Elisa Omodei Lattice

Thierry Poibeau
email: thierry.poibeau@ens.fr

Jean-Philippe Cointet
email: jean-philippe.cointet@polytechnique.edu

Multi-Level Modeling of Quotation Families Morphogenesis

This paper investigates cultural dynamics in social media by examining the proliferation and diversification of clearly-cut pieces of content: quoted texts. In line with the pioneering work of Leskovec et al. [1] and Simmons et al. [2] on meme dynamics we investigate in deep the transformations that quotations published online undergo during their diffusion. We deliberately put aside the structure of the social network as well as the dynamical patterns pertaining to the diffusion process to focus on the way quotations are changed, how often they are modified and to what extent these changes shape diverse families and sub-families of quotations. Following a biological metaphor, we try to understand in which way mutations can transform quotations at different scales and how mutation rates depend on various properties of the quotations.

I. INTRODUCTION

"Memeticists", whose forefather Richard Dawkins [START_REF] Dawkins | The selfish gene[END_REF] was the first to coin the term meme in 1976 in analogy to gene, defend the thesis that culture is subjected to evolutionary processes in the same way that living beings are. This trend of research has suffered strong oppositions especially from ethnographers and anthropologists [START_REF] Sperber | La contagion des idées[END_REF] criticizing in particular the assumption that culture could be divided into individual objects called cultural entities [START_REF] Aunger | Cultural transmission and diffusion[END_REF], and more generally the lack of empirical validation [START_REF] Edmonds | Three challenges for the survival of memetics[END_REF]. Social media offer at last this opportunity for studying social and cultural dynamics in-vivo. In this paper, we claim that tracking the transformations of quotations in the blogosphere is a good way to tackle such empirical endeavour. If quotations admittedly cannot catch the complex properties found in every cultural traits, their atomic structure is, by nature, an opportunity that should be seized to put the memetic program, at least partially, into practice.

We are then interested in the diffusion of quotations which builds on the notion of "intertextuality", frequently used in political discourse studies. Intertextuality refers to the fact that fragments of discourse are repeated, re-used and progressively modified in different ways. It is thus possible to track the stability or the progressive distortion of an utterance over time. Following Kristeva [START_REF] Kristeva | Word, dialogue and novel[END_REF], we assume that these distortions are not neutral and reflect the way ideas diffuse in different communities.

Diffusion studies have attracted much interest from pioneering sociological approaches [START_REF] Rogers | New Product Adoption and Diffusion[END_REF] to more contemporary studies observing diffusion dynamics in online media. Whatever the nature of the diffusive entity: drugs [START_REF] Coleman | The Diffusion of an Innovation Among Physicians[END_REF], books recommandations [START_REF] Leskovec | The dynamics of viral marketing[END_REF], citations [START_REF] Leskovec | Cascading behavior in large blog graphs[END_REF], or URLs [START_REF] Adar | Implicit structure and the dynamics of blogspace[END_REF], the process at stake in each of these studies assumes that objects are perfectly replicated, their stability being a necessary condition for studying their diffusion. Like the previously mentioned objects, quotations can be tracked because whatever their changes they still refer to the same singular external event. But contrarily to them, quotations can undergo transformations, opening the way to the systematic quantitative analysis of regular patterns underlying these changes.

When it comes to characterizing changes that can affect quotations, it may be useful to follow the biological metaphor provided by memetics. A sequence of genes can be altered by mutations which may affect only one nucleotide or a large sequence of nucleotides. Small-scale mutations encompass point mutations (substitute a single nucleotide with another), deletions and insertions. We claim that such an ontology distinguishing between small-scale and large-scale changes is fruitful for addressing quotation transformation dynamics. We then introduce a different typology than the one described by Simmons et al. in their analysis of the same dataset. In [START_REF] Simmons | Memes Online: Extracted, Subtracted, Injected, and Recollected[END_REF] authors discriminate between "reframing" and "alteration" events: if a phrase is transformed into a superstring or substring, then reframing takes place, otherwise one should talk about alteration. The relation of inclusion between two quotations then defines the type of transformation. We prefer to use a different typology directly inspired from the biological mutation process. We will then simply consider on the one hand micro-mutations affecting only one word whatever the type of transformation (i.e. a word can be added, deleted or even replaced by another one) and on the other hand macromutations affecting more deeply the composition of phrases. Intuitively small-scale and large-scale mutation events stand for different underlying cognitive processes. Micro-mutations are small changes in the original quotation which can be introduced voluntarily or simply by error with no special intention to alter the original meaning of the replicated quote.

In the other case, macro-mutations are more probably due to voluntary changes by bloggers or journalists that only want to stress the attention of readers toward a subpart of the original quoted text.

Our goal will then be to describe how micro-scale and macro-scale mutations progressively transform quotations during their diffusion. The first part of the article will be devoted to a very short description of our empirical dataset. An original algorithm for detecting coherent families of quotations is then introduced. Based on these families, we will then introduce stability and diversity indexes which help us describing the transformation process at different levels (words, quotes and families). In the last part we will empirically measure mutation rates according to different properties of the quotations and will propose a morphogenesis model for building realistic families of quotations.

II. DATASET DESCRIPTION

We analyze the MemeTracker corpus which is made of quotations automatically extracted from 90 millions news and blog articles collected over the final three months of the 2008 U.S. Presidential Election and the following three months [START_REF] Leskovec | Meme-tracking and the Dynamics of the News Cycle[END_REF]. More precisely, we downloaded the MemeTracker dataset from the publicly available website memetracker.org, that consists of 310 457 distinct quotations collected from news and blog articles from August 2008 till the end of January 2009. Each quotation had to be mentioned at least 5 times in order to be included in the corpus. As we will primarily focus on characterizing how quotes are being transformed, the effects stemming from the underlying social network are out of the scope of this article. We then decided to neglect all the hyperlinks between articles and concentrate only on the textual data, i.e. the quotes themselves, and their number of mentions.

III. BUILDING QUOTATION FAMILIES

In order to analyze the MemeTracker corpus, it is necessary to identify families of quotations, which means grouping together the different quotations in relation with a same "seed" quotation (i.e. an original quoted text that can be subsequently re-used, duplicated or modified). This analysis is done in three steps: (i) all the quotations are linguistically analyzed and normalized (by lemmatizing the quotes and removing stop words); (ii) similarity between every pair of quotations is calculated and the quotes whose similarity is above a given threshold are linked so as to obtain a graph of quotations; (iii) a clustering algorithm is applied to detect communities (i.e. cohesive subgraphs in the graph) that will correspond to our families. We detail this process in the following subsection, followed by an evaluation of our results and a discussion.

A. A hybrid linguistic and structural approach

While the clustering method of Leskovec and his colleagues [START_REF] Leskovec | Meme-tracking and the Dynamics of the News Cycle[END_REF] builds on structural relations between phrases mainly defined according to their potential string inclusion, we tried to design a proximity measure between quoted phrases following more linguistic hypotheses.

A domain of interest regarding our objective is the paraphrase detection task, which is useful for various natural language applications, including information extraction, automatic summarization and machine translation. Paraphrase detection is highly difficult since it theoretically requires both a semantic and a syntactic analysis of sentences to give valuable results. In practice, most approaches are based on the identification of similar words between couples of sentences, which makes it possible to calculate a similarity value (using a similarity measure like cosine) [START_REF] Mihalcea | Corpus-based and knowledge-based measures of text semantic similarity[END_REF]. Various refinements can be explored in order to get more accurate results, like trying to calculate word similarity (using for example a resource like Wordnet for English) or trying to identify relations between words. For example, Qiu et al. [START_REF] Qiu | Paraphrase recognition via dissimilarity significance classification[END_REF] use the Charniak parser to get a syntactic analysis of the sentence and try to map predicate-argument patterns (for example, a verb with its arguments) between sentences, which makes the method more precise.

In this study, we preferred to design a simple strategy for building quotation families which features basic text processing techniques and makes use of a refined proximity measure. First we substituted every word with its lemma using the TreeTagger software [START_REF] Schmid | Probabilistic part-of-speech tagging using decision trees[END_REF] and eliminated all the stop words. This step is supposed to conserve only the chore semantic part of each quoted text so that our proximity measure only focuses on the most informative part of each phrase. Lemmatization allows to unify into one single class simple variations of the same word like singular/plurals, or verbs at different tenses. Stop words, also called "empty words", are usually considered as noise when comparing the semantic content of two phrases.

We make use of the traditional Levenshtein distance to assess the dissimilarity between two cleaned quotes. But we still need to add some sophistication to take into account word frequency in our measure, considering that rare words are more informative than frequent ones. We then computed the tf*idf score [START_REF] Salton | Introduction to modern information retrieval[END_REF] for every word w of quotation q defined as tf*idf(w, q, Q) = tf(w, q) * idf(w, Q), where tf(w, q) is the word w frequency in the quotation q and idf(w, Q) = log |Q|/{q ∈ Q : w ∈ q} where |Q| is the cardinality of the set Q of all the quotations. The first term gives more weight to frequent words (in the quotation) and the second one adjusts this value by penalizing words that are too frequent in the dataset since these words are considered to be not discriminative enough.

