A Vector-Integration-to-Endpoint Model for Performance of Viapoint Movements

Viapoint (VP) movements are movements to a desired point that are constrained to pass through an intermediate point. Studies have shown that VP movements possess properties, such as smooth curvature around the VP, that are not explicable by treating VP movements as strict concatenations of simpler point-to-point (PTP) movements. Such properties have led some theorists to propose whole-trajectory optimization models, which imply that the entire trajectory is pre-computed before movement initiation. This paper reports new experiments conducted to systematically compare VP with PTP trajectories. Analyses revealed a statistically significant early directional deviation in VP movements but no associated curvature change. An explanation of this effect is offered by extending the Vector-Integration-To-Endpoint (VITE) model (Bullock and Grossberg, 1988), which postulates that voluntary movement trajectories emerge as internal gating signals control the integration of continuously computed vector commands based on the evolving, perceptible difference between desired and actual position variables. The model explains the observed trajectories of VP and PTP movements as emergent properties of a dynamical system that does not precompute entire trajectories before movement initiation. The new model includes a working memory and a stage sensitive to time-to-contact information. These cooperate to control serial performance. The structural and functional relationships proposed in the model are consistent with available data on forebrain physiology and anatomy.Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N0014-95-1-0409

Measuring time preferences

We review research that measures time preferences—i.e., preferences over intertemporal tradeoffs. We distinguish between studies using financial flows, which we call “money earlier or later” (MEL) decisions and studies that use time-dated consumption/effort. Under different structural models, we show how to translate what MEL experiments directly measure (required rates of return for financial flows) into a discount function over utils. We summarize empirical regularities found in MEL studies and the predictive power of those studies. We explain why MEL choices are driven in part by some factors that are distinct from underlying time preferences.National Institutes of Health (NIA R01AG021650 and P01AG005842) and the Pershing Square Fund for Research in the Foundations of Human Behavior

Mechanics of Stimulus & Response Generalization in Signal Detection & Psychophysics: Adaptation of Static Theory to Dynamic Performance.

The area of perceptual decision-making research seeks to understand how our perception of the world affects our judgment. Laboratory investigations of perceptual decision-making concentrate on observers\u27 ability to discriminate among stimuli and their biases towards reporting one stimulus more frequently than others. Choice theories assume that these performance measures are determined by generalization of reinforcement along both stimulus and response dimensions. Historically the majority of research has addressed situations in which the difference among stimuli and resulting consequences of a perceptual decision are static. Consequently, little is known about the dynamics of stimulus and response generalization. The present research investigated the dynamics of discrimination accuracy and response bias by frequently varying differences among stimuli and the outcomes for correct decisions. In Experiment 1, four rats responded in a two-stimulus, two-response detection procedure employing temporal stimuli (short vs. long houselight presentations). Sample stimulus difference was varied over two levels across experimental conditions. A rapid acquisition procedure was employed in which relative reinforcer frequency varied daily. Shifts in response bias were well described by a behavioral model of detection (Davison & Nevin, 1999). Within sessions, bias adjusted rapidly to current reinforcer ratios when the sample stimulus difference was large, but not when the difference was small. In Experiment 2, three rats responded in a five-stimulus, two-response detection procedure employing temporal stimuli. Relative reinforcer frequency was again varied daily. Control by current session reinforcer ratios increased rapidly within sessions in a nearly monotonic fashion. Furthermore, response bias following each sample stimulus was observed within the first few trials of an experimental session. The speed of changes in response bias, especially following an unreinforced probe stimulus, provide strong support for an effective reinforcement process and suggest that this process may operate at a trial-by-trial level. In Experiment 3, three rats responded in a six-stimulus, two-response classification procedure. A repeated-acquisition procedure was employed in which the relationship between classes of short and long sample stimuli and their respective correct comparison locations reversed every 15 sessions. After several reversals, the probabilities of reinforcement for correct classification were also manipulated. In the majority of conditions across subjects, response bias reached half-asymptotic levels more rapidly than did discrimination accuracy. These findings provide some support for a backward chaining account of the acquisition of signal detection performance. An attention-augmented behavioral detection model accurately described the acquisition data; however parameter estimates expressing the probability of attending to sample and comparison stimuli differed widely among subjects. The results of these experiments support the adaptation of dynamic research methodologies to the study of learning in perceptual decision-making tasks. Furthermore, discrimination performance and response bias adapt rapidly to frequent changes in reinforcement contingencies. Quantitative models formulated to describe static performance in detection procedures can be extended to predict dynamic performance. Some theoretical assumptions of these models were supported and others were violated. Overall, this research supports a renewed emphasis on learning in signal detection procedures and suggests that stable behavioral endpoints are at least as much a function of contingency variables as they are of sensory variables

