Bayesian inference of population prevalence

A drift diffusion model analysis of age-related impact on multisensory decision-making processes

Older adults (OAs) are typically slower and/or less accurate in forming perceptual choices relative to younger adults. Despite perceptual deficits, OAs gain from integrating information across senses, yielding multisensory benefits. However, the cognitive processes underlying these seemingly discrepant ageing effects remain unclear. To address this knowledge gap, 212 participants (18-90 years old) performed an online object categorisation paradigm, whereby age-related differences in Reaction Times (RTs) and choice accuracy between audiovisual (AV), visual (V), and auditory (A) conditions could be assessed. Whereas OAs were slower and less accurate across sensory conditions, they exhibited greater RT decreases between AV and V conditions, showing a larger multisensory benefit towards decisional speed. Hierarchical Drift Diffusion Modelling (HDDM) was fitted to participants' behaviour to probe age-related impacts on the latent multisensory decision formation processes. For OAs, HDDM demonstrated slower evidence accumulation rates across sensory conditions coupled with increased response caution for AV trials of higher difficulty. Notably, for trials of lower difficulty we found multisensory benefits in evidence accumulation that increased with age, but not for trials of higher difficulty, in which increased response caution was instead evident. Together, our findings reconcile age-related impacts on multisensory decision-making, indicating greater multisensory evidence accumulation benefits with age underlying enhanced decisional speed.

Speech prosody enhances the neural processing of syntax

Human language relies on the correct processing of syntactic information, as it is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. However, behavioral and neural evidence from adults suggests the idea that prosody and syntax interact, and studies in infants support the notion that prosody assists language learning. Here we analyze a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic representations in the brains of native English speakers. More specifically, to examine whether prosody enhances the cortical encoding of syntactic representations, we decode syntactic phrase boundaries directly from brain activity, and evaluate possible modulations of this decoding by the prosodic boundaries. Our findings demonstrate that the presence of prosodic boundaries improves the neural representation of phrase boundaries, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This work has implications for interactive models of how the brain processes different linguistic features. Future research is needed to establish the neural underpinnings of prosody-syntax interactions in languages with different typological characteristics.

Computational Language Modeling and the Promise of In Silico Experimentation

Language neuroscience currently relies on two major experimental paradigms: controlled experiments using carefully hand-designed stimuli, and natural stimulus experiments. These approaches have complementary advantages which allow them to address distinct aspects of the neurobiology of language, but each approach also comes with drawbacks. Here we discuss a third paradigm-in silico experimentation using deep learning-based encoding models-that has been enabled by recent advances in cognitive computational neuroscience. This paradigm promises to combine the interpretability of controlled experiments with the generalizability and broad scope of natural stimulus experiments. We show four examples of simulating language neuroscience experiments in silico and then discuss both the advantages and caveats of this approach.

Walking modulates visual detection performance according to stride cycle phase

Walking is among our most frequent and natural of voluntary behaviours, yet the consequences of locomotion upon perceptual and cognitive function remain largely unknown. Recent work has highlighted that although walking feels smooth and continuous, critical phases exist within each step for the successful coordination of perceptual and motor function. Here, we test whether these phasic demands impact upon visual perception, by assessing performance in a visual detection task during natural unencumbered walking. We finely sample visual performance over the stride cycle as participants walk along a smooth linear path at a comfortable speed in a wireless virtual reality environment. At the group-level, accuracy, reaction times, and response likelihood show strong oscillations, modulating at approximately 2 cycles per stride (~2 Hz) with a marked phase of optimal performance aligned with the swing phase of each step. At the participant level, Bayesian inference of population prevalence reveals highly prevalent oscillations in visual detection performance that cluster in two idiosyncratic frequency ranges (2 or 4 cycles per stride), with a strong phase alignment across participants.

