Jori Ferran
email: ferran.jori@cirad.fr

G Petit

N Civil

A Decors

F Charrier

F Casabianca

V Grosbois

Ferran Jori

A questionnaire survey for the assessment of wild-domestic pig interactions in a context oedema disease outbreaks among wild boars (Sus scrofa) in South-Eastern France

Keywords: disease transmission, interactions, pig production, Sus scrofa, wild boar

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Wild boar (Sus scrofa) populations have been growing in high numbers for several decades both in Europe and other countries in the northern hemisphere generating serious ecological, economic and sanitary costs [START_REF] Massei | Wild boar populations up, numbers of hunters down? A review of trends and implications for Europe[END_REF]. At the same time, the number of outdoor pig farms is increasing because of consumers' demand for higher quality products originating from animal friendly production practices [START_REF] Åkerfeldt | Health and welfare in organic livestock production systems-A systematic mapping of current knowledge[END_REF]. These two parallel developments facilitate an increasing occurrence of interactions between domestic pig (DP) and wild boar (WB) populations, which can have several sanitary and environmental consequences. Belonging to the same species, WB and DP can share a large diversity of pathogens through a diversity of pathways [START_REF] Jori | Wild and feral pigs: Disease transmission at the interface between wild and domestic pig species in the Old and the New World[END_REF]. In Southern France, several fatal outbreaks have been reported in WB, caused by a strain enterotoxemic Escherichia coli (serotype O139K82), with virulence markers and symptoms characteristic of oedema disease (OD) in DP [START_REF] Decors | Diagnosing a health problem in wildlife: Example of edema disease in wild boar (Sus scrofa) in Ardèche, France[END_REF][START_REF] Petit | Polymorphism of the alpha-1fucosyltransferase (FUT1) gene in several wild boar (Sus scrofa) populations in France and link to edema disease[END_REF]. These emerging events in wild boar populations highlight the need to explore the factors associated with those outbreaks, including the potential importance of direct or indirect interactions between WB and DP.

Therefore, the goal of our study was (i) to report on the predominant pig farming patterns in the continental French region of Ardèche, (ii) to examine the potential importance of DP-WB interactions in this rural context and (iii) to explore the potential association with previous mortality outbreaks attributed to OD among the local wild boar populations.

MATERIAL AND METHODS

Study area

Ardèche is a territory of South-Eastern France (Figure 1a) covering an area of 5.500 km 2 with an altitude ranging between 140 and 1750 m asl and crossed by a large network of rivers and streams, a forest vegetation cover (52%) of exceptional diversity combining oak, acorn, chestnut, wild pine and red beech forests (see Figure 2). Its climate is predominantly Mediterranean (0-28 Animals can therefore eat chestnuts throughout the fall season, at no cost to the farmer. Likewise, forest cover provides ideal conditions for the establishment of wild boar, which feeds in abundance on acorns, chestnuts and beechnuts [START_REF] Baubet | Connaissances sur la biologie du sanglier: Utilisation de l'espace et régime alimentaire[END_REF][START_REF] Schley | Diet of wild boar Sus scrofa in Western Europe, with particular reference to consumption of agricultural crops[END_REF]. Based on the detected cases of wild boar carcasses presenting lesions, clinical signs and bacteriological or genetic evidence of OD between 2013 and 2016 (Perrat et al., 2022), a poly-gon of 1981 km 2 was spatially identified and designed throughout the study, as OD area (Figures 123).

Study design

A cross-sectional study was conducted from March to June 2018, among stakeholders located inside and outside the OD area. The sampling frame was based on databases of hunters and pig farmers provided by the regional authorities. In the case of hunters, a list containing 200 names of the leaders of the local hunting teams was provided by Regional Association of hunters from Ardèche. A total of 30 hunters were randomly drawn from a list of 200 names: 20 hunters from the OD area and 10 hunters from outside this area. As the participation to interviews was volunteer, if some people drawn refused the interview, the next names on the list were selected.

The selection of farmers was targeted towards those individuals practicing at least partial outdoor farming in a part of their facilities and therefore, potentially exposed to wild boar incursions (Figure 1).

Based on this list of people, two farmers were aimed by municipality. The selection of the farmers was based on information obtained from other farmers on potential additional candidates, following a systematic snowball process until reaching a total number of 30 farmers for our survey. Each candidate was preliminarily contacted by phone to request his/her availability to participate in the survey. The 30 pig farms interviewed were distributed within the OD area (n = 21) and outside (n = 9) on a 'first available, first interviewed' basis (Figure 1c).

In addition, to assess environmental conditions facilitating potential interactions, vegetation type and wild boar abundance were assessed in the vicinity of pig farms (1 km radius) based on geographical information and vegetation cover data available and hunting information provided by departmental hunter's association (Supporting Information 1).

Data collection

Data collection was based on semi-structured interviews conducted among hunters and pig farmers giving the opportunity to develop their answers but also to give their point of view [START_REF] Relun | Multivariate analysis of traditional pig management practices and their potential impact on the spread of infectious diseases in Corsica[END_REF]. The questionnaires used during interviews consisted of 30 questions for hunters and 50 for pig farmers, and the time required to answer the various questions of the two questionnaires was estimated at 20 and 30 min, respectively. This questionnaire was previously tested with a hunter and a breeder to verify the understanding of the different questions and the response times. Before beginning the interview, the interviewer explained the aim of the study and emphasized that the information the hunters and the farmers provided would be processed anonymously. Both questionnaires are available as Supporting Information 1.

The questionnaire designed for farmers included a section on biosecurity measures to assess the types of fences used to protect pig farms

F I G U R E

Statistical analysis

A descriptive analysis of farming practices, environmental context (i.e., landscape, wild boar density) and risk factors with regard to potential pathogen transmission characteristics and interactions with WB was first performed. For this analysis, outdoor surface, number of pigs in the farm and the wild boar density indexes were categorized using breaks in the distribution as cut-off points when no break was clearly visible. The farms were then classified in different groups using a multiple factor analysis (MFA) followed by a hierarchical cluster analysis (HCA). This approach allowed the identification of groups of farm types that have similar farming practices, environmental context, biosecurity levels and reported events of incursions or interactions with WB (see Supporting Information 2).

RESULTS AND DISCUSSION

General information on predominant hunting and farming characteristics in the study area is available as Supporting Information 3. The (2/30) between DP and WB. Five farmers (17%) reported experiencing hybrid litters as a result of sexual interactions between male WB and their respective sows. There was a certain seasonality in terms of proximity of WB to farms: incursions of WB in DP farm premises were mainly reported in autumn (29%) and there was a significant difference between interactions reported in that season, compared to other seasons (p < .05). This season coincides with the most active reproductive season of male WB in Europe, reported between November and January [START_REF] Drimaj | Reproductive characteristics of wild boar males (Sus scrofa) under different environmental conditions[END_REF], during which they can be easily attracted by sows on heat in outdoor farming paddocks, as observed in a diversity of locations in the European Union [START_REF] Jori | Questionnaire-based assessment of wild boar/domestic pig interactions and implications for disease risk management in Corsica[END_REF][START_REF] Malmsten | The reproductive pattern and potential of free ranging female wild boars (Sus scrofa) in Sweden[END_REF][START_REF] Wu | Risk factors for contacts between wild boar and outdoor pigs in Switzerland and investigations on potential Brucella suis spill-over[END_REF].

The MFA/HCA approach allowed the identification of three types of DP farms, which are shown in Table 1. Type 1 (n = 17) includes farrow to finish farms with intermediate biosecurity measures against wild boar incursions located in woodlands (Figure 2) areas with high to medium wild boar densities (Figure 3). Half of these farms reported incursions or interactions with WB and most of them were located in the OD outbreak area. Type 2 (n = 6) included farms located in high wild boar density areas and characterized by weak biosecurity measures against wild boar intrusions. Half of them reported incursions or interactions with WB, and all of these farms were located in the OD outbreak area.

Type 3 (n = 7) included farms highly protected against intrusions of WB and located in cultivated areas. None of these farms reported incursions or interactions with WB and most of them were located outside the OD outbreak area.

Most of the farms (Table 1) were significantly located inside the OD area and appeared significantly associated with a forested habitat (p = .025), a higher estimated density of wild boar (p = .06), a lowto-intermediate biosecurity index (p = .0001). Several risky farming practices were found to be more common in the OD area than in the rest of the department. These risky farming practices included abandonment of waste in the wild (n = 4), slurry spreading (n = 4) and presence of WB in the slurry spread area (n = 3). Similarly, a larger proportion of farms with low to intermediate levels of biosecurity (21/30) were located inside the OD area (Table 1, Figures 2 and3). Those included total or partial absence of fence or farms were only partially protected with a single fence or partial electrification.

It is likely that reported figures of interaction through questionnaires are underestimated for several reasons. First, an important proportion of them occurs during night time and are difficult to be witnessed directly by farmers, but rather detected by the observation of damages in the fence or the observation of cross-bred offspring a few weeks later. Further studies using ecological tools (camera traps and/or telemetry devices with proximity loggers) could be useful to confirm and quantify more accurately wild boar incursions in farms from the study area [START_REF] Kukielka | Spatial and temporal interactions between livestock and wildlife in South Central Spain assessed by camera traps[END_REF][START_REF] Triguero-Ocaña | The wildlife-livestock interface on extensive free-ranging pig farms in central Spain during the "montanera" period[END_REF].

In addition, a certain level of recall bias could have influenced some of the responses. In fact, hunters struggled more than farmers to place the observed events in time. In addition, a third of farmers (9/30) expressed some mistrust towards the questionnaire, despite insisting that responses would be anonymous. Indeed, a majority of farmers admitted having observed sexual or trophic interactions, mostly towards the end of the questionnaire, when a higher level of trust had been achieved. Certainly, the fear of sanctions from local authorities for not following legal requirements could have motivated underreporting the frequency of interactions in the farms. Indeed, local animal health authorities can control the farms and engage substantial sanctions if the infrastructure is not up to standard. In addition, since the sexual interactions reported were those leading to the birth of hybrids, a certain number of interactions not resulting in cross-bred offspring could have been underestimated.

