A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.

Tests for consciousness in humans and beyond.

Which systems/organisms are conscious? New tests for consciousness ('C-tests') are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology. Although a number of C-tests have been proposed in recent years, most are of limited use, and currently we have no C-tests for many of the populations for which they are most critical. Here, we identify challenges facing any attempt to develop C-tests, propose a multidimensional classification of such tests, and identify strategies that might be used to validate them.

Toward 'Computational-Rationality' Approaches to Arbitrating Models of Cognition: A Case Study Using Perceptual Metacognition.

Perceptual confidence results from a metacognitive process which evaluates how likely our percepts are to be correct. Many competing models of perceptual metacognition enjoy strong empirical support. Arbitrating these models traditionally proceeds via researchers conducting experiments and then fitting several models to the data collected. However, such a process often includes conditions or paradigms that may not best arbitrate competing models: Many models make similar predictions under typical experimental conditions. Consequently, many experiments are needed, collectively (sub-optimally) sampling the space of conditions to compare models. Here, instead, we introduce a variant of optimal experimental design which we call a computational-rationality approach to generative models of cognition, using perceptual metacognition as a case study. Instead of designing experiments and post-hoc specifying models, we began with comprehensive model comparison among four competing generative models for perceptual metacognition, drawn from literature. By simulating a simple experiment under each model, we identified conditions where these models made maximally diverging predictions for confidence. We then presented these conditions to human observers, and compared the models' capacity to predict choices and confidence. Results revealed two surprising findings: (1) two models previously reported to differently predict confidence to different degrees, with one predicting better than the other, appeared to predict confidence in a direction opposite to previous findings; and (2) two other models previously reported to equivalently predict confidence showed stark differences in the conditions tested here. Although preliminary with regards to which model is actually 'correct' for perceptual metacognition, our findings reveal the promise of this computational-rationality approach to maximizing experimental utility in model arbitration while minimizing the number of experiments necessary to reveal the winning model, both for perceptual metacognition and in other domains.

Consciousness beyond the human case.

There is growing interest in the relationship been AI and consciousness. Joseph LeDoux and Jonathan Birch thought it would be a good moment to put some of the big questions in this area to some leading experts. The challenge of addressing the questions they raised was taken up by Kristin Andrews, Nicky Clayton, Nathaniel Daw, Chris Frith, Hakwan Lau, Megan Peters, Susan Schneider, Anil Seth, Thomas Suddendorf, and Marie Vanderkerckhoeve.

Locus coeruleus integrity predicts ease of attaining and maintaining neural states of high attentiveness.

The locus coeruleus (LC), a small subcortical structure in the brainstem, is the brain's principal source of norepinephrine. It plays a primary role in regulating stress, the sleep-wake cycle, and attention, and its degradation is associated with aging and neurodegenerative diseases associated with cognitive deficits (e.g., Parkinson's, Alzheimer's). Yet precisely how norepinephrine drives brain networks to support healthy cognitive function remains poorly understood - partly because LC's small size makes it difficult to study noninvasively in humans. Here, we characterized LC's influence on brain dynamics using a hidden Markov model fitted to functional neuroimaging data from healthy young adults across four attention-related brain networks and LC. We modulated LC activity using a behavioral paradigm and measured individual differences in LC magnetization transfer contrast. The model revealed five hidden states, including a stable state dominated by salience-network activity that occurred when subjects actively engaged with the task. LC magnetization transfer contrast correlated with this state's stability across experimental manipulations and with subjects' propensity to enter into and remain in this state. These results provide new insight into LC's role in driving spatiotemporal neural patterns associated with attention, and demonstrate that variation in LC integrity can explain individual differences in these patterns even in healthy young adults.

A Novel Hidden Markov Approach to Studying Dynamic Functional Connectivity States in Human Neuroimaging.

Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods: We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging "resting-state" dataset. Results: Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Discussion: Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity "summed" across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.

Comparing random dot motion in MATLAB vs. Inquisit Millisecond.

