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The neural and cognitive architecture for learning from a small sample

Cortese, A; De Martino, B; Kawato, M; (2019) The neural and cognitive architecture for learning from a small sample. Current Opinion in Neurobiology , 55 pp. 133-141. 10.1016/j.conb.2019.02.011. Green open access

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Abstract

Artificial intelligence algorithms are capable of fantastic exploits, yet they are still grossly inefficient compared with the brain’s ability to learn from few exemplars or solve problems that have not been explicitly defined. What is the secret that the evolution of human intelligence has unlocked? Generalization is one answer, but there is more to it. The brain does not directly solve difficult problems, it is able to recast them into new and more tractable problems. Here, we propose a model whereby higher cognitive functions profoundly interact with reinforcement learning to drastically reduce the degrees of freedom of the search space, simplifying complex problems, and fostering more efficient learning.

Type: Article
Title: The neural and cognitive architecture for learning from a small sample
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.conb.2019.02.011
Publisher version: https://doi.org/10.1016/j.conb.2019.02.011
Language: English
Additional information: This is an Open Access article published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/),
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Institute of Cognitive Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10074105
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