Our lab members

1. Principal investigator
2. Post-doctoral researchers
3. PhD researchers
   • Haopeng Chen
   • Xiaoyu Zhang
   • Mengting Xu
4. Visiting researchers
5. Internship students
   • Jelle Goetschalckx

Principal Investigator

Tom Verguts

Personal statement
How do humans learn? And how can they do that so efficiently, despite noisy, sparse, and inconsistent feedback? A key theme in the lab is that meta-learning is an import piece of this puzzle: While learning a task, one is simultaneously already setting the parameters for learning the next one. Because we live in an environment where we switch between tasks with significant overlap, this is an efficient learning strategy. To address such questions, we believe that multiple levels of analysis should be combined: Computational, behavioral, and neural. Thus, we adopt a cognitive computational neuroscience approach.

Post-doctoral researchers

Currently, we have no postdocs working in our lab.

PhD researchers

Haopeng Chen

My research topic is the behavioral and neural nature of the testing effect. The testing effect refers to the phenomenon that testing can help people reinforce the learned materials better than restudying. Although the testing effect has been demonstrated in many studies and holds major educational implications, its origin has remained unclear. Based on earlier empirical work and theory formation, we currently postulate that the testing effect derives from reward prediction error (RPE). To be specific, during testing, people will calculate their confidence in their answers and get feedback, which will trigger the RPE (feedback-confidence). Therefore, it might be the RPE triggered by testing that leads to the testing effect. We will try to investigate this postulation at both behavioral and neural (fMRI) levels.

Xiaoyu Zhang

My research interest is how human attention works in learning different tasks and how to model this procession with neural networks and oscillation. Humans can learn new tasks with transferable knowledge and adapt to different environments quickly. Among these activities, attention plays an important role, which has been proposed to be implemented via oscillation in the brain. We build models with neural networks and oscillations and investigate how the models implement cognitive flexibility, particularly generalization with well-designed complex psychological tasks. Then, we will compare the model performance with the EEG results from human behavioral experiments. Using computational modeling, EEG, and behavioral methods, I am collaborating with Tom Verguts and Mehdi Senoussi for this research.

Mengting Xu

To adapt to an ever-changing environment, humans must maintain, update, and process external information over short time scales while simultaneously learning from experience to guide ongoing behavior. Working memory plays a crucial role in these cognitive processes by temporarily storing and manipulating information to support flexible decision-making and adaptive responses. However, how working memory processes are shaped or optimized over time remains underexplored. One promising perspective is that neural oscillations provide a mechanism for flexibly retaining and binding information in working memory. Inspired by this idea, we aim to develop an artificial neural network that incorporates oscillatory dynamics to gain deeper insights into the implementation of working memory. The model will be tested and refined using neural (e.g., EEG) and behavioral data to better understand how the brain integrates working memory processes across different time scales during cognitive tasks. This research is conducted in collaboration with Tom Verguts and Mehdi Senoussi.

Visiting researchers

Currently, we have no visiting researchers working in our lab.

Internship students

Jelle Goetschalckx

In my internship, I investigate perceptual cycles in human vision using EEG, supervised by Raquel London and Tom Verguts. Specifically, we want to find out whether visual processing is divided into discrete snapshots, just like a movie consists of frames, or whether this is more of a continuous process. This discrete view has historically gained much support from evidence linking perception to alpha band activity, but more recent research is pointing towards a continuous account. Building on this debate, we will also investigate whether individual (psychophysical) differences, such as neural noise, shape this process. Beyond this project, I am also particularly interested in designing intricate experimental designs, coding, and data visualization. Aside from academia, I enjoy playing bass guitar, playing boardgames, and chatting about anything interesting!

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Please get in contact if you are interested in a research stay or an internship in our lab.