Our study did not confirm a direct correlation between wild and domestic suid and the emergence of OD in WB. Nevertheless, the occurrence of factors facilitating interactions (insufficient biosecurity, improper management of carcass remains or slurry spreading) in the OD area was highly significant (Table 1). A recent molecular study on the samples collected from WB in the study area suggests that WB populations in the South of France are genetically predisposed to OD (Perrat, Branchu et al., 2022) and that the strain of E. coli causing mortality in WB, was previously undetected in domestic pigs, and has evolved for a long time in this WB population (Perrat, Petit et al., 2022).

There was no genetic similarity between the isolated WB strain and any of the available E. coli strains isolated from DP farms in France. Nevertheless, it is important to stress that local DP strains from Ardèche

were not available for a potential comparison. Based on this information and the results from this study, DP farms with low biosecurity levels in the study area are exposed to the risk of becoming infected with OD as a result of direct or indirect interactions with WB.

Although our farm sample was small, there was a predominance of infrastructures and practices allowing the incursion of WB into farms in the territory, including those areas with high estimated den-sities and favourable wild boar habitat. Indeed, an intermediate level of biosecurity appeared strongly correlated (p < .05) with the risk of WB incursion and trophic interactions. In that respect, it is important to underline that fences are expensive for farmers, and if the farming surface is important, their maintenance across the whole perimeter can represent a serious financial and technical constraint for the farmer. In the context of the African swine fever threat, it is likely that French outdoor pig farmers will be confronted with the obligation of increasing biosecurity measures, and mediation processes might be required to facilitate their local acceptance and implementation [START_REF] Gisclard | From national biosecurity measures to territorial ASF preparedness: The case of free-range pig farming in Corsica, France[END_REF].

Regarding hunters, only one reported direct (trophic) interactions

CONCLUSIONS

This study allowed us to identify that practices and conditions facilitating interactions between WB and DP and the transmission of shared swine pathogens were common and widespread in our sample of farms from the Ardèche Department. This information can be used to target preparedness for the emergence and circulation of shared swine pathogens such as OD. Despite our sample was limited, many of those factors were more important in the OD emergence area than outside. Those included ecological aspects, but also anthropic factors related to pig farming such as a significantly higher presence of farms with leaky biosecurity measures that were confirmed by the presence of a higher number of reported incursions and episodes of interaction. Moreover, our study highlights the potential importance of abandoned pot-bellied pig pets which can allow the development of free-ranging feral pig populations and the development of a new and unsuspected wild-domestic suid interface, which deserves higher attention and further investigation about its potential epidemiological impact

AUTHOR CONTRIBUTIONS

FJ conceived the presented idea, analyzed the raw data and wrote the manuscript. GP and FCh and FC designed the sampling approach.

NC implemented the questionnaires in the field. VG and FJ analyzed the data. BC encouraged and AD supervised the findings of this work and corrected the manuscript. All authors discussed the results and contributed to the final manuscript.

A

  questionnaire survey for the assessment of wild-domestic pig interactions in a context oedema disease outbreaks among wild boars ( Sus scrofa ) in South-Eastern France Ferran Jori, G. Petit, N. Civil, Anouk Decors, François Charrier, François Casabianca, Vladimir Grosbois To cite this version: Ferran Jori, G. Petit, N. Civil, Anouk Decors, François Charrier, et al.. A questionnaire survey for the assessment of wild-domestic pig interactions in a context oedema disease outbreaks among wild boars ( Sus scrofa ) in South-Eastern France. Transboundary and emerging diseases, 2022, 10.1111/tbed.14704. hal-03792881 1 INTRODUCTION

1

  Maps of the Ardèche Department showing (a) its location within the French territory. (b) The location of the wild boar oedema disease (OD) cases detected between 2013 (labelled 1) and 2016 (labelled 4). The envelope containing most of the detected cases is defined as the OD outbreak area. (c) The distribution of pig farmers in the territory. Black filled symbols stand for surveyed farms (which are all outdoor farms), close grey symbols for outdoor farms that were not surveyed, open symbols for indoor farms, circles for farms rearing pigs for consumption, triangles for pet pig farms and squares for wild boar farms. The envelope defines the OD outbreak area. F I G U R E 2 Locations of surveyed farms characterized by their type in the multiple factor analysis/hierarchical cluster analysis (MFA/HCA) (colour) and reporting of interactions with wild boar (WB) (sign type) on a landcover background from wild boar incursions. A wild boar incursion was defined as the fact of observing a wild boar in the farm or observing any evidence of potential visits (broken fence, tracks, birth of hybrids). An interaction was defined as the direct observation of WB or DP mating, fighting or foraging together within the farm. Reports of incursions and interactions were merged together into a single 'contact' variable for the analysis. Finally, observations of WB by the farmers around their farms were also recorded during the survey. Farm biosecurity information recorded during the farmers' survey (number and type of fence and the electric wires) was integrated into a three-level variable reflecting protection against wild boar intrusions. The hunter's questionnaire assessed the observation of contacts between both populations in hunting areas and the presence of DP or cross-bred individuals in hunting grounds during the last 10 years. For the farmer, a list of criteria suggesting potential hybridization features was provided including white spots in the fur, shape of the body, short snout, falling ears, thick fat layer and large litters. The questionnaire requested to qualitatively characterize the frequency of observation as 'low' if only one observation was reported, 'medium' if less than five observations were reported and 'high' , if more than five observations were reported. In addition to those systematic questionnaires, various interviews were carried out either opportunistically in face-to-face or by telephone interviews and in order to obtain certain additional information F I G U R E 3 Locations of surveyed farms characterized by their type in the multiple factor analysis/hierarchical cluster analysis (MFA/HCA) (colour) and reporting of interactions with wild boar (WB) (sign type) on a wild board density background from animal health authorities, farmers associations or wildlife management authorities. The objective was double and consisted of (i) collecting additional information but also (ii) cross-cutting the preliminary results obtained by the hunters and farmers questionnaires.

  results obtained during this study suggest that small scale outdoor farming initiatives are widespread in the Ardèche territory. A third of the interviewed farmers reported intrusions or attempts from WB to enter the farm, whereas 23% of them observed some evidence of direct interactions. Taking the previous year as a reference, 66% of the farmers had observed WB in proximity of the farm at a distance ranging between 1 and 200 m. Those included reports of sexual interactions (reported by 8/30 farmers), fights (3/30) and trophic interactions

  in the forest. Despite none of them observed direct agonistic interaction, six acknowledged that this kind of contacts could occur. Two hunters had heard or observed of wild boar incursions in some farms in the region and two had observed the presence of DP in hunting grounds. Eighteen hunters (60%) recalled observing the presence or hunting hybrid pigs, and two-thirds of these observations were dated between 2013 and 2016. The large majority of those reports (10/18) referred to animals with a phenotype compatible with pot-bellied pigs.The practice of keeping and breeding pot-bellied pigs, frequent in many EU countries, was identified in eight leisure states among the list of 230 registered farms. Complementary interviews with local authorities confirmed an escape incident with a farm of several specimens of pot-bellied pigs from the study area in 2015. Considering that sexual interactions reported by hunters had occurred in proximity of this farm, the possibility of crossbreeding between WB and feral pot-bellied pigs is a plausible scenario. In many EU countries, there are numerous reports of abandoned pot-bellied pig individuals. However, the purchase and ownership of pot-bellied pigs are rarely regulated by national administrations, despite there is evidence that abandoned pot-bellied pigs have the capacity to form feral sounders that remain in natural habitats in Southern Europe and interact with wild boar populations increasing the population of hybrids. This is becoming a common phenomenon in many countries where pot-bellied pigs are sold as pets and has been currently described in rural areas of Southern and Northern Spain (Delibes-Mateos & Delibes, 2013; Soler, Casas, Closa-Sebastià, Sanz, & Martorell, 2021). Based on our results, this kind of interaction is worth reporting in this study, to raise awareness about this phenomenon and encourage French and EU authorities to monitor similar incidents and their impact among WB populations.

  Classification of the farms in farm types and p-value indicating significant association between the farm types and the categorical and quantitative variables defining the typology

	Typology of domestic pig farms	Type 1	Type 2	Type 3	p-Value
	Number of farms	17	6	7
	Dominant landscape type within a 1 km radius (cultivated/shrublands/	3/1/13	1/2/3	5/1/1	.025
	woodlands)
	Mean wild boar density index within a 10 km radius (low/medium/high)	3/6/ 8	0/1/5	4/2/1	.06
	Speciality (farrow to finish/finisher)	15/2	2/4	3/4	.03
	Protection against wild boar intrusions (low/intermediate/adequate)	0/17/0	6/0/0	1/1/5	<.0001
	Report of wild boar incursions in the farm or of observed interactions between	8/9	3/3	7/0	.05
	DP and WB (no/yes)
	OD epidemics zone (outside/inside)	3/13	0/6	5/2	.008
	Abbreviations: DP, domestic pig; OD, oedema disease; WB, wild boar.

TA B L E 1

ACKNOWLEDGEMENTS

This work was funded by INRAE (French Institute for Agriculture, Food and Development) within the Meta-program GISA (https://colloque. inrae.fr/metaprograms-workshops_eng/Metaprograms/GISA) -Sustainable management of Animal Health (Projet EPIDEWILD-3i). We would like to acknowledge la Fédération des Chasseurs de Ardèche (Fabrice Etienne), the hunters and farmers who were interviewed and the local authorities of animal health and production (DSPP).

Funding information INRAE (French Institute for Agriculture, Food and Development) ; Meta-program

CONFLICT OF INTEREST

The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT

Due to privacy and ethical concerns, neither the data nor the source of the data can be made openly available. Further information about the data origin and analysis is provided as Supporting Information.

In addition, conditions for access can be obtained by contacting the corresponding author.

ETHICS STATEMENT

Ethics statement was not applicable since only questionnaires from humans have been gathered, and no sample collection experimental activities were necessary for this work in France.

SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.


Eddy Caron

Frédéric Desprez

David Loureiro

Adrian Muresan Domaine

Cloud Computing Resource Management through a Grid Middleware: A Case Study with DIET and Eucalyptus

Keywords: Cloud Computing, DIET-Solve, Eucalyptus, resource management Cloud Computing, DIET-Solve, Eucalyptus, gestion des ressources

The Cloud phenomenon is quickly growing towards becoming the de facto standard of Internet Computing, storage and hosting both in industry and academia. The large scalability possibilities offered by Cloud platforms can be harnessed not only for services and applications hosting but also as a raw on-demand computing resource. This paper proposes the use of a Cloud system as a raw computational on-demand resource for a Grid middleware. We illustrate a proof of concept by considering the DIET-Solve Grid middleware and the EUCALYPTUS open-source Cloud platform.

Introduction

Over the last years, Internet Computing and storage have considerably evolved from small isolated nodes to large-scale Cluster-like architectures driven by efficiency and scalability needs which we now know under the name of «Clouds» [START_REF] Armbrust | Above the Clouds: A Berkeley View on Cloud Computing[END_REF][START_REF] Vaquero | A Break in the Clouds: Towards a Cloud Definition[END_REF]. They aim at being dynamically scalable and offer virtualized resources as service over the Internet. Usually solutions deployed in Clouds are web browser targeted and are balanced when it comes to computational power and storage. Clouds can also be used in more computational-intensive domains by using them as scalable computational resources.

Grid platforms offer a large benefit when it comes to computational power, yet they have a drawback caused by their scalability. To get the best of both worlds, a Grid middleware can be used to manage and harness raw Cloud computational resources.

The current paper will only scratch the surface of this interesting topic. We offer a proof of concept of using a Cloud system as computational resource through a Grid middleware. We demonstrate the use of EUCALYPTUS [START_REF] Nurmi | The Eucalyptus Open-source Cloud-computing System[END_REF], the open-source Cloud, as a resource for the DIET 1 Grid middleware [START_REF] Caron | DIET: A Scalable Toolbox to Build Network Enabled Servers on the Grid[END_REF].

The rest of this paper is organized as follow. In Section 2, we present the overall architecture of EUCALYPTUS. Then we present the architecture of the Cloud version of DIET-Solve. After a short description of DIET-Solve, we give details about the way we connected it to EUCALYPTUS, the pro and cons of each approaches we tried and some performance estimations of the overheads. Finally and before a conclusion and future work section, we describe some previous works around this subject.

EUCALYPTUS

In the world of open-source Cloud Computing, EUCALYPTUS [START_REF] Nurmi | Eucalyptus: A Technical Report on an Elastic Utility Computing Architecture Linking Your Programs to Userful Systems[END_REF] is a pioneer. It relies on commonly-available Linux tools and simple Web Service technology and is compatible with the Amazon Elastic Computing Cloud (EC2) SOAP interface [START_REF]Amazon Elastic Compute Cloud[END_REF].

The EUCALYPTUS platform has a three-level hierarchical architecture. At the top of the hierarchy lies the Cloud Controller node (CLC). Its role is to coordinate the Cloud as a whole and to handle client Cloud management requests. This is the only node that is responsible for decision-making.

Halfway between the top and bottom of the hierarchy lies the Cluster Controller (CC). It is responsible for keeping track of resource usage in its Cluster.

At the bottom of the hierarchy lies the Node Controller (NC). Each physical machine that is to be a computing machine needs to have the NC service running. The NC has two main responsibilities: monitoring resource usage and managing virtual resources.

To achieve virtual resource management, EUCALYPTUS uses Xen2 and/or KVM3 virtualization technologies. These offers great benefits when dealing with scalability and application isolation but have also drawbacks due to the necessary start-up and shutdown time of the virtual machines.

We have chosen to use the SOAP interface that EUCALYPTUS privides. This is more flexible than the query interface (the query string has a limited size) and offers better security because it implements the WS-Security standard with an asymmetric RAS key pair. As a result we have successfully manage to programmatically manipulate EUCALYPTUS virtual resources and gain direct access to them once instantiated.

On the basis of the above results we are ready to integrate EUCALYPTUS as a resource into DIET-Solve.

DIET-Solve over a Cloud

DIET-Solve Overview

The DIET-Solve component architecture is hierarchically structured for an improved scalability. The DIET-Solve toolkit [START_REF] Amar | Tunable Scheduling in a GridRPC Framework[END_REF][START_REF] Caron | DIET: A Scalable Toolbox to Build Network Enabled Servers on the Grid[END_REF] is implemented in CORBA and thus benefits from the many standardized, stable services provided by freely-available and high performance CORBA implementations.

The DIET-Solve framework has several components. A Client is an application that uses the DIET-Solve infrastructure to solve problems using a GridRPC approach. A SED (Server Daemon) acts as the service provider, exporting functionality through a standardized computational service interface; a single SED can offer any number of computational services.

The third component of the DIET-Solve architecture, agents, facilitate the service location and invocation interactions of clients and SEDs. Collectively, a hierarchy of agents provides higher-level services such as scheduling and data management. These services are made scalable by distributing them across a hierarchy of agents composed of a single Master Agent (MA) and several Local Agents (LA).

DIET-Solve Cloud Architecture

With the question of how to harness EUCALYPTUS as a DIET-Solve resource in mind we need to consider the architectures of both systems in order to find a suitable answer. From a high-level perspective we have several plausible solutions that differ by how much of the architectures of both systems overlap or are included one in the other. To be more precise we can consider the following two scenarios and any other scenario that is logically between the two.

DIET-Solve is completely outside of EUCALYPTUS:

In this scenario the DIET-Solve and EUCALYPTUS architectures do not overlap at all in the sense that all DIET-Solve agents or SEDs run separately with respect to the EUCALYPTUS controllers. The DIET-Solve SED requests resources (compute nodes) to EUCA-LYPTUS when needed and uses the resources directly (bypassing EUCALYPTUS broker) once they are ready. In this scenario scalability is limited because of the fixed DIET-Solve architecture that cannot scale easily, but the number of compute nodes takes full advantage of EUCALYPTUS's scalability.

DIET-Solve is completely included in EUCALYPTUS:

The DIET-Solve architecture is virtualized inside EUCALYPTUS. This scenario is the other extreme of the previously stated one since DIET-Solve agents and SEDs are virtualized and instantiated on-demand. It is obvious that this scenario offers more flexibility because one can configure the amount of physical resources that are allocated to EUCALYPTUS virtual resources. This is also a more scalable approach because of the on-demand way of use that is typical to Cloud platforms.

The simplest and most natural scenario from the perspective of DIET-Solve is to treat EUCALYPTUS as a new type of resource allocator and scheduler inside the SED since DIET-Solve is easily extensible in this direction.

Figure 1 shows the architecture of DIET-Solve with three kinds of server daemons. SED The basic SED encapsulates a computational server. The information stored on a SED is a list of the data available on it, the list of problems that it can solve and performance-related information. SED Batch is an upgraded version of the previous one. This SED has the capability of submitting requests to different Batch Schedulers (BS) without the user having to know how to submit to the underlying BS. SED Cloud Based on the same idea that the previous one, this version provides the capability to use Cloud resources through the API of the Cloud platform. Knowing which image is associated to which service the SED could then automatically deploy it on the selected number of compute nodes.

Handling of a service call is done in three steps. Obtain the requested virtual machines. This first step involves requesting the virtual machines to EUCALYP-TUS by using its SOAP API. The SED Cloud contains a mapping of services to virtual machines that the user is not aware of (and does not need to be aware of). The start-up of virtual machines is a time consuming operation and is best done asynchronously with the request. As a result, the SED Cloud polls EUCALYPTUS to receive a positive or negative reply related to the requested virtual machines. If the reply is positive then SED Cloud can proceed to the second step in handling the request. Execute the MPI service on the instantiated virtual machines. The end result of the previous step is a list of addresses of the instantiated virtual machines.

Having this low-level information, SED Cloud can now initiate a MPI request on the machines. The result of the calculation is returned and a reply is formed for the service call. Terminating the virtual machines. The SED Cloud initiates another SOAP request to EUCALYPTUS for the termination of the instantiated virtual machines.

In order to test the functionality of the Eucalyptus scheduler and resource allocator, we set up a test architecture and deployed examples. The test architecture has two parts. The DIET-Solve part: a MA that published the services that DIET- INRIA Solve makes available. The MA receives requests for service calls and relays them to the SED. The MA does not broker the request once the request has been relayed. A SED that is responsible for handling service call requests by instantiating EU-CALYPTUS virtual machines. The EUCALYPTUS part: an EUCALYPTUS Cloud Controller (CLC) node that is the front of the cloud. All EUCALYPTUS requests and interactions start throught the CLC node. An EUCALYPTUS Cluster Controller (CC) node that manages the test Cluster. An EUCALYPTUS Node Controller (NC) installed on the physical machine that will be the compute node. Virtual machines will be instantiated on this node.

Performance Estimation

When refering to performance one is interested if the overhead outweigh the scalability increases and efficient use of resources. To get a good estimation we need to take into account the following performance factors: EUCALYPTUS instance throughput (a constant value that is added to the latency calculation and not a multiplication factor), EUCALYPTUS virtual network overhead and Xen virtualization overhead.

Instance startup time is the most difficult overhead to predict as it is influenced by the most varying factors like virtual machine OS and installed packages. However, we can get a good lower bound estimation for this value by considering a practical study done by UCSB Computer Science department [START_REF] Nurmi | Eucalyptus: A Technical Report on an Elastic Utility Computing Architecture Linking Your Programs to Userful Systems[END_REF]. By using "ttylinux" [START_REF] Ttylinux | [END_REF], a compact Linux image that boots very quickly, the startup times obtained by considering the number of requested instances are: 17 seconds for 1 instance and 24 seconds for 8 instances. We can consider these results as lower bounds when estimating instance throughput time.

