Behavioral Data Analysis

This repository is a compact behavioral data analysis portfolio project built with simulated repeated-measures data. The notebook shows a full workflow: data generation, quality control, participant-level summaries, visualization, and inferential statistics.

Key Plotly visuals

1. Mean switch rate by condition

This Plotly bar chart summarizes the condition effect with standard error bars, making the baseline vs. adaptation comparison easy to read at a glance.

2. Participant-level change across conditions

This paired Plotly line chart shows the within-subject pattern directly, which is often the most important behavioral-data view in a repeated-measures design.

What this project does

• Generate a simulated behavioral dataset for baseline and adaptation conditions across multiple participants.
• Apply a simple quality-control exclusion based on minimum completed trials.
• Summarize participant-level switch_rate outcomes.
• Visualize the two most important behavioral patterns with Plotly.
• Run a paired t-test and a repeated-measures ANOVA.

How to run

1. Create and activate a virtual environment.

python -m venv .venv
.venv\Scripts\activate

2. Install dependencies.

pip install -r requirements.txt

3. Generate the Plotly screenshots used in this README.

python generate_plotly_assets.py

4. Open the notebook to explore or rerun the full analysis.

jupyter notebook analysis.ipynb

The script writes both static PNG screenshots and interactive HTML files into assets/.

Contents

File	Description
analysis.ipynb	Main notebook for the behavioral analysis workflow, now using Plotly for the two key graphs.
generate_plotly_assets.py	Recreates the simulated dataset and exports the Plotly figures to assets/.
requirements.txt	Python dependencies required for the notebook and Plotly figure export.