Our lab members

  1. Principal investigator
  2. Post-doctoral researchers
  3. PhD students
  4. Visiting researchers
  5. Internship students

Principal Investigator

Tom Verguts

Tom Verguts

Personal statement
Humans are biological beings. Thus, behavior is generally adaptive, meaning it aims toward optimising 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 reinforcement learning, the use of neural synchrony for neural communication, and the role of confidence in performance adaptation. For this research, we use behavioral, computational, and neural methods.

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Post-doctoral researchers

Anna Marzecova

Information will be added soon.

Cristian Buc Calderon

My research focuses on understanding how humans select one out of many competing actions. My past research tested specific predictions of the affordance competition hypothesis (Cisek & Kalaska, 2010, Ann. Rev. Neurosci.), both at the neural architecture and decision dynamics levels (Calderon et al., 2015, JEPG; Calderon et al., 2017, PNAS; Calderon et al., 2018, Front. Hum. Neurosci.; Calderon et al., 2018, Psychol. Rev.).

More recently, I have been awarded a FWO postdoctoral fellow where I will investigate how the brain learns the optimal timing of individual actions within a sequence of actions (in collaboration with Michael Frank @ Brown University and Tom Verguts @ Ghent University). To carry out this project, I use computational modeling, behavioral and neural methods.

PS: I took the picture of Pieter Verbeke (scroll down ;)).

Elise Lesage

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.

Jacki Janowich

Jacki Janowich

Our brains are simultaneously tasked with acting in the present while predicting and preparing for the future. I am interested in how we proactively optimize neural systems ahead of future goal-directed action. In one ongoing line of work, I use EEG/MEG to investigate the neural activities underlying the computation, communication, and maintenance of future goals. In my current work, I focus on how errors in reward prediction shape learning, using computational modeling and MEG/EEG. In particular, I aim to understand how reward prediction (error) is influenced by timing and hierarchical context. My current research is supported by the EOS project MEMODYN, funded by the FWO and F.R.S.–FNRS.

Kobe Desender

Kobe Desender

In my research, I focus on the question how metacognition is used for further adaptation of behavior. Currently, I examine how subjective confidence in a decision is used to further optimize cognition. Theoretical models of confidence posit an important role for confidence in learning and adapting behavior, and these are the dynamics that I wish to unravel. I combine behavioral measures, computational modeling, and electrophysiological recordings to answer these questions. In previous work performed during my PhD at the VUB (Belgium), I used behavioral and EEG recordings to unravel the relation between metacognition and conflict processing. I am supported by an FWO {PEGASUS} Marie Skłodowska-Curie fellowship.

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Mehdi Senoussi

Mehdi Senoussi

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.

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PhD students

Kate Ergo

Kate Ergo

My research focuses on how reward and reward prediction errors (i.e., mismatches between reward outcome and reward expectation) influence declarative memory (e.g., learning a foreign language). Using a Dutch-Swahili word learning paradigm, we found that word pair recognition increases linearly with larger and more positive RPEs (De Loof et al., 2018). We also found neural oscillatory signatures that confirm the experience of signed RPEs (SRPEs) boosting declarative memory (Ergo et al., 2019). My goal is to further investigate the robustness and generality of these findings. To study how RPEs influence declarative memory, I use both behavioral and EEG studies. Recently, I also became interested in neurostimulation techniques, such as tACS. My PhD is funded by the Flanders Fund for Scientific Research (FWO).

Outside of the lab, I enjoy hanging out with my dog Fellow, playing the guitar, karaoke, gaming and travelling.

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Pieter Huycke

Pieter Huycke

In my Ph.D. project, we aim to take a fresh perspective that emphasizes the study of memory, in its natural dynamic setting, as a journey in the making. The two key characters in this journey are learning and consolidation: both processes are known to interact but have hitherto been studied in isolation. The perspective of this project enables them to be understood, for the first time, under the influence of their inherent interactions, and in the context of brain maturation and aging. The project aims to provide new insight into how much our ability to learn is dictated by the makeup of our neuronal circuitries, how memories are initially formed and later stored for the long haul, how this is made possible by the rewiring of our neuronal circuitries, and how our memory processes and capacities vary as our brain evolves through its lifespan.

For this project I use mainly EEG and computational modeling. I like to perform my EEG analyses using Python’s mne software. Computational modeling is something I try to achieve using Python 3.

Outside my work I find joy in programming, mainly to solve mathematical riddles. Also, I like to learn more about the Python programming language in general. Code for my first and second passion can both be found on my personal GitHub.

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Jonas Simoens

Psychologists tend to be interested not only in understanding, but also in modulating, human cognition and behavior. Correspondingly, it has already been extensively demonstrated that concrete behaviors (such as choosig one type of food over another) can indeed be modulated by selectively rewarding certain behaviors more than others. In line with these findings, my research focuses on whether, in the same way, it is also possible to modulate abstract task execution parameters (such as the learning rate and the discount rate), as described by computational models of learning and decision making. Furthermore, I aim to investigate to what extent modulations of task execution parameters in certain environments transfer to other environments.

I conduct this research in collaboration with Tom Verguts and Senne Braem, using behavioral, computational modeling and neuroimaging techniques.

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Esin Turkakin

Information will be added soon.

Pieter Verbeke

Pieter Verbeke

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.

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Visiting researchers

Fabrice Luyckx

Fabrice Luyckx

I am currently in the final stages of my DPhil at Oxford University in the Summerfield lab. My work focuses on how information is structured and transformed in the brain during decision making, mainly using neurophysiological measures (EEG) and computational modelling. One line of research has looked at how structure (e.g. a line, circle, hierarchy, …) is represented in the brain. For example we studied whether decisions about different categories with a shared underlying structure rely on the same neural signals (Luyckx et al., eLife, 2019). A different line of research has focused on the neural mechanisms of value-based decision making, specifically how neural patterns can shed a light on why we make economically “irrational” choices (Luyckx et al., bioRxiv, 2019).

Outside of work, I like to torment the strings on my guitar or squash racket.

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Internship students

Jacopo Bonazzi

Jacopo Bonazzi

I’m a master student from Milano Bicocca University doing the internship in Ghent thanks to the Erasmus+ program. In this project I’m trying to understand how uncertainty and variability can influence inferences. We live in a world that is intrinsically chaotic, and uncertainty is omnipresent in every aspect of our life. Learning, choosing and predicting in such an environment requires estimating and computing how variable is the world and how reliable is the mental model used to capture this variability. The current project aims to unravel how these two different sources of uncertainty, namely expected and unexpected, influence visual search performance. I plan to collect behavioral data and test the predictions of different computational models.

When I’m not troubled by uncertainties, I love to sail (quite uncertain conditions anyway) and hike in the mountains!

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Please get in contact if you are also interested in an internship in our lab.