Search

This literature review explores the role of attractor neural networks (ANNs) in modeling psychological processes in artificial and biological systems. By synthesizing research from dynamical systems theory, psychology, and computational neuroscience, the review provides an overview of the current understanding of ANN function in memory formation, memory reinforcement, retrieval, and forgetting. Key mathematical foundations of ANNs, including dynamical systems theory and energy functions, are discussed to explain the behavior and stability of these networks. The review also examines empirical applications of ANNs in cognitive processes such as semantic memory and episodic recall, as well as highlighting the hippocampus' role in pattern separation and completion. The review addresses challenges like catastrophic forgetting and noise effects on memory retrieval. By identifying gaps between theoretical models and empirical findings, it highlights the interdisciplinary nature of ANN research and suggests future areas for exploration.

About 30 years ago, the Dynamical Hypothesis instigated a variety of insights and transformations in cognitive science. One of them was the simple observation that, quite unlike trial‐based tasks in a laboratory, natural ecologically valid behaviors almost never have context‐free starting points. Instead, they produce lengthy time series data that can be recorded with dense‐sampling measures, such as heartrate, eye movements, EEG, etc. That emphasis on studying the temporal dynamics of extended behaviors may have been the trigger that led to a rethinking of what a “representation” is, and then of what a “cognitive agent” is. This most recent and perhaps most revolutionary transformation is the idea that a cognitive agent need not be a singular physiological organism. Perhaps a group of organisms, such as several people working on a joint task, can temporarily function as one cognitive agent – at least while they're working adaptively and successfully. [ABSTRACT FROM AUTHOR]

Abstract Municipal solid waste is one of the most significant sources of gas emissions. In a context of renewable energy production and greenhouse gas emission reduction, we focus on the amount of biogas produced by the anaerobic digestion of organic matter in a controlled landfill environment. We present a mathematical model describing this process from a microbiological and biochemical point of view, including the dynamics of the methane and carbon dioxide produced. A qualitative analysis of the dynamic system shows the existence of an infinite number of non-hyperbolic equilibria inducing an attractor that has been identified. Through numerical tests, we explore how the non-connectivity of the attractor resulting from the choice of growth functions can entirely transform the performance of the process, and highlight critical initial stock values that influence the amount of biogas produced.

The complexity of modern societies necessitates that children learn highly abstract material, such as mathematics, which often conflicts with behavioral goals that are innately motivating. For instance, children’s educational success is generally evaluated based on their individual achievements, while humans are motivated to learn by engaging in socially relevant behaviors. Additionally, science-related content typically requires higher-level abstract thinking to comprehend related concepts and perform the underlying cognitive processes, whereas humans evolved primarily to monitor and manipulate the physical environment by moving within it to execute foraging and hunting behaviors. Moreover, school systems inherently prescribe top-down strategies in which teachers transfer knowledge by providing instructions to guide students' knowledge acquisition. By contrast, humans evolved to learn through bottom-up processes motivated by the learner's internal drive to explore their physical and social environment. As a consequence, skeletal cognitive competencies that evolved throughout human history create a mismatch between why children are motivated to learn and how they are expected to learn. This review adopts an evolutionary perspective to examine how the interplay between students’ internal physiological and psychological adaptations and external instructional methods of modern educational systems impacts motivation and learning. Ultimately, the review offers suggestions on how to motivate the learning process by integrating self-determination theory principles into a dynamical systems framework. [ABSTRACT FROM AUTHOR]

Tracy and Robins (Psychological Inquiry, 15(2), 103–125, 2004) theorized that there are two facets of pride: authentic (e.g., "accomplished") and hubristic (e.g., "arrogant"). While these facets are assumed to function simultaneously over time, it remains unclear whether they differ by gender, given that pride is associated with various social behaviors (Magee Journal of Happiness Studies, 16(5), 1091–1115, 2015; Yeung & Shen Journal of Organizational Behavior, 40(6), 605–624, 2019). In this study, we recruited 118 U.S. college students (54.2% female; 49.18% Non-Hispanic White; mean age = 19.12 years) who participated in a group discussion task and reported their levels of authentic and hubristic pride at three-time points. We used the Repeated-Measures Actor-Partner Interdependence Model (RM-APIM, Kenny et al., 2006) and calculated two change scores (e.g., T2 - T1) as outcomes for the dependent variables. We also graphed the topological state of each facet of pride simultaneously (Butner et al. Psychological Methods, 20, 1–25, 2015). Our findings suggest that: (1) both facets significantly predicted their stability over time for both genders, and (2) females had a lower value of authentic pride but reached stability in authentic pride more quickly than males. These results suggest that females may be more quickly guided by what they perceive as socially expected behavior by themselves and others in authentic pride, even at lower values. In contrast, males may prioritize maintaining higher levels of authentic pride over quickly responding to social cues. [ABSTRACT FROM AUTHOR]

