Building Experiments in PsychoPy

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

21 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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This course guides you in building experiments using PsychoPy, covering basic and advanced topics like online studies, stimuli creation, and EEG/fMRI integration. It's ideal for psychology students and researchers. This resource provides a comprehensive guide to designing and building psychological experiments using PsychoPy, with a focus on both foundational and advanced techniques. It equips learners with the skills to create dynamic, interactive experiments and integrate cutting-edge technologies like EEG and eye tracking. The hands-on approach ensures practical application of concepts, making it ideal for those looking to enhance their research methodology. This resource is ideal for psychology students, researchers, and professionals interested in experimental design. It is suitable for beginners with a basic understanding of research methods. Prior knowledge of psychological research and behavioral science is beneficial but not required. This course provides a comprehensive guide to creating psychological experiments with PsychoPy, catering to both beginners and professionals. It covers everything from simple reaction-time tasks to more complex experiments involving fMRI and EEG studies, guiding you through every step from setup to running the experiment. This course is based on Building Experiments in PsychoPy, by Jonathan Peirce, Rebecca Hirst and Michael MacAskill. Copyright ©2022 by Sage Publications Limited. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Published by Sage Publications Limited, London. Used by arrangement with Sage Publications Limited.

Syllabus

Introduction
- This module introduces learners to the PsychoPy software package, covering its features, uses, and the advantages of open-source tools in experimental design. It explores both the graphical Builder and coding Coder interfaces, as well as the integration of PsychoPy with Pavlovia for online studies.
Building your first experiment
- This module guides learners through the process of building a Stroop experiment using PsychoPy Builder, covering essential skills such as creating routines, configuring stimuli, managing responses, and analyzing data. Learners will gain hands-on experience in designing interactive psychological experiments and understanding the flow of experimental tasks.
Using images: A study into face perception
- This module covers the fundamentals of working with images in PsychoPy, including loading images, managing file paths, adjusting image sizes, and using masks for advanced visual manipulation. Learners will gain practical skills in creating consistent and interactive visual stimuli for psychological experiments.
Timing and brief stimuli: Posner cueing
- This module covers the fundamentals of precise timing and stimulus presentation in experimental design, with a focus on optimizing conditions for brief visual tasks. Learners will explore how to set up and analyze the Posner cueing task, including managing cues, stimuli, and response timing. The module emphasizes practical skills in designing experiments using PsychoPy for accurate behavioral research.
Running studies online
- This module covers the process of transitioning experiments from offline to online environments using PsychoPy and Pavlovia. Learners will gain knowledge on adjusting experimental designs for web-based platforms, addressing technical challenges like unit compatibility, and utilizing tools for online recruitment and touch screen optimization.
Creating dynamic stimuli (revealing text and moving stimuli)
- This module teaches how to create dynamic visual stimuli using PsychoPy, focusing on time-based changes such as revealing text, rotating images, and creating pulsing effects. Learners will gain practical skills in controlling stimulus properties over time to enhance participant engagement and experiment design. The content emphasizes both conceptual understanding and hands-on implementation.
Providing feedback: Simple code components
- This module teaches how to integrate custom Python code into experiments for dynamic feedback, performance tracking, and enhanced functionality. Learners will explore code snippets, color-based feedback mechanisms, and different code types for seamless execution. By the end, they'll be able to implement and refine code components effectively.
Collecting survey data using forms
- This module covers the process of creating and configuring survey forms using PsychoPy, emphasizing rating systems, response types, and advanced settings for data collection. Learners will gain practical skills in designing structured surveys and optimizing form functionality for psychological research.
Using sliders
- This module covers the use of sliders in creating interactive experiments, including configuring slider settings, understanding the Müller-Lyer illusion, and adjusting stimuli for data analysis. Learners will gain practical skills in implementing dynamic response mechanisms and interpreting experimental results.
Randomizing and counterbalancing blocks of trials: A bilingual Stroop task
- This module teaches how to structure and organize trials into blocks, control their order through counterbalancing, and implement these techniques in PsychoPy to improve the reliability and validity of experimental designs. Learners will gain hands-on experience with nested loops, block ordering schemes, and strategies for managing trial sequences in psychological experiments.
Using the mouse for input: Creating a visual search task
- This module teaches how to design and implement a visual search task using mouse input, focusing on spatial response tracking, randomized stimulus positioning, and dynamic control through code and conditions files. Learners will gain hands-on experience in creating interactive experiments that respond to user actions.
Implementing research designs with randomization
- This module covers the implementation of randomization techniques in research designs using PsychoPy. Learners will gain skills in managing participant assignment, stimulus presentation, and condition ordering. The content also explores strategies for selecting and balancing subsets of conditions in experiments.
Coordinates and color spaces
- This module provides an in-depth exploration of coordinate systems and color representation in PsychoPy, helping learners understand how to effectively design and manipulate visual stimuli for experiments. It covers unit conversions, normalized coordinates, and various color space specifications, offering practical insights for accurate visual programming.
Understanding your computer timing issues
- This module explores how hardware limitations, such as screen refresh rates and keyboard response delays, impact the accuracy of timing in experiments. Learners will gain an understanding of how to test and improve the precision of stimulus presentation and response timing on their computers. It provides practical strategies for ensuring reliable data collection in scientific studies.
Monitors and Monitor Center
- This module covers the fundamentals of monitor technologies, calibration procedures, and the use of tools like Monitor Center to achieve accurate display output. It explains how to calibrate monitors using both hardware and psychophysical methods, ensuring visual consistency across different devices.
Debugging your experiment
- This module equips learners with essential debugging skills for PsychoPy experiments. It covers common error types, effective troubleshooting techniques, and strategies for communicating issues clearly in online forums. Learners will gain practical knowledge to resolve technical problems and improve their experimental workflow.
Pro tips, tricks and lesser-known features
- This module covers advanced techniques for optimizing PsychoPy experiments, including managing files, using variables effectively, and adjusting display settings. Learners will gain practical skills to enhance organization, control experiment flow, and improve user interaction.
Psychophysics, stimuli and staircases
- This module covers essential techniques in psychophysics, including the use of stimuli like gratings and Gabor masks, procedures such as staircases and QUEST, and the importance of monitor calibration. Learners will gain practical knowledge on designing and implementing psychophysical experiments with precision.
Building an fMRI study
- This module covers essential aspects of designing and implementing fMRI studies, including timing synchronization, trial consistency, and monitor calibration. Learners will gain insights into ensuring accurate data collection and optimizing experimental setups for neuroimaging research.
Building an EEG study
- This module covers the essential techniques for synchronizing trigger signals in neuroimaging systems, focusing on parallel port communication, network connections, and screen refresh synchronization. Learners will gain practical knowledge on how to ensure precise timing in EEG experiments, which is critical for accurate data collection and analysis.
Add eye tracking to your experiment
- This module covers the integration of eye tracking with PsychoPy using the ioHub framework. Learners will gain skills in calibrating eye trackers, using gaze data for stimulus control, and storing eye movement data effectively. The content provides hands-on guidance for implementing eye tracking in experimental designs.