Robert W. Proctor - US grants

Although stimulus-response compatibility and response precuing effects are of major concern in the area of perceptual-motor performance, aging research on these effects has been sporadic and has produced inconsistent findings. The existing aging research is dated and does not reflect the significant theoretical advances on these topics that have occurred in recent years. Some studies of spatial compatibility effects show larger effects for older than younger adults, whereas others do not. Of the studies conducted on response precuing effects, the results obtained for precuing discrete keypress responses are discrepant with those for precuing aimed movement responses: The former suggest that older adults have difficulty preparing two responses on different hands, but the latter show that older adults are able to benefit from all types of precues evaluated. The goal of this pilot project is to initiate a systematic investigation of the influence of aging on response selection in basic perceptual-motor tasks. Four experiments are included as part of this pilot project. For all experiments, performance of college-age adults (18-25 years) will be compared to that of older adults (55-70 years). Experiments 1 and 2 focus on the effects of stimulus response compatibility, examining the role of stimulus and response modalities. Experiments 3 and 4 focus on response precuing effects, examining whether the elderly are less able to prepare responses on the same hand and whether any differences in precuing relative to the young adults reflect simply the need for more preparation time. The participants recruited for the project will be diverse in terms of gender, racial, and ethnic composition. The results from these experiments will resolve fundamental issues regarding aging and response selection, and will provide a solid foundation for the development of a more extensive research program that evaluates a wide range of response selection issues pertaining to aging. This research will also provide information that can be applied to the development of design guidelines for maximizing response speed and accuracy of elderly adults in perceptual-motor tasks. These guidelines will lead to development of products and environments that allow elderly adults to remain more independent and to engage safely in more activities in their daily lives.

Privacy is increasingly a major concern that prevents the exploitation of the Internet's full potential. Consumers are concerned about the trustworthiness of the websites to which they entrust their sensitive information. Although significant industry efforts are seeking to better protect sensitive information online, existing solutions are still fragmented and far from satisfactory. Specifically, existing languages for specifying privacy policies lack a formal and unambiguous semantics, are limited in expressive power and lack enforcement as well as auditing support. Moreover, existing privacy management tools aimed at increasing end-users' control over their privacy are limited in capability or difficult to use.
This project seeks to provide a comprehensive framework for protecting online privacy, covering the entire privacy policy life cycle. This cycle includes enterprise policy creation, enforcement, analysis and auditing, as well as end user agent presentation and privacy policy processing. The project integrates privacy-relevant human, legal and economic perspectives in the proposed framework. This project will develop an expressive, semantics-based formal language for specifying privacy policies, an access control and auditing language for enforcing privacy policies in applications, as well as theory and tools for verifying privacy policies. Additionally, experiments and surveys will be conducted to better understand the axes of users' privacy concerns and protection objectives. Results from this empirical work will be used to develop an effective paradigm for specifying privacy preferences and methods to present privacy policies to end users in an accurate and accessible way.

DESCRIPTION (provided by applicant): The Census Bureau estimates that by the year 2030, there will be 70 million people aged 65 or older, more than twice the number in 1999. These older Americans represent just 13% of the population in the year 2000 but are expected to make up 20% of the population by 2030. Of particular concern in this aging society is the finding that many cognitive functions decline with normal healthy aging. Previous research has indicated that multiple factors are associated with these cognitive impairments, including deficits in the speed of processing, poorer self-initiated processing, reduced working memory capacity, lack of inhibitory control, and reduced perceptual processing. The multiple interactions among these possible causes make it difficult to isolate one particular process and nearly impossible to make predictions based on a purely verbal model. This proposal describes a program of research designed to assess the effects of reduced perceptual processing on memory functioning by looking at detailed error patterns among young and old participants in a wide variety of memory paradigms. These data will then be fit using two existing models of memory that have heretofore been used mainly with younger populations. Because the parameters of these models can be mapped on to each of the factors of interest (speed of processing, perceptual efficiency, etc.), it is possible to determine the relative contribution of each factor and, more importantly, see how these factors interact in a dynamic system. Given this analysis of the dynamic interactions among the factors, specific predictions about the types of tasks and situations which should and should not result in processing difficulties for older adults can be made and will be tested empirically. The long-term goal of these studies is to develop a formal, quantitative model that will allow researchers and practitioners to focus specifically on those areas of functioning that are critical to efficient cognitive processing. In addition, the studies will demonstrate that perceptual processing abilities must be considered and either controlled for or manipulated in cognitive aging research.

