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Abstraction and Generalisation in Human Decision-Making

Total Cost €


EC-Contrib. €






 NEUROABSTRACTION project word cloud

Explore the words cloud of the NEUROABSTRACTION project. It provides you a very rough idea of what is the project "NEUROABSTRACTION" about.

extant    basis    patterns    successful    good    experimental    deal    speak    computational    psychologists    theory    pertaining    centered    representations    representational    form    series    neural    describe    dorsal    dimensional    categories    brain    fmri    population    encoding    unseen    emergence    sciences    domains    decision    environment    behave    generalisation    eeg    seeking    spanish    learning    input    structure    pilot    scaffolded    portuguese    stream    depends    intelligent    building    variables    function    human    codes    decisions    contexts    abstractions    view    model    underpinnings    neuroimaging    encode    unexplored    latent    abstract    unsolved    agents    novelty    implications    individual    machine    bicycle    neuroscience    chart    artificial    largely    humans    populated    environments    data    perform    neuroscientists    similarity    functions    world    category    motorcycle    space    stimuli    rsa    cognitive    generalise    predicts    previously    structured    executive    ride    settings   

Project "NEUROABSTRACTION" data sheet

The following table provides information about the project.


Organization address
city: OXFORD
postcode: OX1 2JD

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 United Kingdom [UK]
 Total cost 1˙999˙775 €
 EC max contribution 1˙999˙775 € (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 2017-07-01   to  2022-06-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Intelligent agents make good decisions in novel environments. Understanding how humans deal with novelty is a key problem in the cognitive and neural sciences, and building artificial agents that behave effectively with novel settings remains an unsolved challenge in machine learning. According to one view, humans form abstract representations that encode latent variables pertaining to the high-level structure of the environment (a “model” of the world). These abstractions facilitate generalisation of extant task and category information to novel domains. For example, an individual who can ride a bicycle, or speak Spanish, will learn more rapidly to ride a motorcycle, or speak Portuguese. However, the neural basis for these abstractions, and the computational underpinnings of high-level generalisation, remain largely unexplored topics in cognitive neuroscience. In the current proposal, we describe 4 experimental series in which humans learn to perform structured decision-making tasks, and then generalise this behaviour to input domains populated by previously unseen stimuli, categories, or tasks. Building on extant pilot work, we will use representational similarity analysis (RSA) of neuroimaging (fMRI or EEG) data to chart the emergence of neural representations encoding abstract structure in patterns of brain activity. We will then assess how the formation of these abstractions at the neural level predicts successful human generalisation to previously unseen contexts. Our proposal is centered around a new theory, that task generalisation depends on the formation of low-dimensional population codes in the human dorsal stream, that are scaffolded by existing neural basis functions for space, value and number. The work will have important implications for psychologists and neuroscientists interested in decision-making and executive function, and for machine learning researchers seeking to build intelligent artificial agents.


year authors and title journal last update
List of publications.
2020 Christopher Summerfield, Fabrice Luyckx, Hannah Sheahan
Structure learning and the posterior parietal cortex
published pages: 101717, ISSN: 0301-0082, DOI: 10.1016/j.pneurobio.2019.101717
Progress in Neurobiology 184 2020-04-15
2019 Yinan Cao, Christopher Summerfield, Hame Park, Bruno Lucio Giordano, Christoph Kayser
Causal Inference in the Multisensory Brain
published pages: 1076-1087.e8, ISSN: 0896-6273, DOI: 10.1016/j.neuron.2019.03.043
Neuron 102/5 2020-04-15
2020 Fabrice Luyckx, Bernhard Spitzer, Annabelle Blangero, Konstantinos Tsetsos, Christopher Summerfield
Selective Integration during Sequential Sampling in Posterior Neural Signals
published pages: , ISSN: 1047-3211, DOI: 10.1093/cercor/bhaa039
Cerebral Cortex 2020-04-15
2019 Keno Juechems, Christopher Summerfield
Where Does Value Come From?
published pages: 836-850, ISSN: 1364-6613, DOI: 10.1016/j.tics.2019.07.012
Trends in Cognitive Sciences 23/10 2020-04-15
2019 Santiago Herce Castañón, Rani Moran, Jacqueline Ding, Tobias Egner, Dan Bang, Christopher Summerfield
Human noise blindness drives suboptimal cognitive inference
published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-019-09330-7
Nature Communications 10/1 2020-04-15
2019 Keno Juechems, Jan Balaguer, Santiago Herce Castañón, María Ruz, Jill X. O’Reilly, Christopher Summerfield
A Network for Computing Value Equilibrium in the Human Medial Prefrontal Cortex
published pages: 977-987.e3, ISSN: 0896-6273, DOI: 10.1016/j.neuron.2018.12.029
Neuron 101/5 2019-06-11
2017 Vickie Li, Santiago Herce Castañón, Joshua A. Solomon, Hildward Vandormael, Christopher Summerfield
Robust averaging protects decisions from noise in neural computations
published pages: e1005723, ISSN: 1553-7358, DOI: 10.1371/journal.pcbi.1005723
PLOS Computational Biology 13/8 2019-06-11
2017 Matthew Botvinick, David G. T. Barrett, Peter Battaglia, Nando de Freitas, Darshan Kumaran, Joel Z Leibo, Timothy Lillicrap, Joseph Modayil, Shakir Mohamed, Neil C. Rabinowitz, Danilo J. Rezende, Adam Santoro, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis
Building machines that learn and think for themselves
published pages: , ISSN: 0140-525X, DOI: 10.1017/s0140525x17000048
Behavioral and Brain Sciences 40 2019-06-11
2019 Fabrice Luyckx, Hamed Nili, Bernhard Spitzer, Christopher Summerfield
Neural structure mapping in human probabilistic reward learning
published pages: , ISSN: 2050-084X, DOI: 10.7554/elife.42816
eLife 8 2019-06-11
2018 Christopher Summerfield, Vickie Li
Perceptual suboptimality: Bug or feature?
published pages: , ISSN: 0140-525X, DOI: 10.1017/s0140525x18001437
Behavioral and Brain Sciences 41 2019-06-11
2018 Timo Flesch, Jan Balaguer, Ronald Dekker, Hamed Nili, Christopher Summerfield
Comparing continual task learning in minds and machines
published pages: E10313-E10322, ISSN: 0027-8424, DOI: 10.1073/pnas.1800755115
Proceedings of the National Academy of Sciences 115/44 2019-06-11
2017 Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick
Neuroscience-Inspired Artificial Intelligence
published pages: 245-258, ISSN: 0896-6273, DOI: 10.1016/j.neuron.2017.06.011
Neuron 95/2 2019-06-11

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