SUBJECT TITLE: Bio-Bayes Predictions -- Coupling Biological and Bayesian Predictive
Models in Neurocognitive Speech Processing
RESEARCH FIELD: Computer science, Cognitive Science, Psychological sciences, Neurosciences
SCIENTIFIC DEPARTMENT (LABORATORY?S NAME): LPNC -- Laboratoire de Psychologie et
NeuroCognition
DOCTORAL SCHOOL?S: MSTII -- Mathématiques, Sciences et technologies de l'information,
Informatique SUPERVISOR?S NAME: Julien Diard, PhD

SUBJECT DESCRIPTION:
The issue of the relationship between perception and production mechanisms is central to
many domains in cognitive science. In speech communication for instance, where
predictions from speech production simulation interact in various ways with perceptual
processes. In this context, we have developed COSMO (Communicating about Objects using
SensoriMotor Operations), a family of Bayesian algorithmic models of communicating
agents. We have previously used such models to study the evolution of phonological
systems (Moulin-Frier et al., 2015), speech perception and learning (Laurent et al.,
2017; Barnaud et al., 2017; 2018), and speech production and adaptation (Patri et al.,
2015; 2018).

However, so far, these models consider greatly simplified temporal dimensions. For
instance, syllable perception was restricted to consonant-vowel syllables, assuming that
the key points of speech trajectories, respectively at the center of the consonant and
the vowel, were previously identified. This, of course, contrasts with natural speech
processing, where sensory inputs and motor controls continuously unfold over time.
Indeed, the neuronal substrates, in the brain, that deal with auditory input are well
described in terms of their oscillatory nature, since they intrinsically have to deal
with temporal properties of speech, and their predictive nature, since they aim at
anticipating events.

In this PhD project, we aim at extending the COSMO framework to define the first Bayesian
perceptuo-motor model of continuous speech communication. In previous work, in the domain
of Bayesian visual word recognition modeling (Phénix et al., 2018; Ginestet et al.,
2019), we have developed mathematical tools to describe the temporal dynamics of
perceptual evidence accumulation across layers of hierarchical representations.
Probability distributions at each layer (letters and words) evolve continuously over
time, as a function of bottom-up sensory evidence and top-down lexical constraints, to
predict upcoming events. Crucially, we have developed mathematical tools to model, on the
one hand, attentional control of these information flows, and, on the other hand,
asynchronous and asymmetric information transfer. Applying these mathematical constructs
to speech communication modeling would yield a novel class of Bayesian hierarchical and
predictive models, able to account for observations of neuronal oscillatory systems in
the brain.

This PhD project is part of an international collaboration with Anne-Lise Giraud and
Itsaso Olasagasti of the ?Auditory, Speech and Language Neuroscience? group at UNIGE
(Université de Genève, Switzerland), with regular meetings and visits to Geneva planned.
This international collaboration with Geneva will provide a unique framework for mixing
the Bayesian approach with neuroscience constraints and data, providing a valuable
multidisciplinary environment for the PhD project. The PhD candidate will integrate the
?Language? research team at LPNC (Laboratoire de Psychologie et NeuroCognition,
Grenoble), will be supervised by Julien Diard (CR CNRS, LPNC) and Jean-Luc Schwartz (DR
CNRS, Gipsa- Lab), and will be registered in the MSTII (Mathématiques, Sciences et
technologies de l'information, Informatique) doctoral school of Univ. Grenoble Alpes.

ELIGIBILITY CRITERIA
Applicants must hold a Master's degree (or be about to earn one) or have a university
degree equivalent to a European Master's (5-year duration),
Applicants will have to send an application letter in English and attach: - their last
diploma;
- their CV;
- letters of recommendation are welcome.
Address to send their application: Julien.Diard@univ-grenoble-alpes.fr

SELECTION PROCESS
Application deadline: June, 21, 2019 at 17:00 (CET) Applications will be evaluated
through a three-step process:
1. Eligibility check of applications before June, 24, 2019
2. 1st round of selection: the applications will be evaluated by a Review Board between
June, 24, and June, 28,
2019. Results will be given June, 28, 2019.
3. 2nd round of selection: shortlisted candidates will be invited for an interview
session in Grenoble on July, 5,
2019 (in Grenoble or by Skype).

TYPE of CONTRACT: temporary-3 years of doctoral contract JOB STATUS: Full time
HOURS PER WEEK: 35
OFFER STARTING DATE: October, 1st, 2019
APPLICATION DEADLINE: June, 21, 2019
Salary: between 1768.55 ? and 2100 ? brut per month (depending on complementary activity
or not)
PROFILE SUMMARY:
The candidate must have a background in computer science, applied mathematics or signal
processing, with a strong affinity for cognitive sciences, or instead a background in
cognitive science or computational neuroscience, with a strong affinity for mathematical
modelling. This training must be validated, or be validated soon (before Summer 2019), by
a Masters level diploma (or equivalent).

The candidate must have previous experience in scientific research in a laboratory.
Mastery of a scientific calculus language, or of a general-purpose programming language
is required (R, Python, Matlab, etc.). The candidate must also have a good command of
scientific English, both spoken and written.

Previous experience in probabilistic modeling, dynamic systems modeling, or connectionist
modeling are a plus but are not required. Previous knowledge of the field of perception
modeling or speech production is also a plus.