Action preparation helps and hinders perception of action.

Several theories of the mechanisms linking perception and action require that the links are bidirectional, but there is a lack of consensus on the effects that action has on perception. We investigated this by measuring visual event-related brain potentials to observed hand actions while participants prepared responses that were spatially compatible (e.g., both were on the left side of the body) or incompatible and action type compatible (e.g., both were finger taps) or incompatible, with observed actions. An early enhanced processing of spatially compatible stimuli was observed, which is likely due to spatial attention. This was followed by an attenuation of processing for both spatially and action type compatible stimuli, likely to be driven by efference copy signals that attenuate processing of predicted sensory consequences of actions. Attenuation was not response-modality specific; it was found for manual stimuli when participants prepared manual and vocal responses, in line with the hypothesis that action control is hierarchically organized. These results indicate that spatial attention and forward model prediction mechanisms have opposite, but temporally distinct, effects on perception. This hypothesis can explain the inconsistency of recent findings on action-perception links and thereby supports the view that sensorimotor links are bidirectional. Such effects of action on perception are likely to be crucial, not only for the control of our own actions but also in sociocultural interaction, allowing us to predict the reactions of others to our own actions.

Urban-scale Environmental Prediction Systems

Weather, climate, water and related environmental conditions, including air quality, all have profound effects on cities. A growing importance is being attached to understanding and predicting atmospheric conditions and their interactions with other components of the Earth System in cities, at multiple scales. We highlight the need for: (1) development of high-resolution coupled environmental prediction models that include realistic city-specific processes, boundary conditions and fluxes; (2) enhanced observational systems to support (force, constrain, evaluate) these models to provide high quality forecasts for new urban services; (3) provision of meteorological and related environmental variables to aid protection of human health and the environment; (4) new targeted and customized delivery platforms using modern communication techniques, developed with users to ensure that services, advice and warnings result in appropriate action; and (5) development of new skill and capacity to make best use of technologies to deliver new services in complex, challenging and evolving city environments. We highlight the importance of a coordinated and strategic approach that draws on, but does not replicate, past work to maximize benefits to stakeholders.

The impact of a thermodynamic sea-ice module in the COSMO numerical weather prediction model on simulations for the Laptev Sea, Siberian Arctic

Previous versions of the Consortium for Small-scale Modelling (COSMO) numerical weather prediction model have used a constant sea-ice surface temperature, but observations show a high degree of variability on sub-daily timescales. To account for this, we have implemented a thermodynamic sea-ice module in COSMO and performed simulations at a resolution of 15 km and 5 km for the Laptev Sea area in April 2008. Temporal and spatial variability of surface and 2-m air temperature are verified by four automatic weather stations deployed along the edge of the western New Siberian polynya during the Transdrift XIII-2 expedition and by surface temperature charts derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. A remarkable agreement between the new model results and these observations demonstrates that the implemented sea-ice module can be applied for short-range simulations. Prescribing the polynya areas daily, our COSMO simulations provide a high-resolution and high-quality atmospheric data set for the Laptev Sea for the period 14-30 April 2008. Based on this data set, we derive a mean total sea-ice production rate of 0.53 km3/day for all Laptev Sea polynyas under the assumption that the polynyas are ice-free and a rate of 0.30 km3/day if a 10-cm-thin ice layer is assumed. Our results indicate that ice production in Laptev Sea polynyas has been overestimated in previous studies.

The relation between action, predictability and temporal contiguity in temporal binding

Previous studies have documented a subjective temporal attraction between actions and their effects. This finding, named intentional binding, is thought to be the result of a cognitive function that links actions to their consequences. Although several studies have tried to outline the necessary and sufficient conditions for intentional binding, a quantitative comparison between the roles of temporal contiguity, predictability and voluntary action and the evaluation of their interactions is difficult due to the high variability of the temporal binding measurements. In the present study, we used a novel methodology to investigate the properties of intentional binding. Subjects judged whether an auditory stimulus, which could either be triggered by a voluntary finger lift or be presented after a visual temporal marker unrelated to any action, was presented synchronously with a reference stimulus. In three experiments, the predictability, the interval between action and consequence and the presence of action itself were manipulated. The results indicate that (1) action is a necessary condition for temporal binding; (2) a fixed interval between the two events is not sufficient to cause the effect and (3) only in the presence of voluntary action do temporal predictability and contiguity play a significant role in modulating the effect.These findings are discussed in the context of the relationship between intentional binding and temporal expectation. (C) 2010 Elsevier B.V. All rights reserved.

