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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.

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

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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