Then for every couple of quotations q and q (with |q| ≥ |q |) we computed an adjusted Levenshtein distance treating words as tokens and weighting them with their tf*idf scores. Classically, the Levenshtein distance -also called edit-distance -computes the miminum number of additions, deletions or substitutions necessary to transform an ordered sequence of object into another. Our weighted Levenshtein distance L then allows to compare two quotations, defined as two ordered sequences of words following this formula: L(q, q ) = min edit path f (w, q, w , q )(1 -δ(w, w ))

min edit path f (w, q, w , q )

where "min edit path" is the minimum edit path found by the algorithm to compute the Levenshtein distance, δ(w, w ) = 1 if w = w , 0 otherwise, and

f (w, q, w , q ) =       
max(tf*idf(w, q, Q), tf*idf(w , q , Q)) if w = w or w substituted w or vice-versa tf*idf(w, q, Q) if w was inserted or w was deleted

The rationale behind this method is to use a proximity measure based on sequences of words since word order clearly matters (as opposed to bag-of-word approaches where words are considered independently of their order of appearance). But we also give more weight to more discriminating words with their tf*idf score. After this pre-processing step, we constructed a similarity network with the set of quotations, in which every quotation is a node. We assign a weighted edge between two quotations q and q if they have at least two (full) words in common and if their similarity score, calculated as 1-L(q, q ), is greater than 0.35, a value that we empirically found to be an appropriate threshold. The weight of the edge equals 1 -L(q, q ). The final step was to apply an algorithm for community detection in networks in order to identify different quotation families. For this purpose we chose the Infomap algorithm by Rosvall and Bergstrom [START_REF] Rosvall | Maps of random walks on complex networks reveal community structure[END_REF], an information theoretic approach algorithm which uses the probability of flow of random walks on a network as a proxy for information flow in the real system and decomposes the graph into communities by compressing a description of the probability flow. Lancichinetti and Fortunato tested various community detection algorithms [START_REF] Lancichinetti | Community detection algorithms: A comparative analysis[END_REF] and found that Infomap has an excellent performance combined with low computational complexity, which enables to analyze large systems like our dataset.

B. Result evaluation and comparison

Clustering methods are widely used for natural language processing applications that require grouping different sets of elements. However, evaluating the output of clustering methods remains challenging since gold standards1 are rarely available and different partitions of the data may often make sense depending on the task and the context.

As for our experiment, no gold standard was available but it is possible to use the result of the MemeTracker project experiment as a comparison point. We chose to rely on a manual evaluation of a relevant sample of clusters randomly selected from those produced by our method and those produced by the MemeTracker method. We randomly selected 30 of our families, and for each family we also selected every MemeTracker family that had at least one quotation in common with the original family. Then for each family we made two lists: the first one containing all the quotations in the family, and the second one containing all the quotations which belonged to the corresponding selected families of the MemeTracker project. Then we did the opposite with 30 families of the MemeTracker project. The size of the initial clusters used for evaluation varies from 3 to 150 text snippets/sentences.

The list was then assessed by two judges, who were told that every first list represents a subset of closely related quotations and to mark any quotation that they thought should not belong to the family. Then they had to look at the second list and mark if any of the quotations should be added to the family.

Before detailing the result of this evaluation, it is necessary to quickly examine some methodological issues. First, a number of text snippets were not real quotations but titles ("high school musical"), short expressions with no clear meaning out of context ("a little bit") or foreign words ("la vida no vale nada") between brackets. The corresponding clusters were excluded from the evaluation2 . Second, the identification of the main information expressed in a set of snippets is a difficult task, especially given the variation in length of the different snippets. The instruction given to the evaluators was to first have a look at the whole set of snippets before determining the prominent information, which seems to have worked pretty well. Lastly, the instruction was to tag as equivalent snippets that were reporting the same main information even if some secondary information was missing. It was possible to tag a snippet as uncertain.

Despite the minimal set of instructions given to our evaluators, we obtained interesting and reliable results. We compared the evaluation produced by two annotators and obtained a high inter-annotator agreement (Cohen's kappa is 0.69, which is surprisingly high given the relative subjectivity of the task and the scarcity of instructions provided to the evaluators).

We obtained the following results for precision and relative recall (the recall is relative since we performed an evaluation based on a comparison between two methods and not with respect to a gold standard), where precision is calculated as the fraction of quotations considered relevant in the first list, recall as the number of relevant quotations in the first list over the same number plus the number of relevant quotations found in the second list. Our method outperforms Leskovec et al.'s approach both in precision and relative recall. This increase in precision is probably due to the linguistic preprocessing step that makes the whole process more precise (our analysis is focused on content words that are themselves weighted according to their discriminative power). The increase in relative recall is probably due to the fact that the MemeTracker clusters contain also a lot of snippets that are very small fractions of the seed quotation and were thus considered by the judges not to carry enough information to be unambiguously part of the family. We also measured the statistical significance between the two F-measure values through the SIGF V2 test [START_REF] Padó | User's guide to sigf: Significance testing by approximate randomisation[END_REF], which implements an assumption-free randomization framework. It allows to assess whether the difference in performance between two sets of predictions is significant. We found a pvalue of 0.049, which means that the F-scores are different with a 5% significance level.

Clustering Method Precision

C. Family filtering

Since the dataset contains many quotations that are either not in English either too short to convey a real unit of meaning, we first decided to filter it by considering only quotations containing at least 5 words in English 3 .

D. Family description

We plotted the distribution of family sizes defined as the total number of quotations mentioned in a family, the distribution of the number of distinct versions found in each family and the distribution of the number of mentions per quotation (Fig. 1). The shapes of all the three distributions can be approximated as a power-law and are comparable to distributions found in [START_REF] Leskovec | Meme-tracking and the Dynamics of the News Cycle[END_REF] although the families were defined differently.

IV. MULTI-LEVEL TRANSFORMATION ANALYSIS

Before going into further details, let's first have a look at an actual family identified as a family gathering 7 different quotes from the MemeTracker dataset. On Sep 3, 2008, Carly Fiorina, former boss of computer-making giant Hewlett-Packard, told a press conference: "The Republican party will not stand by while Sarah Palin is subjected to sexist attacks ... and as women, I think all of us are sensitized and outraged when we see sexist treatment". The quote is genuinely replicated 16 times in the dataset but it can also be found in 6 alternative forms, the most frequent of them being mentioned 56 times 3 For this purpose we used the guess-language software available at http://code.google.com/p/guess-language/. the republican party will not stand by while sarah palin is subjected to sexist attacks the republican party will not stand by while gov palin is subjected to sexist attacks the republican party will not stand by while sarah palin is being subjected to sexist attacks the republican party will not stand by while sarah palin is subjected to sexist attacks and as women i think all of us are sensitized and outraged when we see sexist treatment the republican party will not stand by while sarah palin is subjected to sexist attacks and as women i think all of us are sensitised and outraged when we see sexist treatment as women i think all of us are sensitized and outraged when we see sexist treatment the republican party will not stand by while gov sarah palin is subjected to sexist attacks Fig. 2. Empirical family detected by our algorithm and its associated editdistance graph. Quotes are connected when their edit distance is at most 1.

Sub-families (colored in red, blue and purple in this example) are made of connected components of the edit graph. Node sizes scale with the observed total number of mentions of the corresponding quote.

in a much shorter form than the original one: "the republican party will not stand by while sarah palin is subjected to sexist attack".

Observing the diversity of quotes within the family, it appears that 3 main sub-families clearly emerge gathering either the first, the second or both parts of the original quote. Within each sub-family, we also observe small variations between different versions of the quotations due to different spellings of the same word (sensitised/sensitized), words trimmed or added (sarah palin/gov palin/gov sarah palin, is being subjected/is subjected).

As we wish to take into account the diversity of possible changes, we will distinguish in the following between smallscale transformations observed when a single word is changed, added or removed, and large-scale transformations occurring when the quote is significantly trimmed into a smaller version.

A. Definitions

Before providing a more formal definition of these transformations, we first define the edit-distance graph as the network connecting quotes whose edit-distance is no larger than 1. Edit distance is defined as a Levenshtein distance between two quotes considering words as single characters. The edit distance between two quotes is then the minimum number of edits needed to transform one quote into the other, with the allowable edit operations being insertion, deletion, or substitution of a single term. The maximum number of allowed edits is fixed to one 4 . Please note that in this case and in all the following analysis no preliminary linguistic processing is applied to quotes before computing their edit-distance.

Contrarily to the strategy we adopted for detecting families, we naturally do not wish to alter quotations at this step as we are interested in identifying every possible transformation.

