Arturo Cruz-Maya

Adriana Tapus

Learning Users' and Personality-Gender Preferences in Close Human-Robot Interaction

Robots are expected to interact with persons in their everyday activities and should learn the preferences of their users in order to deliver a more natural interaction. Having a memory system that remembers past events and using them to generate an adapted robot's behavior is a useful feature that robots should have. Nevertheless, robots will have to face unknown situations and behave appropriately. We propose the usage of user's personality (introversion/extroversion) to create a model to predict user's preferences so as to be used when there are no past interactions for a certain robot's task. For this, we propose a framework that combines an Emotion System based on the OCC Model with an Episodic-Like Memory System. We did an experiment where a group of participants customized robot's behavior with respect to their preferences (personal distance, gesture amplitude, gesture speed). We tested the obtained model against preset behaviors based on the literature about extroversion preferences on interaction. For this, a different group of participants was recruited. Results shows that our proposed model generated a behavior that was more preferred by the participants than the preset behaviors. Only the group of introvert-female participants did not present any significant difference between the different behaviors.

I. INTRODUCTION

Robots are expected to interact with persons in their everyday activities, and robots should do it in a way that does not increment the level of stress or anxiety on the users. Therefore, robots should learn the preferences of their users. Having a memory system that remembers past events and using it to predict the outcome of the robot's behavior is a useful feature that robots should possess.

Research in social robotics has shown that robots with emotions and personality are considered to have more believable behavior and are preferred over robots that lack these features [START_REF] Kröse | Lino, the user-interface robot[END_REF]. Also, emotions have proved to be important for cognition, and some models have been designed to represent the emotions and their role on cognition. One of them is the OCC model [START_REF] Ortony | The cognitive structure of emotions[END_REF], which has been used in virtual agents [START_REF] Becker-Asano | Affective computing with primary and secondary emotions in a virtual human[END_REF] and in some robots [START_REF] Kröse | Lino, the user-interface robot[END_REF] to embody them with emotions and make their behavior more appealing to the users.

The OCC model of emotions lacks the specification of the agent's memory and how the agent's behaviors are modulated with the use of emotions. The memory system combined with emotions has been adopted in [START_REF] Leconte | Design and integration of a spatio-temporal memory with emotional influences to categorize and recall the experiences of an autonomous mobile robot[END_REF], where an Episodiclike Memory System (EM) with emotions regulating the intensities of memories on a robot is used. The EM has the advantage of learning online through experience and the capacity to predict expected events based on past experiences.

Here, we propose the usage of the OCC model to synthesize the emotion that regulates the intensities of the memories saved on the EM. Combining the OCC Model with an Episodic Memory system can give to the robots the capability of being more believable to the users, showing behaviors more preferred by their users. These behaviors should be learned through an interaction between the robots and the users. This information can also be used to interact with new users, if for example the robot knows a priori the personality of a new user, then the robot could deliver an appropriate behavior for the type of personality of this new user.

In the field of Social Psychology, it is widely known that individuals with the same personality have similar preferences. Nevertheless, it exists two theories that seem opposite of each other. One of them is called the similarity attraction theory [START_REF] Byrne | Attraction and similarity of personality characteristics[END_REF], which states that people are attracted by other people with similar personality. The second one is called complementary needs theory [START_REF] Winch | The theory of complementary needs in mate-selection: An analytic and descriptive study[END_REF], which states that people are attracted by other people with different personality. Both theories have been supported by works in social robotics. In [START_REF] Lee | Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human-robot interaction[END_REF], participants enjoyed interacting with an AIBO robot when the robot's personality was complementary to their own preferences. In [START_REF] Joosse | What you do is who you are: The role of task context in perceived social robot personality[END_REF], similarity attraction theory was supported when the robot was a tour guide, and the complementary theory was supported when the robot was a cleaner.

Using an adaptive behavior could be useful to predict the preferences of individuals, when there are no past interactions between them and the robot, if the robot has interacted with other people in similar situations. Instead of having assumptions on any of these theories, the robot would create its own model of user's preferences based on previous experiences. This can be used to model different preferences for different tasks. In this work, we describe our proposed framework for an adaptable robot behavior, as well as an experiment divided in two parts. In the first part, the model of the robot's behavior is created. A group of participants interacts with the robot three times, in different days each time, and they specify their preferences for three parameters defining the robot's behavior in a close interaction (as shown in Fig. 1). The preferences are saved individually for each user and also grouped by personality and gender. In the second part of the experiment, the current state of the robot's memory is used to generate the saved behavior to other group of participants with respect to their personality and gender.

There exist many works where a robot is trained from several demonstrations and learn to generate the desired behavior [START_REF] Argall | A survey of robot learning from demonstration[END_REF]. However, there are only a few works with robots learning online, through interactions with their users, which could be a better form of learning due to the constantly changing nature of the human's preferences.

In a previous work, an assistive therapist robot that adjusted its behavior parameters with respect to the personality and performance of the user was presented [START_REF] Tapus | Long term learning and online robot behavior adaptation for individuals with physical and cognitive impairments[END_REF]. A humanoid torso robot mounted on a mobile platform was used with an algorithm based on a policy gradient reinforcement learning for the adaptation. Personalizing a robot by learning the preferences of their users has been approached before in [START_REF] Mason | Robot self-initiative and personalization by learning through repeated interactions[END_REF]. The authors developed a robotic system that interacts with the user, and through repeated interactions, adapts to the user. They used a beta regression classifier for learning and generalization of the task, they tested their system with a "room cleaning" scenario in a simplified world, where a small robot with a holonomic base was navigating and transporting blocks to the desired position of the users.

A recent work on engagement and its relation to personality in human robot interaction, is presented in [START_REF] Salam | Fully automatic analysis of engagement and its relationship to personality in human-robot interactions[END_REF]. They propose an automatic analysis of users' personality in a triadic human-human-robot interaction. The analysis is used to predict the personality of the users, and this information is used as an input for the engagement classification system. They focus on the concept of "group engagement" and found better engagement when the participants and the robot were extroverted, and the worst results were obtained when all were introverted.

Using information about the interaction of the robots with the users so as to personalize the interaction over time, allows for more successful and natural human-robot interactions [START_REF] Leyzberg | Personalizing robot tutors to individuals' learning differences[END_REF]. We propose a framework that includes an Adapted Episodic Memory-like system, which is extended with an Emotion system based on the OCC Model to update the intensity of the memories of past interactions with respect to the user's feedback. This framework has not only to remember the preferences of specific users, but also to generate behaviors for new users based on their personalities. The difference of our proposed model and other existing research works, is that it can generate a specific behavior for each user based on previous interactions, and also, it can generate personality-based behaviors, using the interactions with different users instead of preset information.

This work is structured as follows: section II presents the methods used, section III describes the experimental design setup, results and discussion are showed in section IV, and finally the conclusion is presented in section V.

