This exercise is based on data from a PhD student in cognitive psychology. In his work, this student collects response times (RT) to two simultaneous tasks, and then he has to analyse the response times.

  • At \(t_0\), the stimulus S1 is triggered
  • At \(t_1=t_0 + SOA\), the stimulus S2 is triggered (SOA is the time between the 2 stimuli)
  • At \(t_2=t_0 + RT1\), the subject responds to stimulus S1
  • At \(t_3=t_1 + RT2\), the subject responds to stimulus S2

The inter-response interval (IRI) is defined as the time difference between \(t_3\) and \(t_2\), thus \(IRI=SOA+RT2-RT1\). Negatives IRI therefore mean that the response to S2 is emitted before the response to S1.

For theoretical reasons, one can consider that if \(SOA=1500\) ms, both stimulis are answered independently. One can thus use the RT1 and RT2 values for SOA=1500 ms to estimate the IRI in the case of independent responses to the stimuli.

1 Data wrangling

  • Download the datafiles archive and unzip it, then create a R project in this folder in Rstudio.

  • Load the tidyverse and patchwork packages:

  • Load the .csv file in the Data folder and save it into raw_data. Look into the help of read_csv() to help you get rid of the error.

  • Using successive pipe operations, we will now create a dataclean table from raw_data, where we:

    • Filter raw_data so that Procedure[Trial] is only equal to "EssaisDT"
    • Mutate the table by adding a column IRI containing SOAdur + S2Visuel.RT - S1Audio.RT
    • Filter the table so that some extreme values are excluded:
      • S1Audio.RT is smaller or equal to 2500 and larger or equal to 100
      • S2Visuel.RT is smaller or equal to 2500 and larger or equal to 100
      • S1Audio.ACC, S1response.ACC and S2Visuel.ACC are equal to 1

  • Let’s compute the simulated values in the case the responses to the stimuli are independent, i.e. using SOA=1500ms, and save it into a tibble called IRI_sim. Using successive pipe operations and starting from dataclean:

    • Filter rows so that SOAdur is equal to 1500
    • Delete the SOAdur and IRI columns
    • Mutate the table to add 3 columns IRI_sim_xx, where xx=15, 65 or 250 and IRI_sim_xx = xx + S2Visuel.RT - S1Audio.RT.
    • Using pivot_longer() and the options names_prefix = "IRI_sim_", names_to = "SOAdur", values_to = "IRI_sim", pivot the columns containing "IRI_sim_" into a long table (you need to add the option to select the corresponding columns).

  • We want now to get the averaged IRI per subject and per SOA, and its standard deviation. Using group_by() and summarise(), store the mean and standard deviation of IRI in a table called stats_obs, starting from dataclean. It should look like this:

## # A tibble: 24 × 4
## # Groups:   Subject [6]
##    Subject SOAdur    mean    sd
##      <dbl>  <dbl>   <dbl> <dbl>
##  1       1     15  -64.8   291.
##  2       1     65   20.8   297.
##  3       1    250  218.    217.
##  4       1   1500 1019.    228.
##  5       2     15 -159.    193.
##  6       2     65 -156.    236.
##  7       2    250    4.42  191.
##  8       2   1500 1184.    198.
##  9       3     15 -289.    207.
## 10       3     65 -251.    262.
## # … with 14 more rows
  • We want to do the same for the 3 simulated IRI.
## # A tibble: 18 × 4
## # Groups:   Subject [6]
##    Subject SOAdur   mean    sd
##      <dbl> <chr>   <dbl> <dbl>
##  1       1 15     -466.   228.
##  2       1 250    -231.   228.
##  3       1 65     -416.   228.
##  4       2 15     -301.   198.
##  5       2 250     -65.9  198.
##  6       2 65     -251.   198.
##  7       3 15      -99.0  157.
##  8       3 250     136.   157.
##  9       3 65      -49.0  157.
## 10       4 15     -168.   214.
## 11       4 250      66.9  214.
## 12       4 65     -118.   214.
## 13       5 15     -367.   249.
## 14       5 250    -132.   249.
## 15       5 65     -317.   249.
## 16       6 15     -313.   161.
## 17       6 250     -77.9  161.
## 18       6 65     -263.   161.

2 Plotting

  • We want now to produce a graph showing the histograms of the observed IRI column using ggplot2.
    • Create a ggplot using the dataclean dataset
    • Set the aesthetics to x = IRI
    • Create the histograms using geom_histogram(), with a fill color depending on SOAdur
    • Arrange the plots on a grid depending on the Subject column using facet_wrap()
    • Add a vertical lign marking the average value for each subject using geom_vline(). The data for these lines are stored in the stats_obs dataset.
    • Play with the theme and other ggplot commands to make the plot look like the one below

  • Let’s do the same for the simulated dataset. It should look like this: