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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.