# Cognitive Models

In most RT modelling work, researchers usually want to examine the manifested statistics. Often, these are the average response times (RTs) and accuracy rates. In the following, I used the LNR model LNR (Heathcote & Love, 2012), as an example to illustrate a method to calculate these statistics efficiently. The user wishes to understand and apply LNR model on her / his work can find useful information in the DMC tutorials (Heathcote et al., 2018).

This particular LNR model presumes one stimulus (S) factor, and similar to the LBA model, it has a latent matching (M) factor.

library(data.table); library(ggdmc)

model <- BuildModel(
p.map     = list(meanlog = "M", sdlog = "M", t0 = "1", st0 = "1"),
match.map = list(M = list(left = "LEFT", right = "RIGHT")),
factors   = list(S = c("left", "right")),
responses = c("LEFT", "RIGHT"),
constants = c(st0 = 0),
type      = "lnr")


The arbitrary chosen true parameters generate a reasonable RT distribution, which similar with typical choice RT data, giving approximately 25% errors (I will show you this later).

p.vector <- c(meanlog.true = -1, meanlog.false = 0, sdlog.true = 1,
sdlog.false = 1, t0 = .2)


simulate function takes the first option, model to generate data based on the provided model. ps option expects a true parameter vector that matches the setting in the model object, nsim option expects the number of trial per condition.

dat <- simulate(model, ps = p.vector, nsim = 1024)
d <- data.table(dat)
##           S     R        RT
##    1:  left  LEFT 0.3821405
##    2:  left  LEFT 0.7859101
##    3:  left  LEFT 0.5237262
##    4:  left RIGHT 0.3932804
##    5:  left  LEFT 0.6604592
##   ---
## 2044: right RIGHT 0.7342084
## 2045: right RIGHT 1.3628130
## 2046: right RIGHT 0.3343844
## 2047: right RIGHT 0.4913930
## 2048: right RIGHT 0.6119065

• S is the stimulus factor
• R is the response type
• RT stores response time in second

By using data.table function .N, I confirmed that each condition does has 1024 trials.

d[, .N, .(S)]

##        S    N
## 1:  left 1024
## 2: right 1024


A similar syntax, with S and R factors, I printed out the information regarding the hit, correct rejection, false alarm and miss responses.

d[, .N, .(S, R)]

##        S     R   N  ## assuming the left is signal and right is noise
## 1:  left  LEFT 791  ## hit
## 2:  left RIGHT 233  ## miss
## 3: right RIGHT 786  ## correct rejection
## 4: right  LEFT 238  ## false alarm


I used a ifelse chain to calculate a C column to indicate correct (TRUE) and error (FALSE) responses. In real world data, there would be some responses missing or participants pressing wrong keys, so the last else is “NA” to catch these situations.