This interdisciplinary research program addresses construction industry training needs in equipment operation by drawing upon such knowledge domains as computer graphics, human-machine interaction, psychology, human factors. This research will contribute to the foundation of the visualization, training, and safety topic areas for architecture, engineering, and construction research and establish fundamental knowledge for more effective use of technology tools to improve workforce development methods and outcomes. The proposed integrated activities will advance discovery regarding the cognitive, sensory and motor characteristics of real and virtual training, human-machine interaction, and construction workforce development areas. Crucial to virtual training systems is the issue of skill transfer from the virtual practice experience to real world performance. Through development of a unique experimental facility and a sequence of experiments, the program of research is devised to establish new understandings regarding skill requirements, skill development and learning in varied artificial environments, and transfer of skills between artificial and real environments.
Fundamental research on the comparative effectiveness of virtual technologies in equipment-operator training is limited worldwide and pursued very little in the United States. Given the size of the construction industry and other related industries (e.g., manufacturing), the research contributions in this project are expected to directly impact the US workforce and economy and will potentially enable better design, operation and management of equipment-related operator training programs. Broader impacts of the research will include three societal benefits: 1) the reductions in workforce training costs that can be possible through proper design of virtual training systems; 2) the effective education and training of the future industrial workforce, construction engineers, and human factors experts; and 3) the broad knowledge and technology transfer of equipment training technology and principles from the construction industry to other related industries.

Risk communication is an important part of many cyber security mechanisms. Android's current risk communication mechanism is based on security warnings and has been demonstrated to be ineffective because users become habituated to ignore such warnings and tend to consent to all prompts. This multi-disciplinary research project aims at developing holistic solutions to usable risk communication and control for the Android platform.

This project investigates an approach that presents risk information at multiple granularities, including a high-level numerical risk summary, an intermediate-level summary of risk for different dimensions, and detailed risk information. The high-level risk summary is computed by information integration techniques, using information discovered from multiple sources, e.g., user reviews and app source code. This summary enables proactive risk communication (e.g., when the user searches for apps) so that users can take this information into the decision process.

This project also introduces a multi-mode approach that, in addition to communicating risks, also controls risks in the sense of discouraging risky applications and ensuring that users truly understand the risks. The project develops mechanisms that aggregate, communicate, and control risks incurred by apps at runtime, and ways to personalize risk integration, communicate, and control techniques to accommodate differences among users.

This project is expected to advance the state of the art in principles and techniques to risk communication and control, and has the potential to impact the Android app ecosystem by collaboration with Google researchers.

In the U.S., the building sector accounts for about 40% of primary energy usage, 71% of electricity and 38% of carbon dioxide emissions. For this reason, development of efficient solutions to reduce energy consumption and environmental impact of buildings is of critical societal importance. Occupants play a significant role in energy use of office buildings, affecting up to 30% of the energy use. To manage occupants' energy impact due to their presence and behavior, environmental control systems (e.g., HVAC, shading, lighting) have been automated based on the use of "widely acceptable" visual and thermal comfort metrics. However, occupants have a strong preference for customized indoor climate, and there is a strong relationship between occupants' perception of control over their environment and productivity, health and well-being. To address the challenge of customized control of the environment, the objective of this project is to realize a new paradigm for human-centered sustainable buildings, enabled by conducting research with computing innovations in probabilistic methods and machine learning, linked to sustainability, and with broader impacts in multiple domains of science and engineering. Broader impacts are: (1) New computing methods and algorithms on probabilistic classification, inference and optimal control that may impact a number of scientific communities, including Architectural, Mechanical, Electrical, Computer and Industrial/Human Factors Engineering, Computer and Psychological Sciences. Potential application areas include genomics, traffic flow prediction, infrastructure systems including power, transportation, etc. (2) Integration of the project's modeling, simulation, and experimental platforms into new teaching modules and experiential learning activities that support the curriculum and workforce development in four engineering schools (Civil, Mechanical, Electrical and Computer, Industrial Engineering) and the Department of Psychological Sciences. (3) Dissemination of research outcomes to the academic community and to the industry through publications, workshops, conferences and a customized external evaluation process. (4) Creation of outreach and engagement initiatives for K-12 teachers and students in STEM learning and research.

This project takes a multidisciplinary approach that is grounded in (1) new algorithms for automated identification of the relevant human perception-attributes of buildings; and (2) new concepts for intelligent and self-tuned comfort delivery systems for customized thermal and visual environments in buildings. The research includes: (1) Laboratory and field studies with human test-subjects that map indoor environment conditions, thermal and visual perception and comfort, occupant-building interactions and control actions, as well as corresponding space performance for perimeter building zones. (2) Probabilistic classification of human perception, comfort, and satisfaction profiles for a typical population. (3) Computationally-efficient inference algorithms for online learning of individual and population-level human preferences. (4) Optimal control algorithms and simulation tools for implementation in building management systems. The research outcomes will be integrated into a new cyber-enabled technological solution for self-tuned comfort delivery devices (thermostats, shading and lighting actuators). The experimental prototypes and field demonstrations will achieve improved performance with quantified building energy use reduction and occupant satisfaction, as well as robustness to uncertainty due to the reduction in the frequency of overrides.

