Andrea Caragliu

Peter Nijkamp

The impact of regional absorptive capacity on spatial knowledge spillovers

Keywords: Absorptive capacity, Knowledge spillovers, Total Factor Productivity, Spatial Econometrics JEL classification codes: O33, R11 Absorptive capacity, knowledge spillovers, total factor productivity, spatial econometrics

We design a conceptual framework for linking two approaches: absorptive capacity, and spatial knowledge spillovers (KS). Regions produce new knowledge, but only part of it is efficiently adopted in the economy; the share of efficiently adopted technology depends on cognitive capital. Our dataset is based on a panel of European regions over the period 1999-2006, combining data from EUROSTAT and the European Values Study (EVS). We test the hypothesis that insufficient levels of cognitive capital hamper the capability of regions to fully exploit new knowledge. Results show that a lower regional absorptive capacity increases KS towards surrounding areas, hampering the regions' capability to decode and efficiently exploit new knowledge, both locally produced and originating from outside.

Introduction

Since the publication of [START_REF] Cohen | Absorptive capacity: a new perspective on learning and innovation[END_REF] on the firm's absorptive capacity, much research has focused on understanding the key characteristics of firms, regions, and countries that make it easier to understand and decode information coming from outside in an economically-efficient manner.

The concept of absorptive capacity, whose foundations were originally designed in the context of firm theory, can be extended to complex institutions, such as countries and regions. The idea that a proper knowledge base is needed to understand more and better knowledge is not new and can derives from human capital-based growth models. In the present investigation the focus is not simply on the role of human capital in enhancing the 2 growth capabilities of regions, but on the role of the accumulated stock of cognitive capital in the capability of a region to identify and utilize proper knowledge from outside.

In fact, evidence proves examples of regions producing codified knowledge, but unable to fully absorb, decode and exploit it at its full potential. How does this discrepancy come about? What drives this result, and why are physical factors not sufficient to explain regions' growth in efficiency? We believe these answers must be sought in spatial KS and the absorptive capacities of regions.

Therefore, we need a broad framework, in which relevant growth factors are taken into account; in particular, we assume here that knowledge produced in specific regions where regional receptivity is not sufficient spills over to surrounding areas. Hence, patenting an innovation in a region is no longer sufficient to retain its positive fallouts in that region itself. The area must be endowed with the capability to understand technical innovation and decode it in order to produce more efficiently.

In turn, this capability depends on the area's endowment with cognitive capital. Cognitive capital is the set of all local, spatially-bounded characteristics that determine an area's capability to grow. In particular, it encompasses all "soft" components (social, relational, and human capital) that determine a society's quality, and the density and efficiency of its members' interactions. A region's insufficient endowment of cognitive capital may cause locally-produced knowledge to spill over to surrounding competing areas. To provide evidence supporting this proposition, here we develop an innovative econometric model and test it on a sample of 261 European regions.

The paper is structured as follows. In Section 2 we review highlights from the literature on the topic of absorptive capacity and we state our own definition. In Section 3, we present our conceptual and theoretical framework for KS, framing our definition within the existing literature. In Section 4 we explain our measure of KS. In Section 5 we provide empirical results from a modeling experiment. Finally, we draw conclusions and suggest some relevant policy implications.

A framework for absorptive capacity

The basic lesson from the standard literature on absorptive capacity is that it comes from knowledge accumulation. The basis for this statement originates essentially from a cognitive approach. "Research on memory development suggests that accumulated prior knowledge increases both the ability to put new knowledge into memory, what we would refer to as the acquisition of knowledge, and the ability to recall and use it" (Cohen and Levinthal 1990, p. 129). The development of absorptive capacity requires more than a mere exposition to, and familiarization with, the relevant prior knowledge. Learning crucially depends on the intensity of the effort. Moreover, the ability to assimilate information as a • Learning has a cumulative pattern;

• Learning performance is greatest when the object of learning is related to what is already known.

Regions display similar patterns. If prior knowledge is needed for a firm's staff to understand and decode new knowledge, why shouldn't regions behave similarly? Moreover, if exerting a higher effort and being culturally and socially not too distant helps employees in similar firms to understand new knowledge, why shouldn't more aggregate entities such as regions also obey these rules?

A recent attempt to link the firm's behaviour with regional innovation performance is made by Abreu et al. (2008). Two British firm-level datasets are combined to measure the role of the firms' absorptive capacity in driving regional innovation performance. They find that a larger share of R&D employees, the use of new management techniques, and collaborative behaviour are all positively associated with an increase in regional innovation performance. Their study, however, does not have the same scope as ours. In their study, technical, standardized innovation is automatically assumed to lead to growth. However, this is not always true 1 .

In [START_REF] Abreu | Spatial patterns of technology diffusion: an empirical analysis using TFP Tinbergen Discussion Paper 04-079[END_REF] country level productivity growth is linked to human capital accumulation. TFP is used as a measure of aggregate technology; its rate of change over time is explained with a model that nests the [START_REF] Nelson | Investment in humans, technological diffusion, and economic growth[END_REF] and [START_REF] Lucas | On the mechanics of economic development[END_REF] models. The authors link up with the literature on direct and indirect effects, which forms the basis of our measure of outward knowledge spillovers (OKS). Although their spatial framework resembles ours, and the TFP concept is used, our approach differs in that, in the present study, spatial econometrics techniques are used to obtain the OKS measure.

The existing literature on absorptive capacity states that the stock of knowledge previously accumulated enhances the capability of complex organizations to understand new knowledge. In our view technical, formalized knowledge does not capture the entire set of norms, customs, uses and attitudes that also constitute a society's knowledge. A more comprehensive definition of regional knowledge and absorptive capacity should include all types of knowledge, including not only technical knowledge, but also a region's attitude towards learning. Cognitive capital is capable of encompassing all these different aspects of regional knowledge. Following [START_REF] Henry | Hope, hopelessness, and coping: a framework for class-distinctive cognitive capital[END_REF], we define cognitive capital as the set of cognitive dispositions of agents towards socio-economic interactions. Thus, in our D1. The knowledge of regions, i.e. a region's absorptive capacity, is the set of psychological and cognitive dispositions of regional agents towards socioeconomic interactions.

Therefore, a region's absorptive capacity is best measured with its endowment of cognitive capital.

A framework for knowledge spillovers

The literature on absorptive capacity can be connected to the research carried out on KS. First, one needs to extend the unit of analysis from the firm to the aggregate level. Next, one may wonder what happens if local absorptive capacity lacks the capability to absorb locally-produced knowledge.

The theory of KS assumes that, although knowledge is a public good, appropriability is imperfect. In Michael Spence's words: "Imperfect appropriability means that a fraction of each firm's research leaks out" (Spence 1984, p. 118).

New knowledge can actually take different forms. It can also be the creative adoption of existing knowledge (for instance, the use of satellite phones by Serbian troops as a device of communication to overcome the destruction of their fixed phone lines by the opponents' coalition); new and more efficient managerial techniques (Toyota's Just-in-time system); the creative and artistic combination of old, traditional materials to impose nonstandardized products on the market (French and Italian fashion companies). Clearly, patents are not the only measure of innovative activities in a country. That is why we resort to a different measure of technological change, i.e. TFP. With TFP we assess whether a region is more efficient in combining physical factors, broadly categorized as capital and labour.

The theory of KS has described how, in the absence of sufficient local absorptive capacity, new knowledge spills over to surrounding areas. But what exactly is a knowledge spillover? Through which mechanisms does knowledge travel? How far does it go? Several definitions of KS have been given in the literature. An appropriate definition was given by [START_REF] Grossman | Innovation and growth in the global economy[END_REF]. They define KS by two main characteristics: "By technological spillovers, we mean that (1) firms can acquire information created by others without paying for that information in a market transaction, and (2) the creators (or current owners) of the information have no effective recourse, under prevailing laws, if other firms utilize information so acquired" (Grossman and Helpman 1992, p. 16). We define KS as follows: D2. Knowledge spillovers represent a non-rival knowledge externality implying the passage of knowledge in a non-marketed form, so that somebody using knowledge created elsewhere or by somebody else cannot be subject to legal procedures.

Measuring KS entails a link between the productivity growth of an organization j and a measure of the innovative activity of another organization i, which has a functional relationship with j. Studies differ on the way knowledge can be carried across borders. Usually this happens by patenting. Patent flows between firms involved in the same vertical relationship [START_REF] Nadiri | Innovations and technological spillovers[END_REF] bring knowledge across firms, and hence across administrative and political boundaries. Alternative knowledge carriers include input-output mechanisms, multinational companies, labour force pooling, and migrations. [START_REF] Jaffe | Technological opportunity and spillovers of R&D: evidence from firms' patents, profits and market value[END_REF][START_REF] Jaffe | Real effects of academic research[END_REF] used patent applications to construct a measure of similarity of research activities, and calculated the external R&D pool available to each firm. For the focus of the present paper, however, patents would be misleading. We would mainly replicate an already established estimation procedure on different data and scale. Besides, patent data reflect only a part of real innovation and knowledge, i.e. what can be technically described on a document and revised by peers.2 Knowledge is a complex phenomenon, assuming different forms and being spread by alternative carriers. [START_REF] Blumentritt | Towards a strategy for knowledge management[END_REF] define knowledge according to four categories, viz. codified, common, social, and embodied knowledge. Patents fall into the category of codified knowledge.

