Stress-induced failure of embodied cognition: A general model

We derive the classic, ubiquitous, but enigmatic Yerkes-Dodson effect of applied stress on real-world performance in a highly natural manner from fundamental assumptions on cognition and its dynamics, as constrained by the asymptotic limit theorems of information and control theories. We greatly extend the basic approach by showing how differences in an underlying probability model can affect the dynamics of decision across a broad range of cognitive enterprise. Most particularly, however, this development may help inform our understanding of the different expressions of human psychopathology. A 'thin tailed' underlying distribution appears to characterize expression of 'ordinary' situational depression/anxiety symptoms of conditions like burnout induced by toxic stress. A 'fat tailed' underlying distribution appears to be associated with brain structure and function abnormalities leading to serious mental illness and poor decision making where symptoms are not only emerging in the setting of severe stress but may also appear in a highly punctuated manner at relatively lower levels of stress. A simple hierarchical optimization shows how environmental 'shadow price' constraints can buffer or aggravate the effects of stress and arousal. Extension of the underlying theory to other patterns of pathology, like immune disorders and premature aging, seems apt. Applications to the punctuated dynamics of institutional cognition under stress also appear possible. Ultimately, the probability models studied here can be converted to new statistical tools for the analysis of observational and experimental data.

Bumblebees compensate for the adverse effects of sidewind during visually guided landings

Flying animals often encounter winds during visually guided landings. However, how winds affect their flight control strategy during landing is unknown. Here, we investigated how sidewind affects the landing performance and sensorimotor control of foraging bumblebees (Bombus terrestris). We trained bumblebees to forage in a wind tunnel, and used high-speed stereoscopic videography to record 19,421 landing maneuvers in six sidewind speeds (0 to 3.4 m s-1), which correspond to winds encountered in nature. Bumblebees landed less often in higher windspeeds, but the landing durations from free flight were not increased by wind. By testing how bumblebees adjusted their landing control to compensate for adverse effects of sidewind on landing, we showed that the landing strategy in sidewind resembled that in still air, but with important adaptations. Bumblebees landing in a sidewind tended to drift downwind, which they controlled for by performing more hover maneuvers. Surprisingly, the increased hover prevalence did not increase the duration of free-flight landing maneuvers, as these bumblebees flew faster towards the landing platform outside the hover phases. Hence, by alternating these two flight modes along their flight path, free-flying bumblebees negated the adverse effects of high windspeeds on landing duration. Using control theory, we hypothesize that bumblebees achieve this by integrating a combination of direct aerodynamic feedback and a wind-mediated mechanosensory feedback control, with their vision-based sensorimotor control loop. The revealed landing strategy may be commonly used by insects landing in windy conditions, and may inspire the development of landing control strategies onboard autonomously flying robots.

What are the key stability challenges in high-bandwidth, non-minimum phase systems with time-varying, and non-smooth delays?

The analysis and control of stability in high-bandwidth systems characterized by non-minimum phase delays represent a formidable challenge within the realm of control theory and engineering. This research aims to address the pivotal question of whether it is feasible to enhance the stability of such intricate systems. These systems inherently possess uncertain and swiftly changing delay characteristics, rendering them exceptionally demanding to control effectively. In the course of this investigation, we embark on a comprehensive exploration of the theoretical underpinnings of the stability of high-bandwidth, non-minimum phase delay systems. This encompassing inquiry encompasses a meticulous consideration of both derivative-delay and piecewise continuous delay components. To underpin our analysis, we judiciously incorporate feedback mechanisms, drawing upon mathematical tools such as the Jensen inequality and Lyapunov-based methodologies to rigorously establish stability conditions. Furthermore, our exploration extends to encompass the concept of input-output stability and complements it with the notion of asymptotic stability, thereby ensuring that the systems in question exhibit uniform stability across diverse temporal domains. The outcomes of our investigation furnish compelling evidence that by harnessing the power of discrete-time Lyapunov-Krasovskii functionals, it becomes conceivable to circumscribe the maximum delay within predefined thresholds. This achievement holds the promise of enhancing stability in non-minimum phase delay systems characterized by high bandwidth. These findings have far-reaching implications, profoundly influencing the design and control paradigms across a spectrum of engineering applications. Notably, this impact extends to areas such as communication networks, real-time control systems, and robotics, where the mitigation of instability due to non-minimum phase delays has been an enduring challenge.

Conceptual foundations of physiological regulation incorporating the free energy principle and self-organized criticality

Bettinger, J. S., K. J. Friston. Conceptual Foundations of Physiological Regulation incorporating the Free Energy Principle & Self-Organized Criticality. NEUROSCI BIOBEHAV REV 23(x) 144-XXX, 2022. Since the late nineteen-nineties, the concept of homeostasis has been contextualized within a broader class of "allostatic" dynamics characterized by a wider-berth of causal factors including social, psychological and environmental entailments; the fundamental nature of integrated brain-body dynamics; plus the role of anticipatory, top-down constraints supplied by intrinsic regulatory models. Many of these evidentiary factors are integral in original descriptions of homeostasis; subsequently integrated; and/or cite more-general operating principles of self-organization. As a result, the concept of allostasis may be generalized to a larger category of variational systems in biology, engineering and physics in terms of advances in complex systems, statistical mechanics and dynamics involving heterogenous (hierarchical/heterarchical, modular) systems like brain-networks and the internal milieu. This paper offers a three-part treatment. 1) interpret "allostasis" to emphasize a variational and relational foundation of physiological stability; 2) adapt the role of allostasis as "stability through change" to include a "return to stability" and 3) reframe the model of homeostasis with a conceptual model of criticality that licenses the upgrade to variational dynamics.

Tumour growth control: analysis of alternative approaches

In this work we address the problem of tumour growth control by properly exploiting a low-dimensional model that grounds on the Chemical Reaction Network (CRN) formalism. Originally conceived to work both in deterministic and stochastic frameworks, it is shown that, except for the case of very low number of tumour cells, the deterministic approach is appropriate to characterize the system behaviour, especially for control planning purposes. Two alternative control approaches are here investigated. One trivially assumes a constant infusion of external drug administration, the other is designed according to a state-feedback control scheme, with complete or partial knowledge of the state. Pros and cons of both control laws are investigated, showing that the tumour size at the beginning of the therapy plays a role of paramount importance for fixed infusion therapies, whilst only state-feedback laws can eradicate arbitrarily large tumours.