AbstractBackgroundMethodsResultsConclusionsAbstinence has long been considered the defining feature of recovery from substance use disorder, with a focus on individual level factors associated with abstinence rather than identifying individual and socioecological factors that support recovery. This paper proposes greater consideration of dynamic behavioral ecological influences on recovery and offers an expanded contextualized approach to understanding and promoting recovery and predicting dynamic recovery pathways.Conceptual and empirical bases are summarized that support moving beyond research, treatment, and policy agendas that focus narrowly on the individual as the fundamental change agent in recovery and that emphasize changes in substance use as the primary outcome metric. A model is presented that expands the scope of recovery-relevant variables to include dynamically-varying ecological contexts that variously support or hinder recovery along with an expanded scope of functional and contextual outcome variables.Examining behavior patterns through time in changing environmental contexts that include community and neighborhood-level variables and social determinants of health is critical for understanding recovery and developing multi-level interventions to promote change. Molar behavioral and ecological perspectives are needed to understand how recovery is influenced by broader contextual features in addition to individual determinants. This paper provides concrete recommendations for the pursuit of this broadened research agenda.Individual pathology-based approaches to understanding and promoting substance use disorder recovery are too narrowly focused. This review calls for greater consideration of the dynamic behavioral ecological and temporally extended contexts that contribute to harmful substance use and systemic changes necessary to promote and sustain recovery. [ABSTRACT FROM AUTHOR]

In this work, we investigate the existence and non-existence of traveling wave solutions for a chemotaxis model with two chemoattractants. To prove our main results, we apply the dynamical systems theory by constructing a positively invariant set in the four-dimensional space. Particularly, we analyze the monotonicity of traveling wave solutions. [ABSTRACT FROM AUTHOR]

Psychologism is defined as "the doctrine that the laws of mathematics and logic can be reduced to or depend on the laws governing thinking" (Moran & Cohen, 2012 266). And for Husserl, the laws of logic include the laws of meaning: "logic evidently is the science of meanings as such [Wissenschaft von Bedeutungen als solchen]" (Husserl (1975) 98/2001 225). I argue that, since it is sufficient for a theory to be psychologistic if the empiricistic theory of abstraction is employed, it follows that neural networks are psychologistic insofar as they use this theory of abstraction, which I demonstrate is the case (Husserl (1975) 191/2001 120). It's sufficient for psychologism because, according to Husserl, the theory in question reduces one's phenomenological ability to intend types (or universals) to one's past history of intending tokens (or particulars), usually amalgamated in some fashion (classically via associations; recently via autoencoders) (ibid; Kelleher, 2019). Similarly dynamical systems theory entails psychologism. For dynamical systems theory ties content to the temporal evolution of a system, which, according to Husserl, violates the fact that intentionality toward validities and objectivities does not pertain to "particular temporal experience[s]" (Husserl (1975) 194/2001 121). It follows that neither the species (neural networks), nor the genus (dynamical systems), can avoid psychologism and intend objects "in specie" (ibid). After critiquing these two approaches, I proceed to give an account based on the essentialist school of cognitive psychology of how we may intend objects "in specie" while avoiding the empiricistic theory of abstraction (Keil, 1989, Carey, 2009, Marcus & Davis, 2019). Such an account preserves the type-token distinction without psychologistic reduction to the temporal evolution of a dynamical system (Hinzen, 2006). This opens the way toward a truly unifying account of Husserlian phenomenology in league with cognitive science that avoids Yoshimi's (2016) and neurophenomenology's psychologistic foundation (herein demonstrated) and builds upon Sokolowski's (2003) syntactic account of Husserlian phenomenology. [ABSTRACT FROM AUTHOR]

We consider the susceptible-exposed-infected-removed (SEIR) epidemic model and apply optimal control to it successfully. Here three control inputs are considered, so that the infection rate is decreased and exposed or infected individuals are removed. Our approach is to reduce the computation of the optimal control input to that of the stable manifold of an invariant manifold in a Hamiltonian system. Some numerical examples in which the computer software AUTO is used to numerically compute the stable manifold are given to demonstrate the usefulness of our approach for the optimal control in the SEIR model. Our study suggests how we can decrease the number of infected individuals quickly before a critical situation occurs while keeping social and economic burdens small. Our results for the SEIR model are very different from the previous one for the SIR model, which is similar but simpler than the present one. [ABSTRACT FROM AUTHOR]

For microbiome biology to become a more predictive science, we must identify which descriptive features of microbial communities are reproducible and predictable, which are not, and why. We address this question by experimentally studying parallelism and convergence in microbial community assembly in replicate glucose-limited habitats. Here, we show that the previously observed family-level convergence in these habitats reflects a reproducible metabolic organization, where the ratio of the dominant metabolic groups can be explained from a simple resource-partitioning model. In turn, taxonomic divergence among replicate communities arises from multistability in population dynamics. Multistability can also lead to alternative functional states in closed ecosystems but not in metacommunities. Our findings empirically illustrate how the evolutionary conservation of quantitative metabolic traits, multistability, and the inherent stochasticity of population dynamics, may all conspire to generate the patterns of reproducibility and variability at different levels of organization that are commonplace in microbial community assembly.

