A key element of human flexible behavior concerns the ability to continuously predict and prepare for sudden changes in tasks or actions. Here, we tested whether people can dynamically modulate task preparation processes and decision-making strategies when the identity of a to-be-performed task becomes uncertain. To this end, we developed a new paradigm where participants need to prepare for one of nine tasks on each trial. Crucially, in some blocks, the task being prepared could suddenly shift to a different task after a longer cue-target interval, by changing either the stimulus category or categorization rule that defined the initial task. We found that participants were able to dynamically modulate task preparation in the face of this task uncertainty. A second experiment shows that these changes in behavior were not simply a function of decreasing task expectancy, but rather of increasing switch expectancy. Finally, in the third and fourth experiment, we demonstrate that these dynamic modulations can be applied in a compositional manner, depending on whether either only the stimulus category or categorization rule would be expected to change.

Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.

In nature, gene regulatory networks are a key mediator between the information stored in the DNA of living organisms (their genotype) and the structural and behavioral expression this finds in their bodies, surviving in the world (their phenotype). They integrate environmental signals, steer development, buffer stochasticity, and allow evolution to proceed. In engineering, modeling and implementations of artificial gene regulatory networks have been an expanding field of research and development over the past few decades. This review discusses the concept of gene regulation, describes the current state of the art in gene regulatory networks, including modeling and simulation, and reviews their use in artificial evolutionary settings. We provide evidence for the benefits of this concept in natural and the engineering domains.

In uncertain environments, decision-makers must balance two goals: they must \"exploit\" rewarding options but also \"explore\" in order to discover rewarding alternatives. Exploring and exploiting necessarily change how the brain responds to identical stimuli, but little is known about how these states, and transitions between them, change how the brain transforms sensory information into action. To address this question, we recorded neural activity in a prefrontal sensorimotor area while monkeys naturally switched between exploring and exploiting rewarding options. We found that exploration profoundly reduced spatially selective, choice-predictive activity in single neurons and delayed choice-predictive population dynamics. At the same time, reward learning was increased in brain and behavior. These results indicate that exploration is related to sudden disruptions in prefrontal sensorimotor control and rapid, reward-dependent reorganization of control dynamics. This may facilitate discovery through trial and error.

Cognitive control (with the closely related concepts of attention control and executive function) encompasses the collection of processes that are involved in generating and maintaining appropriate task goals and suppressing task goals that are no longer relevant, as well as the way in which current goal representations are used to modify attentional biases to improve task performance. Here, we provide a comprehensive but nonexhaustive review of this complex literature, with an emphasis on the contributions made by techniques for studying human brain function. The review is divided into five sections: (a) overview and historical perspective of cognitive control, its subcomponent processes, and its neural substrate; (b) most common types of tasks used to assess and/or manipulate the level of control; (c) main research findings obtained with various imaging methodologies, with a focus on ERP data, and briefer overviews of oscillatory (event-related spectral perturbations) and fMRI data; (d) major theories of cognitive control; and (e) discussion of open questions regarding how to integrate the various dimensions of control, as well as the faster versus slower temporal dynamics informing this complex and multifaceted concept.

Mathematical models of parasite transmission provide powerful tools for assessing the impacts of interventions. Owing to complexity and uncertainty, no single model may capture all features of transmission and elimination dynamics. Multi-model ensemble modelling offers a framework to help overcome biases of single models. We report on the development of a first multi-model ensemble of three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and evaluate its predictive performance in comparison with that of the constituents using calibration and validation data from three case study sites, one each from the three major LF endemic regions: Africa, Southeast Asia and Papua New Guinea (PNG). We assessed the performance of the respective models for predicting the outcomes of annual MDA strategies for various baseline scenarios thought to exemplify the current endemic conditions in the three regions. The results show that the constructed multi-model ensemble outperformed the single models when evaluated across all sites. Single models that best fitted calibration data tended to do less well in simulating the out-of-sample, or validation, intervention data. Scenario modelling results demonstrate that the multi-model ensemble is able to compensate for variance between single models in order to produce more plausible predictions of intervention impacts. Our results highlight the value of an ensemble approach to modelling parasite control dynamics. However, its optimal use will require further methodological improvements as well as consideration of the organizational mechanisms required to ensure that modelling results and data are shared effectively between all stakeholders.

The ability to maintain appropriate gaps to objects in one\'s environment is important when navigating through a three-dimensional world. Previous research has shown that the visual angle subtended by a lead/approaching object and its rate of change are important variables for timing interceptions, collision avoidance, continuous regulation of braking, and manual control of headway. However, investigations of headway maintenance have required participants to maintain a fixed distance headway and have not investigated how information about own-speed is taken into account. In the following experiment, we asked participants to use a joystick to follow computer-simulated lead objects. The results showed that ground texture, following speed, and the size of the lead object had significant effects on both mean following distances and following distance variance. Furthermore, models of the participants\' joystick responses provided better fits when it was assumed that the desired visual extent of the lead object would vary over time. Taken together, the results indicate that while information about own-speed is used by controllers to set the desired headway to a lead object, the continuous regulation of headway is influenced primarily by the visual angle of the lead object and its rate of change. The reliance on visual angle, its rate of change, and/or own-speed information also varied depending on the control dynamics of the system. Such findings are consistent with an optimal control criterion that reflects a differential weighting on different sources of information depending on the plant dynamics. As in other judgements of motion in depth, the information used for controlling headway to other objects in the environment varies depending on the constraints of the task and different strategies of control.