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CogEnv: A Reinforcement Learning Environment for Cognitive Tests
Ansarinia, Morteza; Clocher, Brice; Defossez, Aurélien et al.
2022In 2022 Conference on Cognitive Computational Neuroscience
Peer reviewed
 

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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.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
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)
External co-authors :
no
Language :
English
Title :
CogEnv: A Reinforcement Learning Environment for Cognitive Tests
Publication date :
2022
Event name :
2022 Conference on Cognitive Computational Neuroscience
Event place :
San Francisco, United States
Event date :
2022
Main work title :
2022 Conference on Cognitive Computational Neuroscience
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
FnR Project :
FNR11242114 - Scientifically Validated Digital Learning Environments, 2016 (01/06/2017-31/01/2023) - Pedro Cardoso-leite
Funders :
FNR - Fonds National de la Recherche [LU]
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since 26 May 2023

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