Our goal is to study the brain from the viewpoint of the computations that subtend various aspects of cognition, from higher-level perception to decision making, statistical inference and the monitoring of uncertainty. Those topics correspond to the fields of expertise of the teams’ PIs and constitute a representative sample of human faculties. Our team uses the computational viewpoint to unify different levels of description:
- Formalize computational models of various cognitive functions and account for the behavior collected during cognitive tasks.
- Uncover the corresponding algorithms, i.e. the successive steps in information processing, and relate their latent variables to brain recordings (M/EEG and fMRI)
- Identify the underlying neural codes and probe their organization with high resolution fMRI.
Florent MEYNIEL – website – publications
Are you sure about your own judgments? Does it actually matter? I am interested in understanding the computation and roles of uncertainty in the brain. More specifically:
(1) How does the brain estimate the uncertainty about its own judgments, and communicate this subjective confidence to others?
(2) How does subjective confidence regulate learning? In particular, how does it regulate the inference of trends and regularities in dynamic environments?
(3) How does subjective confidence regulate our decisions, such as the decision to explore certain options, or to seek out information by mere curiosity?
I combine several scales and approaches in order to understand how those three processes operate in the brain:
Computational level: the goal is to provide abstract, formal accounts of brain computations. I rely on Bayesian (and more generally probabilistic) inference to this end.
Macro- and meso- scales: the goal is to understand the coordination of brain-scale networks that subserves those computations (macro-level), and how the inferred variables and their uncertainty are encoded by large populations of neurons (meso-scale). I currently use fMRI and MEG to this end.
Chemicals: I am interested in the role of neuromodulators in those computations, which actually bridge gaps between single neurons (of which they modulate the activity and plasticity) and large-scale networks (in which they are released simultaneously). I use fMRI, physiological measures (pupillometry, heat beats) and pharmacological interventions to address those questions.
Evelyn EGER – publications
Visual scenes convey a multitude of different types of information, some defined at the level of individual objects (such as their category or specific identity), and others at the level of groups of objects (such as their number). We understand all of these quickly and intuitively, but this apparent ease does not do justice to the complexity of the computations involved.
What are the brain representations underlying our perceptual experience of such high-level visual entities, and how do they arise from bottom up or top–down processing mechanisms? Which characteristics of neuronal representations along the cortical hierarchy determine aspects of behavioral discrimination ability, and how are they altered in developmental disorders?
To tackle these questions, my work is combining non-invasive brain imaging in humans (such as ultra-high-field fMRI) with psychophysics and aiming to link these to computational approaches based on artificial neural networks.
Philippe DOMENECH – publications
I am interested in how the brain supports flexible and adaptive behavior in changing and uncertain environments.
To tackle this issue, I use tools from computational neurosciences and neurophysiological/imaging recordings (MRI, EEG/MEG, intracranial EEG) of human brain activity while it’s engaged in dynamically controlling decision-making processes.
As a psychiatrist specializing in treatment-resistant OCD/depression, Brain-Computer Interfaces and neuromodulation, I’m also interested in using the same approach to study the computational underpinning of psychiatric diseases.