Genetic assimilation is the process by which a phenotype that is initially induced by an environmental stimulus becomes stably inherited in the absence of the stimulus after a few generations of selection. While the concept has attracted much debate after being introduced by C. H. Waddington 70 y ago, there have been few experiments to quantitatively characterize the phenomenon. Here, we revisit and organize the results of Waddington’s original experiments and follow-up studies that attempted to replicate his results. We then present a theoretical model to illustrate the process of genetic assimilation and highlight several aspects that we think require further quantitative studies, including the gradual increase of penetrance, the statistics of delay in assimilation, and the frequency of unviability during selection. Our model captures Waddington’s picture of developmental paths in a canalized landscape using a stochastic dynamical system with alternative trajectories that can be controlled by either external signals or internal variables. It also reconciles two descriptions of the phenomenon— Waddington’s, expressed in terms of an individual organism’s developmental paths, and that of Bateman in terms of the population distribution crossing a hypothetical threshold. Our results provide theoretical insight into the concepts of canalization, phenotypic plasticity, and genetic assimilation. [ABSTRACT FROM AUTHOR]

Over the past three decades, Van Gelder's dynamical hypothesis has been instrumental in reconceptualizing the ways in which perception‐action‐cognition unfolds over time and in context. Here, I examine how the dynamical approach has enriched the theoretical understanding of social dynamics within cognitive science, with a particular focus on interpersonal coordination. I frame this review around seven principles in dynamical systems: three that are well‐represented in interpersonal coordination research to date (emergent behavior, context‐sensitive behavior, and attractors) and four that could be useful opportunities for future growth (hysteresis, sensitivity to initial conditions, equifinality, and reciprocal compensation). In addition to identifying specific promising lines of theoretical inquiry, I focus on the significant potential afforded by computationally intensive science—especially in naturally occurring data or trace data. Building on the foundation laid over the past three decades, I argue that looking to increasingly situated and naturalistic settings (and data) is not only necessary to realize the full commitment to the dynamical hypothesis but is also critical to building parsimonious and principled theories of social phenomena. [ABSTRACT FROM AUTHOR]

Mechanistic explanations, according to one prominent account, are derived from objective explanations (Craver 2007, 2014). Mechanistic standards of explanation are in turn pulled from nature, and are thereby insulated from the values of investigators, since explanation is an objectively defined achievement grounded in the causal structure of the world (Craver 2014). This results in the closure of mechanism's explanatory standards—it is insulated from the values, norms and goals of investigators. I raise two problems with this position. First, it relies on several ontological claims which, while plausible, fail to guarantee the objectivity of mechanistic explanatory standards to the degree of certainty required. Second, Craver's position itself introduces a value–laden explanatory standard—the 3M requirement (Kaplan & Craver 2011)—which undermines the closure of explanatory standards. I show how in practice mechanistic explanation is in part guided by explanatory taste, shorthand for background contextual values that influence our standards of explanation. Mechanism often has a particular pragmatically-oriented taste for control, and gerrymanders explanatory standards in order to obtain it. I conclude by arguing that objectivity, rather than being obtained through the right set of explanatory standards, is better thought of as the result of processes of intersubjective criticism, which renders visible the contextual values of communities of investigators and allows them to be controlled for (Longino 1990). [ABSTRACT FROM AUTHOR]

Quasi-periodic orbits offer a broad range of flexibly and adaptable destinations in multi-body systems. In this investigation, a computational framework for construction of heteroclinic connections between quasi-periodic orbits is summarized. Subsequently, families of heteroclinic connections are characterized and examples are provided in the Circular Restricted Three-Body Problem (CR3BP). Transition to an ephemeris model is also demonstrated. An understanding of these types of transfers is crucial for a more complete picture of the flow in a three-body system. [ABSTRACT FROM AUTHOR]

The machine-learning paradigm promises traders to reduce uncertainty through better predictions done by ever more complex algorithms. We ask about detectable results of both uncertainty and complexity at the aggregated market level. We analyzed almost one billion trades of eight currency pairs (2007-2017) and show that increased algorithmic trading is associated with more complex subsequences and more predictable structures in bid-ask spreads. However, algorithmic involvement is also associated with more future uncertainty, which seems contradictory, at first sight. On the micro-level, traders employ algorithms to reduce their local uncertainty by creating more complex algorithmic patterns. This entails more predictable structure and more complexity. On the macro-level, the increased overall complexity implies more combinatorial possibilities, and therefore, more uncertainty about the future. The chain rule of entropy reveals that uncertainty has been reduced when trading on the level of the fourth digit behind the dollar, while new uncertainty started to arise at the fifth digit behind the dollar (aka 'pip-trading'). In short, our information theoretic analysis helps us to clarify that the seeming contradiction between decreased uncertainty on the micro-level and increased uncertainty on the macro-level is the result of the inherent relationship between complexity and uncertainty.

