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Paper Detail

Paper: PS-1B.61
Session: Poster Session 1B
Location: H Fl├Ąche 1.OG
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Speed and accuracy in learning: A combined Q-learning diffusion decision model analysis
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Steven Miletic, Russell Boag, Varvara Mathiopoulou, Birte Forstmann, University of Amsterdam, Netherlands
Abstract: Recent advances in cognitive modelling merged two classes of cognitive models: Sequential sampling models of decision-making, and reinforcement learning models of error-driven learning. Such integrated models provide theoretical accounts of the cognitive processes underlying decisions and learning simultaneously. Here, we test whether a classical decision-making phenomenon -the speed-accuracy trade-off- can be observed in an instrumental learning task, and whether an integrated reinforcement learning/sequential sampling model is able to capture this effect. The results show that the model indeed captured the speed-accuracy trade-off effect in empirical data, as well as changes in response times and accuracies due to learning over the course of the experiment. This study further illustrates the great promise of the integration of the reinforcement learning and sequential sampling frameworks for cognitive psychology and cognitive neuroscience.