d$C <- ifelse(d$S == "left"  & d$R == "LEFT", TRUE, ifelse(d$S == "right" & d$R == "RIGHT", TRUE, ifelse(d$S == "left"  & d$R == "RIGHT", FALSE, ifelse(d$S == "right" & d$R == "LEFT", FALSE, NA))))  The data table now looks like below. ## S R RT C ## 1: left LEFT 0.3821405 TRUE ## 2: left LEFT 0.7859101 TRUE ## 3: left LEFT 0.5237262 TRUE ## 4: left RIGHT 0.3932804 FALSE ## 5: left LEFT 0.6604592 TRUE ## --- ## 2044: right RIGHT 0.7342084 TRUE ## 2045: right RIGHT 1.3628130 TRUE ## 2046: right RIGHT 0.3343844 TRUE ## 2047: right RIGHT 0.4913930 TRUE ## 2048: right RIGHT 0.6119065 TRUE  This is one way to calculate average RTs with data.table. d[, .(MRT = round(mean(RT), 2)), .(C)] ## C MRT ## 1: TRUE 0.61 ## 2: FALSE 0.71  The syntax to calculate the response proportions, namely correct and error rates, are less straightforward, but possible. Firstly, I calculated the counts for hit, correct rejection, miss, and false alarm and store them in prop. Then I made up a new column, called NN, to store the total number of trial. Lastly, I divided the four conditions by the total number of trial. I also used a round to print only to the two decimal place below zero. These are almost 25% error rates, as promised. prop <- d[, .N, .(S, R)] prop[, NN := sum(N), .(S)] prop[, acc := round(N/NN, 2)] prop ## S R N NN acc ## 1: left LEFT 791 1024 0.77 ## 2: left RIGHT 233 1024 0.23 ## 3: right RIGHT 786 1024 0.77 ## 4: right LEFT 238 1024 0.23  ## Real-world Example In this section, I will demonstrate more data processing techniques, using the empirical data (Holmes, Trueblood & Heathcote (2016). This data set can be downloaded from my OSF site. One raw data format often found is one subject per text or csv file (*.txt or *.csv). For example, the file, “S125.2014-04-23_6-22-36.txt”, stores the data from participant, S125. There are 47 of them. All are in the same format. In another tutorial, I will illustrate how to handle similar but not identical formatted data files. block trial target CO1 CO2 ST resp RT correct 1 1 R 50 -1 -1 1 1076 1 1 2 L 50 -1 -1 0 733 1 1 3 R 50 -1 -1 1 637 1 1 4 R 50 -1 -1 1 517 1 ...  ### How to read large data sets efficiently I stored data files in a standard location of usual R packaging. The folder, named data unsurprisingly, immediately in a project folder. And the analysis scripts are stored in a folder, called R. I then used list.files function to store all file names in a object, called fn. ?list.files dp <- "data/Holmes_etal_CogPsych_2016_Data"; ## data path fn <- list.files(dp, pattern = "*.txt") ## file name print(fn) ##  "S125.2014-04-23_6-22-36.txt" ##  "S126.2014-04-23_6-25-38.txt" ##  "S127.2014-04-23_6-26-46.txt" ## ... ##  "S169.2014-05-07_6-16-49.txt" ##  "S170.2014-05-07_6-18-16.txt" ##  "S171.2014-05-07_6-38-56.txt"  Next I created a DTLapply function to pipe the text files one after another to the fread of data.table to quickly process them. function(fn, dp) { v <- lapply(seq_along(fn), function(i) { s <- strsplit(fn[i], split = "[.]")[] d <- data.table::fread(file.path(dp, fn[i])) S <- d$target
R  <- d$resp RTSec <- d$RT / 1e3
C  <- d$correct return(d[, c("s", "S", "R", "RT", "C") := list(s, S, R, RTSec, C)]) }) return(data.table::rbindlist(v)) } x0 <- DTLapply(fn, dp)  seq_along function will convert fn, which store 47 file name strings to numerical indices, for the looping. seq_along(fn)  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47  function(i) inside the lapply function is an anonymous function, namely a function used internally by the lappy function. Inside this anonymous function is basically a quicker R for loop. The first line in the anonymous function is to extract the label for a participant. For example, if I just processed the first text file, the strsplit function splits the string whenever it finds a dot symbol. Because strsplit returns an R list, I took the first element in the first list. print(fn) ##  "S125.2014-04-23_6-22-36.txt" strsplit(fn, split = "[.]") ## [] ##  "S125" "2014-04-23_6-22-36" "txt" s <- strsplit(fn[i], split = "[.]")[] print(s) ##  "S125"  Next line uses the convenient function, file.path in R base to construct a file path to a particular file. For example, if I extract the first participant. dp <- "data/Holmes_etal_CogPsych_2016_Data"; ## data path file.path(dp) ##  "data/Holmes_etal_CogPsych_2016_Data" file.path(dp, fn) ##  "data/Holmes_etal_CogPsych_2016_Data/S125.2014-04-23_6-22-36.txt"  The function, file.path returns the complete relative file path to the data file, which is then read by the fread function. Note I can easily use the relative path method, because the folder structure follows strictly R packaging structure. d <- data.table::fread(file.path(dp, fn)) ## block trial target CO1 CO2 ST resp RT correct ## 1: 1 1 R 50 -1 -1 1 1076 1 ## 2: 1 2 L 50 -1 -1 0 733 1 ## 3: 1 3 R 50 -1 -1 1 637 1 ## 4: 1 4 R 50 -1 -1 1 517 1 ## 5: 1 5 L 50 -1 -1 0 476 1 ## --- ## 1276: 20 68 L 15 -1 -1 -1 -1 1 ## 1277: 20 69 L 15 -1 -1 0 1077 0 ## 1278: 20 70 LR 15 15 529 0 1463 1 ## 1279: 20 71 L 15 -1 -1 0 1895 0 ## 1280: 20 72 LR 15 15 529 0 1311 1  The following four lines were simply to temporarily save the columns, “target”, “resp”, “RT” (converted to second), and “correct” to four different R vectors, “S”, “R”, “RTSec”, and “C”. These operations just converted the original column naming to my factor naming convention. For example, target column indicates whether a stimulus was one of the four levels: 1. right (stationary trial), 2. left (stationary trial), 3. left and right (switching trial) or 4. right and left moving dot (switching trial), so I converted it to as stimulus, S, factor. Similarly, R factor is from the resp response column, RT column was converted to second, and correct column was converted to C. S <- d$target
R     <- d$resp RTSec <- d$RT / 1e3
C     <- d\$correct


