The neural correlates supporting our perceptual experience of the world remain largely unknown. Recent studies have shown how stimulus detection and related confidence involve evidence accumulation (EA) processes similar to those involved in perceptual decision-making. Here, we propose that independently from any tasks, percepts are not static but fade in and out of consciousness according to the dynamics of a leaky evidence accumulation process (LEAP), and that confidence corresponds to the maximal evidence accumulated by this process. We discuss the implications and limitations of our proposal, assess how it may qualify as a neural correlate of consciousness, and illustrate how it brings us closer to a mechanistic understanding of phenomenal aspects of perceptual experience like intensity and duration, beyond mere detection.

Numerous studies have shown that humans can successfully correct deviations to ongoing movements without being aware of them, suggesting limited conscious monitoring of visuomotor performance. Here, we ask whether such limited monitoring impairs the capacity to judiciously place confidence ratings to reflect decision accuracy (metacognitive sensitivity). To this end, we recorded functional magnetic resonance imaging data while thirty-one participants reported visuomotor cursor deviations and rated their confidence retrospectively. We show that participants use a summary statistic of the unfolding visual feedback (the maximum cursor error) to detect deviations but that this information alone is insufficient to explain detection performance. The same summary statistics is used by participants to optimally adjust their confidence ratings, even for unaware deviations. At the neural level, activity in the ventral striatum tracked high confidence, whereas a broad network including the anterior prefrontal cortex encoded cursor error but not confidence, shedding new light on a role of the anterior prefrontal cortex for action monitoring rather than confidence. Together, our results challenge the notion of limited action monitoring and uncover a new mechanism by which humans optimally monitor their movements as they unfold, even when unaware of ongoing deviations.

Metacognitive deficits are well documented in schizophrenia spectrum disorders as a decreased capacity to adjust confidence to performance in a cognitive task. Because metacognitive ability directly depends on task performance, metacognitive deficits might be driven by lower task performance among patients. To test this hypothesis, we conducted a Bayesian meta-analysis of 42 studies comparing metacognitive abilities in 1425 individuals with schizophrenia compared to 1256 matched controls. We found a global metacognitive deficit in schizophrenia (g = −0.57, 95 % CrI [−0.72, −0.43]), which was driven by studies which did not control task performance (g = −0.63, 95 % CrI [−0.78, −0.49]), and inconclusive among controlled-studies (g = −0.23, 95 % CrI [−0.60, 0.16], BF01 = 2.2). No correlation was found between metacognitive deficit and clinical features. We provide evidence that the metacognitive deficit in schizophrenia is inflated due to non-equated task performance. Thus, efforts should be made to develop experimental protocols accounting for lower task performance in schizophrenia.

A fundamental scientific question concerns the neural basis of perceptual consciousness and perceptual monitoring resulting from the processing of sensory events. Although recent studies identified neurons reflecting stimulus visibility, their functional role remains unknown. Here, we show that perceptual consciousness and monitoring involve evidence accumulation. We recorded single-neuron activity in a participant with a microelectrode in the posterior parietal cortex, while they detected vibrotactile stimuli around detection threshold and provided confidence estimates. We find that detected stimuli elicited neuronal responses resembling evidence accumulation during decision-making, irrespective of motor confounds or task demands. We generalize these findings in healthy volunteers using electroencephalography. Behavioral and neural responses are reproduced with a computational model considering a stimulus as detected if accumulated evidence reaches a bound, and confidence as the distance between maximal evidence and that bound. We conclude that gradual changes in neuronal dynamics during evidence accumulation relates to perceptual consciousness and perceptual monitoring in humans.

Previous studies have shown that self-generated stimuli in auditory, visual, and somatosensory domains are attenuated, producing decreased behavioral and neural responses compared to the same stimuli that are externally generated. Yet, whether such attenuation also occurs for higher-level cognitive functions beyond sensorimotor processing remains unknown. In this study, we assessed whether cognitive functions such as numerosity estimations are subject to attenuation. We designed a task allowing the controlled comparison of numerosity estimations for self (active condition) and externally (passive condition) generated words. Our behavioral results showed a larger underestimation of self-compared to externally-generated words, suggesting that numerosity estimations for self-generated words are attenuated. Moreover, the linear relationship between the reported and actual number of words was stronger for self-generated words, although the ability to track errors about numerosity estimations was similar across conditions. Neuroimaging results revealed that numerosity underestimation involved increased functional connectivity between the right intraparietal sulcus and an extended network (bilateral supplementary motor area, left inferior parietal lobule and left superior temporal gyrus) when estimating the number of self vs. externally generated words. We interpret our results in light of two models of attenuation and discuss their perceptual versus cognitive origins.

