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Beard, Bettina L.

Publications and source records attributed to Beard, Bettina L..

The Fine Motor Skills and Cognition Test Batteries: Normative Data and Interdependencies

Fine motor skills and cognitive abilities are major contributors to crew performance on essentially all extravehicular and intra-vehicular activities during spaceflight. It is critical for the crew’s safety, and for mission productivity, to know if, and when, motor skills or cognitive abilities are compromised so that countermeasures may be introduced. NASA has developed two test batteries to measure and monitor astronaut cognitive and fine motor skills. The Cognition Test Battery contains 10 sub-tests that assess cognitive behaviors ranging from low level visual perception to high level decision-making. The Fine Motor Skills Test Battery contains 4 sub-tests that assess finger dexterity, manual dexterity and wrist-finger speed. This study sought to determine acceptable norms for both batteries in an astronaut-like population and to identify the extent to which fine motor skills contribute to cognitive test scores.

Normative Data

Human-Automation Allocations for Current Robotic Space Operations

Within the Human Research Program, one risk delineates the uncertainty surrounding crew working with automation and robotics in spaceflight. The Risk of Inadequate Design of Human and Automation/Robotic Integration (HARI) is concerned with the detrimental effects on crew performance due to ineffective user interfaces, system designs and/or functional task allocation, potentially compromising mission success and safety. Risk arises because we have limited experience with complex automation and robotics. One key gap within HARI, is the gap related to functional allocation. The gap states: We need to evaluate, develop, and validate methods and guidelines for identifying human-automation/robot task information needs, function allocation, and team composition for future long duration, long distance space missions. Allocations determine the human-system performance as it identifies the functions and performance levels required by the automation/robotic system, and in turn, what work the crew is expected to perform and the necessary human performance requirements. Allocations must take into account each of the human, automation, and robotic systems capabilities and limitations. Some functions may be intuitively assigned to the human versus the robot, but to optimize efficiency and effectiveness, purposeful role assignments will be required. The role of automation and robotics will significantly change in future exploration missions, particularly as crew becomes more autonomous from ground controllers. Thus, we must understand the suitability of existing function allocation methods within NASA as well as the existing allocations established by the few robotic systems that are operational in spaceflight. In order to evaluate future methods of robotic allocations, we must first benchmark the allocations and allocation methods that have been used. We will present 1) documentation of human-automation-robotic allocations in existing, operational spaceflight systems; and 2) To gather existing lessons learned and best practices in these role assignments, from spaceflight operational experience of crew and ground teams that may be used to guide development for future systems. NASA and other space agencies have operational spaceflight experience with two key Human-Automation-Robotic (HAR) systems: heavy lift robotic arms and planetary robotic explorers. Additionally, NASA has invested in high-fidelity rover systems that can carry crew, building beyond Apollo's lunar rover. The heavy lift robotic arms reviewed are: Space Station Remote Manipulator System (SSRMS), Japanese Remote Manipulator System (JEMRMS), and the European Robotic Arm (ERA, designed but not deployed in space). The robotic rover systems reviewed are: Mars Exploration Rovers, Mars Science Laboratory rover, and the high-fidelity K10 rovers. Much of the design and operational feedback for these systems have been communicated to flight controllers and robotic design teams. As part of the mitigating the HARI risk for future human spaceflight operations, we must document function allocations between robots and humans that have worked well in practice.

robotic allocation

Accounting for the Speed-Accuracy Trade-Off in Quantifying Human-In-The-Loop Error Probabilities

Human-in-the-loop (HITL) simulations cannot collect enough data from human operators to validate estimates of error probabilities for task components. Error rates for tasks have been estimated by using laboratory data for error rates depending on variables such as the cognitive complexity of the task. The limited channel capacity of human operators compels error rates to be strongly related to the time available for the task, the speed-accuracy trade-off. HITL simulations can provide valuable data on the time available for the operator's tasks. We propose that the response times be used in conjunction with measured speed-accuracy curves to estimate the operator error rates contributing to mission failure. Such analyses should be especially important in the estimation of error rates in off-nominal situations.