Literacy in nonspeaking autistic people

LAY ABSTRACT

Many autistic people who do not talk cannot tell other people what they know or what they are thinking. As a result, they might not be able to go to the schools they want, share feelings with friends, or get jobs they like. It might be possible to teach them to type on a computer or tablet instead of talking. But first, they would have to know how to spell. Some people do not believe that nonspeaking autistic people can learn to spell. We did a study to see if they can. We tested 31 autistic teenagers and adults who do not talk much or at all. They played a game on an iPad where they had to tap flashing letters. After they played the game, we looked at how fast they tapped the letters. They did three things that people who know how to spell would do. First, they tapped flashing letters faster when the letters spelled out sentences than when the letters made no sense. Second, they tapped letters that usually go together faster than letters that do not usually go together. This shows that they knew some spelling rules. Third, they paused before tapping the first letter of a new word. This shows that they knew where one word ended and the next word began. These results suggest that many autistic people who do not talk can learn how to spell. If they are given appropriate opportunities, they might be able to learn to communicate by typing.

Sub-second and multi-second dopamine dynamics underlie variability in human time perception

Timing behaviour and the perception of time are fundamental to cognitive and emotional processes in humans. In non-human model organisms, the neuromodulator dopamine has been associated with variations in timing behaviour, but the connection between variations in dopamine levels and the human experience of time has not been directly assessed. Here, we report how dopamine levels in human striatum, measured with sub-second temporal resolution during awake deep brain stimulation surgery, relate to participants' perceptual judgements of time intervals. Fast, phasic, dopaminergic signals were associated with underestimation of temporal intervals, whereas slower, tonic, decreases in dopamine were associated with poorer temporal precision. Our findings suggest a delicate and complex role for the dynamics and tone of dopaminergic signals in the conscious experience of time in humans.

Cultural facial expressions dynamically convey emotion category and intensity information

Communicating emotional intensity plays a vital ecological role because it provides valuable information about the nature and likelihood of the sender's behavior.1,2,3 For example, attack often follows signals of intense aggression if receivers fail to retreat.4,5 Humans regularly use facial expressions to communicate such information.6,7,8,9,10,11 Yet how this complex signaling task is achieved remains unknown. We addressed this question using a perception-based, data-driven method to mathematically model the specific facial movements that receivers use to classify the six basic emotions-"happy," "surprise," "fear," "disgust," "anger," and "sad"-and judge their intensity in two distinct cultures (East Asian, Western European; total n = 120). In both cultures, receivers expected facial expressions to dynamically represent emotion category and intensity information over time, using a multi-component compositional signaling structure. Specifically, emotion intensifiers peaked earlier or later than emotion classifiers and represented intensity using amplitude variations. Emotion intensifiers are also more similar across emotions than classifiers are, suggesting a latent broad-plus-specific signaling structure. Cross-cultural analysis further revealed similarities and differences in expectations that could impact cross-cultural communication. Specifically, East Asian and Western European receivers have similar expectations about which facial movements represent high intensity for threat-related emotions, such as "anger," "disgust," and "fear," but differ on those that represent low threat emotions, such as happiness and sadness. Together, our results provide new insights into the intricate processes by which facial expressions can achieve complex dynamic signaling tasks by revealing the rich information embedded in facial expressions.

Strength of predicted information content in the brain biases decision behavior

Prediction-for-perception theories suggest that the brain predicts incoming stimuli to facilitate their categorization.1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17 However, it remains unknown what the information contents of these predictions are, which hinders mechanistic explanations. This is because typical approaches cast predictions as an underconstrained contrast between two categories18,19,20,21,22,23,24-e.g., faces versus cars, which could lead to predictions of features specific to faces or cars, or features from both categories. Here, to pinpoint the information contents of predictions and thus their mechanistic processing in the brain, we identified the features that enable two different categorical perceptions of the same stimuli. We then trained multivariate classifiers to discern, from dynamic MEG brain responses, the features tied to each perception. With an auditory cueing design, we reveal where, when, and how the brain reactivates visual category features (versus the typical category contrast) before the stimulus is shown. We demonstrate that the predictions of category features have a more direct influence (bias) on subsequent decision behavior in participants than the typical category contrast. Specifically, these predictions are more precisely localized in the brain (lateralized), are more specifically driven by the auditory cues, and their reactivation strength before a stimulus presentation exerts a greater bias on how the individual participant later categorizes this stimulus. By characterizing the specific information contents that the brain predicts and then processes, our findings provide new insights into the brain's mechanisms of prediction for perception.

