Investigating the spatial characteristics of the crossmodal interaction between nociception and vision using gaze direction

The present study investigated the influence of nociceptive stimuli on visual stimuli processing according to the relative spatial congruence between the two stimuli of different sensory modalities. Participants performed temporal order judgments on pairs of visual stimuli, one presented near the hand on which nociceptive stimuli were occasionally applied, the other one either to its left or to its right. The visual hemifield in which the stimulated hand and the near visual stimulus appeared was manipulated by changing gaze direction. The stimulated hemibody and the stimulated visual hemifield were therefore either congruent or incongruent, in terms of anatomical locations. Despite the changes in anatomical congruence, judgments were always biased in favor of the visual stimuli presented near the stimulated hand. This indicates that nociceptive-visual interaction may rely on a realignment of the respective initial anatomical representations of the somatic and retinotopic spaces toward an integrated, multimodal representation of external space

Strong Conscious Cues Suppress Preferential Gaze Allocation to Unconscious Cues

Visual attention allows relevant information to be selected for further processing. Both conscious and unconscious visual stimuli can bias attentional allocation, but how these two types of visual information interact to guide attention remains unclear. In this study, we explored attentional allocation during a motion discrimination task with varied motion strength and unconscious associations between stimuli and cues. Participants were instructed to report the motion direction of two colored patches of dots. Unbeknown to participants, dot colors were sometimes informative of the correct response. We found that subjects learnt the associations between colors and motion direction but failed to report this association using the questionnaire filled at the end of the experiment, confirming that learning remained unconscious. The eye movement analyses revealed that allocation of attention to unconscious sources of information occurred mostly when motion coherence was low, indicating that unconscious cues influence attentional allocation only in the absence of strong conscious cues. All in all, our results reveal that conscious and unconscious sources of information interact with each other to influence attentional allocation and suggest a selection process that weights cues in proportion to their reliability

Disruption of Broca's Area Alters Higher-order Chunking Processing during Perceptual Sequence Learning

Because Broca's area is known to be involved in many cognitive functions, including language, music, and action processing, several attempts have been made to propose a unifying theory of its role that emphasizes a possible contribution to syntactic processing. Recently, we have postulated that Broca's area might be involved in higher-order chunk processing during implicit learning of a motor sequence. Chunking is an information-processing mechanism that consists of grouping consecutive items in a sequence and is likely to be involved in all of the aforementioned cognitive processes. Demonstrating a contribution of Broca's area to chunking during the learning of a nonmotor sequence that does not involve language could shed new light on its function. To address this issue, we used offline MRI-guided TMS in healthy volunteers to disrupt the activity of either the posterior part of Broca's area (left Brodmann's area [BA] 44) or a control site just before participants learned a perceptual sequence structured in distinct hierarchical levels. We found that disruption of the left BA 44 increased the processing time of stimuli representing the boundaries of higher-order chunks and modified the chunking strategy. The current results highlight the possible role of the left BA 44 in building up effector-independent representations of higher-order events in structured sequences. This might clarify the contribution of Broca's area in processing hierarchical structures, a key mechanism in many cognitive functions, such as language and composite actions

Human brain effects of DMT assessed via EEG-fMRI.

Psychedelics have attracted medical interest, but their effects on human brain function are incompletely understood. In a comprehensive, within-subjects, placebo-controlled design, we acquired multimodal neuroimaging [i.e., EEG-fMRI (electroencephalography-functional MRI)] data to assess the effects of intravenous (IV) N,N-Dimethyltryptamine (DMT) on brain function in 20 healthy volunteers. Simultaneous EEG-fMRI was acquired prior to, during, and after a bolus IV administration of 20 mg DMT, and, separately, placebo. At dosages consistent with the present study, DMT, a serotonin 2A receptor (5-HT2AR) agonist, induces a deeply immersive and radically altered state of consciousness. DMT is thus a useful research tool for probing the neural correlates of conscious experience. Here, fMRI results revealed robust increases in global functional connectivity (GFC), network disintegration and desegregation, and a compression of the principal cortical gradient under DMT. GFC × subjective intensity maps correlated with independent positron emission tomography (PET)-derived 5-HT2AR maps, and both overlapped with meta-analytical data implying human-specific psychological functions. Changes in major EEG-measured neurophysiological properties correlated with specific changes in various fMRI metrics, enriching our understanding of the neural basis of DMT's effects. The present findings advance on previous work by confirming a predominant action of DMT-and likely other 5-HT2AR agonist psychedelics-on the brain's transmodal association pole, i.e., the neurodevelopmentally and evolutionarily recent cortex that is associated with species-specific psychological advancements, and high expression of 5-HT2A receptors

Unconscious learning : behavioral evidence, relationship with attention and physiological markers

