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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.

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

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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