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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.

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

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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The information about "COGTOM" are provided by the European Opendata Portal: CORDIS opendata.

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