Effectiveness of a behavioral medicine intervention in physical therapy on secondary psychological outcomes and health-related quality of life in exercise-based cardiac rehabilitation: a randomized, controlled trial

BACKGROUND

Interventions promoting adherence to exercise-based cardiac rehabilitation (exCR) are important to achieve positive physical and psychological outcomes, but knowledge of the added value of behavioral medicine interventions for these measures is limited. The aim of the study was to investigate the added value of a behavioral medicine intervention in physical therapy (BMIP) in routine exCR on psychological outcomes and health-related quality of life (HRQoL) versus routine exCR alone (RC).

METHODS

A total of 170 patients with coronary artery disease (136 men), mean age 62.3 ± 7.9 years, were randomized at a Swedish university hospital to a BMIP plus routine exCR or to RC for four months. The outcome assessments included HRQoL (SF-36, EQ-5D), anxiety and depression (HADS), patient enablement and self-efficacy and was performed at baseline, four and 12 months. Between-group differences were tested with an independent samples t-test and, for comparisons within groups, a paired t-test was used. An intention-to-treat and a per-protocol analysis were performed.

RESULTS

No significant differences in outcomes between the groups were shown between baseline and four months or between four and 12 months. Both groups improved in most SF-36 domains, EQ-VAS and HADS anxiety at the four-month follow-up and sufficient enablement remained at the 12-months follow-up.

CONCLUSION

A BMIP added to routine exCR care had no significant effect on psychological outcomes and HRQoL compared with RC, but significant improvements in several measures were shown in both groups at the four-month follow-up. Since recruited participants showed a better psychological profile than the general coronary artery disease population, further studies on BMIP in exCR, tailored to meet individual needs in broader patient groups, are needed. Trial registration number NCT02895451, 09/09/2016, retrospectively registered.

An optimal control theory approach for freight structure path evolution post-COVID-19 pandemic

After the outbreak of COVID-19, the freight demand fell briefly, and as production resumed, the trucking share rate increased again, further increasing energy consumption and environmental pollution. To optimize the sudden changing freight structure, the study aims on developing an evolution model based on Markov's theory to estimate the freight structure post-COVID-19. The current study applies economic cybernetics to establish a freight structural adjustment path optimization model and solve the problem of how much freight transportation should increase each year under the premise that the total turnover of the freight industry continues to grow, and how many years it will take at least to reach a reasonable freight structure. The freight transport structure of China is used to examine the feasibility of the proposed model. The finding indicates that the development of China's freight transport structure is at an adjustment period and should enter a stable period by 2035 and the COVID-19 makes it harder to adjust the freight structure. Increasing the growth rate of the freight volume of railway and waterway transportation is the key to realizing the optimization of the freight structure, and the freight structure path optimization method can realize the rationalization of the freight structure in advance.

Hamilton's rule, the evolution of behavior rules and the wizardry of control theory

This paper formalizes selection on a quantitative trait affecting the evolution of behavior (or development) rules through which individuals act and react with their surroundings. Combining Hamilton's marginal rule for selection on scalar traits and concepts from optimal control theory, a necessary first-order condition for the evolutionary stability of the trait in a group-structured population is derived. The model, which is of intermediate level of complexity, fills a gap between the formalization of selection on evolving traits that are directly conceived as actions (no phenotypic plasticity) and selection on evolving traits that are conceived as strategies or function valued actions (complete phenotypic plasticity). By conceptualizing individuals as open deterministic dynamical systems expressing incomplete phenotypic plasticity, the model captures selection on a large class of phenotypic expression mechanisms, including developmental pathways and learning under life-history trade-offs. As an illustration of the results, a first-order condition for the evolutionary stability of behavior response rules from the social evolution literature is re-derived, strengthened, and generalized. All results of the paper also generalize directly to selection on multidimensional quantitative traits affecting behavior rule evolution, thereby covering neural and gene network evolution.

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model

In this work, we propose a new mathematical modeling of the spread of COVID-19 infection in an arbitrary population, by modifying the SIQRD model as m-SIQRD model, while taking into consideration the eight governmental interventions such as cancellation of events, closure of public places etc., as well as the influence of the asymptomatic cases on the states of the model. We introduce robustness and improved accuracy in predictions of these models by utilizing a novel deep learning scheme. This scheme comprises of attention based architecture, alongside with Generative Adversarial Network (GAN) based data augmentation, for robust estimation of time varying parameters of m-SIQRD model. In this regard, we also utilized a novel feature extraction methodology by employing noise removal operation by Spline interpolation and Savitzky-Golay filter, followed by Principal Component Analysis (PCA). These parameters are later directed towards two main tasks: forecasting of states to the next 15 days, and estimation of best policy encodings to control the infected and deceased number within the framework of data driven synergetic control theory. We validated the superiority of the forecasting performance of the proposed scheme over countries of South Korea and Germany and compared this performance with 7 benchmark forecasting models. We also showed the potential of this scheme to determine best policy encodings in South Korea for 15 day forecast horizon.

Pandemic control - do's and don'ts from a control theory perspective

Managing a pandemic is a difficult task. Pandemics are part of the dynamics of nonlinear systems with multiple different interactive features that co-adapt to each other (such as humans, animals, and pathogens). The target of controlling such a nonlinear system is best achieved using the control system theory developed in engineering and applied in systems biology. But is this theory and its principles actually used in controlling the current coronavirus disease-19 pandemic? We review the evidence for applying principles in different aspects of pandemic control related to different goals such as disease eradication, disease containment, and short- or long-term economic loss minimization. Successful policies implement multiple measures in concordance with control theory to achieve a robust response. In contrast, unsuccessful policies have numerous failures in different measures or focus only on a single measure (only testing, vaccines, etc.). Successful approaches rely on predictions instead of reactions to compensate for the costs of time delay, on knowledge-based analysis instead of trial-and-error, to control complex nonlinear systems, and on risk assessment instead of waiting for more evidence. Iran is an example of the effects of delayed response due to waiting for evidence to arrive instead of a proper risk analytical approach. New Zealand, Australia, and China are examples of appropriate application of basic control theoretic principles and focusing on long-term adaptive strategies, updating measures with the evolution of the pandemic.