To estimate the network throughput, the UCSB Computer Science department did performance network experiments and obtained the following average values: TCP throughput of 700 ms for 1 availability zone and 100 ms for 2 availability zones; round-trip latency of 1 ms for 1 availability zone and 5.5 ms for 2 availability zones.

For assessing Xen performance we refer to a study of virtualization systems for large scale emulations [START_REF] Cappello | Selecting a virtualization system for grid/p2p large scale emulation[END_REF] that revealed that the CPU and memory overheads for Xen technology are negligible (by comparison to running in native mode). The study also revealed that these overheads are not influenced by the number of virtual machines run by the system, leading to a very good scaling.

In light of the above estimations we have optimistic expectations for the overall performance of the SED Cloud.

Related Work

In [START_REF] Evangelinos | Cloud Computing for parallel Scientific HPC Applications: Feasibility of running Coupled Atmosphere-Ocean Climate Models on Amazon's EC2[END_REF], the authors evaluate the use of Amazon EC2 for the coupling of oceanatmosphere models. Both codes are written in MPI and are able to run on the Amazon Clusters. Performance evaluation show the interest of such approach, even if dedicated supercomputers are still more efficient for such HPC applications.

The analysis of Clouds platforms presented in [START_REF] Jha | Using Clouds to Provide Grids with Higher Levels of Abstraction and Explicit Support for Usage Modes[END_REF] is very interesting. Authors point out several issues that limit the use of Clouds for highly distributed applications. Among them, the level of abstraction at which Clouds are accessed is one of the major issues. Interoperability between different Cloud platforms is also an issue that will become a problem when their use will increase. DIET-Solve could be one of the approaches that will help GridRPC users to forget the underlying architecture on which they are running their programs.

Conclusion and Future Work

Cloud platforms have two advantages over their predecessors: a greater flexibility and an improved scalability. We propose the use of the DIET-Solve Grid middleware on top of the EUCALYPTUS Cloud system to demonstrate general purpose computing using Cloud platforms. We have seen possible ways of connecting the two architectures and the compatibility issues that need to be taken into account to achieve this. We have also described a working demo and performance estimations based on previous studies.

Although we have highlighted the most important issues that arise when dealing with this topic in this paper, our study serves solely as a proof of concept, leaving open questions as further development topics.

DIET-Solve schedulers must take into account the overhead of different SeDs when taking mapping decisions. We did not addressed the problem of data management. This is indeed an issue when running several request having data dependencies between them (for example when processing workflows).

Figure 1 :

 1 Figure 1: DIET architecture with three different kind of server daemons included Cloud server.

DIET is now know under the name DIET-Solve.RR n°7096

http://www.cl.cam.ac.uk/research/srg/netos/xen

http://www.linux-kvm.org INRIA

RR n°7096


domain: scco sdv

text:
Camille Heurteloup

Annabelle Merchie

Sylvie Roux

Frédérique Bonnet-Brilhault

Carles Escera

Marie Gomot
email: gomot@univ-tours.fr

Gomot Marie

Neural repetition suppression to vocal and non-vocal sounds

Keywords: Vocal, Neural adaptation, Repetition Positivity, Habituation, Prediction, ERPs

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

We encounter many sensory regularities in everyday life through sensory adaptation, whether simply at the level of word composition, or more complex scene schemas [START_REF] Turk-Browne | Neural evidence of statistical learning: efficient detection of visual regularities without awareness[END_REF]. At the cerebral level, information must be processed optimally to select the most relevant events. This requires building and continuously updating a sensory memory trace, following the presentation of a stimulus [START_REF] Winkler | Modeling the auditory scene: predictive regularity representations and perceptual objects[END_REF]. A repeated stimulus will be considered as regular, leading to the adaptation of the response, through the habituation process. Such storage is an essential property of the sensory systems, in order to process the incoming flow of information. Identifying the probability that the previous stimulus will reappear, enables the prediction of future events [START_REF] Winkler | Modeling the auditory scene: predictive regularity representations and perceptual objects[END_REF][START_REF] Winkler | The role of large-scale memory organization in the mismatch negativity event-related brain potential[END_REF]. This makes the detection of environmental changes faster, improving our ability to react quickly and optimally. It also underlies our ability to detect modulations of social indices like gaze, facial expressions or voice intonation, and to communicate with others.

One of the most relevant auditory stimuli for our species is voice, which constitutes a very strong social input and tends to be processed as a priority [START_REF] Whitten | Auditory event-related potentials index faster processing of natural speech but not synthetic speech over nonspeech analogs in children[END_REF]. Human beings are considered to be voice experts, because of our experience with and ability to decode such auditory stimuli [START_REF] Latinus | Human voice perception[END_REF]). The voice is acoustically richer than other types of auditory stimuli in terms of harmonics, pitch and intensities, with more details to encode. Voice stimuli may therefore require further processing prior to full adaptation. [START_REF] Belin | Thinking the voice: neural correlates of voice perception[END_REF] proposed a neurocognitive model of vocal perception. In brief, part of this model suggests that all auditory stimuli are processed, in a general low-level auditory analysis, in the primary cortex A1, and that vocal stimuli then enter a specific, voice structural analysis, involving other regions close to primary auditory cortex, such as the superior temporal gyrus. This suggests that vocal stimuli require additional processing, involving additional regions, which may be reflected in different dynamics for establishing a regularity for vocal versus non-vocal stimuli.

The neural process underlying habituation is called 'Repetition Suppression' (RS). RS refers to neural adaptation -a decrease in neural activity during repeated exposure to the same stimuli.

It therefore reflects the formation and continuous updating of sensory memory traces [START_REF] James | The effects of visual object priming on brain activation before and after recognition[END_REF][START_REF] Schacter | Specificity of priming: a cognitive neuroscience perspective[END_REF][START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF] and is translated at the cerebral level by a decrease in neural response with an increasing number of repetitions of the same stimulus (neural fatigue or sharpen activity models) [START_REF] Desimone | Neural mechanisms for visual memory and their role in attention[END_REF][START_REF] Grill-Spector | Repetition and the brain: neural models of stimulus-specific effects[END_REF]Sagaert et al., 2013). RS was initially studied in animals at the level of the individual neuron and was termed 'Stimulus Specific Adaptation' (SSA; see Escera & Malmierca, 2016). SSA refers to the decrease in the response of a single neuron with increasing repetitions of the same stimulus (standard), with no decrease in response to a rare stimulus (deviant) (Ulanovsky et al., 2003), and has been recently dissociated from prediction error [START_REF] Parras | Neurons along the auditory pathway exhibit a hierarchical organization of prediction error[END_REF]. SSA has been observed at cortical (Miller, Li, & Desimone, 1991;[START_REF] Ulanovsky | Multiple time scales of adaptation in auditory cortex neurons[END_REF] and subcortical levels (Perez-Gonzalez & Malmierca, 2012;Ayala & Malmierca 2013), both in visual (Müller et al., 1999;Vogels, 2012Vogels, , 2014) ) and auditory regions (von der Behrens et al., 2009;Nelken et al., 2013). A complementary process of RS, the Repetition Enhancement (RE), reflects the recognition of a stimulus, the anticipation of its appearance, and results in an increase in neural response with repetition [START_REF] Segaert | The suppression of repetition enhancement: a review of fMRI studies[END_REF][START_REF] Vogels | Sources of adaptation of inferior temporal cortical responses[END_REF]. These processes can also be explained by the predictive coding model [START_REF] Friston | A theory of cortical responses[END_REF][START_REF] Bendixen | Early electrophysiological indicators for predictive processing in audition: A review[END_REF][START_REF] Auksztulewicz | Repetition suppression and its contextual determinants in predictive coding[END_REF] which proposes that the first presentation of a stimulus triggers the generation of a prediction of future sensory input. When this stimulus is repeated, it is compared to the prediction. According to Bayesian models of perceptual inference, RS would reflect a decrease in computational demand that occurs as the prediction error decreases, due to the match between sensory inputs and expected information [START_REF] Summerfield | Neural repetition suppression reflects fulfilled perceptual expectations[END_REF].

In parallel an Expectation Suppression (ES) effect has been identified, which would correspond to a diminution of the neural activity while the expectation become strongest. ES would co-exist with RS (deGardelle et al., 2013;[START_REF] Grotheer | The relationship between stimulus repetitions and fulfilled expectations[END_REF] but the two phenomena would be independent as the ES effect occurs slightly later than the RS, as shown in both the visual [START_REF] Summerfield | Human scalp electroencephalography reveals that repetition suppression varies with expectation[END_REF] and the auditory modalities [START_REF] Todorovic | Repetition Suppression and Expectation Suppression Are Dissociable in Time in Early Auditory Evoked Fields[END_REF]. Finely the RE which indexes an increase in the prediction strength when expected, would also be observed with a short delay, in separate frontal brain areas [START_REF] Recasens | Repetition suppression and repetition enhancement underlie auditory memory-trace formation in the human brain: an MEG study[END_REF].

