Motivation can be defined as the process that forms goals and transforms these into action. A goal can be reduced to a situation with anticipated positive (hedonic) value: earning money, performing well, being loved, etc... However, goal achievement typically involves investing effort, which is aversive. Hence, goal-directed behaviour necessarily trades incentive values with effort (and other) costs. This raises three basic questions:
(i) how does the brain computes net value (e.g., from prospective benefits and costs)?
(ii) how do psychological (e.g., cognitive biases) and/or biological (e.g., limited neural firing range) constraints influence value computations?
(iii) how do value computations determine behavioural outputs (choices, allocated effort, etc...)?
We start with the premise that value computations are distributed over large-scale brain circuits (from midbrain nuclei to basal ganglia, to limbic and prefrontal cortices), whose intimate connectivity structure determines information processing pathways. Importantly, this structure is plastic and under the influence of phasic and tonic changes in neuromodulatory activity. When altered, these regulatory processes may yield maladaptive behaviour.
Understanding the mechanics of motivational processes thus requires relating processes of value computation to the neurobiology of brain networks in a quantitative manner. To do this, I rely on formal mathematical theories that I borrow from diverse academic fields, such as Artificial Intelligence, Control Engineering and Statistical Physics.
Current research projects include:
Identifying the biological constraints on value computations in the orbitofrontal cortex (OFC) using fMRI/electrophysiology and artificial neural network models.
Decomposing the metacognitive control of attentional resources using optogenetics in mice (external collab. with E. Burguiere) and Markov decision processes. This work also has applications to ADHD (external collab. with J. Mattout).
Investigating attribution biases in the metacognitive regulation of mental effort using fMRI and Bayesian inference models.
Questioning the role of different kinds of resource constraints on mental fatigue using fMRI and optimal control theory (with applications to burnout and depression).