R/document_behrule.R

In multiRL: Reinforcement Learning Tools for Multi-Armed Bandit

#' @title Behavior Rules
#' @name behrule
#' @description 
#' 
#'  In most instances of the Multi-Armed Bandit (MAB) task, the cue aligns with 
#'    the response. For example, you are required to select one of four bandits 
#'    (A, B, C, or D), receive immediate feedback, and subsequently update the 
#'    expected value of the selected bandit.
#'  
#'  When the cue and the response are inconsistent, the agent needs to form a 
#'    latent rule. For example, in the arrow paradigm of Rmus et al. (2024) 
#'    \doi{10.1371/journal.pcbi.1012119}, 
#'    participants can only choose left or right, but what they actually need 
#'    to learn is the value associated with arrows of different colors. 
#'    
#'  The final case represents my personal interpretation, when participants 
#'    have limited working-memory capacity and an object can be decomposed into 
#'    many elements, they may update the values of only a subset of those 
#'    elements rather than the entire object.
#'    
#' @section Class: 
#' \code{behrule [List]} 
#'    
#' @section Slots: 
#' \itemize{
#'    \item \code{cue [CharacterVector]}
#'    
#'          A \code{cue} refers to the stimulus-or a component of the 
#'          stimulus-presented in the paradigm. It represents the internal 
#'          target the agent selects, which may differ from the actual 
#'          behavioral response. For instance, cue is the color of arrows, 
#'          rather than the direction. 
#'          
#'    \item \code{rsp [CharacterVector]}
#'    
#'          The \code{rsp} represents the action the agent actually makes. 
#'          It typically has a mapping relationship with the cue. For example, 
#'          in the arrow paradigm of Rmus et al. (2024) 
#'          \doi{10.1371/journal.pcbi.1012119}, 
#'          the agent updates the value associated with the arrow's color, but 
#'          the overt response is the direction corresponding to the currently 
#'          chosen color arrow.
#' }
#' 
#' @section Example: 
#' 
#' \preformatted{ # latent rule
#'  behrule = list(
#'    cue = c("Red", "Yellow", "Green", "Blue"),
#'    rsp = c("Up", "Down", "Left", "Right")
#'  )
#' }
#' 
#' @references 
#' Rmus, M., Pan, T. F., Xia, L., & Collins, A. G. (2024). Artificial neural 
#' networks for model identification and parameter estimation in computational 
#' cognitive models. \emph{PLOS Computational Biology, 20}(5), e1012119. 
#' \doi{10.1371/journal.pcbi.1012119}
#' 
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