Metacognition is the set of reflexive processes allowing humans to evaluate the accuracy of their mental operations. Deficits in synthetic metacognition have been described in schizophrenia using mostly narrative assessment and linked to several key symptoms. Here, we assessed metacognitive performance by asking individuals with schizophrenia or schizoaffective disorder (N=20) and matched healthy participants (N = 21) to perform a visual discrimination task and subsequently report confidence in their performance. Metacognitive performance was defined as the adequacy between visual discrimination performance and confidence. Bayesian analyses revealed equivalent metacognitive performance in the two groups despite a weaker association between confidence and trajectory tracking during task execution among patients. These results were reproduced using a bounded evidence accumulation model which showed similar decisional processes in the two groups. The inability to accurately attune confidence to perceptual decisions in schizophrenia remains to be experimentally demonstrated, along with the way such impairments may underpin functional deficits.

Our everyday life is full of rapid decisions such as a driver having to choose which exit to take on the highway. These rapid decisions often come with a certain level of subjective confidence ranging from certainty of having made the right choice to certainty of having chosen wrong and passing through various levels of uncertainty. Since our sense of confidence stems from the monitoring of the decision it relates to, its underlying brain mechanisms have been difficult to study in isolation of the accompanying decisions. We have published a study in which we isolate confidence from decisional processes by comparing confidence related to our own decisions with confidence related to other people’s decisions (e.g. the confidence of the passenger in the car). In our study, decisions were taken about which stimulus contained the highest number of dots. These decisions could be taken either by the study participants by pressing a button, or by the computer by showing a hand on the chosen side. In both cases, participants had to rate their confidence in the previous decision. We found that confidence ratings tracked the correctness of decisions better when those decisions were taken by the participants. Since we recorded electroencephalography while the participants were in a functional magnetic resonance imaging scanner, we were able to precisely locate the brain regions associated to confidence both in time and space. We found only one brain region that was still associated with confidence when participants rated the computer’s decisions: the inferior frontal cortex. One other brain region, the anterior prefrontal cortex, that is well known to relate to confidence was only associated to confidence in participants’ own decisions. Our study thus sheds light on the underlying mechanisms of confidence, highlighting the role of the inferior frontal cortex as a key region for confidence independently from decisions and constraining the role of the anterior prefrontal cortex to self-related monitoring.

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.

Visual attention can be spatially oriented, even in the absence of saccadic eye-movements, to facilitate the processing of incoming visual information. One behavioral proxy for this so-called covert visuospatial attention (CVSA) is the validity effect (VE): the reduction in reaction time (RT) to visual stimuli at attended locations and the increase in RT to stimuli at unattended locations. At the electrophysiological level, one correlate of CVSA is the lateralization in the occipital alpha-band oscillations, resulting from alpha-power increases ipsilateral and decreases contralateral to the attended hemifield. While this alpha-band lateralization has been considerably studied using electroencephalography (EEG) or magnetoencephalography (MEG), little is known about whether it can be trained to improve CVSA behaviorally. In this cross-over sham-controlled study we used continuous real-time feedback of the occipital alpha-lateralization to modulate behavioral and electrophysiological markers of covert attention. Fourteen subjects performed a cued CVSA task, involving fast responses to covertly attended stimuli. During real-time feedback runs, trials extended in time if subjects reached states of high alpha-lateralization. Crucially, the ongoing alpha-lateralization was fed back to the subject by changing the color of the attended stimulus. We hypothesized that this ability to self-monitor lapses in CVSA and thus being able to refocus attention accordingly would lead to improved CVSA performance during subsequent testing. We probed the effect of the intervention by evaluating the pre-post changes in the VE and the alpha-lateralization. Behaviorally, results showed a significant interaction between feedback (experimental–sham) and time (pre-post) for the validity effect, with an increase in performance only for the experimental condition. We did not find corresponding pre-post changes in the alpha-lateralization. Our findings suggest that EEG-based real-time feedback is a promising tool to enhance the level of covert visuospatial attention, especially with respect to behavioral changes. This opens up the exploration of applications of the proposed training method for the cognitive rehabilitation of attentional disorders.