However, knowledge goes beyond codified schemes. Managerial skills, tacit knowledge, relational and social capital play a crucial role in explaining why certain areas are more productive than others, by shaping their cultural context. This is a new element captured in the present paper. We do not refer to any specific form of knowledge, but instead capture all possible spillover effects of local skills towards surrounding areas. TFP captures all the four categories defined by Blumentritt and Johnston, without losing relevant information.

Once our definition of regional absorptive capacity (D1) and KS (D2) have been given, we can state the main research question (RQ) to be answered in the present study:

RQ. Does lower regional absorptive capacity cause higher knowledge spillovers to surrounding areas?

The literature finds that KS are facilitated by geographic proximity and by the human capital endowment of the areas analyzed. What happens if we reverse the reasoning? KS are determined by geographical proximity, but also by the lack of absorptive capacity, and by the absorptive capacity of surrounding areas. A more comprehensive concept of proximity is here needed. Socio-economic proximity, or cultural and relational proximity make the spatial component of this problem more complex to represent but very interesting to investigate. This is where the concept of cognitive capital enters: as a comprehensive Traditional management science studies find that more human capital leads to a greater capability of firms to understand and decode new knowledge. In this context the abovementioned study by [START_REF] Cohen | Absorptive capacity: a new perspective on learning and innovation[END_REF] is important, as they argue that: "The ability to evaluate and utilize outside knowledge is largely a function of the level of prior related knowledge" (p. 128).

Hence, accumulated prior knowledge actually increases the ability of firms to correctly evaluate new information, assimilate it, and apply it for commercial purposes. Cohen and Levinthal build on psychology studies where individuals are shown to be more able to absorb new skills when better endowed with previous knowledge. The passage from the individual to the organizational level is done through aggregation. Similarly, we believe that, if organizations and companies in a region are better endowed with absorptive capacity, they also have higher chances to decode new knowledge, thus preventing OKS.

Its seems therefore plausible a general framework model of the following nature:

OKS i = f (Innovation i , Innovation i * cognitive capital i , cognitive capital j ), (1)
where region i is the region analyzed, while regions j, with j≠i, are all other regions. We expect eq. ( 1) to meet the following reasonable expectations:

• A positive sign for the first variable: more innovation in a region provides scope for OKS. This is in line with similar studies on technology and knowledge transfer, which find that higher investment in knowledge production leads to a stronger knowledge spillover to surrounding areas [START_REF] Landry | Determinants of knowledge transfer: evidence from Canadian university researchers in natural sciences and engineering[END_REF]).

• A negative sign for the second variable: more local capacity (in terms of territorial characteristics and capabilities of understanding and translating knowledge) should make it easier for the area to retain the positive effects of local innovation within its boundaries. Cognitive proximity helps economic agents (firms as well as individuals) in mutual understanding. The economic value of new knowledge is thus more likely to be fully exploited locally when societies own high stocks of cognitive capital.

• Empirical estimation of this relationship has been carried out with various linear techniques. This will be done in the remaining part of the paper. In our application, we use the NUTS2 level for European regional data.

The measure of outward knowledge spillovers and the dataset

Measuring outward knowledge spillovers

To test our model a first step is required: finding a good proxy for OKS. Doing so also requires an ex ante definition of knowledge. The definition of the proper measure of knowledge and KS would ideally be based on two broad theoretical quantities, viz. patents and Total Factor Productivity (TFP).

Patents are a common and much utilized measure of technical change. However, they represent a skewed measure, as patenting is mostly carried out in large firms. Hence patent statistics tend to be higher in regions where firm structure is biased towards large size.

TFP is more reliable as a measure of the type of knowledge we consider. As a residual to a production function, it encompasses everything that is not measured by physical factors. As such, its difference over time also captures the change in non-technical efficiency, i.e. creativity, managerial skills, and all the non-technical knowledge factors that might arise from a global time-improvement of general regional knowledge. If spatially-lagged, it can also measure the extent to which regions are influenced by surrounding areas; hence an inverse function of this spatial lag might represent a good measure of OKS. How this new measure is constructed is explained below.

TFP has also been criticized. 3 It would understate quality improvements, overestimate real productivity improvements, and would include not only productivity gains but also measurement errors, the extent of the black market, noise in the data, and so on.

This last critique is well grounded. However, it is clear that it cannot explain the whole variation in productivity levels. Therefore, TFP must capture at least a significant part of the real gains in productivity.

3 See [START_REF] Hulten | Total factor productivity: a short biography[END_REF] for a comprehensive introduction to this measure. Productivity, in turn, can be explained by a set of determinants. This approach is typically referred to as the "knowledge production function" 5 : it usually entails studying the relationship between R&D inputs and output (measured by some form of benefit from the invention activity such as GDP growth, firm profits or turnover, productivity, or the stock market value of the firm). This approach is only functional for building our measure of OKS.

In this study, TFP actually proxies for generic regional knowledge. In a simple linear framework, if we indicate TFP as Y and the set of its determinants as X, we can write the knowledge production function as:

Y X β ε = + . ( 3
)
Suppose then that productivity levels are correlated across space. Estimating the β coefficients with least squares would yield biased estimates. Spatial econometrics allows the modeling of spatial processes. This is often done with the spatial lag and the spatial error model; in our case, the spatial lag model is preferred. From a theoretical point of view, it is reasonable to assume that regional productivity depends on productivity in neighbouring regions, and is not just related to unmeasured variables that are common to productivity levels both in the region analysed and in nearby regions. This may be due to an underlying spatial correlation of all variables in the stage of knowledge production, and not just through the contagion effect usually assumed in the spatial error setting. Empirically, while both spatial lag and spatial error models are supported by significant Lagrange Multiplier tests [START_REF] Anselin | GeoDa™ 0.9 user's guide, Spatial Analysis Laboratory[END_REF], the spatial error model displays a slightly higher p-value 4 The log-linear transformation allows us to estimate a Cobb-Douglas production function of the form

1 Y AK L α α - =
. Y is regional GDP in constant 1995 prices; L is regional labour force; K is calculated from the regional Gross Fixed Capital Formation dataset, with the perpetual inventory method. The base year is 1995; we assume a yearly depreciation rate equal to 2.5 per cent. 5 [START_REF] Griliches | Issues in assessing the contribution of research and development to productivity growth[END_REF]; [START_REF] Pakes | Patents and R&D at the firm level: a first look[END_REF]. 3) reads now as follows:

Y WY X ρ β ε = + + , ( 4
)
where W is the spatial weight matrix, and ρ is the spatial autocorrelation coefficient. The weight matrix in our specification is row standardized; this does not influence parameter estimates, but does change the estimation of direct and indirect effects. Each entry is the inverse of the distance between two regions. The ρ coefficient can be interpreted in a way similar to the time-autocorrelation coefficient in the time series literature. It also displays similar features: in particular, a value of ρ bigger than 1 in absolute terms implies that spatial correlation becomes larger, the longer the distance.

Eq. ( 4) cannot be correctly estimated with least squares: the dependent variable is also on the right-hand side. To obtain an estimable function, we must rearrange the terms in the usual way, i.e. bring the ρWY term to the left-hand side, isolate Y, and premultiply the matrix (I-ρW) -1 to the X matrix and the ε vector.

The (I-ρW) matrix has an interesting interpretation. It is obtained as follows:

ρ ∧             -            , ( 5
)
where ρ ∧ is the (estimated) autocorrelation parameter, 6 and w ij represent the inverse distances between European regions. Eq. ( 5) shows that the result of this calculation is an (nXn) matrix. This matrix, after being inverted, transforms each variable in the X matrix into its contribution to the dependent variable to and from each region. In other words, it can be interpreted as a sort of input-output matrix, where each element shows the weight to be assigned to each observation in the vectors stacked in the X matrix in order to obtain inward and outward flows of these elements to the region observed. Suppose, in fact, that the matrix (I-ρW) -1 (which we will denote with the Greek upper-case letter Β from the word meaning "weight") takes on the form:

      - = Β =       . ( 6
)
Our estimates of the regional knowledge production function are based on a conceptual framework, laying its foundations in the most recent literature on the topic. Traditionally, regional knowledge production functions relate R&D output (oftentimes measured with TFP) with R&D inputs, including the intensity of investment in R&D (or the level of educated workforce: Abdih and Joutz 2005), and the regional endowment of human capital.

We control for the level of financial development in a region (Guiso et al. 2004, King and[START_REF] King | Finance and growth: Schumpeter might be right[END_REF]) and the quality of its institutions (Morgan 1997). 7 This conceptual framework leads to the following functional form:

, 0 1 , 2 , 3 , 4 , , r t r t r t r t r t r t TFP HRST H FD INST WTFP β β β β β ρ = + + + + + . ( 7
)
where HRST stands for Human Resources in Science and Technology, H for Human Capital, measured as the percentage of people holding a degree from an upper secondary education institution (ISCED system), Financial Development (measured as the share of people in the workforce employed in finance and banking) and Quality of Institutions (measured as the percentage of arable land). All variables are measured in natural logs for each region r at time t. Estimates are carried out by means of maximum likelihood. The results of the estimates are shown in Table 1.