Control Theory Forecasts of Optimal Training Dosage to Facilitate Children's Arithmetic Learning in a Digital Educational Application

Education can be viewed as a control theory problem in which students seek ongoing exogenous input-either through traditional classroom teaching or other alternative training resources-to minimize the discrepancies between their actual and target (reference) performance levels. Using illustrative data from [Formula: see text] Dutch elementary school students as measured using the Math Garden, a web-based computer adaptive practice and monitoring system, we simulate and evaluate the outcomes of using off-line and finite memory linear quadratic controllers with constraintsto forecast students' optimal training durations. By integrating population standards with each student's own latent change information, we demonstrate that adoption of the control theory-guided, person- and time-specific training dosages could yield increased training benefits at reduced costs compared to students' actual observed training durations, and a fixed-duration training scheme. The control theory approach also outperforms a linear scheme that provides training recommendations based on observed scores under noisy and the presence of missing data. Design-related issues such as ways to determine the penalty cost of input administration and the size of the control horizon window are addressed through a series of illustrative and empirically (Math Garden) motivated simulations.

The Use of Fitness-Fatigue Models for Sport Performance Modelling: Conceptual Issues and Contributions from Machine-Learning

The emergence of the first Fitness-Fatigue impulse responses models (FFMs) have allowed the sport science community to investigate relationships between the effects of training and performance. In the models, athletic performance is described by first order transfer functions which represent Fitness and Fatigue antagonistic responses to training. On this basis, the mathematical structure allows for a precise determination of optimal sequence of training doses that would enhance the greatest athletic performance, at a given time point. Despite several improvement of FFMs and still being widely used nowadays, their efficiency for describing as well as for predicting a sport performance remains mitigated. The main causes may be attributed to a simplification of physiological processes involved by exercise which the model relies on, as well as a univariate consideration of factors responsible for an athletic performance. In this context, machine-learning perspectives appear to be valuable for sport performance modelling purposes. Weaknesses of FFMs may be surpassed by embedding physiological representation of training effects into non-linear and multivariate learning algorithms. Thus, ensemble learning methods may benefit from a combination of individual responses based on physiological knowledge within supervised machine-learning algorithms for a better prediction of athletic performance.

In conclusion, the machine-learning approach is not an alternative to FFMs, but rather a way to take advantage of models based on physiological assumptions within powerful machine-learning models.

The Dome: A virtual reality apparatus for freely locomoting rodents

The cognitive map in the hippocampal formation of rodents and other mammals integrates multiple classes of sensory and motor information into a coherent representation of space. Here, we describe the Dome, a virtual reality apparatus for freely locomoting rats, designed to examine the relative contributions of various spatial inputs to an animal’s spatial representation. The Dome was designed to preserve the range of spatial inputs typically available to an animal in free, untethered locomotion while providing the ability to perturb specific sensory cues. We present the design rationale and corresponding specifications of the Dome, along with a variety of engineering and biological analyses to validate the efficacy of the Dome as an experimental tool to examine the interaction between visual information and path integration in place cells in rodents.

'The names have changed, but the game's the same': artificial intelligence and racial policy in the USA

Like the Operations Research models used to justify the ethnic cleansing of minority voting blocs in 1970’s New York City, AI ‘risk assessment’ systems for individuals will be used to reinforce longstanding power relations between ethnic groups within the USA. From the perspective of African–Americans and their abolitionist allies, the central problem with AI risk assessment does not involve ‘corrective’ stabilization of an inadvertently unstable system. On the contrary, that system’s de-facto—if sometimes camouflaged—purpose is enforcing the stability of historic patterns of racial oppression, constitutional formalities notwithstanding. AI, like ‘OR’ before it, becomes, then, simply another tactic in a persistent strategy aimed at reinforcing a stable cultural trajectory with roots deep in human slavery. To the archetypic question ‘what is to be done?’ is the archetypic answer: build countervailing power.

Toward a formal theory of embodied cognition

Since the reduction of uncertainty associated with cognition necessarily implies existence of a 'dual' information source, the Source Coding and Rate Distortion Theorems of information theory, in combination with the Data Rate Theorem of control theory, are sufficient to model much of embodied cognition. An iterated Morse Function 'free energy' allows construction of a 'higher entropy' analog constrained to obey the approximate dynamics of the Onsager gradient model of nonequilibrium thermodynamics. However, since palindromes are unlikely, there is no time reversal symmetry and hence no Onsager reciprocal relations. The group symmetry-breaking associated with physical phase transitions then emerges in terms of groupoids associated with equivalence classes of high probability developmental paths. The formalism is agnostic regarding representation and the Yerkes-Dodson inverted-U relation emerges directly, as do stochastic versions of the underlying dynamics, and a canonical approach to an embodied consciousness model. Contrary to the Western cultural fixation with individual salience at the expense of context, for cognition against and in the real world, context is often central, rather than simply being a screen against which some culturally-specific cartoon is supposed to play.

Nonlinear model predictive control with logic constraints for COVID-19 management

The management of COVID-19 appears to be a long-term challenge, even in countries that have managed to suppress the epidemic after their initial outbreak. In this paper, we propose a model predictive approach for the constrained control of a nonlinear compartmental model that captures the key dynamical properties of COVID-19. The control design uses the discrete-time version of the epidemic model, and it is able to handle complex, possibly time-dependent constraints, logical relations between model variables and multiple predefined discrete levels of interventions. A state observer is also constructed for the computation of non-measured variables from the number of hospitalized patients. Five control scenarios with different cost functions and constraints are studied through numerical simulations, including an output feedback configuration with uncertain parameters. It is visible from the results that, depending on the cost function associated with different policy aims, the obtained controls correspond to mitigation and suppression strategies, and the constructed control inputs are similar to real-life government responses. The results also clearly show the key importance of early intervention, the continuous tracking of the susceptible population and that of future work in determining the true costs of restrictive control measures and their quantitative effects.

