Computational Investigation of Probabilistic Learning Task with Use of Machine Learning

Probabilistic Learning Task is a game that serve psychiatrists and psychologists to measure some cognitive abilities of people having various cognitive disorders. Mathematical models together with machine learning techniques are routinely used to summarize large amount of data produced by players during the game. Parameters of mathematical models are taken to represent behavioral data gathered during the game. However, there is no study of reliability of those parameters available in literature. We investigate how much one can trust the values of models parameters. We proposed a specific method to assess reliability of models parameters, that makes use of the game sessions of human players and their virtual counterparts.

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