I have a special interest for methodology and statistics, and I often serve as a statistician for research projects outside my primary area of research. 

Over the past four years, I have had the privilege of working with multiple universities internationally, assisting in the development of methodological and design plans for upcoming studies. Additionally, I have been responsible for conducting statistical analyses and crafting results reports for scientific peer-reviewed publications. 

This collaborative work has been incredibly fulfilling, allowing me to engage with a wide range of projects across diverse fields (clinical neuropsychology, physiology, nutrition, sleep, to cite a few). It has not only enabled me to acquire new techniques but also apply my expertise in methodology and statistics more broadly. For example, I have been involved in meta-analytic projects, complex modelling, and design of clinical trial protocols. I'm eager to continue this work, maintaining existing partnerships while also exploring opportunities for new collaboration. 

Feel free to contact me if you would like to collaborate and/or use the tasks I have designed! 

PsychoPy: Code for Cognitive Tasks

Vigilance Clock Task

Measure of vigilance (sustained attention). In this task, participants monitor a clock-like display where a hand moves at regular intervals, but occasionally skipping a beat. Feedback is provided through a red flash for incorrect responses and a green flash for correct ones. Contrary to the Mackworth Clock Task, for which reaction time is the primary outcome of interest, the Vigilance Clock Task focuses on accuracy. Every characteristic of the task can be easily changed. 

Click here to access the code files on GitHub. 

Complex Card Matching Task (CCMT)

Task used to investigate the Exploration-Exploitation tradeoff. While it was inspired by the Wisconsin Card Sorting Task, key aspects were changed to make the CCMT more difficult as to tap into higher-order cognitive processes. 

In this task, participants classify cards based on unspecified rules, learning through trial and error with correct-incorrect feedback. It assesses cognitive flexibility by requiring them to alternate between exploration (testing new rules) and exploitation (applying learned rules). Task difficulty is manipulated across two versions: the easy version involves three features (color, shape, number), with six possible classification rules, while the difficult version introduces a fourth feature (size).

This task is available here to download on GitHub, along with more details about the task itself, and Python notebooks to pre-process data.