This paper presents an integrated approach to modeling human competencies by combining the theoretical foundations of cognitive architectures with principles from Human Factors Ergonomics (HFE). Through a comparative analysis of established cognitive models—SOAR, ACT-R, LIDA, and COCOM—we synthesize a tailored architecture designed to address the complexities of human–machine interaction (HMI) in dynamic environments. By contextualizing this model within ergonomic frameworks, we elucidate the mechanisms underlying decision-making, skill acquisition, and adaptive behavior, bridging the gap between cognitive theory and applied system design. Our framework is empirically grounded in industrial robotics applications, where operator expertise, normative knowledge, and real-time feedback loops are critical. The proposed architecture not only enhances the cognitive alignment of HMI systems but also provides a scalable methodology for designing intelligent, human-centered interfaces in high-stakes environments. This work advances both the theoretical understanding of human competencies and the practical implementation of adaptive, ergonomically optimized systems.

This study aims to investigate a misleading scheme that occurred in an aeronautic workshop, and to test possible forms of remediation. A misleading scheme is a scheme activated by deceptive characteristics of the situation which might reduce performance. Such a scheme had been identified among operators in a metal frame stretching workshop: they used the results obtained on one frame to program a stretching machine for subsequent frames. As it proved difficult to study this misleading scheme in the real workshop, the microworld paradigm was used for this research to evaluate two forms of remediation: a brief training course in the inhibition of the misleading scheme and a real-time simulation program. The misleading scheme appeared among the “naïve” participants in the same way it had appeared among the operators in the real workshop. Moreover, in the microworld the misleading scheme led to a decrease in performance. The real-time simulation tool did not lead to the disappearance of the misleading scheme, even though it seemed to allow users to learn how to refine the rules guiding the machine tool settings. The article thus raises the question of the persistence of a misleading scheme and the relevance of using real-time simulation without support to avoid its use in a situation where it is not relevant.

Previous studies have sought to better understand exogenous factors explaining drivers’ ability to regain control or not from highly automated driving. However, few studies have examined the role of individual factors in drivers’ take-over behavior and performance. The present study sought to examine the extent to which take-over performance can be predicted by individual differences in visuo-attentional and executive abilities. After a period of automated driving on a simulator, participants aged 20 to 60 (N = 118) had to regain control of their vehicle in a critical take-over situation (i.e., avoiding an obstacle by changing lanes while other vehicles are approaching at higher speeds). The take-over manoeuvre was considered successful if the participants managed to complete it without colliding with the obstacle or another vehicle. All participants completed a battery of cognitive (working memory, inhibitory control, attentional flexibility, and planning) and visuo-attentional tests (visuomanual coordination, multiple object avoidance, and multiple object tracking), and reported their age and driving experience. Several partial least-squares models predicting the success of the manoeuvre from individual abilities were compared. The most accurate model had an accuracy of 70.79% and identified spatial working memory (measured with the CORSI task), visuomanual coordination (one’s ability to manually track a moving target) and driving experience (annual mileage) as key factors for the success of the take-over. Conversely, higher inhibitory control ability, as measured by the Flanker and Stop-Signal tasks, was negatively related to take-over success, possibly because these participants exerted strong cognitive control over the non-driving task during automated driving, which came at the cost of flexibility.

This study aims to investigate a misleading scheme that occurred in an aeronautic workshop, and to test possible forms of remediation. A misleading scheme is a scheme activated by deceptive characteristics of the situation which might reduce performance. Such a scheme had been identified among operators in a metal frame stretching workshop: they used the results obtained on one frame to program a stretching machine for subsequent frames. As it proved difficult to study this misleading scheme in the real workshop, the microworld paradigm was used for this research to evaluate two forms of remediation: a brief training course in the inhibition of the misleading scheme and a real-time simulation program. The misleading scheme appeared among the "na & iuml;ve" participants in the same way it had appeared among the operators in the real workshop. Moreover, in the microworld the misleading scheme led to a decrease in performance. The real-time simulation tool did not lead to the disappearance of the misleading scheme, even though it seemed to allow users to learn how to refine the rules guiding the machine tool settings. The article thus raises the question of the persistence of a misleading scheme and the relevance of using real-time simulation without support to avoid its use in a situation where it is not relevant.