II. METHODS

The high level framework used here was introduced in a previous work [START_REF] Cruz-Maya | Social facilitation in a gamelike human-robot interaction using synthesized emotions and episodic memory[END_REF], where the OCC Model was used to generate predefined behaviors based on the performance of the user. In this work, the model of emotions is used to regulate the intensities of the memories saved on the Episodic-Memory Like, based on past interactions and new user's feedback. The purpose of the framework is to remember users' preferences with respect to a certain robot's behavior that is saved in the Episodic-like Memory (EM). This allows the memories to be in constant variability of recall, more frequent events have a higher recallability. The intensity with which these events are saved depends on the users' feedback and the emotion generated by the Emotion System. We assume that the robot has a priori information about the user, including the gender and personality of the user. The three parameters defining robot's behavior that users can change are: personal distance, gesture amplitude, and gesture speed, having 5 possible values for each parameter.

The process of the proposed algorithm is showed in Fig. 2. It is started by a search of the user's preferences for a specific behavior of the robot. If there exist information about this in the EM, then the behavior is performed with the preferences of the user, if there is no information about the user's preferences, then a search based on the personality and the gender of the user is done. The behavior based on this information, if there is any, is performed, otherwise a default behavior is executed. The user can modify the behavior of the robot by changing the parameters defining robot's behavior (i.e., personal distance, gesture amplitude, gesture speed) and this is done by voice command. After the user finished with the modifications, the parameters are updated with a new weight, rating them with respect to the new user's preferred parameters. The OCC model is used to calculate this weight using the emotion of Hope, which according to this model, is an emotion belonging to the category Consequences of Events having a prospect relevant.

A. Adapted Episodic-like Memory

The AEM-ART model [START_REF] Wang | Neural modeling of episodic memory: Encoding, retrieval, and forgetting[END_REF] is made of three layers: Input, Events, and Episodes. Our framework uses the AEM-ART implementation presented in [START_REF] Leconte | Design and integration of a spatio-temporal memory with emotional influences to categorize and recall the experiences of an autonomous mobile robot[END_REF]. The input layer is categorized by channels, each channel containing different elements that are called categories. Also, each category has a channel relevance and activation value, the relevance allows the system to save general information in that channel when its value is 0, and save specific information when its value is 1. The activation value is used to match a search depending on the channel relevance.

We are saving the information in two types of episodes, one of them is used to save the specific preferences of the 1) Episodes with specific information: For the episodes containing specific user's preferences we used the following channels in the input layer:

• User: Category = Name of the user (unique), channel relevance = 1.0 (max), activation value = 1.0 (max). Model, Category = 'hope', channel relevance = 1.0 (max), activation value between 0 and 1. The search is done with the channels of the User, Order, and Emotion. The Emotion is used to decrease the importance of past preferences (explained in Section II-B). In this work, we setup the memory to retrieve always the last Episode of the user (Emotion at max. value), meaning his/her last preferences. This gives us episodes with two events, where the first event is used for the search, and the second event contains all the channels described above. A new episode is created for each new user or new preferences of a known user.

2) Episodes with personality/gender information: For the episodes containing information grouped by personality and gender, we used the following channels in the input layer: channel relevance = 0.0, activation value between 0 and 1, generated using the emotion system. • Amplitude: Preferred amplitude of the gesture of the robot's arm, in percentage of the total amplitude, Category = [0.2,0.4,0.6,0.8,1.0], channel relevance = 0.0, activation value = between 0 and 1, generated using the emotion system. • Speed: Preferred speed of the gesture of the robot's arm, in percentage of the total speed, Category = [0.2,0.4,0.6,0.8,1.0], channel relevance = 0.0, activation value = between 0 and 1, generated using the emotion system. The episodes generated using this channel configuration contain two events, one for the memory search (Personality, Gender, Order) and the other one with all the channels presented above. The channels of Distance, Amplitude, and Speed (parameters' channels) are set up with a channel relevance of 0 in order to save general information on them, grouped by personality and gender, which were set up with a channel relevance of 1. The activation value of each Category in the parameters' channels are updated each time a user save his/her preferences, increasing the activation of preferred parameters with a value in an empirically calculated range from 0 to 0.2. This value depends on the emotion generated by the emotion system. The activation of each Category of non preferred parameter is decreased by a factor of 5% of its current value. This is done with the purpose of slowly forgetting not frequently chosen parameters.

B. OCC Model

The OCC Model [START_REF] Ortony | The cognitive structure of emotions[END_REF] is a model commonly used in virtual agents to synthesize emotions, it has 22 emotions and they are divided in three categories: Aspect of Objects, Action of Agents, and Consequences of Events. In this work, we focused on the category of Consequences of Events for Self, especially in the emotion of Hope. This can improve the Adapted-Episodic-like Memory system rating the memories with higher activation values for events more liked by the users. We use the Hope emotion because it is a prospect based emotion, unlike emotions of joy or distress which are well-being emotions having prospects irrelevant or others emotions which are based on Consequences for Others.

We have two types of episodes, one for specific user's preferences and one for personality/gender preferences. We generate the emotion of Hope for each type of episodes.

1) Emotion for episodes with specific information: For the type of episodes with information about specific user's preferences, each time a search is done in the memory system, an emotion of Hope is generated. We used 4 global variables of the OCC model and 2 local variables, all of them normalized with values between 0 and 1.

The global variables are:

• Sense of Reality (SoR): value = 0.75, because this emotion is based on the memories of a specific user. • Proximity (P rox): value= 1, because our scenario presents a close interaction between the robot and the user, • Unexpectedness (U n): value = 1 -EpisodeActivation (memory system) • Arousal (Ar): value = 0.5, in order to set a neutral value for this variable.

The global variables were combined computing the mean of all of them (Eq. 1).

GlobalV ar = (SoR + P rox

+ U n + Ar)/4 (1)
The values of the local variables (Likelihood and Desirability) are computed as follows:

Likelihood = n i=1 ActivationChannel i n ( 2
)
where n is the number of parameters' channels (three in this case)

Desirability = 1 (3)
The value of Desirability was set to 1, because there is only one objective in the task performed by the robot.

In the OCC model, the potential of the emotion is obtained by combining the global variables with the local variables. Each emotion has also a threshold to discard emotions with low potential. In this work, the emotion potential is computed as shown in Eq. 4. The threshold of the emotion was set to 0 to allow an intensity of the emotion ranging from 0 to 1.

EmP = (Des * 0.7 + GlobalV ar * 0.3) * Lk ( 4
)
where EmP is the Emotion Potential, Des is the Desirability, GlobalV ar are calculated using the Equation 1, and Lk is the Likelihood.

After the user gives the feedback to the robot, there are two possible emotions that are consequence of the emotion of Hope. These emotions are Satisfaction Confirmed and Disappointment. Satisfaction confirmed happens if the user's preferences remain unchanged. Then, the synthesized emotion of Hope is assigned to the preferred parameter. Disappointment happens if the user's preferences are different than the remembered preferences. Then, the emotion assigned to the preferred parameter is 1 -Hope. The final emotion is the average of all the emotions of each parameter, which is used as an activation value in the category of Hope in the emotion channel of the memory system. New events, which are the preferences of the user, are set with a high level of emotion in order to be easy remembered.

2) Emotion for episodes with personality/gender information: For the type of episodes with information about personality/gender, we used the same variables, changing the Sense of reality to 0.5, because the memory retrieved is composed by several users. For the same reason, the values of the Desirability and Likelihood need to be computed based on the information in the memory system, as well as a prediction of the preferred parameters.