The establishment of stimulus regularity has previously been studied in both visual and auditory modalities, in electrophysiology [START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF][START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF][START_REF] Recasens | Repetition suppression and repetition enhancement underlie auditory memory-trace formation in the human brain: an MEG study[END_REF][START_REF] Ferrari | Repetition and ERPs during emotional scene processing: A selective review[END_REF] and brain imaging [START_REF] Fiebach | Neuronal mechanisms of repetition priming in occipitotemporal cortex: spatiotemporal evidence from functional magnetic resonance imaging and electroencephalography[END_REF][START_REF] Friston | A theory of cortical responses[END_REF][START_REF] Gagnepain | Spoken word memory traces within the human auditory cortex revealed by repetition priming and functional magnetic resonance imaging[END_REF][START_REF] Andics | FMRI repetition suppression for voices is modulated by stimulus expectations[END_REF][START_REF] Cacciaglia | Auditory predictions shape the neural rsponses to stimulus repetition and sensory change[END_REF]. It was first explored indirectly by studying the electrophysiological response to the detection of change, i.e., the 'Mismatch negativity' (MMN), a negative component, obtained by subtracting the response following the repetition of a standard stimulus from the response evoked by a changing deviant stimulus [START_REF] Näätänen | Early selective-attention effect on evoked potential reinterpreted[END_REF]. MMN indexes deviancy detection, occurring when a stimulus is incongruent with the memory representation of the preceding repeated stimuli [START_REF] Winkler | The role of large-scale memory organization in the mismatch negativity event-related brain potential[END_REF][START_REF] Näätänen | The mismatch negativity (MMN) in basic research of central auditory processing: a review[END_REF]. In the framework of the predictive coding model, MMN is considered as a marker of error detection, caused by a deviation from a learned regularity [START_REF] Garrido | Repetition suppression and plasticity in the human brain[END_REF].

Previous electrophysiological studies in humans, directly investigated auditory regularity encoding through the use of a "roving paradigm", in which a stimulus is repeated a number of times (n), then followed by a new stimulus, which is also repeated n times. This leads to a continuous updating of the memory trace, which is suppressed at the end of each stimulus train [START_REF] Cowan | Memory prerequisites of mismatch negativity in the auditory event-related potential (ERP)[END_REF][START_REF] Baldeweg | Differential changes in frontal and sub-temporal components of mismatch negativity[END_REF]2004[START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF][START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF]. These studies have highlighted RS phenomena, through the modulation of electrophysiological indices. The repeated presentation of a stimulus results in a decrease of the N1 component, between 90 and 150 ms and an increase in the positive components P1 and P2, reflecting the adaptation of responses. Through comparing the responses to a new stimulus and to the same stimulus, after a number of repetitions, it is possible to isolate an electrophysiological index of the neural adaptation. The Repetition Positivity (RP), corresponds to a positive deflection between 50 and 250 ms, increasing with the number of repetitions [START_REF] Baldeweg | Mismatch negativity potentials and cognitive impairment in schizophrenia[END_REF][START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF][START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF].

The present study aims to explore the influence of the information to be encoded on the establishment of an auditory regularity. Although adaptation to vocal sound regularities is essential to extract relevant information for social communication, the formation of auditory regularity in the context of vocal stimuli has received little research interest. To our knowledge, most previous studies focused upon pure tones repetitions. Only one study explored the establishment of regularity to more complex semi synthetized sounds of two categories: vowels sounds representing Finnish vowels, and their vowel-like equivalents with increased formants frequencies, that were unfamiliar sounds [START_REF] Ylinen | Is there a direct neural correlate for memory-trace formation in audition?[END_REF]. Using these complex non-natural sounds in a roving paradigm, this study did not show any RP, possibly because of the very low number of participants and of repetitions used (3-4) compared to a minimum of 12 repetitions in other paradigms [START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF].

Another study compared RP to vocalizations with different emotional valences, and showed that repetition suppression was increased for positive vocal stimuli as compared to neutral or negative vocal sounds [START_REF] Pinheiro | Is laughter a better vocal change detector than a growl[END_REF]. Authors concluded that positive vocalizations lead to enhanced sensory prediction. However although vocal stimuli might be considered as potent predictors, they also contain rich acoustic information. Whether we become accustomed to repeated vocal information in the same way as to non-vocal auditory stimuli thus remains to be explored.

To study brain correlates of auditory regularity in the context of vocal stimuli, trains of different lengths (4, 8, 16 repetitions) comprising either vocal or non-vocal complex sounds are compared. The different train lengths should allow us to determine the minimum number of repetitions required to elicit RP with more social stimuli. We assume that vocal regularity encoding is related to the phenomenon of RS, and anticipate an increase in the positivity (RP) with increasing number of vocal sound repetitions. Analysis of the RP characteristics (amplitude, latency and organization of the response through brain topography analyses) to vocal and non-vocal auditory stimuli will provide information on the RS effect in each condition, that will thereby be compared independently of the sensory response specific to each stimulus category. The comparison of P1 amplitude modulation with increasing number of repetitions in each condition will allow to estimate the dynamic of the neural adaptation, by providing information about the amount of repeated information necessary to attain a plateau in the response (to reach a full neural adaptation). We hypothesize that neural adaptation is likely to be influenced by the vocal/non-vocal aspect of the stimuli, with more repetitions needed for the P1 amplitude to reach a plateau for vocal stimuli. The involvement of memory traces could be weaker and/or slower for vocal stimuli, since these contain richer acoustic complexity than non-vocal stimuli.

Materials and Method

We report how we determined our sample size, all data exclusions (if any), all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study.

Previous EEG studies rarely report all the information needed to calculate sample sizes [START_REF] Larson | Sample size calculations in human electrophysiology (EEG and ERP) studies: A systematic review and recommendations for increased rigor[END_REF]. The sample size for this exploratory EEG study was thus determined based on previous research in the field. We chose a conventional sample size in ERP research (20 participants), and reported all the information needed for sample size estimation in future studies.

Population

Twenty young adults aged 18 to 30 (mean = 24 ± 2 years) participated in the study (12 female).

None had neurological, psychiatric, or metabolic disorders or were under medication at the time of the study. None had a hearing deficit as tested with an audiometer at different frequencies (250, 750 and 1500 Hz). Each participant signed an informed consent form, and the protocol received approval from Ethic Committee (PROSCEA2017/23; ID RCB: 2017-A00756-47).

Stimuli

A total of 8 vocal sounds and 8 non-vocal complex sounds were used to study the possible impact of vocal/non-vocal nature of the information to be encoded. The vocal sounds consisted of the vowel "a" uttered with a neutral prosody by 8 female speakers of different identities, selected from an existing database of vocal sounds validated (on the basis of valence and emotion recognition) on an independent sample of adults (n = 16) [START_REF] Charpentier | Emotional prosodic change detection in autism Spectrum disorder: an electrophysiological investigation in children and adults[END_REF].

Non-vocal stimuli were synthetic complex sounds with acoustic characteristics close to those of the vocal sounds. For this we created complex sounds with a global frequency spectrum similar to that of the voices. Firstly, using speech analysis software (Praat ®; see [START_REF] Boersma | Praat, a system for doing phonetics by computer[END_REF], we measured the values of the fundamental frequency (F0) and the first 4 formants of each of the eight voices selected as vocal stimuli. The second step consisted of synthesizing complex sounds using sinewaves of the corresponding frequency values (Adobe Audition® software). Amplitudes of each harmonic were adapted to best fit the voices frequency spectrums. To achieve the necessary attenuations (spectral slope), each time the frequency doubled we applied a decrease of 12 dB to the amplitude of the harmonic [START_REF] Kreiman | Perceptual interaction of the harmonic source and noise in voice[END_REF]. In both categories, each sound was normalized according to the root mean square of the amplitude, so that all stimuli had the same energy, using Matlab (The MathWorks Inc., Natick, MA, USA); fade-in and fade-out effects of 10% (30ms) were applied.

Global energy was equalized at 65dB SPL. We obtained 8 vocal sounds with F0 ranging from 190 Hz to 230 Hz, with frequency steps of about 5 Hz, and 8 non-vocal sounds with the same F0s and spectral structure (Table 1; Figure 1). Sounds are controlled for their 'speech value' as the synthetized sounds mimic their natural vocal equivalents in terms of main frequencies; the two categories of sounds therefore contain the F0, F1 and F2 allowing the phoneme /a/ automatic categorization as previously demonstrated [START_REF] Jacobsen | Pre-attentive perception of vowel phonemes from variable speech stimuli[END_REF]

Sequences of stimulation

We used a roving paradigm [START_REF] Cowan | Memory prerequisites of mismatch negativity in the auditory event-related potential (ERP)[END_REF][START_REF] Baldeweg | Mismatch negativity potentials and cognitive impairment in schizophrenia[END_REF] in which trains of stimulation of different frequencies/voices and different lengths were presented. Each train was composed of the same stimulus, which was repeated n times (depending on the length of the train), and called standard (S). The first stimulus of each train was considered as deviant compared to the preceding repeated standard. We used trains of 4, 8 and 16 repetitions. These trains were delivered in a pseudo-random order, with the only constraint that the same stimulus could not be delivered in two consecutive trains.

Two sequences were presented, the first consisting of vocal stimuli and the second consisting of the non-vocal stimuli described above. The order of presentation of the sequences was counterbalanced between subjects. Each stimulus was presented for 300 ms, with a SOA of 646 ms, via two speakers located at 1.2 m from the ears of the participants (Logitech Z-2300). 120 repetitions of each train length were presented for each sequence, resulting in a total of 6720 stimuli (3360 per sequence) and a recording time of approximately one hour. A schematic representation of the roving paradigm used in this study is illustrated in Figure 2.

EEG data acquisition and processing

During EEG recording, participants were sitting comfortably in a reclining armchair, located in a sound-attenuated room. Subjects watched a silent movie without subtitles whilst sounds were delivered; they were instructed that they would have to briefly tell the story of the movie at the end of the recording session. This procedure avoided voluntary directing of attention towards the auditory stimulation. The two stimulation sequences were delivered, with a break between, so that the subject could move and relax.

Presentation software (NeuroBehavioral Systems Inc., Berkeley, CA) was used to deliver the stimulation sequences, during which the EEG was recorded from 64 active electrodes (ActiveTwo Systems Biosemi, The Netherlands) with a sampling rate of 512 Hz. Horizontal and vertical eye movements were monitored using electrodes placed on the left and right outer canthi and below the left eye. An electrode was placed on subject's nose for offline rereferencing. The ELAN software package was used for the analysis of EEG-ERP [START_REF] Aguera | ELAN: a software package for analysis and visualization of MEG, EEG, and LFP signals[END_REF]. The EEG signal was amplified and filtered with a 0.1 Hz high-pass filter (Butterworth filter, order 1). Artefacts resulting from eye movements were removed by applying Independent Component Analysis (ICA) as implemented in EEG Lab. Blink artifacts were captured into components and selectively removed via inverse ICA transformation. Sixtyfour components were examined, and one or two components were removed in each subject, to account for vertical and horizontal movements. Motion artefacts, characterized by high frequency or high amplitude signals, were discarded manually by an experimenter blind from trial type. A 30 Hz low-pass filter was applied (Butterworth filter, order 3), and ERPs were averaged over a 700 ms time window, including a 100 ms pre-stimulus baseline.

