🧩 CABLAB-Delay-Discounting

This repository contains the full Delay Discounting (DD) experimental and analysis pipeline developed by the CABLab for use in behavioral and neuroimaging studies examining impulsivity, temporal discounting, and social conformity.

It integrates task design, data decryption, processing scripts, and video-triggered social influence manipulations into one reproducible workflow.

🎯 Overview

The repository provides both the experimental implementation of the Delay Discounting task and all supporting analysis tools required to process and interpret the data.

It supports two complementary paradigms:

1. Standard Delay Discounting Task – assesses impulsive decision-making through choices between smaller-sooner vs. larger-later rewards.

• Analyses scripts included

2. Social Conformity Delay Discounting Task – introduces a peer observation and conformity manipulation, examining whether participants adjust discounting behavior when influenced by a fictional peer.

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🧱 Repository Structure

Folder	Purpose
dd-data/	Data conversion and analysis pipeline. Converts raw E-Prime exports → computes indifference averages and discounting rates (k, ln(k)).
dd-transform/	Handles the decryption and conversion of raw E-Prime .txt files into structured .csv files using the convert_eprime package.
dd-conformity-task/	Implements the social conformity video trigger version of the DD task. After calculating each participant’s indifference average, it launches a corresponding peer-video showing increasingly impulsive choices.
dd-formulas.md	Documents the core mathematical logic behind all DD computations (Indifference Average, k-value, ln(k)) and provides interpretive guidelines.
dd-desktop-setup/	Contains the executable Python and .bat launcher files used by research assistants to run the conformity task locally without code modification.

🧪 Research Context

The CABLab Delay Discounting pipeline enables investigation of:

• Individual differences in impulsive decision-making.
• Effects of social context on temporal discounting and conformity.
• Behavioral correlates of reward valuation in developmental and neuroimaging studies.

The social conformity variant includes a controlled deception element: participants are led to believe they are being observed by a peer and then view a prerecorded “peer” making systematically more impulsive choices before repeating the task.

Workflow

1. Data Collection (E-Prime)
   Participants complete the DD task on E-Prime, producing raw .txt output files.

2. File Consolidation
   Run dd-data/dd-datafile-org.py to flatten the nested participant folders into one directory.

3. Decryption & Conversion
   Run dd-transform/dd--eprim-to-csv.py to convert .txt files to .csv.

4. Metric Computation
   Use dd-data/indiff.avg_k_calculation.py to calculate:

   • Average indifference point (Indiff_Avg)
   • Discount rate (k)
   • Log-transformed discounting (ln_k)

This can be run on both alone and social output.

5. Social Conformity Task Execution
   From the desktop interface, launch dd-desktop-setup/conformity_video_task.py (or the included .bat shortcut).

   • Enter the participant ID when prompted.
   • The script locates and converts the participant’s .txt file.
   • Computes Indiff_Avg.
   • Automatically plays the appropriate peer-video in VLC.

   (Neccesary videos are excluded from github repo due to size restrictions, but can be accessed (contact danny.zweben99@gmail.com))

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💻 Requirements

• Python 3.9+
• Packages:
  pandas, tkinter, platform, subprocess, convert_eprime
• VLC Media Player installed at default path for automatic playback
• Manual install for convert_eprime:

  python -m pip install git+https://github.com/tsalo/convert-eprime.git