Mathematical analysis of spread and control of the novel corona virus (COVID-19) in China

Number of well-known contagious diseases exist around the world that mainly include HIV, Hepatitis B, influenzas etc., among these, a recently contested coronavirus (COVID-19) is a serious class of such transmissible syndromes. Abundant scientific evidence the wild animals are believed to be the primary hosts of the virus. Majority of such cases are considered to be human-to-human transmission, while a few are due to wild animals-to-human transmission and substantial burdens on healthcare system following this spread. To understand the dynamical behavior such diseases, we fitted a susceptible-infectious-quarantined model for human cases with constant proportions. We proposed a model that provide better constraints on understanding the climaxes of such unseen disastrous spread, relevant consequences, and suggesting future imperative strategies need to be adopted. The main features of the work include the positivity, boundedness, existence and uniqueness of solution of the model. The conditions were derived under which the COVID-19 may extinct or persist in the population. Sensitivity and estimation of those important parameters have been carried out that plays key role in the transmission mechanism. To optimize the spread of such disease, we present a control problem for further analysis using two control measures. The necessary conditions have been derived using the Pontryagin's maximum principle. Parameter values have been estimated from the real data and experimental numerical simulations are presented for comparison as well as verification of theoretical results. The obtained numerical results also present the verification, accuracy, validation, and robustness of the proposed scheme.

On the Variety of Cognitive Temperatures and Their Symmetry-Breaking Dynamics

The asymptotic limit theorems of information and control theories permit exploration of a surprising number of temperature-like measures and symmetry-breaking dynamics associated with cognition. Each of several markedly different perspectives produces a distinct temperature-analog, capturing a rich and highly-punctuated behavioral landscape across the complex, hierarchical cognitive phenomena that characterize life at every scale and level of organization. Theories of cognition may be confronted by canonical conundrums similar to those plaguing the study of consciousness and its regulation. In short, there may be a spectrum of interacting cognitive 'temperatures' for organisms, social structures, institutions, information processing machines, and their composite entities, that varies across different systems, and between similar systems having undergone different individual developmental trajectories. The complexities of cognitive failure-leading to a vast array of pathologies-may be far stranger than generally recognized.

Signal transduction in cognitive systems: Origin and dynamics of the inverted-U/U dose-response relations

Cognitive systems are indeed physical systems, but have behavioral patterns extending far beyond familiar physical changes. These behaviors are characterized by underlying symmetries and temperature measures much different from those driving phase transitions in non-cognitive systems. We define a temperature analog dependent on a scalarized synergism between rates of internal crosstalk, external sensory information, and material supply, and show that, if the analog fits a simple concavity relation with the rate index, then the dose-response curve of cognition rate with that index will be an inverted-U. If convex, the relation will be U-shaped. The basic results can be generalized at the expense of further mathematical detail to fit a broad spectrum of empirical patterns.

Fractional order controllers increase the robustness of closed-loop deep brain stimulation systems

We studied the effects of using fractional order proportional, integral, and derivative (PID) controllers in a closed-loop mathematical model of deep brain stimulation. The objective of the controller was to dampen oscillations from a neural network model of Parkinson's disease. We varied intrinsic parameters, such as the gain of the controller, and extrinsic variables, such as the excitability of the network. We found that in most cases, fractional order components increased the robustness of the model multi-fold to changes in the gains of the controller. Similarly, the controller could be set to a fixed set of gains and remain stable to a much larger range, than for the classical PID case, of changes in synaptic weights that otherwise would cause oscillatory activity. The increase in robustness is a consequence of the properties of fractional order derivatives that provide an intrinsic memory trace of past activity, which works as a negative feedback system. Fractional order PID controllers could provide a platform to develop stand-alone closed-loop deep brain stimulation systems.

Accounting for uncertainty in forest management models

Forests and their ecosystem services are subjected to uncertain factors, causing drastic changes in forest production and/or market conditions, the impacts of which cannot be precisely estimated beforehand. We introduce a theoretical framework, based on control theory, for robust optimization of forest management under uncertainty. Forest owners herein regard their decision support system only as an approximation to an unknown, true, model. Concerns about model misspecification incite them to seek the single harvesting rule that works well over a set of models statistically similar to their approximation. Accounting for mistrust of decision support systems in modelling harvesting behavior is particularly relevant in view of uncertainty induced by climate change. We use a stylized forest model to explore the effects of uncertainty on harvesting decisions, also considering the role of information aimed at reducing, or making forest owners aware of, such uncertainty. The simulation results demonstrate that model uncertainty affects harvesting intensity and thus forest development. Harvest levels are lower for forest managers concerned with model uncertainty, favoring stand volume over harvest revenues. Further, information release affects the level of perceived uncertainty and thus harvesting behavior and forest development, reaffirming the importance of information as a forest policy instrument.

Perceived availability of future care and depressive symptoms among older adults in China: evidence from CHARLS

BACKGROUND

Major concerns have arisen about the challenges facing China in providing sufficient care to its older population in light of rapid population ageing, changing family structure, and considerable rates of internal migration. At the family level, these societal changes may produce care uncertainty which may adversely influence the psychological wellbeing of older individuals. This paper applies social support and control theories to examine the relationship between perceived availability of future care and psychological wellbeing of older adults in China, and how this relationship is moderated by economic insufficiency, health vulnerability, and urban/rural context.

METHODS

Analyses are based on data from the China Health and Retirement Longitudinal Study, a multi-panel nationally representative household survey of the Chinese population aged 45 years and older. Data are taken from 2013 and 2011 waves of the study, with an initial sample size around 17,000, in which around 11,000-14,000 respondents are used for our final regression model. The score of depressive symptoms was measured in both waves with the Center for Epidemiologic Studies Depression Scale (CES-D10), and perceived availability of future care was measured in 2013 by asking respondents the question "Suppose that in the future, you need help with basic daily activities like eating or dressing, do you have relatives or friends (besides your spouse/partner) who would be willing and able to help you over a long period of time (yes/no)?"

RESULTS

Multivariate regression analysis revealed that uncertainty regarding future care support was associated with greater depressive symptoms even after controlling for factors confounded with care uncertainty such as family structure, socio- economic status, and a lagged measure of depression. Further, older adults without an anticipated source of care faced double jeopardy in their depressive symptoms if they also experienced functional limitations.

CONCLUSIONS

Considering rapid aging of the Chinese population, anticipated increases in chronic disease burden, and possible attenuation of filial care, this analysis suggests that older adults in China may increasingly face health and social conditions detrimental to their mental health. Polices that remedy these concerns should be discussed, developed and implemented.

Emerging roles of network analysis for epilepsy

In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.

