R/med_dec-data.R

In fddm: Fast Implementation of the Diffusion Decision Model

#' Medicial decision data
#'
#' Part of the accuracy and response time data presented in Trueblood et al.
#' (2017) investigating medical decision making among medical professionals
#' (pathologists) and novices (i.e., undergraduate students). The task of
#' participants was to judge whether pictures of blood cells show cancerous
#' cells (i.e., blast cells) or non-cancerous cells (i.e., non-blast cells). The
#' current data set contains 200 decisions per participant (the "accuracy"
#' condition from Trueblood et al.).
#'
#' @docType data
#' @keywords dataset
#' @name med_dec
#' @usage data(med_dec)
#'
#' @details
#'
#' At the beginning of the experiment, both novices and medical experts
#' completed a training to familiarize themselves with blast cells. After that,
#' each participant performed the main task in which they judged whether or not
#' presented images were blast cells or non-blast cells. Among them, some of the
#' cells were judged as easy and some as difficult trials by an additional group
#' of experts. The current data set only contains the data from the "accuracy"
#' condition (i.e., Trueblood et al. considered additional conditions that are
#' not part of the current data set).
#'
#' The relevant part of the method section for the accuracy condition from the
#' original paper is as follows:
#'
#' "The main task consisted of six blocks with 100 trials in each block. The
#' main task was the same as the practice block, where participants were asked
#' to identify single images. However, participants did not receive
#' trial-by-trial feedback about their choices. They received feedback about
#' their performance at the end of each block. The 100 trials in each block were
#' composed of equal numbers of easy blast images, hard blast images, easy
#' non-blast images, and hard non-blast images, fully randomized.
#'
#' There were three manipulations across blocks: accuracy, speed, and bias. In
#' the accuracy blocks, participants were instructed to respond as accurately as
#' possible and were given 5 s to respond. [...] If they responded after the
#' deadline, they received the message "Too Slow!" The 5-s [...] response
#' windows for the accuracy [...] [condition was] based on the response time
#' data from the three expert raters. The 0.975 quantile of the expert raters'
#' response times was 4.96 s; thus, we set the accuracy response window to 5 s.
#'
#' The order of the first three blocks was randomized but with the constraint
#' that there was one block for each type of manipulation (i.e., accuracy,
#' speed, and bias). The order of the last three blocks was identical to the
#' order of the first three blocks."
#'
#' Note that this dataset contains some negative response times that indicate a
#' missing response (i.e., the response value for that trial is `NA`). Take care
#' in removing these values before using this dataset. See our Validity vignette
#' for an example of use in an optimization setting.
#'
#' @format A data frame with 11000 rows and 9 variables:
#' \describe{
#'   \item{id}{identification number of the participant}
#'   \item{group}{expertise of participant; "experienced", "inexperienced", or "novice". The first two levels refer to different type of medical professional (i.e., experts).}
#'   \item{block}{block number}
#'   \item{trial}{index of trial for each participant}
#'   \item{classification}{true classification of the pictured cell; i.e. the correct response}
#'   \item{difficulty}{adjudged difficulty of the task for the particular image}
#'   \item{response}{response given by the participant; either "blast" or "non-blast"}
#'   \item{rt}{the response time associated with the response, in seconds}
#'   \item{stimulus}{the image file used for the specific trial}
#' }
#' @example examples/examples.med_dec.R
#'
#' @source Trueblood, J.S., Holmes, W.R., Seegmiller, A.C. et al. The impact of
#'   speed and bias on the cognitive processes of experts and novices in medical
#'   image decision-making. Cogn. Research 3, 28 (2018).
#'   https://doi.org/10.1186/s41235-018-0119-2
"med_dec"