Flight experience and executive functions predict unlike professional pilots who are limited by the FAA's age rule, no age limit is defined in general aviation (GA)

Unlike professional pilots who are limited by the FAA's age rule, no age limit is defined in general aviation (GA). Some studies revealed significant aging issues on accident rates but these results are criticized. Our overall goal is to study how the effect of age on executive functions (EFs), high level cognitive abilities, impacts on the flying performance in GA pilots. This study relies on three components: EFs assessment, pilot characteristics (age, flight experience), and the navigation performance on a flight simulator. The results showed that contrary to age, reasoning, working memory (WM) and total flight experience were predictive of the flight performance. These results suggest that "cognitive age", derived in this study by the cognitive evaluation, is a better mean than "chronological age" consideration to predict the ability to pilot, in particular because of the inter-individual variability of aging impact and the beneficial effect of the flight experience

Adaptive Neural Networks for Control of Movement Trajectories Invariant under Speed and Force Rescaling

This article describes two neural network modules that form part of an emerging theory of how adaptive control of goal-directed sensory-motor skills is achieved by humans and other animals. The Vector-Integration-To-Endpoint (VITE) model suggests how synchronous multi-joint trajectories are generated and performed at variable speeds. The Factorization-of-LEngth-and-TEnsion (FLETE) model suggests how outflow movement commands from a VITE model may be performed at variable force levels without a loss of positional accuracy. The invariance of positional control under speed and force rescaling sheds new light upon a familiar strategy of motor skill development: Skill learning begins with performance at low speed and low limb compliance and proceeds to higher speeds and compliances. The VITE model helps to explain many neural and behavioral data about trajectory formation, including data about neural coding within the posterior parietal cortex, motor cortex, and globus pallidus, and behavioral properties such as Woodworth's Law, Fitts Law, peak acceleration as a function of movement amplitude and duration, isotonic arm movement properties before and after arm-deafferentation, central error correction properties of isometric contractions, motor priming without overt action, velocity amplification during target switching, velocity profile invariance across different movement distances, changes in velocity profile asymmetry across different movement durations, staggered onset times for controlling linear trajectories with synchronous offset times, changes in the ratio of maximum to average velocity during discrete versus serial movements, and shared properties of arm and speech articulator movements. The FLETE model provides new insights into how spina-muscular circuits process variable forces without a loss of positional control. These results explicate the size principle of motor neuron recruitment, descending co-contractive compliance signals, Renshaw cells, Ia interneurons, fast automatic reactive control by ascending feedback from muscle spindles, slow adaptive predictive control via cerebellar learning using muscle spindle error signals to train adaptive movement gains, fractured somatotopy in the opponent organization of cerebellar learning, adaptive compensation for variable moment-arms, and force feedback from Golgi tendon organs. More generally, the models provide a computational rationale for the use of nonspecific control signals in volitional control, or "acts of will", and of efference copies and opponent processing in both reactive and adaptive motor control tasks.National Science Foundation (IRI-87-16960); Air Force Office of Scientific Research (90-0128, 90-0175

Visual Search and Target Selection Using a Bounded Optimal Model of State Estimation & Control

Visual attention and motor control are tightly coupled in domains requiring a human operator to interact with a visual interface. Here, we integrate a boundedly optimal visual attention model with two separate motor control models and compare the predictions made by these models against perceptual and motor data collected from human subjects engaged in a parafoveal detection task. The results indicate that humans use an optimal motor control policy limited by precision constraints – humans executed ballistic movements using near-optimal velocity (i.e., bang-bang control), but imprecision in those movements often caused participants to overshoot their targets, necessitating corrective action. Motor movements did not reflect response hedging, but rather a perceptual-motor policy permitting ballistic movements to a target only after localization confidence exceeded a threshold. We conclude that a boundedly-optimal perceptual-motor model can predict aspects of human performance visual search tasks requiring motor response

The role of goal representations in action control

Actions are goal-directed. It can be the goal of an action to change the environment (i.e. to produce an effect), but also to change one´s own situation in the environment (i.e. to move to a physical target). Previous research has shown that kinematics of actions directed towards physical targets are not only mere reactions to such targets. Instead, targets evoke intentional goals. Representations of such intentional goals influence action execution. However, thus far, most studies in the context of the ideomotor theory of action control have focused on the influence of anticipated action effects on action planning. The role of targets as action goals as well as the role of goal anticipations on overt action execution has mostly been neglected. In this dissertation the role of goal representations in action control was investigated. The ideomotor theory served as a theoretical framework. It was assumed that targets function as action goals similar to action effects and that action goals influence action execution by the anticipation of upcoming events. Action execution towards targets and towards effects was compared. This was done in the temporal and the spatial domain. Furthermore, goal representations were manipulated in order to evaluate their influence on action execution and to disentangle the role of physical target characteristics and the role of goal representations. The findings obtained strengthen the assumption that goal representations play an important role in action control. First, both targets and effects can be viewed as goals of an action in the temporal and spatial domain. Second, movement kinematics are shaped by the way targets are represented as action goals, rather than by physically target properties. In conclusion, as goal representations are formed before the action is actually executed they influence action execution by the anticipation of upcoming events. The ideomotor theory of action control should incorporate action targets as goals similar to action effects

