Scaling laws in cognitive sciences

Zipfian distributions facilitate children's learning of novel word-referent mappings

The word-frequency distributions children hear during language learning are highly skewed (Zipfian). Previous studies suggest that such skewed environments confer a learnability advantage in tasks that require the learner to discover the units that have to be learned, as in word-segmentation or cross-situational learning. This facilitative effect has been attributed to contextual facilitation from high frequency items in learning lower frequency items, and to better learning under the increased predictability (lower entropy) of skewed distributions. Here, we ask whether Zipfian distributions facilitate learning beyond the discovery of units, as expected under the predictability account. We tested children's learning of novel word-referent mappings in a learning task where each mapping was presented in isolation during training, and did not need to be dicovered. We compared learning in a uniform environment to two skewed environments with different entropy levels. Children's learning was overall better in the two skewed environments, even for low frequency items. These results extend the facilitative effect of Zipfian distributions to additional learning tasks and show they can facilitate language learning beyond the discovery of units.

Quantum Logic Automata Generate Class IV-like patterns and 1/f noise

Owing to recent advancements in brain science and AI, researchers tend to focus on the concept of self-organized criticality or the edge of chaos. On the other hand, quantum cognition, which is rooted in quantum mechanics, is promising for resolving various cognitive illusions. However, until recently, no connection between criticality and quantum mechanics was proposed. Gunji et al. (2024) recently introduced a linkage termed quantum logic automata, which encompasses not only quantum logic but also criticality characterized by power-law distributions. While quantum logic automata can be derived from various structures, only one of them has been proposed and discussed. Here, we define another type of quantum logic automata involving quantum logic and demonstrate that symmetric quantum logic automata lead to complex Class IV-like patterns and power-law distributions. Our findings support the association between criticality and quantum theory.

The statistics of cognitive variability: Explaining common patterns in individuals, groups and financial markets

Psychological variability (i.e., "noise") displays interesting structure which is hidden by the common practice of averaging over trials. Interesting noise structure, termed 'stylized facts', is observed in financial markets (i.e., behaviors from many thousands of traders). Here we investigate the parallels between psychological and financial time series. In a series of three experiments (total N = 202), we successively simplified a market-based price prediction task by first removing external information, and then removing any interaction between participants. Finally, we removed any resemblance to an asset market by asking individual participants to simply reproduce temporal intervals. All three experiments reproduced the main stylized facts found in financial markets, and the robustness of the results suggests that a common cognitive-level mechanism can produce them. We identify one potential model based on mental sampling algorithms, showing how this general-purpose model might account for behavior across these very different tasks.

Nonlinear analysis of neuronal firing modulated by sinusoidal stimulation at axons in rat hippocampus

Introduction

Electrical stimulation of the brain has shown promising prospects in treating various brain diseases. Although biphasic pulse stimulation remains the predominant clinical approach, there has been increasing interest in exploring alternative stimulation waveforms, such as sinusoidal stimulation, to improve the effectiveness of brain stimulation and to expand its application to a wider range of brain disorders. Despite this growing attention, the effects of sinusoidal stimulation on neurons, especially on their nonlinear firing characteristics, remains unclear.

Methods

To address the question, 50 Hz sinusoidal stimulation was applied on Schaffer collaterals of the rat hippocampal CA1 region in vivo. Single unit activity of both pyramidal cells and interneurons in the downstream CA1 region was recorded and analyzed. Two fractal indexes, namely the Fano factor and Hurst exponent, were used to evaluate changes in the long-range correlations, a manifestation of nonlinear dynamics, in spike sequences of neuronal firing.

Results

The results demonstrate that sinusoidal electrical stimulation increased the firing rates of both pyramidal cells and interneurons, as well as altered their firing to stimulation-related patterns. Importantly, the sinusoidal stimulation increased, rather than decreased the scaling exponents of both Fano factor and Hurst exponent, indicating an increase in the long-range correlations of both pyramidal cells and interneurons.

Discussion

The results firstly reported that periodic sinusoidal stimulation without long-range correlations can increase the long-range correlations of neurons in the downstream post-synaptic area. These results provide new nonlinear mechanisms of brain sinusoidal stimulation and facilitate the development of new stimulation modes.

Reorienting psychological science

Psychological phenomena occur across a wide range of scales, ranging from small, quick events of neurology and biology, to broader, more prolonged unfoldings typical of extended cultural practices. Although theories deployed by psychologists of different stripes have tended to incorporate these different scales, this is typically done in a manner that is implicit, and often unsystematic. That is, typical psychological research is conducted in a manner that is 'scale-blind'. In this article, I explore some of the historical and more recent recognition of this scale-blindness and place it in the context of recent work on the concept and implications of scale. I conclude by elucidating some of the important ways in which behaviour settings theory, and the researchers who developed it, are explicit and disciplined in their approach to scale, and how such scale-aware work promises practical value in improving scientific practice. This article is part of the theme issue 'People, places, things, and communities: expanding behaviour settings theory in the 21st century'.