Random Dot Motion (RDM) displays refer to clouds of independently moving dots that can be parametrically manipulated to provide a perception of the overall cloud moving coherently in a specified direction of motion. As a well-studied probe of motion perception, RDMs have been widely employed to understand underlying neural mechanisms of motion perception, perceptual decision-making, and perceptual learning, among other processes. Despite their wide use, RDM stimuli implementation is highly dependent on the parameters and the generation algorithm of the stimuli; both can greatly influence behavioral performance on RDM tasks. With the advent of the COVID pandemic and an increased need for more accessible platforms, we aimed to validate a novel RDM paradigm on Inquisit Millisecond, a platform for the online administration of cognitive and neuropsychological tests and assessments. We directly compared, in the same participants using the same display, a novel RDM paradigm on both Inquisit Millisecond and MATLAB with Psychtoolbox. We found that psychometric functions of Coherence largely match between Inquisit Millisecond and MATLAB, as do the effects of Duration. These data demonstrate that the Millisecond RDM provides data largely consistent with those previously found in laboratory-based systems, and the present findings can serve as a reference point for expected thresholds for when these procedures are used remotely on different platforms.

Recent Advances at the Interface of Neuroscience and Artificial Neural Networks.

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.

Towards characterizing the canonical computations generating phenomenal experience.

Science and philosophy have long struggled with how to even begin studying the neural or computational basis of qualitative experience. Here I review psychological, neuroscience, and philosophical literature to reveal how perceptual metacognition possesses five unique properties that provide a powerful opportunity for studying the neural and computational correlates of subjective experience: (1) Metacognition leads to subjective experiences (we "feel" confident); (2) Metacognition is "about" internal representations, formalizing introspection; (3) Metacognitive computations are "recursive" (applying to meta-cognition and meta-meta-cognition), so we might discover "canonical computations" preserved across processing levels and implementations; (4) Metacognition is anchored to observable behavior; and (5) Metacognitive computations are unobservable yet hierarchically dependent, requiring development of sensitive, specific models. Given these properties, computational models of metacognition provide an empirically-tractable early step in characterizing the generative process that constructs qualitative experience. I also present practical ways to make progress in this vein, applying decades of developments in nearby fields to perceptual metacognition to reveal new and exciting insights about how the brain constructs subjective conscious experiences.

Interaction of prior category knowledge and novel statistical patterns during visual search for real-world objects.

Two aspects of real-world visual search are typically studied in parallel: category knowledge (e.g., searching for food) and visual patterns (e.g., predicting an upcoming street sign from prior street signs). Previous visual search studies have shown that prior category knowledge hinders search when targets and distractors are from the same category. Other studies have shown that task-irrelevant patterns of non-target objects can enhance search when targets appear in locations that previously contained these irrelevant patterns. Combining EEG (N2pc ERP component, a neural marker of target selection) and behavioral measures, the present study investigated how search efficiency is simultaneously affected by prior knowledge of real-world objects (food and toys) and irrelevant visual patterns (sequences of runic symbols) within the same paradigm. We did not observe behavioral differences between locating items in patterned versus random locations. However, the N2pc components emerged sooner when search items appeared in the patterned location, compared to the random location, with a stronger effect when search items were targets, as opposed to non-targets categorically related to the target. A multivariate pattern analysis revealed that neural responses during search trials in the same time window reflected where the visual patterns appeared. Our finding contributes to our understanding of how knowledge acquired prior to the search task (e.g., category knowledge) interacts with new content within the search task.

Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery.

Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed "subjective inflation." Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise (i.e., "variance misperception"). However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics, which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here, we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors-but used them incorrectly-showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results, which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system.

Social relevance modulates multivariate neural representations of threat generalization in children and adults.

