METHODS

To better understand human behaviors and their neural bases, I use descriptive and normative computational models which aim to capture the mechanisms underlying measured behaviors.

Subjective value can be easily measured with rating tasks, choice tasks and effort tasks. I use various utility functions to understand how several dimensions can be combined into a unique subjective value. For instance, when choosing an appartement, we need to consider several dimensions, such as geographical localization or surface area. Individuals weight those dimensions differently to make their final choices. This variability can be accounted for by utility functions.

Choices can be described by softmax functions and logistic regressions, they can also be modelled as evidence accumulation processes such as in the Drift Diffusion Model (DDM). I use this kind of models to understand how biases can be expressed in choices, and to understand the relationship between response time and choices.

To understand knowledge representation and exploration, I use graph theory and search algorithms.

A simple behavior can be captured by different types of model and algorithm, which correspond to distinct mechanistic hypotheses. To untangle those different possibilities, I always conduct model comparisons, mainly using Variational Bayesian Analyses (VBA).

To investigate the neural bases of cognitive processes, I use functional Magnetic Resonance Imaging (fMRI) and intra-electro-encephalography (iEEG) in humans. My approach is essentially parametric as I usually look for correlations between recorded neural activity and task or model variables. I investigate the functional connectivity between regions and large-scale networks with resting-state MRI (rsMRI). Antomical MRI (aMRI) has an excellent spatial resolution and allows me to investigate and take into account the large inter-individual variability in the sulcal organization of some brain regions, such as the vmPFC or the OFC.