The predicted parameters are computed by using the information contained in the Episodic-like Memory as follows:

P aram k = n i=1 Ch k [Cat i ] * Ch k [Act i ] n i=1 Ch k [Act i ] (5)
where k is the channel of the predicted parameter, Cat i is the Category i of the channel k, and Act i is the activation i of the channel k. Each P aram k is rounded to a value multiple of 0.2 in order to discretize them. When there are no preferences saved in memory, the predicted value is set to 0.6. P aram k is associated with an activation value P aram k [Act], corresponding with the category with the same value of the channel k.

The likelihood of each predicted parameter is:

lk k = P aram k [Act] n i=1 Ch k [Act i ] (6)
where k is the channel of the predicted parameter, and n is the number of categories in the channel k and Act i is the Activation i of the channel k.

The local variables were computed as follows:

Likelihood = n k=1 lk k n (7) Desirability = n i=1 Ch i [M ax(Act)] n ( 8
)
where n is the number of parameter channels. The emotion is computed as described above.

III. EXPERIMENTAL DESIGN SETUP

A. Hypothesis

Remembering user's preferences is one of the basic capacities that personal robots should have, but these preferences should not be fixed over time. Robots should be able to change these parameters with respect to the preferences of the users. The inter-individual differences based on personality and gender need to be integrated and the robot's behaviors should best match the preferences of new users.

Our hypothesis is stated as follows: H1. The robot's behavior generated after interactions with individuals of different personalities and genders will best match with the preferences of new users than by using fixed behaviors based on the theories of Similarity attraction or Complementarity attraction.

B. Scenario Description

The purpose of this work was to customize a robot's behavior to users' preferences. This learned behavior was shown to new users, using the information saved in the Episodic Memory to perform the behavior with the most common preferred parameters by individuals with the same gender and personality. For this reason, we did an experiment divided in two parts. The first part was done to test the Episodic-like Memory (EM) and the behavior of the robot, and to train a model of users' preferences based on personality and gender. The second part was done in order to test the models created by the EM. The behavior of the robot was expressed through three parameters configured by the user: personal distance, gesture amplitude, and gesture speed. We chose these parameters because of the their link and corelation with the extroversion/introversion personality trait. It is well known that extroverted individuals tend to do wider and faster gestures [START_REF] Knapp | Nonverbal communication in human interaction[END_REF] [13], and also prefer closer distances than introverted individuals [START_REF] Williams | Personal space and its relation to extraversionintroversion[END_REF]. The gesture used was a wave of the left arm of the robot, when the robot was greeting the user.

C. Robot Capabilities

The robot used was a Pepper robot, which has the capabilities of Face Detection and Face Tracking, Voice Recognition, and movement of the body and head to track the user. These capabilities are provided by the NaoQI Framework. We used the naoqi ROS driver to communicate with the Episodiclike Memory and Emotion systems that were located on an external computer.

D. Training the robot

In order to train our model based on personality and gender, 16 Participants were recruited to perform this experiment, 8 male and 8 female participants, with ages between 25 and 56 years old.

The experiment was done as follows. The participants were instructed to interact one by one with the robot in a dyadic interaction, the robot was able to recognize a list of voice commands (see below). Participants were standing in front of the robot. The robot greeted the participant with a hand wave and verbal greeting, "Hello, I'm learning about personalities preferences for which I will interact with you for some minutes. You can modify three parameters of my behavior: personal distance, gesture's amplitude and gesture's speed". The robot listed the voice commands and showed them on the tablet mounted on its torso (see Fig. 3). Then the participants were free to give the desired commands to modify the parameters of the robot. It was repeated 3 times for each participant in different days each time.

The voice commands and their purpose are as follows: To finish the modifications; (h) Yes: To confirm the "Done" command; (i) No: To cancel the "Done" command. Each time the user said one of the commands to modify a parameter, one image was illustrated on the torso tablet that showed the robot and the current value of the parameter in terms of percentages for the speed and amplitude, and in meters for the distance. The used images are shown in Fig. 3. The distance had values in a range of 40 cm to 120 cm (as stated by Hall [START_REF] Hall | The hidden dimension[END_REF]). The minimum and maximum values for the amplitude of the wave of the robot's arm are shown in Table I, a full schema of the joints and angles is shown in the documentation website1 of the robot. The speed had values between 40% to 100% of the maximum speed provided by SoftBank Robotics, details of the motor of each joint are shown in the documentation website2 of the robot. The robot performed the behavior each time the user said "increase" or "decrease" the chosen parameter. Learning is done after the first interaction for each user of each personality/gender group.

E. Pre-experiment Questionnaire

The participants were chosen with respect to their scores on a personality test (Big 5 [START_REF] Goldberg | An alternative 'description of personality': the bigfive factor structure[END_REF]). The personality test was filled-in by the participants before the experiments. Individuals with a score <= 3 on a personality trait were considered in the low category of that specific personality trait. The training phase was done with 16 participants: 4 in each group (introverted/extroverted and male/female). The testing phase was done with 26 participants: 8 introverted males, 9 extroverted males, 4 introverted females, and 5 extroverted females.

F. Post-Experiment Questionnaire -Training Phase

A post-experiment questionnaire (10 items) was applied to the participants that modified the parameters of the robot's behavior. The first 6 questions were designed to know if individuals preferred lower or higher values for the min and max parameters, and the other 4 questions were designed to know if individuals considered the parameters they chosen as appropriate, with answers on a 5 points Likert scale. Most of the participants did not like lower minimum values or higher values for the parameters of the robot's behavior. The percentages of the participants for the answers of the questions 1 to 6 are shown in Table II. Also, most of the participants rated the chosen parameters as appropriate (4 on a scale from 1 to 5) in the questions 7 to 10 (as shown in Table III). The final behavior was most of the times rated as neutral.

G. Testing of the model

In order to test our model based on personality and gender, 26 Participants were recruited to perform this experiment, 17 male and 9 female participants, with ages ranging from 20 to 47 years old. Participants taking part in the training phase were not part of the testing group. We designed two robot behaviors based on the literature about introverts and extroverts, the values of our interaction parameters were chosen using the minimum and maximum values chosen by the participants in the training phase. The values of each parameter for each personality of the robot are shown in Table IV. Each participant observed the robot performing the behavior generated by our model with respect to his/her personality and gender, the introverted behavior of the robot, and the extroverted behavior of the robot. The robot's behaviors were presented in a random order to each participant. At the end of each robot's behavior, the participant completed a questionnaire with three questions about his/her preferences about the three parameters to evaluate (distance/amplitude/speed). The answers were on a 5 points Likert scale, where 1 was "too close/narrow/slow" for the distance/amplitude/speed respectively, "3" was "appropriate", and 5 was "too far/wide/fast", respectively. Other three questions were used to rate the perceived stress caused by each parameter of robot on a 5 points Likert scale.

