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Cognitive tomography of mental representations

Total Cost €


EC-Contrib. €






 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.

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

Project "COGTOM" data sheet

The following table provides information about the project.


Organization address
address: NADOR UTCA 9
postcode: 1051

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


Take a look of project's partnership.

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


 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.


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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