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COGTOM SIGNED

Cognitive tomography of mental representations

Total Cost €

0

EC-Contrib. €

0

Partnership

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 COGTOM project word cloud

Explore the words cloud of the COGTOM project. It provides you a very rough idea of what is the project "COGTOM" about.

learning    decay    social    variety    implementations    series    cognitive    off    visual    tomography    percepts    infer    reconstruction    piecemeal    simply    stringent    cognition    consistent    statistical    quantitative    release    ranging    systematically    mental    movements    memory    central    interacts    hypothesis    rigorously    prior    synthetic    limited    experimental    time    principled    mind    model    dimension    humans    constructs    doubly    quantifiable    observations    lack    quantitatively    fundamental    machine    usable    decisions    formalised    techniques    data    accumulates    single    models    estimate    patterns    superior    methodological    transformations    conclusively    discovering    analytical    heuristic    simultaneously    internal    representations    distributions    collected    controls    collaborators    itself    algorithms    shelf    content    behavioural    paradigms    track    perceptual    takes    strength    structure    criteria    bayesian    multiple    changing    structured    motor    interference    dimensions   

Project "COGTOM" data sheet

The following table provides information about the project.

Coordinator
KOZEP-EUROPAI EGYETEM 

Organization address
address: NADOR UTCA 9
city: BUDAPEST
postcode: 1051
website: www.ceu.hu

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Hungary [HU]
 Total cost 1˙179˙462 €
 EC max contribution 1˙179˙462 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-COG
 Funding Scheme ERC-COG
 Starting year 2017
 Duration (year-month-day) from 2017-05-01   to  2022-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KOZEP-EUROPAI EGYETEM HU (BUDAPEST) coordinator 1˙179˙462.00

Map

 Project objective

Internal models are fundamental to our understanding of how the mind constructs percepts, makes decisions, controls movements, and interacts with others. Yet, we lack principled quantitative methods to systematically estimate internal models from observable behaviour, and current approaches for discovering their mental representations remain heuristic and piecemeal. I propose to develop a set of novel 'doubly Bayesian' data analytical methods, using state-of-the-art Bayesian statistical and machine learning techniques to infer humans' internal models formalised as prior distributions in Bayesian models of cognition. This approach, cognitive tomography, takes a series of behavioural observations, each of which in itself may have very limited information content, and accumulates a detailed reconstruction of the internal model based on these observations. I also propose a set of stringent, quantifiable criteria which will be systematically applied at each step of the proposed work to rigorously assess the success of our approach. These methodological advances will allow us to track how the structured, task-general internal models that are so fundamental to humans' superior cognitive abilities, change over time as a result of decay, interference, and learning. We will apply cognitive tomography to a variety of experimental data sets, collected by our collaborators, in paradigms ranging from perceptual learning, through visual and motor structure learning, to social and concept learning. These analyses will allow us to conclusively and quantitatively test our central hypothesis that, rather than simply changing along a single 'memory strength' dimension, internal models typically change via complex and consistent patterns of transformations along multiple dimensions simultaneously. To facilitate the widespread use of our methods, we will release and support off-the-shelf usable implementations of our algorithms together with synthetic and real test data sets.

 Publications

year authors and title journal last update
List of publications.
2018 Rodrigo Echeveste, Máté Lengyel
The Redemption of Noise: Inference with Neural Populations
published pages: 767-770, ISSN: 0166-2236, DOI: 10.1016/j.tins.2018.09.003
Trends in Neurosciences 41/11 2019-02-28

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