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See detailCogEnv: A Reinforcement Learning Environment for Cognitive Tests
Ansarinia, Morteza UL; Clocher, Brice UL; Defossez, Aurélien et al

in 2022 Conference on Cognitive Computational Neuroscience (2022)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 17 (2 UL)
See detailThe Structure of Behavioral Data
Defossez, Aurélien; Ansarinia, Morteza UL; Clocher, Brice UL et al

E-print/Working paper (2020)

For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend ... [more ▼]

For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results. Although behavioral data is fundamental to many scientific fields, there is currently no widely adopted standard for formatting, naming, organizing, describing or sharing such data. This lack of standardization is a major bottleneck for scientific progress. Not only does it prevent the effective reuse of data, it also affects how behavioral data in general are processed, as non-standard data calls for custom-made data analysis code and prevents the development of efficient tools. To address this problem, we develop the Behaverse Data Model (BDM), a standard for structuring behavioral data. Here we focus on major concepts in behavioral data, leaving further details and developments to the project's website (https://behaverse.github.io/data-model/). [less ▲]

Detailed reference viewed: 93 (9 UL)