med_dec: Medicial decision data

In fddm: Fast Implementation of the Diffusion Decision Model

Medicial decision data

Description

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.).

Usage

data(med_dec)

Format

A data frame with 11000 rows and 9 variables:

id

identification number of the participant

group

expertise of participant; "experienced", "inexperienced", or "novice". The first two levels refer to different type of medical professional (i.e., experts).

block

block number

trial

index of trial for each participant

classification

true classification of the pictured cell; i.e. the correct response

difficulty

adjudged difficulty of the task for the particular image

response

response given by the participant; either "blast" or "non-blast"

rt

the response time associated with the response, in seconds

stimulus

the image file used for the specific trial

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.

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

Examples

data("med_dec", package = "fddm")
str(med_dec)

## number of participants per expertise condition:
aggregate(id ~ group, med_dec, function(x) length(unique(x)))

## number of trials per participant
aggregate(rt ~ group + id, med_dec, length)

fddm documentation built on Sept. 10, 2022, 1:06 a.m.