Our lab members
- Principal investigator
- Post-doctoral researchers
- PhD researchers
- Visiting researchers
- Internship students
Humans are biological agents. Thus, behavior is generally adaptive, meaning that it aims to optimise some goal function (e.g., to minimise error or to maximise reward). At the same time, humans live in rapidly changing environments, so this ideal will often not be reached.
From this perspective, human behavior is often not optimal, but humans learn to gradually achieve their goal (function). In summary, humans learn adaptive behavior, and this defines the topic of our research group.
Although this is a fairly broad topic, there are a few recurring themes, such as learning to use inter-areal synchrony for neural communication; learning what to store in declarative memory; and learning the (meta-)parameters of decision processes. For this research, we use behavioral, computational, and neural methods.
I am interested in neurocognitive and -modulary mechanisms of attention and decision-making under uncertainty. In the current project, I explore how Bayesian estimates about uncertainty guide attention using computational modeling in combination with electroencephalography and pupillometry.
My research is supported by an FWO (Research Foundation – Flanders) post-doctoral fellowship.
My current project studies how behavior becomes habitual and how habits can be overcome. I use computational modeling, fMRI, and brain stimulation techniques to investigate these questions.
My broader research interests are pretty varied and can be described as: finding out how humans think . Before and during my PhD I studied the role of the cerebellum in non-motor functions such as language and cognition. In my postdoc at NIDA (NIH) I switched gears and learned more about addiction and reward processing. My present Fellowship integrates some of the key ideas behind reward processing, automaticity, and cognition.
My Fellowship is funded by the Flanders Fund for Scientific Research (FWO) and the European Commission’s Marie Curie Actions through the Pegasus scheme.
Irene Cogliati Dezza
I am a postdoctoral researcher funded by the FWO-postdoctoral fellowship at Ghent University (BE) and University College London (UK). I am also associate editor at In-Mind Italy and co-founder of BeBrain. I hold a BA in Biology, a MA in Neurobiology, a university certificate in data science and a PhD in computational cognitive neuroscience. My research focuses on understanding how people decide what they want to know and how they explore novel and unknown courses of action. I conduct my research in adults and children in both healthy and clinical populations. My approach combines state-of-the-art methods from diverse disciplines including psychology, neuroscience and computer science.
My research currently focuses on top-down/cognitive control mechanisms that allow us to manipulate and use perceptual and/or mnesic representations. For instance our ability implement a task rule (bind a percept to a motor command), attend to a specific feature, switch between sensory modalities or create and manipulate a mental image. I use Multivariate Pattern Analysis (MVPA) and modeling methods to better understand which and when relevant representations emerge in neural activity. And to know how (and when) such representations are bound together (e.g. perceptual <-> task representations) I use connectivity measures (phase-locking, cross-frequency coupling). In previous work during my PhD at the CerCo lab (Toulouse) I used fMRI, EEG and MVPA to measure the effects of learning on neural representations of visual objects and how it affects perception.
Esin Turkakin is a doctoral working at Verguts Lab.
In her research, she mainly focuses on perceptual decision-making, computational modeling of decision processes, neuromodulation and gamification of decision-making tasks.
Psychologists tend to be interested not only in understanding, but also in improving, human cognition and behavior. Correspondingly, it has already been extensively demonstrated that concrete behaviours can indeed be modulated by selectively rewarding certain behaviours more than others. Inspired by computational models of cognitive control, I investigate whether, in the same way, it is also possible to modulate abstract task execution parameters, such as learning rate, as described by computational models of learning and decision making. Moreover, I investigate whether these parameters can be adapted to multiple environments (in terms of reward contingencies) simultaneously, guided by associated contextual features.
I conduct this research in collaboration with Tom Verguts and Senne Braem, using a combination of computational modeling, behavioural and neuroimaging techniques.
My PhD project focuses mostly on the impact of extensive learning on both behavioral performance and neural oscillations. In our first study, we investigated whether alpha and theta neural oscillations co-exist and interact on the same timescale, or whether they impact learning on distinct timescales. Our results suggested that theta power decreases when learning took place on a slow timescale, while alpha increases on a fast timescale, suggesting that these frequencies operate at different timescales. In a second study, we use computational models to investigate two different types of binding: learning and synchronization. We show that learning, but not synchronization is essential for behavioral improvement. Synchrony boosts performance, but only for linearly inseparable tasks. Theta power was found to decrease with practice but increase with task difficulty. Our simulation results bring new insights in how different types of binding interact in different types of tasks, and how this is translated in both behavioral and neural metrics.
Previous studies of human cognition at different time scales have reported two mechanisms to alter connectivity between brain regions. First, the brain can adjust the strength of the synaptic connections between different neurons. Second, regions can be bound together by synchronizing their oscillations (Fries, 2005). In this PhD project, we investigate how interactions between these two dynamics lead to the remarkable flexibility (and also stability) in human behavior. Additionally, we investigate how the brain is able to know which regions to synchronize by exerting learning at a higher hierarchical level. For these purposes, we aim at describing computational models and test these models empirically with EEG.
Currently, we have no visiting researchers working in our lab.
Since I am fascinated by both computational modelling and statistics, I am happy to be able to do my research internship in this lab. The project I am working on relates to the estimation of free parameters, such as the learning rate, in computational models. More specifically, we investigate whether exact free parameter estimation is always necessary and how this varying need can be implemented in power estimations. The goal is to find the optimal balance between high power studies and a parsimonious design in different research contexts. In addition, I hope to learn much and acquire new skills during this research internship.