D— Cognitive Aging and Adaptive Technologies
Donald L. Fisher
Adaptivity is an important aspect of human behavior, frequently determining those who will and will not perform a given task successfully. As adults grow older, they respond more slowly to simple stimuli and take longer to learn new material, thus potentially decreasing their ability to adapt. Their vision, speech, and hearing can become impaired. In addition, they often exhibit larger temporal variations in sensory, motor, and more abstract cognitive abilities than do younger and middle-aged adults. Until recently, technology could not address most of these decreases in potential adaptivity. However, as computers become smaller, more powerful, and more easily embedded in other objects and processes, they provide the opportunity to construct technology that can augment greatly the adaptivity and functionality of the older adult user.
Examples of current and future adaptive technologies include computers that can be worn—for example, eyeglasses that enhance the peripheral field of vision (Jebara et al., 1998)—and microclectromechanical systems, which can easily and unobtrusively be placed in objects that an individual might normally carry—for example, an ultrasound sensor embedded in a cane, which provides information about the nearby structures and terrain for older adults with vision impairments (Gao and Cai, 1998)—or with which an individual might normally interact—for example, head-up displays in an automobile (Tufano, 1997). Taking advantage of these advances will require a greatly expanded understanding of the effects of aging on cognition, since such ef-
fects are often dependent on the context, and the adaptive technologies will radically change this context.
Federal and private initiatives to advance adaptive technologies are extensive in the fields of biomedicine and bioengineering. They include everything from the support of the development of full-scale doctoral degree programs (the Whitaker Foundation) to the support of small projects to be undertaken by undergraduates in engineering with clients who have one or more disabilities (the National Science Foundation). However, no programs currently exist at the federal level that specifically target research that advances understanding of the basic cognitive behaviors in older adults that could serve as the foundation for the design of adaptive technologies.
The chapter is divided into four sections. The first section discusses the most promising of the new technologies (hardware and software) that have been developed for sensing environmental and behavioral information. The second section identifies a similarly promising group of new technologies that can be used to display information to individuals. The third section discusses powerful modeling tools that can be used to infer the behavior of individuals from the much more detailed record that the new sensing technologies provide. It also shows how these tools can lead directly to the development of a next generation of personalized, highly interactive interfaces that themselves increase the adaptivity of existing technologies. The final section discusses current and future applications of these technologies and identifies the basic research in cognitive aging that would be needed to carry forward the applications.
SENSING, INTEGRATING AND PLANNING
Advances in technologies are making it possible to put sensors and processors in locations that heretofore had been inaccessible. Advances in algorithms are making it possible to process intelligently and in real time the extraordinary amounts of data that can now be gathered by these devices. Increases in the raw speed of processing information are one major factor contributing to these advances. There is every likelihood that these increases will continue into the foreseeable future. Equally important in the domain of sensing is the development of microelectromechanical systems. They are defined as three-dimensional mechanical or electromechanical components/ devices with sizes in the micrometer range, such as micro gears, beams, pumps, motors, hinges, and switches. These microscopic devices are designed to create controllable mechanical motions, which are the basis for a wide range of sensors, actuators, or mechanical structures that have been used in both industrial and commercial applications. Discussed below are new technologies for sensing environmental variables, integrating environmental informa-
tion, and planning possible actions in order to make an older adults' behavior more broadly context sensitive and therefore more adaptive.
Sensors can now be placed in cars. Together with the associated image analysis algorithms and the necessary models of vehicle dynamics and traffic scenarios, they can provide selected, critical information to the driver about the roadway ahead (e.g., upcoming regulatory or warning signs). For example, existing systems have a range of 1 to 200 meters, can resolve detail as fine as 1 meter, and are about 150 mm in diameter and 100 mm in length. Each beam usually covers 5 to 8 degrees, leading typically to 3 or 4 beams in total for complete forward coverage. Doppler information can be read directly for the determination of velocity. Such sensors could potentially play a critical role in reducing the number of collisions for older adults. This is especially important given that older adults are more likely to be involved in a fatal crash than any other cohort (Barr, 1991). However, before sensor technology can be implemented, much must be learned about the basic visual search strategies of older adults, in order to know what information to present and when to present it, reducing to a minimum the number of false or useless warnings. And much must be learned about older adults' ability to handle successfully multiple forms of input when the load on the cognitive systems is especially high—as it will be during a potential collision.
