I am currently Maître de Conférences (Assistant Professor with tenure) of computer science at Aix-Marseille University.

I am also a Research Fellow at the Institute of Language, Communication and the Brain (ILCB) — of which I am also a member of the governing board.

Founder of the Computational Communicative Development team (CoCoDev), where we study children's communicative development in natural, interactive, and multimodal contexts.

I have a background in Mathematics and Physics (Ecole Polytechnique), Computational Cognitive Science (Ecole Normale Supérieure), and Experimental Psychology (Stanford University).

Recent Updates:

I am co-editing, and welcoming contributions, to the following two journal special issues:

- "Language Learning, Representation, and Processing in Humans and Machines" (Call for papers), in Computational Linguistics

- "Neural and Behavioral Mechanisms of Social Learning" (Topic description), in Frontiers in Human Neuroscience

Other activities and events:

Representative publications

  • Bodur, K., Nikolaus, M., Prévot, L., & Fourtassi, A. (2023). Using video calls to study children’s conversational development: The case of backchannel signaling. Frontiers in Computer Science, 5.

      Understanding how children’s conversational skills develop is crucial for understanding their social, cognitive, and linguistic development, with important applications in health and education. To develop theories based on quantitative studies of conversational development, we need (i) data recorded in naturalistic contexts (e.g. child-caregiver dyads talking in their daily environment) where children are more likely to show much of their conversational competencies, as opposed to controlled laboratory contexts which typically involve talking to a stranger (e.g., the experimenter); (ii) data that allows for clear access to children’s multimodal behavior in face-to-face conversations; and (iii) data whose acquisition method is cost-effective with the potential of being deployed at a large scale to capture individual and cultural variability. The current work is a first step to achieve this goal. We built a corpus made of video chats involving children in middle childhood (6-12 years old) and their caregivers using a weakly structured word-guessing game to prompt spontaneous conversation. The manual annotations of these recordings have shown the similarity of the frequency distribution of multimodal communicative signals from both children and caregivers. As a case study, we capitalize on this rich behavioral data to study how both verbal and non-verbal cues contribute to the development of conversational coordination. In particular, we looked at how children learn to engage in coordinated conversations not only as speakers but also as listeners by analyzing children’s use of backchannel signaling (e.g., verbal ’mh’ or head nods) during these conversations. Contrary to results from previous in-lab studies, our use of both more natural/spontaneous conversational settings and more adequate controls allowed us to reveal that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. Our work demonstrates the usefulness of recent technology in video calling for acquiring quality data that can be used for research on children’s conversational development in the wild.

      Link Repo

  • Nikolaus, M., Maes, E., Auguste, J., Prevot, L., & Fourtassi, A. (2022). Large-scale study of speech acts’ development in early childhood. Language Development Research, 2(1).

      Studies of children’s language use in the wild (e.g., in the context of child-caregiver social interaction) have been slowed by the time- and resource- consuming task of hand annotating utterances for communicative intents/speech acts. Existing studies have typically focused on investigating rather small samples of children, raising the question of how their findings generalize both to larger and more representative populations and to a richer set of interaction contexts. Here we propose a simple automatic model for speech act labeling in early childhood based on the INCA-A coding scheme (Ninio, Snow, Pan, & Rollins, 1994). After validating the model against ground truth labels, we automatically annotated the entire English-language data from the CHILDES corpus. The major theoretical result was that earlier findings generalize quite well at a large scale. Further, we introduced two complementary measures for the age of acquisition of speech acts which allows us to rank different speech acts according to their order of emergence in production and comprehension.Our model will be shared with the community so that researchers can use it with their data to investigate various question related to language use both in typical and atypical populations of children.

      Link Preprint Repo

  • Fourtassi, A., Regan, S., & Frank, M. C. (2021). Continuous developmental change explains discontinuities in word learning. Developmental Science, 24(2).

      Cognitive development is often characterized in terms of discontinuities, but these discontinuities can sometimes be apparent rather than actual and can arise from continuous developmental change. To explore this idea, we use as a case study the finding by Stager and Werker (1997) that children’s early ability to distinguish similar sounds does not automatically translate into word learning skills. Early explanations proposed that children may not be able to encode subtle phonetic contrasts when learning novel word meanings, thus suggesting a discontinuous/stage-like pattern of development. However, later work has revealed (e.g., through using more precise testing methods) that children do encode such contrasts, thus favoring a continuous pattern of development. Here, we propose a probabilistic model that represents word knowledge in a graded fashion and characterizes developmental change as improvement in the precision of this graded knowledge. Our model explained previous findings in the literature and provided a new prediction – the referents’ visual similarity modulates word learning accuracy. The models’ predictions were corroborated by human data collected from both preschool children and adults. The broader impact of this work is to show that computational models, such as ours, can help us explore the extent to which episodes of cognitive development that are typically thought of as discontinuities may emerge from simpler, continuous mechanisms.

      Link Preprint Repo

  • Fourtassi, A., Bian, Y., & Frank, M. C. (2020). The growth of children’s semantic and phonological networks: Insight from 10 languages. Cognitive Science, 44(7), e12847.

      Children tend to produce words earlier when they are connected to a variety of other words along the phonological and semantic dimensions. Though these semantic and phonological connectivity effects have been extensively documented, little is known about their underlying developmental mechanism. One possibility is that learning is driven by lexical network growth where highly connected words in the child’s early lexicon enable learning of similar words. Another possibility is that learning is driven by highly connected words in the external learning environment, instead of highly connected words in the early internal lexicon. The present study tests both scenarios systematically in both the phonological and semantic domains across 10 languages. We show that phonological and semantic connectivity in the learning environment drives growth in both production- and comprehension-based vocabularies, even controlling for word frequency and length. This pattern of findings suggests a word learning process where children harness their statistical learning abilities to detect and learn highly connected words in the learning environment.

      Link Preprint Repo