Promising results from clinical trials have ignited a resurgence of scientific interest in serotonergic psychedelic compounds. Functional neuroimaging investigations of acute psychedelic brain action have broadly revealed that they reduce integration within, and segregation between, most large-scale networks. However, spatially specific findings have shown poor convergence. Complex systems approaches characterize the brain as a dynamically evolving system of interacting elements. In doing so, they abstract from localized patterns of activity and instead focus on the dynamical properties of the brain as a whole. Drawing from complexity science, we propose a new 'meta' perspective in which psychedelics are catalysts of a distinct mode of brain functioning that is best characterized by dynamical whole-brain features rather than by region- or network-specific changes. Recent findings suggesting the potential transdiagnostic efficacy of psychedelic-assisted therapy have fostered the need to deepen our understanding of psychedelic brain action. Functional neuroimaging investigations have found that psychedelics reduce the functional segregation of large-scale brain networks. However, beyond this general trend, findings have been largely inconsistent. We argue here that a perspective based on complexity science that foregrounds the distributed, interactional, and dynamic nature of brain function may render these inconsistencies intelligible. We propose that psychedelics induce a mode of brain function that is more dynamically flexible, diverse, integrated, and tuned for information sharing, consistent with greater criticality. This 'meta' perspective has the potential to unify past findings and guide intuitions toward compelling mechanistic models. [ABSTRACT FROM AUTHOR]

The repeated co‐occurrence of cold spells over Eastern North America and wet or windy extremes over Western Continental Europe during recent winters, has led to hypothesize a link between the two. Here, we analyze the interplay between the large‐scale atmospheric circulation and co‐occurring cold spells in North America and wet or windy extremes in Europe. We collectively term these occurrences compound cold–wet–windy extremes. We leverage a recent approach grounded in dynamical systems theory, which provides an analytically and computationally efficient analysis of spatially resolved, multivariate climate extremes. We find that there are specific, recurrent large‐scale atmospheric circulation patterns systematically associated with both the individual extremes and co‐occurring cold–wet–windy anomalies. Evidence for this is also found when focusing on compound cold–wet–windy extremes, although with a weaker signal. This motivates further analyses focusing specifically on the statistics and drivers of these compound extreme occurrences. Plain Language Summary: In recent winters, very cold weather over the eastern part of North America and stormy weather or heavy rainfall in Europe have often made the news. One may think that these events are independent, since they occur several thousands of kilometres apart. However, researchers have hypothesized that there may be weather patterns that connect these different episodes. Here we test this idea. We find that there is indeed a connection between unusually cold weather in Eastern North America and unusually stormy weather and heavy rainfall in Western Continental Europe. We also find a link, albeit weaker, when focusing specifically on extreme events—namely only the coldest of the cold spells, the windiest of the stormy days and the wettest of the heavy rainfall days. The strongest connection, however, emerges when looking at unusual but not extreme weather episodes. Key Points: North American cold spells and European wet or windy extremes are very strongly coupled to recurrent large‐scale atmospheric patternsThe compound occurrence of North American cold spells and European wet or windy extremes is associated with common atmospheric patternsExtremes show weaker evidence of common atmospheric patterns than all temperature, precipitation and wind anomalies [ABSTRACT FROM AUTHOR]

This article extends the ideas expressed in a special section on theories of psychopathology by expounding on heterotypic patterns in which different arrangements of symptoms appear over time. With heterotypic continuity, the different arrangements are somewhat predictable; with discontinuity, they are not. Among the reasons the articles in the special section give for heterotypic patterns are the lack of central controllers for producing symptom clusters, the importance of transdiagnostic factors, and the dynamics of gene-environment correlations. The articles also consider what more there is to psychopathology than symptoms—largely by modeling normal, adaptive psychology as flexible and maladaptive psychology too rigid or too pliable. The articles espouse a variety of views on the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD) legacy disorders moving forward, with some of them seeking to eliminate DSM-ICD categories from the classification of psychopathology and others seeing the DSM-ICD constructs as having continued roles to play. I use Lakatos' notion that elimination of a theory requires that an alternative theory demonstrate competitive superiority to account for why legacy constructs have not been eliminated. I examine a debate about the existence or not of basic emotions and apply it to psychopathology to identify a common ground or potential point of agreement between those who want to eliminate DSM-ICD categories and those who believe DSM-ICD constructs can continue to be useful moving forward. General Scientific Summary: Contemporary theories of psychopathology can help explain why individuals sometimes express different and shifting symptom patterns over time. This can be aided by viewing psychopathology as being about more than symptoms. Some patterns do conform to recognized syndromes, but they need not. [ABSTRACT FROM AUTHOR]

Abstract The repeated co‐occurrence of cold spells over Eastern North America and wet or windy extremes over Western Continental Europe during recent winters, has led to hypothesize a link between the two. Here, we analyze the interplay between the large‐scale atmospheric circulation and co‐occurring cold spells in North America and wet or windy extremes in Europe. We collectively term these occurrences compound cold–wet–windy extremes. We leverage a recent approach grounded in dynamical systems theory, which provides an analytically and computationally efficient analysis of spatially resolved, multivariate climate extremes. We find that there are specific, recurrent large‐scale atmospheric circulation patterns systematically associated with both the individual extremes and co‐occurring cold–wet–windy anomalies. Evidence for this is also found when focusing on compound cold–wet–windy extremes, although with a weaker signal. This motivates further analyses focusing specifically on the statistics and drivers of these compound extreme occurrences.

