R Exercises - Cognitive Psychology

Download data files

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.

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.
# ℹ 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.

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
  - Try plotting a normalized histogram by looking up on the Internet how to do this

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