Philippe Rauffet

Clément Guérin

Christine Chauvin

Eric Martin

Contribution of Industry 4.0 to the emergence of a joint cognitive and physical production system

The digital and technological revolutions of Industry 4.0 aim at increasing the flexibility of companies, the mass customization of products and the improvement of working conditions. Thus, IoT and biofeedback sensors become new sources of information on the production context and the state of resources, big data and cloud computing offer increased processing capacity (for learning and simulation), virtual and augmented realities, as well as vocal or gestural commands, make the interactions more contextual, natural, and customized. The integration of these Industry 4.0 technologies must however reconsider the place of the operator in the production, to tend towards a joint cognitive and physical system. Interference between operators and the cyber-physical production system must be optimized, by fostering the emergence of a real know-how to cooperate on the different production management activities (line operations, supervision, planning). In this perspective, the paper provides an overview on the Industry 4.0 technologies and their impact on the human-systems cooperation. A synthesis model proposes to position these technologies around the processes of building common frame of reference and distributing functions between humans and the cyber-physical system. This model is finally illustrated by a conditioning activity, shared between a human operator and cyberphysical components (cobot, augmented reality, etc.).

Introduction

The term Industry 4.0 emerged in the early 2010s and refers to the advent of a fourth industrial revolution (Drath & Horth, 2014) and the application of the Cyber-Physical Systems concept to the field of industrial production systems. For Wang, Wan ans Zhang (2016), Industry 4.0 is one type of industry which incorporates technological advances for addressing major societal challenges: improving the quality of life with high-quality customized products, participating in the sustainable development by reducing non-renewable energy consumption, and offering better working conditions to employees. Rüssman et al. (2015, Boston Consulting Group report) identify nine technology pillars related to the digital revolution of industry.

On the one hand, Internet of Things, horizontal and vertical integration of Information System, Big Data, Cloud computing, cybersecurity, will facilitate management of companies' information by linking or decentralizing data, making data reliable, or also gave rise new knowledge becomes available (Hozdic, 2015). These technologies contribute to the advent of cyber-physical systems, defined as interactive networks of physical and computer components, characterized by their evolutionary and decentralized nature, and their properties of openness and context sensitivity [START_REF] Fantini | Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems[END_REF]. In addition, they offer enhanced capabilities in monitoring, modeling, analysis, and calculations, using data mining and machine learning techniques [START_REF] Longo | Smart operators in industry 4.0: A humancentered approach to enhance operators' capabilities and competencies within the new smart factory context[END_REF].

On the other hand, simulation, virtual or augmented reality, improve production verification and supervision capabilities. [START_REF] Longo | Smart operators in industry 4.0: A humancentered approach to enhance operators' capabilities and competencies within the new smart factory context[END_REF] argue that the industry 4.0 will allow the emergence of the virtual enterprise, thanks to the constitution of digital twins. According to Rosen, Wichert & Bettenhausen (2015), this notion of 'digital copying' can be used to reduce design costs, to detect errors early in the preliminary design phase of products or production systems. Digital twins could also help for supervision, displaying in real time in virtual environments the state and behavior of machines and production. This immersion of the digital twin in reality could be done using augmented reality. Augmented reality, today often dedicated to one or a few specific tasks, will become ubiquitous [START_REF] Grubert | Towards pervasive augmented reality: Context-awareness in augmented reality[END_REF]). It will be adaptive depending on the context (location, type of activity, situation encountered), to project operational information on the immediate environment of the task to be performed, but also more tactical information on the general environment of production. Longo et al. (Ibid.) believe that this combination of virtual and real worlds is carrier and could bring interesting applications for maintenance (remote assistance) or training to operational situations.

Finally, additive manufacturing and self-adjusting robotics will support more flexible production. According Rüssmann et al. (2015), robotics becomes a more sustainable means of collaborative work. At the same time, additive manufacturing, now confined to prototyping, is becoming more industrial, moving from mass production to "mass customization". All of this contributes to the emergence of autonomous and self-organized manufacturing systems [START_REF] Longo | Smart operators in industry 4.0: A humancentered approach to enhance operators' capabilities and competencies within the new smart factory context[END_REF], making production more agile. These opportunities, brought by the implementation of new technologies in manufacturing systems, raise new challenges (Hozdic, 2015;NIST, 2016;[START_REF] Longo | Smart operators in industry 4.0: A humancentered approach to enhance operators' capabilities and competencies within the new smart factory context[END_REF]. In particular, the cyber-physical production system must be resilient, responsive and flexible in the face of critical situations. Manufacturing must become intelligent and holonic (NIST, Ibid.) and rely on artificial intelligence that can automatically adapt to the changing environments and demands of the process. Above all, we must move from the era of intelligent products (for example with RFID), to that of intelligent machines and the optimization of joint systems, combining operator and cyberphysical systems. The emergence of these joint systems [START_REF] Hollnagel | Joint cognitive systems: Foundations of cognitive systems engineering[END_REF] also called "symbiosis" between operators and digital assistances (Romero, Bernus, Noran, Stahre, Fast & Berglund, 2016), is a major challenge of the industry 4.0. It addresses key questions about the future place of human in industry 4.0 (Hozdic, 2015, Hirsch-Kreinsen, 2014[START_REF] Longo | Smart operators in industry 4.0: A humancentered approach to enhance operators' capabilities and competencies within the new smart factory context[END_REF].

The purpose of the paper is to contribute to the understanding and modeling of this joint industry 4.0 production system. The first part presents a review of the literature on the place of human in industry 4.0 and develops the concept of operator 4.0. A state of the art on human-machine cooperation models is also conducted in this section, in order to better understand the emergence of the joint, cognitive and physical system of production. The second part then presents a synthesis framework, to model the possibilities of man-machine cooperation between the operator and these new assistances of industry 4.0. This framework is applied to a use case (the activity of order picking). Finally, these proposals are discussed, in terms of the prospects for the design and adaptation of Industry 4.0 to future operators.

I. Related works

I.1. Towards operator 4.0, a joint cognitive and physical system of production

The place of operators in future smart factory Several authors identify various issues related to the role of operators within these new sociotechnical systems. Hirsch-Kreinsen (2014) describes the impact of this new "industrial revolution" on the skills and qualifications of operators as well as the evolution of the organization of work  Skills and knowledges. Operators will be required to demonstrate high adaptive capacity. They will act on complex, interconnected and autonomous technical systems that they will have to understand and control. Operators will manipulate more abstract information and will often be more "remote" from the process to be controlled, with an increased scope of supervision (Hoc, 1996).  Qualification. Industry 4.0 will employ fewer unskilled workers than the traditional factory. Indeed, as Fantini, Pinzone and Taisch (2018) recall, it is the routine activities, characterized by a low level of requirements of manual dexterity or social interaction, which are most likely to be replaced by the technology. On the other hand, it will need qualified and welltrained workers (Lorentz, Russmann, Strack, Lueth & Bolle, 2015).  Work organization. Hirsch-Kreinsen identifies two extreme forms of organization and division of work between humans: a "polarized" organization with few disqualified tasks and a large group of highly qualified experts and specialists versus a "distributed" organization aiming for flexibility and based on a high level of qualification which enables operators to face unanticipated situations.