Thin-slice methodology has provided us with abundant behavioral streams that self-reported measures would fail to capture, but traditional analytical paradigms in social and personality psychology cannot fully capture the temporal trajectories of person perception at zero acquaintance. At the same time, empirical investigations into how persons and situations jointly predict behavior enacted in situ are scarce, despite the importance of examining real-world behavior to understand any phenomenon of interest. To complement existing theoretical models and analyses, we propose the dynamic latent state-trait model blending dynamical systems theory and person perception. We present a data-driven case study using thin-slice methodology to demonstrate the model. This study provides direct empirical support for the proposed theoretical model on person perception at zero acquaintance highlighting the target, the perceiver, the situation, and time. The results of the study demonstrate that dynamical systems theory approaches can be leveraged to provide information about person perception at zero acquaintance above and beyond that of more traditional approaches. Classification code: 3040 (Social Perception & Cognition)

Growing evidence suggests that identifying movement variability alterations in pathological vs. healthy gait may further understanding of injury mechanisms related to gait biomechanics; however, in the context of running and musculoskeletal injuries the role of movement variability remains unclear. What is the impact of a previous musculoskeletal injury on running gait variability? Medline, CINAHL, Embase, Cochrane library and SPORTDiscus were searched from inception until February 2022. Eligibility criteria were (a) included a musculoskeletal injury group, (b) compared running biomechanics data to a control group, (c) measured movement variability for at least one dependent variable, (d) provided a statistical between-group comparison of variability outcomes. Exclusion criteria were neurological conditions impacting gait, upper body musculoskeletal injuries and age < 18 years old. A summative synthesis was performed instead of a meta-analysis due to methodological heterogeneity. Seventeen case-control studies were included. The most common deviations in variability observed among the injured groups were: (1) high and low knee-ankle/foot coupling variability and (2) low trunk-pelvis coupling variability. Significant (p < 0.05) between-group differences in movement variability were identified in 8 of 11; 73% of studies of runners with injury-related symptoms, and 3 of 7; 43% of studies of recovered or asymptomatic populations. This review identified limited to strong evidence that running variability is altered in adults with a recent history of injury for specific joint couplings only. Individuals with ankle instability or pain employed altered running strategies more often than those who have recovered from injury. Altered variability strategies have been proposed to contribute to future running-related injuries, therefore these findings are relevant to clinicians managing active populations. • Individuals who run with injury-related symptoms employ altered gait variability. • Pain or ligamentous instability may underly injurious adjustments in gait. • Knee-ankle/foot coupling variability commonly altered among runners with injury. [ABSTRACT FROM AUTHOR]

The goal of this paper is to highlight considerations and provide recommendations for analytical issues that arise when applying entropy methods, specifically Sample Entropy (SampEn), to temporally correlated stochastic datasets, which are representative of a broad range of biomechanical and physiological variables. To simulate a variety of processes encountered in biomechanical applications, autoregressive fractionally integrated moving averaged (ARFIMA) models were used to produce temporally correlated data spanning the fractional Gaussian noise/fractional Brownian motion model. We then applied ARFIMA modeling and SampEn to the datasets to quantify the temporal correlations and regularity of the simulated datasets. We demonstrate the use of ARFIMA modeling for estimating temporal correlation properties and classifying stochastic datasets as stationary or nonstationary. We then leverage ARFIMA modeling to improve the effectiveness of data cleaning procedures and mitigate the influence of outliers on SampEn estimates. We also emphasize the limitations of SampEn to distinguish among stochastic datasets and suggest the use of complementary measures to better characterize the dynamics of biomechanical variables. Finally, we demonstrate that parameter normalization is not an effective procedure for increasing the interoperability of SampEn estimates, at least not for entirely stochastic datasets. [ABSTRACT FROM AUTHOR]

In current philosophy of mind, there is lively debate over whether emotions, moods, and other affects can extend to comprise elements beyond one’s organismic boundaries. At the same time, there has been growing interest in the nature and significance of so-called existential feelings, which, as the term suggests, are feelings of one’s overall being in the world. In this article, I bring these two strands of investigation together to ask: can the material underpinnings of existential feelings extend beyond one’s skull and skin? To begin, I introduce and adopt a componential-systemic view of extended affectivity. In doing so, I specify the vehicle externalist criteria for extension employed in my analysis. I then define what existential feelings are and pinpoint several key issues in their potential extension. More specifically, I identify sensorimotor ‘know-how’ as a possibly extending component of existential feeling and posit it as the fulcrum of my argument. Finally, I move on to consider the extension of existential feeling via sensory substitution, especially through so-called tactile visual sensory substitution (TVSS) devices. Informed by both philosophical and empirical studies, I argue that, under certain conditions, (a) the agent’s implicit sensorimotor processing and a TVSS device can become coupled into a new systemic whole, which in turn (b) reconfigures the material underpinnings of the agent’s preintentional world-experience. This, I conclude, counts as a cogent case for extended existential feeling. [ABSTRACT FROM AUTHOR]

