RL-Memory-Specificity-Analysis

Brief Overview

Project done as part of Behavioral Research: Statistical Methods course at IIIT Hyderabad.

In this project we aimed to analyze behavioral research data to understand adaptive behavior and memory formation across different life stages. By analyzing experimental data and addressing developmental questions, we aim to seek insights into the complex relationship between age and its influence on memory specificity and learning specificity. In the study, the participants engaged in a specialized game where they make decisions based on varying levels of detail, earning rewards accordingly, to explore how this adaptation influences subsequent memory. Through two reinforcement-learning tasks across different age groups, researchers examine how learning representations adjust based on rewards and how this impacts memory recall.

Objectives

The key objectives of our project are:

1. Conduct thorough data analysis on the dataset from the experiment. Use statistical tools to validate the results.
2. Address developmental questions about adaptive behavior and its significance for memory formation.
3. Gain insights into the complex relationship between experience, cognition, and memory throughout life.

Folder Structure

• EDA: This directory contains exploratory data analysis for the project, including separate folders for analysis of each experiment (eda_exp1, eda_exp2).

• Data: Contains data tables for each experiment (exp1, exp2), including rl_data.csv, memory_data.csv, learning_data.csv, and subject_data.csv.

• Docs: Contains project documents such as the final report (report.pdf), initial proposal (initial_proposal.pdf), and presentation slides.

• Src: Contains the code for hypothesis testing, with separate scripts for each test (hypothesis_test_1.py, hypothesis_test_2.py, etc.).

Getting Started

To replicate the analyses or explore the data, follow these steps:

1. Clone this repository to your local machine.
2. Ensure you have Python 3 or above installed. If not, download and install it from Python's official website.
3. Download all the data files from the Data folder for experiments (exp1 and exp2).
4. Use Jupyter Notebooks to view the analysis. To install, use the command pip install jupyterlab or pip install notebook.

Team Members

• Hariharan Kalimuthu
• Shreeya Singh
• Srujana Vanka

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