A recurrent neural network framework for flexible and adaptive decision making based on sequence learning
Fig 1
Sequence learning and decision making.
a. Pavlovian learning. Different cues predict different reward outcomes. b. Instrumental learning. Different actions lead to different reward outcomes. c. An example match-to-sample task. Moving the lever leftward after a pair of matching cues leads to a reward. d. Reversal learning. Two options are presented. The left choice is initially rewarded, but the reward switches to the rightward choice in the second trial. Notice both the contingencies between events within each trial and events across trials are essential for the learning. e. In more complicated decision making, the contingencies can be between many different types of sensory, action, and reward events distributed across time. Black brackets indicate contingencies that exist.