A recurrent neural network framework for flexible and adaptive decision making based on sequence learning
Fig 2
The network framework and Task 1: Probabilistic reasoning.
a. The framework diagram. The network has three layers: the input layer, the hidden layer, and the output layer. The input layer receives the input sequences of sensory, action, and reward events. The hidden layer has 128 gated recurrent units. The output layer units mirror the input layer units and represent the prediction of future events. The diagram illustrates the particular input and output units for Task 1. b. Task 1: the reaction-time version of probabilistic reasoning task. The subject fixates at a central point and views a series of shapes to make a response by moving the eyes toward one of the two choice targets on the peripheral. Each shape confers information regarding which target will be rewarded. The optimal strategy is to integrate the information and to make a choice when the integrated information hits a bound. The inset shows the sampling distributions.