How we consciously experience the world remains a mystery in science. To tackle this problem, scientific works on perceptual consciousness contrast brain activity when participants consciously perceive a stimulus versus when they are unaware of it. To report stimulus awareness, participants need to make decisions. However, the extent to which the well-studied mechanisms of decision-making apply to consciousness is unclear. One possible reason is that standard neuroimaging methods lack the sensitivity to observe whether the mechanisms of decision-making also operate in the absence of task-relevance, as when participants become conscious of a stimulus irrespective of any task.

In this project, we will test the hypothesis that a mechanism of decision-making –evidence accumulation– explains how perceptual consciousness unfolds over time. First, we will develop a computational model of a latent evidence accumulation process (LEAP) and test it on behavioral measures of phenomenal aspects of perceptual experience: its duration and intensity. Second, we will search for single neuron activity in humans that instantiates evidence accumulation and test whether it also determines these phenomenal aspects of perceptual experience. Third, we will stimulate the corresponding brain regions to disentangle their causal role in either solely triggering perceptual experience or shaping it. Last, we will use the LEAP model to explain hallucinatory-like experiences in patients with Parkinson's disease and test whether deep-brain stimulation affects only decision-making –as previously shown– or also perceptual experience.

By combining computational modeling and cutting-edge electrophysiology, the LEAP project will provide unique mechanistic insights on how neuronal activity determines perceptual experience and guides its temporal dynamics. It will also provide a tool to better understand hallucinations, which remain today a major debilitating symptom in numerous psychiatric disorders.

Upon making decisions, one usually "feels" that a given choice was correct or not, which allows deciding whether to commit to the choice, to seek more evidence under uncertainty, or to change one’s mind and go for another option. This crucial aspect of decision making relies on the capacity to monitor and report one’s own mental states, which is commonly referred to as metacognitive monitoring. Similarly for conscious perception, we can be sure to see a cat, or doubt whether it is a cat or a rock, for example at dusk.

We know that sensory evidence is accrued over time until a bound that leads to a perceptual choice. However, it is still unclear how confidence or doubt arises from this evidence accumulation process and where this is implemented in the brain. Does evidence accumulation continue after the decision? How are prior beliefs about the stimulus integrated?

To study metacognitive monitoring, we develop computational models of confidence based on evidence accumulation. We also record scalp EEG and intracranial EEG from deep-brain stimulation electrode in patients with obsessive-compulsive disorder. Our goal is to better understand the mechanisms of confidence in neurotypical human participants and how they might differ in psychiatric conditions.

On the side, we are interested in the development of neuro-technologies. We develop new algorithms to decode brain states from electrophysiological activity, sometimes post-hoc, somethimes in real-time. In the later case, we use the output of the decoders to adapt the task, or to adjust the stimulation. This closed-loop technology can be harnessed to induce lasting changes in brain activity, either by adapting the task, or by stimulating the brain at the best moment. Our long-term goal is to develop neuro-technologies to reduce hallucinations and obsessive-compulsive symptoms.