probability

The Insertion of Human Factors Concerns into NextGen Programmatic Decisions

Since the costs of proposed improvements in air traffic management exceed available funding, FAA decision makers must select and prioritize what actually gets implemented. We discuss a set of methods to help forecast operational and human performance issues and benefits before new automation is introduced. This strategy could minimize the impact of politics, assist decision makers in selecting and prioritizing potential improvements, make the process more transparent and strengthen the link between the engineering and human factors domains.

air traffic control

NextGen Operational Improvements: Will they Improve Human Performance

Modernization of the National Airspace System depends critically on the development of advanced technology, including cutting-edge automation, controller decision-support tools and integrated on-demand information. The Next Generation Air Transportation System national plan envisions air traffic control tower automation that proposes solutions for seven problems: 1) departure metering, 2) taxi routing, 3) taxi and runway scheduling, 4) departure runway assignments, 5) departure flow management, 6) integrated arrival and departure scheduling and 7) runway configuration management. Government, academia and industry are simultaneously pursuing the development of these tools. For each tool, the development process typically begins by assessing its potential benefits, and then progresses to designing preliminary versions of the tool, followed by testing the tool's strengths and weaknesses using computational modeling, human-in-the-loop simulation and/or field tests. We compiled the literature, evaluated the methodological rigor of the studies and served as referee for partisan conclusions that were sometimes overly optimistic. Here we provide the results of this review.

Beard, Bettina L.

A Visual Detection Learning Model

Our learning model has memory templates representing the target-plus-noise and noise-alone stimulus sets. The best correlating template determines the response. The correlations and the feedback participate in the additive template updating rule. The model can predict the relative thresholds for detection in random, fixed and twin noise.

Beard, Bettina L.

Parafoveal Target Detectability Reversal Predicted by Local Luminance and Contrast Gain Control

This project is part of a program to develop image discrimination models for the prediction of the detectability of objects in a range of backgrounds. We wanted to see if the models could predict parafoveal object detection as well as they predict detection in foveal vision. We also wanted to make our simplified models more general by local computation of luminance and contrast gain control. A signal image (0.78 x 0.17 deg) was made by subtracting a simulated airport runway scene background image (2.7 deg square) from the same scene containing an obstructing aircraft. Signal visibility contrast thresholds were measured in a fully crossed factorial design with three factors: eccentricity (0 deg or 4 deg), background (uniform or runway scene background), and fixed-pattern white noise contrast (0%, 5%, or 10%). Three experienced observers responded to three repetitions of 60 2IFC trials in each condition and thresholds were estimated by maximum likelihood probit analysis. In the fovea the average detection contrast threshold was 4 dB lower for the runway background than for the uniform background, but in the parafovea, the average threshold was 6 dB higher for the runway background than for the uniform background. This interaction was similar across the different noise levels and for all three observers. A likely reason for the runway background giving a lower threshold in the fovea is the low luminance near the signal in that scene. In our model, the local luminance computation is controlled by a spatial spread parameter. When this parameter and a corresponding parameter for the spatial spread of contrast gain were increased for the parafoveal predictions, the model predicts the interaction of background with eccentricity.

Ahumada, Albert J., Jr.

Image Discrimination Models With Stochastic Channel Selection

Many models of human image processing feature a large fixed number of channels representing cortical units varying in spatial position (visual field direction and eccentricity) and spatial frequency (radial frequency and orientation). The values of these parameters are usually sampled at fixed values selected to ensure adequate overlap considering the bandwidth and/or spread parameters, which are usually fixed. Even high levels of overlap does not always ensure that the performance of the model will vary smoothly with image translation or scale changes. Physiological measurements of bandwidth and/or spread parameters result in a broad distribution of estimated parameter values and the prediction of some psychophysical results are facilitated by the assumption that these parameters also take on a range of values. Selecting a sample of channels from a continuum of channels rather than using a fixed set can make model performance vary smoothly with changes in image position, scale, and orientation. It also facilitates the addition of spatial inhomogeneity, nonlinear feature channels, and focus of attention to channel models.

Ahumada, Albert J., Jr.