constructivist" positioning, i.e. evolutionist, pragmatic and driven by the cesses and states of the subject as "cognitive schemas" carried out at different "levels of control". Our results consist of the methodology that we have developed, the software tool that we have built, the models of the activity that we have produced, and the cognitive schemas that we have modeled.Comment décrire les processus et états "mentaux" qu’un sujet humain met en œuvre lorsqu’il réalise une activité ? C'est-à-dire : qu’est-ce que des processus et états "mentaux" ? Comment les inférer à partir des comportements et des déclarations de ce sujet ? Quelle description obtient-on ? Notre objectif est d’apporter des réponses à ces questions dans le contexte de la conduite automobile. Pour cela nous avons collecté des "traces d’activité" pendant une expérimentation de conduite avec un véhicule instrumenté. Ce sont des séquences d’événements qui décrivent l’interaction du sujet avec son environnement. Nous avons mis au point un processus progressif de modélisation de ces traces. Il est basé sur un outil d’ingénierie des connaissances, que nous avons spécifiquement développé. Ce processus nous permet de produire des modèles de l’activité à différents niveaux d’abstraction. A partir de ces modèles, nous montrons comment un ergonome peut expliquer l’activité, en référence à son propre cadre théorique explicatif, notamment avec les notions de "schémas cognitifs", mis en œuvre à différents "niveaux de contrôle".Notre approche nous permet de repérer la mise en œuvre de schémas cognitifs dans l’activité et d’en proposer une modélisation. Sur le plan épistémologique, cette démarche se fonde sur un positionnement "constructiviste", c'est-à-dire évolutionniste, pragmatique, et dirigé par l’ergonome. Les résultats obtenus sont constitués de la méthodologie que nous avons mise au point, de l’outil informatique que nous avons conçu et réalisé, des modèles de l’activité que nous avons produits, et des schémas cognitifs de conduite automobile que nous avons modélisés

Analyzing traces of activity for modeling cognitive schemes of operators

International audienceModern design of human/machine interfaces requires a better understanding of how operators control their interaction with machines. To understand these interactions, cognitive ergonomists seek to construct cognitive models of operators. These models generally depict operator activity as a process of information- collecting, computing, decision-making, and action. While this symbolic approach effectively describes formal reasoning, it becomes ambiguous when con- sidering an activity in which operators are physically involved, such as driving a car. Here, operators’ cognitive process accompanies their actions and can be equally viewed as a cause or as a consequence of their activity. Perception, cognition, and action can hardly be separated, because expectations drive perception, and the feeling of comprehension relies on possibilities of action.Where interaction and perception are so tightly coupled, we take inspiration from psychologists like Piaget, who have proposed to keep perception and action embedded into schemes. We consider schemes and cognitive schemas as the basic elements of our cognitive modelling, and we seek to highlight and model them from “traces of activity” (Georgeon, 2008). To do this, we have implemented knowledge engineering software and a method of cognitive modeling, which derives from “traces of activity”. This software includes graph processing and visualization, symbolic inference, as well as ontology manipulatio

Little AI: Playing a constructivist robot

Little AI is a pedagogical game aimed at presenting the founding concepts of constructivist learning and developmental Artificial Intelligence. It primarily targets students in computer science and cognitive science but it can also interest the general public curious about these topics. It requires no particular scientific background; even children can find it entertaining. Professors can use it as a pedagogical resource in class or in online courses. The player presses buttons to control a simulated “baby robot”. The player cannot see the robot and its environment, and initially ignores the effects of the commands. The only information received by the player is feedback from the player’s commands. The player must learn, at the same time, the functioning of the robot’s body and the structure of the environment from patterns in the stream of commands and feedback. We argue that this situation is analogous to how infants engage in early-stage developmental learning (e.g., Piaget (1937), [1]). Keywords: Pedagogical game, Artificial intelligence, Developmental learning, Constructivist learnin