For every family, we then build the edit-distance graph G connecting its quotations, and extract its connected components. These connected components define the sub-families, i.e. sets of quotes which can only be differentiated by smallscale changes. Applied to our former example, edit distance graph indeed allows us to exhibit three different sub-families including various micro-level variants (see Fig. 2). The edit distance graph is not only useful to define sub-families since it will also be used to identify -given a target quote q -which quotes are found in its immediate neighbourhood V q 5 (i.e. quotes that are directly competing for the attention of bloggers or journalists). We then introduce three different measures that will help us to assess the transformation dynamics at different levels.

a) Term level: we are first interested in the relative stability of terms 6 found in a quotation. Given a quote q and a term t ∈ q, we define the stability of this term in this quote s(t|q) as the proportion of quotes in V q that share the same term 7 . The global stability of a term t is then defined as the weighted average of term stabilities computed for every quote it belongs to, that is to say: s(t) = q,t∈q w q s(t|q), where w q stands for the total number of mentions of q.

b) Quote level: at the quote level, we simply define the stability of a quote q as the proportion between the number of mentions of q and the total number of mentions of quotes in its immediate neighborhood: S(q) = wq q ∈Vq w q

. At this stage, we voluntarily focus on micro changes, excluding large-scale transformations that may occur when copying only a subpart of a quotation for example. c) Family level: we also wish to appraise how much a family or a sub-family composition is heterogeneous. We compute the entropy of the distribution of number of mentions for every quote in the family / sub-family: H F = -q∈F p q log(p q ) where p q holds for the proportion of mentions of quotes q in its family / sub-family: p q = wq q∈F wq .

B. Term level

The observation of the list of the most unstable words exhibits some well known patterns, the first one being the slight orthographic variations that exist for certain words in English. We thus observe the following alternations: "defence/defense", "programme/program" and, among many others, "behaviour/behavior". Other variations include words with a dash ("cease-fire/ceasefire"), abbreviations ("gov/governor") and foreign words ("al-qaeda/al-qaida"). Lastly, slang words 5 Vq will define the set of quotes whose edit-distance with q is less than or equal to 1 and that belong to q's family. Note that q ∈ Vq. 6 In this study we define terms as simple words. 7 More formally the stability of t in a given quote q is defined as: s(t|q) = q ∈Vq ,t∈q w q q ∈Vq w q

, where wq stands for the total number of mentions of q. are frequently omitted, which makes them more subject to variation than ordinary words (for example, "fuck" is in the top 20 most instable words in the corpus; it can be either suppressed or replaced by a simple "f" in indirect quotes). Besides these qualitative observations, more systematic patterns emerged when analyzing term stability according to different properties. We also wish to describe which features can provide quotes or terms higher-fidelity replication rates. Put differently, we are asking which properties at the term or quote level may systematically account for a higher or lower mutation rates? Formally, the stability of any feature attached to a term/quote is defined as the weighted average of every terms/quotes sharing this property 8 .

Figure 3 shows that word frequency significantly affects term stability. More frequent terms are significantly more likely to be stable. More precisely terms with more than 100 occurrences in our quote corpus have a stability higher than 99%, this value reaching up to 99.5% for the most frequent terms. The rarer the term the more dramatically its stability falls. We checked that this pattern is still present after removing stop-words from our set of terms. The same pattern can be observed even when selecting only certain grammatical types of terms, suggesting that frequency of terms plays a central role when it comes to memory issues or when one has to decide whether a given term should undergo a change. This observation may seem counter-intuitive if one considers that less frequent terms may convey more specific meaning. Yet rare terms may also be more prone to change as they may be misspelled or simply more difficult to spell precisely because of their scarcity. The same kind of observation has actually been made in studies examining the long-term evolution of language. For example, in [START_REF] Lieberman | Quantifying the evolutionary dynamics of language[END_REF], 8 For example for assessing the stability of terms with a given total frequency in the corpus n we will compute:

s(f = n) = t,f (t)=n
q,t∈q wq s(t|q) authors showed that the regularization rate of irregular English verbs was rapidly decreasing with their frequency, indicating that low frequency irregulars verbs are subject to more errors, leading to their "rapid" regularization. Besides, specificity is not synonymous with stability. A recent study analyzing the same dataset used Wordnet 9 to rank terms according to their genericity. They showed that the more specific the terms the more likely they are to be replaced, especially by more generic ones [START_REF] Lerique | How do brains copy and paste? the semantic drift of quotes in blogspace[END_REF]. This "natural preference" for more generic terms may also account for the shape of the curve we observe as it is probable that more specific terms occur less frequently than more polysemic ones. We also computed the average stability of terms according to their grammatical type (see Fig. 4). Results are as expected. Most common grammatical types approximately have the same level of stability. Yet we note that besides interjections which we could expect to feature lower stability, proper noun tend to be more than twice more unstable than average. Indeed proper nouns may produce more mutations as they can be misspelled or undergo more transformations as illustrated in our example in Fig. 2.

C. Quote level

We computed quote stability and plotted stability against quote length, i.e the number of words that it contains (see Fig. 5). Quote stability is minimal for a certain length (around 8 words). Smaller quotes are less keen to change, while longer quotes also feature higher stability. On such a digital medium, two processes may be in competition when it comes to editing quotes: i) a blogger may read/hear a quote in a newspaper, Twitter, or more broadly catch it from any media and try to replicate it by memory or ii) he can simply copy & paste the quote from a digital source. The first copy process is probably more used for quotes that are not too long (a 10-15 words long quote seems already quite difficult to memorize), and is also more keen to introduce variations than a pure copy & paste process (note that variations depend on the length of the quote: very short quotes are easier to memorize and are thus quite stable; longer quotes are more unstable, 8 words 9 Wordnet is a lexical database of English featuring synonymous relations between words (http://wordnet.princeton.edu/). long quotes being the most unstable ones). On the other hand, it seems plausible that longer quotes have greater chances to have been replicated from an existing source (for quotes over 8 words long, copy & paste is probably the preferred option, hence the increase in stability for longer quotes). This competition between low and high-fidelity replication along with the uneven probability to introduce errors according to the size of the quote may explain the particular shape of the curve.

We also investigated whether a quote stability is affected or not by its total frequency. In Figure 6 we plotted the weighted average stability of quotes according to their frequency and observe that the curve increases logarithmically. Two explanations may account for the higher stability of high frequency quotes. People trying to replicate them may produce less errors simply because they are inherently better replicators (this property also explaining their popularity). Another cause of their stability may simply be that they are being copied -because of their spread -significantly more often than their alternative forms, increasing in the long-run the disparity between their frequency and alternative forms which are less and less likely to get copied. The two processes may also be taking place at the same time: popular quotes tend to be naturally copied more frequently, while their number may decrease the chances to introduce mistakes in the copying process as it would seem more unlikely for someone to alter a quote she/he has already read several times.

D. Family / sub-family level

To better understand how families are composed and more precisely their inner diversity we also measured their entropy. We both measured the entropy of sub-families and of families as a whole. We recall that families group togther every quote related to an original quote whatever the scale of transformations it may have undergone, while sub-families gather quotes from the family that can be connected through micro- level changes. The Shannon entropy, which was originally applied to letters [START_REF] Shannon | A Mathematical Theory of Communication[END_REF], is classically used as a diversity index. Applied to quotes, the entropy measures the diversity of quotes composing a family or a sub-family. Entropy at the sub-family and family levels exhibits very different patterns.

Figures 7 shows how entropy correlates with the family / subfamily size. We observe that the value of the entropy for the families with a certain number of mentions is always much higher than the value of the entropy of the sub-families of the dataset gathering the same number of mentions. Put differently sub-families exhibit less diversity than families, suggesting that -at the micro-level -the competition among different versions of the same phrase eventually leads to a situation in which there is one version that is predominant regarding the number of mentions with respect to other versions, whereas -at the macro-level -there is more heterogeneity due to the coexistence of different relatively independent sub-families.

V. MODELING QUOTATION FAMILY GENERATION

We now propose a model of quotation family morphogenesis that accounts for their composition in terms of sub-family size distribution, and regarding their diversity at both levels.

To design a realistic model, we still need to precisely define how quotations are being changed when the family is growing. In this section we will then have a closer look at the temporal evolution of families, examining how many and which type of mutations are introduced during the process.

A. Mutation rates

We have shown that two types of mutations could occur when copying a quote. When the quotation is not perfectly replicated, one may observe a macro-level mutation -only a subpart of the original quotation is selected -or a microlevel mutation -small changes affecting only one word in the quotation. In the first case, we will consider that a new subfamily is produced, in the second case that the sub-family the original quote belongs to is simply enriched by a new version. We make the assumption that the chances that a mutation produces a version that had been already published before is negligible.