Dynamic ecological system analysis: A holistic analysis of compartmental systems

This article develops a new mathematical method for holistic analysis of nonlinear dynamic compartmental systems in the context of ecology. The method is based on the novel dynamic system and subsystem partitioning methodologies through which compartmental systems are decomposed to the utmost level. The dynamic system and subsystem partitioning enable tracking the evolution of the initial stocks, environmental inputs, and intercompartmental system flows, as well as the associated storages derived from these stocks, inputs, and flows individually and separately within the system. Moreover, the transient and the dynamic direct, indirect, acyclic, cycling, and transfer (diact) flows and associated storages transmitted along a given flow path or from one compartment, directly or indirectly, to any other are analytically characterized, systematically classified, and mathematically formulated. Further, the article develops a dynamic technique based on the diact transactions for the quantitative classification of interspecific interactions and the determination of their strength within food webs. Major concepts and quantities of the current static network analyses are also extended to nonlinear dynamic settings and integrated with the proposed dynamic measures and indices within the proposed unifying mathematical framework. Therefore, the proposed methodology enables a holistic view and analysis of ecological systems. We consider that this methodology brings a novel complex system theory to the service of urgent and challenging environmental problems of the day and has the potential to lead the way to a more formalistic ecological science.

Maximization of fertility transfers from rangeland to cropland: The contribution of control theory

In traditional mixed farming systems, soil fertility in cropland relies on the transfer of fertility from rangeland through the transfer of manure produced by livestock that grazes in rangeland. In this work, we introduce a simple meta-ecosystem model in which the mixed farming system is represented by a cropland sub-system connected to a rangeland sub-system by nutrient fluxes. The livestock plays the role of nutrient-pump from the rangeland sub-system to the cropland sub-system. We use this model to study how spatial organization and practices of livestock management such as the control of grazing pressure and night corralling can help optimize both nutrient transfers and crop production. We argue that addressing the optimization of crop production requires different methods, depending on whether the agricultural practice in focus is constant or variable over time. We first used classical optimization methods at equilibrium to address optimization when the grazing pressure was assumed to be constant over time. Second, we address optimization for a more realistic configuration of our model, where grazing pressure was assumed to vary over the course of a year. In this case, we used methods developed in the field of the control theory. Classical methods showed the existence of an optimal level of constant grazing pressure that maximizes the transfers from rangeland to cropland, leading to the maximization of crop production. Control methods showed that by varying the grazing pressure adequately an additional gain of production is possible, with higher crop production and lower nutrient transfer from rangeland to cropland. This additional gain arises from the fact that the requirement of nutrient by crops is variable along the year. Consequently, a constant adjustment of the grazing pressure allows a better match between nutrient transfer and nutrient requirement over time, leading to a substantial gain of crop biomass. Our results provide new insights for a "smarter" management of fertility transfers leading to higher crop production with less rangeland surface.

Combined mechanisms of neural firing rate homeostasis

Spikes in the membrane potential of neurons comprise the currency of information processing in the brain. The ability of neurons to convert any information present across their multiple inputs into a significant modification to the pattern of their emitted spikes depends on the rate at which they emit spikes. If the mean rate is near the neuron's maximum, or if the rate is near zero, then changes in the inputs have minimal impact on the neuron's firing rate. Therefore, a neuron needs to control its mean rate. Protocols that either dramatically increase or decrease a neuron's firing rate lead to multiple compensatory changes that return the neuron's mean rate toward its prior value. In this primer, first as a summary of our previous work (Cannon and Miller in J Neurophysiol 116(5):2004-2022, 2016; Cannon and Miller in J Math Neurosci 7(1):1, 2017), we describe the advantages and disadvantages of having more than one such control mechanism responding to the neuron's firing rate. We suggest how problems of two, coexisting, potentially competing mechanisms can be overcome. Key requirements are: (1) the control be of a distribution of values, which the controlled variable achieves over a fast timescale compared to the timescale of the control system; (2) at least one of the control mechanisms be nonlinear; and (3) the two control systems are satisfied by a stable distribution or range of values that can be achieved by the variable. We show examples of functional control systems, including the previously studied integral feedback controller and new simulations of a "bang-bang" controller, that allow for compensation when inputs to the system change. Finally, we present new results describing how the underlying signal processing pathways would produce mechanisms of dual control, as opposed to a single mechanism with two outputs, and compare the responses of these systems to changes of input statistics.

Control of Pyragas Applied to a Coupled System with Unstable Periodic Orbits

We apply a Pyragas-type control in order to synchronize the solutions of a glycolytic model that exhibits an aperiodic behavior. This delay control is used to stabilize the orbits of ordinary differential nonlinear equations systems. Inspired by several works that studied the chaotic behavior of diverse systems for the enzymatic reactions in the presence of feedbacks, the control to two of these models is analyzed.

Optimizing target control of the vessel rich group with volatile anesthetics

The ability to monitor the inspired and expired concentrations of volatile anesthetic gases in real time makes these drugs implicitly targetable. However, the end-tidal concentration only represents the concentration within the brain and the vessel rich group (VRG) at steady state, and very poorly approximates the VRG concentration during common dynamic situations such as initial uptake and emergence. How should the vaporization of anesthetic gases be controlled in order to optimally target VRG concentration in clinical practice? Using a generally accepted pharmacokinetic model of uptake and redistribution, a transfer function from the vaporizer setting to the VRG is established and transformed to the time domain. Targeted actuation of the vaporizer in a time-optimal manner is produced by a variable structure, sliding mode controller. Direct mathematical application of the controller produces rapid cycling at the limits of the vaporizer, further prolonged by low fresh gas flows. This phenomenon, known as "chattering", is unsuitable for operating real equipment. Using a simple and clinically intuitive modification to the targeting algorithm, a variable low-pass boundary layer is applied to the actuation, smoothing discontinuities in the control law and practically eliminating chatter without prolonging the time taken to reach the VRG target concentration by any clinically significant degree. A model is derived for optimum VRG-targeted control of anesthetic vaporizers. An alternate and further application is described, in which deliberate perturbation of the vaporization permits non-invasive estimation of parameters such as cardiac output that are otherwise difficult to measure intra-operatively.