INSERT TABLE 1 ABOUT HERE

Pre-multiplying each vector, for instance "HRST", by Β yields an input-output matrix, where entries in each column represent the contribution of each region's TFP to the allocation of human resources to science and technology in other regions, and vice versa; and the entries in the rows represent contributions of HRST in each region to the analysed region's TFP.

Hence, we can operate both row and column sums to inspect, respectively, TFP outward and inward spillovers. It suffices to premultiply the TFP vector by the Β matrix to obtain a weighted sum of TFP spillovers to surrounding regions. In other words, we can assess the average OKS of the i-th region which contributes to the surrounding regions' productivity, or the extent to which each region's productivity spills over to its neighbours.

We might interpret this new approach in terms of economic value. While paper track (i.e. patents) KS imply the passage of standardized knowledge, productivity spillovers imply the creative resonance process through which people of neighbouring regions, by commuting to the workplace where better work processes can be learnt, by trading, reverse engineering and buying semi-manufactured products from commercial partners, can learn and add economic value to he knowledge embedded in economic processes.

The dataset

Data cover a sample of 261 European NUTS2 regions. The dataset has been collected from two main sources:

1. EUROSTAT (regional GDP, population, labour force, gross fixed capital formation);

2. EVS, a comprehensive survey of Europeans and their beliefs about broad life categories, including trust, religion, politics, and society.

The dataset comprises the following variables (Table 2 8 ):

INSERT TABLE 2 ABOUT HERE

This study uses the concept of cognitive capital as a novel way to model spatial cognitive impacts. Regions are endowed with a set of spatially-bounded resources, which contribute to their capability to understand, decode, and creatively adopt new technologies. These resources, both hard (physical and human capital, infrastructure) and soft (social and relational capital, knowledge transfer mechanisms, governance), define the notion of cognitive capital. Regions with insufficient levels of cognitive capital are less prone to use new knowledge in an efficient manner. This causes higher KS to surrounding areas.

Our ex ante choice of the determinants of OKS can be related to the literature on economic growth. Traditional neoclassical economics has often focused on the role of factor accumulation in explaining long-run economic performance [START_REF] Solow | A contribution to the theory of economic growth[END_REF][START_REF] Mankiw | A contribution to the empirics of economic growth[END_REF]. The literature on human capital has only recently stressed the role of soft elements in explaining how efficiently hard components (capital, labour, land, and infrastructure) are combined. More recently, endogenous growth theorists have found a way to incorporate externalities in constant returns to scale growth models. Theoretically speaking, these externalities are conceived as the mechanisms which magnify the effects of factor accumulation. Practically, they have been identified, from among many cases, in aggregate human capital [START_REF] Lucas | On the mechanics of economic development[END_REF] and R&D [START_REF] Romer | Endogenous technological change[END_REF].

We believe these to be only partial explanations of the emergence of increasing returns.

Cognitive elements in a broad sense also play a major role in explaining economic mechanisms. In this research, we focus on the role of governance, R&D transfer agencies, relational capital, management of collective goods, and district economies in shaping the 8 The PCA is performed on indicators of cognitive capital listed in chances that regions have to retain the positive effects of new knowledge within their own boundaries.

Empirical results

KS are expected to depend on regional innovation inputs. The higher the expenditure and commitment to R&D, the higher the chances that produced knowledge spills over its positive effects to surrounding areas. Thus, for instance, regions bordering Oberbayern or Stuttgart in Germany are expected to gain from their neighbours being committed to R&D. Their proximity represents a positive externality and translates into higher productivity even in neighbouring regions. This variable is expected to have the strongest effect on outward spillovers.

A specific role is played by cognitive elements. New knowledge, although locally produced, might not be understood by local agents in the real economy [START_REF] Capello | Territorial capital and regional growth: increasing returns in knowledge use[END_REF]. Lack of human and social capital, in particular of its trust component, might be detrimental to the efficient understanding and exploitation of this knowledge (La [START_REF] Porta | Trust in large organizations[END_REF]. Thinness of social networks provide disincentives to the fluid transfer of knowledge, and limit the capability of regions to fully achieve their long-run growth potential [START_REF] Beugelsdijk | Social capital and growth in European regions: an empirical test[END_REF]. Lack of R&D transcoding agencies might hamper the likelihood that knowledge is fully exploited, even by firms who did not take part in the knowledge production process [START_REF] Camagni | Regional specificities: towards a theory of territorial capital, in Modelling regional scenarios for the enlarged Europe: European competitiveness and global strategies[END_REF]. This can be summarized by a generalized lack of cognitive capital. Local insufficient endowment of cognitive capital might cause increased OKS. By the same token, a high endowment of cognitive capital might help regions to retain the positive effects of R&D activity in the local economy. This effect is expected to be of a smaller magnitude than that of R&D expenditure.

Cognitive capital might also work as a force contrary to the local retention of knowledge: if neighbouring regions have a higher endowment of cognitive capital, provided that space imposes less impedance to knowledge transfer, they might be more capable of understanding and decoding new local knowledge. Hence, neighbours' cognitive capital is expected to exert a positive influence (pull effect) on OKS.

Our KS measure is a weighted sum of the region's relative TFP contributions to and from its neighbours. As such, it can also take on negative values; therefore, we can represent the relative net balance of inward and OKS. If the variable is negative, the region should be a net knowledge recipient. Figure 1 represents this measure for the last available year (2006).

The map shows OKS divided into five subclasses. The first category with values above zero is shown in light grey. Grey regions are net knowledge exporters: this shows some spatial patterns, with peripheral regions tending to "import" knowledge from outside, and Nordic regions and the Pentagon area 9 being net producers.

INSERT FIGURE 1 ABOUT HERE

Least squares estimates

We are now ready to test our main hypothesis. Does higher cognitive capital lead to lower OKS? The tested equation is a simple linear functional form:

, , , , , , & *
& r t r t r t r t j t r t OKS R D CC R D CC α β γ δ ε = + + + + , (8)
where r =(1…261), j=(all regions: j≠r), t= (1999,…,2006).

OKS are measured as described above; R&D intensity is measured by the number of patent applications to the EPO per 1,000 inhabitants; cognitive capital is proxied by the PCA-built measure indicated in Table 2; and cognitive capital in neighbouring regions is measured by its spatial lags. The results of this first test are shown in Table 3.

INSERT TABLE 3 ABOUT HERE

In Model 1 the constant term is included, while in Model 2 it is not. Estimates meet our theoretical expectations. Both the R&D intensity and spatially-lagged cognitive capital elements are positive, while the interaction between local cognitive capital and R&D intensity is negative. Results suggest that R&D intensity and external cognitive capital exert a pull effect on OKS, while the higher the endowment of local cognitive capital, the lower OKS are. The significance of the R&D intensity term varies with the exclusion of the constant term, which is also rejected in the larger model. Thus, it is possible that large nonlinearities may determine the optimality of dropping the constant term from the regression.

The inclusion of fixed effects does not improve estimation precision: this is probably caused by the correct accounting for region-specific effects (which have been captured by cognitive capital elements in the regression). An alternative interpretation might be that most of the variance in the sample is cross-sectional. In this case, most cross-sectional variance would be wiped out by regional dummies, resulting in poor panel estimates.

Further use of spatial econometric techniques is not necessary. By construction, our measure of OKS removes spatial autocorrelation patterns in the data: hence all usual tests for the presence of spatial autocorrelation in the dependent variable have a negative outcome. This reinforces the interpretation of the lack of significance of regional fixed effects: most of the cross-sectional variation is captured by the measure of KS.

Our results are consistent with different choices of the weight matrix. In particular, the first and second coefficient tend to have the same sign and significance when distance is defined 9 The area comprising London, Paris, Milan, Munich and Hamburg. according to a queen and rook contiguity criterion, a threshold criterion with respectively, a 500, 1500, and 3,000 kilometre threshold, and a nearest neighbour criterion, k, being set at, respectively, 4, 30, and 50. Thus, we can safely rely on the consistency of our construction: geographical distance does play a major role in our framework, but is not sufficient per se to explain OKS. On the contrary, the coefficient associated with the interaction term remains remarkably stable. Consistency is not perfect, though. The significance of the third coefficient varies with different definitions of distance, which might suggest the need for further inspection of this topic. In particular, we could assess which is the threshold beyond which external cognitive capital fails to exert a pull effect on each region.

Identification strategy: instrumental variables estimates

The direction of causality among variables in the main empirical model (eq. 7) may be reversed. Not only cognitive capital in the region producing knowledge helps the region in retaining its positive effects: also, it may be that the flow of knowledge may induce a high endowment of cognitive capital. In order to rule out this possibility, we resort on instrumental variables.

Following [START_REF] Guiso | Does culture affect economic outcomes?[END_REF], we believe cultural characteristics to be deeply embedded in a society, and strongly persistent over time. As the capacity to absorb new knowledge depends, according to our conceptual framework, on the set of cognitive skills (mutual trust, relational capital, sense of belonging) that are strongly space-specific and pertain to the cultural domain, we base our second set of analysis on this a priori.

