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PsychoPy Task Development

Overview​

The PsychoPy Task Development initiative provides a standardized approach to creating experimental tasks using PsychoPy, ensuring compatibility with the Library of Tasks and enabling easy integration across research projects.

Status: 🚀 Ongoing
Platform: PsychoPy
Integration: Library of Tasks compatible

Philosophy & Approach​

Standardization Through PsychoPy​

PsychoPy provides a robust, open-source platform for creating behavioral experiments. Our approach emphasizes:

  • Compatibility: All tasks designed for seamless Library of Tasks integration
  • Reusability: Standardized components that can be adapted across projects
  • Accessibility: User-friendly development for researchers with varying technical backgrounds
  • Reproducibility: Version-controlled, well-documented implementations

Why PsychoPy?​

Advantages:

  • Widely used in psychological and neuroscience research
  • Excellent timing precision for stimulus presentation
  • Cross-platform support (Windows, macOS, Linux)
  • Built-in integration with various input/output devices
  • Strong community and documentation
  • Python-based for extensibility
  • Native support for neuroimaging paradigms (fMRI, MEG, EEG)

Current PsychoPy Tasks​

MEG/EEG Tasks​

Status: 🔄 In Development
Collaboration: Valentina Borghesani's Laboratory
Modalities: MEG and EEG

MEG/EEG Overview​

These tasks are specifically designed for magnetoencephalography (MEG) and electroencephalography (EEG) research, with precise timing requirements and event marker integration.

MEG/EEG Key Features​

  • Timing Precision: Millisecond-accurate stimulus presentation for neuroimaging
  • Event Markers: Automatic trigger generation synchronized with MEG/EEG systems
  • Flexible Paradigms: Adaptable experimental designs for various research questions
  • Library Integration: Compatible with the central task repository
  • Hardware Integration: Seamless communication with MEG/EEG acquisition systems

MEG/EEG Technical Specifications​

  • Platform: Desktop (optimized for lab MEG/EEG setups)
  • Framework: PsychoPy 3.x
  • Timing: Frame-locked presentation with photodiode validation
  • Data Output: BIDS-compatible event files
  • Trigger System: Parallel port, serial port, or network-based triggers

Research Applications​

  • Language processing studies
  • Cognitive neuroscience experiments
  • Event-related potential (ERP) paradigms
  • Oscillatory dynamics investigations
  • Multi-modal neuroimaging protocols

Development Approach​

The laboratory of Valentina Borghesani collaborates with TTF-DDG to ensure:

  • Scientific validity of experimental paradigms
  • Technical robustness for neuroimaging environments
  • Documentation for replication and adaptation
  • Training materials for laboratory staff

Task-Form-Meaning Task​

Status: ✅ Completed
Collaboration: Julie Franco
Work Package: Meaning

Task-Form-Meaning Overview​

The Task-Form-Meaning task was developed to investigate the relationship between linguistic form and semantic meaning. This task represents a successful collaboration between researchers and TTF-DDG technical development.

Key Features​

  • Semantic Processing: Experimental paradigms targeting meaning comprehension
  • Linguistic Stimuli: Carefully designed stimulus sets
  • Response Collection: Multiple response modalities
  • Data Quality: Built-in validation and quality checks
  • Adaptability: Configurable parameters for different populations

Technical Specifications​

  • Platform: Cross-platform (Desktop/Web)
  • Framework: PsychoPy (with potential web export via PsychoJS)
  • Stimulus Types: Text, images, audio (as applicable)
  • Response Modes: Keyboard, mouse, touchscreen
  • Data Format: CSV/JSON for analysis pipeline integration

Research Impact​

This task enables the Meaning work package to:

  • Conduct standardized meaning processing experiments
  • Compare results across different populations
  • Share methodology with other research groups
  • Build on existing experimental frameworks

Availability​

  • ✅ Available in the Library of Tasks
  • ✅ Comprehensive documentation provided
  • ✅ Example datasets available
  • ✅ Training materials prepared

Citations & Use​

When using this task in research, please acknowledge:

  • Julie Franco (task design and scientific development)
  • TTF-DDG (technical implementation)
  • NCCR Evolving Language (funding and infrastructure)

Future PsychoPy Task Contributions​

Expansion Strategy​

The TTF-DDG welcomes new PsychoPy task contributions from across the NCCR Evolving Language community. We prioritize PsychoPy development whenever appropriate for:

  • Neuroimaging studies: fMRI, MEG, EEG paradigms
  • Behavioral experiments: Laboratory and field research
  • Cross-platform needs: Desktop to web deployment
  • Standardization requirements: Replicable methodologies

Upcoming Tasks​

Additional PsychoPy tasks are in planning stages for:

  • Language acquisition assessments
  • Comparative cognition paradigms
  • Multi-modal stimulus presentation
  • Adaptive experimental designs

Contributing Your Task​

Have a research task that could benefit from PsychoPy development?

