Feedback information transfer in the human brain reflects bistable perception in the absence of report.

In the search for the neural basis of conscious experience, perception and the cognitive processes associated with reporting perception are typically confounded as neural activity is recorded while participants explicitly report what they experience. Here, we present a novel way to disentangle perception from report using eye movement analysis techniques based on convolutional neural networks and neurodynamical analyses based on information theory. We use a bistable visual stimulus that instantiates two well-known properties of conscious perception: integration and differentiation. At any given moment, observers either perceive the stimulus as one integrated unitary object or as two differentiated objects that are clearly distinct from each other. Using electroencephalography, we show that measures of integration and differentiation based on information theory closely follow participants' perceptual experience of those contents when switches were reported. We observed increased information integration between anterior to posterior electrodes (front to back) prior to a switch to the integrated percept, and higher information differentiation of anterior signals leading up to reporting the differentiated percept. Crucially, information integration was closely linked to perception and even observed in a no-report condition when perceptual transitions were inferred from eye movements alone. In contrast, the link between neural differentiation and perception was observed solely in the active report condition. Our results, therefore, suggest that perception and the processes associated with report require distinct amounts of anterior-posterior network communication and anterior information differentiation. While front-to-back directed information is associated with changes in the content of perception when viewing bistable visual stimuli, regardless of report, frontal information differentiation was absent in the no-report condition and therefore is not directly linked to perception per se.

Network Communications Flexibly Predict Visual Contents That Enhance Representations for Faster Visual Categorization.

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N = 11, both sexes). Each was cued to the spatial location (left vs right) and contents [low spatial frequency (LSF) vs high spatial frequency (HSF)] of a predicted Gabor stimulus that they then categorized. Using each participant's concurrently measured MEG, we reconstructed networks that predict and categorize LSF versus HSF contents for behavior. We found that predicted contents flexibly propagate top down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF versus HSF representations of the stimulus, all the way from occipital-ventral-parietal to premotor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e., 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.SIGNIFICANCE STATEMENT An enduring cognitive hypothesis states that our perception is partly influenced by the bottom-up sensory input but also by top-down expectations. However, cognitive explanations of the dynamic brain networks mechanisms that flexibly predict and categorize the visual input according to task-demands remain elusive. We addressed them in a predictive experimental design by isolating the network communications of cognitive contents from all other communications. Our methods revealed a Prediction Network that flexibly communicates contents from temporal to lateralized occipital cortex, with explicit frontal control, and an occipital-ventral-parietal-frontal Categorization Network that represents more sharply the predicted contents from the shown stimulus, leading to faster behavior. Our framework and results therefore shed a new light of cognitive information processing on dynamic brain activity.

Neural Encoding of Active Multi-Sensing Enhances Perceptual Decision-Making via a Synergistic Cross-Modal Interaction.

Most perceptual decisions rely on the active acquisition of evidence from the environment involving stimulation from multiple senses. However, our understanding of the neural mechanisms underlying this process is limited. Crucially, it remains elusive how different sensory representations interact in the formation of perceptual decisions. To answer these questions, we used an active sensing paradigm coupled with neuroimaging, multivariate analysis, and computational modeling to probe how the human brain processes multisensory information to make perceptual judgments. Participants of both sexes actively sensed to discriminate two texture stimuli using visual (V) or haptic (H) information or the two sensory cues together (VH). Crucially, information acquisition was under the participants' control, who could choose where to sample information from and for how long on each trial. To understand the neural underpinnings of this process, we first characterized where and when active sensory experience (movement patterns) is encoded in human brain activity (EEG) in the three sensory conditions. Then, to offer a neurocomputational account of active multisensory decision formation, we used these neural representations of active sensing to inform a drift diffusion model of decision-making behavior. This revealed a multisensory enhancement of the neural representation of active sensing, which led to faster and more accurate multisensory decisions. We then dissected the interactions between the V, H, and VH representations using a novel information-theoretic methodology. Ultimately, we identified a synergistic neural interaction between the two unisensory (V, H) representations over contralateral somatosensory and motor locations that predicted multisensory (VH) decision-making performance.SIGNIFICANCE STATEMENT In real-world settings, perceptual decisions are made during active behaviors, such as crossing the road on a rainy night, and include information from different senses (e.g., car lights, slippery ground). Critically, it remains largely unknown how sensory evidence is combined and translated into perceptual decisions in such active scenarios. Here we address this knowledge gap. First, we show that the simultaneous exploration of information across senses (multi-sensing) enhances the neural encoding of active sensing movements. Second, the neural representation of active sensing modulates the evidence available for decision; and importantly, multi-sensing yields faster evidence accumulation. Finally, we identify a cross-modal interaction in the human brain that correlates with multisensory performance, constituting a putative neural mechanism for forging active multisensory perception.