The place of human in the productive systems is thus to rethink in depth. It is in this sense that Hozdic (2015) calls for the updating of the operator model. The author specifies that it is necessary to reflect on new human activities, taking into account the new possibilities of the mixed reality combining the physical world and the digital / cybernetic world (through interfaces, techniques of representation of knowledge, augmented reality, etc.). In this perspective, Hozdic (Ibid.) also emphasizes the need to model new architectures of cyber-physical production systems (CPS), ranging from sensors to the presentation and contextualization of decision support, and that keep the human in the loop, or even increase its capabilities.

In order to better define the place of the human in the context of Industry 4.0, some authors have recently sought to build typologies of the operator, qualified as "operator 4.0" (Romero et al., 2016), to from the new technological revolutions (see §I.1). They have formalized in a detailed way the interactions with the cyber-physical system of production (Romero et al., 2016[START_REF] Romero | Social Factory Architecture: Social Networking Services and Production Scenarios Through the Social Internet of Things, Services and People for the Social Operator 4.0[END_REF], or proposed a framework to design and evaluate these interactions [START_REF] Fantini | Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems[END_REF]. Operator 4.0: a symbiosis between human and machines Romero, Bernus, Noran, Stahre and Fast Berglund (2016) explain that the operator 4.0 is part of a new approach to designing adaptive production systems. Automation is considered as a means to increase human capacities at the physical, sensory and cognitive levels. The "symbiosis" between the human and the cyber-physical system, will allow to emerge a new intelligent hybrid agent (human and artificial). This vision proposed by Romero et al. (2016) somehow joins the current of cognitive systems [START_REF] Hollnagel | Joint cognitive systems: Foundations of cognitive systems engineering[END_REF]. This approach follows and extends the concept of "Balanced Automation System" (Romero et al., 2016[START_REF] Fantini | Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems[END_REF]: it is no longer only a question of finding a balance between manual tasks and automated tasks, but to design an adaptive and dynamic automation, based on a dynamic allocation of functions between the man and the machine.

According to [START_REF] Fantini | Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems[END_REF], robots and algorithms are not intended to replace the human in industry 4.0, but are instead intended to become assistance that allows the operator to continue working, despite age, disabilities or inexperience, which favor the maintenance of human "in the control loop", and which improve the performance and comfort of workers.

A The operator 4.0 can be physically increased ("super-strength operator" and "collaborative operator"), using cobots and exoskeletons. This will contribute to the increase in performance and the reduction of musculoskeletal disorders (the physical load can be shared between the man and the robot) and will also allow the insertion of disabled workers.

The operator 4.0 can also be increased on the cognitive level (to better treat and interpret the information, and to solve in a more optimal way the problems met) and on the sensory level (to better perceive the environment and to detect new signs):

 Biofeedback sensors will bring a sensory increase ("healthy operator").

Like the IoT sensors, they will allow a reflexive evaluation of the operator on the physical and mental effort that he provides, and thus contribute to the detection of risk situations, when the man is physically overloaded or cognitive  New visualization interfaces, such as virtual reality and augmented reality, will contribute to sensory and cognitive enhancement of the operator ("augmented operator" and "virtual operator"). The diagnosis of the situation can indeed be enriched, through the display of information from the cyber-physical system, and this contextualized (depending on the location, the role of the operators, the situation encountered). The perception of the environment will also be increased, thanks to the subjective vision proposed by these visualization means, and to the display of visual alerts or indexes highlighting the important elements of the environment.

Enterprise social networks will participate in a cognitive increase of the operator ("social operator"). They will notably improve the diagnosis of the real situation and help the problem solving thanks to the knowledge of other experts accessible online, synchronously (chat, video), or asynchronous (with the information "traces" left on the platforms of wiki type, forum). The analysis of big data and cloud computing will lead to a cognitive increase of the operator ("analytical operator"). They will extend the capabilities of calculation, classification, analysis and synthesis of information of the company, and thus enrich the analysis of the situation by the operator, whether at the strategic, tactical or operational level.

Finally, the operator 4.0 will benefit from increased interactions with the cyberphysical system ("smarter operator"), in support of the various increases of the operator listed above. Human-machine interactions will be improved by the use of personal assistants and artificial intelligence. The queries and commands can then be transmitted in natural language between the man and the machine, and the understanding of the needs of the operator will be contextualized and enriched (through a learning process where the personal assistant will consolidate a model of the operator).

Operator 4.0, a social agent In addition to this vision based on the "symbiotic" assistance and increases of the operator, we must also approach human-machine cooperation with a perspective of social interactions [START_REF] Fantini | Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems[END_REF]. In this sense, Romero, Wuest, Stahre and Gorecky (2017) refine their vision of the operator 4.0, placing it at the center of a social network, made up of other social operators, but also of machines and software qualified them also of "social". These authors propose a multi-agent architecture of the social factory to better formalize these social interactions and this holonic production system. This is based in particular on the introduction of two types of agents:

 The interface agents correspond to a set of rules and interaction conditions that support interactions between human or technical agents and the rest of the system. These interface agents are qualified as active, in the sense that they evolve continuously, by learning (based on observation, imitation, return of other agents or programming). They thus make it possible to personalize the assistance provided to the agent, making it dynamic and adaptive, and thus contributes to keeping the social operator or the social machine in the loop when they encounter difficulties.  The broker agents correspond to the rules of allocation of functions and sharing or delegation of authority. They thus make it possible to adapt the level of automation to optimize man-machine cooperation.

Optimization of joint production systems: a human-machine cooperation perspective Attempts to model the place of the operator in Industry 4.0 and its "social" interactions with other agents of the organisation highlight the emergence of a joint cognitive system, symbiosis of human and of the cyber-physical system. Obtaining a "symbiotic" system must go through an optimization of man-machine cooperation. This optimization is conditioned by:  The creation of a common frame of reference (Clark, 1996[START_REF] Hoc | Towards a cognitive approach to human-machine cooperation in dynamic situations[END_REF], including both a shared awareness of the environment, but also a representation of each agent and the team, seen as resources. It is in this sense that the interface agents allow adaptive assistance. This common frame of reference must also be constructed taking into account the questions of transparency, which will refine and guide the dialogue between the different agents (Chen, Procci, Boyce, Wright, Garcia & Barnes, 2014).  The design of a dynamic allocation of functions, allowing a sharing of work between the man and the machine that responds to the hazards and keeps the operator in the loop.

