Visual confidence refers to our ability to estimate the correctness of our visual perceptual decisions. As compared to other forms of metacognition, meta-perception has attracted a burst of studies recently, no doubt because perception already benefits from strong theoretical frameworks. We have recently refined these existing frameworks by proposing to clearly distinguish sensory evidence from some “confidence evidence” that drives the confidence decision. The problem now is to characterize the properties and consequences of this confidence evidence, and this is the aim of the present proposal. As the number of studies grows, it becomes clear that visual confidence is not simply a noisy estimate of the perceptual decision, but instead depends on a large number of factors. We have identified four axes that we believe will contribute to shape confidence evidence: (1) individual variability, (2) task accessibility, (3) global confidence, and (4) perceptual learning. The purpose of the first axis is to understand which cues are used for confidence, and for this purpose, we will study confidence variability across individuals. Some of the idiosyncratic variability in confidence judgment efficiency might come from a variable temptation to exaggerate the impact of stimulus noise on the estimation of one own performance. In the second axis, we will try to understand what in a task determines the accessibility to visual confidence. In particular, we will test the hypothesis that more high-level tasks, such as face identification, lead to better confidence efficiency that low-level tasks, such as detecting whether two line segments are aligned. The aim of the third axis is to understand how individuals construct a sense of confidence for a task as a whole, not for a single isolated judgment. We will start by carefully studying how confidence builds up within a set of stimuli and compare how such a global confidence compares with a single decision confidence. Finally, in the fourth axis, we will study how perceptual learning benefits from visual confidence. In particular, we will test the extent to which confidence evidence can be seen as an internal error signal that can act as a proxy for an external feedback. We believe that a better understanding of these four fundamental aspects of confidence evidence will help us derive an accurate and useful model of visual confidence, and ultimately of metacognition.

The dynamics of large-scale brain networks are at the origin of the signals measured via human invasive and non-invasive recordings and imaging. The recent decade saw the emergence of a novel generation of mathematical network models acknowledging the large-scales, using personalized connectivity matrices derived from individual structural imaging data and reproducing functional brain imaging signals. These innovative modelling approaches have recently entered in the clinical domain including stroke, epilepsy and neuro-degenerative diseases. Key requirements for these models to be successful in applications of personalized medicine are high-quality connectomes, biologically realistic population models and good paradigms for proper validation. Our project aims to directly address two of these latter requirements. We propose to extend to multiscale neural circuits a novel mathematical formalism, Exact Reduced Methodology (ERM), able to reproduce exactly the collective dynamics of large spiking neural networks, while taking into account properties of the constituent neurons and circuits. Notably we will focus on the ubiquitously observed coherent patterns of multi-frequency brain oscillations, including as well cross frequency-coupled (CFC) dynamics, that have been linked to cognitive function. We will use the novel population models as network nodes in large-scale brain networks, which we will build based on realistic human connectomes (eventually, personalized when single patient tractography data are available). So far connectome-based models were tested for resting state dynamics. Here we increase the predictive power significantly by introducing a new paradigm and performing systematic perturbation studies, thereby testing the response dynamics of the models far beyond the resting state. Experimentally we realize this paradigm by the systematic exploration of the individual brain signal transients in the cohort using: on one side intracranial electric stimulation and stereo-electroencephalography (SEEG) in epileptic patients in pre-surgical evaluation; and, on another side simultaneous transcranial magnetic stimulation (TMS) and electroencephalography (EEG). We will link the novel mathematical population models and empirical data by determining the Phase Resetting Curve (PRC) for the stimulated brain areas for each subject of the experiment and we will study the emergence of CFC and its stability to perturbations at population level. This information will enable us to infer the properties of the ERM-based connectome for the collective dynamics localised at the stimulated node as well as phase coupling to other locations recorded in EEG. Together the new theoretical framework, the novel paradigm and the link to human brain electrophysiological and imaging data, will enable us to examine central hypotheses on how the flexible inter-regional coordination is shaped by large-scale brain network dynamics. Since inter-regional oscillatory coherence is disrupted in a variety of psychiatric and neurological diseases, our research will enable: novel PRC- and CFC-based diagnostic tools; and, in perspective, the model-assisted design of non-invasive stimulation protocols for the selective “repair” of altered functional connections. Here we will explore this possibility by manipulating resting state functional connectivity with TMS and by studying the functional connectivity alterations in the language system induced by intracranial stimulation, putting it in systematic relation with the severity of the observed transient aphasia symptoms.