Within-participant statistics for cognitive science

Overlapping Cortical Substrate of Biomechanical Control and Subjective Agency

Every movement requires the nervous system to solve a complex biomechanical control problem, but this process is mostly veiled from one's conscious awareness. Simultaneously, we also have conscious experience of controlling our movements-our sense of agency (SoA). Whether SoA corresponds to those neural representations that implement actual neuromuscular control is an open question with ethical, medical, and legal implications. If SoA is the conscious experience of control, this predicts that SoA can be decoded from the same brain structures that implement the so-called "inverse dynamics" computations for planning movement. We correlated human (male and female) fMRI measurements during hand movements with the internal representations of a deep neural network (DNN) performing the same hand control task in a biomechanical simulation-revealing detailed cortical encodings of sensorimotor states, idiosyncratic to each subject. We then manipulated SoA by usurping control of participants' muscles via electrical stimulation, and found that the same voxels which were best explained by modeled inverse dynamics representations-which, strikingly, were located in canonically visual areas-also predicted SoA. Importantly, model-brain correspondences and robust SoA decoding could both be achieved within single subjects, enabling relationships between motor representations and awareness to be studied at the level of the individual.Significance Statement The inherent complexity of biomechanical control problems is belied by the seeming simplicity of directing movements in our subjective experience. This aspect of our experience suggests we have limited conscious access to the neural and mental representations involved in controlling the body - but of which of the many possible representations are we, in fact, aware? Understanding which motor control representations percolate into awareness has taken on increasing importance as emerging neural interface technologies push the boundaries of human autonomy. In our study, we leverage machine learning models that have learned to control simulated bodies to localize biomechanical control representations in the brain. Then, we show that these brain regions predict perceived agency over the musculature during functional electrical stimulation.

Individual differences do not mask effects of unconscious processing

A wave of criticisms and replication failures is currently challenging claims about the scope of unconscious perception and cognition. Such failures to find unconscious processing effects at the population level may reflect the absence of individual-level effects, or alternatively, the averaging out of individual-level effects with opposing signs. Importantly, only the first suggests that consciousness may be necessary for the tested process to take place. To arbitrate between these two possibilities, we tested previously collected data where unconscious processing effects were not found (26 effects from 470 participants), using five frequentist and Bayesian tests that are robust to individual differences in effect signs. By and large, we found no reliable evidence for unconscious effects being masked by individual differences. In contrast, when we examined 136 non-significant effects from other domains, two novel non-parametric tests did reveal effects that were hidden by opposing individual results, though as we show, some of them might be driven by design-related factors. Taken together, five analysis approaches provide strong evidence for the restricted nature of unconscious processing effects not only across participants, but also across different trials within individuals. We provide analysis code and best-practice recommendations for testing for non-directional effects.

Decoding the Spatiotemporal Dynamics of Neural Response Similarity in Auditory Processing: A Multivariate Analysis Based on OPM-MEG

The brain represents information through the encoding of neural populations, where the activity patterns of these neural groups constitute the content of this information. Understanding these activity patterns and their dynamic changes is of significant importance to cognitive neuroscience and related research areas. Current studies focus more on brain regions that show differential responses to stimuli, but they lack the ability to capture information about the representational or process-level dynamics within these regions. In this study, we recorded neural data from 10 healthy participants during auditory experiments using optically pumped magnetometer magnetoencephalography (OPM-MEG) and electroencephalography (EEG). We constructed representational similarity matrices (RSMs) to investigate the similarity of neural response patterns during auditory decoding. The results indicate that RSA can reveal the dynamic changes in pattern similarity during different stages of auditory processing through the neural activity patterns reflected by OPM-MEG. Comparisons with EEG results showed that both techniques captured the same processes during the early stages of auditory decoding. However, differences in sensitivity at later stages highlighted both common and distinct aspects of neural representation between the two modalities. Further analysis indicated that this process involved widespread neural network activation, including the Heschl's gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, parahippocampal gyrus, and orbitofrontal gyrus. This study demonstrates that the combination of OPM-MEG and RSA is sufficiently sensitive to detect changes in pattern similarity during neural representation processes and to identify their anatomical origins, offering new insights and references for the future application of RSA and other multivariate pattern analysis methods in the MEG field.

Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence

Much research in the behavioral sciences aims to characterize the "typical" person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions.

Fractal cycles of sleep, a new aperiodic activity-based definition of sleep cycles

Sleep cycles are defined as episodes of non-rapid eye movement (non-REM) sleep followed by an episode of REM sleep. Fractal or aperiodic neural activity is a well-established marker of arousal and sleep stages measured using electroencephalography. We introduce a new concept of 'fractal cycles' of sleep, defined as a time interval during which time series of fractal activity descend to their local minimum and ascend to the next local maximum. We assess correlations between fractal and classical (i.e. non-REM - REM) sleep cycle durations and study cycles with skipped REM sleep. The sample comprised 205 healthy adults, 21 children and adolescents and 111 patients with depression. We found that fractal and classical cycle durations (89±34 vs 90±25 min) correlated positively (r=0.5, p<0.001). Children and adolescents had shorter fractal cycles than young adults (76±34 vs 94±32 min). The fractal cycle algorithm detected cycles with skipped REM sleep in 91-98% of cases. Medicated patients with depression showed longer fractal cycles compared to their unmedicated state (107±51 vs 92±38 min) and age-matched controls (104±49 vs 88±31 min). In conclusion, fractal cycles are an objective, quantifiable, continuous and biologically plausible way to display sleep neural activity and its cycles.

The speed and phase of locomotion dictate saccade probability and simultaneous low-frequency power spectra

Every day we make thousands of saccades and take thousands of steps as we explore our environment. Despite their common co-occurrence in a typical active state, we know little about the coordination between eye movements, walking behaviour and related changes in cortical activity. Technical limitations have been a major impediment, which we overcome here by leveraging the advantages of an immersive wireless virtual reality (VR) environment with three-dimensional (3D) position tracking, together with simultaneous recording of eye movements and mobile electroencephalography (EEG). Using this approach with participants engaged in unencumbered walking along a clear, level path, we find that the likelihood of eye movements at both slow and natural walking speeds entrains to the rhythm of footfall, peaking after the heel-strike of each step. Compared to previous research, this entrainment was captured in a task that did not require visually guided stepping - suggesting a persistent interaction between locomotor and visuomotor functions. Simultaneous EEG recordings reveal a concomitant modulation entrained to heel-strike, with increases and decreases in oscillatory power for a broad range of frequencies. The peak of these effects occurred in the theta and alpha range for slow and natural walking speeds, respectively. Together, our data show that the phase of the step-cycle influences other behaviours such as eye movements, and produces related modulations of simultaneous EEG following the same rhythmic pattern. These results reveal gait as an important factor to be considered when interpreting saccadic and time-frequency EEG data in active observers, and demonstrate that saccadic entrainment to gait may persist throughout everyday activities.

The shift of attention: Salience modulates the local vs global processing of auditory scenes in musicians and non-musicians

This study addresses how salience shapes the perceptual organization of an auditory scene. A psychophysical task that was introduced previously by Susini, Jiaouan, Brunet, Houix, and Ponsot [(2020). Sci. Rep. 10(1), 16390] was adapted to assess how the ability of non-musicians and expert musicians to detect local/global contour changes in simple hierarchically-organized tone sequences is affected by the relative salience of local information in the timbre dimension. Overall, results show that salience enhanced local processing capacities, at the cost of global processing, suggesting a bottom-up reallocation of attention. Interestingly, for non-musicians, salience caused a reversal of the basic global-over-local processing prioritization as it is typically observed in expert musicians.

