Reference : CogEnv: A Reinforcement Learning Environment for Cognitive Tests

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/55225

Title :	CogEnv: A Reinforcement Learning Environment for Cognitive Tests
Language :	English
Author, co-author :	Ansarinia, Morteza [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)]
	Clocher, Brice [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)]
	Defossez, Aurélien [> >]
	Schmück, Emmanuel [> >]
	Cardoso-Leite, Pedro [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)]
Publication date :	2022
Main document title :	2022 Conference on Cognitive Computational Neuroscience
Peer reviewed :	Yes
Event name :	2022 Conference on Cognitive Computational Neuroscience
Event date :	2022
Event place (city) :	San Francisco
Event country :	US
Abstract :	[en] Understanding human cognition involves developing computational models that mimic and possibly explain behavior; these are models that “act” like humans and produce similar outputs when facing the same inputs. To facilitate the development of such models and ultimately further our understanding of the human mind we created CogEnv: a reinforcement learning environment where artificial agents interact with and learn to perform cognitive tests and can then be directly compared to humans. By leveraging CogEnv, cognitive and AI scientists can join efforts to better understand human cognition: the relative performance profiles of human and artificial agents may provide new insights on the computational basis of human cognition and on what human-like abilities artificial agents may lack.
Funders :	Fonds National de la Recherche - FnR
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/55225
DOI :	10.32470/CCN.2022.1198-0
Other URL :	https://2022.ccneuro.org/proceedings/0000205.pdf
FnR project :	FnR ; FNR11242114 > Pedro Cardoso-leite > DIGILEARN > Scientifically Validated Digital Learning Environments > 01/06/2017 > 31/01/2023 > 2016

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