Cortical tracking of speech in noise accounts for reading strategies in children.

Humans' propensity to acquire literacy relates to several factors, including the ability to understand speech in noise (SiN). Still, the nature of the relation between reading and SiN perception abilities remains poorly understood. Here, we dissect the interplay between (1) reading abilities, (2) classical behavioral predictors of reading (phonological awareness, phonological memory, and rapid automatized naming), and (3) electrophysiological markers of SiN perception in 99 elementary school children (26 with dyslexia). We demonstrate that, in typical readers, cortical representation of the phrasal content of SiN relates to the degree of development of the lexical (but not sublexical) reading strategy. In contrast, classical behavioral predictors of reading abilities and the ability to benefit from visual speech to represent the syllabic content of SiN account for global reading performance (i.e., speed and accuracy of lexical and sublexical reading). In individuals with dyslexia, we found preserved integration of visual speech information to optimize processing of syntactic information but not to sustain acoustic/phonemic processing. Finally, within children with dyslexia, measures of cortical representation of the phrasal content of SiN were negatively related to reading speed and positively related to the compromise between reading precision and reading speed, potentially owing to compensatory attentional mechanisms. These results clarify the nature of the relation between SiN perception and reading abilities in typical child readers and children with dyslexia and identify novel electrophysiological markers of emergent literacy.

History biases reveal novel dissociations between perceptual and metacognitive decision-making.

Human decision-making and self-reflection often depend on context and internal biases. For instance, decisions are often influenced by preceding choices, regardless of their relevance. It remains unclear how choice history influences different levels of the decision-making hierarchy. We used analyses grounded in information and detection theories to estimate the relative strength of perceptual and metacognitive history biases and to investigate whether they emerge from common/unique mechanisms. Although both perception and metacognition tended to be biased toward previous responses, we observed novel dissociations that challenge normative theories of confidence. Different evidence levels often informed perceptual and metacognitive decisions within observers, and response history distinctly influenced first- (perceptual) and second- (metacognitive) order decision-parameters, with the metacognitive bias likely to be strongest and most prevalent in the general population. We propose that recent choices and subjective confidence represent heuristics, which inform first- and second-order decisions in the absence of more relevant evidence.

Neurocomputational mechanisms underlying cross-modal associations and their influence on perceptual decisions.