Motivated by the near-future re-exploration of the cislunar space, this paper investigates dynamical substitutes of the Earth-Moon's resonant Near-Rectilinear Halo Orbits (NRHOs) under the Elliptic-Circular Restricted Four-Body Problem formulation of the Earth-Moon-Sun system. This model considers that the Earth and Moon move in elliptical orbits about each other and that a third body, the Sun, moves in a circular orbit about the Earth-Moon barycenter. By making use of this higher-fidelity dynamical model, we are able to incorporate the Sun's influence and the Moon's eccentricity, two of the most significant perturbations of the cislunar environment. As a result of these perturbations, resonant periodic NRHOs of the Earth-Moon Circular Restricted Three-Body Problem (CR3BP) are hereby replaced by two-dimensional quasi-periodic tori that better represent the dynamical evolution of satellites near the vicinity of the Moon. We present the steps and algorithms needed to compute these dynamical structures in the Elliptic-Circular model and subsequently assess their utility for spacecraft missions. We focus on the planned orbit for the NASA-led Lunar Gateway mission, a 9:2 synodic resonant L 2 southern NRHO, as well as on the 4:1 synodic and 4:1 sidereal resonances, due to the proximity to the nominal orbit and their advantageous dynamical properties. We verify that the dynamical equivalents of these orbits preserve key dynamical attributes such as eclipse avoidance and near-linear stability. Furthermore, we find that the higher dimensionality of quasi-periodic solutions offers interesting alternatives to mission designers in terms of phasing maneuvers and low-altitude scientific observations. [ABSTRACT FROM AUTHOR]

Balance and postural control exercises are generally included as a part of exercise programs, during which movement practitioners can provide instructions to facilitate the performance of motor skills. Instructions can be used as cues to direct attentional focus, which has been found to affect the performance of motor skills, including balance and postural control tasks. However, no known studies to date have investigated the effect of both internal and external attentional focus instructions on static single leg balance performance, and it seems unclear whether effects of such instructions are related specifically to the direction of attention. The purpose of this study was to investigate the effect of instructing the direction of attentional focus on single leg static balance performance as reflected by the complexity of the center of pressure (COP) profile. Participants (N = 46) between the ages of 19–28 years old were randomly assigned to one of three group conditions: internal focus (INTn=15), external focus (EXTn=16) and control (CONn=15). Participants performed a thirty-five second static single leg balance task. Outcome measures were the scaling exponent determined from a detrended fluctuation analysis (DFA) to infer complexity of the COP profile in the anterior-posterior (AP) and medial-lateral (ML) directions, and root mean square error (RMSE) of the COP profile in AP and ML directions. A one-way analysis of variance (ANOVA) determined there were no statistically significant differences in the measured variables among groups. The results do not support the claim that manipulating the direction of attentional focus affects static single leg balance performance. [ABSTRACT FROM AUTHOR]

In this paper we argue that our comprehension of musical participation—the complex network of interactive dynamics involved in collaborative musical experience—can benefit from an analysis inspired by the existing frameworks of dynamical systems theory and coordination dynamics. These approaches can offer novel theoretical tools to help music researchers describe a number of central aspects of joint musical experience in greater detail, such as prediction, adaptivity, social cohesion, reciprocity, and reward. While most musicians involved in collective forms of musicking already have some familiarity with these terms and their associated experiences, we currently lack an analytical vocabulary to approach them in a more targeted way. To fill this gap, we adopt insights from these frameworks to suggest that musical participation may be advantageously characterized as an open, non-equilibrium, dynamical system. In particular, we suggest that research informed by dynamical systems theory might stimulate new interdisciplinary scholarship at the crossroads of musicology, psychology, philosophy, and cognitive (neuro)science, pointing toward new understandings of the core features of musical participation. [ABSTRACT FROM AUTHOR]