Aperiodic neural activity distinguishes between phasic and tonic REM sleep

Traditionally categorized as a uniform sleep phase, rapid eye movement sleep exhibits substantial heterogeneity with its phasic and tonic constituents showing marked differences regarding many characteristics. Here, we investigate how tonic and phasic states differ with respect to aperiodic neural activity, a marker of arousal and sleep. Rapid eye movement sleep heterogeneity was assessed using either binary phasic-tonic (n = 97) or continuous (in 60/97 participants) approach. Slopes of the aperiodic power component were measured in the low (2-30 Hz, n = 97) and high (30-48 Hz, n = 60/97) frequency bands with the Irregularly Resampled Auto-Spectral Analysis applied on electroencephalography. Rapid eye movement amplitudes were quantified with the YASA applied on electrooculography (n = 60/97). The binary approach revealed that the phasic state is characterized by steeper low-band slopes with small effect sizes and some topographical heterogeneity over datasets. High-band aperiodic slopes were flatter in the phasic versus tonic state with medium-to-large effect sizes over all areas in both datasets. The continuous approach confirmed these findings. The temporal analysis within rapid eye movement episodes revealed that aperiodic activity preceding or following EM events did not cross-correlate with eye movement amplitudes. This study demonstrates that aperiodic slopes can serve as a reliable marker able to differentiate between phasic and tonic constituents of rapid eye movement sleep and reflect phasic rapid eye movement event intensity. However, rapid eye movement events could not be predicted by preceding aperiodic activity and vice versa, at least not with scalp electroencephalography.

Concurrent perception of competing predictions: A "split-stimulus effect"

Visual illusions are systematic misperceptions that can help us glean the heuristics with which the brain constructs visual experience. In a recently discovered visual illusion (the "frame effect"), it has been shown that flashing a stimulus inside of a moving frame produces a large misperception of that stimulus's position. Across two experiments, we investigated a novel illusion (the "split stimulus effect") where the symmetrical motion of two overlaid frames produces two simultaneous positional misperceptions of a single stimulus. That is, one stimulus is presented but two are perceived. In both experiments, a single red dot was flashed when the moving frames reversed direction, and participants were asked to report how many dots they saw. Naïve participants sometimes reported seeing two dots when only one was presented, indicating spontaneous perception of the illusion. A Bayesian analysis of the population prevalence of this effect was conducted. The dependence of this effect on the frames' speed, the dot's opacity, spatial attention, as the presence/absence of pre-flash motion ("postdiction") was also investigated, and the features of this illusion were compared to similar motion position illusions within a predictive processing framework. In demonstrating this illusory "splitting" effect, this study is the first to show that it is possible to be simultaneously aware of two opposing perceptual predictions about a single object and provides evidence of the hyperpriors that limit and inform the structure of visual experience.

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.

International Experience of Implementing Cognitive Remediation for People With Psychotic Disorders

BACKGROUND

Cognitive remediation (CR) is an effective therapy for the cognitive impact of mental illness, especially schizophrenia. Global efforts are being made to implement CR into routine mental health services with the aim of improving functional outcomes for the population of people recovering from mental illness. Implementation and dissemination of CR in heterogeneous settings require knowledge gleaned from formal implementation research and pragmatic experiential learning. This article describes cross-cultural approaches to CR implementation, focusing on initiatives in France, the United States, Australia, and Japan.

METHOD

Key leaders in the implementation of CR in France, the United States, Australia, and Japan were asked to describe the implementation and dissemination process in their settings with respect to the categories of context, implementation, outcomes, facilitators, and barriers.

RESULTS

All 4 sites noted the role of collaboration to leverage the implementation of CR into mental health rehabilitation services. In France, high-level, government organizational backing enhanced the dissemination of CR. Academic and clinical service partnerships in the United States facilitated the dissemination of programs. The advocacy from service users, families, and carers can aid implementation. The support from international experts in the field can assist in initiating programs but maintenance and dissemination require ongoing training and supervision of staff.