I.2. Human-machine cooperation models to understand operator 4.0

Human-machine cooperation can be understood by structural approaches that define the structure of the relationships between cooperating agents, or functional approaches that describe the cooperative activities that develop between agents [START_REF] Chauvin | Integration of Ergonomics in the Design of Human-Machine Systems[END_REF] Human-machine cooperation models [START_REF] Millot | Men-Machine Cooperative Organizations: Formal and Pragmatic implementation methods[END_REF] defined two generic organizational structures for cooperation:

 Vertical cooperation in which an agent at a higher level supervises and has authority. The lower level agent can provide advice;  Horizontal cooperation where the agents are at the same hierarchical level and the tasks are shared by allocation, the sharing being defined by the higher hierarchical level. [START_REF] Schmidt | Cooperative Work: A Conceptual Framework[END_REF] takes a functional-structural approach by distinguishing three types of cooperation, which can be observed when agents are mutually dependent to carry out their task (their individual activities must then be articulated in order to achieve the work objectives):  Augmentative cooperation that aims to increase physical or intellectual abilities since additional agents with identical skills perform the task when the workload increases and can not be managed by a single agent;  Debating cooperation makes it possible to confront points of view between agents in order to make the solutions more reliable and reduce the errors. This type of cooperation requires some agents to check and control other agents;  Integrative cooperation joins complementary skilled agents.

According to [START_REF] Hoc | Towards a cognitive approach to human-machine cooperation in dynamic situations[END_REF] functional approach, two autonomous agents are in a cooperative situation if two minimum conditions are met. First, each agent pursues goals, and each may interfere with goals, resources, or procedures. Secondly, each agent strives to deal with these interferences in order to facilitate the accomplishment of the individual activities of each or the accomplishment of the common task if it exists. This interference management can be observed at three levels, defining three levels of cooperation:

 L1: cooperation in action (or execution). This level distinguishes between different operational cooperative real-time and short-term goal and procedure management activities: local interference creation (eg disagreement), local interference detection (eg redundancy), anticipation and interference resolution.  L2: cooperation in planning. This level is characterized by cooperative activities for developing or maintaining a common reference system: maintaining and developing a common objective, a common plan, a distribution of functions  L3: metacooperation. It facilitates the two previous levels by developing a common code of communication and models of oneself and the partner

The central concept of common ground in human-machine cooperation To move towards joint cognitive systems based on a symbiosis between operator 4.0 and technologies of industry, shared representations of situations, adapted to the contexts, must be supported [START_REF] Salas | Situation awareness in team performance: Implications for measurement and training[END_REF][START_REF] Kaber | Team situation awareness for process control safety and performance[END_REF]. [START_REF] Millot | A common Work Space for a mutual enrichment of Human-machine Cooperation and Team-Situation Awareness[END_REF] propose to extend the work of humanmachine cooperation from framework of collective situation awareness (team-SA, shared-SA and distributed-SA) defined as the shared understanding of a situation between agents. Although each individual has an individual representation of a given situation, one can identify a level of "recovery" of individual SA whose key elements facilitating the construction of the team-SA are to share the goals and future states of the system. (Salmon et al., 2008). The 4.0 technologies will thus facilitate a level of mutual transparency between the partner agents, by making it possible to build, feed, update and adapt the common repository of agents. The common frame of reference becomes the "linchpin" of cooperating agents since it allows: (a) cooperation in action and planning [START_REF] Hoc | Towards a cognitive approach to human-machine cooperation in dynamic situations[END_REF] through effective coordination mechanisms [START_REF] Schmidt | Cooperative Work: A Conceptual Framework[END_REF]. By generalizing the joint activity concepts developed by Clark (1996), [START_REF] Klein | Common ground and coordination in joint activity[END_REF] indicate that this coordination is facilitated when team members are inter-predictable (they can predict the actions of others) and have a sufficient common frame of reference; (b) enrichment of self and partner models. This enrichment of models helps to develop the "know-cooperate" component of cooperation [START_REF] Rogalski | Cooperation processes in dynamic environment management: evolution through training experienced pilots in flying a highly automated aircraft[END_REF].

Know-How to Cooperate and processes for building common ground [START_REF] Millot | A common Work Space for a mutual enrichment of Human-machine Cooperation and Team-Situation Awareness[END_REF] propose a model of human-machine cooperation that articulates the coordination mechanism to the concept of knowing how to cooperate. Their model of cooperation is based on two principles: (1) the agents have knowhow in the control of processes or the accomplishment of tasks (2) the agents have knowledge to cooperate with other agents. From the definition of cooperation proposed by [START_REF] Hoc | Towards a cognitive approach to human-machine cooperation in dynamic situations[END_REF], the authors consider two processes contributing to the know-how to cooperate: (a) the coordination which makes it possible to manage the interferences between the aims pursued by the agents (detect, create, solve); (b) facilitating the goals of other agents. Their common reference concept, the Common Work Space [START_REF] Pacaux-Lemoine | A Common Work Space to support the Air Traffic Control[END_REF][START_REF] Millot | A common Work Space for a mutual enrichment of Human-machine Cooperation and Team-Situation Awareness[END_REF] then makes it possible to support cooperative activities.

Industry 4.0, a support for the enhancement iof human-machine cooperation Technologies of Industry 4.0 will help to build and adapt the common frame of reference of cooperating agents by supporting the processes of coordination and facilitation of goals. These adaptive mechanisms of the joint human-machine systems may vary according to spatial and temporal parameters, making it possible to define different modes of cooperation [START_REF] Schmidt | Cooperative Work: A Conceptual Framework[END_REF]: close or distant cooperation, synchronous or asynchronous cooperation, collective or distributed cooperation, direct or indirect cooperation. mediated. The "state" or "quality" of the common frame of reference could then be a criterion allowing to favor a real-time flexible human-machine cooperation [START_REF] Hoc | Towards a cognitive approach to human-machine cooperation in dynamic situations[END_REF] and thus "activate", according to the paradigm of the dynamic allocation of functions, modes of assistance to optimize resources or recover a degraded situation.

II. A framework for modeling joint cognitive and physical production systems

II.1. Development of a human-machine cooperation framework for operator 4.0

The functions of production to model Operators perform functions of different natures to perform their tasks. In the current definition of jobs of operators, there is indeed a combination of several aspects of production management that an operator must manage. We can find for example:  The scheduler, which does both short-term planning and supervision, possibly correcting the planning of the manufacturing orders of the day according to the encountered hazards (related to the demand, or the shortcomings of the production system provoked by machine breakdowns or the absence of employees);

 In empowering organizations (e.g. in Michelin or French Post Office), flow facilitators do both production operations and supervision. They will carry out support activities (quality control, training) or managerial activities (point 5 minutes, transmission of directives and feedback of field problems) in addition to their implementation tasks.