User authentication is an important part of most information systems that require some level of security. Due to their ease of use, wide deployment, and user familiarity, passwords have been the most widely adopted user authentication mechanism in the past and are likely to continue to be an important part of cybersecurity for the foreseeable future. At the same time, it is well known that there is a tension between the security and usability of passwords. Often times, secure passwords are difficult to memorize, making them less usable, whereas passwords that are memorable tend to be predictable and discoverable. This project aims to improve the complex password ecosystem, including ways to help both human users and websites that require passwords. One research thrust focuses on developing techniques to help human users, and in particular, ways that effectively train humans in the skills to create and remember secure passwords. Another research thrust focuses on studying how to improve the password-generation interface of the website, which plays a decisive role in users' performance of password generation.

To help human users, the project aims to develop and evaluate mental password generation strategies--cognitive algorithms that can be executed by humans--for generating high-entropy passwords that can be acquired and implemented by human users. An effective generation strategy should be easy to use, and the resulting passwords should be both unpredictable and easy to recall. Another major challenge a user faces is the large number of accounts that need passwords. The researchers are studying effective mental password management systems, in which passwords for different accounts are organized in a hierarchical manner and related to the website domain name to make recall easier, while it remains difficult for an attacker who possesses such a password to easily guess another. To help websites promote user-centered and safe password generation, this project studies how to improve the password-generation interface of the website by developing effective password strength communication and embedded training methods. The project poses the research question of how should websites check for weak passwords and effectively warn against or forbid their use, without imposing excess effort on the user.

Differential privacy (DP) has been accepted as the de facto standard for data privacy in the research community and beyond. Both companies and government agencies are trying to deploy DP technologies. Broader deployments of DP technology, however, face challenges. This project aims to understand the needs of different stakeholders in data privacy, and to develop algorithms and software to enable broader deployment of private data sharing. The project's novelty is combining the expertise of social science researchers with that of computer scientists who have both theoretical and system research experiences related to DP to develop a hybrid approach to private data sharing to achieve better privacy-utility tradeoff. The project's impacts are in advancing the state-of-the-art with regard to DP deployment in particular and privacy protection in general. More specifically the project identifies the workflow of DP data sharing, improve understanding of DP communication, and develop new algorithms, privacy concepts, and privacy mechanisms to support deployment of DP.

The project has four tasks that will advance the understanding of user-centered DP and lay a foundation for its deployment. (1) Examine individual human users' perception, comprehension and acceptance of the concept and guarantee of DP and the effect of privacy parameter, and to investigate effective ways to communicate those concepts. (2) Implement methods from the domains of human factors and human-computer interaction to identify tasks, goals, and workflow in private data sharing. (3) Develop key algorithms and software for a hybrid approach of private data sharing. In the hybrid approach, one first publishes a private synopsis of dataset using carefully selected low-degree marginals. From these marginals, one can either synthesize new datasets, or answer queries directly using inference under the maximum entropy principle. The hybrid approach enhances this with interactive query answering, enabling extraction of information not covered by low-degree marginals. (4) Develop techniques to further improve the privacy-utility tradeoff in private data sharing, including a theory of differential privacy under publishable information.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.

Self-driving vehicles, or connected and automated vehicles (CAVs), are advocated as a solution to improve the safety of the transportation system. However, current transportation infrastructure is only designed for human drivers, without considering the characteristics of self-driving vehicles or their interactions with other self-driving and human-driven vehicles (HDVs). Miscommunication and improper interactions between self-driving and human-driven vehicles may lead to more accidents during the transition period when the two vehicle types coexist on the roadway. Despite the fact that adaptations of transportation infrastructure are as critical as the technological advances of the vehicles, most research on traffic with both driverless and human-driven vehicles has disregarded the role of transportation infrastructure. This project seeks to strengthen American transportation infrastructure by investigating future infrastructure design methods that support communication among self-driving vehicles, infrastructure, and human-driven vehicles to enhance safety and speed widespread adoption of self-driving vehicles.

The “smart” transportation infrastructure of the future must support communication among connected and automated vehicles (CAVs) and between CAVs and human-driven vehicles (HDVs) if the goal of efficient and relatively error-free vehicle transportation is to be attained. This project aims to advance knowledge in infrastructure design by integrating the cognition and actions of humans with a system-of-systems approach, viewing roadway transportation as an overall system consisting of CAVs, human-driven vehicles, and infrastructure subsystems. The central hypothesis of this project is that transportation infrastructure that is optimized based on the new features of CAVs will appreciably reduce accidents and traffic delays. The methods take a human-centered design framework that focuses on the perceptions and actions of HDV operators in relation to interactions with CAVs. The goal is to develop an empirical research base that will guide the future vehicle transportation system design under mixed traffic conditions. To this end, the multidisciplinary team of investigators will 1) Identify root causes of accidents between HDVs and CAVs from accident reports, interviews of CAV experts, surveys of drivers, and studies of human cognition and actions in a driving simulator; 2) Propose countermeasure solutions to deliver necessary information to human drivers and CAVs based on identified root causes, information needs, and human-information processing; and 3) Use the countermeasure solutions and information needs to understand improvements in current infrastructure that would better support communication and interactions between HDVs and CAVs. As a final step, the PIs will evaluate the proposed solutions at a real-world roundabout.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.