As endogeneity may affect the second and third independent variable in our testable equation (viz. the interaction term and the spatially lagged value of the cognitive capital measure), we look for instruments which are correlated with these two terms but orthogonal to the knowledge spillover measure. Our choice of instruments focuses on a set of timelagged cultural characteristics: in other words, we look for the roots of the differentials in cognitive capital levels in European regions, by inspecting the level of trust, sense of belonging, and relational capital ten years before our analysis. Fortunately the EVS also entails a second wave dating back to the year 1990. We selected four variables which should be correlated with the levels of cognitive capital in 2000, but not with the knowledge spillover measure:

1. The level of trust;

2. The self-stated interest in fellow region citizens;

3. The citizens' subjective justification of tax evasion; . The average regional interest in political issues. 10 By nature, parameter estimates tend to be on average higher in absolute terms with the IV estimator with respect to least squares estimates. Using four instruments to correct for endogeneity of two original variables, we obtain overidentified estimated parameters.

The results of the 2SLS estimation are presented in Table 4.

INSERT TABLE 4 ABOUT HERE

While all signs and order of magnitude of the estimated coefficients are retained, 2SLS estimates confirm a relatively higher estimated coefficient for all three parameters of interest. Investment in R&D exerts a higher push effect on KS, which is partially (and significantly) offset by a high local endowment of cognitive capital. Neighbouring regions endowed with cognitive capital exert a significant and positive attraction effect on locally produced knowledge, increasing KS. A joint Wald test on the overall significance of the model cannot be rejected at all conventional levels. Finally, a higher level of fit also supports the choice of this estimator.

Conclusions and policy implications

This paper has aimed to provide a bridge between two different, but complementary, approaches: absorptive capacity, and KS.

By identifying a new measure of KS we tested the assumption that local cognitive capital, which according to our conceptual framework properly measures absorptive capacity at the regional level, helps in retaining locally the positive effects of knowledge creation. The relatively low endowment of cognitive capital is found to be associated with higher OKS. Time processes are found to be insignificant in the dataset, but this last result may crucially depend on the short time span we can observe.

This last observation introduces a crucial issue in our conclusions. We find that the evidence demonstrating the role of cognitive capital (including cognitive proximity, relational capital, and a wise management of collective goods) in exploiting knowledge is strong. However, from the policy maker's perspective this may not be sufficient. If cognitive capital, and in particular those elements pertaining to the trust and governance domains, only accumulates at a slow pace, investment in such capital may require a longrun perspective which may be at odds with the short-run political cycle.

F

  richness of the pre-existing knowledge structure highlights two important factors:

  capital properly measures a region's absorptive capacity. We state it as follows (D1):

  spatially-bounded elements, it encompasses all local, qualitative characteristics, from social to human capital, that determine the capability of a region in decoding knowledge.

  robust version of the test. In addition, the lag model achieves the lowest levels for the Akaike and Schwartz criteria and the highest R 2 , which equals 0.74 for the spatial lag model. Therefore, both the theoretical expectations and conventional spatial tests point at the spatial lag model. Eq. (

Figure

  Figure 1 -OKS in 2006

  A positive sign for surrounding areas' endowments of cognitive capital: this is in line with what the absorptive capacity literature suggests. In a way, this last expectation is the reverse of the previous point: when neighbouring regions own large stocks of cognitive capital, they tend to exert a pull effect on locally produced knowledge. Their socio-economic soil is more fertile and ready to reap the positive effects of externally produced knowledge, through commuting patterns, input output formal and informal exchange of new ideas. Finally, this last sign is expected to be positive for one more reason. KS happen through several different channels, one of which is trade: through reverse engineering, firms can acquire technology embedded in traded goods[START_REF] Macgarvie | The determinants of international knowledge diffusion as measured by patent citations[END_REF][START_REF] Padilla-Pérez | A regional approach to study technology transfer through foreign direct investment: the electronics industry in two Mexican regions[END_REF]. Trade is a negative function of geographical, technological, and cognitive distance: thus, when neighbouring regions own a consistent stock of cognitive capital, they are better suited to understand and exploit externally-produced knowledge.

	mechanisms, and
	F o
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	7

Page 8 of 22 Editorial

  The innovative component of this database is our measure of OKS. Industrial economists traditionally identify patent citations as the best measure for technological transfer. TFP shows instead the efficiency with which physical factors are combined. It measures not only the outcome of the R&D production process, but also improvements in managerial and organizational techniques, creativity, growth of tacit knowledge, and all non-technical change factors that contribute to improved economic efficiency.

					Submitted Manuscript
	We calculate TFP as the residual in an OLS regression over the function:
	ln( ) y	=	α	ln( ) k	,	(2)
	where lower-case letters indicate labour-intensive variables. 4 The calculated TFP vector is
	affected by spatial autocorrelation: the Moran's I global autocorrelation index equals 0.43
	(pseudo p-value equal to 0.001) for 2006.
				F o
					r
					P
					e e r
					R e v i e w
					8

Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 2

 2

	Submitted Manuscript

. The vector of cognitive capital is identified with the first PCA vector; it explains 40% of total variance. Details on the performed PCA are available upon request.

Page 12 of 22

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Page 13 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	F o
	r
	P
	e e r
	R e v i e w
	13

Page 14 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	F o
	r
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	e e r
	R e v i e w
	14

Table 1 : results of the estimates of the knowledge production function (eq. 7)

 1

	Abreu, M., Grinevich, V, Kitson, M, Savona M, (2008) Absorptive capacity and regional patterns of
	innovation DIUS RR-08-11.
	o
	r
	P
	e e r
	R e v i e w
	16

Notes: * = significant at the 90% level; ** = significant at the 95% level; *** = significant at the 99% level; Standard errors are shown in brackets.

Page 19 of 22

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 2 : variables in the dataset Variable Raw data Source

 2

		Submitted Manuscript	Page 20 of 22
		Y: regional GDP in constant prices EUROSTAT
	Outward knowledge spillovers	K: stock of capital estimated with the perpetual inventory method	EUROSTAT
		L: regional labor force	EUROSTAT
	R&D intensity	Patent applications to the European Patent Office per 1,000 inhabitants	EUROSTAT
		Principal Components Analysis (PCA) on five elements measuring	EUROSTAT;
	Cognitive capital	the set of norms, customs, uses,
		psychological dispositions towards socio-economic interactions.	EVS.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 3 -OLS estimates of equation (8)

 3

	Submitted Manuscript
	Dep. variable: outward knowledge spillovers	OLS estimates
		(1)	(2)
	R&D intensity	0.026 (0.02)	0.030* (0.016)
	Cognitive capital*R&D intensity	-0.021*** -0.022*** (0.007) (0.006)
	Cognitive capital in neighboring regions	0.034* 0.035*** (0.02) (0.02)
	Constant term	0.001 (0.004)	-
	R 2	0.07	0.08
	Joint F test	5.79	5.95
	Local Moran's I	-0.0041*** (0.001)
	Number of obs.	1756	1756

Notes: * = significant at the 90% level; ** = significant at the 95% level; *** = significant at the 99% level; Standard errors are shown in brackets.

Page 22 of 22

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 4 -Instrumental variables estimates of equation (8)

 4 Notes: * = significant at the 90% level; ** = significant at the 95% level; *** = significant at the 99% level

	Dep. variable: outward knowledge spillovers	Instrumental variables estimates (1) (2)
	R&D intensity	0.12* (0.06)	0.09*** (0.029)
	Cognitive capital*R&D intensity	-0.07*** (0.022)	-0.07*** (0.017)
	Cognitive capital in neighboring regions	0.02* (0.011)	0.04** (0.019)
	Constant term	-0.004 (0.009)	-
	R 2	0.12	0.13
	Wald χ 2 (3) test	20.18***	21.27***
	Number of obs.	1756	1756

Page 23 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

[START_REF] Griliches | Patent statistics as economic indicators: a survey[END_REF].

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

In general, the rich literature of the learning region explains in detail the role of institutions in shaping regional productivity patterns, as well as the underlying microfoundations of such a relationship.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


Ph.D Markus Kiefer
email: markus.kiefer@uni-ulm.de

Ph.D Gerda ; Videsott

Ph.D.; Klaus Bärbel Herrnberger

Ph.D Edgar Hoenig

Jo Schilly

Werner Grothe

Ph.D Manfred Wiater

M.D Spitzer

Bärbel Herrnberger

Klaus Hoenig

Edgar Schilly

Jo Grothe

Werner Wiater

Manfred Spitzer

Speaking in Multiple Languages: Neural Correlates of Language Proficiency in Multilingual Word Production

Keywords: multilingualism, picture naming, word retrieval, right prefrontal cortex, functional magnetic resonance imaging multilingualism, picture naming, word retrieval, right prefrontal cortex, functional magnetic resonance imaging

The human brain has the fascinating ability to represent and to process several languages. Although the first and further languages activate partially different brain networks, the linguistic factors underlying these differences in language processing have to be further specified. We investigated the neural correlates of language proficiency in a homogeneous sample of multilingual native Ladin speakers from a mountain valley in South Tyrol, Italy, who speak Italian as second language at a high level, and English at an intermediate level. In a constrained word production task under functional magnetic resonance imaging (fMRI), participants had to name pictures of objects in Ladin, Italian and English in separate blocks. Overall, multilingual word production activated a common set of brain areas dedicated to known subcomponents of picture naming. In comparison to English, the fluently spoken languages Ladin and Italian were associated with enhanced right prefrontal activity. In addition, the MR signal in right prefrontal cortex correlated with naming accuracy as a measure of language proficiency. Our results demonstrate the significance of right prefrontal areas for language proficiency. Based on the role of these areas for cognitive control, our findings suggest that right prefrontal cortex supports language proficiency by effectively supervising word retrieval.