CONCLUSIONS

CR is an effective intervention for the cognitive impact of schizophrenia. Programs can be implemented in diverse settings globally. Adaptations of CR centering upon the core components of effective CR therapy enhance outcomes and enable programs to integrate into diverse settings.

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.

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.

The superior colliculus motor region does not respond to finger tapping movements in humans

Electrophysiological studies in macaques and functional neuroimaging in humans revealed a motor region in the superior colliculus (SC) for upper limb reaching movements. Connectivity studies in macaques reported direct connections between this SC motor region and cortical premotor arm, hand, and finger regions. These findings motivated us to investigate if the human SC is also involved in sequential finger tapping movements. We analyzed fMRI task data of 130 subjects executing finger tapping from the Human Connectome Project. While we found strong signals in the SC for visual cues, we found no signals related to simple finger tapping. In subsequent experimental measurements, we searched for responses in the SC corresponding to complex above simple finger tapping sequences. We observed expected signal increases in cortical motor and premotor regions for complex compared to simple finger tapping, but no signal increases in the motor region of the SC. Despite evidence for direct anatomical connections of the SC motor region and cortical premotor hand and finger areas in macaques, our results suggest that the SC is not involved in simple or complex finger tapping 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.

Reconstructing visual illusory experiences from human brain activity

Visual illusions provide valuable insights into the brain's interpretation of the world given sensory inputs. However, the precise manner in which brain activity translates into illusory experiences remains largely unknown. Here, we leverage a brain decoding technique combined with deep neural network (DNN) representations to reconstruct illusory percepts as images from brain activity. The reconstruction model was trained on natural images to establish a link between brain activity and perceptual features and then tested on two types of illusions: illusory lines and neon color spreading. Reconstructions revealed lines and colors consistent with illusory experiences, which varied across the source visual cortical areas. This framework offers a way to materialize subjective experiences, shedding light on the brain's internal representations of the world.

Activity in the human superior colliculus associated with reaching for tactile targets

The superior colliculus (SC) plays a major role in orienting movements of eyes and the head and in the allocation of attention. Functions of the SC have been mostly investigated in animal models, including non-human primates. Differences in the SC's anatomy and function between different species question extrapolations of these studies to humans without further validation. Few electrophysiological and neuroimaging studies in animal models and humans have reported a role of the SC in visually guided reaching movements. Using BOLD fMRI imaging, we sought to decipher if the SC is also active during reaching movements guided by tactile stimulation. Participants executed reaching movements to visual and tactile target positions. When contrasted against visual and tactile stimulation without reaching, we found increased SC activity with reaching not only for visual but also for tactile targets. We conclude that the SC's involvement in reaching does not rely on visual inputs. It is also independent from a specific sensory modality. Our results indicate a general involvement of the human SC in upper limb reaching movements.

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.

Network targets for therapeutic brain stimulation: towards personalized therapy for pain

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.

Commonalities between mind wandering and task-set switching: An event-related potential study

Previous research has established that mind wandering does not necessarily disrupt one's task-switching performance. Here we investigated the effects of mind wandering on electrophysiological signatures, measured using event-related potentials (ERPs), during a switching task. In the current study, a final sample of 22 young adults performed a task-switching paradigm while electroencephalography was continuously recorded; mind wandering was assessed via thought probes at the end of each block. Consistent with previous research, we found no significant disruptive effects of mind wandering on task-switching performance. The ERP results showed that at the posterior electrode sites (P3, Pz, and P4), P3 amplitude was higher for mind-wandering switch trials than on-task switch trials, thus opposing the typical pattern of P3 attenuation during periods of mind wandering relative to on-task episodes. Considering that increased P3 amplitude during higher-order switch trials (e.g., response rule switching) may reflect the implementation of new higher-order task sets/rules, the current findings seem to indicate similar executive control processes underlie mind wandering and task-set switching, providing further evidence in favor of a role for switching in mind wandering.