Hand grasping is a sophisticated motor task that has received much attention by the neuroscientific community, which demonstrated how grasping activates a network involving parietal, pre-motor and motor cortices using fMRI, ECoG, LFPs and spiking activity. Yet, there is a need for a more precise spatio-temporal analysis as it is still unclear how these brain activations over large cortical areas evolve at the sub-second level. In this study, we recorded ten human participants (1 female) performing visually-guided, self-paced reaching and grasping with precision or power grips. Following the results, we demonstrate the existence of neural correlates of grasping from broadband EEG in self-paced conditions and show how neural correlates of precision and power grasps differentially evolve as grasps unfold. 100 ms before the grasp is secured, bilateral parietal regions showed increasingly differential patterns. Afterwards, sustained differences between both grasps occurred over the bilateral motor and parietal regions, and medial pre-frontal cortex. Furthermore, these differences were sufficiently discriminable to allow single-trial decoding with 70% decoding performance. Functional connectivity revealed differences at the network level between grasps in fronto-parietal networks, in terms of upper-alpha cortical oscillatory power with a strong involvement of ipsilateral hemisphere. Our results supported the existence of fronto-parietal recurrent feedback loops, with stronger interactions for precision grips due to the finer motor control required for this grasping type.

Non-invasive and invasive electrical neurostimulation are promising tools to better understand brain functionandultimately treat its malfunction. In current open-loop approaches, a clinician chooses a fixed set of stimulation parameters, informed by observed therapeutic benefits and previous empirical evidence. However, this procedure leads to a large intra- and inter-subject variability often introducing side-effects and low effect sizes. Closed-loop electrical neurostimulation (CLENS) approaches strive to alleviate these limitations by tailoring the stimulation parameters to an ongoing electrophysiological biomarker. Here, we review the current status of closed-loop, supraspinal elec- trical stimulation in humans, presenting our vision of potential control frameworks, and support the idea of creating synergies with the field of brain-machine interfacing. Finally, we pinpoint two pivotal challenges that, in our view, need to be overcome for this technology to become a reality: dealing with the electrical stimulation artifacts, and dissociating the pathological from physiological information within the targeted biomarker.

Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns. However, somatosensory feedback should not interfere with the recorded MI brain pattern. In this study we propose a novel feedback modality to guide subjects during MI based on sensory threshold neuromuscular electrical stimulation (St-NMES). St-NMES depolarizes sensory and motor axons without eliciting any muscular contraction. We hypothesize that St-NMES does not induce detectable ERD brain patterns and fosters MI performance. Twelve novice subjects were included in a cross-over design study. We recorded their EEG, comparing St-NMES with visual feedback during MI or resting tasks. We found that St-NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns. Moreover, classification accuracy and stability were significantly higher with St-NMES. Importantly, St-NMES alone did not induce detectable artifacts, but rather the changes in the detected patterns were due to an increased MI performance. Our findings indicate that St-NMES is a promising feedback in order to foster MI performance and cold be used for BMI online applications.

Numerous studies have examined neural correlates of the human brain’s action-monitoring system during experimentally segmented tasks. However, it remains unknown how such a system operates during continuous motor output when no experimental time marker is available (such as button presses or stimulus onset). We set out to investigate the electrophysiological correlates of action monitoring when hand position has to be repeatedly monitored and corrected. For this, we recorded high-density electroencephalography (EEG) during a visuomotor tracking task during which participants had to follow a target with the mouse cursor along a visible trajectory. By decomposing hand kinematics into naturally occurring periodic submovements, we found an event-related potential (ERP) time-locked to these submovements and localized in a sensorimotor cortical network comprising the supplementary motor area (SMA) and the precentral gyrus. Critically, the amplitude of the ERP correlated with the deviation of the cursor, 110 ms before the submovement. Control analyses showed that this correlation was truly due to the cursor deviation and not to differences in submovement kinematics or to the visual content of the task. The ERP closely resembled those found in response to mismatch events in typical cognitive neuroscience experiments. Our results demonstrate the existence of a cortical process in the SMA, evaluating hand position in synchrony with submovements. These findings suggest a functional role of submovements in a sensorimotor loop of periodic monitoring and correction and generalize previous results from the field of action monitoring to cases where action has to be repeatedly monitored.

Non-invasive brain stimulation has shown promising results in neurorehabilitation for motor-impaired stroke patients, by rebalancing the relative involvement of each hemisphere in movement generation. Similarly, brain-computer interfaces have been used to successfully facilitate movement-related brain activity spared by the infarct. We propose to merge both approaches by using BCI to train stroke patients to rebalance their motor-related brain activity during motor tasks, through the use of online feedback. In this pilot study, we report results showing that some healthy subjects were able to learn to spontaneously up- and/or down-regulate their ipsilateral brain activity during a single session.