The behaviour of different reinforcement-learning models in a task environment in which unexpected and expected uncertainties were independently manipulated.

<p>All models converge reasonably well with the actual mean of variable rewards. The learning rate for the Rescorla-Wagner model (η, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013445#pcbi.1013445.e001" target="_blank">Eq 1</a>) i...

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Bibliographic Details
Main Author:	Boluwatife Ikwunne (22238697) (author)
Other Authors:	Jolie Parham (22238700) (author), Erdem Pulcu (517414) (author)
Published:	2025
Subjects:	Cell Biology Science Policy Mental Health Biological Sciences not elsewhere classified readily available datasets g ., linear dynamically changing environments agent &# 8217 xlink "> reinforcement logarithmic etc .) learning rates space learning rates accumulate learning </ p agents perform learning novel rl model learning rates novel experiment logarithmic function human reinforcement temporal predictions prediction errors physiological correlates learners observe key components exact nature coefficient used based adaptions
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The behaviour of different reinforcement-learning models in a task environment in which unexpected and expected uncertainties were independently manipulated.

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