Working Memory Secondary Memory (SM) Task Analysis

Overview

These scripts compute mean accuracy and total trial counts for working memory performance in the Secondary Memory (SM) tasks (letter position and word-word) developed by:

• Oliver Wilhelm (University of Ulm, Germany)
• Andrea Hildebrandt (Humboldt-Universität zu Berlin, Germany)
• Klaus Oberauer (University of Zurich, Switzerland)

The tasks were adapted as a measure of Long-Term Memory (LTM) retrieval for the study:

"The Psychometric Structure of Working Memory: An Analysis Utilizing Network Modeling"
Presented at the 65th Annual Meeting of the Psychonomic Society
Principal Investigators: Kevin P. Rosales & Jason F. Reimer (California State University, San Bernardino) (A copy of the poster is included in the readings folder.)

Reference

For more details on the Secondary Memory (SM) tasks, refer to:
📄 Wilhelm et al. (2013): "What is working memory capacity, and how can we measure it?"
(A copy is included in the readings folder.)

Repository Structure

📂 example_data/         # Sample participant data (Inquisit .csv files)
📂 example_output/      # Sample output (.xlsx files)
📂 readings/     # Relevant articles and study materials
sm_letterpos_block1_analysis.py   # Script 1
sm_letterpos_block2_analysis.py   # Script 2
sm_wordword_task_analysis.py      # Script 3
README.md        # This document

Data Collection & Processing

• Participant data was collected using Inquisit (Millisecond Software).
• Raw data was downloaded and saved as .csv files for processing.
• The scripts filter real trials, compute accuracy metrics, and export summary statistics.

Usage

1. Specify input and output file paths:

   • The scripts take input specified by the datafile_PATH variable.
   • Processed results are saved at the location specified by desired_output_PATH.

2. Run the script:

   • Ensure your .csv data files are inside a data/ folder. example_data/ is included.
   • Modify datafile_PATH and desired_output_PATH as needed.
   • Execute the script in Python.

3. View the output:

   • Processed results will be stored in Excel (.xlsx) format at the designated output path. example_output/ is included.

Example (Python Script)

# Define file paths
datafile_PATH = r"/path/to/your/input_data.csv"
desired_output_PATH = r"/path/to/your/output_results.xlsx"

# Read the csv file
df = pd.read_csv(datafile_PATH)

# Process and export results
df.to_excel(desired_output_PATH, index=False)
print("Processing Complete.")