Simple Summary: The individual's ability to conceive and regulate the broad spectrum of their human emotions is closely linked to their mental health. The implications of a serious disease such as cancer represent an extraordinary burden to these internal coping mechanisms, especially in the case of young patients. Regarding their well-being and support, it is therefore of particular interest for caregivers to be able to follow the dynamics of the patient's emotional world and perceptions. Technical progress enables new possibilities for data collection through tools for digital patient self-reports while simultaneously creating new challenges. Within the scope of this article, we provide an overview of the literature on this topic, outlining the current strengths and weaknesses and possible perspectives on digital aids, especially in terms of capturing the flexibility, fluctuations and early detection of symptom changes. Emotion dysregulation is regarded as a driving mechanism for the development of mental health problems and psychopathology. The role of emotion regulation (ER) in the management of cancer distress and quality of life (QoL) has recently been recognized in psycho-oncology. The latest technological advances afford ways to assess ER, affective experiences and QoL in child, adolescent and young adult (CAYA) cancer patients through electronic patient-reported outcomes (ePRO) in their daily environment in real-time. Such tools facilitate ways to study the dynamics of affect and the flexibility of ER. However, technological advancement is not risk-free. We critically review the literature on ePRO in cancer existing models of ER in pediatric psycho-oncology and analyze strength, weaknesses, opportunities and threats of ePRO with a focus on CAYA cancer research and care. Supported by personal study-based experiences, this narrative review serves as a foundation to propose a novel methodological and metatheoretical framework based on: (a) an extended notion of ER, which includes its dynamic, adaptive and flexible nature and focuses on processes and conditions rather than fixed categorical strategies; (b) ePRO as a means to measure emotion regulation flexibility and affect dynamics; (c) identifying early warning signals for symptom change via ePRO and building forecasting models using dynamical systems theory. [ABSTRACT FROM AUTHOR]

A proposed Gateway facility in a lunar near rectilinear halo orbit (NRHO) will serve as an outpost in cislunar space, with various spacecraft periodically arriving and departing. The dynamics in the proximity of an NRHO are complex; the motion is primarily influenced by the Earth and Moon within the vicinity of the NRHO, but spacecraft are significantly impacted by solar gravity upon departure from the lunar vicinity. The current investigation explores two aspects of the trajectory dynamics after a separation maneuver from Gateway: departure dynamics and escape dynamics. The departure dynamics analysis focuses on the immediate fate of the trajectory after a maneuver, while the object remains close to the NRHO. The escape dynamics analysis details the outcome of the trajectory, that is, whether it remains in the Earth-Moon vicinity or escapes to heliocentric space. This investigation identifies a range of parameters and metrics at the different stages along the departed trajectory that inform the trajectory design process. [ABSTRACT FROM AUTHOR]

Many common tasks require or are made more efficient bycoordinating with others. In this paper we investigate thecoordination dynamics of a joint action pick-and-place task inorder to identify the behavioral dynamics that underlie theemergence of human coordination. More precisely, weintroduce a task dynamics approach for modeling multi-agentinteraction in a continuous pick-and-place task where twoagents must decide to work together or alone to move an objectfrom one location to another. Our aims in the current paper areto identify and model (1) the relevant affordance dynamics thatunderlie the selection of the different action modes required bythe task and (2) the trajectory dynamics of each actor’s handmovements when moving to grasp, relocate, or pass the object.We demonstrate that the emergence of successful coordinationcan be characterized in terms of behavioral dynamics modelswhich may have applications for artificial agent design.

This paper proposes the sparse optimal control in the circular restricted three-body problem to exploit the dynamical structure of the linearized motion around equilibrium points or periodic orbits. First, optimal transfer problem is formulated for a linearized system where the objective function and constraints are given by convex sets. For a long time transfer problem in the vicinity of periodic orbits, the Floquet-Lyapunov transformation and a normal form are introduced to enable numerical calculations. Then, the proposed method is applied to several transfer problems around an equilibrium point or periodic orbits and a parameter analysis is performed to understand the optimal transfer by changing the maximum thrust acceleration and time of flight. Finally, a new station-keeping strategy is proposed to implement the sparse optimal solution in a nonlinear dynamics. [ABSTRACT FROM AUTHOR]

This aim of this paper is two-fold: it critically analyses and rejects accounts blending active inference as theory of mind and enactivism; and it advances an enactivist-dynamic understanding of social cognition that is compatible with active inference. While some social cognition theories seemingly take an enactive perspective on social cognition, they explain it as the attribution of mental states to other people, by assuming representational structures, in line with the classic Theory of Mind (ToM). Holding both enactivism and ToM, we argue, entails contradiction and confusion due to two ToM assumptions widely known to be rejected by enactivism: that (1) social cognition reduces to mental representation and (2) social cognition is a hardwired contentful 'toolkit' or 'starter pack' that fuels the model-like theorising supposed in (1). The paper offers a positive alternative, one that avoids contradictions or confusion. After rejecting ToM-inspired theories of social cognition and clarifying the profile of social cognition under enactivism, that is without assumptions (1) and (2), the last section advances an enactivist-dynamic model of cognition as dynamic, real-time, fluid, contextual social action, where we use the formalisms of dynamical systems theory to explain the origins of socio-cognitive novelty in developmental change and active inference as a tool to demonstrate social understanding as generalised synchronisation. [ABSTRACT FROM AUTHOR]