Few studies have examined threat generalization across development and no developmental studies have compared the generalization of social versus nonsocial threat, making it difficult to identify contextual factors that contribute to threat learning across development. The present study assessed youth and adults' multivoxel neural representations of social versus nonsocial threat stimuli. Twenty adults (Mage  = 25.7 ± 4.9) and 16 youth (Mage  = 14.1 ± 1.7) completed two conditioning and extinction recall paradigms: one social and one nonsocial paradigm. Three weeks after conditioning, participants underwent a functional magnetic resonance imaging extinction recall task that presented the extinguished threat cue (CS+), a safety cue (CS-), and generalization stimuli (GS) consisting of CS-/CS+ blends. Across age groups, neural activity patterns and self-reported fear and memory ratings followed a linear generalization gradient for social threat stimuli and a quadratic generalization gradient for nonsocial threat stimuli, indicating enhanced threat/safety discrimination for social relative to nonsocial threat stimuli. The amygdala and ventromedial prefrontal cortex displayed the greatest neural pattern differentiation between the CS+ and GS/CS-, reinforcing their role in threat learning and extinction recall. Contrary to predictions, age did not influence threat representations. These findings highlight the importance of the social relevance of threat on generalization across development.

Tuned inhibition in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior.

Current dominant views hold that perceptual confidence reflects the probability that a decision is correct. Although these views have enjoyed some empirical support, recent behavioral results indicate that confidence and the probability of being correct can be dissociated. An alternative hypothesis suggests that confidence instead reflects the magnitude of evidence in favor of a decision while being relatively insensitive to the evidence opposing the decision. We considered how this alternative hypothesis might be biologically instantiated by developing a simple neural network model incorporating a known property of sensory neurons: tuned inhibition. The key idea of the model is that the level of inhibition that each accumulator unit receives from units with the opposite tuning preference, i.e. its inhibition 'tuning', dictates its contribution to perceptual decisions versus confidence judgments, such that units with higher tuned inhibition (computing relative evidence for different perceptual interpretations) determine perceptual discrimination decisions, and units with lower tuned inhibition (computing absolute evidence) determine confidence. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature where confidence and decision accuracy dissociate. By comparing model fits, we further demonstrate that a full complement of behavioral data across several previously published experimental results-including accuracy, reaction time, mean confidence, and metacognitive sensitivity-is best accounted for when confidence is computed from units without, rather than units with, tuned inhibition. Finally, we discuss predictions of our results and model for future neurobiological studies. These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by relying on the diversity of neural resources available.

Towards Democratizing and Automating Online Conferences: Lessons from the Neuromatch Conferences.

Legacy conferences are costly and time consuming, and exclude scientists lacking various resources or abilities. During the 2020 pandemic, we created an online conference platform, Neuromatch Conferences (NMC), aimed at developing technological and cultural changes to make conferences more democratic, scalable, and accessible. We discuss the lessons we learned.

An investigation of how relative precision of target encoding influences metacognitive performance.

Detection failures in perceptual tasks can result from different causes: sometimes we may fail to see something because perceptual information is noisy or degraded, and sometimes we may fail to see something due to the limited capacity of attention. Previous work indicates that metacognitive capacities for detection failures may differ depending on the specific stimulus visibility manipulation employed. In this investigation, we measured metacognition while matching performance in two visibility manipulations: phase-scrambling and the attentional blink. As in previous work, metacognitive asymmetries emerged: despite matched type 1 performance, metacognitive ability (measured by area under the ROC curve) for reporting stimulus absence was higher in the attentional blink condition, which was mainly driven by metacognitive ability in correct rejection trials. We performed Signal Detection Theoretic (SDT) modeling of the results, showing that differences in metacognition under equal type I performance can be explained when the variance of the signal and noise distributions are unequal. Specifically, the present study suggests that phase scrambling signal trials have a wider distribution (more variability) than attentional blink signal trials, leading to a larger area under the ROC curve for attentional blink trials where subjects reported stimulus absence. These results provide a theoretical basis for the origin of metacognitive differences on trials where subjects report stimulus absence, and may also explain previous findings where the absence of evidence during detection tasks results in lower metacognitive performance when compared to categorization.

Normal observers show no evidence for blindsight in facial emotion perception.

