Computational modeling of reinforcement learning using probabilistic selection task and instructional probabilistic selection task - 13/04/16
Résumé |
Introduction |
Humans learn how to behave both through rules and instructions as well as through environmental experiences. It has been shown that instructions can powerfully control people's choices, often leading to a confirmation bias.
Aim |
To compare learning parameters in reinforcement learning task with and without instructions.
Methods |
We recruited 52 healthy adult control subjects (21 males, 31 females, age 30±6.5 years). Participants completed Repeatable Battery of Neuropsychological Status (RBANSS). Twenty-seven participants completed additionally Probabilistic Selection Task (PST) while twenty-five participants completed Instructional Probabilistic Selection Task (IPST). To analyze learning parameters, we used Q-learning model with 3 parameters: learning rate due to positive and negative reinforcements as well as exploration-exploitation parameter.
Results |
Both groups did not differ with respect to cognitive functioning measured with RBANSS (immediate and delayed memory, visuospatial abilities, language and attention); however, participants who completed PST had trend-level statistically faster learning rates due to positive (P=0.099) and negative reinforcements (0.057) in comparison to participants who completed IPST. Both groups did not differ with respect to exploration-exploitation parameter (0.409).
Conclusion |
In healthy adults, interference of confirmation bias can influence learning speed independent of cognitive functioning (immediate and delayed memory, visuospatial abilities, language and attention).
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Vol 33 - N° S
P. S138 - mars 2016 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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