Human-machine interaction is increasingly important in industry, and this trend will only intensify with the rise of Industry 5.0. Human operators have skills that need to be adapted when using machines to achieve the best results. It is crucial to highlight the operator's skills and understand how they use and adapt them [19]. A rigorous description of these skills is necessary to compare performance with and without robot assistance. Predicate logic, used by Vergnaud within Piaget's scheme concept, offers a promising approach. However, this theory doesn't account for cognitive system constraints, such as the timing of actions, the limitation of cognitive resources, the parallelization of tasks, or the activation of automatic gestures contrary to optimal knowledge. Integrating these constraints is essential for representing agent skills understanding skill transfer between biological and mechanical structures. Cognitive architectures models [2] address these needs by describing cognitive structure and can be combined with the scheme for mutual benefit. Welding provides a relevant case study, as it highlights the challenges faced by operators, even highly skilled ones. Welding's complexity stems from the need for constant skill adaptation to variable parameters like part position and process. This adaptation is crucial, as weld quality, a key factor, is only assessed afterward via destructive testing. Thus, the welder is confronted with a complex perception-decisionaction cycle, where the evaluation of the impact of his actions is delayed and where errors are definitive. This dynamic underscores the importance of understanding and modeling the skills of operators.

The industry of the future, also known as Industry 5.0, aims to modernize production tools, digitize workshops, and cultivate the invaluable human capital within the company. Industry 5.0 can't be done without fostering a workforce that is not only technologically adept but also has enhanced skills and knowledge. Specifically, collaborative robotics plays a key role in automating strenuous or repetitive tasks, enabling human cognitive functions to contribute to quality and innovation. In manual manufacturing, however, some of these tasks remain challenging to automate without sacrificing quality. In certain situations, these tasks require operators to dynamically organize their mental, perceptual, and gestural activities. In other words, skills that are not yet adequately explained and digitally modeled to allow a machine in an industrial context to reproduce them, even in an approximate manner. Some tasks in welding serve as a perfect example. Drawing from the knowledge of cognitive and developmental psychology, professional didactics, and collaborative robotics research, our work aims to find a way to digitally model manual manufacturing skills to enhance the automation of tasks that are still challenging to robotize. Using welding as an example, we seek to develop, test, and deploy a methodology transferable to other domains. The purpose of this article is to present the experimental setup used to achieve these objectives.

Effective collaboration is essential in high-stakes environments where poor teamwork can lead to critical errors and adverse outcomes. This Work in Progress aims to contribute to research by providing real-time feedback to prevent critical situations arising from inadequate collaboration. We are developing an experiment to compare seven indicators of collaboration for their effectiveness in real-time context. Using a collaborative virtual environment, we can control the situation and environmental effects, allowing for precise and reliable assessment of each indicator. The goal is to identify the most efficient indicators for real-time assessment of collaboration, thereby enhancing team performance and preventing critical failures. This research will contribute to optimizing teamwork and operational success in critical fields, such as industrial applications, where collaboration is crucial.

Shared spaces are urban areas without physical separation between motorised and non-motorised users. Previous research has suggested that it is difficult for users to appropriate these spaces and that the advent of self-driving cars could further complicate interactions. It is therefore important to study the perception of these spaces from the users' perspectives to determine which conditions may promote their acceptance of the vehicles. This study investigates the perceived collision risk of a self-driving car's passenger when pedestrians cross the vehicle's path. The experiment was conducted with a driving simulator. Seven factors were manipulated to vary the dynamics of the crossing situations in order to analyse their influence on the passenger's perception of collision risk. Two measures of perceived risk were obtained. A continuous subjective assessment, reflecting an explicit risk evaluation, was reported in real time by participants. On the other hand, their skin conductance responses, which reflects implicit information processing, were recorded. The relationship between the factors and the risk perception indicators was studied using Bayesian networks. The best Bayesian networks demonstrate that subjective collision risk assessments are primarily influenced by the factors that determine the relative positions of the vehicle and the pedestrian as well as the distance between them when they are in close proximity. The analysis further reveals that variations in skin conductance response indicators are more likely to be explained by variations in subjective assessments than by variations in the manipulated factors. These findings could benefit the development of self-driving navigation among pedestrians by improving understanding of the factors that influence passengers' feelings.