Some researchers have argued that normal human observers can exhibit "blindsight-like" behavior: the ability to discriminate or identify a stimulus without being aware of it. However, we recently used a bias-free task to show that what looks like blindsight may in fact be an artifact of typical experimental paradigms' susceptibility to response bias. While those findings challenge previous reports of blindsight in normal observers, they do not rule out the possibility that different stimuli or techniques could still reveal perception without awareness. One intriguing candidate is emotion processing, since processing of emotional stimuli (e.g. fearful/happy faces) has been reported to potentially bypass conscious visual circuits. Here we used the bias-free blindsight paradigm to investigate whether emotion processing might reveal "featural blindsight," i.e. ability to identify a face's emotion without introspective access to the task-relevant features that led to the discrimination decision. However, we saw no evidence for emotion processing "featural blindsight": as before, whenever participants could identify a face's emotion they displayed introspective access to the task-relevant features, matching predictions of a Bayesian ideal observer. These results add to the growing body of evidence that perceptual discrimination ability without introspective access may not be possible for neurologically intact observers.

Divergence in cortical representations of threat generalization in affective versus perceptual circuitry in childhood: Relations with anxiety.

Children at risk for anxiety display elevated threat sensitivity and may inaccurately classify safe stimuli as threatening, a process known as overgeneralization. Little is known about whether such overgeneralization might stem from altered sensory representations of stimuli resembling threat, especially in youth. Here we implement representational similarity analysis of fMRI data to examine the similarity of neural representations of threat versus ambiguous or safe stimuli in threat and perceptual neurocircuitry among children at varying levels of anxiety traits. Three weeks after completing threat conditioning and extinction, children underwent an fMRI extinction recall task, during which they viewed the extinguished threat cue (CS+), safety cue (CS-) and generalization stimuli (GS) consisting of CS-/CS+ blends. Multivoxel BOLD signal patterns were measured in seven regions of interest: four affective areas (ventromedial prefrontal cortex (vmPFC), anterior insular cortex (AIC), dorsomedial prefrontal cortex (dmPFC), and amygdala) and three perceptual areas (inferior temporal cortex (ITC) and visual areas V1 and V4). Compared to low anxious children, children with high trait anxiety evidenced less neural pattern differentiation between the CS+ and similar GS, particularly in the vmPFC. Together, these results demonstrate the utility of multivariate neuroimaging approaches in arbitrating the relative contributions of perceptual versus affective sources to threat generalization.

Anodal Transcranial Direct Current Stimulation to the Left Rostrolateral Prefrontal Cortex Selectively Improves Source Memory Retrieval.

Functional neuroimaging studies have consistently implicated the left rostrolateral prefrontal cortex (RLPFC) as playing a crucial role in the cognitive operations supporting episodic memory and analogical reasoning. However, the degree to which the left RLPFC causally contributes to these processes remains underspecified. We aimed to assess whether targeted anodal stimulation-thought to boost cortical excitability-of the left RLPFC with transcranial direct current stimulation (tDCS) would lead to augmentation of episodic memory retrieval and analogical reasoning task performance in comparison to cathodal stimulation or sham stimulation. Seventy-two healthy adult participants were evenly divided into three experimental groups. All participants performed a memory encoding task on Day 1, and then on Day 2, they performed continuously alternating tasks of episodic memory retrieval, analogical reasoning, and visuospatial perception across two consecutive 30-min experimental sessions. All groups received sham stimulation for the first experimental session, but the groups differed in the stimulation delivered to the left RLPFC during the second session (either sham, 1.5 mA anodal tDCS, or 1.5 mA cathodal tDCS). The experimental group that received anodal tDCS to the left RLPFC during the second session demonstrated significantly improved episodic memory source retrieval performance, relative to both their first session performance and relative to performance changes observed in the other two experimental groups. Performance on the analogical reasoning and visuospatial perception tasks did not exhibit reliable changes as a result of tDCS. As such, our results demonstrate that anodal tDCS to the left RLPFC leads to a selective and robust improvement in episodic source memory retrieval.