IV. RESULTS AND DISCUSSION

A. Training the robot

The values for each parameter, obtained at the end of the training of the robot are shown in Fig. 4, and in Table V, where the values in parenthesis are the values that the robot can perform, and that are the actual values that were used in the second part of the experiment. The model predicts that extroverted female participants will prefer a closer distance than the other groups of participants, also that female participants will prefer gestures with higher amplitude than male participants with the same personality as theirs (extroversion/introversion) and that extroverted male participants will prefer a faster speed for the gestures than the other groups of participants. As our model is based on a weighted mean of the activation values of each parameter in the EM, we applied a weighted t-test using the software R for a statistical analysis on the differences of the predicted parameters. The only significant difference was found in the preferred amplitude between the extrovert female group (0.83 m) and the introvert male group (0.44 m) with t = 2.327, df = 7.905, p = 0.048, and Std.err = 0.167. VI. We obtained different results for each personality-gender group. For each parameter (distance/speed/gesture) defining the behavior of the robot a two-way ANOVA was performed to identify main effects or interaction effects between the parameters and the personality and gender of the participants. We did not found any significant effect. A possible cause of this is the small number of participants that took part in the experiment. For this reason, we divided the data by personality/gender and analyzed it separately with a pairwise t-test for each group. 1) Extrovert-Male: The distance of the proposed model was perceived as "more appropriate" than the distance of the extroverted robot behavior (p = 0.0087). The amplitude of the introverted behavior was perceived as "more appropriate" than the amplitude of the extroverted behavior (p = 0.02). In Table VI, Extro M section shows the means of each parameter of the different behaviors of the robot. The model has a mean closer to 3 ("appropriate") than the introverted and extroverted behaviors in the parameters of distance and amplitude. This can also be seen in Figs. 5b and5c. The speed of the extroverted robot was chosen as "appropriate" more times than the other behaviors (see Fig. 5a).

B. Testing the model

2) Introvert-Male: The distance of the introverted robot behavior was perceived as "less appropriate" than the distance of the proposed model (p = 0.0022), and as "more appropriate" than the extroverted robot behavior (0.0014). In Table VI, Intro M section shows the means of each parameter of the different behaviors of the robot. The model has a mean closer to 3 ("appropriate") than the introverted and extroverted behaviors in the parameters of distance and speed.

3) Extrovert-Female: The distance of the introverted robot behavior was perceived as "more appropriate" than the distance of the proposed model (p = 0.0269), and than the extroverted robot behavior (0.0012). The speed of the introverted behavior was perceived different as "somewhat far", while the extroverted behavior was perceived as "somewhat close / very close" (p = 0.0087). In Table VI, Extro F section shows the means of each parameter of the different behaviors of the robot, the model has a mean closer to 3 ("appropriate") than the introverted and extroverted behaviors in the parameter of speed. 4) Introvert-Female: There were no significant differences on the parameters of the different robot behaviors. In Table VI, Intro F section shows the means of each parameter of the different behaviors of the robot.

The behavior of the robot generated by the proposed model got an advantage over the introverted behavior and the extroverted behavior in some parameters in most of the personality/gender groups. Even when the differences were not significantly different, we can see a better performance of it. A larger group of participants would be needed so as to verify the differences. The parameter of distance of the proposed model was rated as "appropriate" more than the distance of the other behaviors with a difference that showed to be statistically significant as stated above. The accuracy of the model can be improved by training with more users. Also, for the sake of simplicity, the robot's behavior was restricted to 5 values for each parameter. Otherwise, the model could had be better rated by the participants in the testing phase. We can see that introverted male participants rated with a mean of 2.87 the speed of the robot's gesture generated with the model (see Table VI), meaning something slower than an appropriate value, the used value was 60%, but the value given by the model was 70%, which was a higher speed. Similar to this, the extroverted participants rated the speed of the robot's gesture generated with the model as faster than an appropriate value (mean 3.55), the used value was 100%, but the value given by the model was 93%, which was a slower speed. This shows that personality and gender can be used to predict user's preferences of robot's behaviors. Our hypothesis, that the robot's behavior generated with the interaction of individuals of different personalities and gender will best match with preferences of new users, was partially confirmed, as our model did not fit all the parameters of the different groups. Nevertheless, we are convinced that if we train our model with more individuals it can give better results.

V. CONCLUSION AND FUTURE WORK

In this work, we presented a framework based on an Episodic Memory-Like and a cognitive model of emotions (OCC) to create a robot model behavior based on the personality and gender of its users. We trained the model with 16 participants, 4 participants for each group of personality/gender, and tested it with other 26 participants. Results are promising as per the discussions above. Future works include the use of different behaviors occurring in a consecutive time and different contexts, taking advantage of the episodic memory and the proposed model.

Fig. 1 :

 1 Fig. 1: Interaction scenario with a Pepper Robot

Fig. 2 :

 2 Fig. 2: Process of the proposed framework

•

  Order: Used to facilitate the memory search, Category = 1: search, 2: saved preferences, channel relevance = 1.0, activation value = 1.0 • Distance: Preferred distance between the robot and the user, given in meters, Category = [0.4,0.6,0.8,1.0,1.2], channel relevance = 1.0, activation value = 1.0 • Amplitude: Preferred amplitude of the gesture of the robot's arm, given in percentage of the total amplitude, Category = [0.2,0.4,0.6,0.8,1.0], channel relevance = 1.0, activation value = 1.0 • Speed: Preferred speed of the gesture of the robot's arm, given in percentage of the total speed, Category = [0.2,0.4,0.6,0.8,1.0], channel relevance = 1.0, activation value = 1.0 • Emotion: Emotion of Hope generated using the OCC

•

  Personality: Category = Personality of the user (Extrovert/Introvert), channel relevance = 1.0 (max), activation value = 1.0 (max). • Gender: Category = Gender of the user (Male/Female), channel relevance = 1.0, activation value = 1.0 • Order: Used to facilitate the memory search, Category = 1: search, 2: saved preferences, channel relevance = 1.0, activation value = 1.0 • Distance: Preferred distance between the robot and the user, given in meters, Category = [0.4,0.6,0.8,1.0,1.2],

  (a) Show Commands: To show on the tablet the list of voice commands; (b) Modify distance: To modify the distance between the robot and the user; (c) Modify gesture: To modify the gesture's amplitude; (d) Modify Speed: To modify the gesture's speed; (e) Reduce: To reduce the chosen parameter; (f) Increase: To increase the chosen parameter; (g) Done:

Fig. 3 :

 3 Fig. 3: Images showed on the tablet of the robot. a) Image to show the voice commands, b) c) d) Images displayed when the user was modifying each parameter.

Fig. 4 :

 4 Fig. 4: Predicted parameters of gesture by Personality/Gender. a) distance b) Amplitud c) Speed.

Fig. 5 :

 5 Fig. 5: Parameter preferences of the Extrovert-Male participants in the different robot behaviors. Extro: Extroverted robot, Intro: Introverted robot, Model: Proposed Model. a) Speed b) Amplitud c) Distance

TABLE I :

 I Amplitude of the wave of the robot's arm

		Minimum	Maximum
	Shoulder Roll 40.1 Elbow Yaw 0 •	0 •
	Wrist Yaw	34.4 •	34.4 •

• to 42.4 • 40.1 • to 51.5 • Shoulder Pitch 68.75 • 68.75 • Elbow Roll -74.5 • to -51.5 • -74.5 • to -5.7 •

TABLE II :

 II Answers of participants in the Training phase -Questions 1-6

		Yes	No
	Lower Distance	6.2 % 93.8 %
	Higher Distance	37.5 %	62.5%
	Lower Amplitude	25.0 % 75.0 %
	Higher Amplitude 18.8 % 81.2 %
	Lower Speed	6.2 % 93.8 %
	Higher Speed	43.8 % 56.2 %

TABLE III

 III

	: Answers of participants in the Training phase -
	Questions 7-10, Likert Scale: 1 (not at all appropiate) to 5
	(very appropriate)
		1	2	3	4	5
	Chosen Distance	6.25 %	12.5 % 0.00 % 75.0 % 6.25 %
	Chosen Amplitude 0.00 %	0.00 % 31.3 % 50.0 % 18.7 %
	Chosen Speed	0.00 %	18.7 % 25.0 % 43.8 % 12.5 %
	Final Behavior	06.25 % 0.00 % 56.2 % 37.5 % 0.00 %

TABLE IV :

 IV Parameters for the introvert and the extrovert robot's behavior

		Distance (m) Amplitude (%) Speed (%)
	Extro Robot 1.20	100.0	100.0
	Intro Robot	0.40	20.0	40.0

TABLE VI :

 VI Comparison of users' preferences of the generated model and the introvert and the extrovert robot's behaviors. Means and Variance of the 5 points Likert scale questionnaire.