We can help with:

  • Converting existing paradigms to PsychoPy
  • Developing new tasks from specifications
  • Optimizing timing and performance
  • Integrating with neuroimaging systems
  • Library of Tasks integration
  • Documentation and training

Development Process:

  1. Initial Consultation: Discuss research requirements and technical needs
  2. Design Phase: Collaborative planning of task architecture
  3. Development: Iterative implementation with researcher feedback
  4. Testing: Validation with pilot data and equipment
  5. Documentation: Comprehensive guides for users and developers
  6. Library Integration: Addition to central repository
  7. Training: User training and support materials
  8. Deployment: Release to research community

Contact us to propose a task →

Technical Standards​

PsychoPy Development Guidelines​

All TTF-DDG PsychoPy tasks adhere to:

Code Quality​

  • Version Control: Git repository with clear commit history
  • Documentation: Inline comments and external documentation
  • Structure: Modular, reusable components
  • Testing: Validation across target platforms
  • Dependencies: Clearly specified requirements

Timing Precision​

  • Frame-locked presentation: Critical stimuli locked to screen refresh
  • Timing validation: Photodiode or similar verification
  • Dropped frame detection: Monitoring and logging
  • Hardware latency: Documented and minimized
  • Event logging: Millisecond-accurate timestamps

Data Management​

  • Standardized formats: CSV, JSON, or BIDS-compatible
  • Metadata recording: Complete experimental parameters
  • Data validation: Built-in checks for completeness
  • Privacy compliance: Anonymization and secure storage
  • Analysis-ready output: Minimal preprocessing required

Integration Requirements​

  • Library compatibility: Follows Library of Tasks specifications
  • Configuration files: External parameter specification
  • Logging standards: Consistent across all tasks
  • Error handling: Graceful failures with informative messages
  • Documentation: User manual, technical specs, and troubleshooting guide

Cross-Platform Considerations​

Desktop Deployment​

Advantages:

  • Maximum timing precision
  • Full hardware integration
  • Offline operation
  • Powerful processing capabilities

Use cases: Laboratory experiments, neuroimaging, specialized hardware

Web Deployment (PsychoJS)​

Advantages:

  • Browser-based accessibility
  • No installation required
  • Wide participant reach
  • Automatic data collection

Considerations:

  • Timing less precise than desktop
  • Browser compatibility testing required
  • Network dependency

Use cases: Online studies, remote data collection, screening tasks

Platform Selection​

We help researchers choose the appropriate platform based on:

  • Timing precision requirements
  • Hardware integration needs
  • Participant population and location
  • Data collection volume
  • Resource availability

Integration with Neuroimaging​

MEG/EEG Compatibility​

PsychoPy tasks can integrate with neuroimaging systems via:

  • Trigger systems: Parallel port, serial port, network triggers
  • Synchronization: Clock alignment and drift correction
  • Event markers: Automated marker insertion
  • Timing verification: External validation methods

fMRI Compatibility​

  • MRI-safe input devices: Keyboard, button box integration
  • Scanner synchronization: TR pulse detection
  • Sparse sampling: Quiet periods for auditory stimuli
  • Motion correction: Consideration in task design

Behavioral Synchronization​

  • Eye tracking: Integration with eye-tracking systems
  • Physiological monitoring: Heart rate, GSR, respiration
  • Motion capture: Coordination with tracking systems
  • Audio recording: Synchronized speech collection

Training & Support​

Available Resources​

For Researchers​

  • User Manuals: Step-by-step task operation guides
  • Parameter Guides: Configuration and customization options
  • Data Analysis: Example scripts and pipelines
  • Troubleshooting: Common issues and solutions

For Developers​

  • Code Documentation: Technical architecture details
  • Development Guidelines: Standards and best practices
  • Testing Protocols: Validation procedures
  • Integration Guides: Library of Tasks compatibility

Training Opportunities​

The TTF-DDG provides:

  • Task-specific training: Learning to run specific experiments
  • PsychoPy workshops: General PsychoPy skill development
  • Technical consultation: One-on-one support for customization
  • Documentation review: Ensuring clarity and completeness

Request training →

Quality Assurance​

Testing Protocol​

Every PsychoPy task undergoes:

  1. Unit Testing: Component-level validation
  2. Integration Testing: Full task workflow verification
  3. Timing Validation: Precision measurements with hardware
  4. Cross-platform Testing: Verification on target systems
  5. User Acceptance Testing: Researcher feedback and approval
  6. Pilot Data Collection: Real-world validation

Performance Metrics​

  • Timing accuracy: Frame drops, latency measurements
  • Data quality: Completeness, validity checks
  • User experience: Ease of use, clarity of instructions
  • Reliability: Stability across sessions and systems
  • Reproducibility: Consistency of results

Continuous Improvement​

  • User feedback: Ongoing collection and incorporation
  • Bug tracking: Issue management and resolution
  • Version control: Documented updates and changes
  • Performance optimization: Regular efficiency improvements

Case Studies​

Success Story: Task-Form-Meaning​

Challenge: Develop a robust task for investigating form-meaning relationships in language processing.

Solution: Collaborative development with Julie Franco, iterative design based on research requirements, thorough testing with pilot participants.

Outcome:

  • ✅ Successfully integrated into Library of Tasks
  • ✅ Used across multiple studies
  • ✅ Adapted for different populations
  • ✅ Cited in publications

In Progress: MEG/EEG Tasks​

Challenge: Create timing-precise tasks for neuroimaging research with complex hardware integration requirements.

Approach: Close collaboration with Valentina Borghesani's lab, rigorous timing validation, comprehensive trigger system integration.

Status: Active development with hardware testing phase underway.

External Resources​

Contact & Collaboration​

Get Involved​

Interested in PsychoPy task development?

  • Propose a new task: Share your research needs
  • Contribute expertise: Collaborate on development
  • Request training: Learn PsychoPy best practices
  • Report issues: Help improve existing tasks

TTF-DDG Support​

For PsychoPy-related questions:

Contact the TTF-DDG team →

Acknowledgments​

Current Collaborators​

  • Valentina Borghesani's Laboratory - MEG/EEG task development
  • Julie Franco - Task-Form-Meaning task design and validation
  • NCCR Evolving Language Work Packages - Research requirements and feedback

Technology Partners​

  • PsychoPy Development Team - Excellent open-source platform
  • NCCR Technical Infrastructure - Support and resources

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