As we assume that every new version is triggered by a mutation event, we can assess mutation rates a posteriori at both levels by comparing the number of different versions in a given subset and the total number of mentions these different versions received. More precisely, the average mutation rate can be computed at both micro / macro level as the ratio between the number of micro / macro-mutation events (number of versions in the sub-family minus one / number of subfamilies in the family minus one), and the total number of copying steps (mentions in the sub-family / family minus one).

But accessing average mutation rates is not enough to realistically reconstruct family and sub-family morphogenesis: we also need to take into account the relevant properties affecting mutation rates. In the previous section, we showed that quote stability is modified according to their length and their popularity, which suggests that mutation rates could strongly differ according to those two properties. Besides, those properties may critically depend on the diffusion dynamics. That is the reason why we should dynamically assess the rate at which new versions are being produced in the empirical process according to those different conditions.

We then define the following strategy to dynamically measure mutation rates. Each family is considered as a growing set progressively populating the various sub-families with new quotes. Each time a new quotation is produced, we record whether it is a perfect copy of a previously mentioned quotation, or a new version that had never been observed before. In the latter case we also record whether the original quotation is enriching an existing sub-family or creating a completely new sub-family. We compile those events according to the original state of the family and sub-family 10 , i.e. we enumerate microchanges, macro-changes and perfect copying events according to the average sub-family size and average quotation length.

From there on, it is straightforward to define the micro / macro-mutation rate according to a given average length or a given total number of mentions as the proportion of replication events producing a micro / macro change. We will call the so computed micro and macro mutation rates ρ µ and ρ M respectively.

From figure 6 we can suspect that the number of mentions is crucial for determining the precise mutation rate of a quotation. Therefore we plotted (Fig. 8) the micro (and respectively macro) mutation rates according to the total number of mentions observed in the sub-family. As expected we find that both mutation rates decrease with the number of mentions (see figure caption for further details about the fitting functions used). This behaviour confirms our hypothesis that more popular quotations are less keen to changes. Very popular quotations may be so ubiquitous in the environment that the probability to introduce micro-mutations by error is lowered (many copies can recall the agents how the correct version should be spelled) or that "successful" quotations have such high "fitness" that any further refinements is unnecessary.

Figure 5 showed that quotation stability is sensibly modified according to their length l. We plotted in Fig. 9 the mutation rates according to the average length of the family / subfamily quotations. We observe that the macro-mutation rate is growing with quotation length. While small quotations (less than 5 words) can naturally not undergo any macro mutation given the definition of our family categorization, the macro-mutation rate reaches a threshold for quotations over 20 words. In our model, we use an exponential function to express the dependence of the macro mutation rate with 10 We make the hypothesis that a new quotation enriching a pre-existing sub-family was necessarily copied from one member of this sub-family quotation size (see Fig. 9 caption for further details). A plausible explanation is that a quotation can hardly be trimmed before reaching a certain critical length. Above 20 words the quotation is certainly made of different phrases or sentences that individually carry some autonomous meaning even when separated from their original environment. The correlation between l and the micro mutation rate seems more complex. A peak of the micro mutation rate is reached for mid-size quotations around 8 words. After the maximal value is reached, we observe an exponential decrease until the curve plateau at the minimal mutation rate. As already hypothesized when commenting the shape of quotation stability with length, micro-change dynamics seems to be driven by two processes acting in different directions regarding the number of words. First, it seems clear that mistakes introduced during the copying process are possible only when the quotation is not simply copy & paste. It seems reasonable to postulate that the probability for a quotation to be retrieved from memory rather than copy & paste is exponentially decreasing with quotation length. If the quotation was retrieved from memory then chances are that some mistakes will be introduced. If we refer to classical works in psychology [START_REF] Miller | The magical number seven, plus or minus two: Some limits on our capacity for processing information[END_REF], human brain can hold up to a certain number of objects or chunks in memory. This so-called "magic number" below which short term memory is almost perfectly accurate is precisely around 5 for words. This is the reason why we chose to fit the correlation between micro mutation rate and length with a more complex equation made of the product of two probabilities: the probability that the quoted phrase is not replicated by a copy & paste event (which is exponentially decreasing with l) and the probability that an error is introduced by chance (which is assumed to be linear for quotations larger than the magical number 5). This product models the probability that the quotation is replicated with an error. We also add a baseline in the fitting function to account for the constant probability that a blogger or a journalist q or q' q'' q q q' q mutation proobability (Micro change) voluntarily introduces a single change to the quotation (see Fig. 9 caption for further details).

B. Model Design

We now propose a generative process producing families of quotations. Our aim is to find a realistic agent-based process that accounts for the distribution of the size of subfamilies as well as for the shape of the increase of diversity at the family and sub-family level in time. We rely on a classical Polya urn principle like in [START_REF] Simmons | Memes Online: Extracted, Subtracted, Injected, and Recollected[END_REF]. We assume that each sub-family is centered around a specific kernel of meaning and is then characterized with its own autonomous dynamical process. This is the reason why we randomly select a sub-family from which a quotation is picked for replication. The quotation may then undergo a mutation according to its length l and its number of mentions n.

More formally, a family F is initialized with a quotation q of a given length l (F = {q}). This first quotation is also assigned a sub-family f . The simulation then iterates over every time step as follows (see Figure 10 for an illustration):

1) One randomly chooses a sub-family f of F and then a quotation q ∈ f with probability proportional to the number of mentions of q 2) With a probability given by the combination of the two micro mutations rates ρ µ (l) and ρ µ (n) 11 the quotation undergoes a micro-change, resulting in a new quotation q that differs from the original one by only one edit (deletion, insertion or substitution). If above this probability the quote is not modified, 3) Then, if long enough, the quotation can also be trimmed into a smaller quotation with a probability given by the the macro mutation rates which depend on the quotation length l and number of mentions n according to the fitted values of ρ M (l) and ρ M (n) 11 . If so, a new shorter quotation q is created. 4) If the quotation did not undergo any mutation on steps 2 and 3, it is perfectly replicated (copy&paste) and a new quotation q is produced. 5) The possibly mutated (q or q ) or unchanged (q) version of the original quotation is added to the family. The process is repeated from step 1 until the family is considered complete, i.e. when it has received the total number of quotations we observed in the empirical distribution.

C. Model Results

Our simulation almost produced the same number of distinct versions and sub-families than in our original dataset (less than 1% error). We also observe that the proposed model accounts for the size distribution of sub-families and for the diversity of families and sub-families. Figure 11 shows a very good fit between the empirical and the simulated sub-family size distributions, suggesting that our model succeeds in reproducing the 11 Precisely the global probability to observe a mutation given the total number of mentions and average length is given by ρ = ρ(l)ρ(n) <ρ> . As one can not simply infer which was the original quote from which a new quote was replicated, we computed the stylized behaviour linking mutation rates with l and n considering average lengths and estimated cumulated number of mentions in the sub-family. We are now making the hypothesis that mutation rates can be directly computed based on the quotation length and number of mentions. Lengths being homogeneously distributed, the approximation seems reasonable. Since the distribution of mentions is heterogeneous and given that our process preferentially selects the most cited quote, it is very likely that the number of mentions of a random quote is well approximated by the total number of mentions in the sub-family. observed feature that families are composed of sub-families of more similar quotation versions. Moreover, Figure 12 shows that our model is also able to reproduce the difference that we observed in the values of entropy between families and subfamilies. The model creates quite homogeneous sub-families that put together into families account for their larger diversity.

VI. CONCLUSIONS

In this paper we introduced a new algorithm for quotation clustering as a first step towards the analysis of quotation family structure and transformations. We showed how these families can be characterized at a meso level by retrieving the corresponding sub-families, i.e. the connected components of a graph of edit distances of at most one edit. This multi-level analysis allowed us to find new interesting results, such as the difference in the entropy level at the two scales, suggesting that the strong competition among very similar quotations leads to a more homogeneous situation with respect to the co-existence of the different sub-families. Moreover, we presented a model that attempts to describe the morphogenesis of these families of quotations and accounts for their composition in terms of sub-family size distribution and for the difference of diversity measured at both levels. The model relies on the analysis of quotation stability, through which we showed that quotations undergo a different number of mutations according to their length and their number of mentions, concluding that quotations that are already very popular have less chances to be transformed. In future work we would like to take into account the underlying social network in order to enrich the analysis of the driving forces determining quotations transformation. Moreover, our model could be significantly more realistic with a finer description of temporal patterns pertaining to quotation diffusion.

Fig. 1 .

 1 Fig.1. Distribution of family sizes defined as the total number of mentions of the quotations in the family (black dots), distribution of the number of distinct quotations in a family (pink full diamonds) and distribution of the number of mentions per quotation (blue diamonds).

Fig. 3 .

 3 Fig. 3. Stability in function of term frequency. The figure is obtained by creating 20 equally populated quantiles and averaging the stability values corresponding to each quantile. Error bars stand for confidence interval (5%). Inset: stability of verbs and nouns only.

Fig. 4 .

 4 Fig. 4. Stability values for different grammatical types.

Fig. 5 .

 5 Fig. 5. Quote stability in function of quote length (number of words). The figure is obtained by creating 10 equally populated quantiles and averaging the stability values corresponding to each quantile. Error bars stand for confidence interval (5%).

Fig. 6 .

 6 Fig. 6. Quote stability in function of their number of mentions. The figure is obtained by creating 30 equally populated quantiles and averaging the stability values corresponding to each quantile. Error bars stand for confidence interval (5%).

Fig. 7 .

 7 Fig. 7.Family and sub-family entropy in function of the total number of mentions of their quotes. The figure is obtained by creating 10 equally populated quantiles and averaging the entropy values corresponding to each quantile. Error bars stand for confidence interval (5%).

Fig. 8 .

 8 Fig. 8. Micro and Macro mutation rates according to the sub-family total number of mentions, along with their fitted model. ρ M (n) = 0.225n -0.763 and ρµ(n) = 0.057n -0.739 . The figure is obtained by creating 15 equally populated quantiles and averaging the mutation rate values corresponding to each quantile. Error bars stand for confidence interval (5%).

Fig. 9 .

 9 Fig. 9. Micro and Macro mutation rates according to quotation lengths, along with their fitted model. ρ M (l) = 0.020 -0.292 exp (-0.499l) and ρµ(l) = 0.004 + 0.046(l -5) exp (-0.423l). The figure is obtained by creating 15 equally populated quantiles and averaging the mutation rate values corresponding to each quantile. Error bars stand for confidence interval (5%).

Fig. 10 .

 10 Fig. 10. Schematic representation of the model dynamics.

Fig. 11 .

 11 Fig. 11. Comparison of the distribution of sub-family sizes in the empirical data (black dots) and produced by the model (blue diamonds).

Fig. 12 .

 12 Fig. 12. Family and sub-family entropy produced by the model in function of the total number of mentions of their quotations. The figure is obtained by creating 10 equally populated quantiles. Error bars stand for confidence interval (5%).

In natural language processing, a gold standard is a set of manually annotated data. Most of the time, the data is annotated by several annotators to ensure a reliable result based on a high inter-annotator agreement.

Note that our families will be filtered in a second phase in order to delete these kinds of pathologic families.

We have chosen to consider only the edit-distance graph connecting quotes at the smallest possible edit-distance i.e. 1. However, we have checked that our results are qualitatively unchanged when considering larger transformations (edit distance at most 2 or 3).

ACKNOWLEDGMENT

The authors would like to thank Tommaso Brotto and Zorana Ratkovic for serving as judges for the clustering method evaluation. We also warmly thank the reviewers as well as Sébastien Lerique, Camille Roth, Andreï Mogoutov, Benjamin Fagard, Isabelle Tellier and Jonathan Platkiewicz for fruitful comments. Elisa Omodei's work is supported by a PhD grant from the Région Ile-de-France. This work has also been partially supported by the French National Agency of Research (ANR) through the grant Algopol (ANR-12-CORD-0018).


Chantal Delon-Martin
email: chantal.delon@ujf-grenoble.fr

Jane Plailly
email: plailly@olfac.univ-lyon1.fr

Pierre Fonlupt
email: pierre.fonlupt@inserm.fr

Alexandra Veyrac
email: alexandra.veyrac@u-psud.fr

Jean-Pierre Royet
email: royet@olfac.univ-lyon1.fr

Jean- Pierre Royet

Jean-Pierre Royet

Perfumers' expertise

Keywords: Olfaction, Perfumer, Expertise, Gray-matter volume, Voxel-based morphometry, Structural reorganization

Introduction

Numerous studies in humans have indicated that functional and anatomical modifications occur in the brain because of learning and training. In experts with enhanced visual, auditory or motor skills, such as musicians and athletes, greater performance is associated with functional and structural brain changes in modality-specific brain areas, which supports the view that brain reorganization is associated with expertise.

What about olfactory expertise? Could the brain reorganization observed with expertise in other modalities be generalized to olfaction? This question has been addressed only once before , probably because human olfactory abilities are less essential for survival than other senses and are more poorly developed compared to those of other mammals, and because olfactory experts, such as perfumers, are rare. While exploring brain process related to odor mental imagery, we observed an expertise-dependent functional reorganization in olfactory and mnesic brain areas, such as the primary olfactory (piriform) cortex (PC) and hippocampus, which was concomitant to enhanced behavioral performances with expertise. To the best of our knowledge, brain structural reorganization in olfactory expert has never been investigated. However, focusing on alterations of olfactory processes, several studies have shown gray matter (GM) atrophy in olfactory-related areas in patients suffering from anosmia, or hyposmia because of peripheral dysfunction or neurological disease . Whether subjects with olfactory expertise present modifications of GM volume in olfactory areas is an open question.

Computational morphometry tools allow for investigation of structural brain changes related to development, learning, expertise and pathology . The voxel-based morphometry (VBM) technique is broadly used and remains an active field of methodological research . Several pitfalls in image preprocessing, including inter-individual misregistration and errors in the segmentation procedure , skull-stripping and bias correction , and bias induced by improper templates and priors, are the subjects of vigorous debate in the neuroimaging community. However, methodological improvements have recently been developed that may limit possible errors . First, the segmentation algorithm can include bias correction, and can now model not only brain tissues but also non-brain tissues. This allows removing potential contamination from soft tissues outside the brain, large vessels and skull. Second, a study-specific template can be created. Third, algorithms for diffeomorphic image registration with a high number of degrees of freedom can be employed to obtain accurate registration of each individual brain with a given template. In particular, the algorithm for "Diffeomorphic Anatomical Registration using Exponentiated Lie algebra" better known under the DARTEL acronym is considered as being among the best algorithms available .

Fourth, the segmentation and the spatial registration procedure can be combined in a unified approach to improve preprocessing steps . A recent VBM test-retest study showed that when using all above-mentioned improvements as implemented in Statistical Parametric Mapping 8 (SPM8) using DARTEL, this permits to avoid obtaining most of the false positives . Fifth, voxels which feature low effect-size and very low variance can be artifactually significant . In order to reduce false positives, the newest SPM8 release (v.4010, April 04, 2011) includes a scheme to force such voxels to have low significance in VBM statistical analysis.

In the current study, we used structural magnetic resonance imaging (MRI) and the VBM pipeline with DARTEL in statistical parametric mapping (SPM8) software. We scanned 14 perfumers renowned for creating perfumes, 13 students from an international school of perfumery and 21 control subjects. We took advantage of variability in length of expertise in professional perfumers to identify structural brain reorganization that is associated with experience under the hypothesis that intensive olfactory training may lead to increased GM volumes in olfactory-and memory-related areas.

Materials and Methods

Subjects

Forty-eight healthy right-handed subjects participated in our study. The perfumers' group included 13 young experts (YE; 3 men; mean age, 23 years; range, 21-26 years) who were students at an international school of perfumery (Institut Supérieur International de la Parfumerie, de la Cosmétique et de l'Aromatique, Versailles, France), and 14 older experts (OE; 10 men; mean age, 42 years; range, 29-59 years) who were professionals known for creating perfumes. While the YE had been trained for 2 years at most as part of their education, all OE had 5-35 years of careerrelevant business experience. The control group was composed of 21 untrained subjects, including 8 young controls (YC; 2 men; mean age, 25 years; range, 24-28 years) and 13 older controls (OC; 6 men; mean age, 40 years; range, 30-55 years). Subjects from both groups were matched in age (F (1,47) = 0.10, p = 0.712) and gender ( χ ² = 0.34, p = 0.560).

The exclusion criteria were rhinal disorders (e.g., a history of nasal-sinus surgery), pregnancy, ferrous implants (e.g., pacemakers and cochlear implants), claustrophobia, or any neurological disease. This study was conducted according to French regulations on biomedical experiments using healthy volunteers and according to the principles outlined in the Declaration of Helsinki. All subjects gave written informed consent, as required by the local Institutional Review Board.

Structural Data Acquisition

All structural images were acquired on a Philips NT 1.5-Tesla MRI scanner (Philips Medical Systems, Best, Netherlands) with a birdcage head coil. The high resolution anatomical images of the olfactory experts were acquired during an fMRI study investigating the neural substrates of olfactory imagery and on controls during a visual retinotopy protocol that was conducted on the same scanner with the same sequence and at the same period of time as the scanning of olfactory experts . A high-resolution (1 mm 3 ) structural image was acquired with the same sequence for all subjects using a 3-dimensional, T1-weighted gradient echo sequence [repetition time = 23.7 ms, echo time = 6.9 ms, flip angle = 28°, number of accumulations = 2]. This acquisition sequence type has been shown to be particularly efficient when studying the inner part of the cortex .

Structural Data Preprocessing

We processed and analyzed the structural data using a VBM approach and SPM8 software (www.fil.ion.ucl.ac.uk/spm/software/spm8/) with default parameters.

For the first step, each individual image was segmented using the 'New Segment' tool provided with the DARTEL toolbox . This procedure performs segmentation and spatial normalization in a unified generative model based on a mixture of Gaussians with spatial priors and bias correction. In addition, the algorithm includes non-brain tissues (dura, scalp and large vessels), which ensures that the calculation of GM images is uncontaminated by either the skull or large veins. This approach permits to skip the bias correction and skull-stripping procedure, which improves VBM preprocessing .

In a second step, a study-specific template was calculated from all subjects' GM and white matter (WM) images using the DARTEL framework . This diffeomorphic registration algorithm iteratively estimates the non-linear deformations that best align the GM and WM images together, which provides a common study-specific template and deformation field that parameterizes the deformations for each subject's image.

In a third step, to spatially normalize the images to the Montreal Neurological Institute (MNI) standard brain, we calculated the affine registration that realigned our study-specific template generated using DARTEL with the GM tissue probability map in the MNI space. For each subject, we applied a combination of the deformation field and affine registration to the GM and structural images of each individual. The spatially normalized GM segmented images were further modulated with the Jacobian of the deformation field to adjust for the resulting volume changes and derive GM volume-related images. The realigned structural images of all subjects were averaged for display purposes.

In a fourth step, to improve the sensitivity of the analysis to regional differences at a small spatial scale (required by small structures, such as the PC), we smoothed the GM volume images using a small isotropic Gaussian kernel with a 6-mm full width at half maximum. This kernel width ensures that any non-normality in the error term is sufficiently attenuated in balanced designs to render the tests valid .

Olfactory-related regions of interest

Statistical analyses were performed on brain areas known to play a role in olfactory and memory processing. As no probabilistic maps have been proposed for the olfactory areas, anatomical volume of interests (VOIs) in the PC, amygdala, hippocampus, insula and thalamus were drawn from the study-specific template realigned to the MNI using MRIcron (www.mccauslandcenter.sc.edu/mricro/mricron/) and human brain atlases . The VOIs were drawn from coronal slices for the PC (from y = 12.5 anterior to -2.7 posterior to the anterior commissure), amygdala (from y = 0 to -13.3), insula (from y = -22 to 36), and thalamus (from y = 8 to 29.2), and from sagittal slices for the hippocampus (from x = -24 to -38 on the left, and from x = 24 to 38 on the right). Based on previous data , the PC was divided along the y-axis into two areas, specifically, the anterior area (spreading from 12.5 to 4 mm) and posterior area (spreading from 4 to -2.7 mm).

Orbitofrontal cortex (OFC) VOIs were spheres 10 mm in diameter located at coordinates previously identified by Gottfried and Zald in the left and right hemispheres (Talairach coordinates: -22 30 -17 and 24 33 -12) as being the putative secondary olfactory cortex. These areas defining the human olfactory network were combined to form a binary mask to which we added orbital areas (Brodmann areas (BA) 11, 20 and 34) using the WFU_PickAtlas v2.4 software based on the Talairach Daemon database . This mask was further dilated by 6 mm (the same width as the smoothing kernel employed for GM volume images) for use as an explicit mask in subsequent VBM statistical analyses.

Statistical Analysis

Voxel-based morphometry analysis

As it was reported that the anatomical effects of expertise in musicians increase as a function of years of practice , we hypothesized that the effects of expertise could also be correlated with age in perfumers and not in naïve controls who have never undergone any olfactory training. Given this hypothesis, the effect of age was inhomogeneous among the four groups of subjects and had to be modeled separately for each sub-group. Voxelwise GM volume differences between experts and control subjects were statistically tested using multiple regression analysis, including four regressors of interest (age of older experts, older controls, young experts and young controls). To eliminate possible confounding factors associated with gender or with total GM volume , we applied two regressors of no-interest (gender and total GM volume, the latter being obtained using the 'get-totals.m' matlab function, www.cs.ucl.ac.uk/staff/g.ridgway/vbm/). For statistical analysis of the GM images, we excluded all voxels with GM values below 0.2 (absolute threshold masking) to avoid possible edge effects between different tissue types and to only account for GM voxels with high GM volume content. The GM-volume statistical analysis was constrained within the explicit GM olfactory mask defined above.

We tested whether GM volumes differed globally between experts and controls [(YE + OE) -(YC + OC)] and also assessed the effect of expertise in the younger and older experts compared to their respective controls (contrasts [YE-YC] and [OE-OC], respectively). To identify the areas commonly different between the two sub-groups of experts and their respective controls, we computed the voxels that reached the criterion of significance for both contrasts [YE-YC] and [OE-OC]. Finally, to test for differences in brain volumes between the expert sub-groups, we performed contrasts for [(YE-YC) -(OE-OC)] and [(OE-OC) -(YE-YC)]. For all the contrasts, we applied Student's t-test with a height of threshold on individual voxels such that p uncorrected < 0.001 and a threshold on spatial extent such that the probability on the extent was p k < 0.05.

VOI-based analysis

For each olfactory-related VOI and each of the 48 participants, we derived the adjusted values from the previous general linear model analysis, by removing any contribution from no-interest variables within the VOI. For each VOI, three-way ANOVA (Group x Age x Side) and mean posthoc comparisons were performed using Statistica 6.0 (StatSoft®, Tulsa, OK, USA) to compare the degree of change in GM volume as a function of the groups (controls and experts), age (younger and older) and brain sides (left and right). We tested the null hypothesis that there were no significant changes of GM in each of the seven VOIs (the aPC, pPC, OFC, amygdala, hippocampus, insula and thalamus). To control for Type I error rate, we applied the Bonferroni correction by dividing the probability α per the number of VOIs (pcorrected = 0.007).

Regression analyses

On the bilateral aPC and pPC VOIs, and two GR/MOG clusters evidenced on the left side (-9 23 -15 and -8 26 -24) with classical general linear model analysis as being dependent of expertise, we performed linear regression analyses to test for significant relationships between the years of age and volume of GM either in older experts or in older controls using Statistica 6.0 (StatSoft®, Tulsa, OK, USA). In older experts, years from 20 years-old (when their training started) were considered as years of practice and thus reflected the length of expertise. In the controls matched in age, we also removed 20 years to their age in order to compare with the professional experts. The regression lines of the expert and control groups were compared for parallelism and common intercepts to respectively test whether the relationships between expertise and GM volume (slopes of the regression lines) were different, and whether the volume variations began at identical levels of expertise (intersection of the regression lines with the y-axis). As an effect of expertise on the greymatter volume could be predicted for each of these 6 VOIs, we applied the Bonferroni correction (p = 0.0083).

Results

Statistical results of the analyses of GM volume in the four groups of subjects are presented in Table 1. When contrasting the GM volumes between all experts and controls [(YE + OE) -(YC + OC)], we observed a bilateral pattern of increased volume spreading from the gyrus rectus (GR) to the medial orbital gyrus (MOG), and an increased GM volume in the anterior cingulate gyrus and the caudate nucleus (Fig. 1A). In YE compared with YC (YE -YC), we observed an increased GM volume in the left and right GR/MOG and in the left anterior cingulate gyrus. In OE compared with OC (OE -OC), a higher GM volume was observed in an anterior area of the left GR/MOG and in the left caudate nucleus. Although reaching the criterion of significance at peak level only, the conjunction analysis between contrasts applied in both sub-groups of experts when compared to their respective controls [(YE -YC) and (OE -OC)] revealed a common pattern of increased GM volume in the right gyrus rectus (x y z coordinates, 9 27 -24). The interactions [(OE -OC) -(YE -YC)] and [(YE -YC) -(OE -OC)] did not reveal any significant differences in GM volume, demonstrating no significant influence of sub-groups on the effect of expertise. No significant decrease in GM volume was observed in experts when compared with controls regardless of the comparisons.

Anatomical VOI analyses did not reveal any significant group effect in the VOIs (p corrected ), although a group effect was noted in the posterior PC with a p uncorrected (F(1,44) = 4.00, p = 0.044), suggesting a volume of GM tending to be higher in experts than in control participants. Significant Group x Side interactions were observed for the amygdala (F(1,44) = 13.01, p = 0.001), insula (F(1,44) = 56.99, p < 0.001) and thalamus (F(1,44) = 16.45, p < 0.001); however, post-hoc comparisons did not show any significant difference between groups for a given side (p's > 0.007), but only significant differences between both sides in experts and/or controls.