Rather than focusing on roles, we will discuss in terms of production functions, by distinguishing operational, planning and supervisory functions. These different functions can be analyzed according to the level of control that operators can have on operations. This control can be characterized following two axes (cf. figure 2):  Control can be reactive (during production) or anticipatory (before production),  The control level depends on the margins that operators can have on production load and capacity (in other words, on the tradeoff that an operator will be able to find between the demand of production and the implementation of the human and technical resources).

During production (reactive control), we will therefore distinguish two kinds of function: (a) production operations (product manufacturing, maintenance, etc.) consist in implementing resources to meet the demand. Production capacity and load are fixed, and the operator must remain in this constraint field, with a reflexive control of the action in progress; (b) production supervision consists of monitoring and reconfiguring the production system, if necessary, to ensure that the resources (capacity) can adequately meet the demand for production (load). Capacity can therefore be redefined (redistribution of tasks, prioritization of manufacturing or intervention orders), but demand is a constraint.

Before production (anticipatory control), planning tasks aim at monitoring and adjusting the load / capacity ratio in the management of production operations.

Operator plays on load AND capacity, with a more or less distant horizon (at the level of Industrial and Commercial Planning or Master Production Scheduling).

Figure 2 -Mapping of functions of production and existing cyberphysical components

Categorization of industry 4.0 technologies The technologies for industry 4.0 can be grouped into four categories (Tech1 to Tech4), according to their impact on information processing or implementation of the action (Table 1).

Table 1 -Characterization of industry 4.0 technologies

A framework for modeling operator 4.0

The figure 3 below synthesizes the human-machine cooperation mechanisms that should be developed for designing future operator 4.0, by positioning the different industry 4.0 technologies and the different levels of functions of production. The four categories of technology 4.0 in Table 1 (Tech1 to Tech 4) can be positioned according to the two main levels of industrial activity (Figure 3): at the upper level, with tactical activities of planning and supervision, and at the lower level, the operational activities of product manufacturing.

Industrial activities at the operational level are performed by a combination of human agents and machines (H or M or H-M or M-M). At this level, sensor technologies (Tech1) can dynamically collect information related to the state of material resources (IoT: temperature, hydrometry, presence, etc.), to the operator functional states (biofeedback: heart rate variation) or to the characteristics of the production processes. At this level too, the human can be assisted in performing physical tasks (Tech4).

The collected data of the situation of production are treated at the tactical level by human or artificial agents, which manage the diagnosis and the adaptation of the operational level. This process is performed by comparison with models (individual's long-term memory, organizational memory and computer database). This comparison process, supported and enriched by big data and machine learning (Tech2), allows:  To evaluate the current situation (diagnosis), by measuring the quality of the COFOR ("interface" property of supervisory agents 1 and 2) and the relevance in the distribution of the functions at the operational level ("broker" property of supervision agents 1 and 2). The diagnosis is based on the mutual understanding of goals and the compliance with procedures, and focused on short term operational activities of production (corresponding to cooperation in action of [START_REF] Hoc | Towards a cognitive approach to human-machine cooperation in dynamic situations[END_REF];  To generate system adaptations from the results of these assessments. The adaptation criteria must satisfy the maintenance of a common reference system (Hoc's cooperation in planning, 2001) and may lead to re-allocations of functions at the operational level (broker property of supervisory agents);  To infer new rules to improve models and the know-how to cooperate (Hoc's meta-cooperation, 2001) through a learning process. , 2013, Fig. 11). This dialogue between supervisory agents then makes it possible to determine the cooperation structure, horizontal or vertical (see Millot & Mendiaux, 1995[START_REF] Millot | A common Work Space for a mutual enrichment of Human-machine Cooperation and Team-Situation Awareness[END_REF], which will define the role and authority of each agent for the operational regulation of the industrial system (information sharing, task sharing). The results of this "horizontal" dialogue, resulting in the delegation of authority to one or the other of the supervisory agents, will then be transmitted "vertically" at the operational level, in order to:  Coordinate the operating agents with each other according to a mode of cooperation adapted to the situation (augmentative, debatable or integrative, see the Schmidt taxonomy);  Maintain COFOR by facilitating perception, understanding and projection of future states of the situation, including the context of the task in which the information can be transmitted (Tech 3), based on the same information transparency rules.

This model of operator 4.0, with a human-machine cooperation perspective, replaces the new technologies of industry 4.0 as input or assistance for managing the dialogue between agents and coordinating the the joint activity. It aims at supporting the definition and the analysis of new activities of operators 4.0. The next section will notably study the order picking activity. This specific activity will include both planning and organization tasks for the different production batches to be performed, as well as physical production tasks related to batch packaging.

II.2. Application of the framework to the specific activity of order picking

We consider the activity of an order picker, who can be assisted by all the different kind of technologies presented in table 1. Tech 1 will be composed of biofeedback sensors (smart watch on operator) and IoT sensors (RFID, smart sensors for detecting the presence of empty parcel boxes in storage). Tech 2 consists in calculation capabilities provided by the digital twin (predicting breakdown of production agents from Tech 1 sensors) and the integration of other data (related to customer demand). Tech 3 will support dialogue and communication with augmented reality glasses, pick-to-light and voice-to-pick technologies. Finally, Tech 4 corresponds to a cobot, able to interact with a human agent in a parallel or a sequential process.

The activity of an order picker 4.0, considered as an agent in symbiosis with the four technologies, could be described through the following scenario, designed with the aid of the framework proposed in Figure 3:  Let consider that the customer demand is increasing. The activity of conditioning (filling and closing parcel boxes according to the customer orders) could be therefore shared between human and cobot (Tech 4) with a parallel organization (in the augmentative mode of Schmidt's cooperation taxonomy, where each agent is making a complete box at the same time).



The detection of production problems is enhanced with the aid of Tech 1 sensors. A Problem can be related to operator overload, or wrong cobot behaviour for specific production to fill in the boxes.  Based on the history of incidental situations experienced at this picking workstation, machine learning and simulation capabilities (Tech3) will help for deciding between different improvement strategies. Considering a problem of wrong cobot behaviour on the filling of specific products into boxes, a choice could be proposed between: o Switching to a sequential organization (the human agent, in addition to his/her own parcel boxes, will also fill the boxes of cobot with the specific products, transforming this task into an integrative cooperation), o Stopping the production, to teach the cobot with the correct behaviour The data mining achieved of the factory database thus help for enrich situation knowledge, and it can be therefore considered a component of the metacooperation level of Hoc's HMC model.  Finally, the operator can be alerted with augmented reality (smart glasses) that there is a performance decrease of the cobot, or that a new function allocation is proposed between agents. It is also assisted in his/her manual actions by voice or by light to choose the correct products or the rightsized parcel box.

