Function: Learning Rate — func

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, where eta[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.

default: var1 = "Extra_Var1"

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.

default: var2 = "Extra_Var2"

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, 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.

The structure of eta depends on the model type:

For the Temporal Difference (TD) model, where a single learning rate is used throughout the experiment $$V_{new} = V_{old} + \eta \cdot (R - V_{old})$$
For the Risk-Sensitive Temporal Difference (RDTD) model, where two different learning rates are used depending on whether the reward is lower or higher than the expected value: $$V_{new} = V_{old} + \eta_{+} \cdot (R - V_{old}), R > V_{old}$$ $$V_{new} = V_{old} + \eta_{-} \cdot (R - V_{old}), R < V_{old}$$

TD: eta = 0.3

RSTD: eta = c(0.3, 0.7)

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

if (FALSE) { # \dontrun{
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)
}
} # }