Adaptive Drone Mapping Task

This repository contains the implementation of the Adaptive Drone Mapping behavioral experiment developed using jsPsych. Participants explore a hidden 15×15 landscape by scanning patches, receive noisy observations, and finally make a single-cell choice to maximize the underlying true signal.

📁 Repository Structure adaptive-drone-mapping/ │ ├── task/ │ └── experiment.html # Main experiment file (final version) │ ├── grids/ │ └── participant_1/ # Animation, practice, and task grids (JSON) │ ├── vendor/ │ ├── jspsych.css # jsPsych styling │ └── plugin-call-function.js │ ├── docs/ │ └── design_decisions.md # Notes and design documentation │ ├── .gitignore ├── .gitattributes ├── CITATION.cff └── README.md

▶️ Running the Task Locally

To run the experiment locally:

Navigate into the repository folder:

cd adaptive-drone-mapping

Start a local server (Python):

python -m http.server 8000

Open the experiment in your browser:

http://localhost:8000/task/experiment.html

This is the standard and recommended way to run jsPsych tasks.

🎯 Purpose of the Task

The goal is to investigate how people seek information under uncertainty when only noisy observations are available.

Participants:

Select square patches to scan the landscape

See noisy measurements

Infer the hidden true field

Make a final single-cell guess

This design builds on Gaussian-process–generated fields with varying smoothness and noise levels.

👥 Contributors

Selin Doğaner