A potential mechanism for Gibsonian resonance: behavioral entrainment emerges from local homeostasis in an unsupervised reservoir network

While the cognitivist school of thought holds that the mind is analogous to a computer, performing logical operations over internal representations, the tradition of ecological psychology contends that organisms can directly "resonate" to information for action and perception without the need for a representational intermediary. The concept of resonance has played an important role in ecological psychology, but it remains a metaphor. Supplying a mechanistic account of resonance requires a non-representational account of central nervous system (CNS) dynamics. Towards this, we present a series of simple models in which a reservoir network with homeostatic nodes is used to control a simple agent embedded in an environment. This network spontaneously produces behaviors that are adaptive in each context, including (1) visually tracking a moving object, (2) substantially above-chance performance in the arcade game Pong, (2) and avoiding walls while controlling a mobile agent. Upon analyzing the dynamics of the networks, we find that behavioral stability can be maintained without the formation of stable or recurring patterns of network activity that could be identified as neural representations. These results may represent a useful step towards a mechanistic grounding of resonance and a view of the CNS that is compatible with ecological psychology.

Fluctuation in cortical excitation/inhibition modulates capability of attention across time scales ranging from hours to seconds

Sustained attention, as the basis of general cognitive ability, naturally varies across different time scales, spanning from hours, e.g. from wakefulness to drowsiness state, to seconds, e.g. trial-by-trail fluctuation in a task session. Whether there is a unified mechanism underneath such trans-scale variability remains unclear. Here we show that fluctuation of cortical excitation/inhibition (E/I) is a strong modulator to sustained attention in humans across time scales. First, we observed the ability to attend varied across different brain states (wakefulness, postprandial somnolence, sleep deprived), as well as within any single state with larger swings. Second, regardless of the time scale involved, we found highly attentive state was always linked to more balanced cortical E/I characterized by electroencephalography (EEG) features, while deviations from the balanced state led to temporal decline in attention, suggesting the fluctuation of cortical E/I as a common mechanism underneath trans-scale attentional variability. Furthermore, we found the variations of both sustained attention and cortical E/I indices exhibited fractal structure in the temporal domain, exhibiting features of self-similarity. Taken together, these results demonstrate that sustained attention naturally varies across different time scales in a more complex way than previously appreciated, with the cortical E/I as a shared neurophysiological modulator.

Scattering spectra models for physics

Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of samples is limited. In this paper, we introduce scattering spectra models for stationary fields and we show that they provide accurate and robust statistical descriptions of a wide range of fields encountered in physics. These models are based on covariances of scattering coefficients, i.e. wavelet decomposition of a field coupled with a pointwise modulus. After introducing useful dimension reductions taking advantage of the regularity of a field under rotation and scaling, we validate these models on various multiscale physical fields and demonstrate that they reproduce standard statistics, including spatial moments up to fourth order. The scattering spectra provide us with a low-dimensional structured representation that captures key properties encountered in a wide range of physical fields. These generic models can be used for data exploration, classification, parameter inference, symmetry detection, and component separation.

Relation of connectome topology to brain volume across 103 mammalian species

The brain connectome is an embedded network of anatomically interconnected brain regions, and the study of its topological organization in mammals has become of paramount importance due to its role in scaffolding brain function and behavior. Unlike many other observable networks, brain connections incur material and energetic cost, and their length and density are volumetrically constrained by the skull. Thus, an open question is how differences in brain volume impact connectome topology. We address this issue using the MaMI database, a diverse set of mammalian connectomes reconstructed from 201 animals, covering 103 species and 12 taxonomy orders, whose brain size varies over more than 4 orders of magnitude. Our analyses focus on relationships between volume and modular organization. After having identified modules through a multiresolution approach, we observed how connectivity features relate to the modular structure and how these relations vary across brain volume. We found that as the brain volume increases, modules become more spatially compact and dense, comprising more costly connections. Furthermore, we investigated how spatial embedding shapes network communication, finding that as brain volume increases, nodes' distance progressively impacts communication efficiency. We identified modes of variation in network communication policies, as smaller and bigger brains show higher efficiency in routing- and diffusion-based signaling, respectively. Finally, bridging network modularity and communication, we found that in larger brains, modular structure imposes stronger constraints on network signaling. Altogether, our results show that brain volume is systematically related to mammalian connectome topology and that spatial embedding imposes tighter restrictions on larger brains.

Natural brain state change with E/I balance shifting toward inhibition is associated with vigilance impairment

The delicate balance between cortical excitation and inhibition (E/I) plays a pivotal role in brain state changes. While previous studies have associated cortical hyperexcitability with brain state changes induced by sleep deprivation, whether cortical hypoexcitability is also linked to brain state changes and, if so, how it could affect cognitive performance remain unknown. Here, we address these questions by examining the brain state change occurring after meals, i.e., postprandial somnolence, and comparing it with that induced by sleep deprivation. By analyzing features representing network excitability based on electroencephalogram (EEG) signals, we confirmed cortical hyperexcitability under sleep deprivation but revealed hypoexcitability under postprandial somnolence. In addition, we found that both sleep deprivation and postprandial somnolence adversely affected the level of vigilance. These results indicate that cortical E/I balance toward inhibition is associated with brain state changes, and deviation from the balanced state, regardless of its direction, could impair cognitive performance.