The recordings of the two stimulation sequences were carried out separately for each subject, and vocal and non-vocal stimulations were averaged separately.

Data analysis

Measurements

Responses to each stimulus, in positions 1 to 16, were averaged individually for each sequence, so that the effect of repetition for each position could be observed. Trials presented at the same position, but in trains of different lengths (e.g., stimulus 3 in a train of 4, 8 and 16 repetitions)

were also averaged together. This resulted in three times more stimuli from 1 to 4, and twice as many stimuli from 5 to 8, than stimuli from positions 9 to 16. Responses to stimuli in positions 2 to 4 were averaged, as well as those in positions 5 to 8, those in positions 9 to 12 and finally those in positions 13 to 16, in order to obtain more robust responses by increasing the number of trials. For each group of stimuli, the mean number of artifact-free trials was: 925 ± 72 (S2-4), 820 ± 63 (S5-8), 404 ± 45 (S9-12) and 410 ± 31 (S13-16) for the non-vocal sequence and 912 ± 81 (S2-4), 807 ± 77 (S5-8), 401 ± 44 (S9-12) and 400 ± 41 (S13-16) for the vocal sequence.

For the 4 groups of repeated standards, only the P1 component was clearly identified and measured for both conditions. To isolate the RP response, the difference between the average stimuli 13 to 16 (long train) and the averaged stimuli 2 to 4 was calculated for both conditions.

In addition, to isolate MMN responses after each length of train, the differences between the first stimulus (deviant) and last stimulus (standard) of each length of train (4, 8 and 16) were calculated for both conditions. For these stimuli, the mean number of artifact-free trials were:

307 ± 27 (S1), 104 ± 9 (S4), 103 ± 10 (S8) and 102 ± 9 (S16) for the non-vocal sequence and 302 ± 29 (S1), 100 ± 9 (S4), 102 ± 11 (S8) and 100 ± 10 (S16) for the vocal sequence.

The P1 (positive peak at ~ 100 ms) was identified as the first positive deflection occurring between 70 and 120 ms. This time-window was selected on the basis of studies with similar stimuli (Sheerer et al., 2013;[START_REF] Charpentier | Emotional prosodic change detection in autism Spectrum disorder: an electrophysiological investigation in children and adults[END_REF]. Amplitudes and latencies of the P1 were measured in each participant in a 70-120 ms time window, centered on the peak of the grand mean average of the group. RP and MMN measure time windows were selected on the basis of previous studies with similar stimuli [START_REF] Pinheiro | Is laughter a better vocal change detector than a growl[END_REF][START_REF] Bishop | Is auditory discrimination mature by middle childhood? A study using time-frequency analysis of mismatch responses from 7 years to adulthood[END_REF][START_REF] Charpentier | Emotional prosodic change detection in autism Spectrum disorder: an electrophysiological investigation in children and adults[END_REF]. RP was identified as a positive deflection occurring between 90 and 200 ms and the MMN as a negative deflection occurring in a 130-190 ms time-window. Amplitudes and latencies of the RP and the MMN were measured in a 90-200 ms and a 130-190 ms time window, respectively, centered on the peak of the grand mean average of the group.

Statistical analyses

After visual inspection of the responses scalp distributions, and based on previous studies [START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF][START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF], amplitudes and latencies were analyzed at We performed permutation tests based on randomizations [START_REF] Edgington | Randomization tests[END_REF] over a 50-300 ms time-window at each electrode and at each time point, to assess difference in the RP between conditions (vocal vs. non-vocal). These analyses provide supplementary information on condition differences, by confirming peak analyses or by affording additional topographical findings or amplitude statistical comparisons for responses whose peak is barely measurable.

Each permutation test involved the random permutation of the values for the 20 pairs of data compared (corresponding to the 20 participants), then the calculation of the sum of squared sums of values in each of the two obtained samples, and finally the computation of the difference between these two statistical values. For each analysis we performed 10,000 such randomizations, to obtain an estimate of the distribution of this difference under the null hypothesis. This distribution was then compared to the actual difference between the values in the two conditions (vocal vs. non-vocal). Correction for multiple comparisons was performed using the statistical-graphical method of [START_REF] Guthrie | Significance testing of difference potentials[END_REF] which tabulates the minimum number of consecutive time samples that need to be significant in the ERP differences, in order to have a significant effect over a given time window. For the analyses of RP (250 ms: 50-300, i.e., 125 sampling points), the minimum number corresponded to 12 consecutive time points (i.e., 24 ms) with p values below the .05 significance level.

Results

The grand mean ERPs to standard after 2-4, 5-8, 9-12 and 13-16 repetitions, for vocal and nonvocal conditions are illustrated in Figure 3A. The P1 component (positive peak at ~ 100 ms)

was clearly observed, and modulated by repetition, for both conditions. The mean peak amplitudes and latencies of the P1 for each condition are reported in Table 2. Auditory evoked potentials were acquired over the entire scalp, revealing a large fronto-central positivity. Analyses were performed at the Fz electrode, where the responses culminated.

Difference in repetition effects between conditions

P1 component

A significant repetition effect on P1 amplitudes was found (F (3, 57) = 17.635, p < .001 Ɛ =

.875, η²p = .48) due to an increase in P1 amplitude with increased repetition. In addition, a significant condition effect was observed (F (1, 19) = 6.287, p = .021, Ɛ = 1.000, η²p = .25) due to larger amplitudes for the vocal versus non-vocal condition. An interaction between the condition and the number of repetitions was highlighted (F (3, 57) = 3.514, p = .021, Ɛ = .848, η²p = .16).

Post-hoc analyses (Bonferroni) revealed different repetition effects between the two conditions, with a significant increase in P1 amplitude between 2-4 and the three other groups of stimuli

(2-4 vs 5-8, 9-12 and 13-16 respectively: p < .001, p < .001, p < .001) but not between 5-8 and 9-12, nor between 5-8 and 13-16 repetitions and between 9-12 and 13-16 repetitions (p = 0.45)

for the non-vocal condition. For the vocal condition, a significant increase in P1 amplitude was revealed between 2-4 and 9-12 repetitions (p = 0.011) and between 2-4 and 13-16 repetitions (p < .001) but not between 2-4 and 5-8 repetitions, 5-8 and 9-12 nor between 9-12 and 13-16.

Statistical difference in the P1 amplitude was found for vocal versus non-vocal conditions, for 2-4 (p = .003) and 13-16 (p = .008) repetitions, with larger amplitudes observed in the vocal condition.

A significant repetition effect was found for P1 latencies (F (3, 57) = 7.18, p = .001, Ɛ = .743, η²p = .27) due to an increase in P1 latencies as repetitions increased. There was no condition effect (F (1, 19) = .18, p = .680, Ɛ = 1.000, η²p = .01 and no interaction between the condition and the number of repetitions (F (3, 57) =, 69, p = .526, Ɛ = .776, η²p = .04).

Statistical analyses performed on a larger set of electrodes surrounding Fz (FCz, F1, F2, FC1, FC2), revealed similar results for both P1 amplitude and P1 latency.

The scalp distribution of the P1 remained stable across the two conditions, corresponding to a large fronto-central positivity, increasing with repetition.

An example of the P1 scalp potential distribution at 100 ms after 13-16 for non-vocal and vocal conditions with the statistical maps of the topographical differences is illustrated in Figure 3C.

Repetition Positivity

To better understand the activity associated with stimulus repetition we isolated the RP by subtracting the average responses to stimuli 2-4 (short trains) from the average response to

Effect of repetition on deviance detection

The repetition effect on deviance detection was analyzed after 4, 8 and 16 repetitions. The mean peak amplitudes and latencies of the MMN for each condition are reported in Table 3. The scalp distribution of the MMN, corresponding to a large fronto-central negativity, remained stable between the two conditions. Topographical distributions of MMN observed after 4, 8 and 16 repetitions are presented in Figure 5C.

Discussion

This study aimed to explore the possible influence of the information to be encoded (vocal versus non-vocal) on the establishment of auditory regularity indexed by the repetition positivity.

An increase in the number of repetitions generated a significant increase in positivity at the fronto-central electrodes, between 90 and 200 ms, for both vocal and non-vocal stimuli. Results are consistent with previous studies using pure tones, highlighting an RP between 50 and 250 ms [START_REF] Baldeweg | Mismatch negativity potentials and cognitive impairment in schizophrenia[END_REF][START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF][START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF][START_REF] Recasens | Repetition suppression and repetition enhancement underlie auditory memory-trace formation in the human brain: an MEG study[END_REF]. It should be noted that the latencies observed are later than expected from the literature, a delay that might be due to the nature of the stimuli used. Indeed responses to vocal stimuli have later components latencies than pure tones, possibly explained by a later encoding due to their complexity. Both the scalp topographies; characterized by a large positivity spreading over fronto-central regions, and the shape of the RP curves obtained, were very similar in the two conditions. A permutations analysis revealed no significant differences between conditions.

This might be explained by the very close spectral composition between the two types of stimuli used, since the non-vocal stimuli were based on the vocal stimuli. Alternatively, the brain activity associated with RS may display the same organization, regardless of the stimuli used.

