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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.

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

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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