Surface and Contextual Linguistic Cues in Dialog Act Classification: A Cognitive Science View

What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency-based, machine learning, and deep learning methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition.

Scale in Language

A central concern of the cognitive science of language since its origins has been the concept of the linguistic system. Recent approaches to the system concept in language point to the exceedingly complex relations that hold between many kinds of interdependent systems, but it can be difficult to know how to proceed when "everything is connected." This paper offers a framework for tackling that challenge by identifying *scale* as a conceptual mooring for the interdisciplinary study of language systems. The paper begins by defining the scale concept-simply, the possibility for a measure to be larger or smaller in different instances of a system, such as a phonemic inventory, a word's frequency value in a corpus, or a speaker population. We review sites of scale difference in and across linguistic subsystems, drawing on findings from linguistic typology, grammatical description, morphosyntactic theory, psycholinguistics, computational corpus work, and social network demography. We consider possible explanations for scaling differences and constraints in language. We then turn to the question of *dependencies between* sites of scale difference in language, reviewing four sample domains of scale dependency: in phonological systems, across levels of grammatical structure (Menzerath's Law), in corpora (Zipf's Law and related issues), and in speaker population size. Finally, we consider the implications of the review, including the utility of a scale framework for generating new questions and inspiring methodological innovations and interdisciplinary collaborations in cognitive-scientific research on language.

How the Complexity of Psychological Processes Reframes the Issue of Reproducibility in Psychological Science

In the past decade, various recommendations have been published to enhance the methodological rigor and publication standards in psychological science. However, adhering to these recommendations may have limited impact on the reproducibility of causal effects as long as psychological phenomena continue to be viewed as decomposable into separate and additive statistical structures of causal relationships. In this article, we show that (a) psychological phenomena are patterns emerging from nondecomposable and nonisolable complex processes that obey idiosyncratic nonlinear dynamics, (b) these processual features jeopardize the chances of standard reproducibility of statistical results, and (c) these features call on researchers to reconsider what can and should be reproduced, that is, the psychological processes per se, and the signatures of their complexity and dynamics. Accordingly, we argue for a greater consideration of process causality of psychological phenomena reflected by key properties of complex dynamical systems (CDSs). This implies developing and testing formal models of psychological dynamics, which can be implemented by computer simulation. The scope of the CDS paradigm and its convergences with other paradigms are discussed regarding the reproducibility issue. Ironically, the CDS approach could account for both reproducibility and nonreproducibility of the statistical effects usually sought in mainstream psychological science.

The synchronizing role of multiplexing noise: Exploring Kuramoto oscillators and breathing chimeras

The synchronization of spatiotemporal patterns in a two-layer multiplex network of identical Kuramoto phase oscillators is studied, where each layer is a non-locally coupled ring. Particular focus is on the role played by a noisy inter-layer communication. It is shown that modulating the inter-layer coupling strength by uncommon noise has a significant impact on the dynamics of the network, in particular, that modulating the interlayer coupling by noise can counter-intuitively induce synchronization in networks. It is further shown that increasing the noise intensity has many other analogous effects to that of increasing the interlayer coupling strength. For example, the noise intensity can also induce state transitions in a similar way, in some cases causing the layers to completely synchronize within themselves. It is discussed how such disturbances may in many cases be beneficial to multilayer systems. These effects are demonstrated both for white noise and for other kinds of colored noise. A "floating" breathing chimera state is also discovered in this system.

Dynamical biomarkers in teams and other multiagent systems

Effective team behavior in high-performance environments such as in sport and the military requires individual team members to efficiently perceive the unfolding task events, predict the actions and action intents of the other team members, and plan and execute their own actions to simultaneously accomplish individual and collective goals. To enhance team performance through effective cooperation, it is crucial to measure the situation awareness and dynamics of each team member and how they collectively impact the team's functioning. Further, to be practically useful for real-life settings, such measures must be easily obtainable from existing sensors. This paper presents several methodologies that can be used on positional and movement acceleration data of team members to quantify and/or predict team performance, assess situation awareness, and to help identify task-relevant information to support individual decision-making. Given the limited reporting of these methods within military cohorts, these methodologies are described using examples from team sports and teams training in virtual environments, with discussion as to how they can be applied to real-world military teams.

Understanding the Effects of Constraint and Predictability in ERP

Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.

The fractal brain: scale-invariance in structure and dynamics

The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.

Cognitive Science Progresses Toward Interactive Frameworks

Despite its many twists and turns, the arc of cognitive science generally bends toward progress, thanks to its interdisciplinary nature. By glancing at the last few decades of experimental and computational advances, it can be argued that-far from failing to converge on a shared set of conceptual assumptions-the field is indeed making steady consensual progress toward what can broadly be referred to as interactive frameworks. This inclination is apparent in the subfields of psycholinguistics, visual perception, embodied cognition, extended cognition, neural networks, dynamical systems theory, and more. This pictorial essay briefly documents this steady progress both from a bird's eye view and from the trenches. The conclusion is one of optimism that cognitive science is getting there, albeit slowly and arduously, like any good science should.