In the field of large scale robotic, which is often remotely operated, having direct interaction can be disruptive for applications such as moving heavy loads or 3D printing. A Cable-Driven Parallel Robot (CDPR) is used here in physical Human–Robot Interactions (pHRI) with an admittance-based control strategy to physically interact with a user in tele-operation or in co-manipulation mode. A user experiment involving participants is designed to assess the performance of the human–robot team in a given task completion. Task performance and interaction quality metrics are defined and recorded during experiments with different robot configurations. The novelty is to provide a methodology to compare the configurations based on the performance metrics. The methodology accounts for variations of the metrics along time of use and assert a training effect leading to a progression or a regression of the performances. The experiment apparatus includes a CDPR, a user task composed of targets to reach with the robot and a handle equipped with a force sensor acting as a control input of a fixed admittance control strategy of the robot. Collected data show that the task performances and the interaction quality vary during the experiments and denote different variation profiles among the user population. Distribution of these profiles among configurations are analysed to determine the configuration that has the best training effect on users.

Résumé : Les robots parallèles à câbles utilisent des câbles pour déplacer et orienter une plateforme mobile dans l’espace de travail du robot. Les câbles sont guidés depuis les actionneurs jusqu’à la plateforme mobile au travers de poulies. Les actionneurs les plus répandus consistent en des enrouleurs motorisés qui permettent de contrôler l’enroulement ou le déroulement des câbles et ainsi contrôler la situation de la plateforme mobile. La géométrie et la position relative des enrouleurs par rapport aux poulies ont une influence sur la modélisation géométrique du robot. Ce papier présente la modélisation avancée d’un enrouleur et son influence sur l’erreur commise en terme de longueur de câble déroulée. Abstract : Cable-Driven Parallel Robots (CDPRs) use cables instead of rigid legs to connect the Moving-Platform (MP) to the base frame. Cables are routed from the actuators to the MP trough pulleys. The actuators commonly used to control the cable length, and therefore the MP pose, are winch actuated by motors that coil and uncoil the cable. The winch geometry and relative position to the pulleys have an influence on the geometric modelling of the robot. This paper presents the geometric modelling of the winch and its influence on the error of uncoiled cable.

The subject of this paper is about the relationship between the stiffness and the transparency of Cable-Driven Parallel Robots (CDPRs) used as human-machine interfaces in object comanipulation tasks. An index quantifying the transparency of a CDPR is first introduced. The stiffness of the robot is determined in simulation which parameters have been experimentally identified. Particular attention is paid to the effect of the Moving-Platform pose and cable tension management on CDPR stiffness. Then, the relationship between the stiffness and the transparency is analysed. Finally, the transparency index is traced throughout the constant-orientation static workspace and throughout the cable tension feasibility polygon for a given MP pose.

When manually steering a car, the driver's visual perception of the driving scene and his or her motor actions to control the vehicle are closely linked. Since motor behaviour is no longer required in an automated vehicle, the sampling of the visual scene is affected. Autonomous driving typically results in less gaze being directed towards the road centre and a broader exploration of the driving scene, compared to manual driving. To examine the corollary of this situation, this study estimated the state of automation (manual or automated) on the basis of gaze behaviour. To do so, models based on partial least square regressions were computed by considering the gaze behaviour in multiple ways, using static indicators (percentage of time spent gazing at 13 areas of interests), dynamic indicators (transition matrices between areas) or both together. Analysis of the quality of predictions for the different models showed that the best result was obtained by considering both static and dynamic indicators. However, gaze dynamics played the most important role in distinguishing between manual and automated driving. This study may be relevant to the issue of driver monitoring in autonomous vehicles.

Autonomous navigation becomes complex when it is performed in an environment that lacks road signs and includes a variety of users, including vulnerable pedestrians. This article deals with the perception of collision risk from the viewpoint of a passenger sitting in the driver's seat who has delegated the total control of their vehicle to an autonomous system. The proposed study is based on an experiment that used a fixed-base driving simulator. The study was conducted using a group of 20 volunteer participants. Scenarios were developed to simulate avoidance manoeuvres that involved pedestrians walking at 4.5 kph and an autonomous vehicle that was otherwise driving in a straight line at 30 kph. The main objective was to compare two systems of risk perception: These included subjective risk assessments obtained with an analogue handset provided to the participants and electrodermal activity (EDA) that was measured using skin conductance sensors. The relationship between these two types of measures, which possibly relates to the two systems of risk perception, is not unequivocally described in the literature. This experiment addresses this relationship by manipulating two factors: The time-to-collision (TTC) at the initiation of a pedestrian avoidance manoeuvre and the lateral offset left between a vehicle and a pedestrian. These manipulations of vehicle dynamics made it possible to simulate different safety margins regarding pedestrians during avoidance manoeuvres. The conditional dependencies between the two systems and the manipulated factors were studied using hybrid Bayesian networks. This relationship was inferred by selecting the best Bayesian network structure based on the Bayesian information criterion. The results demonstrate that the reduction of safety margins increases risk perception according to both types of indicators. However, the increase in subjective risk is more pronounced than the physiological response. While the indicators cannot be considered redundant, data modeling suggests that the two risk perception systems are not independent.

Cable-Driven Parallel Robots (CDPRs) offer high payload capacities, large translational workspace and high dynamic performances. The rigid base frame of the CDPR is connected in parallel to the moving platform using cables. However, their orientation workspace is usually limited due to cable/cable and cable/moving platform collisions. This paper deals with the design, modelling and prototyping of a hybrid robot. This robot, which is composed of a CDPR mounted in series with a Parallel Spherical Wrist (PSW), has both a large translational workspace and an unlimited orientation workspace. It should be noted that the six degrees of freedom (DOF) motions of the moving platform of the CDPR, namely, the base of the PSW, and the three-DOF motion of the PSW are actuated by means of eight actuators fixed to the base. As a consequence, the overall system is underactuated and its total mass and inertia in motion is reduced.

In autonomous cars, the automation systems assume complete operational control. In this situation, it is essential that passengers always feel comfortable with the vehicle's decisions. In this project, we are specifically interested in risk assessment by the passenger of an autonomous car navigating among pedestrians in a shared space. A driving simulator experiment was conducted with 27 participants. The challenge was twofold: on the one hand, to find a link between the pedestrians' avoidance behavior of the vehicle and the risk felt by the passenger; and on the other hand, to try to predict this perceived risk in real time. The study revealed a significant effect of two factors on the risk assessed by the participants: (1) the value of the TTC at the moment the vehicle begins a pedestrian avoidance maneuver; (2) the lateral distance it leaves to the pedestrian. The proposed real-time prediction model is based on the principle of impulse response operation. This new paradigm assumes that the passenger's risk assessment is the result of a quantifiable unconscious internal phenomenon that has been estimated using the dynamics of the perceived pedestrian approach. The results showed that this approach was predictive of risk for isolated avoidance maneuvers, but was insufficient to explain the variability in the risk assessment behavior of the participants.

During highly automated driving, drivers no longer physically control the vehicle but they might need to monitor the driving scene. This is true for SAE level 2, where monitoring the external environment is required; it is also true for level 3, where drivers must react quickly and safely to a take-over request. Without such monitoring, even if only partial, drivers are considered out-of-the-loop (OOTL) and safety may be compromised. The OOTL phenomenon may be particularly important for long automated driving periods during which mind wandering can occur. This study scrutinized drivers' visual behaviour for 18 min of highly automated driving. Intersections between gaze and 13 areas of interest (AOIs) were analysed, considering both static and dynamic indicators. An estimation of self-reported mind wandering based on gaze behaviour was performed using partial least squares (PLS) regression models. The outputs of the PLS regressions allowed defining visual strategies associated with good monitoring of the driving scene. This information may enable online estimation of the OOTL phenomenon based on a driver's spontaneous visual behaviour.

