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#' @templateVar MODEL_FUNCTION bandit4arm_4par
#' @templateVar CONTRIBUTOR
#' @templateVar TASK_NAME 4-Armed Bandit Task
#' @templateVar TASK_CODE bandit4arm
#' @templateVar TASK_CITE
#' @templateVar MODEL_NAME 4 Parameter Model, without C (choice perseveration)
#' @templateVar MODEL_CODE 4par
#' @templateVar MODEL_CITE (Seymour et al., 2012)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS "subjID", "choice", "gain", "loss"
#' @templateVar PARAMETERS \code{Arew} (reward learning rate), \code{Apun} (punishment learning rate), \code{R} (reward sensitivity), \code{P} (punishment sensitivity)
#' @templateVar REGRESSORS
#' @templateVar POSTPREDS "y_pred"
#' @templateVar LENGTH_DATA_COLUMNS 4
#' @templateVar DETAILS_DATA_1 \item{subjID}{A unique identifier for each subject in the data-set.}
#' @templateVar DETAILS_DATA_2 \item{choice}{Integer value representing the option chosen on the given trial: 1, 2, 3, or 4.}
#' @templateVar DETAILS_DATA_3 \item{gain}{Floating point value representing the amount of currency won on the given trial (e.g. 50, 100).}
#' @templateVar DETAILS_DATA_4 \item{loss}{Floating point value representing the amount of currency lost on the given trial (e.g. 0, -50).}
#' @templateVar LENGTH_ADDITIONAL_ARGS 0
#'
#' @template model-documentation
#'
#' @export
#' @include hBayesDM_model.R
#' @include preprocess_funcs.R
#' @references
#' Seymour, Daw, Roiser, Dayan, & Dolan (2012). Serotonin Selectively Modulates Reward Value in Human Decision-Making. J Neuro, 32(17), 5833-5842.
#'
bandit4arm_4par <- hBayesDM_model(
task_name = "bandit4arm",
model_name = "4par",
model_type = "",
data_columns = c("subjID", "choice", "gain", "loss"),
parameters = list(
"Arew" = c(0, 0.1, 1),
"Apun" = c(0, 0.1, 1),
"R" = c(0, 1, 30),
"P" = c(0, 1, 30)
),
regressors = NULL,
postpreds = c("y_pred"),
preprocess_func = bandit4arm_preprocess_func)
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