The future in action: neurophysiological and behavioral evidence of anticipatory motor simulation.

The motor system can no longer be considered as a mere passive executive system of motor commands generated elsewhere in the brain. On the contrary, it is deeply involved in perceptual and cognitive functions and acts as an “anticipation device”. The present thesis investigates the anticipatory motor mechanisms occurring in two particular instances: i) when processing sensory events occurring within the peripersonal space (PPS); and ii) when perceiving and predicting others’actions. The first study provides evidence that PPS representation in humans modulates neural activity within the motor system, while the second demonstrates that the motor mapping of sensory events occurring within the PPS critically relies on the activity of the premotor cortex. The third study provides direct evidence that the anticipatory motor simulation of others’ actions critically relies on the activity of the anterior node of the action observation network (AON), namely the inferior frontal cortex (IFC). The fourth study, sheds light on the pivotal role of the left IFC in predicting the future end state of observed right-hand actions. Finally, the fifth study examines how the ability to predict others’ actions could be influenced by a reduction of sensorimotor experience due to the traumatic or congenital loss of a limb. Overall, the present work provides new insights on: i) the anticipatory mechanisms of the basic reactivity of the motor system when processing sensory events occurring within the PPS, and the same anticipatory motor mechanisms when perceiving others’ implied actions; ii) the functional connectivity and plasticity of premotor-motor circuits both during the motor mapping of sensory events occurring within the PPS and when perceiving others’ actions; and iii) the anticipatory mechanisms related to others’ actions prediction.

Can outcome prediction data change patient outcomes and organizational outcomes?

PURPOSE OF REVIEW: Intensive care medicine consumes a high share of healthcare costs, and there is growing pressure to use the scarce resources efficiently. Accordingly, organizational issues and quality management have become an important focus of interest in recent years. Here, we will review current concepts of how outcome data can be used to identify areas requiring action. RECENT FINDINGS: Using recently established models of outcome assessment, wide variability between individual ICUs is found, both with respect to outcome and resource use. Such variability implies that there are large differences in patient care processes not only within the ICU but also in pre-ICU and post-ICU care. Indeed, measures to improve the patient process in the ICU (including care of the critically ill, patient safety, and management of the ICU) have been presented in a number of recently published papers. SUMMARY: Outcome assessment models provide an important framework for benchmarking. They may help the individual ICU to spot appropriate fields of action, plan and initiate quality improvement projects, and monitor the consequences of such activity.

Using a Prediction and Option Generation Paradigm to Understand Decision Making

In many complex and dynamic domains, the ability to generate and then select the appropriate course of action is based on the decision maker's "reading" of the situation--in other words, their ability to assess the situation and predict how it will evolve over the next few seconds. Current theories regarding option generation during the situation assessment and response phases of decision making offer contrasting views on the cognitive mechanisms that support superior performance. The Recognition-Primed Decision-making model (RPD; Klein, 1989) and Take-The-First heuristic (TTF; Johnson & Raab, 2003) suggest that superior decisions are made by generating few options, and then selecting the first option as the final one. Long-Term Working Memory theory (LTWM; Ericsson & Kintsch, 1995), on the other hand, posits that skilled decision makers construct rich, detailed situation models, and that as a result, skilled performers should have the ability to generate more of the available task-relevant options. The main goal of this dissertation was to use these theories about option generation as a way to further the understanding of how police officers anticipate a perpetrator's actions, and make decisions about how to respond, during dynamic law enforcement situations. An additional goal was to gather information that can be used, in the future, to design training based on the anticipation skills, decision strategies, and processes of experienced officers. Two studies were conducted to achieve these goals. Study 1 identified video-based law enforcement scenarios that could be used to discriminate between experienced and less-experienced police officers, in terms of their ability to anticipate the outcome. The discriminating scenarios were used as the stimuli in Study 2; 23 experienced and 26 less-experienced police officers observed temporally-occluded versions of the scenarios, and then completed assessment and response option-generation tasks. The results provided mixed support for the nature of option generation in these situations. Consistent with RPD and TTF, participants typically selected the first-generated option as their final one, and did so during both the assessment and response phases of decision making. Consistent with LTWM theory, participants--regardless of experience level--generated more task-relevant assessment options than task-irrelevant options. However, an expected interaction between experience level and option-relevance was not observed. Collectively, the two studies provide a deeper understanding of how police officers make decisions in dynamic situations. The methods developed and employed in the studies can be used to investigate anticipation and decision making in other critical domains (e.g., nursing, military). The results are discussed in relation to how they can inform future studies of option-generation performance, and how they could be applied to develop training for law enforcement officers.