	Extro M	Model	Intro R	Extro R
	Speed	3.55 (0.77)	2.66 (2.25)	3.22 (0.19)
	Amplitude 3.22 (0.19) 2.66 (1.25)	3.55 (0.27)
	Distance	2.77 (0.44) 3.33 (0.50)	1.88 (0.36)
	Intro M	Model	Intro R	Extro R
	Speed	2.87 (0.69) 2.75 (0.50)	3.25 (0.50)
	Amplitude 2.75 (0.78)	3.00 (0.85)	3.50 (0.57)
	Distance	3.00 (0.28) 3.37 (0.83)	1.87 (0.12)
	Extro F	Model	Intro R	Extro R
	Speed	3.00 (0.5)	2.40 (0.8)	3.80 (0.2)
	Amplitude 3.60 (0.30) 3.00 (1.00)	2.80 (1.20)
	Distance	2.60 (1.30)	3.37 (0.83)	1.80 (0.20)
	Intro F	Model	Intro R	Extro R
	Speed	2.50 (0.33)	2.50 (0.33)	3.25 (0.25)
	Amplitude 3.25 (0.25) 2.75 (0.91)	3.50 (0.33)
	Distance	3.25 (0.25)	3.75 (0.25)	2.75 (0.25)

http://doc.aldebaran.com/2-0/family/juliette technical/joints juliette.html

http://doc.aldebaran.com/2-0/family/juliette technical/motors juliette.html

ACKNOWLEDGMENT

The authors thank to the Mexican Council of Science and Technology for the grant CONACYT-French Government n.382035. This work was also funded by EU Horizon2020 ENRICHME project grant agreement no. 643691C.


Simon Mallard -Phd
email: mallardsimon@hotmail.com

Jerome Eneau
email: jerome.eneau@univ-rennes2.fr

Training managers: a case study of a French corporate university

Keywords: Vocational education and training, apparatus, emotion, managers, learning design

This paper will analyze the learning design from the learning designers' and trainers' point of view. Focusing on the training system, we use a threefold model: "ideational", "functional" and "experienced" dimensions of a training system.

In the French public mail company, training for all managers is provided by a department of the Corporate University: the "School of Managers". How has the project evolved since the original plan? If there are some gaps, how can we analyze the fact that, when it comes to innovation, "things never go exactly as planned"? Using interviews with designers and trainers involved in this project (N=7), we present the results of a discourse analysis performed with computer-assisted software. Answering the main goals of the research, the results will help us to better understand the prior ideas of the people in charge of the original system about (a) professionalization of managers, and (b) emotional and socioaffective dimensions of learning.

Introduction

Skill development is hugely important for the Human Resources department at La Poste Courrier, one of the main postal operators in Europe. The aim of their HR policy and more generally of their corporate policy is to develop their workers' employability, meet the challenges of the present and best prepare for the future in order to ensure the growth of new postal offers while at the same time improving working conditions and improving the economic performance of the business.

In 2009, a special entity, the École des Managers, or School of Managers was created to assist managers as the business underwent change, in particular the increase in the size of the sorting centres and the new, expanded role and implemented practices that the managers there have to play. The School of Managers was focused on creating, putting into place and evaluating a training programme for managers at La Poste.

These training programmes take into account managers' experience and focus on workplace situations for learning. This position means the programme has to look at the complete human being and understand learning in various dimensionscognition, conation and emotion [START_REF] Barbier | Action, affects et transformation de soi[END_REF]. The study of the training courses presented herein is part of a doctoral research project examining the emotional and socioaffective dimensions of the training these managers receive.

In this paper, we will look at how these issues are taken into account in the development and implementation of these training courses, from the point of view of the designers and trainers.

We will not examine here the learners' point of view. The context for this study will be briefly presented to highlight how original the School of Manager's educational project is (École des Managers, 2009). The theoretical framework for the analysis of the training "system", proposed by Albero (2010a[START_REF] Albero | Approche trilogique des dispositifs en formation : pourquoi « les choses ne fonctionnent-elles jamais comme prévu[END_REF], will then be reviewed. Two types of data will be used to illustrate the theoretical basis: observing participation and the analysis of semi-structured interviews. All of this data will allow us to answer the following questions: How do the designers and trainers view the emotional and socioaffective dimensions in professional training? How are they taken into account during the design and implementation of the training system? What differences are there between the system as it was originally designed and the system as it is implemented?

Context of the Study: training managers at La Poste

La Poste is seeking to decentralize its organization with the goal of delegating greater power to the field in order to meet the strategic, human, financial and legal aims of the business. The organisational model is intended to be both top-down and bottom-up. The decentralization they are seeking should be taken on board by the employees, starting with the current organi culture: the slogan used widely in the company is "La Poste of tomorrow is built by the postal workers 1 of today". Faced with the changes of decentralization, the School of Managers works with managers in their new roles and on their position with regard to their superiors, the people under them, their peers and their work environment. Since 2009, more than 3000 managers have been through this training.

The School of Managers is currently located in the La Poste corporate university, which is responsible for training all postal workers in La Poste, meaning nearly 150,000 people. Up until 1991, the training departments remained attached to the ideals of teaching, school and managers, all notions that belonged to the bureaucratic, governmental structure in place. This is because La Poste in France was a public administration up until 1991. Since 1991, La Poste has been moving towards a private business model (reflected in its current status as a Société Anonyme, or public limited company) while still retaining its particular culture. As for the new training policies and the "manager factory", "adult education" for the company's managers is now called "continuous professional education for managers" [START_REF] Cristol | La fabrique des managers[END_REF][START_REF] Wildemeersch | Editorial: The effects of policies for the education and learning of adults -from "adult education" to "lifelong learning", from "emancipation" to "empowerment[END_REF]. At the organisational level, institutionalizing these changes, the term Corporate University (Université d'Entreprise) was first used beginning in 1994 and became official in 2005. In the courses, the "professional positioning" has become a more important concept than the "organisational culture". In terms of pedagogy, the methods used focus on action, interaction, games and experiential education. In addition, the School of Managers' pedagogical project includes emotions as one of the elements that encourage learning and in particular memorization.