Introduction

Proficiency in two or more languages is common in regions or countries with several native languages. How the brain subserves this fascinating ability is an important question in the neuroscience of language and increasingly relevant in intercultural and multilinguistic societies.

Several neuroimaging studies have investigated the cortical representation of the first language (L1) in comparison to further languages (L2, L3, etc.) acquired at the same time, or later in life.

The majority of these studies asked whether or not L1 and the further languages share the same neuronal substrate or whether each language is distinctively represented in the brain.

As languages spoken in a specific socio-cultural setting differ with regard to linguistic code (e.g., phonology, syntax), age of acquisition, intensity of exposure or speakers' proficiency [START_REF] Perani | The bilingual brain: Proficiency and age of acquisition of the second language[END_REF]), activation differences between L1 and further languages could depend on one or more of these linguistic factors. In fact, previous neuroimaging studies suggested an influence of these variables for the neural representation of multiple languages and particularly emphasized the role of language proficiency (for a review see [START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF][START_REF] Kotz | A critical review of ERP and fMRI evidence on L2 syntactic processing[END_REF]. Although a lot of progress has been made in elucidating the brain structures underlying multilingualism [START_REF] Perani | The neural basis of first and second language processing[END_REF][START_REF] Indefrey | A meta-analysis of hemodynamic studies on first and second language processing: Which suggested differences can we trust and what do they mean?[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF], the role of proficiency has to be better enlightened.

There is converging evidence that the processing of further languages (L2, L3 etc.) generally recruits the network of classical language areas in inferior frontal and temporo-parietal cortex that also subserves L1 [START_REF] Indefrey | A meta-analysis of hemodynamic studies on first and second language processing: Which suggested differences can we trust and what do they mean?[END_REF][START_REF] Abutalebi | Bilingual language production: The neurocognition of language representation and control[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF]. While several neuroimaging studies found entirely overlapping activation patterns for the first and further languages in production and comprehension tasks (e.g. Illes et al. 1995;[START_REF] Chee | Processing of visually presented sentences in Mandarin and English studied with fMRI[END_REF][START_REF] Hernandez | In search of the language switch: An fMRI study of picture naming in Spanish-English bilinguals[END_REF][START_REF] Hernandez | Language switching and language representation in Spanish-English bilinguals: An fMRI study[END_REF][START_REF] Rodriguez-Fornells | Second language interferes with word production in fluent bilinguals: brain potential and functional imaging evidence[END_REF], a stronger recruitment of some brain areas for the later acquired and less fluently spoken languages has been sometimes obtained. One of the most robust findings is the stronger involvement of left inferior prefrontal areas, including the inferior frontal gyrus, in the production and comprehension of languages that are spoken at a relatively low proficiency level [START_REF] Indefrey | A meta-analysis of hemodynamic studies on first and second language processing: Which suggested differences can we trust and what do they mean?[END_REF][START_REF] Abutalebi | Bilingual language production: The neurocognition of language representation and control[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF][START_REF] Abutalebi | Control mechanisms in bilingual language production: Neural evidence from language switching studies[END_REF].

For instance, in free language production, [START_REF] Kim | Distinct cortical areas associated with native and second languages[END_REF] found increased activity in left inferior frontal gyrus (IFG) for L2 when it was acquired later in life than L1. Similarly, in picture naming, stronger activation in left IFG was again found for L2, but only when subjects were less proficient than in L1 [START_REF] De Bleser | The organisation of the bilingual lexicon: a PET study[END_REF][START_REF] Vingerhoets | Multilingualism: an fMRI study[END_REF]. In word generation, activity in left IFG decreased with increasing proficiency of L2 [START_REF] Yetkin | Use of functional MR to map language in multilingual volunteers[END_REF]. Increased activity in left IFG during L2 processing was also obtained for sentence comprehension [START_REF] Yokoyama | Cortical activation in the processing of passive sentences in L1 and L2: An fMRI study[END_REF], or processing of syntactic structure [START_REF] Suh | Effects of syntactic complexity in L1 and L2; an fMRI study of Korean-English bilinguals[END_REF]). As the left IFG is involved in several language tasks including phonetic, syntactic and semantic processing [START_REF] Indefrey | The neural correlates of language production[END_REF], its specific importance for processing a less fluently spoken L2 has not been unequivocally determined yet. It has been proposed that particularly the posterior portions of IFG subserve syllabification of speech which is more demanding for languages spoken at a low proficiency level [START_REF] Indefrey | A meta-analysis of hemodynamic studies on first and second language processing: Which suggested differences can we trust and what do they mean?[END_REF]. Alternatively, Abutalebi and colleagues [START_REF] Abutalebi | Bilingual language production: The neurocognition of language representation and control[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF][START_REF] Abutalebi | Control mechanisms in bilingual language production: Neural evidence from language switching studies[END_REF] proposed in their language control theory of bi-und multilingualism that enhanced left inferior prefrontal activation for low proficient languages reflects the increased demands on cognitive control processes. As prefrontal activation for low proficient languages was frequently observed outside the classical frontal language area (e.g., Broca's area) and encompassed regions typically involved in cognitive control and conflict resolution [START_REF] Carter | Anterior cingulate cortex, error detection, and the online monitoring of performance[END_REF]Kiefer et al., 2005a), Abutalebi and colleagues assume that at lower levels of language proficiency word retrieval is more effortful due to a strong lexical competition between languages (Abutalebi et al. 2008). This competition between languages is resolved with the intervention of left prefrontal circuits supporting cognitive control.

In addition to left prefrontal cortex, there are preliminary reports suggesting that word production in languages spoken at a high proficiency might depend on neural circuits in right prefrontal areas [START_REF] Calabrese | Wortgenerierung bei Bilingualeneine fMRT-Studie mit Implikationen für Sprach-und Gedächtnisprozesse[END_REF][START_REF] Vingerhoets | Multilingualism: an fMRI study[END_REF]. As right prefrontal cortex plays an important role in action control during response inhibition [START_REF] De Zubicaray | Motor response suppression and the prepotent tendency to respond: A parametric fMRI study[END_REF][START_REF] Garavan | Dissociable executive functions in the dynamic control of behavior: Inhibition, error detection, and correction[END_REF][START_REF] Rubia | Right inferior prefrontal cortex mediates response inhibition while mesial prefrontal cortex is responsible for error detection[END_REF] and during memory retrieval [START_REF] Tulving | On the uniqueness of episodic memory[END_REF][START_REF] Vallesi | Prefrontal involvement in source memory: an electrophysiological investigation of accounts concerning confidence and accuracy[END_REF], enhanced activity in this region might reflect efficient control processes that support successful word production [START_REF] Abutalebi | Bilingual language production: The neurocognition of language representation and control[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF]. As previous evidence for the role of right prefrontal cortex in high language proficiency was only tentative, the neural correlates of proficiency in multilingual language production deserve further systematic investigation.

Figure 1 about here

The present fMRI study therefore investigated the effects of language proficiency on the neural substrate of word production in multiple languages within a unique homogenous multilingual group of participants who grew up in the same social context and used their different languages in a comparable manner. Our participants were native Ladin speakers from South Tyrol in Italy who live in a circumscribed area of the Badia Valley in the Dolomites mountain range close to the border to Austria (see Figure 1). Their first language Ladin (L1) is a Roman Language that is spoken by a population of about 30,000 individuals, particularly in the rural villages of the Dolomites mountain valleys. Typically, during their school career, native Ladin speaking children start to learn Italian as the first second language (L2) at an age of four to five, and German as their next second language (L3) at an age of six to seven. Both Italian and German are acquired at a similar age and spoken with a proficiency similar to Ladin. At an age of thirteen to fourteen English (L4) is acquired but less practiced and mostly performed at an intermediate proficiency level. Hence, this specific participant population is ideally suited to address differential language proficiency within the same subjects: We assessed word production in highly proficient Ladin (L1) and Italian (L2) as well as in low proficient English (L4). German (L3) was not investigated because its proficiency level is comparable with Italian.

We used a picture naming task to study multilingual word production, because the processes underlying task performance are well defined and highly controllable within an experiment [START_REF] Humphreys | Cascade processes in picture identification[END_REF][START_REF] Levelt | A theory of lexical access in speech production[END_REF]. It is generally accepted that picture naming encompasses the following stages: (i) perceptual encoding of the picture, (ii) semantic analysis and object recognition, (iii) retrieval and selection of the word form, (iv) phonological encoding, and finally (v) articulation. As sentences and discourses at the intermediate and higher levels of language, respectively, are composed of words as elementary units, language proficiency can be addressed already at this elementary level.

7

We expect that picture naming in Ladin, Italian and English recruits a neural network known to be involved in perceptual, semantic and language processing. Based on previous findings of a left IFG recruitment in languages spoken at a low proficiency level, English (L4) as the least fluent language is expected to activate left IFG more strongly than native Ladin (L1) and highly proficient Italian (L2). Previous preliminary findings are suggestive of an involvement of right prefrontal cortex in high language proficiency, presumably due to its general role in cognitive and language control. We therefore predict that the highly proficient languages Ladin and Italian activate right prefrontal cortex more strongly than English. Hence, low and high language proficiency in multiple languages should depend on partially dissociable neural pathways.