An Extended Hierarchical Task Analysis for Error Prediction in Medical Devices

This paper introduces an extended hierarchical task analysis (HTA) methodology devised to evaluate and compare user interfaces on volumetric infusion pumps. The pumps were studied along the dimensions of overall usability and propensity for generating human error. With HTA as our framework, we analyzed six pumps on a variety of common tasks using Norman’s Action theory. The introduced method of evaluation divides the problem space between the external world of the device interface and the user’s internal cognitive world, allowing for predictions of potential user errors at the human-device level. In this paper, one detailed analysis is provided as an example, comparing two different pumps on two separate tasks. The results demonstrate the inherent variation, often the cause of usage errors, found with infusion pumps being used in hospitals today. The reported methodology is a useful tool for evaluating human performance and predicting potential user errors with infusion pumps and other simple medical devices.

Does Social Support Really Help to Eat a Low-Fat Diet? Main Effects and Gender Differences of Received Social Support within the Health Action Process Approach

BACKGROUND: Most theories of health-behavior change focus exclusively on individual self-regulation without taking social factors, such as social support, into account. This study's first aim was to systematically test the added value of received instrumental and emotional social support within the Health Action Process Approach (HAPA) in the context of dietary change. In the social support literature, gender effects emerge with regard to the effectiveness of social support. Thus, a second aim was the examination of gender differences in the association of social support with dietary behavior. METHODS: Participants were 252 overweight and obese individuals. At baseline and 12 months later, participants completed questionnaires on HAPA variables; diet-specific received social support and low-fat diet. RESULTS: For the prediction of intentions 12 months later, instrumental support was more beneficial for men than for women over and above individual self-regulation. In terms of dietary behavior at T2, a moderate main effect of instrumental support emerged. Moreover, received emotional social support was beneficial for men, but not for women in terms of a low-fat diet 12 months later. CONCLUSIONS: Effects of received instrumental social support found in this study provide new evidence for the added value of integrating social support into the HAPA.

Predicting Physical Activity in Adolescents: The Role of Compensatory Health Beliefs within the Health Action Process Approach

Objective: Compensatory health beliefs (CHBs), defined as beliefs that healthy behaviours can compensate for unhealthy behaviours, may be one possible factor hindering people in adopting a healthier lifestyle. This study examined the contribution of CHBs to the prediction of adolescents’ physical activity within the theoretical framework of the Health Action Process Approach (HAPA). Design: The study followed a prospective survey design with assessments at baseline (T1) and two weeks later (T2). Method: Questionnaire data on physical activity, HAPA variables and CHBs were obtained twice from 430 adolescents of four different Swiss schools. Multilevel modelling was applied. Results: CHBs added significantly to the prediction of intentions and change in intentions, in that higher CHBs were associated with lower intentions to be physically active at T2 and a reduction in intentions from T1 to T2. No effect of CHBs emerged for the prediction of self-reported levels of physical activity at T2 and change in physical activity from T1 to T2. Conclusion: Findings emphasise the relevance of examining CHBs in the context of an established health behaviour change model and suggest that CHBs are of particular importance in the process of intention formation.