Beyond the semantic, institutional and pedagogical changes, the most important change was from initial, obligatory employee training after they successfully completed a competitive, national testmeaning they became civil servants after an internship period -to a "lifelong learning" approach. Up until then, training went with beginning a job and made it possible to transmit a particular culture from the top down. Now training methods and their aims are much more variedface-to-face 'classroom' courses, blended learning, adapting to a position, skill development, etc. In this context, the fact that the School of Managers takes emotions into consideration represents a clear break with the traditional pedagogical models that formerly underlay training policy, design and implementation.

However, the change, like all innovations in training, has not gone without a hitch and modifications have been made between the initial intentions, the training's implementation and its current form. The School of Managers did indeed develop an ambitious project for the business, considering its past. But how has the project evolved since its earliest design? To what extent have emotional and socioaffective dimensions been taken into account in developing and implementing the project? More generally, what differences do we see between the initial project and the current reality?

Theoretical framework: emotions and training systems

7th European Research Conference -Humboldt-University Berlin -4 -7 September 2013 The School of Managers here will be examined as a "system" in the sense used by Albero (dispositif in French often translated in Foucault's work by apparatus), in terms of technical organization and the strategic implementation of resources for a specific goal [START_REF] Albero | Petite fabrique de l'innovation à l'université -Quatre parcours de pionniers[END_REF]Albero, 2010a[START_REF] Albero | De l'idéel au vécu : le dispositif confronté à ses pratiques[END_REF]. [START_REF] Agamben | Qu'est-ce qu'un dispositif ? Paris[END_REF], like Foucault, defines the "system" as (a) "a heterogeneous series that includes almost every thing, both discursive and otherwise […]; (b) The system always has a concrete strategic function and is always within a structured power relationship […]; (c) as such, it is the result of the combination of power relations and knowledge" (Agamben, 2007, pp. 10-11).

The use of the term system is justified by the mechanisms, the procedures and the ends-means relation that serves as a logical control of action, all of which "give the training course the status of an engineered thing with a highly technical connotation" (Albero, 2011, p. 59). On one hand, technique is to be considered a part of culture; on the other hand, according to the Heideggerian approach, technique should also be considered in an ontological way, in relation to the "being" rather than existence, and also in relation to "technical action" [START_REF] Albero | De l'idéel au vécu : le dispositif confronté à ses pratiques[END_REF].

It is the relationship between technical action and being that needs to be elucidated here, in order to understand the gap between the innovation sought and how that innovation is implemented, between prescribed activity, real activity and the reality of the activity [START_REF] Clot | La fonction psychologique du travail[END_REF]. In other words, by analysing the system in its two-fold "technical" and "social" dimension as well as on different "ideational, functional and experienced" levels we can observe and understand why, between the plan and the implementation, when it comes to innovating in professional training just as in other fields, "things never go exactly as planned" [START_REF] Albero | Approche trilogique des dispositifs en formation : pourquoi « les choses ne fonctionnent-elles jamais comme prévu[END_REF].

With this in mind, the study of the system is organised along three lines (Albero, 2010a). The first, the "ideational dimension" includes the guiding values and the principles and goals of the system. The examination here sheds light on the School of Managers' ideas, principles, models and values. The second dimension of the system is the "design references". This means studying how the ideals are implemented, that is to say how the ideals are translated into action by operators, in our case the design and implementing of the training system and how this system is rolled out through the decision makers and how it functions. The third line is the experience of the people who go through this system. Every person interprets the situations experienced subjectively given his/her character, life experience, needs, explicit goals or implicit aims and even how they view others as they try to "work together" (Albero, 2010a). In this paper, we will only discuss the experience of designers and trainers; we will not go into the learners' experience, which will be dealt with in another portion of the research that is currently under way.

Concerning the specific role of emotions in this training system, the emotional and socioaffective dimensions will be dealt with here as "adaptive phenomena" and include subjective feelings, psycho-physiological responses and a cognitive evaluation, as well as a behavioural dimension and expressive aspects [START_REF] Channouf | Émotions et cognitions[END_REF][START_REF] Sander | Traité de psychologie des émotions[END_REF]. They are often brief and changing; they could be compared here with a physical "gesture" which the body makes for itself and others to see [START_REF] Dumouchel | Émotions. Essai sur le corps et le social[END_REF]. In this biopsycho-sociological view, emotions and feelings are seen as an individual and a social phenomenon of adaptation. Lastly, the role of speech and verbalisation should be considered in terms of emotional manifestations as they allow us to understand ourselves and others (Averil & Rodis, 1998).

The emotional and socioaffective dimensions, little examined in research on adult education and even less in workplace training, are often suspected of being counterproductive to the "rationality" of learning. In terms of methodology, the difficulty of capturing emotionsand because of their nature, controlling themlimits the extent to which they can be considered when studying adult education, particularly within an organization. The role they play is therefore often ignored despite the fact that numerous authors in different fields stress the very weighty even determining role, in some cases, that emotions play in human conduct [START_REF] Damasio | L'autre moi-même : les nouvelles cartes du cerveau, de la conscience et des émotions[END_REF][START_REF] Darwin | The Expression of the Emotions in Man and Animals[END_REF][START_REF] Gardner | Frames of Mind[END_REF][START_REF] Breton | Anthropologie des passions[END_REF]. We could not possibly examine all the causes and characteristics here, so we will limit ourselves in this paper to studying how they are taken into account (or rather not) in the training system proposed by the School of Managers, from the point of view of the designers and the trainers in particular.

Data Collection and Analysis Methodology

To answer the questions originally posed about the changes in manager training at La Poste and how emotional and socioaffective dimensions were included in designing and implementing the training, we established a protocol for gathering and analysing data.

As for gathering data, the presence of a doctoral student (S. Mallard) at various committees, meetings and discussions with the School of Managers team can be considered a "pre-existing detail" [START_REF] Van Der Maren | La recherche appliquée en pédagogie. Des modèles pour l'enseignement[END_REF], which exists independently of the research. The researcher's positions shifts between "participating observation" and "observing participation" [START_REF] Soulé | Observation participante ou participation observante? Usages et justifications de la notion de participation observante en sciences sociales[END_REF]: participation in the team (meetings and other discussions), training development activities, etc. For all of these activities, the doctoral student's research gave a justifiable reason for his presence.

In addition, a series of semi-structured interviews (7 in all) was held between March and April 2013 with different people from the School of Managers; all of them were involved in developing the course and three of them also implement it as trainers. The interview chart was drawn up based on [START_REF] Albero | Petite fabrique de l'innovation à l'université -Quatre parcours de pionniers[END_REF] with a view to describing and analysing the design of "innovative" training systems. These interviews were transcribed and analysed using the text analysis software Alceste 2 , and the detailed analysis reports were interpreted.

To start, the software breaks down the text to be analysed (all of the interviews) into relatively homogeneous fragments called "context units". Then, the software seeks to classify them hierarchically; the fragments are divided into two distinct "classes" according to the vocabulary used. The fragments in the larger of the two classifications are then distributed again until two new classifications are found. This distribution and classification operation continues until a stable number of classifications is achieved [START_REF] Kalampalikis | Une approche pragmatique de l'analyse Alceste[END_REF]. In the end, each lexical classification is described using a specific term and includes "reduced forms". These make it possible to understand the signifiers behind the words, or the "sound-image" to use Saussure's terms (1975).