Materials and Methods

Subjects. 20 healthy right-handed [START_REF] Oldfield | The assessment and analysis of handedness: The Edinburgh Inventory[END_REF]) multilingual volunteers (11 females, 9 males; mean age = 27.3 years, range = 20-35 years) with normal or corrected to normal visual acuity and no neurological or psychiatric disorders participated in the fMRI study after giving written informed consent. The study was conducted in accordance with the Declaration of Helsinki and approved by the local Ethical Committee. Subjects were paid for their participation. All participants were native Ladin speakers from the Badia mountain valley in South Tyrol, Italy (402 km²; about 10,000 inhabitants). They spent their early childhood in an almost exclusively Ladin environment and grew up in the same socio-cultural environment with both parents being Ladin speakers. Many participants were also relatives. All participants have the certificate of the Autonomous Province of Bozen-Bolzano that classifies their language competence in Ladin, Italian and German at the highest proficiency level A (16 participants) or second highest level B (4 participants). Due to a lack of objective language proficiency tests that allow for comparing competence in the four languages at a common scale, we had to rely on an in-house questionnaire, with which we assessed history of language acquisition and proficiency. All participants had a similar order and age of language acquisition (Ladin: 0 years; Italian: ~5 years; German: ~7 years and English: ~14 years) and a similar language use and exposure (mostly Ladin, then Italian, then German and then English). Proficiency ratings in language production on a ten-point scale (lowest proficiency = 1, highest proficiency = 10) revealed highest for proficiency for Ladin in comparison to the other languages (t(19) > 3.52, p < .01), similar high proficiency for Italian and German (t(19) < .26, p > .80) and lowest proficiency for English in comparison to the other languages (t(19) > 7.01, p < .0001): Ladin = 9.6, SD .94; Italian = 8.4, SD .82; German = 8.3, SD 1.05; English = 4.7, SD 1.96). During their early childhood, all subjects spoke almost exclusively Ladin at home and with their friends with only a predominantely passive competence of Italian and German through mass media and tourism. At primary school, they all attended the same paritetic school model (instructions half in Italian and half in German with Ladin as a support language) and had the same language curriculum. With respect to the Common European Framework of Reference for Languages, participants' proficiency level for second and further languages was highest for Italian (range C1-C2) and German (range B2-C1-C2) and lowest for English (range B1-B2-C1-C2).

Materials.

The stimulus material for the picture naming task consisted of a well-matched picture set of 144 color pictures, 48 per language (Ladin, Italian, English) depicting common objects from a variety of categories in a canonical view. The pictures were selected on the basis of existing standardized picture sets [START_REF] Snodgrass | A Standardized Set of 260 Pictures: Norms for Name Agreement, Image Agreement, Familiarity, and Visual Complexity[END_REF][START_REF] Berman | Developmental picture norms: Relationships between name agreement, familiarity, and visual complexity for child and adult ratings of two sets of line drawings[END_REF][START_REF] Cycowicz | Picture naming by young children: Norms for name agreement, familiarity, and visual complexity[END_REF] the "International Picture Naming Project" of the CRL, University of California/San Diego, http://crl.ucsd.edu/~aszekely/ipnp/) on the principle that stimulus objects should unequivocally express a lexical concept [START_REF] Indefrey | The neural correlates of language production[END_REF], reducing subjective interpretations as much as possible and admitting only one precise and well-defined answer as word output. In order to avoid repetition effects [START_REF] Kiefer | Repetition priming modulates category-related effects on event-related potentials: Further evidence for multiple cortical semantic systems[END_REF], object pictures were presented only once in the experiment and hence were not repeated as stimulus in another language. An initial set of 400 pictures was tested in a pilot study in order to match the final pictures for the different languages in several important variables which are known to influence naming performance. In the pilot study, 20 participants from the Badia Valley different from the participants in the main experiment were asked first to name the picture and second to rate the familiarity and visual complexity of the objects on six-point scales (minimum = 1 / maximum = 6). Hereby, the material was matched for name agreement (F(2,147) = .81, p >.44), word length (F(2,147) = 0, p = 1, familiarity (F(2,147) = 2.42, p>.07) and visual complexity (F(2,147) = 1.2; p >.31) across a) standard Ladin of Badia Valley, b) standard Italian, and c) standard British English.

Procedure. The object pictures were presented visually with Presentation® (Neurobehavioral Systems, Inc. Albany, CA, U.S.A.) in separate sessions for each language (Ladin, Italian, and English). The required language was indicated at the beginning of the session. Picture naming in one of the three languages was assigned to separate sessions in order to minimize effects of language switching, which were not of theoretical interest. Within each session, active blocks, in which pictures had to be named in a specified language, were alternated with fixation blocks, in which only a fixation cross was shown. Each block had a duration of 24 s and comprised six picture naming trials. One trial of an active block first showed a fixation cross for 600 ms, then an object picture for 500 ms. After a pseudo-randomly drawn interstimulus interval of 2600, 2900 or 3200 ms showing a fixation cross, the next trial started. Each scanning session consisted of 9 fixation blocks and 8 active blocks. Sessions started and ended with a fixation block. There was a break of 3 min. between scanning sessions. Two different sequences of active blocks were created and each of them assigned to half of the participant group. The order of the language sessions (Ladin, Italian and English) were individually randomized for each participant in order to control for sequence effects. Each session started with training trials similar to the one used in the main experiment so that subjects could adapt to the new language. Prior to the scanning sessions, subjects were instructed to spontaneously name the object without article or adjectives in the required language as fast and as accurately as possible by using the first name which came to their mind. Furthermore, they were trained to name the object at the basic level of abstraction (e.g., "dog", "table") using ten pictures not shown in the main experiment. Subjects were told that they were neither required to name the object at a subordinate (e.g. "poodle"), nor at a superordinate level (e.g. "animal", "furniture"). All instructions before and during fMRI scanning were given in Ladin. The produced names were digitally recorded and stored to disk.

From these recordings, the background noise from the scanner was removed by digital filtering in order to obtain the undistorted naming response. In the analysis of naming performance appropriate names (including synonyms) at the basic or subordinate level were treated as correct responses, whereas inappropriate names as well as superordinate names were treated as errors.

MR scanning and data analysis. Functional and structural MR images were recorded with a 3 Tesla Allegra MRI system (Siemens, Erlangen, Germany). For the functional scans, a T2*weighted single-shot gradient-echo EPI sequence (TE = 38 ms, TR = 2000 ms, flip angle = 90°, matrix 64 64 pixels, field of view (FOV) 210 210 mm 2 , voxel size 3.3 3.3 4.9 mm 3 ) was used. Starting from the bottom of the brain, 30 transversal slices were acquired in interleaved order. Slice orientation was parallel to a line connecting the bases of the frontal lobe and the cerebellum. The entire fMRI experiment consisted of three imaging sessions (Ladin, Italian, English) of about 7 minutes each, resulting in a total of 618 functional volumes. Structural images were acquired with a T1-weighted MPRAGE sequence (TR = 2300 ms, TE = 3.9 ms, flip angle = 12°, matrix 256 256 pixels, FOV = 256 256 mm 2 , voxel size 1 1 1 mm 3 ). temporal high-pass filter with cutoff frequency 1/128 Hz was used in order to remove physiological noise, and temporal autocorrelation in the fMRI time series was estimated and corrected for using a first-order autoregressive model. For each subject, the main effect of picture naming (L1+L2+L4>baseline) as well as the pairwise contrasts between languages were determined.

To allow for inferences at the population level, second level analyses treated subjects as a random effect and tested all first level contrasts in separate one-sample t-tests against zero. In a separate correlation analysis, accuracy in the picture naming task as a performance measure of proficiency, self-rated proficiency and age of language acquisition were entered as regressors in the different language conditions, leading to three correlation coefficients per language. The main effect of picture naming was thresholded at p < .001 (corrected for multiple comparisons across the entire brain). The pairwise contrasts between languages and the correlation analyses were thresholded at a significance level of p < .001 (uncorrected). Given that conjunction analyses are more conservative, commonalities between language contrasts (L1-L4 and L2-L4)

were assessed at p < .005 (uncorrected).

Results

Behavioral data. We assessed picture naming performance in Ladin, Italian, and English by determining accuracy of the produced object names. A repeated measures analysis of variance (ANOVA) revealed that naming accuracy significantly varied between languages (F(2,38) = 9.112, p < .001): Accuracy was highest for the first language Ladin (89%, SD 6.5) and for the second language Italian, which was acquired early in life (86%, SD 7.4). Accuracy was worst in English, which was acquired during puberty (79%, SD 11.6). Least significant differences (LSD) post-hoc tests showed that accuracy in Ladin and Italian was significantly higher than in English Precentral gyrus, supplementary motor area and cerebellum reflect motor processes during articulation. We also compared the MR signal in each language with the baseline condition. This analysis revealed that picture naming in Ladin, Italian and English largely activated the same set of brain areas (Supplementary Tables 234).

Figures 3, 4 and 5 and Table 1 about here

Differences between languages. Contrasting the fluently spoken languages Ladin (L1) and Italian (L2) with English (L4), which is spoken at an intermediate level, reveals the brain network that supports high language proficiency (L1-L4, L2-L4). The reversed comparisons yield brain areas more strongly recruited in a less fluently spoken language (L4-L1, L4-L2).