1RM prediction: a data mining case study

In the last few years there has been a heightened interest in data treatment and analysis with the aim of discovering hidden knowledge and eliciting relationships and patterns within this data. Data mining techniques (also known as Knowledge Discovery in Databases) have been applied over a wide range of fields such as marketing, investment, fraud detection, manufacturing, telecommunications and health. In this study, well-known data mining techniques such as artificial neural networks (ANN), genetic programming (GP), forward selection linear regression (LR) and k-means clustering techniques, are proposed to the health and sports community in order to aid with resistance training prescription. Appropriate resistance training prescription is effective for developing fitness, health and for enhancing general quality of life. Resistance exercise intensity is commonly prescribed as a percent of the one repetition maximum. 1RM, dynamic muscular strength, one repetition maximum or one execution maximum, is operationally defined as the heaviest load that can be moved over a specific range of motion, one time and with correct performance. The safety of the 1RM assessment has been questioned as such an enormous effort may lead to muscular injury. Prediction equations could help to tackle the problem of predicting the 1RM from submaximal loads, in order to avoid or at least, reduce the associated risks. We built different models from data on 30 men who performed up to 5 sets to exhaustion at different percentages of the 1RM in the bench press action, until reaching their actual 1RM. Also, a comparison of different existing prediction equations is carried out. The LR model seems to outperform the ANN and GP models for the 1RM prediction in the range between 1 and 10 repetitions. At 75% of the 1RM some subjects (n = 5) could perform 13 repetitions with proper technique in the bench press action, whilst other subjects (n = 20) performed statistically significant (p < 0:05) more repetitions at 70% than at 75% of their actual 1RM in the bench press action. Rate of perceived exertion (RPE) seems not to be a good predictor for 1RM when all the sets are performed until exhaustion, as no significant differences (p < 0:05) were found in the RPE at 75%, 80% and 90% of the 1RM. Also, years of experience and weekly hours of strength training are better correlated to 1RM (p < 0:05) than body weight. O'Connor et al. 1RM prediction equation seems to arise from the data gathered and seems to be the most accurate 1RM prediction equation from those proposed in literature and used in this study. Epley's 1RM prediction equation is reproduced by means of data simulation from 1RM literature equations. Finally, future lines of research are proposed related to the problem of the 1RM prediction by means of genetic algorithms, neural networks and clustering techniques. RESUMEN En los últimos años ha habido un creciente interés en el tratamiento y análisis de datos con el propósito de descubrir relaciones, patrones y conocimiento oculto en los mismos. Las técnicas de data mining (también llamadas de \Descubrimiento de conocimiento en bases de datos\) se han aplicado consistentemente a lo gran de un gran espectro de áreas como el marketing, inversiones, detección de fraude, producción industrial, telecomunicaciones y salud. En este estudio, técnicas bien conocidas de data mining como las redes neuronales artificiales (ANN), programación genética (GP), regresión lineal con selección hacia adelante (LR) y la técnica de clustering k-means, se proponen a la comunidad del deporte y la salud con el objetivo de ayudar con la prescripción del entrenamiento de fuerza. Una apropiada prescripción de entrenamiento de fuerza es efectiva no solo para mejorar el estado de forma general, sino para mejorar la salud e incrementar la calidad de vida. La intensidad en un ejercicio de fuerza se prescribe generalmente como un porcentaje de la repetición máxima. 1RM, fuerza muscular dinámica, una repetición máxima o una ejecución máxima, se define operacionalmente como la carga máxima que puede ser movida en un rango de movimiento específico, una vez y con una técnica correcta. La seguridad de las pruebas de 1RM ha sido cuestionada debido a que el gran esfuerzo requerido para llevarlas a cabo puede derivar en serias lesiones musculares. Las ecuaciones predictivas pueden ayudar a atajar el problema de la predicción de la 1RM con cargas sub-máximas y son empleadas con el propósito de eliminar o al menos, reducir los riesgos asociados. En este estudio, se construyeron distintos modelos a partir de los datos recogidos de 30 hombres que realizaron hasta 5 series al fallo en el ejercicio press de banca a distintos porcentajes de la 1RM, hasta llegar a su 1RM real. También se muestra una comparación de algunas de las distintas ecuaciones de predicción propuestas con anterioridad. El modelo LR parece superar a los modelos ANN y GP para la predicción de la 1RM entre 1 y 10 repeticiones. Al 75% de la 1RM algunos sujetos (n = 5) pudieron realizar 13 repeticiones con una técnica apropiada en el ejercicio press de banca, mientras que otros (n = 20) realizaron significativamente (p < 0:05) más repeticiones al 70% que al 75% de su 1RM en el press de banca. El ínndice de esfuerzo percibido (RPE) parece no ser un buen predictor del 1RM cuando todas las series se realizan al fallo, puesto que no existen diferencias signifiativas (p < 0:05) en el RPE al 75%, 80% y el 90% de la 1RM. Además, los años de experiencia y las horas semanales dedicadas al entrenamiento de fuerza están más correlacionadas con la 1RM (p < 0:05) que el peso corporal. La ecuación de O'Connor et al. parece surgir de los datos recogidos y parece ser la ecuación de predicción de 1RM más precisa de aquellas propuestas en la literatura y empleadas en este estudio. La ecuación de predicción de la 1RM de Epley es reproducida mediante simulación de datos a partir de algunas ecuaciones de predicción de la 1RM propuestas con anterioridad. Finalmente, se proponen futuras líneas de investigación relacionadas con el problema de la predicción de la 1RM mediante algoritmos genéticos, redes neuronales y técnicas de clustering.