Discussion

The conceptual proposals in this paper aim at complementing the rather technocentric approach of recent research works (Romero et al., 2016and 2017[START_REF] Fantini | Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems[END_REF] on the place of humans in future smart, connected and agile factories. The proposed modeling framework aims to better position, or even design, the new activities of operators 4.0, seen as a joint physical and cognitive production system.

However, we could go further in this approach, by analyzing in more detail the cognitive dimension of the joint activity. The question of the "social" organization of the operator 4.0 [START_REF] Romero | Social Factory Architecture: Social Networking Services and Production Scenarios Through the Social Internet of Things, Services and People for the Social Operator 4.0[END_REF] could thus be deepened with methods such as the CWA of Rasmussen, and in particular the phase of socio-organizational analysis.

The management of human-machine dialogue should also be further explored, so that the common ground can be optimized between the two agents, thus guaranteeing the performance of the joint system and the quality of decisionmaking. In this sense, it would be interested to work on an enrichment of the model presented here with the concept of informational transparency, developed in particular around 2 models [START_REF] Lyons | Being transparent about transparency: a model for human-robot interaction[END_REF][START_REF] Chen | Situation awareness-based agent transparency[END_REF]:  The first model concerns the transparency of the autonomous and intelligent agent towards the operator ("robot-to-human transparency") and can be detailed using the SAT of [START_REF] Chen | Situation awareness-based agent transparency[END_REF]. Situational Awareness-Based Transparency (SAT), uses Endsley's Situation Awareness model to define three levels of agent-tohuman transparency, ranging from disclosure of basic information (reporting of actions and goals), sharing of explanations on the reasoning used (constraints considered, methods chosen), until the transmission of elements on the consequences of actions in progress (projection, uncertainty, risk);  The second model concerns the level of information that the agent has and can communicate about the state and the behaviour of the human operator ("robotof-human transparency"). It must include modeling and assessment of the operator's or team's condition (stress, fatigue), goals and behavior (social purpose), and must also be based on an understanding of environment and the structure of the performend tasks.

Conclusion

Ultimately, the reflection on the industry of the future is in full swing. It mobilizes a lot of research, to address both the technical challenges of implementing new technologies in the plant, but also to consider the sociological, cognitive and ergonomic aspects of this transformation. While some authors have recently reflected on the place of the man in the industry 4.0, the existing frameworks however remain rather technocentric. This is why the current paper attempts to combine these recent works on operator 4.0 with more cognitive approaches, by especially using well-known models of human-machine cooperation.

  typology of operator 4.0, based on digital revolutions Romero, Bernus, Noran, Stahre, Fast and Berglund (2016) detail the different "increase" of the operator 4.0. They propose a typology based on the technological pillars of industry 4.0 listed by Rüssmann et al. (2015), and secondly on the nature of the assistance (physical, sensory or cognitive) provided to the operator (Figure1).

Figure 1 -

 1 Figure 1 -A typology of operator 4.0 (fromRomero et al., 2016)

Figure 3 -

 3 Figure 3 -Framework for modeling operator 4.0 with a human-machine cooperation perspective, according to industry 4.0technologies and functions of productionAt this tactical level, decision-making on adaptations of the industrial system also involve a dialogue between supervisory agents, which feeds a Common Work Space or CWS (for more details on the construction dynamics of the CWS, seeMillot & Pacaux). -Lemoine, 2013, Fig.11). This dialogue between supervisory agents then makes it possible to determine the cooperation structure, horizontal or vertical (seeMillot & Mendiaux, 1995[START_REF] Millot | A common Work Space for a mutual enrichment of Human-machine Cooperation and Team-Situation Awareness[END_REF], which will define the

Acknowledgements

Authors acknowledge the French National Agency of Research, which supports and funds Humanism Project (ANR-17-CE10-0009) and the current research works. This project involves three laboratories in Automatics Sciences and in Human Factors, CReSTIC (Reims), Lab-STICC (Lorient), and LAMIH (Valenciennes).


Amine Laouar-Zouyed

Philippe Rauffet

Christine Chauvin

Marie- Christine Bressolle

A step toward decision-making assistance: Understanding pilots' decision making and workload impact

Keywords: Naturalistic decision making, Cognitive control, Mental workload, Cockpit, Aviation

Aviation is a dynamic, time-pressured domain, where pilots are required to make quick and accurate decisions using multi-source information and in line with potentially multiple and changing goals. Despite pilots' highlevel training and optimized cockpit automation, however, decision-making errors are observed. We conducted a review of multiple decision-making models and highlighted the influence of cognitive control modes on the quality of the decision-making process. The different frameworks of cognitive control incorporate external factors, such as time pressure or the number of goals, and individual factors, such as expertise, experience, and skills. These elements seem to be congruent with some mental workload models emphasizing regulation mechanisms, which are also based on cognitive trade-off. Accordingly, we can expect an impact of the workload level on the selected control mode involved in decision making. The instantiated cognitive control mode is assessed through the evaluation of the situation requirements and the physiological measurements of the use of attentional resources.

Introduction

To understand human activities in complex dynamic situations, several features need to be considered, such as the uncertain and time-pressured nature of the environment, ill-structured problems, the amount and diversity of information, multiple and potentially changing goals, workflow interruptions, multiple actors, high stakes, and organizational rules [START_REF] Zsambok | Naturalistic Decision Making[END_REF] [START_REF] Hoc | Adaptation et gestion des risques en situation dynamique[END_REF]. Aviation can be viewed as a complex dynamic situation where some of these characteristics can be found. As a consequence, and despite high-level pilot training and a highly optimized cockpit design, decisionmaking errors are still observed [START_REF] Wiegmann | A Human Error Approach to Aviation Accident Analysis[END_REF] [START_REF] Orasanu | Errors in aviation decision making: a factor in accidents and incidents[END_REF].

Accordingly, greater understanding of the degradation of pilots' decision making remains a significant challenge. After a review of decision-making models, highlighting the influence of cognitive control on the decision-making quality, we consider the link between mental workload and cognitive control. This perspective will enable us to investigate the nature of pilots' decision-making via the measurement of the mental workload level.

Theoretical framework: Exploring the relationships between the decision-making process, cognitive control mode, and mental workload 2.1 Decision making

As our goal in this segment of the study was to understand the decision-making process, we examined relevant research that has been conducted over the last decades, including the main two decision-making research streams, namely analytical models for decision making and the Naturalistic Decision Making (NDM [START_REF] Zsambok | Naturalistic Decision Making[END_REF]) framework. While analytical models for decision making were investigated in controlled environments, NDM models describe the decision-making process as it takes place in natural environments, where experienced people actually work. These two approaches complement each other for greater understanding of the whole decision-making process.

