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Sensorimotor_Adaptation

Experiment design

This repository contains the full experimental codebase for a sensorimotor adaptation study involving visual-proprioceptive calibration, sensory switching, and reaching under varying sensory contexts. The experimental logic follows the design shown below.

Experimental Design

Participants undergo five sequential phases:

  1. Sensory Pre-Test

    • Measures baseline alignment across sensory channels
    • Tasks: Visual (V), Proprioceptive (P), and Combined (VP) pointing
    • 40 trials per block

  2. Pre-Adaptation Phase

    • 50 trials to establish reaching behavior before prism shift
    • Conducted under either eyes open (EO) or eyes closed (EC) conditions

  3. Adaptation Phase

    • 100 trials under prism lens perturbation
    • Participants adapt to shifted visual feedback
    • Sensory context remains EO or EC depending on the group

  4. Sensory Post-Test

    • Re-assessment of V/P/VP shifts after adaptation
    • Identical to the pre-test (40 trials per block)

  5. De-Adaptation & Re-Adaptation Phases

    • Participants undergo de-adaptation (50 trials) and re-adaptation (100 trials)
    • Design allows investigation of learning retention and switching cost across contexts

The study includes four groups (repeat/switch × EO/EC), as shown in the figure.

App Architecture

This application is built using native Python with Tkinter, enabling lightweight deployment without external front-end dependencies. Any Python installation with Tkinter support can run the experiment interface without requiring web servers or browsers.

Why Tkinter?

  • Lightweight & native to Python
  • No need for Flask, Streamlit, or Electron
  • Stable GUI across platforms (Windows, macOS, Linux)

Hardware Integration

The experimental protocol includes Arduino-controlled electrochromic film, which temporarily blocks visual feedback before movement initiation, ensuring control over visual input during sensorimotor adaptation.

Communication with the Arduino is handled via serial communication.

電路圖_english

📂 Repository Structure

├── Pre_Adapt_De
│   └── main/
│       ├── main.ino               
│       ├── beep.wav                
│       └── main.py                 
├── Re_Adaptation
│   ├── shift_pilot_10pixel/
│   │   └── shift_pilot/
│   │       ├── shift_pilot.ino
│   │       ├── beep.wav
│   │       └── shift_pilot_10pixel.py
│   └── shift_pilot_smallrectangle/
│       └── shift_pilot/
│           ├── shift_pilot.ino
│           ├── beep.wav
│           ├── generate_new_shift_dot.ipynb
│           └── shift_pilot_smallrectangle.py
├── Sensory_PrePost/
│   └── prepost/
│       ├── prepost.ino
│       ├── beep.wav
│       ├── noice_generate.py
│       └── prepost.py
├── LICENSE
├── README.md
├── requirements.txt

Dependencies

To run this experiment suite, first install the required Python packages. You can do this using the following command:

pip install -r requirements.txt

Required Packages

  • pandas — for data logging and CSV handling
  • pyserial — for serial communication with Arduino
  • tkinter — for GUI interface (comes built-in with most Python distributions)

Note: tkinter is included by default in standard Python installations (e.g., Anaconda, python.org). If you're on Linux and it’s not installed, you may need to run: sudo apt-get install python3-tk

Related Research Paper/Conference

This experiment is part of the study:

Recommended Citation: Liang Lee, Yi-Zhen Hsu, Shang-Hua N. Lin, Ching-Po Lin, Li-Hung Chang. (2025). Investigating Implicit and Explicit Motor Adaptation Using Explainable Deep Learning Approach. Taiwan Society of Cognitive Neuroscience Annual Meeting.

Award: High Distinction Award, Excellent Student Paper Competition

Click to view/download presentation slides

Contributors

sizluluEZ liangleeTW

License

This project is licensed under the MIT License. See LICENSE for details.