Biologically inspired perching for aerial robots

2021 Spring.Includes bibliographical references.Micro Aerial Vehicles (MAVs) are widely used for various civilian and military applications (e.g., surveillance, search, and monitoring, etc.); however, one critical problem they are facing is the limited airborne time (less than one hour) due to the low aerodynamic efficiency, low energy storage capability, and high energy consumption. To address this problem, mimicking biological flyers to perch onto objects (e.g., walls, power lines, or ceilings) will significantly extend MAVs' functioning time for surveillance or monitoring related tasks. Successful perching for aerial robots, however, is quite challenging as it requires a synergistic integration of mechanical and computational intelligence. Mechanical intelligence means mechanical mechanisms to passively damp out the impact between the robot and the perching object and robustly engage the robot to the perching objects. Computational intelligence means computation algorithms to estimate, plan, and control the robot's motion so that the robot can progressively reduce its speed and adjust its orientation to perch on the objects with a desired velocity and orientation. In this research, a framework for biologically inspired perching is investigated, focusing on both computational and mechanical intelligence. Computational intelligence includes vision-based state estimation and trajectory planning. Unlike traditional flight states such as position and velocity, we leverage a biologically inspired state called time-to-contact (TTC) that represents the remaining time to the perching object at the current flight velocity. A faster and more accurate estimation method based on consecutive images is proposed to estimate TTC. Then a trajectory is planned in TTC space to realize the faster perching while satisfying multiple flight and perching constraints, e.g., maximum velocity, maximum acceleration, and desired contact velocity. For mechanical intelligence, we design, develop, and analyze a novel compliant bistable gripper with two stable states. When the gripper is open, it can close passively by the contact force between the robot and the perching object, eliminating additional actuators or sensors. We also analyze the bistability of the gripper to guide and optimize the design of the gripper. At the end, a customized MAV platform of size 250 mm is designed to combine computational and mechanical intelligence. A Raspberry Pi is used as the onboard computer to do vision-based state estimation and control. Besides, a larger gripper is designed to make the MAV perch on a horizontal rod. Perching experiments using the designed trajectories perform well at activating the bistable gripper to perch while avoiding large impact force which may damage the gripper and the MAV. The research will enable robust perching of MAVs so that they can maintain a desired observation or resting position for long-duration inspection, surveillance, search, and rescue

Renewable Energies for Sustainable Development

In the current scenario in which climate change dominates our lives and in which we all need to combat and drastically reduce the emission of greenhouse gases, renewable energies play key roles as present and future energy sources. Renewable energies vary across a wide range, and therefore, there are related studies for each type of energy. This Special Issue is composed of studies integrating the latest research innovations and knowledge focused on all types of renewable energy: onshore and offshore wind, photovoltaic, solar, biomass, geothermal, waves, tides, hydro, etc. Authors were invited submit review and research papers focused on energy resource estimation, all types of TRL converters, civil infrastructure, electrical connection, environmental studies, licensing and development of facilities, construction, operation and maintenance, mechanical and structural analysis, new materials for these facilities, etc. Analyses of a combination of several renewable energies as well as storage systems to progress the development of these sustainable energies were welcomed

Modelado de transmisión eficiente de datos para eventos multivariantes basados en umbral

This doctoral thesis delves into the optimization of communications in sensor networks for a specific purpose: to evaluate threshold-based events that depend on multiple distributed variables. This motivation is behind the detailed research presented here in the form of a compendium of papers. The developed work is structured in 3 scientific contributions in articles. Out of those 3 contributions, the most theoretical work has been described in 2 of them, leaving the third article for the presentation of a methodological support tool with great scientific impact and relevance in this doctoral thesis. Due to the two theoretical and large–scale contributions in the proposed field, a solution is proposed which is stated as an hypotheses. The first contribution is the mathematical foundations for modelling data reduction in the sensor network and measuring its influence on the quality of the event evaluation. For this purpose, a set of functions and parameters is defined. This logic modifies the cardinality of the mathematical domains in which information is defined in order to save traffic. Specific metrics that consider the time delays in the state changes of the evaluated condition are also defined. The second contribution is an adaptive algorithm that, taking into account the logical context of the system information, parameterizes the proposed model at runtime. As a result, this technique maximizes traffic reduction and minimizes error in the evaluation of the event simultaneously, obtaining promising results. As a methodological contribution, a procedure for generating pseudo-realistic random signals is also described, a useful tool for easily obtaining large datasets suitable for experimentation, which has been applied in the described contributions.Esta tesis doctoral profundiza en la optimización de las comunicaciones en redes de sensores con un propósito específico: evaluar eventos basados en umbral que dependen de múltiples variables distribuidas. Con esta motivación se desarrolla la investigación detallada aquí en forma compendio de artículos. El trabajo desarrollado se estructura en 3 aportaciones científicas en artículos. De esas 3 aportaciones, el trabajo en su vertiente más teórica se desarrolla en 2 de ellas, quedando el tercer artículo para la presentación de una herramienta de soporte metodológico con gran impacto científico y de relevancia en esta tesis doctoral. Gracias a las dos aportaciones teóricas y de gran calado en el ámbito propuesto se propone una solución que se plantea en forma de hipótesis. La primera aportación son los fundamentos matemáticos para modelar la reducción de datos en la red de sensores y medir su incidencia en la calidad de la evaluación del evento. Para ello define una serie de funciones y parámetros que alteran la cardinalidad de los dominios matemáticos en los que se define la información, así como métricas específicas que tienen en cuenta los desfases temporales en los cambios de estado de la condición evaluada. La segunda aportación es un algoritmo adaptativo que, considerando el contexto lógico de la información del sistema, parametriza el modelo propuesto en tiempo de ejecución. Como resultado, esta técnica maximiza la reducción de tráfico y minimiza el error en la evaluación del evento simultáneamente, obteniendo resultados prometedores. Como tercera aportación se describe también un procedimiento para generar señales aleatorias pseudo–realistas, una herramienta útil para disponer fácilmente de grandes conjuntos de datos adecuados para experimentación, que ha sido utilizada en las aportaciones descritas

Multiscale computation and dynamic attention in biological and artificial intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence