CultureLab Projects

Junior Excellence Chair Projects                                 PhD Theses                                         ERC Grant                                           ANR Funded Project
 

Junior Excellence Chairs Projetcs

  • Automatically Identifying Inter-Cultural Metaphor Variation [ongoing]

    The way that people in different cultures think is encoded in the language they use. In particular, patterns of metaphorical language, known as conceptual metaphors (Lakoff and Johnson, 1980), may offer a lens into the thought patterns of different cultures in different time periods. In this project, we will be exploring how conceptual metaphors vary between languages, both modern and ancient, in order to investigate whether multilingual metaphor differences are predictive of cultural variation. To achieve these analyses, we are developing develop computational methods to identify conceptual metaphors automatically across a diverse collection of languages.

           Principal investigator: Rowan Hall Maudslay

  • Computational Approach to the Cultural Evolution of Food [ongoing]

    This project investigates how food culture evolves over time, combining perspectives from computational, historical, and cultural research. It explores how cuisines, including recipes, ingredients, techniques, and tools, emerge, spread, and change across societies and historical periods. In addition, this study seeks to understand how cultural, social, and technological factors influence cuisine. It integrates methods from computational linguistics and agent-based modelling to uncover the mechanisms driving culinary change, adaptation, and innovation. In this respect, this project focuses on three main research axes: classifying and comparing cuisines across time and space, tracing the integration of new food elements into existing traditions, and studying how digital media contribute to the viral spread of recipes and culinary trends. By bridging computational methods with cultural theory, this research aims to build a scientific foundation for understanding the cultural evolution of food and to provide new tools and datasets for future interdisciplinary studies.

            Principal investigator: Alexandre Bluet



PhD Theses

  • When Fear Goes Viral : Computational Analysis of the Evolution of Horrific Content Online [ongoing]

    Emerging from early 21st century forums and social networks, creepypastas represent a new literary phenomenon at the crossroads of folklore, digital culture, and literature. These short and frightening stories, often accompanied by images or videos, have given rise to a new imaginary and poetics based on virality, interactivity, and media hybridity.
    This dissertation analyzes the genesis and evolution of the genre through a computational approach combining the study of texts and images. By using large-scale textual analysis and computer vision tools, it seeks to identify the forms, motifs, and codes specific to creepypastas, while examining the processes of canonization and literary legitimation in the digital age.

           PhD student: Alexandre Lionnet
           Supervisors : Daniel Stockholm (EPHE-PSL) & Florian Cafiero (ENC-PSL)

  • The Medieval Transmission of Merovingian Historiography. A Computational-philological Study of Texts and their Manuscripts [ongoing]

The project examines the manuscript circulation of the earliest surviving works of Frankish historiography (the Historiae by Gregory of Tours, the so-called Chronicle of Fredegar, and the Liber Historiae Francorum), mapping their chronology, geography, cultural milieux of diffusion and their role in the development of a distinct historiographical tradition.
It further examines the effects of the dynamics of transmission had on the circulating works themselves, in terms of cumulative modifications, contamination, textual instability or fixation of canonical recensions, attempting to shed light on the overall process of (re)circulation of historical knowledge.
The research project follows a transdisciplinary approach, drawing upon computational stemmatic, manuscript studies and history of historiography.     
      
            PhD student: Alessandro Gnasso
            Supervisor: Jean-Baptiste Camps (ENC-PSL) & Katarzyna Kapitan (ENC-PSL)

 

ERC Grant

  • The Lost Manuscripts of Medieval Europe: Modelling the Transmission of Texts [ongoing]

    In the currents of cultural evolution, the fate of written artefacts hangs on the delicate balance between cultural preferences and chance. How do texts, like living organisms, experience a process of preservation, transformation or extinction? To answer this question, the ERC-funded LostMA project will blend AI, complexity science and philological expertise to unravel the mysteries behind the deviation of textual transmission from pure chance. Focusing on chivalric literature in a European context, the team utilises deep learning for large-scale data collection on 4 000 documents. This groundbreaking approach not only scrutinises the transmission of texts but also challenges the role of chance in shaping cultural canons.
           Principal investigator: Jean-Baptiste Camps (ENC)
 

ANR Funded Project

  • The role of cognition applied to unpredictable content in the evolution of writing (UNPREDICTABLE) [starts in January 2026]

    The appearance of writing was a turning point in the evolution of human communication and cognition; yet writing did not evolve independently in most human societies. For millennia, many societies only used specialised graphic codes representing specific types of information—personal emblems, numbers, calendric units, among others. In the societies that invented writing independently, the notation of speech sounds is preceded by such specialised notations, and is first used for telegraphic writing (encoding only some parts of speech), giving rise to full sentences notation only centuries later. This project attempts to describe this phenomenon systematically, to provide a cognitively plausible explanation for it, and to explore the broader implications of the underlying psychological mechanisms. We hypothesise that contextually unpredictable content is more likely to be encoded: we are more likely to explicitly say or write what cannot be guessed. We will test three hypotheses: (H1) some types of content are consistently more predictable than others, in a way that is to a certain extent robust to historical and cross-linguistic variations; (H2) people can detect unpredictable content and they ascribe cognitive value to it; (H3) the evolution of writing and other graphic codes should follow a sequence, with the invention and use of codes for highly unpredictable content (e.g. numerical quantities) occurring first, followed by the encoding of predictable content (e.g. articles or prepositions). The project is based on three different methodologies: Computational linguistic methods on big text corpora will test H1; H2 will be tested by conducting experiments to test whether participants detect unpredictable content types as such, and whether mentioning unpredictable content enhances the perceived value of explanations and arguments; we will test H3 through a systematic investigation of the evolution of graphic codes dedicated to various types of content.
           Principal investigator: Olivier Morin (ENS)