High language proficiency was associated with enhanced activity in right prefrontal areas (Figure 3 and Table 1). Native Ladin elicited higher activity than less fluent English (L1-L4) in the right anterior (orbital superior frontal gyrus, BA 11) and dorsolateral (middle frontal gyrus, BA 46/BA 10) prefrontal cortices (Figure 3A). Similarly, highly fluent Italian (Figure 3B) elicited higher activity than English (L2-L4) in the right dorsolateral (middle frontal gyrus, BA 46) and inferior (inferior frontal gyrus, BA 47) prefrontal cortices as well as in the right insula (BA 47). Figure 4 demonstrates that clusters activated to Ladin and Italian, respectively, considerably overlapped in right prefrontal areas. In a conjunction analysis, we determined brain areas that showed stronger activation for both Ladin and Italian in comparison to English (L1-L4 and L2-L4). This analysis revealed the right dorsolateral prefrontal cortex (middle frontal gyrus, BA 46/BA 10) as the region common to both highly proficient languages in comparison to least proficient English (Figure 3C). As shown in Figure 5, right prefrontal cortex was generally deactivated in comparison to the resting state baseline condition, but showed more activation (i.e. was less deactivated) for the highly proficient languages (L1, L2) than for L4.

Figure 6 about here

Reversed contrasts demonstrated that least proficient English elicited a higher MR signal than Ladin (L4-L1) in the left inferior frontal gyrus (BA 44/BA 6) and in the cerebellum, and a higher signal than Italian (L4-L2) in the cerebellum only (Figure 6 and Table 1). Italian showed greater activity than Ladin (L2-L1) in the right middle temporal gyrus. The opposite contrast (L1-L2)

did not yield any significant differences.

Correlation analysis. We investigated the relation between the fMRI signal and linguistic variables as well as proficiency measures of the multilingual participant group. Accuracy in the picture naming task as a performance measure of proficiency, self-rated proficiency and age of language acquisition were entered as regressors in these analyses. Significant correlations were obtained for naming accuracy as regressor. With English as the language with highest subject variability in naming accuracy, we found a correlation between the MR signal and naming accuracy in the right dorsolateral prefrontal cortex (Figure 3D) encompassing the middle frontal gyrus (BA 46). For Italian, the MR signal in the same region was also found to correlate with naming performance but at a lower significance level p < .005, presumably due to smaller subject variance (see the behavioral data section). For Ladin, with the lowest interindividual variability, we did not find a significant correlation. None of the other variables showed any significant relation to brain activity even at p < .005.

Discussion

In a picture naming task, we investigated the neural correlates of multilingual word production within a unique homogeneous participant group of native Ladin speakers from the Badia mountain valley in South Tyrol, Italy. Their exceptional language abilities allow us to study, in the same subject, the brain areas involved in word production at high and low proficiency levels.

Our subjects acquired fluent Italian as their first foreign language early in their school career and English as their last and least proficient foreign language in puberty. Sequence of language acquisition as well as exposure to the different languages was hence quite comparable in all participants. Furthermore, the well-controllable picture naming task is ideally suited to investigate language production processes at the word level. Given this unique multilingual sample and the constrained language production task, our study provides significant evidence regarding the network of brain areas involved in word production at high and low proficiency levels.

Picture naming in the three languages (Ladin, Italian and English) generally recruited brain areas dedicated to perceptual, semantic and language-related processes. Most importantly, differences in the MR signal between languages were observed, which were related to proficiency. As these language-related differences were found outside the classical language areas mainly in prefrontal regions dedicated to cognitive control, our data suggest that speaking in multiple languages differ with regard to processing demands, but not with regard to the neural representation of languages per se. We found that the least fluently spoken language English activated left inferior frontal gyrus (IFG) and the cerebellum more strongly than the native language Ladin (L4-L1). The cerebellum was also more strongly activated in comparison to Italian (L4-L2). In contrast, the fluently spoken languages Ladin and Italian recruited right prefrontal areas more strongly than English (L1-L4, L2-L4). This area appears to play a particular role in high language proficiency because activity was functionally related to naming performance: The MR signal in right prefrontal cortex positively correlated with naming accuracy in English and Italian. Hence, we showed for the first time that right prefrontal cortex is involved in processes underlying proficiency irrespective of the language in question. In the following, we first discuss the brain network generally involved in picture naming before we move on to functional activation differences related to language proficiency.

Brain network of multilingual picture naming

Multilingual picture naming generally activated a large network of brain areas that are typically involved in visual perceptual, semantic, and word generation tasks. The bilateral activation of the occipital lobe and neighboring inferior temporal areas reflects visual perceptual processing during object recognition [START_REF] Vandenberghe | Functional anatomy of a common semantic system for words and pictures[END_REF][START_REF] Bly | Functional anatomy of object recognition in humans: Evidence from positron emission tomography and functional magnetic resonance imaging[END_REF]. Activity in the hippocampus indexes long-term memory processes (Fernández et al. 1999 Nobre and[START_REF] Nobre | Language-related field potentials in the anterior-medial temporal lobe: II. Effects of word type and semantic priming[END_REF] presumably subserving word retrieval. Activity in middle temporal cortex has been associated with lexical retrieval and lexical-semantic processing (Indefrey and Level 2000).

Picture naming also elicited widespread activity in left frontal areas including the inferior frontal gyrus, the adjacent insula and the anterior cingulate cortex. A part of inferior frontal gyrus (BA 44, Broca's area) has been related to articulatory speech planning (Indefrey and Level 2000).

However, the observed activity in left frontal regions was quite extended (BA 44,BA 47,BA 48) and also encompassed medial prefrontal areas (e.g., anterior cingulate gyrus, BA 32) typically involved in executive control and response monitoring [START_REF] Norman | Attention to action: Willed and automatic control of behavior[END_REF][START_REF] Posner | Executive attention: Conflict, target detection and cognitive control[END_REF]. Therefore, prefrontal activity during multilingual word production has been assumed to index language control processes [START_REF] Abutalebi | Bilingual language production: The neurocognition of language representation and control[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF][START_REF] Abutalebi | Control mechanisms in bilingual language production: Neural evidence from language switching studies[END_REF]. Activity in the precentral gyrus, the supplementary motor area and the cerebellum might reflect the execution of the articulatory motor program [START_REF] Paulesu | The neural correlates of the verbal component of working memory[END_REF][START_REF] Crinion | Language control in the bilingual brain[END_REF]. In addition to this distributed network underlying word production in general, several brain areas showed differential activation between languages thereby forming neural signatures of proficiency.

Brain activity related to low language proficiency

English (L4) as the least proficient language recruited the cerebellum more strongly in comparison to Ladin (L1) and Italian (L2). Similarly, activation of the cerebellum in response to English as the first foreign language was also observed in bilingual Japanese participants [START_REF] Yokoyama | Cortical activation in the processing of passive sentences in L1 and L2: An fMRI study[END_REF]). As the cerebellum plays an important role in articulation-related motor processes [START_REF] Ackermann | Does the cerebellum contribute to cognitive aspects of speech production? A functional magnetic resonance imaging (fMRI) study in humans[END_REF][START_REF] Silveri | Language, memory, and the cerebellum[END_REF], increased activity may reflect the larger demands on motor planning in a language at a low proficiency level. English also elicited stronger activity in the left inferior frontal gyrus (IFG) in comparison to the first language Ladin. Stronger activity of left inferior frontal areas including the IFG in low proficient speakers of a given language is one of the most robust findings in the research of bi-and multilingualism [START_REF] Indefrey | A meta-analysis of hemodynamic studies on first and second language processing: Which suggested differences can we trust and what do they mean?[END_REF][START_REF] Rodriguez-Fornells | Second language interferes with word production in fluent bilinguals: brain potential and functional imaging evidence[END_REF][START_REF] Abutalebi | Bilingual language production: The neurocognition of language representation and control[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF][START_REF] Abutalebi | Control mechanisms in bilingual language production: Neural evidence from language switching studies[END_REF]. IFG activity seems to reflect varying levels of proficiency and/or age of acquisition rather than intrinsic differences between L1 and further languages: Increased activity of left IFG was also observed for words of the first language, which are learned later in life [START_REF] Fiebach | Distinct brain representations for early and late learned words[END_REF] and for late learners of a second language in comparison to early learners [START_REF] Hernandez | Age of acquisition modulates neural activity for both regular and irregular syntactic functions[END_REF]). As age of language acquisition, language exposure and proficiency are correlated [START_REF] Indefrey | A meta-analysis of hemodynamic studies on first and second language processing: Which suggested differences can we trust and what do they mean?[END_REF], it is difficult to differentiate between these variables. Although there is a general agreement that increased left inferior frontal activity to low levels of language proficiency reflects the higher processing demands, the precise role of this brain area for multilingualism has been a matter of debate. [START_REF] Indefrey | A meta-analysis of hemodynamic studies on first and second language processing: Which suggested differences can we trust and what do they mean?[END_REF] suggests that left IFG is engaged in postlexical syllabification processing during word production. He proposes that this process in the IFG is optimized for early acquired languages and thus less efficient for later acquired and less fluent languages. For that reason, activity of this region is enhanced when a word has to be pronounced in a language spoken at a low proficiency level. Alternatively, Abutalebi and colleagues [START_REF] Abutalebi | Bilingual language production: The neurocognition of language representation and control[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF][START_REF] Abutalebi | Control mechanisms in bilingual language production: Neural evidence from language switching studies[END_REF] propose that left inferior prefrontal cortex resolves competition and conflict between languages in congruency with its general role in action control and executive function. According to this language control theory of bi-and multilingualism, speakers who are low proficient in a given language (e.g., L2) receive strong competition from a more fluent language (e.g., L1) thereby increasing the demands on control processes subserved by left prefrontal regions. In line with this interpretation, we found increased left inferior frontal activity in comparison to L1 Ladin only for the least fluent English (L4), but not for the highly fluent Italian (L2). Furthermore, peak activation was dorsal to the classical anterior language area. Presumably, language conflict is highest for English due to the conjoint competitive influence from the other three languages spoken at a high proficiency level.