Evaluating Human Performance for Image-Guided Surgical Tasks

The following work focuses on the objective evaluation of human performance for two different interventional tasks; targeted prostate biopsy tasks using a tracked biopsy device, and external ventricular drain placement tasks using a mobile-based augmented reality device for visualization and guidance. In both tasks, a human performance methodology was utilized which respects the trade-off between speed and accuracy for users conducting a series of targeting tasks using each device. This work outlines the development and application of performance evaluation methods using these devices, as well as details regarding the implementation of the mobile AR application. It was determined that the Fitts’ Law methodology can be applied for evaluation of tasks performed in each surgical scenario, and was sensitive to differentiate performance across a range which spanned experienced and novice users. This methodology is valuable for future development of training modules for these and other medical devices, and can provide details about the underlying characteristics of the devices, and how they can be optimized with respect to human performance

Reinforcement learning control of a biomechanical model of the upper extremity

Among the infinite number of possible movements that can be produced, humans are commonly assumed to choose those that optimize criteria such as minimizing movement time, subject to certain movement constraints like signal-dependent and constant motor noise. While so far these assumptions have only been evaluated for simplified point-mass or planar models, we address the question of whether they can predict reaching movements in a full skeletal model of the human upper extremity. We learn a control policy using a motor babbling approach as implemented in reinforcement learning, using aimed movements of the tip of the right index finger towards randomly placed 3D targets of varying size. We use a state-of-the-art biomechanical model, which includes seven actuated degrees of freedom. To deal with the curse of dimensionality, we use a simplified second-order muscle model, acting at each degree of freedom instead of individual muscles. The results confirm that the assumptions of signal-dependent and constant motor noise, together with the objective of movement time minimization, are sufficient for a state-of-the-art skeletal model of the human upper extremity to reproduce complex phenomena of human movement, in particular Fitts' Law and the 2/3 Power Law. This result supports the notion that control of the complex human biomechanical system can plausibly be determined by a set of simple assumptions and can easily be learned.Comment: 19 pages, 7 figure

Human-computer interaction in e-business

E-business has brought much change to our daily life and will become a necessary part of business, economy, and society. At least for the foreseeable future, e-business will keep growing. Each study of this dissertation was devoted to human-computer interaction (HCI) in e-business to improve website usability. First, data input tools were compared and optimal design characteristics were suggested for usable web based interaction. When proper input tools are employed, higher usability can be achieved. Second, a practical design process and the use of web elements were studied through the simulation of an e-bookstore. Web design influences e-business traffic and sales. Third, a grid menu was designed and examined for situations in which a menu contains a larger number of options. The grid menu was observed to be both robust and efficient. Fourth, an interaction model for the pull-down menu, including perceptive, cognitive, and motor behavior processes, was studied. The resulting model fit the experimental data well. Fifth, problems with iconic interfaces on e-business websites were reported and a methodology suggested to improve user interface design

Human factors in instructional augmented reality for intravehicular spaceflight activities and How gravity influences the setup of interfaces operated by direct object selection

In human spaceflight, advanced user interfaces are becoming an interesting mean to facilitate human-machine interaction, enhancing and guaranteeing the sequences of intravehicular space operations. The efforts made to ease such operations have shown strong interests in novel human-computer interaction like Augmented Reality (AR). The work presented in this thesis is directed towards a user-driven design for AR-assisted space operations, iteratively solving issues arisen from the problem space, which also includes the consideration of the effect of altered gravity on handling such interfaces.Auch in der bemannten Raumfahrt steigt das Interesse an neuartigen Benutzerschnittstellen, um nicht nur die Mensch-Maschine-Interaktion effektiver zu gestalten, sondern auch um einen korrekten Arbeitsablauf sicherzustellen. In der Vergangenheit wurden wiederholt Anstrengungen unternommen, Innenbordarbeiten mit Hilfe von Augmented Reality (AR) zu erleichtern. Diese Arbeit konzentriert sich auf einen nutzerorientierten AR-Ansatz, welcher zum Ziel hat, die Probleme schrittweise in einem iterativen Designprozess zu lösen. Dies erfordert auch die Berücksichtigung veränderter Schwerkraftbedingungen

Virtual Dynamic Tunnel: A Target-Agnostic Assistive User Interface Algorithm for Head-Operated Input Devices

Today the effective use of computers (e.g. those with Internet browsers and graphical interfaces) involves the use of some sort of cursor control like what a mouse provides. However, a standard mouse is not always the best option for all users. There are currently many devices available to provide alternative computer access. These devices may be divided into categories: brain-computer interfaces (BCI), mouth-based controls, camera-based controls, and head-tilt controls. There is no single solution as each device and application has to be tailored to each user\u27s unique preferences and abilities. Furthermore, each device category has certain strengths and weaknesses that need to be considered when making an effective match between a user and a device. One problem that remains is that these alternative input devices do not perform as well when compared to standard mouse devices. To help with this, assistive user interface techniques can be employed. While research shows that these techniques help, most require that modifications be made to the user interfaces or that a user\u27s intended target be known beforehand by the host computer. In this research, a novel target-agnostic assistive user interface algorithm intended to improve usage performance for both head-operated and standard mouse devices is designed, implemented (as a mouse device driver and in host computer software) and experimentally evaluated. In addition, a new wireless head-operated input device requiring no special host computer hardware, is designed, built and evaluated. It was found that the Virtual Dynamic Tunnel algorithm improved performance for a standard mouse in straight tunnel trials and that nearly 60% of users would be willing to use the head-tilt mouse as a hands-free option for cursor control