This perspective is reinforced by the fact that the RP obtained in the present work is also similar to the RP observed in response to pure tones [START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF][START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF]. However, the only other study which attempted to explore regularity encoding by using a roving paradigm and comparing more complex stimuli like vocal and non-vocal sounds [START_REF] Ylinen | Is there a direct neural correlate for memory-trace formation in audition?[END_REF], did not reveal any RP in response to stimuli repetition. Contrary to our initial assumptions, Ylinen and Huotilainen's negative results would not be related to the low number of repetitions used (2-3 versus 5-6 repetitions) since we showed that 5-8 repetitions are sufficient to observe an effect. The contradictory results could instead, be explained by the use of divers vowels instead of a single vowel -as in our study. Previous studies have shown differences in brain responses between vowels, due to the differences in their acoustic compositions [START_REF] Obleser | Cortical representation of vowels reflects acoustic dissimilarity determined by formant frequencies[END_REF][START_REF] Shestakova | Orderly cortical representation of vowel categories presented by multiple exemplars[END_REF], which could have led to inconsistent results.

The observation of a polarity reversal at mastoid electrodes, confirmed the involvement of temporal auditory areas in the RS phenomenon, in response to vocal and non-vocal complex sounds repetition. This is similar to previous observations in response to pure tones [START_REF] Cooper | Event-related potentials reveal modelling of auditory repetition in the brain[END_REF]. Although our methodological approach did not aim to accurately locate the regions generating the observed responses, findings are consistent with [START_REF] Recasens | Repetition suppression and repetition enhancement underlie auditory memory-trace formation in the human brain: an MEG study[END_REF] who used MEG to identify the regions involved in RP to pure tones repetition. Their study allowed to target the regions involved in RS and RE separately, and highlighted the contribution of both temporal regions and anterior frontal insula in these two phenomena.

A few studies have highlighted that RP results from the joint modulation of the P1, N1 and P2 components (increase of P1 and P2 with a decrease of N1) [START_REF] Baldeweg | Differential changes in frontal and sub-temporal components of mismatch negativity[END_REF][START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF][START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF]. Due to the specific pattern of brain potentials elicited by our stimuli, results did not allow for the isolation of these three individual components, for the vocal and non-vocal conditions. However, the latency and morphology of the RP obtained, confirms the joint modulation of components in the same latency range.

Although our results revealed similar RPs for vocal and non-vocal conditions, different dynamics were observed in terms of stimulus adaptation, with different profiles of variation in responses amplitude as repetitions increase for each condition. Statistical analysis of the P1 component revealed an increase in amplitude with repetition, just as previous studies have shown modulation of the canonical ERPs in response to pure tones [START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF][START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF] and to more complex auditory stimuli [START_REF] Jacobsen | Is there pre-attentive memory-based comparison of pitch?[END_REF].

Moreover, we showed that the effect of repetition no longer changes after 5-8 repetitions for the non-vocal condition, but remains stable only after 9-12 repetitions for the vocal condition.

Based on these observations, the establishment of regularity appears to be faster for non-vocal stimuli, with only 5-8 repetitions required, than for vocal stimuli, where regularity is only fully established after 9 to 12 repetitions. This is consistent with our initial assumption, that adaptation to vocal stimuli would be slower. Moreover it seems that the complexity of voice, which is acoustically richer, requires further processing steps, and subsequently leads to slower neural adaptation. At the basic processing level, the spectral richness of the sounds could explain differences in neural adaptation. Although they have been controlled on several physical features, the sounds used in the present study do not contain exactly the same acoustic information. One mechanistic explanation for the repetition effect is stimulus-specific adaptation, i.e. neurons specialized in processing specific physical features (e.g. frequency)

have higher activity when processing identical features of the stimulus and are therefore subject to the so-called "refractory process" [START_REF] Näätänen | Memory-based or afferent processes in mismatch negativity (MMN): A review of the evidence[END_REF]. The acoustic richness of vocal sounds in terms of spectral frequencies compared to non-vocal sounds with more focused frequency components would explain the observed difference in electrophysiological responses, as the complexity of the physical features to be encoded could recruit a larger number of neurons and ultimately lead to reduced dimension-specific neural adaptation (or given the observed data, to a neural adaptation that would be slower to take place). This is in line with previous NIRS studies on complex speech structure detection in infants, which found that stimulus complexity influences repetition effects [START_REF] Gervain | The neonate brain detects speech structure[END_REF][START_REF] Bouchon | Hemispheric asymmetries in repetition enhancement and suppression effects in the newborn brain[END_REF].

On the other hand, the human brain is efficient in making predictions and in automatically detecting slight changes in voice, due to its expertise [START_REF] Latinus | Human voice perception[END_REF]. And indeed voices are processed faster and more automatically compared to synthetic sounds [START_REF] Whitten | Auditory event-related potentials index faster processing of natural speech but not synthetic speech over nonspeech analogs in children[END_REF]. In the same line, studies have shown that the familiarity of a stimulus has an impact on automatic auditory processing [START_REF] Jacobsen | Familiarity Affects the Processing of Task-irrelevant Auditory Deviance[END_REF]. Social cues are more familiar to us because they contain more relevant information than simpler stimuli such as the non-vocal sounds used in this study. According to this voice preference, one could have expected a faster or larger neural adaptation for vocal sounds than for non-vocal synthetized unfamiliar sounds.

Analyses of ERPs and time-frequency features to repeated vocal emotions [START_REF] Pinheiro | Is laughter a better vocal change detector than a growl[END_REF] showed that both induced pre stimulus beta power and RP amplitude were increased for happy vocalizations, suggesting that positive vocalizations lead to strong sensory prediction.

Moreover, as described earlier, the repetition positivity includes a predictive coding component [START_REF] Cacciaglia | Auditory predictions shape the neural rsponses to stimulus repetition and sensory change[END_REF] From the predictive coding perspective, the RP and the P1 modulation we recorded to repeated stimuli, probably reflect the combined effects of both RS, ES and RE. The classical roving paradigm and the ERPs method used do not allow to spatially and temporally distinguish between these different phenomena. However, as RS is thought to be low-level and automatic [START_REF] Kouider | Activity in face-responsive brain regions is modulated by invisible, attended faces: evidence from masked priming[END_REF] and ES more sensitive to attention and stimulus familiarity (Grotheer & Novacs, 2014), one can assume that even if vocal stimuli represent potent predictors compared to non-vocal stimulation (which should increase ES), their acoustic complexity would slow down the RS effect, resulting in a decreased global response. Alternatively, the impact of the ES effect might also depend on the social relevance of the vocalization. Based on the present results, we were able to draw conclusions on these different hypotheses. Similar RP were recorded for both non-vocal and vocal sounds, but stimulus adaptation was different, with the latter being faster for non-vocal sounds than for vocal sounds. These observations may indicate that regarding neural adaptation, the acoustic simplicity of a sound takes precedence over its familiarity and/or social value.

According to the present results, a maximum of 9 to 12 repetitions is sufficient to establish auditory regularity, regardless of the type of sound. This is consistent with the results obtained by [START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF], who highlighted a maximum effect at 12 repetitions, with pure sounds. It would therefore not be necessary to use trains of stimulation with 36 repetitions, as done by [START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF]. This is confirmed by the RP response, which shows the same morphology found in previous studies [START_REF] Costa-Faidella | Interactions between "what" and "when" in the auditory system: temporal predictability enhances repetition suppression[END_REF][START_REF] Haenschel | Eventrelated brain potential correlates of human auditory sensory memory-trace formation[END_REF], and also displays a similar amplitude (about 1.5μV), suggesting that the effect no longer evolves between 12 and 36 repetitions.

In addition to the RP analysis, which directly correlates with regularity encoding, we studied the effect of repetition on deviance detection by analyzing the MMN response to each train change. For that, MMN difference waveforms were measured after 4, 8 and 16 repetitions in each condition. Results showed an increase in MMN amplitude with repetition for both vocal and non-vocal complex sounds, as previously shown for tones [START_REF] Baldeweg | Mismatch negativity potentials and cognitive impairment in schizophrenia[END_REF][START_REF] Garrido | Repetition suppression and plasticity in the human brain[END_REF]. These observations support the fact that deviance detection is impacted by previous regularity encoding. However, one study using pure tones in a roving paradigm, did not reveal an increase in MMN amplitude with an increasing number of preceding standard repetitions [START_REF] Cooper | Event-related potentials reveal modelling of auditory repetition in the brain[END_REF]. This could be due to the deviant used, which was the last stimulus of each train, varying in duration. Few studies have focused on the impact of longterm memory traces on short-term memory traces formation. A previous MMN study, presented native-vowel and non-native vowel deviants to highlight the existence of language-dependent memory traces [START_REF] Näätänen | Language-specific phoneme representations revealed by electric and magnetic brain responses[END_REF]. Results showed that the MMN elicited by native deviant sounds was larger than the MMN elicited by non-native deviant sounds. Another study using a roving paradigm, studied the impact of long-term memory trace related to native language characteristics on the short-term memory trace formed by phonemes, according to the number of preceding stimulus repetitions [START_REF] Huotilainen | Long-term memory traces facilitate short-term memory trace formation in audition in humans[END_REF]. Three different types of non-vocal stimuli were used: (1) pure tones (2) prototype-vowels: complex synthetic sounds based on the frequency composition of 8 vowels and (3) non-prototype vowels: complex synthetic sounds based on the same vowels, with their first two formants shifted upwards. Results showed that a reduced number of repetitions was required to produce the most prominent MMN for prototypevowels than for non-prototype vowels, probably due to stronger long-term memory traces for the former. In the present study we used non-vocal, complex, synthetic sounds based on vocal vowel composition, but also natural-vocal sounds, in order to study the impact of the vocal component. We found no statistical differences in MMN amplitudes, between the conditions.