This article presents an activity analysis carried out in an aeronautical factory. The representation of the real process and the operators' representations were identified. The representation of the real process was elaborated using an abstraction hierarchy, as described by the ecological interface design framework. The operators' representations were extracted through interviews and observations and described in terms of schemes. The analysis revealed that operators in the studied factory used misleading schemes (i.e., false representations) to organize their activities, resulting in poor performance. We conclude by offering possible remedies, including training aimed at inhibiting the misleading scheme and a simulation tool to convey a more accurate representation.

Numeracy is the ability to use and to reason with numbers and other mathematical concepts, and to apply these in a range of everyday activities. Good numeracy appears today the best protection against unemployment, low wages and poor health. Innumeracy can then be simply defined as the absence of numeracy. That is, innumeracy refers to the growing trend in the inability of people to understand numbers, statistics, probabilities, and to being familiar with mathematical concepts. In France, the fight against innumeracy was erected as "national priority" by the law (8 July 2013). However, few empirical studies investigate the concept of innumeracy (not one to our knowledge). The program for the international assessment of adult competencies (PIAAC), which involved 7000 French participants, does only allows an indirect approach of the concept of innumeracy. Therefore, the 2011's investigation, in about 16000 households by the French national institute of statistical and economic information (Insee), seems an invaluable source of knowledge about the concept of innumeracy. This not only because the investigation included a numeracy test, but also because the participants had to understand a medical prescription (with numerical data) and were directly asked about their difficulties to read an invoice. The two main results of the present study are (1) a prevalence of innumeracy computed on the basis of a deviation of more than 1.645 standard deviation from the mean (below it) of about 7% in the French population, and (2) that two highly significant factors of innumeracy are level of education and gender. The understanding of a medical prescription unsurprisingly showed some supplementary difficulties in the innumeracy subgroup. However, the questioning, on a declarative basis on the difficulties to read an invoice, presumably leads to a considerable amount of avoidance strategies. The quantitative estimation of these strategies supports the idea that, for numeracy, possibly up to four times as many people show to have poor skills as those who acknowledge difficulties (Bynner & Parsons, 2006). Finally, the relation between innumeracy and dyscalculia was investigated. Dyscalculia is usually perceived as a specific learning difficulty for mathematics, or, more appropriately, arithmetic, and affects a wide range of lifelong learning involving mathematics. The prevalence of dyscalculia was estimated less than 3%. This estimated prevalence is lower than the prevalence of about 5 to 7% put forward by Butterworth, Varma and Laurillard ( 2011). Furthermore, these dyscalculia are not necessarily real dyscalculia, but only "potential" dyscalculia (i.e., which, at least, should be corroborated by clinical investigation). (C) 2018 Societe Francaise de Psychologie. Published by Elsevier Masson SAS. All rights reserved.

L’innumérisme, que l’on peut définir comme la non-acquisition ou la perte d’un niveau minimal de numératie par une personne, constitue un véritable handicap social et professionnel. Pour estimer la prévalence de l’innumérisme, nous nous sommes servis des données de l’enquête IVQ (informations sur la vie quotidienne) de l’Insee sur les personnes de 16 à 65 ans, en 2011. Les résultats montrent que l’estimation pour la population de France métropolitaine est de 7 %, avec une surreprésentation des femmes à faible niveau d’études. Les conséquences négatives de cette situation d’innumérisme sur la lecture des factures ou des ordonnances médicales sont examinées. Nous précisons aussi sa relation avec la dyscalculie qui, en tant que trouble spécifique du calcul, est moins courante (prévalence inférieure à 3 %). Enfin, grâce à l’étude IVQ de 2004, nous avons pu vérifier que ces pourcentages sont stables sur les sept ans (approximativement) séparant les deux enquêtes.