Data-projects-with-R-and-GitHub

Project Description

This project will use publicly available behavioral data from Kalbe & Schwabe (2022). The original study looked at the memory performance of the study participants, operationalised as corrected recognition. The unprocessed data can be found here, the preprocessed data which you will use, can be found in the folder project-data.

In the study participants were presented with a series of initially neutral stimuli. The presented stimuli were images of animals and tools, images from one of the two categories were paired with an aversive event (electric shock). The item category paired with the aversive event is characterized as CS*+ (conditioned category, CSm) and the item category not associated with the aversive event as *CS− (CSm).

The purpose of this pairing was to see if the association with the shock would affect the participants’ memory for the images. This kind of experimental design helps to understand hoe emotional experiences can influence memory.

About the Data

The preprocessed data are saved as .csv files. You can find a .csv file for each experiment separately in the above mentioned folder.

Variables:

• study_id

  • Indicates experiemnts number

• participant_id

  • The variable indicates the participants ID / number of the participant

• condition -

  • Describes the item category

  • CSm - CS- category is the category which is not associated with the aversive event

  • CSp - CS+ category is the conditioned category, item category associated with aversive event

• phase - experimental phase items were first presented

  • conditioning - items that were first presented during conditioning phase
  • post-conditioning - items that were first presented during post-conditioning phase
  • pre-conditioning - items that were first presented during pre-conditioning phase

• hit - Hit rate

  • Porportion of previously seen images correctly classified as ‘old’

• fa - False alarm rate

  • Porportion of new images incorrectly classified as ‘old’

Data Manipulation

1. Read the data into your local R environment

2. Compute corrected recognition scores and save them as a new variable (e.g. “cr”)

   • Corrected recognition is computed by subtraction false alarms from hits

   • The new variable should be part of the data frame, this makes plotting easier

3. If you decide to plot all four experiments, pool the data

   • Pooling the data means combining the data from all four experiments, which are currently stored in separate .csv files, into one large file.
   • The pooled data then contains all four experiments and you can filter by ‘study_id’ in order to create the indivudual plots for each experiment.
   • Note: If you pool the data keep in mind that the participants id starts from 1 for each experiement. If you simply merge the four file into one, you will have participant no. 1 four times. To avoid this duplication a continuous count is needed. This means that

Visualization

Here you can find the published paper. Have a look at Figure 3. Try to recreate the figure using the provided data. You can choose which of the four experiments you want to plot.