Event-based visual servoing with features’ prediction

Event-based visual servoing is a recently presented approach that performs the positioning of a robot using visual information only when it is required. From the basis of the classical image-based visual servoing control law, the scheme proposed in this paper can reduce the processing time at each loop iteration in some specific conditions. The proposed control method enters in action when an event deactivates the classical image-based controller (i.e. when there is no image available to perform the tracking of the visual features). A virtual camera is then moved through a straight line path towards the desired position. The virtual path used to guide the robot improves the behavior of the previous event-based visual servoing proposal.

Mimicking the action of GroEL in molecular dynamics simulations: Application to the refinement of protein structures

Bacterial chaperonin, GroEL, together with its co-chaperonin, GroES, facilitates the folding of a variety of polypeptides. Experiments suggest that GroEL stimulates protein folding by multiple cycles of binding and release. Misfolded proteins first bind to an exposed hydrophobic surface on GroEL. GroES then encapsulates the substrate and triggers its release into the central cavity of the GroEL/ES complex for folding. In this work, we investigate the possibility to facilitate protein folding in molecular dynamics simulations by mimicking the effects of GroEL/ES namely, repeated binding and release, together with spatial confinement. During the binding stage, the (metastable) partially folded proteins are allowed to attach spontaneously to a hydrophobic surface within the simulation box. This destabilizes the structures, which are then transferred into a spatially confined cavity for folding. The approach has been tested by attempting to refine protein structural models generated using the ROSETTA procedure for ab initio structure prediction. Dramatic improvements in regard to the deviation of protein models from the corresponding experimental structures were observed. The results suggest that the primary effects of the GroEL/ES system can be mimicked in a simple coarse-grained manner and be used to facilitate protein folding in molecular dynamics simulations. Furthermore, the results Sur port the assumption that the spatial confinement in GroEL/ES assists the folding of encapsulated proteins.