When exposed to complementary features of information across sensory modalities, our brains formulate cross-modal associations between features of stimuli presented separately to multiple modalities. For example, auditory pitch-visual size associations map high-pitch tones with small-size visual objects, and low-pitch tones with large-size visual objects. Preferential, or congruent, cross-modal associations have been shown to affect behavioural performance, i.e. choice accuracy and reaction time (RT) across multisensory decision-making paradigms. However, the neural mechanisms underpinning such influences in perceptual decision formation remain unclear. Here, we sought to identify when perceptual improvements from associative congruency emerge in the brain during decision formation. In particular, we asked whether such improvements represent 'early' sensory processing benefits, or 'late' post-sensory changes in decision dynamics. Using a modified version of the Implicit Association Test (IAT), coupled with electroencephalography (EEG), we measured the neural activity underlying the effect of auditory stimulus-driven pitch-size associations on perceptual decision formation. Behavioural results showed that participants responded significantly faster during trials when auditory pitch was congruent, rather than incongruent, with its associative visual size counterpart. We used multivariate Linear Discriminant Analysis (LDA) to characterise the spatiotemporal dynamics of EEG activity underpinning IAT performance. We found an 'Early' component (∼100-110 ms post-stimulus onset) coinciding with the time of maximal discrimination of the auditory stimuli), and a 'Late' component (∼330-340 ms post-stimulus onset) underlying IAT performance. To characterise the functional role of these components in decision formation, we incorporated a neurally-informed Hierarchical Drift Diffusion Model (HDDM), revealing that the Late component decreases response caution, requiring less sensory evidence to be accumulated, whereas the Early component increased the duration of sensory-encoding processes for incongruent trials. Overall, our results provide a mechanistic insight into the contribution of 'early' sensory processing, as well as 'late' post-sensory neural representations of associative congruency to perceptual decision formation.

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data.

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites1 that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

Characterising cognitive heterogeneity in individuals at clinical high-risk for psychosis: a cluster analysis with clinical and functional outcome prediction.

Schizophrenia is characterised by cognitive impairments that are already present during early stages, including in the clinical high-risk for psychosis (CHR-P) state and first-episode psychosis (FEP). Moreover, data suggest the presence of distinct cognitive subtypes during early-stage psychosis, with evidence for spared vs. impaired cognitive profiles that may be differentially associated with symptomatic and functional outcomes. Using cluster analysis, we sought to determine whether cognitive subgroups were associated with clinical and functional outcomes in CHR-P individuals. Data were available for 146 CHR-P participants of whom 122 completed a 6- and/or 12-month follow-up; 15 FEP participants; 47 participants not fulfilling CHR-P criteria (CHR-Ns); and 53 healthy controls (HCs). We performed hierarchical cluster analysis on principal components derived from neurocognitive and social cognitive measures. Within the CHR-P group, clusters were compared on clinical and functional variables and examined for associations with global functioning, persistent attenuated psychotic symptoms and transition to psychosis. Two discrete cognitive subgroups emerged across all participants: 45.9% of CHR-P individuals were cognitively impaired compared to 93.3% of FEP, 29.8% of CHR-N and 30.2% of HC participants. Cognitively impaired CHR-P participants also had significantly poorer functioning at baseline and follow-up than their cognitively spared counterparts. Specifically, cluster membership predicted functional but not clinical outcome. Our findings support the existence of distinct cognitive subgroups in CHR-P individuals that are associated with functional outcomes, with implications for early intervention and the understanding of underlying developmental processes.

Representational interactions during audiovisual speech entrainment: Redundancy in left posterior superior temporal gyrus and synergy in left motor cortex.

Integration of multimodal sensory information is fundamental to many aspects of human behavior, but the neural mechanisms underlying these processes remain mysterious. For example, during face-to-face communication, we know that the brain integrates dynamic auditory and visual inputs, but we do not yet understand where and how such integration mechanisms support speech comprehension. Here, we quantify representational interactions between dynamic audio and visual speech signals and show that different brain regions exhibit different types of representational interaction. With a novel information theoretic measure, we found that theta (3-7 Hz) oscillations in the posterior superior temporal gyrus/sulcus (pSTG/S) represent auditory and visual inputs redundantly (i.e., represent common features of the two), whereas the same oscillations in left motor and inferior temporal cortex represent the inputs synergistically (i.e., the instantaneous relationship between audio and visual inputs is also represented). Importantly, redundant coding in the left pSTG/S and synergistic coding in the left motor cortex predict behavior-i.e., speech comprehension performance. Our findings therefore demonstrate that processes classically described as integration can have different statistical properties and may reflect distinct mechanisms that occur in different brain regions to support audiovisual speech comprehension.

Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals.

Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience.