Cognitive modeling with context sensitive reinforcement learning
Christian Balkenius, Stefan Winberg · Lund University Publications (Lund University) · 2004
We describe how a standard reinforcement learning algorithm can be changed to include a second contextual input that is used to modulate the learning in the original algorithm. The new algorithm takes the context into account during relearning when the previously learned actions are no longer valid. The algorithm was tested on a number of cognitive experiment and shown to reproduce the learning in both a task switching test and in the Wisconsin Card Sorting Test. In addition, the algorithm was able to learn a context sensitive categorization of objects in the Labov experiment.