Incomplete information games of conflict and collective action

My thesis consists of three essays that investigate strategic interactions between individuals engaging in risky collective action in uncertain environments. The first essay analyzes a broad class of incomplete information coordination games with a wide range of applications in economics and politics. The second essay draws from the general model developed in the first essay to study decisions by individuals of whether to engage in protest/revolution/coup/strike. The final essay explicitly integrates state response to the analysis. The first essay, Coordination Games with Strategic Delegation of Pivotality, exhaustively analyzes a class of binary action, two-player coordination games in which players receive stochastic payoffs only if both players take a ``stochastic-coordination action''. Players receive conditionally-independent noisy private signals about the normally distributed stochastic payoffs. With this structure, each player can exploit the information contained in the other player's action only when he takes the “pivotalizing action”. This feature has two consequences: (1) When the fear of miscoordination is not too large, in order to utilize the other player's information, each player takes the “pivotalizing action” more often than he would based solely on his private information, and (2) best responses feature both strategic complementarities and strategic substitutes, implying that the game is not supermodular nor a typical global game. This class of games has applications in a wide range of economic and political phenomena, including war and peace, protest/revolution/coup/ strike, interest groups lobbying, international trade, and adoption of a new technology. My second essay, Collective Action with Uncertain Payoffs, studies the decision problem of citizens who must decide whether to submit to the status quo or mount a revolution. If they coordinate, they can overthrow the status quo. Otherwise, the status quo is preserved and participants in a failed revolution are punished. Citizens face two types of uncertainty. (a) non-strategic: they are uncertain about the relative payoffs of the status quo and revolution, (b) strategic: they are uncertain about each other's assessments of the relative payoff. I draw on the existing literature and historical evidence to argue that the uncertainty in the payoffs of status quo and revolution is intrinsic in politics. Several counter-intuitive findings emerge: (1) Better communication between citizens can lower the likelihood of revolution. In fact, when the punishment for failed protest is not too harsh and citizens' private knowledge is accurate, then further communication reduces incentives to revolt. (2) Increasing strategic uncertainty can increase the likelihood of revolution attempts, and even the likelihood of successful revolution. In particular, revolt may be more likely when citizens privately obtain information than when they receive information from a common media source. (3) Two dilemmas arise concerning the intensity and frequency of punishment (repression), and the frequency of protest. Punishment Dilemma 1: harsher punishments may increase the probability that punishment is materialized. That is, as the state increases the punishment for dissent, it might also have to punish more dissidents. It is only when the punishment is sufficiently harsh, that harsher punishment reduces the frequency of its application. Punishment Dilemma 1 leads to Punishment Dilemma 2: the frequencies of repression and protest can be positively or negatively correlated depending on the intensity of repression. My third essay, The Repression Puzzle, investigates the relationship between the intensity of grievances and the likelihood of repression. First, I make the observation that the occurrence of state repression is a puzzle. If repression is to succeed, dissidents should not rebel. If it is to fail, the state should concede in order to save the costs of unsuccessful repression. I then propose an explanation for the “repression puzzle” that hinges on information asymmetries between the state and dissidents about the costs of repression to the state, and hence the likelihood of its application by the state. I present a formal model that combines the insights of grievance-based and political process theories to investigate the consequences of this information asymmetry for the dissidents' contentious actions and for the relationship between the magnitude of grievances (formulated here as the extent of inequality) and the likelihood of repression. The main contribution of the paper is to show that this relationship is non-monotone. That is, as the magnitude of grievances increases, the likelihood of repression might decrease. I investigate the relationship between inequality and the likelihood of repression in all country-years from 1981 to 1999. To mitigate specification problem, I estimate the probability of repression using a generalized additive model with thin-plate splines (GAM-TPS). This technique allows for flexible relationship between inequality, the proxy for the costs of repression and revolutions (income per capita), and the likelihood of repression. The empirical evidence support my prediction that the relationship between the magnitude of grievances and the likelihood of repression is non-monotone.

Using multivariate sequential patterns to improve survival prediction in intensive care burn unit

Resuscitation and stabilization are key issues in Intensive Care Burn Units and early survival predictions help to decide the best clinical action during these phases. Current survival scores of burns focus on clinical variables such as age or the body surface area. However, the evolution of other parameters (e.g. diuresis or fluid balance) during the first days is also valuable knowledge. In this work we suggest a methodology and we propose a Temporal Data Mining algorithm to estimate the survival condition from the patient’s evolution. Experiments conducted on 480 patients show the improvement of survival prediction.