func_eta {binaryRL} | R Documentation |
Function: Learning Rate
Description
The structure of eta
depends on the model type:
-
Temporal Difference (TD) model:
eta
is a single numeric value representing the learning rate. -
Risk-Sensitive Temporal Difference (RSTD) model:
eta
is a numeric vector of length two, whereeta[1]
represents the learning rate for "good" outcomes, which means the reward is higher than the expected value.eta[2]
represents the learning rate for "bad" outcomes, which means the reward is lower than the expected value.
Usage
func_eta(
i,
L_freq,
R_freq,
L_pick,
R_pick,
L_value,
R_value,
var1 = NA,
var2 = NA,
value,
utility,
reward,
occurrence,
eta,
alpha,
beta
)
Arguments
i |
The current row number. |
L_freq |
The frequency of left option appearance |
R_freq |
The frequency of right option appearance |
L_pick |
The number of times left option was picked |
R_pick |
The number of times left option was picked |
L_value |
The value of the left option |
R_value |
The value of the right option |
var1 |
[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.
|
var2 |
[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.
|
value |
The expected value of the stimulus in the subject's mind at this point in time. |
utility |
The subjective value that the subject assigns to the objective reward. |
reward |
The objective reward received by the subject after selecting a stimulus. |
occurrence |
The number of times the same stimulus has been chosen. |
eta |
[numeric]
Parameters used in the Learning Rate Function, The structure of
|
alpha |
[vector] Extra parameters that may be used in functions. |
beta |
[vector] Extra parameters that may be used in functions. |
Value
learning rate eta
Note
When customizing these functions, please ensure that you do not modify the arguments. Instead, only modify the 'if-else' statements or the internal logic to adapt the function to your needs.
Examples
## Not run:
func_eta <- function(
# Trial number
i,
# Number of times this option has appeared
L_freq,
R_freq,
# Number of times this option has been chosen
L_pick,
R_pick,
# Current value of this option
L_value,
R_value,
# Extra variables
var1 = NA,
var2 = NA,
# Expected value for this stimulus
value,
# Subjective utility
utility,
# Reward observed after choice
reward,
# Occurrence count for this stimulus
occurrence,
# Free Parameter
eta,
# Extra parameters
alpha,
beta
){
################################# [ TD ] ####################################
if (length(eta) == 1) {
eta <- as.numeric(eta)
}
################################ [ RSTD ] ###################################
else if (length(eta) > 1 & utility < value) {
eta <- eta[1]
}
else if (length(eta) > 1 & utility >= value) {
eta <- eta[2]
}
################################ [ ERROR ] ##################################
else {
eta <- "ERROR" # Error check
}
return(eta)
}
## End(Not run)