Examining these models led us to combine significant features of each model with the integrative Dynamic Situation Management (DSM [START_REF] Hoc | Diagnostic et prise de décision dans les situations dynamiques[END_REF]) model. The latter describes the way operators take decisions based on a mental image of the situation, called "occurring representation". DSM is based on the double-ladder structure [START_REF] Rasmussen | The human data as a system component: Bits and pieces of a model[END_REF] with a situation diagnosis phase on the left side, an identification and evaluation phase at the top, and an action-planning phase on the right. In the double-ladder model, if operators act on the system, they must perform a new detection and identification phase to take into account the effects of their action. In the DSM model, the effects of the action are immediately perceived and taken into account in the operators' representation, which reflects human behavior more accurately.

The double-ladder structure introduces the Skill-Rule-Knowledge (SRK) performance levels [START_REF] Rasmussen | Skills, Rules, and Knowledge; Signals, Signs, and Symbols, and Other Distinctions in Human Performance Models[END_REF] into DSM. Automated processing, which goes directly from detection to action execution, is represented at the bottom of the model. It is the equivalent of the "skills" performance mode in the SRK model. The medium level, which goes from identification to action planning, is the equivalent of the "rules" performance mode. At this level, operators recognize a situation for which they have a set of rules and apply these rules. The upper level is the equivalent of the "knowledge" performance mode, where operators, using their knowledge, elaborate a representation of an unfamiliar situation for which no know-how or rules are available [START_REF] Rasmussen | Skills, Rules, and Knowledge; Signals, Signs, and Symbols, and Other Distinctions in Human Performance Models[END_REF]. Fig. 1 shows how the integrative DSM model integrates different factors. The DSM model can also include the Data-Frame model of Sensemaking [START_REF] Klein | A Data-Frame Theory of Sensemaking[END_REF], which explains how people proceed to understand a situation. Sensemaking involves connecting external data with a frame that is in the operators' mind. A frame is "an explanatory structure that defines entities by describing their relationship to other entities" (p. 118) [START_REF] Klein | A Data-Frame Theory of Sensemaking[END_REF]. External data (entities) are first detected and perceived at the bottom left of the DSM model. Then, operators try to understand these data using their knowledge: they try to connect the perceived data with an explanatory frame. This process takes place in the representation they construct. The situation is identified when there is congruence between the frame and the data (Fig. 2). Once found, the right frame can even provide some additional data from the operators' knowledge base. This review of decision-making models highlights the issue of how the various models characteristics are used to implement a decision process that is suitable for each situation. Hence, we focused next on cognitive control models, as these use the same features.

Cognitive control modes

Theoretical models

Cognitive control refers to processes that enable information processing and behaviors to vary adaptively from moment to moment, depending on current goals, instead of remaining rigid and inflexible [START_REF] Debanne | Modes of cognitive control in official game handball coaching[END_REF]. There are multiple models that describe operators' cognitive control. They do not all take the same factors into consideration, and they do not all describe the same processes; hence, there is not "one" sufficient model. Consequently, it was necessary to combine several models in order to take more factors into account.

Hollnagel's Contextual Control Model (COCOM) [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF] focuses on the complexity of the situation, regardless of the operators' competence. Four control modes are presented: scrambled, opportunistic, tactical attended and unattended, and strategic. Their use is determined by the complexity of the situation, which is function of the subjective available time, the number of tasks, and the match between expected and actual events. Hence, when there is an unlimited amount of time, operators are able to plan their actions on the long term and to take into account multiple goals: this is the strategic mode. At the other end of the spectrum, if there is no sufficient available time to understand the situation, operators will implement random, non-planned and non-considered actions: this is the scrambled mode. All four modes are detailed in a later section of this article.

The metacognitive control model presented by Hoc and Amalberti [START_REF] Hoc | Cognitive control dynamics for reaching a satisfying performance in complex dynamic situations[END_REF] shows a synthesis between two models. It takes into account the operators' internal data presented in the SRK model and the environmental data on which the COCOM is built. It also takes into account the symbolic/subsymbolic nature of data. When operators perceive data at a symbolic level, they understand its significance for the system they are dealing with. If the data are perceived at a subsymbolic level, operators just treat the data as they are, with no added significance [START_REF] Hoc | Adaptation et gestion des risques en situation dynamique[END_REF]. Depending on operator expertise and the complexity of the situation, four control modes are available. Metacognitive control is present, ensuring the constant selection of the correct control mode depending upon the situation. Going beyond a mere synthesis, this model adds a significant feature by describing the fact that an expert facing a new situation will act like a novice, and that a novice encountering an already known situation will act more like an expert [START_REF] Debanne | Modes of cognitive control in official game handball coaching[END_REF].

In sum, the instantiation of a cognitive control mode is the result of multiple external and internal factors. The amount of subjective available time is taken into account in addition to the number of tasks or the familiarity of the situation.

Synthesis

A synthesis of the various features of these models is necessary for greater efficiency and ease of use. In Table 1, we associated some aspects of the COCOM with the SRK levels and the origin and type of data from Hoc and Amalberti's model [START_REF] Hoc | Cognitive control dynamics for reaching a satisfying performance in complex dynamic situations[END_REF].

Table 1. Links between the COCOM cognitive control modes, the SRK levels, and the origin and type of data.

In the left column, the four control modes (scrambled, opportunistic, tactical attended and unattended, and strategic) are presented. The tactical mode is divided into two modes (attended and unattended) as proposed by Hollnagel [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF].

First, the link between the cognitive control modes and SRK levels is easy to establish. When there is an abundant amount of time available, operators can use the strategic mode. This mode is used to plan an elaborate course of actions, using the operators' general knowledge to understand an unknown situation. Hollnagel describes this mode as providing "a more efficient and robust performance than the other modes" (p. 15) [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF]. Thus, it is reasonable to assume that in this mode, operators will use the K performance level. We can, however, hypothesize that the strategic mode can be instantiated even in a known situation, when operators want to enhance their habitual course of actions to obtain a more optimal result, if there is an abundant available subjective time.

The tactical attended mode is not as reflective as the strategic mode, but still requires a non-negligible amount of attention. The tactical attended mode is instantiated in quite familiar situations. It is used when there is less available time than in the strategic mode. It is "the meticulous and careful execution of procedures and plans" (p. 14) [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF]. In this mode, the operator will use the R performance mode.

