[Comp-neuro] PhD student position in Decision Making, Sorbonne Universite, Paris (B. Girard, O. Davidenko)

Benoît Girard benoit.girard at isir.upmc.fr
Tue Sep 18 17:40:17 CEST 2018

Fully funded PhD: Cognitive and computational neurosciences studies to 
decipher how the social context affects individual food decisions

Institution: Sorbonne Université (Paris, France)
Disciplins: Computational Neurosciences, Behavioral Neuroscience, Brain 
Hosting laboratory: ISIR, Institut des Systèmes Intelligents et de 
Robotique (SU/CNRS)

Thesis Supervisors:
Benoît Girard (ISIR, UPMC/CNRS) , supervisor
Olga Davidenko (INRA/AgroParisTech/UPSay PNCA), co-supervisor

Starting date: fall 2018
Key Words: Reinforcement learning, Human eating behavior, Brain imaging

Scientific environment of the project:

Excessive intake of sugars and salt are linked to deleterious 
consequences on Human health. Current strategies to design and favor 
healthier options have limited effects due to their poor acceptability. 
SHIFT  proposes a multidisciplinary approach, combining life, social and 
computer sciences to understand the determinants, mechanisms, and levers 
to modulate the acceptability of a meal option. This project will 
address precise alimentary situations where margins of improvement with 
respect to salt and sugar reduction are possible, ie. acceptability of 
(i) water or low energy beverages in substitution of sodas and (ii) 
sugar- or salt-reduced options at the end of main meals. 
Interdisciplinary research activities will be conducted at the 
populational, contextual and individual scales to decipher criteria 
driving the acceptability foods.  SHIFT gathers 5 academic partners: The 
“Physiologie de la Nutrition et du Comportement Alimentaire” lab (UMR 
PNCA; INRA-AgroParisTech-Université Paris-Saclay, project leader), the 
“Institut des Systèmes Intelligents et de Robotique” (ISIR, Sorbonne 
Université-CNRS-INSERM), the “Mathématiques et Informatique Appliquées” 
lab (UMR MIA; INRA-AgroParisTech-Université Paris-Saclay), the 
“Alimentation et Sciences Sociales” lab (ALISS; INRA), the School of 
Psychology of the University of Birmingham and an industrial partner: 
Danone Nutricia Research (Global Nutrition Department)

Work Programme:

In the selection of foods as in many other decision processes, 
individuals tend to conform to social norms (i.e. a 
collectively-established acceptable behaviour). For instance, when 
eating with others, if individuals are offered to choose between two 
foods, they will take the decision that conforms to the choices made by 
the others guests. In some cases, for instance when all guests need to 
make a choice within a very short time lapse, there might be an 
uncertainty on this social norm. Before making their decision, 
individuals infer the appropriate choice from the available information 
of the choices made by others and after taking their decision, subjects 
judge the appropriateness of their own decision the basis of the overall 
choices of commensals. The acceptability of a food option is modulated 
by whether or not it is imitated by the commensals.

The aim of this thesis is to explore how the social context affects 
individual decisions. The student will study the question from the point 
of view of the individual: what are the contributions of various types 
of social modulation (imitation, conformity to a social norm) to food 
choices? Fundamental to every decision is the brain’s ability to 
internally evaluate subjective values. The theoretical framework of this 
thesis, the “value-based decision-making” (Pessiglione et al., 2006, 
Johnson & Ratcliff, 2014), suggests that, although from an external 
point of view, the outcome of a decision can be reduced to a binary 
phenomenon (acceptance or rejection) the underlying mental computation 
of the value is represented as a continuous function. Our working 
hypotheses are that the aforementioned social modulations to food 
choices can be integrated in an individual-centric modeling approach of 
decision-making (as has been shown in simpler contexts by Burke et al. 
(2010)), that the influences of these various types of modulations can 
be disentangled, so as to help identifying the possibly specific neural 
substrates. In this thesis, we will design a behavioural task dedicated 
to investigate the different causes of social modulations, and we will 
then explore which brain circuits are involved, using fMRI. We will use 
the theoretical tools of value-based decision-making to model the 
mechanisms by which social modulations affect decision. This will allow 
us to derive time series of internal variables of the model as 
regressors to refine the analysis of fMRI data.

The student will work to establish a fully specified model of a decision 
task incorporating mathematical formalizations of the social 
modulations. The model will be based on the so-called model-free 
reinforcement learning algorithms (Sutton & Barto, 1998), to which 
additional value update modalities will be added in order to take into 
account the influence of commensals.

Burke, C.J., Tobler, P.N., Baddeley, M., and Schultz, W. (2010). Neural 
mechanisms of observational learning. PNAS 201003111.
Johnson, E.J., and Ratcliff, R. (2014). Chapter 3 - Computational and 
Process Models of Decision Making in Psychology and Behavioral 
Economics. In Neuroeconomics (Second Edition), P.W. Glimcher, and E. 
Fehr, eds. (San Diego: Academic Press), pp. 35–47.
Pessiglione, M., Seymour, B., Flandin, G., Dolan, R.J., and Frith, C.D. 
(2006). Dopamine-dependent prediction errors underpin reward-seeking 
behaviour in humans. Nature 442, 1042–1045.
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An 
introduction. MIT press.

To apply

Applicants should send a CV, letter of motivation (max 2 pages), and 
references via e-mail to benoit.girard at sorbonne-universite.fr and 
olga.davidenko at agroparistech.fr, with [SHIFT] in the subject of the 
mail. Review of  applicants will begin immediately, and will continue 
until the position is filled. The earliest start date is November 2018.

Benoît Girard, ISIR, UPMC/CNRS
Pyramide T55/65, CC 173
4 place Jussieu, 75252 Paris Cedex
Submit your replications to ReScience: https://rescience.github.io/

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