In this case, analysis of the interviews yielded five lexical classifications (1) participants'

Results: what the system designers and instructors say

In the first lexical classification "participants' emotions and subjectivity", there is a high chisquared value for adjectives and adverbs, meaning a high occurrence rate or a high cooccurrence of these terms. They are used to describe the properties of nouns or nuance the meaning of verbs, respectively. They provide the precision in discourse that socioaffective and emotional dimensions require. This classification is a set of words that describe the dimensions mentioned in the interviews; emotions are therefore "subjective" and "individual" according to one of the people interviewed 3 . The reduced forms found here are for the majority produced by men while there is a significant lack of them from women and the trainers (negative chi-squared value). These forms are used for the majority by the designers in connection with their role in the design, thus removed from the "manager learners". For them, the ideas of "enjoyment", "emotion" and "feelings" are very subjective and related to the individual, to every subject and for that reason, it would be difficult to qualify these subjective dimensions for someone else. Moreover, as one of the subjects interviewed stated, there are different ways of imagining training. One way would be to "follow the objectives in a concrete way", but "disconnecting the human aspect" and leaving aside any emotional dimension. The second way would be to take into account the "individual's subjectivity". In order to do that, they would have to constantly ask a question that is almost impossible to answer: "does the way I am designing this bring out particular emotions?" In the designers' view, the emotional experience is subjective and constitutes a challenge when designing a system. However, this classification does not reflect the discourses in their entirety because it is the most isolated of the classifications and is significantly more represented in the discourse of one designer in particular.

The second classification, known as "trainers' interpersonal skills in managing emotions" is the most well represented in our study (47% of all of the context units) and includes a set of adverbs, nouns and words indicating a spatial relationship. This classification specifies the context for the training's implemented as a combination of a particular time and place ("moment" and words indicating a spatial relationship) and persons ("people") which needs to be a "true" or real experience (very frequent occurrence), described by one of the subjects as "being a part of real life". Here, managing emotions in a training course depends upon a certain skill on the part of the trainer: an emotional know-how, being able to create relations, to make them make sense. One person spoke of the trainer as the keystone, i.e. the one element that brings together different parts and makes the construction (in the architectural sense) hold together. In other words, the trainer creates cohesion for multiple dimensions, including emotions, in learning. This know-how is distinct from what is required when designing a training system. In this classification there is a distinction and even perhaps opposition between the statements by trainers and designers (significant presence and absence, respectively). At certain points, the designers even distance themselvesthey are not the ones implementing it and, in their opinion, the system that they designed and constructed has less influence on how the participants in the training system learn than the trainer's technical knowledge. Some say there is almost a separation or independence between the two levels, designing and implementing training systems. How do these two activities overlap and complement each other? From an organizational point of view, development and implementation are two separate entities. This classification poses the question of creating learning resources and the resources (human, technical or other) that need to be mobilized to foster an emotional climate that facilitates managers' learning. In other words, they implicitly ask "how do you create a climate that fosters learning?". The significant absence of intensity markers shows perhaps the trainers' inability to describe the "ingredients" that encourage a "good" emotional atmosphere in the training room. The participating observation confirms these elements and highlights the wide range of practices and diversity in how trainers take emotions into account in training. The observations also show how important the spatial arrangement for the training area and the use of space and resources are, and these depend heavily on the trainer.

The third classification corresponds to the "theories included in the design", which is closest to the second classification ("trainers' interpersonal skills in managing emotions"), contains the following vocabulary: "framework, concept, start, limit". There is a significant presence of the French auxiliary verbs "to be" and "to have" and interjections. There is a significant absence of modal verbs, adjectives, adverbs, modal markers and person markers. This classification includes a somewhat nebulous subject. Some of those interviewed noted that the framework for the School of Managers' pedagogical project seemed "fuzzy" and "informal" for the designers because it is not specific enough or shared broadly enough. In addition, some of the participants regretted that they had no chance to discuss the theoretical references used (called the "framework" in the interview), or the theories put forward: this lack of dialogue is even likened to "censorship" by one of the people interviewed. For another person, the pedagogical project is seen as being related to the political orientations of the business, at least as he understands them. There may also be differences in different participants' perceptions. The theories about management and the pedagogy were not discussed within the group and were selected outside of the group of system designers. Concerning emotions, no theory held by designers was discussed because emotions constituted a "non-subject". Lastly, on the implementation side of things, the participating observation found that the trainers each created their own individual definition of what a La Poste manager should be, based on their own personal and professional experience, sometimes even as a former manager or sometimes from more "private" theoretical knowledge. But in general, no one was able to answer the question "What makes a La Poste manager?". It is the same for the question of experiential learning or learning from experienceaccording to the results from the participating observation neither the designers nor the trainers agreed upon a single view. Experience was considered at best something that shapes an adult and the trainers work de facto in experiential education. Others feel that experiential education is more a form of "feedback on experience" provided by managers.

The fourth classification, concerning a "necessary questioning of the issue of emotions in the design", closest to classification 1 ("participants' emotions and subjectivity") and furthest from classification 3 ("theories included in the design"), includes modal verbs, person markers, modal markers and markers for intensity. Conversely, adjectives and nouns are significantly absent. The questions that all of these people were asked concerned a particular subject, emotions, which was not brought up during the design and yet was implicitly included ("we have the elements in mind"). For two of the people participating in the project, the subject of emotions, although it was not explicitly present when developing the course, was nonetheless clearly included. For one of the people interviewed, the fact that they cooperated on developing the project ("being together" and "doing together") helped to "include emotions". The selection of those who played a role seems to be another implicit factor in the projecteveryone who helped design the system was chosen for their "individual" profile, their individuality and the sensitivity they would in their own way include in the design.

Finally, the fifth classification covers the "role of emotions in learning activities" and shows a high presence of verbs, adjectives and adverbs and a significant absence of "-ment" adverbs (French suffix corresponding to "-ly" adverbs in English) and nouns. In other words, the people interviewed are talking about action, and more specifically learning activities ("finding out", "memorizing", "inciting", "making possible", etc.) that can encourage emotional and socioaffective dimensions. Here, the activities that incorporate the most emotions are those most often cited by the people interviewed. In this classification, the learners also make their entrance (the term "intern" although its use is relative). The notion of "trust" is highlighted because it is a requisite, according to the subjects interviewed, for expressing emotions in a group. In response to the question they were asked, the interviewees particularly associated emotions with memorisation (which is part of the pedagogical project). However, the responses were much broader and took into account emotions both in the system's design and its implementation. The question the interviewer asked was clearly seeking to find examples of activities using emotions. The way the training is organised into seminars and modules would seem to make it "fertile" ground for taking the emotional dimensions into account. Some seminars (the "first" one in particular) encourage emotions being taken into account.

However, one of the people interviewed held a different view; in his opinion emotion is precisely "what's missing" in the system. The only emotions present in this system were expressed during the design phase and not in its implementation; that was in the debate in which each person hoped to put in place their chosen theory. Classification 5 seems to be the most distant from the others and illustrates for some examples of how the pedagogical activity includes emotions, or quite the contrary, for others, how subjectivity and emotions are missing. There is then a tension here between two opposite views.

To summarize, according to classification 1, the issue of emotions is an individual question.

However, it is up to the trainer to manage this emotional dimension in the training situation (indicated in classification 2). Although, as indicated in classification 4, emotions were implicitly included in the development (presence of verbs and absence of tool-words), class 5 includes a certain number of examples in which emotions were gradually taken into account.