Sustainable thresholds for cooperative epidemiological models

In this paper, we introduce a method for computing sustainable thresholds for controlled cooperative models described by a system of ordinary differential equations, a property shared by a wide class of compartmental models in epidemiology. The set of sustainable thresholds refers to constraints (e.g., maximal "allowable" number of human infections; maximal "affordable" budget for disease prevention, diagnosis and treatments; etc.), parameterized by thresholds, that can be sustained by applying an admissible control strategy starting at the given initial state and lasting the whole period of the control intervention. This set, determined by the initial state of the dynamical system, virtually provides useful information for more efficient (or cost-effective) decision-making by exhibiting the trade-offs between different types of constraints and allowing the user to assess future outcomes of control measures on transient behavior of the dynamical system. In order to accentuate the originality of our approach and to reveal its potential significance in real-life applications, we present an example relying on the 2013 dengue outbreak in Cali, Colombia, where we compute the set of sustainable thresholds (in terms of the maximal "affordable" budget and the maximal "allowable" levels of active infections among human and vector populations) that could be sustained during the epidemic outbreak.

Towards a human self-regulation system: Common and distinct neural signatures of emotional and behavioural control

Self-regulation refers to controlling our emotions and actions in the pursuit of higher-order goals. Although research suggests commonalities in the cognitive control of emotion and action, evidence for a shared neural substrate is scant and largely circumstantial. Here we report on two large-scale meta-analyses of human neuroimaging studies on emotion or action control, yielding two fronto-parieto-insular networks. The networks’ overlap, however, was restricted to four brain regions: posteromedial prefrontal cortex, bilateral anterior insula, and right temporo-parietal junction. Conversely, meta-analytic contrasts revealed major between-network differences, which were independently corroborated by clustering domain-specific regions based on their intrinsic functional connectivity, as well as by functionally characterizing network sub-clusters using the BrainMap database for quantitative forward and reverse inference. Collectively, our analyses identified a core system for implementing self-control across emotion and action, beyond which, however, either regulation facet appears to rely on broadly similar yet distinct subnetworks. These insights into the neurocircuitry subserving affective and executive facets of self-control suggest both processing commonalities and differences between the two aspects of human self-regulation.

How to mathematically optimize drug regimens using optimal control

This article gives an overview of a technique called optimal control, which is used to optimize real-world quantities represented by mathematical models. I include background information about the historical development of the technique and applications in a variety of fields. The main focus here is the application to diseases and therapies, particularly the optimization of combination therapies, and I highlight several such examples. I also describe the basic theory of optimal control, and illustrate each of the steps with an example that optimizes the doses in a combination regimen for leukemia. References are provided for more complex cases. The article is aimed at modelers working in drug development, who have not used optimal control previously. My goal is to make this technique more accessible in the biopharma community.

Understanding and reducing complex systems pharmacology models based on a novel input-response index

A growing understanding of complex processes in biology has led to large-scale mechanistic models of pharmacologically relevant processes. These models are increasingly used to study the response of the system to a given input or stimulus, e.g., after drug administration. Understanding the input-response relationship, however, is often a challenging task due to the complexity of the interactions between its constituents as well as the size of the models. An approach that quantifies the importance of the different constituents for a given input-output relationship and allows to reduce the dynamics to its essential features is therefore highly desirable. In this article, we present a novel state- and time-dependent quantity called the input-response index that quantifies the importance of state variables for a given input-response relationship at a particular time. It is based on the concept of time-bounded controllability and observability, and defined with respect to a reference dynamics. In application to the brown snake venom-fibrinogen (Fg) network, the input-response indices give insight into the coordinated action of specific coagulation factors and about those factors that contribute only little to the response. We demonstrate how the indices can be used to reduce large-scale models in a two-step procedure: (i) elimination of states whose dynamics have only minor impact on the input-response relationship, and (ii) proper lumping of the remaining (lower order) model. In application to the brown snake venom-fibrinogen network, this resulted in a reduction from 62 to 8 state variables in the first step, and a further reduction to 5 state variables in the second step. We further illustrate that the sequence, in which a recursive algorithm eliminates and/or lumps state variables, has an impact on the final reduced model. The input-response indices are particularly suited to determine an informed sequence, since they are based on the dynamics of the original system. In summary, the novel measure of importance provides a powerful tool for analysing the complex dynamics of large-scale systems and a means for very efficient model order reduction of nonlinear systems.

Control blindness: Why people can make incorrect inferences about the intentions of others

There is limited evidence regarding the accuracy of inferences about intention. The research described in this article shows how perceptual control theory (PCT) can provide a “ground truth” for these judgments. In a series of 3 studies, participants were asked to identify a person’s intention in a tracking task where the person’s true intention was to control the position of a knot connecting a pair of rubber bands. Most participants failed to correctly infer the person’s intention, instead inferring complex but nonexistent goals (such as “tracing out two kangaroos boxing”) based on the actions taken to keep the knot under control. Therefore, most of our participants experienced what we call “control blindness.” The effect persisted with many participants even when their awareness was successfully directed at the knot whose position was under control. Beyond exploring the control blindness phenomenon in the context of our studies, we discuss its implications for psychological research and public policy.

Brain and cognitive reserve: Translation via network control theory

Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive "reserve," associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention.

Can an incentive-based intervention increase physical activity and reduce sitting among adults? the ACHIEVE (Active Choices IncEntiVE) feasibility study

Background

Despite recent interest in the potential of incentivisation as a strategy for motivating healthier behaviors, little remains known about the effectiveness of incentives in promoting physical activity and reducing sedentary behavior, and improving associated health outcomes.

This pre-post-test design study investigated the feasibility, appeal and effects of providing non-financial incentives for promoting increased physical activity, reduced sedentary time, and reduced body mass index (BMI) and blood pressure among inactive middle-aged adults.

Methods

Inactive men (n = 36) and women (n = 46) aged 40–65 years were recruited via a not-for-profit insurance fund and participated in a 4 month pre-post design intervention. Baseline and post-intervention data were collected on self-reported physical activity and sitting time (IPAQ-Long), BMI and blood pressure. Participants were encouraged to increase physical activity to 150 mins/week and reduce sedentary behavior by 150 mins/week in progressive increments. Incentives included clothing, recipe books, store gift vouchers, and a chance to win one of four Apple iPad Mini devices. The incentive component of the intervention was supported by an initial motivational interview and text messaging to encourage participants and provide strategies to increase physical activity and reduce sedentary behaviors.