We subsequently examined whether the GM volume in older experts depended on the duration of practice (and therefore on the level of expertise). In the left gyrus rectus (Lower part: -9 23 -15;

Upper part: -8 26 -24; Fig. 2A), the volume of the GM was significantly positively correlated with the age (and therefore the level of expertise) in OE (Upper part: r = 0.685, F(1,12) = 10.62, p = 0.007; Lower part: r = 0.677, F(1,12) = 10.13, p = 0.008), while it tended to be significantly negatively correlated with age in EC in the upper part (r = -0.649, F(1,11) = 8.00, p = 0.016) and it was not significant in the lower part (r = -0.50, F(1,11) = 3.58, p = 0.085). Regression lines obtained in OE and OC were significantly not parallel in both areas (Upper part: T( 27

Discussion

By combining the structural imaging and VBM technique with the best and most recent improvements, we showed structural modifications related to olfactory expertise. Compared with naïve subjects, the student and professional perfumers mainly presented a bilateral GM increase in the GR/MOG, an area that marks the borders of the olfactory sulcus and that is adjacent to the putative secondary olfactory cortex. Furthermore, when considering the professional experts only, expertise seems to counteract the effect of age in the GR/MOG and anterior PC, as the GM volumes increased with years of practice, whereas they decreased with age in naïve subjects.

Structural and functional characteristics of the GR/MOG

All olfactory experts showed an increased GM volume in the GR/MOG compared with control subjects who had no expertise in olfaction. Volume increase was bilateral in the GR/MOG more posterior part and was left-sided in the more anterior part.

Very few studies have shown an increase of GM volume in olfactory areas in relation with olfactory abilities in normosmic volunteers. They demonstrated that the size of the olfactory bulb and/or the depth of the left olfactory sulcus significantly correlated with olfactory performances in healthy subjects and in individuals who became blind early in life . If the olfactory bulb is mostly considered to be a peripheral structure, the depth of the olfactory sulcus can reflect the size of the adjacent cerebral cortex. Using an automated method for extracting the cortical thickness, Frasnelli et al. found an increase in cortical thickness with higher olfactory abilities in an area surrounding the left olfactory sulcus in healthy volunteers. This region is very close to the GR/MOG evidenced in our study, and allows dismissing the interpretation of the current results as resulting from methodological artifact. Recently, applying classical volumetry and VBM methodologies in healthy subjects, Seubert et al. dissociated the functional contributions of olfactory peripheral and central areas. They found that the volume of the olfactory bulb increased with higher identification scores, while the volume of GM in the OFC was enhanced with better olfactory threshold and discrimination performances. Because the authors limited their investigation to a unique area of the OFC that approximately covered the putative human olfactory OFC evidenced by Gottfried and Zald but did not include the GR/MOG regions, the comparison with our data is impossible. However, it is surprising that we did not observe any change of GM volume in this region in the odor experts.

What is the role of the GR/MOG region? In monkeys, the GR/MOG is referred as being area 11m and is a zone located at the level of the olfactory sulcus delimited by the dysgranular areas 13m, 13b and 14r . Studying architectonic maps and the intrinsic cortico-cortical connections within the orbital and the medial prefrontal cortex, Carmichael and Price distinguished two distinct networks, the "orbital network" (including in particular areas 13b and 13m) that receives inputs related to most of the sensory modalities and the "medial network" (including in particular areas 11m and 14r) that receives a few direct olfactory inputs, but does not receive inputs from other sensory modalities. Because all of the areas recognized in the macaque medial prefrontal cortex have their counterparts in humans , the question is to know what is the role of the GR/MOG in humans. In an attempt to explain the respective role of these two networks, the authors suggested that the orbital network appears to be related to feeding and reward, whereas the medial network seems to participate in the guidance of emotional behavior . Rather than a coarse medial/lateral distinction, Kahnt et al. recently identified 6 subdivisions in the human OFC from their functional connectivity profiles with other brain regions. They suggested a role of the medial and centralposterior regions in reward processing and reward learning, respectively.

In cerebral imaging studies on human olfactory processes, the GR/MOG was found to be activated in several studies , although often described with different labels (such as the rostromedial or posterior OFC, ventromedial prefrontal cortex, inferior frontal gyrus, cingulate gyrus, or subcallosum), depending on the atlases and nomenclatures. Interestingly, this brain region is different from the putative olfactory OFC evidenced by Gottfried and Zald . The GR/MOG could therefore be associated to more higher-order cognitive process in comparison to this putative olfactory OFC which was identified from data for which no cognitive olfactory judgment and no aversive odorants were used. A functional segregation of valence neural representations has been observed in the OFC, the medial part being more strongly activated by pleasant odors, and the lateral part being more notably activated by unpleasant odors , a representation that holds even when the attributed hedonic properties are modulated by cognitive information . Similar findings were observed with taste and flavor , the processing of faces, and monetary reward and punishment . Since the perfumers intensively and daily use pleasant odorants in order to make fragrances, as suggested by testimonies, the question is whether this hyper-use can explain structural changes observed in the GR/MOG. Further investigations are needed to clarify this point.

We further observed additional GM changes in the anterior cingulate gyrus in student perfumers and in the caudate nucleus in professional perfumers, which are two areas that are commonly activated in olfaction . Structural modifications in these regions are in accordance with the cognitive functions that are widely used in experts of all types including perfumers. The anterior cingulate gyrus is predicted to play an important role in tasks requiring cognitive control in the presence of emotional stimuli while the caudate nucleus is highly involved in learning and memory .

Dynamic changes of GM volume in olfactory areas

Here, we observed that intensive olfactory training counteracts the effect of age in several brain regions. Thus, although the increases were not systematically significantly positively correlated with experience in these areas in older perfumers, they were strengthened by the fact that the GM volume was reduced with age in controls and that the respective slopes of both regression lines were then systematically significantly different. Thus, the older an individual gets, the more the anterior PC and GR/MOG volumes relatively increase in experts and the more they decrease in untrained subjects. Volume reduction in controls is consistent with the well-known decline of global GM volume with age . Hedman et al. have shown that after 35 years-old, a steady volume loss of 0.2 % per year is found, which accelerates gradually to an annual brain volume loss of 0.5 % at age 60.

Overall, these double morphometry data demonstrate a causal direction between olfactory abilities and GM volume of olfactory areas. When the two lines of regression present a common intercept but different slopes, this means that experts and controls had the same GM volume at the same age (approximately 20 years old), but that this volume changes differently with respect to age by increasing in odor experts and decreasing in controls. These data also allow claiming that longer olfactory training leads to larger GM volumes, a finding that must be related to our previous results observed in the same subjects and showing behavioral performances enhanced with expertise .

Structural changes in odor experts are further consistent with cross-sectional VBM studies that demonstrate learning-dependent changes in the adult human brain and suggest anatomical correlates for navigation, arithmetic, linguistic, procedural, and musical learning abilities . Structural changes have even been observed after brief periods of learning, such as after studying for three months after an exam or even after a very short spatial learning of 2 hours .

Neuronal mechanisms related to olfactory learning

Perceptual learning refers to a phenomenon whereby experience refines sensory perception through the differentiation of stimulus features, dimensions, or categories of items that was previously experienced. It has previously been showed that experience and familiarity enhance odor The current experiment is the first to investigate the structural impact of odor long-term training.

Perfumers live in an enriched olfactory environment and learn to characterize and recognize numerous stimuli daily, and to discriminate often minute differences between them. This paper follows its functional counterpart in which we demonstrate a signal decrease in hyper-trained perfumers in olfactory (piriform cortex and OFC) and memory (hippocampus) brain regions when they generate a mental image of odor. A signal decrease could be because of a greater selectivity of neurons due to a decorrelation of their activity . Similar mechanisms have been shown in the antennal lobe of the honeybees trained on one odorant, the sensorial representation of that odorant becoming smaller, more compact, and nonoverlapping with other odorants and in rats trained to discriminate highly overlapping odorous mixtures . As an attempt to conciliate the structural and functional reorganizations of olfactory experts, we speculate that although cells become more selective after perceptual learning, perfumers could present an enlargement of the cortical surface dedicated to odor representation, and correlatively present a GM volume increase.

Cellular events underlying structural changes

The nature of the cellular events that underlie structural changes in the human brain is still unknown , although it is widely assumed that GM loss in neurodegeneration corresponds to neural loss . First, GM increase can be due to fast morphological changes such as the formation of new connections by dendritic spine growth . Thus, a three-day olfactory learning in rats is accompanied by a dendritic spine density increase (15%) along apical dendrites of PC pyramidal neurons, suggesting an increased number of excitatory synapses . Second, GM increase can be related to slow mechanisms, such as adult neurogenesis that has been reported in the olfactory bulb and the PC . In the olfactory bulb, it was associated with an improvement in short-term olfactory memory when mice were exposed daily to a novel enriched olfactory environment . Here, we suggest that a GM volume increase in the anterior PC could mainly result both from a fast remodeling of the intracortical neuronal network, but genesis of new neurons in this brain area cannot be excluded. Table 1. Whole-brain structural modifications as a function of expertise in 1) all experts vs. all controls; 2) Younger experts vs. their controls; 3) Older experts vs. their controls.

Legends

  ) = 4.21, p < 0.001; Lower part: T(27) = 3.42, p = 0.002), and they had common intercepts (T(27) = 1.90, p = 0.068 and T(27) = 1.52, p = 0.139, respectively). The GM volume of the anterior PC (Fig. 3B) was not significantly positively correlated with expertise level in OE in the left (r = 0.486, F(1,12) = 3.71, p = 0.078) and the right (r = 0.401, F(1,12) = 2.30, p = 0.155) sides, but tended to be significantly negatively correlated (p uncorrected ) in OC in both sides (Left: r = -0.582, F(1,11) = 5.64, p = 0.037; Right: r = -0.623, F(1,11) = 6.69, p = 0.023). Regression lines for OE and OC data were significantly not parallel (Left: T 27 = 3.03, p = 0.005; Right: T(27) = 2.84, p = 0.009) and had common intercepts for the right anterior PC (T(27) = -1.74, p = 0.093).

  quality discrimination . Training to taste wine and beer also improves the ability to discriminate flavors . Recently, Li et al. showed that prolonged exposure (3.5 min) to a floral-smelling odorant enhances perceptual differentiation only for novel odorants that are related in odor quality or functional group, indicating that subjects become floral "experts." This effect was paralleled by increased responses in the posterior PC and interestingly an area close to the medial OFC (-14 30 -14), as observed here. The authors speculated that this learning-induced plasticity could reflect two neuronal mechanisms, an enlargement in cortical receptive fields, which results in the recruitment of more neurons (spatial summation), or alternatively a synchronization of neuronal activity (temporal summation) .

Fig. 1 .

 1 Fig. 1. Whole-brain structural modifications as a function of expertise. Increased GM volumes were observed in (A) the gyrus rectus in all experts (Exp) when compared with all controls (Cont), (B) the cingulate gyrus in young experts (YE) when compared with young controls (YC), and (C) the gyrus rectus and caudate nucleus in older experts (OE) when compared with older controls (OC).Graphs: data adjusted for gender and total GM volume in experts and controls. Vertical bars, standard error of the mean. GM: gray matter.

Fig. 2 .

 2 Fig. 2. Relationship between structural modifications and years of age. Positive slope in older experts (OE, green) and negative slope in older controls (OC, blue) of regression lines between the GM volume and years of age (from 20 to 60 years old) in A) the left GR/MOG and B) left and right anterior piriform cortex. GR/MOG, gyrus rectus/medial orbital gyrus.

Figure 1

 1 Figure 1 to be reproduced in color on the Web only

Table 1

 1 uncorrected < 0.001 at voxel-level, k > 170 (pk < 0.05).Abbreviations: Exp, All experts; Cont, All controls; OE, older expert; OC, older control; YE, young expert; YC, young control; GR/MOG, Gyrus rectus/Medial orbital gyrus; BA, Brodmann area L, left; R, right.

	Contrast	Region	BA	Side	Statistics	Coordinates
					k	T	x	y	z
	Exp -Cont GR/MOG	11m	L	2021 6.83 -8 41 -24
		GR/MOG	14r/13b R		6.00 9 27 -23
		MOG	13b/13m L		4.90 -15 35 -23
		Anterior cingulate gyrus	32ac	L	355 5.13 -3 39 16
		Caudate nucleus		L	552 4.91 -14 6 21
	YE -YC	GR/MOG	11m	L	296 6.65 -9 42 -26
		MOG	13m	L		4.57 -15 35 -23
		GR/MOG	14r/13b R	516 4.87 9 27 -20
		GR/MOG	11m	R		4.07 9 36 -20
		Anterior cingulate gyrus	32ac	L	264 4.27 -3 39 16
	OE -OC	Caudate nucleus		L	503 5.57 -14 14 16
				L		5.47 -15 6 21
		GR/MOG	14r/13b L	395 4.12 -8 26 -24
			14r	L			-9 23 -15

p

Acknowledgments

This work was supported by the Roudnitska Foundation, the Centre National de la Recherche Scientifique (CNRS), and the Institut National de la Santé et de la Recherche Médicale (INSERM).

We thank the editors of American Journal Experts for careful checking of English of the manuscript.

Delon-Martin et al.

The current study showed that olfactory expertise is concomitant with a structural reorganization in primary areas and the OFC. We revealed a GM volume increase in the bilateral GR/MOG and a trend for a GM increase in the posterior PC in odor experts compared with naïve subjects. More strikingly, the GM volume in the left GR/MOG and bilateral anterior PC was positively correlated with experience. These findings are consistent with our previous data showing that perfumers present an expertise-dependent functional reorganization in olfactory and memory areas. In summary, together with our previous experiment , long-term olfactory training leads to the acquisition of highly specific knowledge, which enables the generation of odor mental images and is paralleled by the structural and functional reorganization of olfactory areas.


Event Report: Computational Aesthetics 2011 in Vancouver, Canada, August 5-7, 2011, sponsored by Eurographics, in collaboration with ACM SIG-GRAPH

The Eurographics Workshop on Computational Aesthetics in Graphics, Visualization, and Imaging (CAe) took place in 2011 for the seventh time. This year, however, for the first time it was held as a joint symposium in Vancouver, Canada, together with the International Symposium on Non-Photorealistic Animation and Rendering (NPAR) and the International Symposium on Sketch-Based Interfaces and Modeling (SBIM). In addition, the three symposia were co-located with ACM SIGGRAPH.

Computational Aesthetics seeks to bridge the analytic and synthetic by integrating aspects of computer science, philosophy, psychology, and the fine, applied & performing arts. It seeks to facilitate both the analysis and the augmentation of creative behaviors. CAe also investigates the creation of tools that can enhance the expressive power of the fine and applied arts and furthers our understanding of aesthetic evaluation, perception, and meaning. As such it shares some of the same audience as the two other symposia, NPAR and SBIM, who are also interested in creating tools that foster creativity as well as draw from inspirations in artistic expression.

This year, CAe received 24 paper submissions in the technical track, which were reviewed by the international program committee, leading to all papers receiving at least three reviews, with almost all papers receiving four reviews. This lead to ten papers being accepted for presentation at the symposium (42% acceptance rate). The posters track had eight posters which were on display throughout the symposium and which were presented by the authors during a posters session.

An essential goal of the CAe symposium is to bring together participants from two domains: those who use computers in producing their art and those who write computer programs that purport to do something aesthetic. Thus we continued last years' tradition of pairing the technical part of the CAe program with an artistic part, and thus also issued a call for art papers as well as artworks. The art papers track received 15 submissions, of which six were accepted (40% acceptance rate) based on reviews by an arts program committee. Moreover, the artworks track received 40 submissions, out of which 17 were accepted for presentation at the conference (43% acceptance rate).

The accepted technical and arts papers and a catalog of the presented artworks appear in the CAe 2011 proceedings, which this year for the first time were published by the ACM in an electronic-only format. In addition, the papers are available in the ACM digital library (http://dl.acm.org/citation.cfm?id=2030441) and will be co-listed in the Eurographics digital library. This year for the first time, CAe gave out a best paper award which was awarded to Matt Lockyer, Lyn Bartram, and Bernhard E. Riecke for their paper "Simple Motion Textures for Ambient Affect." Also, honorable mention awards were given to Marc Alexa and Wojciech Matusik for their paper "Images from Self-Occlusion," to Tiffany C. Inglis and Craig S. Kaplan for their paper "Generating Op Art Lines," and to Jonathan Haber, Sean Lynch, and Sheelagh Carpendale for their paper "ColourVis: Exploring Colour Usage in Paintings Over Time."

We thank all people who helped to make CAe 2011 a successful event. In particular, we thank Andres Wanner and Allen Bevans for putting together the arts track of this year's conference which was a great success. Also, we thank John Collomosse and Paul Asente who chaired NPAR 2011 and Ellen Do, Jean-Claude Leon, Tracy Hammond, and Andy Nealen who co-chaired SBIM 2011, all of whom helped share the organizational load and to make the symposium a successful event. In addition, we thank Christian Richardt for running the website and being the publicity chair, Bernhard Riecke for being the posters chair, Brian Wyvill for being the ACM SIGGRAPH contact, Joaquim Jorge for being the Eurographics contact, and Stephen Spencer for producing the proceedings. Furthermore, we acknowledge the support from our main sponsors Adobe and IBM as well as from our production sponsors FCAT, iWeave, SIAT, Digitech Printing Inc., KeyTown, and The Home Depot.

We are delighted to announce that next year's event will take place in Annecy, France. Following this year's experience, CAe 2012 will again be run jointly with the related workshops on Non-Photorealistic Animation and Rendering (NPAR 2012) and Sketch-Based Interaction and Modeling (SBIM 2012), and the event will be co-located with the Annecy Animated Film Festival 2012. More information will be posted on the CAe homepage at http://www.computational-aesthetics.org/ Tobias Isenberg and Douglas Cunningham CAe 2011 co-chairs