Scale-Free Dynamics in Instantaneous Alpha Frequency Fluctuations: Validation, Test-Retest Reliability and Its Relationship with Task Manipulations

Previous studies showed that scale-free structures and long-range temporal correlations are ubiquitous in physiological signals (e.g., electroencephalography). This is supposed to be associated with optimized information processing in human brain. The instantaneous alpha frequency (IAF) (i.e., the instantaneous frequency of alpha band of human EEG signals) may dictate the resolution at which information is sampled and/or processed by cortical neurons. To the best of our knowledge, no research has examined the scale-free dynamics and potential functional significance of IAF. Here, through three studies (Study 1: 25 participants; Study 2: 82 participants; Study 3: 26 participants), we investigated the possibility that time series of IAF exhibit scale-free property through maximum likelihood based detrended fluctuation analysis (ML-DFA). This technique could provide the scaling exponent (i.e., DFA exponent) on the basis of presence of scale-freeness being validated. Then the test-retest reliability (Study 1) and potential influencing factors (Study 2 and Study 3) of DFA exponent of IAF fluctuations were investigated. Firstly, the scale-free property was found to be inherent in IAF fluctuations with fairly high test-retest reliability over the parietal-occipital region. Moreover, the task manipulations could potentially modulate the DFA exponent of IAF fluctuations. Specifically, in Study 2, we found that the DFA exponent of IAF fluctuations in eye-closed resting-state condition was significantly larger than that in eye-open resting-state condition. In Study 3, we found that the DFA exponent of IAF fluctuations in eye-open resting-state condition was significantly larger than that in visual n-back tasks. The DFA exponent of IAF fluctuations in the 0-back task was significantly larger than in the 2-back and 3-back tasks. The results in studies 2 and 3 indicated that: (1) a smaller DFA exponent of IAF fluctuations should signify more efficient online visual information processing; (2) the scaling property of IAF fluctuations could reflect the physiological arousal level of participants.

Word-producing brain: Contribution of the left anterior middle temporal gyrus to word production patterns in spoken language

Vocabulary is based on semantic knowledge. The anterior temporal lobe (ATL) has been considered an essential region for processing semantic knowledge; nonetheless, the association between word production patterns and the structural and functional characteristics of the ATL remains unclear. To examine this, we analyzed over one million words from group conversations among community-dwelling older adults and their multimodal magnetic resonance imaging data. A quantitative index for the word production patterns, namely the exponent β of Heaps' law, positively correlated with the left anterior middle temporal gyrus volume. Moreover, β negatively correlated with its resting-state functional connectivity with the precuneus. There was no significant correlation with the diffusion tensor imaging metrics in any fiber. These findings suggest that the vocabulary richness in spoken language depends on the brain status characterized by the semantic knowledge-related brain structure and its activation dissimilarity with the precuneus, a core region of the default mode network.

Scale-free behavioral dynamics directly linked with scale-free cortical dynamics

Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure. Scale-free dynamics of both brain and behavior are important because each is associated with functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here, we show that scale-free dynamics of mouse behavior and neurons in the visual cortex are strongly related. Surprisingly, the scale-free neural activity is limited to specific subsets of neurons, and these scale-free subsets exhibit stochastic winner-take-all competition with other neural subsets. This observation is inconsistent with prevailing theories of scale-free dynamics in neural systems, which stem from the criticality hypothesis. We develop a computational model which incorporates known cell-type-specific circuit structure, explaining our findings with a new type of critical dynamics. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity.

Scale-free dynamics of core-periphery topography

The human brain's cerebral cortex exhibits a topographic division into higher-order transmodal core and lower-order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale-free dynamics during resting-state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f-like pink noise manifestation of scale-free dynamics in the core-periphery topography during rest and task states applying infra-slow inter-trial intervals up to 1 min falling inside the BOLD's infra-slow frequency band. The results demonstrate (1) higher resting-state power-law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task-related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter-trial interval = 52-60 s; 0.016-0.019 Hz). A computational model and a replication dataset that used similar infra-slow inter-trial intervals provide further support for our main findings. Altogether, the results show that scale-free dynamics differentiate core and periphery regions in the resting-state and mediate task-related effects.

From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans

Brain function is a product of the balance between excitatory and inhibitory (E/I) brain activity. Variation in the regulation of this activity is thought to give rise to normal variation in human traits, and disruptions are thought to potentially underlie a spectrum of neuropsychiatric conditions (e.g., Autism, Schizophrenia, Downs' Syndrome, intellectual disability). Hypotheses related to E/I dysfunction have the potential to provide cross-diagnostic explanations and to combine genetic and neurological evidence that exists within and between psychiatric conditions. However, the hypothesis has been difficult to test because: (1) it lacks specificity-an E/I dysfunction could pertain to any level in the neural system- neurotransmitters, single neurons/receptors, local networks of neurons, or global brain balance - most researchers do not define the level at which they are examining E/I function; (2) We lack validated methods for assessing E/I function at any of these neural levels in humans. As a result, it has not been possible to reliably or robustly test the E/I hypothesis of psychiatric disorders in a large cohort or longitudinal patient studies. Currently available, in vivo markers of E/I in humans either carry significant risks (e.g., deep brain electrode recordings or using Positron Emission Tomography (PET) with radioactive tracers) and/or are highly restrictive (e.g., limited spatial extent for Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Spectroscopy (MRS). More recently, a range of novel Electroencephalography (EEG) features has been described, which could serve as proxy markers for E/I at a given level of inference. Thus, in this perspective review, we survey the theories and experimental evidence underlying 6 novel EEG markers and their biological underpinnings at a specific neural level. These cheap-to-record and scalable proxy markers may offer clinical utility for identifying subgroups within and between diagnostic categories, thus directing more tailored sub-grouping and, therefore, treatment strategies. However, we argue that studies in clinical populations are premature. To maximize the potential of prospective EEG markers, we first need to understand the link between underlying E/I mechanisms and measurement techniques.

Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model

Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network.

Assessing Team Effectiveness by How Players Structure Their Search in a First-Person Multiplayer Video Game

People working as a team can achieve more than when working alone due to a team's ability to parallelize the completion of tasks. In collaborative search tasks, this necessitates the formation of effective division of labor strategies to minimize redundancies in search. For such strategies to be developed, team members need to perceive the task's relevant components and how they evolve over time, as well as an understanding of what others will do so that they can structure their own behavior to contribute to the team's goal. This study explored whether the capacity for team members to coordinate effectively can be related to how participants structure their search behaviors in an online multiplayer collaborative search task. Our results demonstrated that the structure of search behavior, quantified using detrended fluctuation analysis, was sensitive to contextual factors that limit a participant's ability to gather information. Further, increases in the persistence of movement fluctuations during search behavior were found as teams developed more effective coordinative strategies and were associated with better task performance.

How critical is brain criticality?

Criticality is the singular state of complex systems poised at the brink of a phase transition between order and randomness. Such systems display remarkable information-processing capabilities, evoking the compelling hypothesis that the brain may itself be critical. This foundational idea is now drawing renewed interest thanks to high-density data and converging cross-disciplinary knowledge. Together, these lines of inquiry have shed light on the intimate link between criticality, computation, and cognition. Here, we review these emerging trends in criticality neuroscience, highlighting new data pertaining to the edge of chaos and near-criticality, and making a case for the distance to criticality as a useful metric for probing cognitive states and mental illness. This unfolding progress in the field contributes to establishing criticality theory as a powerful mechanistic framework for studying emergent function and its efficiency in both biological and artificial neural networks.

Self-organized criticality as a framework for consciousness: A review study

Objective

No current model of consciousness is univocally accepted on either theoretical or empirical grounds, and the need for a solid unifying framework is evident. Special attention has been given to the premise that self-organized criticality (SOC) is a fundamental property of neural system. SOC provides a competitive model to describe the physical mechanisms underlying spontaneous brain activity, and thus, critical dynamics were proposed as general gauges of information processing representing a strong candidate for a surrogate measure of consciousness. As SOC could be a neurodynamical framework, which may be able to bring together existing theories and experimental evidence, the purpose of this work was to provide a comprehensive overview of progress of research on SOC in association with consciousness.

Methods

A comprehensive search of publications on consciousness and SOC published between 1998 and 2021 was conducted. The Web of Science database was searched, and annual number of publications and citations, type of articles, and applied methods were determined.

Results

A total of 71 publications were identified. The annual number of citations steadily increased over the years. Original articles comprised 50.7% and reviews/theoretical articles 43.6%. Sixteen studies reported on human data and in seven studies data were recorded in animals. Computational models were utilized in n = 12 studies. EcoG data were assessed in n = 4 articles, fMRI in n = 4 studies, and EEG/MEG in n = 10 studies. Notably, different analytical tools were applied in the EEG/MEG studies to assess a surrogate measure of criticality such as the detrended fluctuation analysis, the pair correlation function, parameters from the neuronal avalanche analysis and the spectral exponent.

Conclusion

Recent studies pointed out agreements of critical dynamics with the current most influencing theories in the field of consciousness research, the global workspace theory and the integrated information theory. Thus, the framework of SOC as a neurodynamical parameter for consciousness seems promising. However, identified experimental work was small in numbers, and a heterogeneity of applied analytical tools as a surrogate measure of criticality was observable, which limits the generalizability of findings.

Augmenting Human Selves Through Artificial Agents – Lessons From the Brain

Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for functional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.

Infinite density and relaxation for Lévy walks in an external potential: Hermite polynomial approach

Lévy walks are continuous-time random-walk processes with a spatiotemporal coupling of jump lengths and waiting times. We here apply the Hermite polynomial method to study the behavior of LWs with power-law walking time density for four different cases. First we show that the known result for the infinite density of an unconfined, unbiased LW is consistently recovered. We then derive the asymptotic behavior of the probability density function (PDF) for LWs in a constant force field, and we obtain the corresponding qth-order moments. In a harmonic external potential we derive the relaxation dynamic of the LW. For the case of a Poissonian walking time an exponential relaxation behavior is shown to emerge. Conversely, a power-law decay is obtained when the mean walking time diverges. Finally, we consider the case of an unconfined, unbiased LW with decaying speed v(τ)=v_{0}/sqrt[τ]. When the mean walking time is finite, a universal Gaussian law for the position-PDF of the walker is obtained explicitly.

Structural Variability Shows Power-Law Based Organization of Vowel Systems

Speech sounds are an essential vehicle of information exchange and meaning expression in approximately 7,000 spoken languages in the world. What functional constraints and evolutionary mechanisms lie behind linguistic diversity of sound systems is under ongoing debate; in particular, it remains conflicting whether there exists any universal relationship between these constraints despite of diverse sounds systems cross-linguistically. Here, we conducted cross-linguistic typological and phylogenetic analyses to address the characteristics of constraints on linguistic diversity of vowel systems. First, the typological analysis revealed a power-law based dependence between the global structural dispersion and the local focalization of vowel systems and validated that such dependence was independent of geographic region, language family, and linguistic affiliation. Second, the phylogenetic analysis further illustrated that the observed dependence resulted from correlated evolutions of these two structural properties, which proceeded in an adaptive process. These results provide empirical evidence that self-organization mechanisms helped shape vowel systems and common functional constraints took effect on the evolution of vowel systems in the world’s languages.

Daylong Mobile Audio Recordings Reveal Multitimescale Dynamics in Infants' Vocal Productions and Auditory Experiences

The sounds of human infancy-baby babbling, adult talking, lullaby singing, and more-fluctuate over time. Infant-friendly wearable audio recorders can now capture very large quantities of these sounds throughout infants' everyday lives at home. Here, we review recent discoveries about how infants' soundscapes are organized over the course of a day based on analyses designed to detect patterns at multiple timescales. Analyses of infants' day-long audio have revealed that everyday vocalizations are clustered hierarchically in time, vocal explorations are consistent with foraging dynamics, and musical tunes are distributed such that some are much more available than others. This approach focusing on the multi-scale distributions of sounds heard and produced by infants provides new, fundamental insights on human communication development from a complex systems perspective.

Putative rhythms in attentional switching can be explained by aperiodic temporal structure

The neural and perceptual effects of attention were traditionally assumed to be sustained over time, but recent work suggests that covert attention rhythmically switches between objects at 3-8 Hz. Here I use simulations to demonstrate that the analysis approaches commonly used to test for rhythmic oscillations generate false positives in the presence of aperiodic temporal structure. I then propose two alternative analyses that are better able to discriminate between periodic and aperiodic structure in time series. Finally, I apply these alternative analyses to published datasets and find no evidence for behavioural rhythms in attentional switching after accounting for aperiodic temporal structure. The techniques presented here will help clarify the periodic and aperiodic dynamics of perception and of cognition more broadly.

The role of comorbid depressive symptoms on long-range temporal correlations in resting EEG in adults with ADHD

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder, characterized by core symptoms of inattention, hyperactivity and impulsivity. Comorbid depression is commonly observed in ADHD-patients. Psychostimulants are recommended as first-line treatment for ADHD. Aberrant long-range temporal correlations (LRTCs) of neuronal activities in resting-state are known to be associated with disorganized thinking and concentrating difficulties (typical in ADHD) and with maladaptive thinking (typical in depression). It has yet to be examined whether (1) LRTC occur in ADHD-patients, and if so, (2) whether LRTC might be a competent biomarker in ADHD comorbid with current depression and (3) how depression affects psychostimulant therapy of ADHD symptoms. The present study registered and compared LRTCs in different EEG frequency bands in 85 adults with ADHD between groups with (n = 28) and without (n = 57) additional depressive symptoms at baseline. Treatment-related changes in ADHD, depressive symptoms and LRTC were investigated in the whole population and within each group. Our results revealed significant LRTCs existed in all investigated frequency bands. There were, however, no significant LRTC-differences between ADHD-patients with and without depressive symptoms at baseline and no LRTC-changes following treatment. However, depressed ADHD patients did seem to benefit more from the therapy with psychostimulant based on self-report.

Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations

Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.

Stationary EEG pattern relates to large-scale resting state networks - An EEG-fMRI study connecting brain networks across time-scales

Relating brain dynamics acting on time scales that differ by at least an order of magnitude is a fundamental issue in brain research. The same is true for the observation of stable dynamical structures in otherwise highly non-stationary signals. The present study addresses both problems by the analysis of simultaneous resting state EEG-fMRI recordings of 53 patients with epilepsy. Confirming previous findings, we observe a generic and temporally stable average correlation pattern in EEG recordings. We design a predictor for the General Linear Model describing fluctuations around the stationary EEG correlation pattern and detect resting state networks in fMRI data. The acquired statistical maps are contrasted to several surrogate tests and compared with maps derived by spatial Independent Component Analysis of the fMRI data. By means of the proposed EEG-predictor we observe core nodes of known fMRI resting state networks with high specificity in the default mode, the executive control and the salience network. Our results suggest that both, the stationary EEG pattern as well as resting state fMRI networks are different expressions of the same brain activity. This activity is interpreted as the dynamics on (or close to) a stable attractor in phase space that is necessary to maintain the brain in an efficient operational mode. We discuss that this interpretation is congruent with the theoretical framework of complex systems as well as with the brain's energy balance.

Simulations of Lévy Walk

The simulation of stable distributions was performed to study an ideal movement pattern for the spread of a virus using an autonomous carrier. It has been observed that Lévy walks are the most ideal way to spread and further study was done on how the parameters in Lévy distribution affect the spread.