Results

Only two participants withdrew during the program, demonstrating the feasibility of recruiting and retaining inactive middle-aged participants. While two-thirds of the sample qualified for the easiest physical activity incentive (by demonstrating 100 mins physical activity/week or 100 mins reduced sitting time/week), only one third qualified for the most challenging incentive. Goals to reduce sitting appeared more challenging, with 43% of participants qualifying for the first incentive, but only 20% for the last incentive. More men than women qualified for most incentives. Mean leisure-time physical activity increased by 252 mins/week (leisure-time), with 65% of the sample achieving at least 150 mins/week; and sitting time decreased by 3.1 h/day (both p < 0.001) between baseline and follow-up. BMI, systolic and diastolic (men only) blood pressure all significantly decreased. Most participants (50–85%) reported finding the incentives and other program components helpful/motivating.

Conclusions

Acknowledging the uncontrolled design, the large pre-post changes in behavioral and health-related outcomes suggest that the ACHIEVE incentives-based behavior change program represents a promising approach for promoting physical activity and reducing sitting, and should be tested in a randomized controlled trial.

Trial registration

Australian New Zealand Clinical Trials Registry IDACTRN12616000158460, registered 10/2/16.

The power law of movement: an example of a behavioral illusion

The curved movements produced by living organisms follow a power law where the velocity of movement is a power function of the degree of curvature through which the movement is made. The exponent of the power function is close to either 1/3 or 2/3 depending on how velocity and curvature are measured. This power law is thought to reflect biological and/or kinematic constraints on how organisms produce movements. The present paper shows that the power law is actually a statistical artifact that results from mistaking a correlational for a causal relationship between variables. The power law implies that curvature influences the velocity of movement. In fact, the power law is a mathematical consequence of the way that these variables are calculated. The appearance that curvature affects the velocity of movement is shown to be an example of a "behavioral illusion" that results from ignoring the purpose of behavior.

A test of the core process account of psychopathology in a heterogenous clinical sample of anxiety and depression: A case of the blind men and the elephant?

Many cognitive and behavioral processes, such as selective attention to threat, self-focused attention, safety-seeking behaviors, worry and thought suppression, have their foundations in research on anxiety disorders. Yet, they are now known to be transdiagnostic, i.e. shared across a wide range of psychological disorders. A more pertinent clinical and theoretical question is whether these processes are themselves distinct, or whether they reflect a shared 'core' process that maintains psychopathology. The current study utilized a treatment-seeking clinical adult sample of 313 individuals with a range of anxiety disorders and/or depression who had completed self-report measures of widely ranging processes: affect control, rumination, worry, escape/avoidance, and safety-seeking behaviors. We found that only the first factor extracted from a principal components analysis of the items of these measures was associated with symptoms of anxiety and depression. Our findings supported the 'core process' account that had its origins in the field of anxiety disorders, and we discuss the implications for theory, clinical practice and future research across psychological disorders.

Optimal trajectories of brain state transitions

The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering-based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury.

Development of a Web-Based Quality Dashboard Including a Toolbox to Improve Pain Management in Dutch Intensive Care

Audit and feedback (A&F) is a common strategy to improve quality of care. Meta-analyses have indicated that A&F may be more effective in realizing desired change when baseline performance is low, it is delivered by a supervisor or colleague, it is provided frequently and in a timely manner, it is delivered in both verbal and written formats, and it includes specific targets and an action plan. However, there is little information to guide operationalization of these factors. Researchers have consequently called for A&F interventions featuring well-described and carefully justified components, with their theoretical rationale made explicit. This paper describes the rationale and development of a quality dashboard including an improvement toolbox for four previous developed pain indicators, guided by Control Theory.

Orexin/Hypocretin and Organizing Principles for a Diversity of Wake-Promoting Neurons in the Brain

An enigmatic feature of behavioural state control is the rich diversity of wake-promoting neural systems. This diversity has been rationalized as 'robustness via redundancy', wherein wakefulness control is not critically dependent on one type of neuron or molecule. Studies of the brain orexin/hypocretin system challenge this view by demonstrating that wakefulness control fails upon loss of this neurotransmitter system. Since orexin neurons signal arousal need, and excite other wake-promoting neurons, their actions illuminate nonredundant principles of arousal control. Here, we suggest such principles by reviewing the orexin system from a collective viewpoint of biology, physics and engineering. Orexin peptides excite other arousal-promoting neurons (noradrenaline, histamine, serotonin, acetylcholine neurons), either by activating mixed-cation conductances or by inhibiting potassium conductances. Ohm's law predicts that these opposite conductance changes will produce opposite effects on sensitivity of neuronal excitability to current inputs, thus enabling orexin to differentially control input-output gain of its target networks. Orexin neurons also produce other transmitters, including glutamate. When orexin cells fire, glutamate-mediated downstream excitation displays temporal decay, but orexin-mediated excitation escalates, as if orexin transmission enabled arousal controllers to compute a time integral of arousal need. Since the anatomical and functional architecture of the orexin system contains negative feedback loops (e.g. orexin ➔ histamine ➔ noradrenaline/serotonin-orexin), such computations may stabilize wakefulness via integral feedback, a basic engineering strategy for set point control in uncertain environments. Such dynamic behavioural control requires several distinct wake-promoting modules, which perform nonredundant transformations of arousal signals and are connected in feedback loops.

Who is in control of road safety? A STAMP control structure analysis of the road transport system in Queensland, Australia

Despite significant progress, road trauma continues to represent a global safety issue. In Queensland (Qld), Australia, there is currently a focus on preventing the 'fatal five' behaviours underpinning road trauma (drug and drink driving, distraction, seat belt wearing, speeding, and fatigue), along with an emphasis on a shared responsibility for road safety that spans road users, vehicle manufacturers, designers, policy makers etc. The aim of this article is to clarify who shares the responsibility for road safety in Qld and to determine what control measures are enacted to prevent the fatal five behaviours. This is achieved through the presentation of a control structure model that depicts the actors and organisations within the Qld road transport system along with the control and feedback relationships that exist between them. Validated through a Delphi study, the model shows a diverse set of actors and organisations who share the responsibility for road safety that goes beyond those discussed in road safety policies and strategies. The analysis also shows that, compared to other safety critical domains, there are less formal control structures in road transport and that opportunities exist to add new controls and strengthen existing ones. Relationships that influence rather than control are also prominent. Finally, when compared to other safety critical domains, the strength of road safety controls is brought into question.

