Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: a meta-analytical approach using diffusion decision modeling.
Reinforcement learning (RL) models describe how humans and animals learn by trial-and-error to select actions that maximize rewards and minimize punishments. Traditional RL models focus exclusively on choices, thereby ignoring the interactions between choice preference and response time (RT), or how...
| Published in: | Cognitive, Affective & Behavioral Neuroscience Vol. 19; no. 3; pp. 490 - 503 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jun2019
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| Online Access: | View this record in EBSCOhost |