These four R vectors were then grouped together as a R list.

list(s, S, R, RTSec, C)

## []
##  "S125"
## []
##     "R"  "L"  "R"  "R"  "L"  "R"  "R"  "R"  "L"  "L"  "L"  "R"  "L"  "L"
##    "L"  "R"  "L"  "R"  "R"  "L"  "L"  "R"  "R"  "L"  "R"  "L"  "L"  "R"
##    "L"  "R"  "L"  "R"  "L"  "R"  "L"  "L"  "R"  "R"  "R"  "L"  "R"  "R"
##   ...
## []
##      1  0  1  1  0  1  1  1  0  0  0  1  0  0  0  1  0  0  1  0  0  1  1  0
##     1  0  0  1  0  1  0  1  0  1  0  0  1  0  1  0  1  1  1  1  0  1  1  1
##     1  0 -1  1  1  0  0  1  0  1  1  0  1  1  1  1  1  0  1  1  1  1  0  1
##   ...
## []
##      1.076  0.733  0.637  0.517  0.476  0.419  0.493  0.486  0.685  0.581
##     0.460  0.462  0.666  0.557  0.446  0.438  0.549  0.358  0.486  0.516
##     0.484  0.589  0.679  0.743  0.550  0.646  0.516  0.486  0.588  0.463
## []
##     1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##    0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 1 1 1 1 1 0 1 1
##    1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 0 0 1 1 0 1 0 1 1
##   ...


This list was directly inserted into the data.table, d, which created five new columns on top the original ones (i.e., block, trial, etc.).

d[, c("s", "S", "R", "RT", "C") := list(s, S, R, RTSec, C)]


The result was then returned to the lapply function as its output. := is just the assignment symbol in data.table syntax.

return(d[, c("s", "S", "R", "RT", "C") := list(s, S, R, RTSec, C)])


Each participant file was looped through in a fastest possible way in R language and finally, each of them was glued together via the data.table function, rbindlist, which return as the output for my homemade DTLapply function.

## # A tibble: 60,160 x 13
##    block trial target   CO1   CO2    ST  resp    RT correct s     S         R
##    <int> <int> <chr>  <int> <int> <dbl> <int> <dbl>   <int> <chr> <chr> <int>
##  1     1     1 R         50    -1    -1     1 1.08        1 S125  R         1
##  2     1     2 L         50    -1    -1     0 0.733       1 S125  L         0
##  3     1     3 R         50    -1    -1     1 0.637       1 S125  R         1
##  4     1     4 R         50    -1    -1     1 0.517       1 S125  R         1
##  5     1     5 L         50    -1    -1     0 0.476       1 S125  L         0
##  6     1     6 R         50    -1    -1     1 0.419       1 S125  R         1
##  7     1     7 R         50    -1    -1     1 0.493       1 S125  R         1
##  8     1     8 R         50    -1    -1     1 0.486       1 S125  R         1
##  9     1     9 L         50    -1    -1     0 0.685       1 S125  L         0
## 10     1    10 L         50    -1    -1     0 0.581       1 S125  L         0
## # ... with 60,150 more rows, and 1 more variable: C <int>



### How to trim off irregular participants

It is not uncommon in a data set to have few participants who do not engage in performing a task or drop out in the middle of an experiment. With convincing evidence, we can exclude these participants. Here I demonstrated one way to conduct this operation.

I used the match function, which has a symbol form, %in%. This is an R internal function, which uses efficient algorithm.

## Excluding 3 + 13 participants from model fitting, due to
## (1) a computer error and,
## (2) less than 70% accuracy on the stationary trials in 4-18  blocks.
## They are 126, 129, 130, 133, 134, 138, 139, 140, 143, 147, 148, 150, 155,
## 156, 161, 162
badsubjs <- c("S126", "S129", "S130", "S133", "S134", "S138", "S139", "S140",
"S143", "S147", "S148", "S150", "S155", "S156", "S161", "S162")
x1 <- x0[ !(s %in% badsubjs) ]


## Reference

• Heathcote A., and Love J. (2012) Linear deterministic accumulator models of simple choice. frontiers in Psychology, 23. https://doi.org/10.3389/fpsyg.2012.00292.

• Holmes, W.R. et al, A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model”, 2015, Cognitive Psychology 85, 1-29.