Discussion and openness

From the five lexical classifications given by the Alceste analysis, two major views take shape: the first concerns designers and the second the trainers who implement the system. Everyone makes a distinction among teaching activities according to the "emotional level". While [START_REF] Isen | Positive Affect, Cognitive Processes, and Social Behavior[END_REF] finds that emotion is "intimately" tied to cognition, the interviewees, for the most part, separate cognition and emotion. Thus for the system's designers as well as for the trainers, cognition excludes emotion and emotion excludes cognition.

The diagram below shows the tensions between (a) the designed forms, from design to implementation and (b) the degree of subjectivity. The first axis could be described as the designed axis and the second one as the subjectivity axis. This allows us to examine the learning activities (analysis of practices, skills portfolio, games, etc.) that are implemented according to the views of the designers and the designer-instructors. What is designed to have a high emotional content does not necessarily have that content in practice and vice versa.

7th European Research Conference -Humboldt-University Berlin -4 -7 September 2013 Lastly, a certain number of activities, such as those arranged on the diagram and some that are included in the course, either may not be cited or may not be known to the subjects interviewed given that not everyone is aware of the end result or the pedagogical process and everything that "really" happens during the training.

Diagram: Tensions in how emotions are included

Given the different analyses provided by Alceste, and particularly those in the fifth classification, we can infer that the policy and the design, in principle, provide a framework that fosters emotions. Despite that, the positioning of various activities is very approximate. In the interviews, the subjects could not justify why and how the specific activities can encourage emotions. According to the views of the people interviewed, the effectiveness of the system depends principally on the trainer. There was then no connection between the 7th European Research Conference -Humboldt-University Berlin -4 -7 September 2013 designers' and the trainers' "design references" (including all the theories put forward, which should also be found in the realization of the system's "ideational dimension"). What is striking for their absence from the interviews is the training group and the learners, for whom the system was in fact created. They only appear in the fifth classification, a small portion of our body of data. The final target is only rarely mentioned in the interviews. Yet while the role of the trainer as a mediator does seem key, the space allowed the learner in the system is every bit as important since they are the key figure in the learning act.

Furthermore, it is important to highlight that the emotional and socioaffective dimension had never been discussed before the interviews done for this research. Yet this aspect was indeed included in the School of Managers' pedagogical plan. In other words, the plan does not seem to be entirely understood or broadly shared. The lack of a defined theoretical framework, or one that can be defined collectively, seems to be a problem for cooperative work. Working with the threefold approach used by Albero, the ideational dimension, i.e. the guiding values, principles and aims of the system, may not be known or shared by everyone. Similarly, in the development, the "design references" are not sufficiently discussed or even defined according to some of the interviewees.

Concerning the role of the trainers, their freedom in the training activities is subject to the constraints of the design. There are in fact even two sets of constraintsthose imposed by trainers based on their "ideals" and that given by the system designers through the "design references" contained within the training system. The differences in the views and by extension the experiences of the people interviewed seem to stem from a difference between the trainers' and designers' "ideational dimension" and the "design references" of the trainers who are called upon to implement the system as constructed.

To use Austin's terms (1970), creating a system is not at all a "performative utterance": it is not enough to simply write or say something (or even to suppose, on a yet more implicit level), to cause the trainer and learners to do, think, feel or cause emotions. Constructing the training system is a necessary step that lays the framework and describes it so that the trainers and learners may make use of it. Put differently, the "content of the system" which will be defined here as the ability of the system to welcome and symbolically contain emotional and socioaffective dimensions, is only guaranteed on the condition that the system's policy, design and implementation are articulated and made to be "containers" in their turn. The "ideational dimension", the "design references" and "experience" (every figure interprets the situations they experience differently) seem to be quite mixed up here.

The emotional dimension was not explicitly included in development and implementation and can therefore only partially be part of the training system. To make it so, it would be necessary to include individual and group reflection on the subject during the development phase; a debate concerning the implications of the "cognition, conation and emotion" triptych [START_REF] Barbier | Action, affects et transformation de soi[END_REF] certainly seems necessary for including them. If these conditions are met, we feel that the emotional and socioaffective dimensions can foster a system that works as "a space for (re)creation and experiential appropriation (as part of) sharing and exchange" (Klein & Brackelaire, 1999, p. 68), or in a word, with intersubjectivity. The aim would be to develop systems that are "places to create and reclaim experiences, bringing about involvement and integration in social life, 'moving towards the social'" (ibid). Emotions, previously defined as physical "gestures" for oneself and for others would then be welcomed within the group and could "anchor" transformations in the "body" (physical or symbolic) of the group and the individual.

In the training that the School of Managers proposes, and according to what the participating observer noted, participants express a strong need to be recognised and appreciated by others.

Each participant's job may not be appreciated in their own workplace; then in the training their peers accept it with a range of emotions and more or less verbalised expressions.

  emotions and subjectivity; (2) trainers' interpersonal skills in managing emotions; (3) implicit or explicit theories included in the design; (4) necessary questioning of the issue of emotions in the design; (5) the role of emotions in learning activities. The data in these five classes adds to, supports or calls into question the data from the "observing participation" mentioned above.

Hospitality, i.e. how what is foreign or a stranger is welcomed and especially how emotions are welcomed, must be possible and has to have been the subject of prior preparation during the development of the system. This study then encourages a view that includes "hospitality for the stranger", that is to say "the challenge of moving from hostis to hospes, from the stranger to simply one not known, and from this unknown to a guest" (Cornu, 2008, p. 26). In other words, the issue is finding how to "make room for the other, for their emotions". In order to connect the "ideational" dimension, the "design references" and later the participants "experience", the design step needs to include a more explicit and more broadly shared theoretical framework. Otherwise, as this study shows, the different views that individuals hold, what is left implicit and the gap between designers and trainers leaves a lot of room for subjectivity and reinterpretation of the role that emotions could (or should?) play in manager training.

In terms of practical steps to take, while the ideals are clearly stated in the School of Managers' policy, it is probably necessary to state these ideals more clearly and to make sure that they are shared more generally by the different figures responsible for policy and especially by those responsible for developing training. For that reason it seems necessary to better define the theoretical framework concerning management, managers and emotions (as shown in diagram); this will make it possible to bring the two extremes of the designed axis in the diagram, design and implementation, closer together. Beyond the "vertical" connection between the ideational, functional and experienced dimensions, it seems a "horizontal" adjustment is necessary. For example, the design references for trainers and designers are different and should probably be discussed. This should be beneficial to constructing a system that "contains" and welcomes what is unknown, strangers and the unknown.

In terms of theoretical implications, while this study does not include participants' experiences, further thesis work will examine their experiences and seek to answer the result in part from differences in interpretations, too much left unspoken and ideas and theories that are not shared broadly enough. We may well expect the differences between participants' experiences, of managers following the training course planned specifically for them, to be even greater.

Endnotes

1. By "postal workers", they are referring to everyone working for La Poste, not simply mail carriers.

2. The software Alceste analyses the data as a "possible discourse". It isolates cooccurrences and "lexical 'worlds" that form the basis of the discourse or represent the speaker's "mental environment" (see: http://www.image-zafar.com/en/alcestesoftware).

3. Words in quotation marks are from the analysis report produced by Alceste.