This could be due to inter-individual variability, or to the close composition of the two groups of stimuli. Regarding latencies, [START_REF] Huotilainen | Long-term memory traces facilitate short-term memory trace formation in audition in humans[END_REF] showed a longer MMN latency for non-prototype than for prototype vowels and pure tones, but only after few repetitions. In the present study, an earlier MMN was obtained for the non-vocal condition than the vocal condition, with no effect of repetition. Again, such results could be due to the specificity of vocal sounds, which are more complex to encode and process. In the present study, comparing vocal and non-vocal regularity encoding, findings do not strengthen the link between long-term and short-term memory traces, as in previous studies, but support early discrimination of simplest sounds deviancy. This result is consistent with the earlier establishment of regularity in response to non-vocal than to vocal sounds, and reinforces the link between the process of regularity encoding and deviance detection [START_REF] Baldeweg | Mismatch negativity potentials and cognitive impairment in schizophrenia[END_REF][START_REF] Garrido | Repetition suppression and plasticity in the human brain[END_REF].

Conclusion

The purpose of this study was to explore the possible impact of the nature of the information

Ethics Statement

The protocol received approval from Ethics Committee (PROSCEA2017/23; ID RCB: 2017-A00756-47). Each participant signed an informed consent form.

Figure 1 :

 1 Figure 1 : Physical features of a vocal sounds and of its non-vocal counterpart. (A) spectrogram of the non-vocal sound, (B) spectrogram of the vocal sound, (C) energy of the vocal and the non-vocal sound as a function of time (green: vocal sound, red: non-vocal).

Figure 2 :

 2 Figure 2 : Roving paradigm used in this study. Trains of four, eight or sixteen repetitions of the same stimulus were pseudo-randomly delivered. The SOA was constant at 646 ms. Vocal condition: run composed of vocal stimuli, the vowel "a", uttered by 8 different female speakers with progressive increasing of fundamental frequencies (190≤F0≤229). Non-vocal condition comprised eight synthetic complex sounds with acoustic characteristics close to those of the vocal sounds, with the same F0 progressive increase (190≤F0≤229).

  ANOVAs were performed on P1 amplitudes and latencies, with the Repetition (2-4, 5-8, 9-12 and 13-16) and Condition (vocal vs non-vocal) as within subject factors. Two-way ANOVAs were performed on MMN amplitudes and latencies with the Repetition (4, 8 and 16) and Condition (vocal vs non-vocal) as within subject factors. Since the factor Repetition displayed more than 2 levels, a Greenhouse-Geisser correction was applied to correct for potential violations of the sphericity assumption. Additional Bonferroni post-hoc tests were performed to examine the direction of the interactions. The effects sizes are shown as η²p.

Figure 3 :

 3 Figure 3 : (A) Standard ERPs at Fz electrode after 2-4, 5-8, 9-12 and 13-16 repetitions in vocal (top) and non-vocal (below) conditions. (B) P1 amplitude values plotted for 2-4, 5-8, 9-12 and 13-16 repetitions in vocal (blue) and non-vocal (red) conditions (error bars represent SEM) ; * p < 0.05, ** p < 0.01. (C) P1 scalp potential distribution at 100 ms after 13-16 for non-vocal and vocal conditions and Statistical map of the topographical differences calculated using permutation analyses.

  stimuli 13-16 (long trains). Positive deflections corresponding to the RP were observed between 90 and 200ms over fronto-central electrodes in both vocal and non-vocal conditions. The RP grand-average difference waveforms (13-16 minus 2-4 repetitions) for vocal and non-vocal conditions are illustrated in Figure 4A. Permutation analyses performed between the two conditions, revealed no difference between RP for vocal and non-vocal sounds, in the reported time-window. The scalp distribution of RP, characterized by a large fronto-central positivity, remained stable across the two conditions. Scalp distributions of RP, for vocal and non-vocal conditions, based on the results of the permutation analyses, are shown in Figure 4B. Comparison of the responses obtained on mastoid sites, where the negative activity associated with the fronto-central RP is observed, was also performed. Results revealed no significant differences in the amplitude of the mastoid negativity, between vocal and non-vocal conditions. This reverse polarity between the Fz electrode and the mastoids, confirms the location of RP generators in the supra temporal plane, at the level of the auditory regions.

Figure 4 :

 4 Figure 4 : (A) Repetition Positivity (RP) resulting from the difference wave between 13-16 and 2-4 repetitions for vocal (blue) and non-vocal (red) conditions. (B) RP scalp potential distribution at 130 ms for vocal (right) and non-vocal (left) conditions and Statistical map of the topographical differences calculated using permutation analyses.

Figure 5 :

 5 Figure 5 : (A) Mismatch Negativity (MMN) difference waves after 4 (grey), 8 (dotted line) and 16 (black) repetitions for non-vocal (left) and vocal (right) conditions. (B) Standard after 4, 8 and 16 repetitions and deviant ERPs at Fz electrode for non-vocal (left) and vocal (right) conditions. (C) MMN scalp potential maps at 160 ms for non-vocal (top) and vocal (below) conditions.

(

  vocal versus non-vocal) to be encoded, on the establishment of auditory regularity. Once established, auditory regularity leads to adaptation, a phenomenon known as repetition suppression. We highlighted a Repetition Positivity (RP) between 90 and 200ms, following repeated trains of vocal and non-vocal complex stimulations, confirming the results of previous studies using simple auditory stimuli. As predicted, repetition suppression seems to be sensitive to the nature/complexity of the stimuli to be encoded. Indeed, there are different underlying dynamics for establishing regularity for vocal and non-vocal stimulations, with more repetition required for responses to vocal sounds to stabilize. Adaptation to the auditory environment would therefore follow a different pattern, depending on the type of stimuli, with voice requiring additional processing due to the more complex composition of vocal stimuli. In terms of social communication, this adaptation is essential to quickly detect vocal variations and ultimately, to identify the emotional state of others. This high predictive role of vocal emotion is suggested by the study of[START_REF] Pinheiro | Is laughter a better vocal change detector than a growl[END_REF] which highlighted the impact of positive emotions on neural adaptation. Moreover, one can assume that this adaptation to sensory environment may increase throughout development to become optimal. Deficits in such adaptation may lead to difficulties in everyday life, particularly in detecting relevant environmental changes or in developing adjusted social interactions. Carrying out similar studies in neuro-typical children may help to establish the optimal developmental pathway of repetition suppression, and related neural adaptation. In neurodevelopmental disorders such as Autism Spectrum Disorders, where adaptation deficits have been recently reported at the subcortical level(Font-Alaminos et al., 2020), repetition suppression studies would allow us to determine whether difficulties in social interaction and adaptation to change, result from deficits in establishing a regular context.

Table 1 :

 1 .

	Sound	Fundamental frequency F0	Formant 1	Formant 2	Formant 3	Formant 4
	1	190	905	1515	2830	3716
	2	199	845	1358	2732	4228
	3	205	975	1326	2698	3477
	4	210	705	1480	2847	4479
	5	215	877	1522	2971	4094
	6	220	806	1502	2322	3021
	7	226	797	1483	2943	4096
	8	229	886	1465	3005	3640

Frequency composition of each non-vocal sounds based on the identification of F0, F1, F2, F3 and F4 of the corresponding vocal sounds.

Table 2 :

 2 Summary of P1 mean amplitudes and latencies according to the number of

	repetitions (m±SEM)
	Number of	Amplitude (µV ± SEM) at Fz	Latency (ms ± SEM) at Fz
	repetitions
		Non-vocal	Vocal	Non-vocal	Vocal
	2-4	1.13 ± 0.25	1.62 ± 0.24	91.50 ± 2.52	90.15 ± 2.31
	5-8	1.74 ± 0.26	1.86 ± 0.24	95.34 ± 3.25	96.62 ± 3.52
	9-12	1.98 ± 0.31	2.05 ± 0.28	96.15 ± 3.12	99.73 ± 3.03
	13-16	1.70 ± 0.29	2.15 ± 0.27	101.31 ± 3.21 101.85 ± 2.33

Table 3 :

 3 Summary of MMN mean amplitudes and latencies according to the number of

	repetitions (m±SEM)
	Number of	Amplitude (µV ± SEM) at Fz	Latency (ms ± SEM) at Fz
	repetitions
		Non-vocal	Vocal	Non-vocal	Vocal
	4	-1.60 ± 0.47	-1.01 ± 0.30	155.56 ± 2.93 168.75 ± 3.48
	8	-2.41 ± 0.41	-1.45 ± 0.44	157.81 ± 3.13 174.71 ± 3.17
	16	-2.91 ± 0.42	-1.81 ± 0.34	156.84 ± 2.47 170.01 ± 3.47

  and vocal sounds, which involve larger neural networks (those encoding for speech, language, social context) than non-vocal sounds, in turn will elicit stronger predictions. These may have affected the RP more strongly as suggested by Costa-Faidella et al. (2011) who showed larger RP in the predictable than in the unpredictable condition. Yet in the present study, although comparison of vocal and non-vocal repetitions showed that neural adaptation takes longer for vocal stimuli, RPs were similar in amplitude.

Acknowledgments

We thank all the volunteers for their time and participation in this study and Remy Magne for technical support.

Funding

This work was supported by the INSERM (National Institute for Health and Medical Research) and the ANR (ANR-19-CE37-0022-01). C.H was supported by a grant of the "Ministère de la recherche". The funding sources had no involvement in the study design, data collection and analysis, or the writing of the report.

Availability of data and material

The conditions of our ethics approval do not permit public archiving of the data supporting this study. Readers seeking access to this data stimuli should contact the lead author Marie Gomot at the UMR 1253 Inserm, University of Tours. Access will be granted unconditionally to named individuals in accordance with ethical procedures governing the reuse of sensitive data.

Stimuli, procedures and scripts for main analyses are available in open access at osf.io/afr4c.

Author Contributions

Camille Heurteloup, Carles Escera, Sylvie Roux & Marie Gomot designed the study. Camille

Heurteloup & Annabelle Merchie performed data acquisition. Camille Heurteloup, Annabelle Merchie & Marie Gomot were responsible for data and statistical analyses. All authors were involved in preparing and reviewing the manuscript.

Conflict of interest

All authors declare that they have no conflicts of interest.

Preregistration

No part of the study was pre-registered prior to the research being conducted.