Rideshares increase launch capabilities and decrease the launch costs. However, the range of orbits available for secondary payloads is dependent on launch constraints for the primary mission. Additionally, communications constraints and limited propellant options must be incorporated in the preliminary mission design strategy for secondary payloads. Ridesharing opportunities are now available for orbit destinations beyond Low Earth Orbit (LEO). In this investigation, transfers from Geosynchronous Transfer Orbits (GTO) to Sun-Earth libration point orbits are constructed by leveraging stable manifold structures and Poincaré maps. [ABSTRACT FROM AUTHOR]

This aim of this paper is two-fold: it critically analyses and rejects accounts blending active inference as theory of mind and enactivism; and it advances an enactivist-dynamic understanding of social cognition that is compatible with active inference. While some social cognition theories seemingly take an enactive perspective on social cognition, they explain it as the attribution of mental states to other people, by assuming representational structures, in line with the classic Theory of Mind (ToM). Holding both enactivism and ToM, we argue, entails contradiction and confusion due to two ToM assumptions widely known to be rejected by enactivism: that (1) social cognition reduces to mental representation and (2) social cognition is a hardwired contentful ‘toolkit’ or ‘starter pack’ that fuels the model-like theorising supposed in (1). The paper offers a positive alternative, one that avoids contradictions or confusion. After rejecting ToM-inspired theories of social cognition and clarifying the profile of social cognition under enactivism, that is without assumptions (1) and (2), the last section advances an enactivist-dynamic model of cognition as dynamic, real-time, fluid, contextual social action, where we use the formalisms of dynamical systems theory to explain the origins of socio-cognitive novelty in developmental change and active inference as a tool to demonstrate social understanding as generalised synchronisation.

Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition, and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using functional magnetic resonance imaging (fMRI) data from an open-source database, this study investigated differences between the dynamic exploration of resting-state networks in 71 schizophrenia patients and 74 healthy controls. Focusing on recurrent states of phase coherence in fMRI signals, brain activity was examined for intergroup differences through the lens of dynamical systems theory. Results showed reduced fractional occupancy and dwell time of a globally synchronized state in schizophrenia. Conversely, patients exhibited increased fractional occupancy, dwell time and limiting probability of being in states during which canonical functional networks—i.e., Limbic, Dorsal Attention and Somatomotor—synchronized in anti-phase with respect to the rest of the brain. In terms of state-to-state transitions, patients exhibited increased probability of switching to Limbic, Somatomotor and Visual networks, and reduced probability of remaining in states related to the Default Mode network, the Orbitofrontal network and the globally synchronized state. All results revealed medium to large effect sizes. Combined, these findings expose pronounced differences in the temporal expression of resting-state networks in schizophrenia patients, which may relate to the pathophysiology of this disorder. Overall, these results reinforce the utility of dynamical systems theory to extend current knowledge regarding disrupted brain dynamics in psychiatric disorders. [ABSTRACT FROM AUTHOR]

Musical rhythm abilities—the perception of and coordinated action to the rhythmic structure of music—undergo remarkable change over human development. In the current paper, we introduce a theoretical framework for modeling the development of musical rhythm. The framework, based on Neural Resonance Theory (NRT), explains rhythm development in terms of resonance and attunement , which are formalized using a general theory that includes non-linear resonance and Hebbian plasticity. First, we review the developmental literature on musical rhythm, highlighting several developmental processes related to rhythm perception and action. Next, we offer an exposition of Neural Resonance Theory and argue that elements of the theory are consistent with dynamical, radically embodied (i.e., non-representational) and ecological approaches to cognition and development. We then discuss how dynamical models, implemented as self-organizing networks of neural oscillations with Hebbian plasticity, predict key features of music development. We conclude by illustrating how the notions of dynamical embodiment, resonance, and attunement provide a conceptual language for characterizing musical rhythm development, and, when formalized in physiologically informed dynamical models, provide a theoretical framework for generating testable empirical predictions about musical rhythm development, such as the kinds of native and non-native rhythmic structures infants and children can learn, steady-state evoked potentials to native and non-native musical rhythms, and the effects of short-term (e.g., infant bouncing, infant music classes), long-term (e.g., perceptual narrowing to musical rhythm), and very-long term (e.g., music enculturation, musical training) learning on music perception-action. [ABSTRACT FROM AUTHOR]

Rapid trajectory design in multi-body systems often leverages individual arcs along natural dynamical structures that exist in an approximate dynamical model. To reduce the complexity of this analysis in a chaotic gravitational environment, a motion primitive set is constructed to represent the finite geometric, stability, and/or energetic characteristics exhibited by a set of trajectories and, therefore, support the construction of initial guesses for complex trajectories. In the absence of generalizable analytical criteria for extracting these representative solutions, a data-driven procedure is presented. Specifically, k-means and agglomerative clustering are used in conjunction with weighted evidence accumulation clustering, a form of consensus clustering, to construct sets of motion primitives in an unsupervised manner. This data-driven procedure is used to construct motion primitive sets that summarize a variety of periodic orbit families and natural trajectories along hyperbolic invariant manifolds in the Earth–Moon circular restricted three-body problem. [ABSTRACT FROM AUTHOR]

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS). [ABSTRACT FROM AUTHOR]