The work aims at investigating unconscious processing in the human brain. It is composed by an introduction (chapter I), the experimental section composed by four studies (chapters II to V) and the discussion/conclusion. The introduction consists of two parts: the first one provides an overview of unconscious processes from a critical perspectives, whereas the second part presents an overview about physiological mechanisms driving the pupillary response and its relationship with unconscious cognitive mechanisms. The main core of the thesis is the experimental part, composed of 4 studies. The first study introduces a novel framework to reliably study unconscious processing, addressing the main criticisms discussed in the first part of the introduction. The second and third study exploit this novel framework to investigate the relationship between unconscious learning and overt visual attention. Finally, the last study reveals how pupillary responses can be used to track statistical implicit learning, suggesting that pupil dilates at the occurrence of unconscious surprising events. The conclusion of the thesis contextualizes the finding in the literature and discusses the main limitations.(BIFA - Sciences biomédicales et pharmaceutiques) -- UCL, 201

Surfing cognition like a traveling wave: from unconscious learning to predictive coding

This manuscript summarizes my previous work in cognitive neuroscience and delineates the short-term steps and the global vision for my future research interests. The overarching goal of my research career is to investigate human cognition using different tools and methods. Inter- and multi- disciplinarity are two key aspects that characterize my scientific journey, and it’s essential to keep these elements in mind when navigating through this manuscript. Several topics and methods sparkled my scientific curiosity, from unconscious learning to brain oscillations, from computational modeling to neural networks. Besides my neverending interest in human cognition, the pole star that has always set the course is the quest to find a comprehensive framework to understand the human mind. I started my journey during my phd when I explored unconscious and sequence learning, running several experiments on healthy humans (often medicine students). After establishing a methodologically sound framework to investigate unconscious processes, I explored how these influence eye movements, pupil size, and EEG recordings. Next, I set the course toward more computational shores, investigating how predictive coding could give rise to neural oscillations and traveling waves during my postdocs. Moving through differential equations and neural networks, I compared the performance of models with humans in different tasks, such as visual reasoning or artificial grammar learning. In the next years, combining all I learned along the way, I will dive into brain dynamics at different scales, understanding whether predictive coding could be the critical framework for understanding brain dynamics or, at least, traveling waves. In the long term, I plan to set sail toward more clinical-oriented applications, exploring the fascinating new world of computational psychiatry

GAttANet: Global attention agreement for convolutional neural networks

Paper accepted to ICANN 2021 - The 30th International Conference on Artificial Neural NetworksInternational audienceTransformer attention architectures, similar to those developed for natural language processing, have recently proved efficient also in vision, either in conjunction with or as a replacement for convolutional layers. Typically, visual attention is inserted in the network architecture as a (series of) feedforward self-attention module(s), with mutual key-query agreement as the main selection and routing operation. However efficient, this strategy is only vaguely compatible with the way that attention is implemented in biological brains: as a separate and unified network of attentional selection regions, receiving inputs from and exerting modulatory influence on the entire hierarchy of visual regions. Here, we report experiments with a simple such attention system that can improve the performance of standard convolutional networks, with relatively few additional parameters. Each spatial position in each layer of the network produces a key-query vector pair; all queries are then pooled into a global attention query. On the next iteration, the match between each key and the global attention query modulates the network's activations -- emphasizing or silencing the locations that agree or disagree (respectively) with the global attention system. We demonstrate the usefulness of this brain-inspired Global Attention Agreement network (GAttANet) for various convolutional backbones (from a simple 5-layer toy model to a standard ResNet50 architecture) and datasets (CIFAR10, CIFAR100, Imagenet-1k). Each time, our global attention system improves accuracy over the corresponding baseline

Statistical Regularities Attract Attention when Task-Relevant

Visual attention seems essential for learning the statistical regularities in our environment, a process known as statistical learning. However, how attention is allocated when exploring a novel visual scene whose statistical structure is unknown remains unclear. In order to address this question, we investigated visual attention allocation during a task in which we manipulated the conditional probability of occurrence of colored stimuli, unbeknown to the subjects. Participants were instructed to detect a target colored dot among two dots moving along separate circular paths. We evaluated implicit statistical learning, i.e., the effect of color predictability on reaction times (RTs), and recorded eye position concurrently. Attention allocation was indexed by comparing the Mahalanobis distance between the position, velocity and acceleration of the eyes and the two colored dots. We found that learning the conditional probabilities occurred very early during the course of the experiment as shown by the fact that, starting already from the first block, predictable stimuli were detected with shorter RT than unpredictable ones. In terms of attentional allocation, we found that the predictive stimulus attracted gaze only when it was informative about the occurrence of the target but not when it predicted the occurrence of a task-irrelevant stimulus. This suggests that attention allocation was influenced by regularities only when they were instrumental in performing the task. Moreover, we found that the attentional bias towards task-relevant predictive stimuli occurred at a very early stage of learning, concomitantly with the first effects of learning on RT. In conclusion, these results show that statistical regularities capture visual attention only after a few occurrences, provided these regularities are instrumental to perform the task