Fifty Years of Physics of Living Systems

The equilibrium-point hypothesis and its more recent version, the referent configuration hypothesis, represent the physical approach to the neural control of action. This hypothesis can be naturally combined with the idea of hierarchical control of movements and of synergic organization of the abundant systems involved in all actions. Any action starts with defining trajectories of a few referent coordinates for a handful of salient task-specific variables. Further, referent coordinates at hierarchically lower levels emerge down to thresholds of the tonic stretch reflex for the participating muscles. Stability of performance with respect to salient variables is reflected in the structure of inter-trial variance and phenomena of motor equivalence. Three lines of recent research within this framework are reviewed. First, synergic adjustments of the referent coordinate and apparent stiffness have been demonstrated during finger force production supporting the main idea of control with referent coordinates. Second, the notion of unintentional voluntary movements has been introduced reflecting unintentional drifts in referent coordinates. Two types of unintentional movements have been observed with different characteristic times. Third, this framework has been applied to studies of impaired movements in neurological patients. Overall, the physical approach searching for laws of nature underlying biological movement has been highly stimulating and productive.

Disease elimination and re-emergence in differential-equation models

Traditional differential equation models of disease transmission are often used to predict disease trajectories and evaluate the effectiveness of alternative intervention strategies. However, such models cannot account explicitly for probabilistic events, such as those that dominate dynamics when disease prevalence is low during the elimination and re-emergence phases of an outbreak. To account for the dynamics at low prevalence, i.e. the elimination and risk of disease re-emergence, without the added analytical and computational complexity of a stochastic model, we develop a novel application of control theory. We apply our approach to analyze historical data of measles elimination and re-emergence in Iceland from 1923 to 1938, predicting the temporal trajectory of local measles elimination and re-emerge as a result of disease migration from Copenhagen, Denmark.

Constructing a philosophy of science of cognitive science

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information-processing mechanisms, cognitive science needs an account of how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher-level mechanisms from their components and situating them in their environments.

Inertial control as novel technique for in vitro gait simulations

In vitro gait simulations are a preferential platform to study new intervention techniques or surgical procedures as they allow studying the isolated effect of surgical interventions. Commonly, simulations are performed by applying pre-defined setpoints for the kinetics and kinematics on all degrees of freedom (DOFs) of the cadaveric specimen. This however limits the applicability of the experiment to simulations for which pre-defined kinematics and kinetics can be measured in vivo. In this study we introduce inertial control as a new methodology for gait simulations that omits the need for pre-defined setpoints for the externally applied vertical ground reaction force (vGRF) and therefore allows the effect of interventions to be reflected upon it. Gait simulations of stance (1 s) were performed in 10 cadaveric specimens under three clinically relevant conditions: native ankle, total ankle prosthesis (TAP) and total ankle prosthesis plus triple arthrodesis (TAP+TA). In the native ankle, simulated vGRF was compared against the vGRF measured in vivo in 15 healthy volunteers and high correlations were found (R(2)=0.956, slope of regression line S=1.004). In TAP and TAP+TA, vGRF changed, therefore confirming the sensitivity of the method to kinematic constrains imposed with surgery. Inertial control can replicate in vivo kinetic conditions and allows investigating the isolated effect of surgical interventions on kinematic as well as kinetics.

Cueing an unresolved personal goal causes persistent ruminative self-focus: an experimental evaluation of control theories of rumination

BACKGROUND AND OBJECTIVES

Control theory predicts that the detection of goal discrepancies initiates ruminative self-focus (Martin & Tesser, 1996). Despite the breadth of applications and interest in control theory, there is a lack of experimental evidence evaluating this prediction. The present study provided the first experimental test of this prediction.

METHODS

We examined uninstructed state rumination in response to the cueing of resolved and unresolved goals in a non-clinical population using a novel measure of online rumination.

RESULTS

Consistent with control theory, cueing an unresolved goal resulted in significantly greater recurrent intrusive ruminative thoughts than cueing a resolved goal. Individual differences in trait rumination moderated the impact of the goal cueing task on the extent of state rumination: individuals who had a stronger tendency to habitually ruminate were more susceptible to the effects of cueing goal discrepancies.

LIMITATIONS

The findings await replication in a clinically depressed sample where there is greater variability and higher levels of trait rumination.

CONCLUSIONS

These results indicate that control theories of goal pursuit provide a valuable framework for understanding the circumstances that trigger state rumination. Additionally, our measure of uninstructed online state rumination was found to be a valid and sensitive index of the extent and temporal course of state rumination, indicating its value for further investigating the proximal causes of state rumination.

A mathematical model of pulse-coded hormone signal responses in pituitary gonadotroph cells

Cells in the pituitary that synthesize luteinizing and follicle-stimulating hormones regulate the relative production of these two key reproductive hormones in response to signals from the hypothalamus. These signals are encoded in the frequency of gonadotrophin-releasing-hormone pulses. In vitro experiments with a murine-derived cell line have identified key elements of the processes that decode the signal to regulate transcription of the subunits encoding these hormones. The mathematical model described in this paper is based on the results of those experiments and advances quantitative understanding of the biochemical decoder. The model consists of non-linear differential equations for each of six processes that lead to the synthesis of follicle-stimulating hormone. Simulations of the model exhibit key characteristics found in the experiments, including a preference for follicle-stimulating hormone synthesis at low pulse frequencies and a loss of this characteristic when a mutation is introduced.

Control of perception should be operationalized as a fundamental property of the nervous system

This commentary proposes that "cognitive control" is neither componential nor emergent, but a fundamental feature of behavior. The term "control" requires an operational definition. This is best provided by the negative feedback loop that utilizes behavior to control perception; it does not control behavior per se. In order to model complex cognitive control, Perceptual Control Theory proposes that loops are organized into a dissociable hierarchical network (PCT; Powers, Clark, & McFarland, 1960; Powers, 1973a, 2008). In this way, behavior is dynamically adaptive to environmental disturbances, rather than being formed by, or superimposed upon, learned associations between stimulus and response.