Function: Learning Rate
func_eta(value, utility, reward, occurrence, var1 = NA, var2 = NA, eta, lambda)learning rate eta
The expected value of the stimulus in the subject's mind at this point in time.
The subjective value that the subject assigns to the objective reward.
The objective reward received by the subject after selecting a stimulus.
The number of times the same stimulus has appeared.
[character] column name of extra variable 1. If your model uses more than just reward and expected value, and you need other information, such as whether the choice frame is Gain or Loss, then you can input the 'Frame' column as var1 into the model. e.g., `var1 = "Extra_Var1"`
[character] column name of extra variable 2. If one additional variable, var1, does not meet your needs, you can add another additional variable, var2, into your model. e.g., `var2 = "Extra_Var2"`
[vector] Parameters used in the Learning Rate Function `rate_func` representing the rate at which the subject updates the difference (prediction error) between the reward and the expected value in the subject's mind. In the TD model, there is a single learning rate throughout the experiment. In the RSTD model, two different learning rates are used when the reward is higher or lower than the expected value. e.g., `eta = c(0.3, 0.7)`
[vector] Extra parameters that may be used in functions. e.g., `lambda = c(0.4, 0.7, 20, 60)`