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PREF LEARNHEUR

Learning heuristics in preference elicitation tasks: insights from behavioural, computational and neurobiological investigations

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 PREF LEARNHEUR project word cloud

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Project "PREF LEARNHEUR" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITEIT VAN AMSTERDAM 

Organization address
address: SPUI 21
city: AMSTERDAM
postcode: 1012WX
website: www.uva.nl

contact info
title: n.a.
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surname: n.a.
function: n.a.
email: n.a.
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 Coordinator Country Netherlands [NL]
 Project website https://sites.google.com/site/maellebreton/home
 Total cost 165˙598 €
 EC max contribution 165˙598 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2015
 Duration (year-month-day) from 2015-05-01   to  2017-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITEIT VAN AMSTERDAM NL (AMSTERDAM) coordinator 165˙598.00

Map

 Project objective

Although decision theory assumes that when making a choice, individuals attribute values to available options, compare those values and select the option with the highest, the succession of choices faced during classical preference elicitation tasks might trigger the emergence of additional heuristics, implemented to perform those tasks in a fast, yet adaptive manner. This project aim at pioneering the isolation of such heuristic development, in a dynamical framework where both the task features and the agents’ own preferences are learned from previous trials, and influence subsequent behavior. This framework suggests that agents’ preferences will depend on the choice sequence, thus vary according to predictable patterns through different instantiations of the same task. Thereby, it captures a new component of individual preferences, which we refer to as task-related preferences. Combining behavioral experiments, computational modelling and functional brain imaging, we propose to reveal and measure the behavioral variance accounted by the task-related preferences, to model their emergence during the task, and to incorporate them in a coherent neuro-cognitive model of decision-making. Overall, this project will contribute to 1) refine current neurocognitive and economic models of decision-making, 2) train a promising cognitive neuroscientist to tackle human decision issues relevant to social sciences, with advanced quantitative economic/computational tools, and 3) foster fruitful cross-talks between scholars from economics, psychology, and neuroscience at the host institution. The scientific contribution seems particularly important given that preferences are one of the current conceptual cornerstones used to understand our society at the micro- and macroeconomic level, to guide and assess public policies aiming to maximize people’s well-being, to characterize normal and pathological behaviors, and to unravel the neurobiological mechanisms underlying decision-making.

 Publications

year authors and title journal last update
List of publications.
2016 Mael Lebreton, Stefano Palminteri
When are inter-individual brain-behavior correlations informative?
published pages: , ISSN: , DOI:
2019-06-14
2016 Leendert van Maanen, Joaquina Couto, Mael Lebreton
Three Boundary Conditions for Computing the Fixed-Point Property in Binary Mixture Data
published pages: e0167377, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0167377
PLOS ONE 11/11 2019-06-14
2017 Germain Lefebvre, Maël Lebreton, Florent Meyniel, Sacha Bourgeois-Gironde, Stefano Palminteri
Behavioural and neural characterization of optimistic reinforcement learning
published pages: 67, ISSN: 2397-3374, DOI: 10.1038/s41562-017-0067
Nature Human Behaviour 1 2019-06-14

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