Adaptive Capacities and Complexity of Heart Rate Variability in Patients With Chronic Obstructive Pulmonary Disease Throughout Pulmonary Rehabilitation

Introduction

The complexity of bio-signals, like R-R intervals, is considered a reflection of the organism's capacity to adapt. However, this association still remains to be consolidated. We investigated whether the complexity of R-R intervals at rest and during perturbation [6-minute walking test (6MWT)], yielded information regarding adaptive capacities in Chronic Obstructive Pulmonary Disease (COPD) patients during pulmonary rehabilitation (PR).

Methods

In total, 23 COPD patients (64 ± 8 years, with forced expiratory volume in 1 s of 55 ± 19% predicted) were tested three times at the start (T1), middle (T2), and end (T3) of 4 weeks PR. Each time, R-R intervals were measured at rest and during 6MWT. The complexity of R-R intervals was assessed by evenly spaced Detrended Fluctuations Analysis and evaluated by the fractal exponent α and deviation from maximal complexity |1-α|.

Results

The 6MWT distance was significantly increased at T2 and T3 compared to T1. Neither α nor |1-α| at rest and during perturbation significantly changed throughout PR, nor were they consistently associated with 6MWT distances at each time. Throughout the PR program, complexity during the 6MWT was significantly lower compared to the rest. The level of α during 6MWT at T1 was positively correlated with the improvement of the 6MWT distance throughout the PR program.

Discussion

Reduced complexity in COPD patients during acute perturbation at the beginning of PR supports a decreased improvement of the 6MWT distance throughout PR. This result seems consistent with the notion that the complexity reflects the patients' adaptive capacities and could therefore become a clinical indicator in an applied perspective.

The Accuracy of Causal Learning Over Long Timeframes: An Ecological Momentary Experiment Approach

The ability to learn cause-effect relations from experience is critical for humans to behave adaptively - to choose causes that bring about desired effects. However, traditional experiments on experience-based learning involve events that are artificially compressed in time so that all learning occurs over the course of minutes. These paradigms therefore exclusively rely upon working memory. In contrast, in real-world situations we need to be able to learn cause-effect relations over days and weeks, which necessitates long-term memory. 413 participants completed a smartphone study, which compared learning a cause-effect relation one trial per day for 24 days versus the traditional paradigm of 24 trials back- to- back. Surprisingly, we found few differences between the short versus long timeframes. Subjects were able to accurately detect generative and preventive causal relations, and they exhibited illusory correlations in both the short and long timeframe tasks. These results provide initial evidence that experience-based learning over long timeframes exhibits similar strengths and weaknesses as in short timeframes. However, learning over long timeframes may become more impaired with more complex tasks.

No evidence of rhythmic visuospatial attention at cued locations in a spatial cuing paradigm, regardless of their behavioural relevance

Recent evidence suggests that visuospatial attentional performance is not stable over time but fluctuates in a rhythmic fashion. These attentional rhythms allow for sampling of different visuospatial locations in each cycle of this rhythm. However, it is still unclear in which paradigmatic circumstances rhythmic attention becomes evident. First, it is unclear at what spatial locations rhythmic attention occurs. Second, it is unclear how the behavioural relevance of each spatial location determines the rhythmic sampling patterns. Here, we aim to elucidate these two issues. Firstly, we aim to find evidence of rhythmic attention at the predicted (i.e. cued) location under moderately informative predictor value, replicating earlier studies. Secondly, we hypothesise that rhythmic attentional sampling behaviour will be affected by the behavioural relevance of the sampled location, ranging from non-informative to fully informative. To these aims, we used a modified Egly-Driver task with three conditions: a fully informative cue, a moderately informative cue (replication condition), and a non-informative cue. We did not find evidence of rhythmic sampling at cued locations, failing to replicate earlier studies. Nor did we find differences in rhythmic sampling under different predictive values of the cue. The current data does not allow for robust conclusions regarding the non-cued locations due to the absence of a priori hypotheses. Post-hoc explorative data analyses, however, clearly indicate that attention samples non-cued locations in a theta-rhythmic manner, specifically when the cued location bears higher behavioural relevance than the non-cued locations.

Long range temporal correlations (LRTCs) in MEG-data during emerging psychosis: Relationship to symptoms, medication-status and clinical trajectory

Long-Range Temporal Correlations (LRTCs) index the capacity of the brain to optimally process information. Previous research has shown that patients with chronic schizophrenia present altered LRTCs at alpha and beta oscillations. However, it is currently unclear at which stage of schizophrenia aberrant LRTCs emerge. To address this question, we investigated LRTCs in resting-state magnetoencephalographic (MEG) recordings obtained from patients with affective disorders and substance abuse (clinically at low-risk of psychosis, CHR-N), patients at clinical high-risk of psychosis (CHR-P) (n = 115), as well as patients with a first episode (FEP) (n = 25). Matched healthy controls (n = 47) served as comparison group. LRTCs were obtained for frequencies from 4 to 40 Hz and correlated with clinical and neuropsychological data. In addition, we examined the relationship between LRTCs and transition to psychosis in CHR-P participants, and the relationship between LRTC and antipsychotic medication in FEP participants. Our results show that participants from the clinical groups have similar LRTCs to controls. In addition, LRTCs did not correlate with clinical and neurocognitive variables across participants nor did LRTCs predict transition to psychosis. Therefore, impaired LRTCs do not reflect a feature in the clinical trajectory of psychosis. Nevertheless, reduced LRTCs in the beta-band over posterior sensors of medicated FEP participants indicate that altered LRTCs may appear at the onset of the illness. Future studies are needed to elucidate the role of anti-psychotic medication in altered LRTCs.

Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks

Brain activity is composed of oscillatory and broadband arrhythmic components; however, there is more focus on oscillatory sensorimotor rhythms to study movement, but temporal dynamics of broadband arrhythmic electroencephalography (EEG) remain unexplored. We have previously demonstrated that broadband arrhythmic EEG contains both short- and long-range temporal correlations that change significantly during movement. In this study, we build upon our previous work to gain a deeper understanding of these changes in the long-range temporal correlation (LRTC) in broadband EEG and contrast them with the well-known LRTC in alpha oscillation amplitude typically found in the literature. We investigate and validate changes in LRTCs during five different types of movements and motor imagery tasks using two independent EEG datasets recorded with two different paradigms-our finger tapping dataset with single self-initiated asynchronous finger taps and publicly available EEG dataset containing cued continuous movement and motor imagery of fists and feet. We quantified instantaneous changes in broadband LRTCs by detrended fluctuation analysis on single trial 2 s EEG sliding windows. The broadband LRTC increased significantly (p < 0.05) during all motor tasks as compared to the resting state. In contrast, the alpha oscillation LRTC, which had to be computed on longer stitched EEG segments, decreased significantly (p < 0.05) consistently with the literature. This suggests the complementarity of underlying fast and slow neuronal scale-free dynamics during movement and motor imagery. The single trial broadband LRTC gave high average binary classification accuracy in the range of 70.54±10.03% to 76.07±6.40% for all motor execution and imagery tasks and hence can be used in brain-computer interface (BCI). Thus, we demonstrate generalizability, robustness, and reproducibility of novel motor neural correlate, the single trial broadband LRTC, during different motor execution and imagery tasks in single asynchronous and cued continuous motor-BCI paradigms and its contrasting behavior with LRTC in alpha oscillation amplitude.

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: "is there a more optimal way to optimize?". Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.

Scaling laws in natural conversations among elderly people

Language is a result of brain function; thus, impairment in cognitive function can result in language disorders. Understanding the aging of brain functions in terms of language processing is crucial for modern aging societies. Previous studies have shown that language characteristics, such as verbal fluency, are associated with cognitive functions. However, the scaling laws in language in elderly people remain poorly understood. In the current study, we recorded large-scale data of one million words from group conversations among healthy elderly people and analyzed the relationship between spoken language and cognitive functions in terms of scaling laws, namely, Zipf's law and Heaps' law. We found that word patterns followed these scaling laws irrespective of cognitive function, and that the variations in Heaps' exponents were associated with cognitive function. Moreover, variations in Heaps' exponents were associated with the ratio of new words taken from the other participants' speech. These results indicate that the exponents of scaling laws in language are related to cognitive processes.

Critical integration in neural and cognitive systems: Beyond power-law scaling as the hallmark of soft assembly

Inspired by models of self-organized criticality, a family of measures quantifies long-range correlations in neural and behavioral activity in the form of self-similar (e.g., power-law scaled) patterns across a range of scales. Long-range correlations are often taken as evidence that a system is near a critical transition, suggesting interaction-dominant, softly assembled relations between its parts. Psychologists and neuroscientists frequently use power-law scaling as evidence of critical regimes and soft assembly in neural and cognitive activity. Critics, however, argue that this methodology operates at most at the level of an analogy between cognitive and other natural phenomena. This is because power-laws do not provide information about a particular system's organization or what makes it specifically cognitive. We respond to this criticism using recent work in Integrated Information Theory. We propose a more principled understanding of criticality as a system's susceptibility to changes in its own integration, a property cognitive agents are expected to manifest. We contrast critical integration with power-law measures and find the former more informative about the underlying processes.

Irregular Metronomes as Assistive Devices to Promote Healthy Gait Patterns

Older adults and people suffering from neurodegenerative disease often experience difficulty controlling gait during locomotion, ultimately increasing their risk of falling. To combat these effects, researchers and clinicians have used metronomes as assistive devices to improve movement timing in hopes of reducing their risk of falling. Historically, researchers in this area have relied on metronomes with isochronous interbeat intervals, which may be problematic because normal healthy gait varies considerably from one step to the next. More recently, researchers have advocated the use of irregular metronomes embedded with statistical properties found in healthy populations. In this paper, we explore the effect of both regular and irregular metronomes on many statistical properties of interstride intervals. Furthermore, we investigate how these properties react to mechanical perturbation in the form of a halted treadmill belt while walking. Our results demonstrate that metronomes that are either isochronous or random break down the inherent structure of healthy gait. Metronomes with statistical properties similar to healthy gait seem to preserve those properties, despite a strong mechanical perturbation. We discuss the future development of this work in the context of networked augmented reality metronome devices.