Inability to move one's face dampens facial expression perception

Humans rely heavily on facial expressions for social communication to convey their thoughts and emotions and to understand them in others. One prominent but controversial view is that humans learn to recognize the significance of facial expressions by mimicking the expressions of others. This view predicts that an inability to make facial expressions (e.g., facial paralysis) would result in reduced perceptual sensitivity to others' facial expressions. To test this hypothesis, we developed a diverse battery of sensitive emotion recognition tasks to characterize expression perception in individuals with Moebius Syndrome (MBS), a congenital neurological disorder that causes facial palsy. Using computer-based detection tasks we systematically assessed expression perception thresholds for static and dynamic face and body expressions. We found that while MBS individuals were able to perform challenging perceptual control tasks and body expression tasks, they were less efficient at extracting emotion from facial expressions, compared to matched controls. Exploratory analyses of fMRI data from a small group of MBS participants suggested potentially reduced engagement of the amygdala in MBS participants during expression processing relative to matched controls. Collectively, these results suggest a role for facial mimicry and consequent facial feedback and motor experience in the perception of others' facial expressions.

Influences of local and global context on local orientation perception

Visual context modulates perception of local orientation attributes. These spatially very localised effects are considered to correspond to specific excitatory-inhibitory connectivity patterns of early visual areas as V1, creating perceptual tilt repulsion and attraction effects. Here, orientation misperception of small Gabor stimuli was used as a probe of this computational structure by sampling a large spatio-orientation space to reveal expected asymmetries due to the underlying neuronal processing. Surprisingly, the results showed a regular iso-orientation pattern of nearby location effects whose reference point was globally modulated by the spatial structure, without any complex interactions between local positions and orientation. This pattern of results was confirmed by the two perceptual parameters of bias and discrimination ability. Furthermore, the response times to stimulus configuration displayed variations that further provided evidence of how multiple early visual stages affect perception of simple stimuli.

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.

Coherence and divergence in autonomic-subjective affective space

A central tenet of many theories of emotion is that emotional states are accompanied by distinct patterns of autonomic activity. However, experimental studies of coherence between subjective and autonomic responses during emotional states provide little evidence of coherence. Crucially, previous studies investigating coherence have either adopted univariate approaches or made limited use of multivariate analytic approaches by investigating subjective and autonomic responses separately. The current study addressed this question using a multivariate dimensional approach to build a common autonomic-subjective affective space incorporating subjective responses and three different autonomic signals (heart rate, skin conductance response, and pupil diameter), measured during an emotion-inducing task, in 51 participants. Results showed that autonomic and subjective responses could be adequately described in a two-dimensional affective space. The first dimension included contributions from subjective and autonomic responses, indicating coherence, while contributions to the second dimension were almost exclusively of autonomic covariance. Thus, while there was a degree of coherence between autonomic and subjective emotional responses, there was substantial structure in autonomic responses that did not covary with subjective emotional experience. This study, therefore, contributes new insights into the relationship between subjective and autonomic emotional responses, and provides a framework for future multimodal emotion research, enabling both hypothesis- and data-driven testing.

