Computational simulation of reading acquisition

The BRAID-Acq bayesian model developed by Steinhilber, Valdois et Diard simulates the second phase of reading acquisition, whereby the child who already knows a substantial number of written words (>1000), can read autonomously and progressively grow their orthographic lexicon as they are exposed to new words (unsupervised learning). The goal of this internship is to use, and if necessary adapt this model in order to simulate the first phase of reading acquisition, whereby the child explicitly learns grapheme-phoneme correspondences to decode words (supervised learning), by progressively storing decoded words in their orthographic lexicon. The model will then be used to compare different reading tuition methods: different progressions of grapheme/phoneme correspondences, analytic vs. synthetic methods, whole word methods. Perspectives include the study of individual differences in reading acquisition, and the optimisation of the learning regime as a function of individual characteristics.

Candidate profile:
The candidate should ideally have a strong background in computer programming and simulation, and a taste for mathematical modelling and experimental psychology: students in computer science, engineering and cognitive science. Knowledge in probability would be an asset. The model is developed in Python, so familiarity with this language is a prerequisite.

Contacts
Franck Ramus (LSCP, CNRS) : franck.ramus@ens.psl.eu
Julien Diard (LPNC, CNRS) : julien.diard@univ-grenoble-alpes.fr

In practice:
Standard allowance (~600€/month for a full-time internship).
The student can be based either in Paris at Laboratoire de sciences cognitives et psycholinguistique (ENS/EHESS/CNRS) or in Grenoble at Laboratoire de Psychologie et NeuroCognition (UGA/CNRS/USMB). The collaboration between the two sites will involve visits to the other lab and online meetings.