Inter-individual deep image reconstruction via hierarchical neural code conversion

The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. Although anatomical and functional alignment methods have been proposed in functional magnetic resonance imaging (fMRI) studies, it remains unclear whether and how hierarchical and fine-grained representations can be converted between individuals while preserving the encoded perceptual content. In this study, we trained a method of functional alignment called neural code converter that predicts a target subject's brain activity pattern from a source subject given the same stimulus, and analyzed the converted patterns by decoding hierarchical visual features and reconstructing perceived images. The converters were trained on fMRI responses to identical sets of natural images presented to pairs of individuals, using the voxels on the visual cortex that covers from V1 through the ventral object areas without explicit labels of the visual areas. We decoded the converted brain activity patterns into the hierarchical visual features of a deep neural network using decoders pre-trained on the target subject and then reconstructed images via the decoded features. Without explicit information about the visual cortical hierarchy, the converters automatically learned the correspondence between visual areas of the same levels. Deep neural network feature decoding at each layer showed higher decoding accuracies from corresponding levels of visual areas, indicating that hierarchical representations were preserved after conversion. The visual images were reconstructed with recognizable silhouettes of objects even with relatively small numbers of data for converter training. The decoders trained on pooled data from multiple individuals through conversions led to a slight improvement over those trained on a single individual. These results demonstrate that the hierarchical and fine-grained representation can be converted by functional alignment, while preserving sufficient visual information to enable inter-individual visual image reconstruction.

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.

Overcoming the phenomenological Perpetuum mobile in clinical cognitive neuroscience for the benefit of replicability in research and the societal view on mental disorders

Cognitive neuroscience comes in many facets, and a particularly large branch of research is conducted in individuals with mental health problems. This article outlines why it is important that cognitive neuroscientists re-shape their role in mental health research and re-define directions of research for the next decades. At present, cognitive neuroscience research in mental health is too firmly rooted in categorial diagnostic definitions of mental health conditions. It is discussed why this hampers a mechanistic understanding of brain functions underlying mental health problems and why this is a problem for replicability in research. A possible solution to these problems is presented. This solution affects the strategy of research questions to be asked, how current trends to increase replicability in research can or cannot be applied in the mental health field and how data are analyzed. Of note, these aspects are not only relevant for the scientific process, but affect the societal view on mental disorders and the position of affected individuals as members of society, as well as the debate on the inclusion of so-called WEIRD and non-WEIRD people in studies. Accordingly, societal and science political aspects of re-defining the role of cognitive neuroscientists in mental health research are elaborated that will be important to shape cognitive neuroscience in mental health for the next decades.

Systematic errors in the perception of rhythm

One hypothesis for why humans enjoy musical rhythms relates to their prediction of when each beat should occur. The ability to predict the timing of an event is important from an evolutionary perspective. Therefore, our brains have evolved internal mechanisms for processing the progression of time. However, due to inherent noise in neural signals, this prediction is not always accurate. Theoretical considerations of optimal estimates suggest the occurrence of certain systematic errors made by the brain when estimating the timing of beats in rhythms. Here, we tested psychophysically whether these systematic errors exist and if so, how they depend on stimulus parameters. Our experimental data revealed two main types of systematic errors. First, observers perceived the time of the last beat of a rhythmic pattern as happening earlier than actual when the inter-beat interval was short. Second, the perceived time of the last beat was later than the actual when the inter-beat interval was long. The magnitude of these systematic errors fell as the number of beats increased. However, with many beats, the errors due to long inter-beat intervals became more apparent. We propose a Bayesian model for these systematic errors. The model fits these data well, allowing us to offer possible explanations for how these errors occurred. For instance, neural processes possibly contributing to the errors include noisy and temporally asymmetric impulse responses, priors preferring certain time intervals, and better-early-than-late loss functions. We finish this article with brief discussions of both the implications of systematic errors for the appreciation of rhythm and the possible compensation by the brain’s motor system during a musical performance.

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.