Reward prospect affects strategic adjustments in stop signal task

Interaction with the environment requires us to predict the potential reward that will follow our choices. Rewards could change depending on the context and our behavior adapts accordingly. Previous studies have shown that, depending on reward regimes, actions can be facilitated (i.e., increasing the reward for response) or interfered (i.e., increasing the reward for suppression). Here we studied how the change in reward perspective can influence subjects’ adaptation strategy. Students were asked to perform a modified version of the Stop-Signal task. Specifically, at the beginning of each trial, a Cue Signal informed subjects of the value of the reward they would receive; in one condition, Go Trials were rewarded more than Stop Trials, in another, Stop Trials were rewarded more than Go Trials, and in the last, both trials were rewarded equally. Subjects participated in a virtual competition, and the reward consisted of points to be earned to climb the leaderboard and win (as in a video game contest). The sum of points earned was updated with each trial. After a learning phase in which the three conditions were presented separately, each subject performed 600 trials testing phase in which the three conditions were randomly mixed. Based on the previous studies, we hypothesized that subjects could employ different strategies to perform the task, including modulating inhibition efficiency, adjusting response speed, or employing a constant behavior across contexts. We found that to perform the task, subjects preferentially employed a strategy-related speed of response adjustment, while the duration of the inhibition process did not change significantly across the conditions. The investigation of strategic motor adjustments to reward’s prospect is relevant not only to understanding how action control is typically regulated, but also to work on various groups of patients who exhibit cognitive control deficits, suggesting that the ability to inhibit can be modulated by employing reward prospects as motivational factors.

Degrees of algorithmic equivalence between the brain and its DNN models

Deep neural networks (DNNs) have become powerful and increasingly ubiquitous tools to model human cognition, and often produce similar behaviors. For example, with their hierarchical, brain-inspired organization of computations, DNNs apparently categorize real-world images in the same way as humans do. Does this imply that their categorization algorithms are also similar? We have framed the question with three embedded degrees that progressively constrain algorithmic similarity evaluations: equivalence of (i) behavioral/brain responses, which is current practice, (ii) the stimulus features that are processed to produce these outcomes, which is more constraining, and (iii) the algorithms that process these shared features, the ultimate goal. To improve DNNs as models of cognition, we develop for each degree an increasingly constrained benchmark that specifies the epistemological conditions for the considered equivalence.

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

Human behaviours are guided by how confident we feel in our abilities. When confidence does not reflect objective performance, this can impact critical adaptive functions and impair life quality. Distorted decision-making and confidence have been associated with mental health problems. Here, utilising advances in computational and transdiagnostic psychiatry, we sought to map relationships between psychopathology and both decision-making and confidence in the general population across two online studies (N's = 344 and 473, respectively). The results revealed dissociable decision-making and confidence signatures related to distinct symptom dimensions. A dimension characterised by compulsivity and intrusive thoughts was found to be associated with reduced objective accuracy but, paradoxically, increased absolute confidence, whereas a dimension characterized by anxiety and depression was associated with systematically low confidence in the absence of impairments in objective accuracy. These relationships replicated across both studies and distinct cognitive domains (perception and general knowledge), suggesting that they are reliable and domain general. Additionally, whereas Big-5 personality traits also predicted objective task performance, only symptom dimensions related to subjective confidence. Domain-general signatures of decision-making and metacognition characterise distinct psychological dispositions and psychopathology in the general population and implicate confidence as a central component of mental health.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Within-participant statistics for cognitive science

Experimental studies in cognitive science typically focus on the population average effect. An alternative is to test each individual participant and then quantify the proportion of the population that would show the effect: the prevalence, or participant replication probability. We argue that this approach has conceptual and practical advantages.

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.

Different computations over the same inputs produce selective behavior in algorithmic brain networks

A key challenge in neuroimaging remains to understand where, when, and now particularly how human brain networks compute over sensory inputs to achieve behavior. To study such dynamic algorithms from mass neural signals, we recorded the magnetoencephalographic (MEG) activity of participants who resolved the classic XOR, OR, and AND functions as overt behavioral tasks (N = 10 participants/task, N-of-1 replications). Each function requires a different computation over the same inputs to produce the task-specific behavioral outputs. In each task, we found that source-localized MEG activity progresses through four computational stages identified within individual participants: (1) initial contralateral representation of each visual input in occipital cortex, (2) a joint linearly combined representation of both inputs in midline occipital cortex and right fusiform gyrus, followed by (3) nonlinear task-dependent input integration in temporal-parietal cortex, and finally (4) behavioral response representation in postcentral gyrus. We demonstrate the specific dynamics of each computation at the level of individual sources. The spatiotemporal patterns of the first two computations are similar across the three tasks; the last two computations are task specific. Our results therefore reveal where, when, and how dynamic network algorithms perform different computations over the same inputs to produce different behaviors.