The tactical unattended mode is instantiated when operators encounter a routine situation for which they know exactly what to do. The S performance mode is used, even if there is a great amount of time. The opportunistic mode is instantiated when the context is not clearly understood, and the operator's knowledge is inadequate [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF]. There is less available time than in the tactical mode, and the operator does no planning or anticipating. In this mode, the R performance mode is used.

Finally, in the scrambled mode, operators do not recognize the situation they are facing; this would require the use of general knowledge to go through the Sensemaking process. However, there is not enough available subjective time to do that. Operators implement random actions, and this mode matches with none of the defined performance levels. The operator is in a "blind trial-and-error type of performance", and there is almost "no reflection or cognition involved" (p. 14) [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF].

We can also make a link between the SRK levels and the origin and type of data explained in the metacognitive control model. The K performance level is used for both known and unknown situations. In known situations, mostly internal data (data in the operators' mind) will be used, whereas external data (available in the environment) will be used in unknown situations. These data will be perceived at the symbolic level. At the other end, in scrambled mode, external data will be used since operators do not recognize the situation, and the data will be perceived at the subsymbolic level. In the case of the tactical unattended mode, even if the situation is known and the available time sufficient to cope with the situation, data will be perceived at the subsymbolic level. This feature shows the cognitive economy principle; even if there is enough time to elaborate a plan, as in the strategic mode, the situation is so familiar to operators that the required actions are known and executed without further consideration [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF].

After detailing the decision-making process and the cognitive control modes, it is worth exploring the existing links between them.

Relationships between cognitive control and decision making

We have shown that the cognitive control mode depends on multiple factors, either internal to operators (e.g. expertise, experience, skills) or external, relative to the situation (e.g. available time, data, number of tasks). These factors are important to the integrity of the decision-making process.

Operators need to understand the situation they are facing, given their knowledge, the environmental data, and the available time. If there is a considerable amount of data to take into account, operators will need more time to process the data. This amount of time increases if the situation is new to operators, since the Sensemaking process then takes more time to complete due to the absence, in the operators' mind, of the frame that needs to be constructed [START_REF] Klein | A Data-Frame Theory of Sensemaking[END_REF]. Hence, if the available time is shorter than the time operators need to understand the situation, their occurring representation is incomplete or incorrect. As the planning of a course of action relies on a reliable occurring representation, the actions operators implement may not be adapted to the situation.

In this respect, an efficient control mode implies correct decision making, reducing the risk of errors. This is the case, for example, of the strategic mode, where there is abundant available subjective time, enabling operators to understand a situation correctly and plan a course of actions. On the contrary, a less efficient cognitive control mode can induce the degradation of the decision-making process. This is the case of the scrambled mode, where operators do not have enough time to understand the situation and implement random actions [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF].

These arguments highlight the fact that the quality of the decision-making process depends, through cognitive control, upon the demands of the situation (available time, number of tasks, number and type of data) and on the processing capacity of operators. In his 1977 study, Sperandio [START_REF] Sperandio | La régulation des modes opératoires en fonction de la charge de travail chez les contrôleurs de trafic aérien[END_REF] defined these two elements as responsible for the modulation of the variable called workload. This notion is examined in the following section.

Workload

The workload depends upon the combination of external (relative to the situation) and internal (relative to the operator) factors. The task demands indicate the difficulty of the task, the number of tasks, and the available time. The internal factors are the operators' characteristics that make the task more or less complex for them. The workload thus induces a limit to the operators' processing capacity and the acceptable load for them [START_REF] Sperandio | La régulation des modes opératoires en fonction de la charge de travail chez les contrôleurs de trafic aérien[END_REF].

Operators adjust their strategies in line with these parameters. If the task demands are quite low, given the elements to take into account, the task difficulty, and the available time, operators can make optimal decisions. If the task demands increase, either through task difficulty, number of tasks, or reduced available time, operators adapt their strategy to avoid the overload phenomenon, namely the moment when the task demands exceed their acceptable load, given their processing capacity [START_REF] Sperandio | La régulation des modes opératoires en fonction de la charge de travail chez les contrôleurs de trafic aérien[END_REF]. Operators prioritize tasks, consider fewer secondary objectives, and implement actions that just work, even if they are not optimal. This strategy illustrates the cognitive economy principle whereby operators adapt their strategy in order to avoid overload and keep some resources available to process other tasks that may eventually arise [START_REF] Sperandio | La régulation des modes opératoires en fonction de la charge de travail chez les contrôleurs de trafic aérien[END_REF].

We hypothesized that these adaptation strategies demonstrate the instantiated cognitive control mode. In view of the task demands and the operators' state, a more or less efficient mode is used in order to keep an acceptable performance level while saving resources for unexpected events. If the task demands exceed the operators' capacities, the cognitive control mode instantiates a less adapted and efficient mode than necessary. This outcome involves a course of action that is not based on a correct representation of the situation, either not understood or with missing data, or random actions in the case of the scrambled mode.

It is then possible to establish a relationship between the instantiated cognitive control mode and the workload level, which can be shown by the mental workload level [START_REF] Sperandio | La régulation des modes opératoires en fonction de la charge de travail chez les contrôleurs de trafic aérien[END_REF] [13].

Mental workload

A state of the art study conducted by Young, Karel, Brookhuis, Wickens and Hancock [START_REF] Young | State of science: mental workload in ergonomics[END_REF] considers the mental workload concept as a multidimensional construct. Like the workload, it is determined by the characteristics of the task (e.g. demands, performance), the operators (e.g. skill, attention), and the environmental context in which the performance occurs.

This study assumes that the mental workload is suboptimal if the operators' performance is below a required, desired, or imposed minimum level. A suboptimal mental workload is the result of overload or underload [START_REF] Brookhuis | Assessment of Drivers' Workload: Performance, Subjective and Physiological Indices[END_REF]. Overload occurs when operators are faced with more stimuli than they can handle maintaining their own standards of performance. This effective load can affect selective attention, among other consequences. Conversely, underload is the result of too little stimulation and allocation of resources elsewhere [START_REF] Young | Malleable Attentional Resources Theory: A New Explanation for the Effects of Mental Underload on Performance[END_REF]. Hence, it is possible to assume that both overload and underload can lead to performance degradation, attentional lapses, and errors [START_REF] Wilson | Human-Machine Interfaces for Systems Control[END_REF], and that there is an optimum range of mental workload that is associated with best performance.

The mental workload can be examined through an attentional perspective; it entails a balance between the automatic and controlled processing involved in a task. Automatic processing is associated with expert performance; it is fast, unconscious, and does not use attentional resources. Controlled processing is the opposite [START_REF] Schneider | Controlled and Automatic Human Information Processing: 1. Detection, Search, and Attention[END_REF]. This approach assumes that automatic processing releases attentional resources for other tasks, which decreases the mental workload level. These two kinds of cognitive processing are in line with the SRK performance levels that are involved in cognitive control and decision-making processes, where the skill level entails automated processing, and rules and knowledge levels entail more controlled processing.

Many mental workload models have been developed. For instance, one study by Kostenko, Rauffet, Chauvin and Coppin [START_REF] Kostenko | A dynamic closed-looped and multidimensional model for Mental Workload evaluation[END_REF] synthesizes different mental workload approaches to create a dynamic multi-dimensional model. This model posits that operators select a strategy according to their perception of three criteria: their behavior, their performance and the situational constraints. The pairwise comparison of these criteria reveals regulation loops that enable operators to select a strategy and regulate their activity.

Another model focuses upon a few regulation loops. Cegarra [START_REF] Cegarra | De la gestion de la complexité à son assistance : contribution en psychologie ergonomique (Unpublished Authorization to Lead Research)[END_REF] has developed a mental workload model (Fig. 3) that takes into account multiple factors such as the requirements of the situation, the self-assessed performance, the operators' strategies and perception of the system that are known to have an effect on mental workload modulation, and operators' mental workload experience. In addition to these factors, Cegarra examines the regulation loops that help operators maintain their mental workload level in the optimum range associated with the best performance, adapting their strategies in line with their perception of the system and their mental workload experience.

Links with cognitive control modes

Cegarra's model shows that operators' mental workload depends on a set of external factors (e.g. time pressure, number of goals), and operators need to commit resources to deal with these factors. This feature to a modulation of attentional resources use. Similarly, Hollnagel [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF] asserts that each cognitive control mode is related to a certain level of constraints that affect the level of attentional resources use. Table 2 shows these links. The level of constraints shows the combination of the external factors mentioned above. For example, if there are multiple goals to achieve in a limited of time, both the level of constraints and the use of attentional resources will be high; thus, we can assume that the opportunistic mode is instantiated. In contrast, if there is only one goal to achieve within ample time, we can assume that the tactical unattended mode is instantiated. The factors involved in the level of constraints seem to be line with those involved in Cegarra's mental workload model.

The use of attentional resources predicts the instantiated control mode. For example, Hollnagel describes the strategic mode as the "deliberate and careful planning of actions" that "cannot be achieved without paying attention" (p. 15) [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF]. Similarly, the tactical unattended mode is instantiated when "people know what to do but may not bother follow it in detail" because "the situation is very familiar or there seems to be plenty of time" (p. 15) [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF]; this explains the low attentional resources use. The level of attentional resources use thus appears to be equivalent to the level of constraints.

From these observations, we deduce that we can measure the mental effort of operators through physiological measurements, and analyze the external constraints. The combination of external constraints and mental effort provides information regarding the mental workload level experienced by operators. We hypothesize that this mental workload level can help us assess the cognitive control mode instantiated by operators, hence the quality of the decision-making process.

It would be possible to detect in real time the moments when the probability of the degradation of decision making increases. For example, if we measure a low level of mental workload in the cruise phase in the cockpit, which is a phase with a low level of constraints, we could expect that operators are in the tactical unattended mode. Hollnagel explains that this is a mode where performance failure may occur because of the false sense of safety procured by these situations [START_REF] Hollnagel | Context, cognition and control. Co-operative process management, cognition and information technology[END_REF]. Similarly, if we measure a high level of workload associated with a high level of constraints such as what happens during an unusual situation with high time pressure and multiple tasks, we can assume that operators will be in the opportunistic mode, not far from the scrambled mode that would be detected if the mental workload level becomes excessive.

Discussion

Our ultimate goal is to use different kinds of measures to have a real-time estimate of the operators' mental workload level. In their study, Young, Brookhuis, Wickens and Hancock [START_REF] Young | State of science: mental workload in ergonomics[END_REF] suggest that recent research in neurosciences offers the most promise to define thresholds for mental overload and underload. Many studies recommend the use of brain oxygenation metrics, which could represent a quantitative measure of attentional resources [START_REF] Perrey | NIRS in Ergonomics: Its Application in Industry for Promotion of Health and Human Performance at Work[END_REF] [START_REF] Shaw | Using Cerebral Hemovelocity to Measure Workload during a Spatialised Auditory Vigilance Task in Novice and Experienced Observers[END_REF]. Since attentional resources use can indicate the mental workload level in combination with the measurement of external constraints, we plan to use functional near-infrared spectroscopy (fNIRS) to assess the activity in the prefrontal cortex, which is correlated with mental workload in numerous studies [START_REF] Durantin | Using near infrared spectroscopy and heart rate variability to detect mental overload[END_REF] [START_REF] Causse | Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS[END_REF]. We plan correlations with heart-rate variability [START_REF] Borghini | Measuring neurophysiological signals in aircrafts pilots and car drivers for the assessment of mental workload, fatigue and drowsiness[END_REF] and ocular activity [START_REF] Greef | Eye movement as indicators of mental workload to trigger adaptive automation[END_REF] [26] measurements.

We intend to conduct two experiments. The first one will take place in a laboratory and will help us calibrate and validate the measurement protocol of mental workload using the three indicators just mentioned. Participants will complete a multitasking computer activity with various levels of difficulty. This experiment will enable us to determine the mental overload and underload thresholds.

The second experiment will be more ecological. It will take place in a realistic flight simulator where we will measure pilots' mental workload in real time using different predefined scenarios. These scenarios will place pilots in complex situations defined through exhaustive cognitive work analysis and task analysis.

Conclusion and perspectives

This study of decision-making, cognitive control, and mental workload models has enabled us to highlight some similarities and links between these notions. In particular, it highlights the fact that the quality of decision making depends upon the cognitive control mode instantiated by operators. This control mode is the result of multiple factors (internal and external) and could be assessed by measuring the mental workload level in terms of the situation operators are facing.

The goal of these experiments is to be able to determine the cognitive control mode instantiated by pilots so as to explore innovative ways to assist pilots in their tasks to mitigate excessive mental workload levels. This could take the form of new kinds of assistance in the cockpit.

Fig. 1 .

 1 Fig. 1. The integrative Dynamic Situation Management model (based on Hoc and Amalberti 1994)

Fig. 2 .

 2 Fig. 2. Partial view of the Dynamic Situation Management model showing the integration of Sensemaking.

Fig. 3 .

 3 Fig. 3. Cegarra's model of mental workload [19].

Table 2 .

 2 Links between the COCOM cognitive control modes, attentional resources use, and the level of constraints[START_REF] Debanne | Modes of cognitive control in official game handball coaching[END_REF].

	COCOM mode	Attentional resources use	Level of constraints
	Strategic	Medium-High	Low-Medium
	Tactical attended	Medium-High	Medium
	Tactical unattended	Low	Low
	Opportunistic	High	High
	Scrambled	Very High	Very High