Brain activity related to high language proficiency

Picture naming in the fluently spoken languages Ladin and Italian (L1, L2) elicited enhanced activity in right prefrontal areas in comparison to English (L4). Activation clusters for Ladin and Italian were not entirely identical, but highly overlapping. They encompassed right dorsolateraland ventrolateral (Ladin and Italian) as well as anterior prefrontal areas (Ladin only). All word production tasks were associated with a relative deactivation in comparison to the resting state baseline, presumably because in the resting condition subjects were engaged in thinking of past events, which highly involves right prefrontal cortex (e.g., Binder et al., 1999; see also [START_REF] Schafer | Modulation of functional connectivity with the syntactic and semantic demands of a Noun Phrase Formation Task: A possible role for the Default Network[END_REF]. However, proficient languages Ladin and Italian showed more activation (i.e. less deactivation) than the least proficient language English. Given that right prefrontal activity was obtained for the fluently spoken languages, our data suggest that proficiency in word production depends on neural circuits in this area. This interpretation is further supported by the correlation analysis: The MR signal in right dorsolateral prefrontal cortex (middle frontal gyrus)

specifically correlated with naming accuracy as a measure of language proficiency. Other variables, such as age of language acquisition, did not modulate the MR signal. It should also be noted that the correlation between naming accuracy and brain activity was present for both English and Italian. In fact, it was more pronounced for English than for Italian, presumably because performance in Italian was more homogenous and relatively close to ceiling. In line with preliminary earlier data [START_REF] Calabrese | Wortgenerierung bei Bilingualeneine fMRT-Studie mit Implikationen für Sprach-und Gedächtnisprozesse[END_REF][START_REF] Vingerhoets | Multilingualism: an fMRI study[END_REF], our results suggest that activity in right prefrontal areas supports successful word production and is related to interindividual differences in proficiency irrespective of the language under investigation. Most importantly, right prefrontal activity was apparent both between languages (contrast of Italian or Ladin to English) and within a language (correlation of naming accuracy and MR signal for English and Italian). Right prefrontal activity therefore most likely relates to language proficiency rather than to any intrinsic aspects of a language or the age of language acquisition.

The present fMRI data, of course, do not allow deciding whether activity in right prefrontal areas is a cause or an effect of language proficiency. In addition, the precise functional role of right prefrontal cortex in language proficiency has to be further specified in future work. Previous neuroimaging studies suggest that right prefrontal cortex supports cognitive control and monitoring processes. For instance, several studies associated right prefrontal cortex with monitoring during episodic memory retrieval [START_REF] Fletcher | Right prefrontal cortex responds to item familiarity during a memory encoding task[END_REF][START_REF] Henson | Right prefrontal cortex and episodic memory retrieval: A functional MRI test of the monitoring hypothesis[END_REF][START_REF] Allan | The role of the right anterior prefrontal cortex in episodic retrieval[END_REF][START_REF] Grady | An examination of the effects of stimulus type, encoding task, and functional connectivity on the role of right prefrontal cortex in recognition memory[END_REF][START_REF] Ranganath | Dissociable correlates of two classes of retrieval processing in prefrontal cortex[END_REF]): Right prefrontal cortex is thought to support memory retrieval through evaluating whether the retrieved information belongs to the intended target episode or not. Right prefrontal cortex has also been shown to be active during cognitive control processes that require the inhibition of unintended response tendencies (de [START_REF] De Zubicaray | Motor response suppression and the prepotent tendency to respond: A parametric fMRI study[END_REF][START_REF] Garavan | Dissociable executive functions in the dynamic control of behavior: Inhibition, error detection, and correction[END_REF][START_REF] Rubia | Right inferior prefrontal cortex mediates response inhibition while mesial prefrontal cortex is responsible for error detection[END_REF]. In particular, right prefrontal cortex activity was increased when inhibitory control of prepotent response tendencies was successful [START_REF] Rubia | Right inferior prefrontal cortex mediates response inhibition while mesial prefrontal cortex is responsible for error detection[END_REF]. According to the language control theory of multilingualism [START_REF] Abutalebi | Bilingual language production: The neurocognition of language representation and control[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF][START_REF] Abutalebi | Control mechanisms in bilingual language production: Neural evidence from language switching studies[END_REF], cognitive control is necessary to resolve conflict and competition between languages thereby supporting language proficiency. Presumably, neural circuits in right prefrontal cortex foster word retrieval by monitoring and controlling the eligibility of competing object names. Hence, high language proficiency may depend on efficient cognitive control as indicated by the presently observed increased activity in right prefrontal areas. The interesting question arises whether explicitly training language proficiency enhances activity in right prefrontal areas during word production.

This would further substantiate the role of right prefrontal cortex for proficiency in multilingual word production.

We are aware that our study only captures fluency of word production as one out of many aspects of language proficiency. Of course, language proficiency is a much more complex construct and also comprises syntactic, phonetic and pragmatic competence during both language production and comprehension. These heterogeneous aspects cannot be assessed within one study and were also beyond the scope of this work. However, larger linguistic units such as sentences and discourses necessarily build upon words so that findings from word production studies are relevant for these higher levels of language proficiency. Furthermore, we believe that our strategy of investigating a highly homogenous multilingual participant group with respect to linguistic and socio-cultural variables within a cognitively well-defined language paradigm could also be a promising approach for studying language proficiency at higher levels of complexity.

In conclusion, the present study relates high language proficiency to activity in right prefrontal areas. As right prefrontal activity was observed for two fluently spoken languages (L1, L2) and was functionally related to interindividual differences in language proficiency within languages (L2, L4), it is unlikely that it is contingent to intrinsic linguistic aspects of the languages or the age of language acquisition. Our results therefore suggest that the differential activation of right prefrontal cortex does not reflect differences in language representation per se, but rather differences in higher-level cognitive processing associated with language use. Based on the known functionality of right prefrontal cortex, we propose that proficiency in multilingual word production depends on efficient cognitive control processes [START_REF] Abutalebi | Bilingual language production: The neurocognition of language representation and control[END_REF][START_REF] Abutalebi | Neural aspects of second language representation and language control[END_REF].

Figure captions

  //www.fil.ion.ucl.ac.uk/spm/software/spm5). Preprocessing included correction for differences in slice-timing, spatial realignment to the first volume of the first session, normalization to the MNI reference brain (re-sampled to a voxel size of 2×2×2 mm 3) , and smoothing with an isotropic Gaussian kernel of 8 mm FWHM.Statistical analysis used a hierarchical random-effects model with two levels. At the first level, single-subject fMRI responses were modelled within a block design where the design matrix had volumes of the picture naming blocks coded with one and zero otherwise, convolved with the canonical hemodynamic response function. The fixation blocks served as an implicit baseline. A

  Videsott et al.: Neural correlates of language proficiency13 (all ps < .004). In contrast, accuracy in Ladin and Italian did not differ from each other significantly (p > .30).

Figure

  Figure 2 about here

Figure 1 :

 1 Figure 1: Geographical location of the Ladin speaking valleys (colored areas) in the Dolomites

Figure 2 :

 2 Figure 2: Functional brain activation in multilingual word production during a picture naming

Figure 3 :

 3 Figure 3: Functional brain activation related to high language proficiency in the right prefrontal

  Figure 1 Click here to download high resolution image

Table 1 :

 1 Functional brain activation to languages at different levels of proficiency. The significance level for each contrast was p < .001, uncorrected.

	Tables
	Anatomical description	No. of voxels	Z score		MNI
		Hem	BA			x	y	Z
	Ladin > English
	SFG	R	11	16	3.72	20	36 -20
	MFG	R	46/10	19	3.57	28	56	14
	Italian > English
	Insula	R	47	21	3.49	34	16	0
	MFG	R	47	37	4.09	46	42	-8
	MFG	R	46	67	3.80	42	48	8
	English > Ladin
	Cerebellum L	-	32	3.73	-20 -68 -20
		R	-	29	3.69	24 -76 -50
	IFG	L	44/6	14	3.52	-58	10	32
	English > Italian
	Cerebellum L	-	23	3.95	-20 -66 -34
	Italian > Ladin
	MTG	R	39	37	4.07	46 -70	14

Acknowledgements

This work was supported by an award from the Association L. Heilmann to G.V. and by a grant from the German Research Foundation (DFG Ki 804/5-1) to M.K.. The authors thank Gerwin Müller for his help with data acquisition and two anonymous reviewers for their helpful comments.

Supplementary Material

Click here to download Supplementary Material: Kiefer_SupplementaryMaterial_rev.doc