For aided orientation and mobility, the majority of blind people use the long cane, which provides an extended spatial sensing of about 0.5 meter ahead of the user, within an arc of about 120 degrees. However, the use of a long cane does not provide protection for the body parts above the waist. Using microelectromechanical system technologies, miniaturized ultrasound sensors and on-board microelectronics have recently been developed that can be embedded in the shaft of the long cane, thereby providing added sensing capability for overhanging obstacle detection (Cai and Gao, in press). The ultrasound sensor has a detection range of 4–5 meters (or 12–15 feet), a distance resolution of about 20 mm (about an inch), and a scanning range that basically covers the user's body, roughly +15 to -15 degrees ahead. Improvements in the sensing capability of the long cane could have a large impact on older adults. More than 2 million Americans are blind or have severe visual impairments. Each year, around 35,000 adults become blind as a result of accidents, cataracts, glaucoma, diabetes, and other diseases. Many are older adults. Although the sensing technology is well developed, progress still needs to be made on the interface. A simple warning sound could be presented, but
this would lack important location information. A synthesized voice could provide users with the azimuth, distance, and height of an object, but it is not yet clear how effective this would be. Of course, at some point, three-dimensional virtual auditory displays may make it possible to synthesize the warning at a point in virtual space coincident with its point in actual space (although see the section below on displays). More basic research is needed on sound localization in older adults in order to take full advantage of the blind cane with an ultrasound sensor.
Global positioning systems (GPS), which consist of both a satellite network and a receiver, can now be used to give latitude and longitude positions to within 100 m without differential positioning and to within millimeters with differential positioning (referencing one GPS signal with the signal from a transmitter at a known location). GPS receivers have been miniaturized to just a few integrated circuits (Dye and Baylin, 1997). Such systems can be located in an automobile or carried on the person. Systems with the capability for differential positioning are relatively expensive; much cheaper systems exist that do not have this capability. The major disadvantage of using GPS to obtain location information while in transit is that the satellite transmissions can be blocked by tall structures and overhanging trees and other leafy vegetation. A minor disadvantage in most cases is that the accuracy of elevation information is considerably less, some 3–4 m, even with differential positioning.
GPS technologies have obvious applications to navigation systems in automobiles, and they may be especially useful for older adults. For example, older adults are overrepresented in crashes at signalized left turn intersections (Staplin and Fisk, 1991; Szymkowiak et al., 1997). Many older adults recognize this problem and so may take three right turns around a block rather than a single left turn. A GPS could let them know when this strategy is possible. Older adults would also prefer routes with less traffic and slower speeds. Again, a GPS, together with advances in intelligent transportation systems, could remedy this problem, at least in part. However, it still remains to be determined whether older adults can easily both drive and process input from an in-vehicle navigation system. Basic research on time sharing must be undertaken in order to make the best use of the GPS technologies.
GPS technologies may also have applications as reminder systems. Location can serve as a cue for helpful retrieval cues, which may assist an older adult who has difficulty remembering the names of people at particular locations (grocer, pharmacist, a neighbor). Basic research on the tip-of-the-tongue phenomenon and more general memory could prove especially helpful in this area (Burke and Harrold, 1988; Burke et al., 1988).
Eye trackers have existed for some time but have placed too many constraints on the individual to be useful in applied settings, requiring the individual either to remain motionless or to wear a bulky head-mounted device. However, such is no longer the case. The particular area of a scene or display at which an individual is looking can now be inferred from eye and head trackers that are totally removed from the individual. Many use a video-based technology and can sample the eye position at up to 240 Hz. The accuracy of the position so sampled is within a degree or so. Realistically, the head must remain in a relatively small volume of space. But at least in the cabin of an automobile, this is not a problem. Nor would it be a problem tracking someone using any of the various displays, computer or electromechanical, that now exist (e.g., ATMs).
Eye position information could be critical to the realization of the full potential of adaptive technologies, especially for older adults, since many difficulties that older adults have can only be remedied if more is known in real time about the behaviors of the individuals involved. For example, many older adults have great difficulty with automated teller machines (ATMs) (Rogers et al., 1996). We could better understand the problems if we knew where the older adult was focusing when the problems occurred. Older drivers also have particular difficulty negotiating left turns, as was noted above. We could better understand why this is the case if we knew when the older adult fixated each of the various sections of the roadway ahead and to the side. Basic research is needed that can be used to infer the behavior of the individual using an adaptive technology from the information on the position of the eyes, which is being gathered in real time. Such inferences have been possible when individuals are undertaking a relatively simple task, like reading (Rayner, 1998), and the extensions to adaptive technologies is a reasonable next step. Hidden Markov models may play a particularly important role in this context (Reichle et al., 1998).
Advances in miniaturization are not confined to sensor technologies. Such advances also alter greatly what can be displayed and when it can be displayed. These advances are being fueled by the same factors that are moving forward the development of sensor technologies. Increases in speed make it possible to expand the bandwidth of the channels that are processing the information displayed. Increases in miniaturization make it possible to produce displays so small that they can be mounted in the frames of eyeglasses.
Head-up displays have been used for some time in aviation, and their benefits and disbenefits have been studied extensively in that context (Weintraub and Ensing, 1992). They make it possible for the pilot to keep his or her eyes focused on the outside world and simultaneously to monitor the critical instruments. Head-up displays can now be used in the automobile as well (Gish and Staplin, 1995). In such displays, images are projected onto the windshield in front of the driver. The driver then sees the image superimposed on the roadway outside the automobile. The images can be focused at optical infinity or at any point closer to the driver. Images are typically of the dials and gauges. Such displays are now available on the Corvette. It is also possible to project radar images of objects onto the window at the exact place they would appear were they truly visible. This is especially useful during poor weather or nighttime driving. The images could also be displayed in a separate window. For example, Cadillac now includes as an option a display of the roadway ahead at the bottom of the windshield. The roadway image is generated by infrared radar and can detect objects at night that might not otherwise be visible (e.g., deer crossing some distance in front of the driver). These displays may have the same benefits for drivers that they have for pilots. And they may benefit older adults most, since older adults take longer to scan for information on the standard dials and gauges of a car. However, the technology is being brought to market without extensive discussions in the open literature on the benefits and costs to all drivers, let alone to older drivers. Fundamental increases in understanding of the effects of aging on perception are needed before we can truly be confident that older adults will show a net benefit from these technologies (see the section below on future research areas).
Head-mounted displays make it possible for a user to view a display while performing other tasks (Feiner et al., 1993). Typically, the display is located immediately in front of the eye, either overlaid (Starner et al., 1997) or not overlaid (Rhodes, 1997) on the real world. A relatively high resolution, 720 by 280, is possible. This produces an easily readable 80 by 25 character screen. One can imagine many uses for such technologies by older adults. For example, many older adults miss taking their medications (Park, 1992). This is of particular concern because the health needs of older adults are so much greater than those of younger adults (U.S. Department of Health and Human Services, 1991). A head-mounted display could alert an older adult that it was time to take a particular medication. The actual medication and location of the medication could be presented on the head-mounted display as well as
any instructions relevant to the administration (e.g., take with water). Basic research is needed to identify the best format for presenting complex printed information to the older adult, one that conveys the various risks adequately as well. This research should build on the existing research in medical decision making (e.g., Cho et al., 1999; Miyamoto, 1999).
Variable Message Signs
Electronic variable message signs located over or by the side of the highway can be used to deliver to drivers in real time information about traffic, parking, weather, and other conditions (Kahn, 1992). Although such displays are not new, the technology used to implement the displays is changing rapidly. The displays are becoming much cheaper to produce and, as a consequence, are being implemented on a much broader basis. For example, variable message signs are now used to display the predicted travel time along a driver's current route and several alternative routes to a common destination. Research with younger adults suggests that the proportion diverting along the alternative route can be predicted and, more importantly (at least in the current context), the proportion diverting changes as the load on the driver increases (Katsikopoulos et al., 2000). The effect of load on the decision-making process is of some concern here, because older adults may have particular difficulty with the extra cognitive demands placed on them while driving (Sit and Fisk, 1999). Thus, these systems as proposed may be of little if any use to the older driver. And they could lead to more incidents (crashes, slowdowns) and therefore, ultimately, to more congestion, since it is such incidents that normally choke the progress of traffic. Clearly more needs to be known about how one can use intelligent transportation systems technologies without overwhelming the older driver (Kantowitz et al., 1997; Santiago, 1992; Sobbi, 1995; Walker et al., 1997). And this needs to build on the large body of work that already exists in transportation science (Ben-Akiva and Lerman, 1985).
Advanced digitizing capabilities and a greatly increased understanding of the exact interaural differences and spectral cues that help individuals localize a sound now make it possible to present a three-dimensional soundscape over headphones (Wightman and Kistler, 1989). Specifically, head-related transfer functions are computed for a particular individual (or group of individuals). These functions preserve both the pattern of interaural differences and the spectral cues that are needed to make location judgments. The use of three-dimensional virtual sound environments has been confined primarily to the military and, in particular, to the generation of auditory collision warn-
ings for pilots. The auditory warning is located in virtual space at the same location as the object with which a collision is imminent. Such warnings can decrease greatly the time that it takes users to locate a potential threat, both when the warnings are generated externally (Perrott et al., 1990) and when the warnings are generated over headphones (Begault, 1993).
Three-dimensional audio displays may also have importance in the automobile for older drivers, both those with and without hearing impairments. It is now very difficult, even for drivers with good hearing, to localize in the saggital plane sounds that are either directly in front or directly in back of the driver (Caelli and Porter, 1980). For the hearing impaired, it is even more difficult. Wearable audio computing extends the range of applications (Roy et al., 1997), perhaps to collision warning systems for blind users of a long cane. Basic research on sound localization is needed in order to determine whether older adults can more quickly find a visual target and react appropriately when a sound is generated that occupies a position in virtual space identical to the target. This may be particularly problematic for older adults, who become less sensitive with time to high-frequency sounds; it is such frequencies that are important to localization. If this is the case, then perhaps basic research could identify other ways to cue location.
Digitized and Synthesized Voice
It is now possible to broadcast widely over the telephone personalized messages using either synthetic speech or digitized voice clips. Such a technology could have many different applications for older adults. One potential application is the automating of medical appointment information (Leirer et al., 1993). This is critical, since older adults frequently fail to attend their medical appointments (Deyo and Inui, 1980). Information about a medical appointment can be complex. Thus, it is not all that surprising that older adults have difficulty keeping track of their medical appointments. Early studies indicated that automated telephone reminders can help (Leirer et al., 1989). More recent studies have indicated that the format of the automated message (Morrow, 1997) and the number of repetitions (Morrow et al., 1999) have an effect on older adults' memory for automated messages. However, although both younger and older adults benefit from improvements in the format and increases in the number of message repetitions, the advantage that younger adults generally enjoy in such tasks is not reduced when the measure of message comprehension is free recall. Thus, room for improvement still exists and research is needed that identifies procedures that can increase further older adults' recall of medical appointments.
Advances in sensors and display technologies can lead by themselves to increasingly adaptive interfaces. However, the adaptivity of an interface is not controlled by these technologies alone. Increases in adaptivity can be obtained by displaying to older adults an interface that is tailored to them as a group or to each individually. Increases in the adaptivity of an interface can also be achieved by predicting in real time the future behavior of an individual and adjusting the response of a system appropriately.
Increasingly personalized interfaces are made possible by rapid advances in wireless communication (to download a user's profile to an interface), much more sophisticated models of performance (for predicting response times and errors), and faster optimizing algorithms (for identifying the best interface). Most users of personal data assistants are familiar with at least one form of wireless communication. Specifically, infrared radiation is now used to send data from one device (e.g., computer or personal data assistant) to another at speeds that are fast enough to transmit easily tens of kilobytes of information in a second or less. With the advent of wearable computers and the recent development of flexible transistors (Markoff, 1999), this makes it possible to consider adaptive technologies that tune themselves to the individual at start-up. For example, as noted above, older adults generally have difficulty with ATMs. This might change greatly if the interface could be tailored to the user, which it could be if the relevant information on the user (say the relative duration of various cognitive processes) were beamed to the ATM from a user's wearable computer.
Quantitative models then make possible the individualization of the interface from the initialization data. Specifically, an analytic or computer model can be used to predict performance for any given design of an interface (e.g., the response time for that design). For example, consider the menu hierarchy that might be displayed to a user of an ATM. In this case, one can vary in each menu the option that is assigned to a key (assuming that a central display of options is surrounded by keys that point to each option). Suppose that the menu hierarchy has just two levels with six options in each menu (i.e., one menu with six options at the top level and six menus each with six options at the bottom level). The time on average that it takes an older adult to access an option in the bottom level will depend both on the time that it takes an individual to strike a pair of keys, one at the first level and one at the second, and on the frequency with which a given pair of options is used. Models exist that can predict the average access time for any given assignment (Rumelhart and Norman, 1982).
Finally, an optimal design of an interface needs to be established. Continuing with the example, note that there are 6! (six factorial) different assignments of options to keys in each menu, or a total of 720 different assignments for just the single menu in the top level of the hierarchy. Since there is one menu with 6 options in the highest level of the hierarchy and 6 menus with 6 options each in the next highest level, there are a total of 7207 different assignments of options to keys in a simple ATM! Methods clearly are needed to identify the assignment that minimizes the average access time for an individual.
Point and click hierarchies also present the same opportunity for optimization (Fisher et al., 1990). A very, very large number of different organizations are possible, any one of which is semantically acceptable. The optimal organization in this case depends on the time that it takes an individual to analyze each option in a menu (or on a screen). A simple model can be used to predict the average terminal option access time. And then dynamic programming can be used to identify the hierarchy that minimizes the average terminal option access time. These are but a few of the many possible ways in which one can potentially individualize an interface for older adults and thereby adapt it to the user (Fisher, 1993).
Increasingly, interactive interfaces are made possible by many of the same improvements that are leading to the individualized interfaces described above: improvements in wireless communications, mathematical models of older adults' behavior, and real-time optimization algorithms. For example, older adults have difficulties maneuvering their vehicles that younger adults do not have (arthritis being one of the leading causes of these problems). This might change if the profile from an individual older adult could be beamed to devices in the vehicle that offered steering, braking, and throttle assistance. Models of the older adult can then be used both to initialize the interface and to predict the behavior of the older adult, intervening when it appears that assistance is needed, either passively (by providing information) or actively (by taking over the controls). These interventions can in many cases be optimized as well.
Passive interventions have been used for some time, at least on computers. For example, on the basis of the sequence of keystrokes that a user enters, help windows open at particular points to suggest possible actions the user might take. However, the range of assistance that can be provided is greatly expanded once we consider the new sensing technologies, especially the ability to track a user's eyes in real time (Byrne and Anderson, 1998; Salvucci and Anderson, 1998). For example, consider the potential assistance that might be provided were one to track the eyes of an older driver. Such information
could be used to warn inattentive or sleepy drivers (Allen et al., 1994; Knipling and Wierwille, 1994; Wierwille, 1994; Wierwille et al., 1994). This information could also be used to alert drivers that attention should be paid to important warning or regulatory information. Specifically, knowing where an individual is fixating is critical to determining whether the individual sees a warning or regulatory sign. However, it is not the case that the fixation position can by itself be used to indicate just what has and has not been seen (Rayner, 1998). Information on the details of a stimulus that is not being fixated can also be identified, information such as the shape, color, and sometimes even the accompanying text and symbols. Thus, were one to warn drivers about signs to which attention must be paid immediately based solely on the fact that the sign had not yet been fixated, one would generate many false alarms. Soon, drivers would not heed the warning. What one needs in this case is a model of the visual scanning process that predicts at each point in time the likelihood a particular object has been identified given the prior sequence of fixations. Such models exist for simple scenes (Ranney, 1998). Basic research is needed to extend these models to the more complex scenes that confront the driver.
Consider now a more active intervention. Sensors can give to individuals important information about the environment around them. Sometimes, actions must be taken on that information very quickly, perhaps in order to avoid a collision. The sensor technology designed to yield such real-time information has great potential for older adults, aiding in nighttime driving, parallel parking maneuvers, intersection maneuvers, and steering and braking during potential collision scenarios. In order to assist efficiently the older driver, one must be able to predict the path of the vehicle (or vehicles) in a given scenario in real time. Sensor management systems that include real-time path planning and control algorithms for an automobile are likely soon. Such systems are available right now for indoor environments. They detect and model geometry in that environment and track other moving objects (Connolly and Grupen, 1993, 1994; Grupen et al., 1995; Stan et al., 1994). The major advances in technology that are needed at present are for sensor management systems operating in real time in the environment of an automobile that would detect and model the geometry in the roadway and track other vehicles on the roadway. One must also be able to predict the actions of the older driver. Hidden Markov models of lane changing have already been successfully developed and tested (Liu, 1998; Liu and Pentland, 1997). They can predict ahead when a driver is changing lanes. Models of braking are also available (Lee, 1976). The major advances in person-machine models needed at present are discrete state descriptions of a much wider range of driving behaviors, a better understanding of how to distribute control between the driver and the car, and a better appreciation for just what drivers will and will not accept as assistance in the car. Hidden Markov models and more general
discrete state models have been used successfully to model a number of person-machine systems similar to the automobile (Miller, 1985; Rabiner, 1989; Wickens, 1982). Thus, there is reason to believe that the basic research will succeed.
FUTURE RESEARCH AREAS
It has been argued at length that the advances in adaptive technologies cannot be fully realized unless more is understood about the effects of aging on cognition. It is clear that we need to know more about perception, visual search, memory, decision making, learning and problem solving, and models of cognitive processing. Unfortunately, there is not the space needed to describe all of the many research advances that are necessary. Below, the areas of basic research in cognitive aging that are likely to bear most critically on the implementation of adaptive technologies are discussed. An argument is made at the end that much of the research needs to be interdisciplinary in nature.
The more one knows about the underlying behavior of an individual, the more one can potentially increase the individualization and interactivity of a given interface. The sensor technology most likely to yield this information in the future is the development of eye trackers that are transparent to the user. Currently, much is understood about both the components of visual search when the eyes remain stationary (or move little, if at all; e.g., Schneider and Shiffrin, 1977; Wolfe, 1994) and the effects of aging on these components (e.g., Plude and Doussard-Roosevelt, 1989). Much less is understood about the components of visual search when the eyes must move broadly across a static scene (Rayner and Pollatsek, 1992) or the effects of aging on these components (Whiteside, 1974). And even less is understood about the components of visual search and the effects of age on these components when the scenes being scanned are dynamic ones, as in driving (Szlyk et al., 1995) or when the observer is changing his or her point of view (Tarr, 1995). Interestingly, across all types of search, one observes a strong and consistent effect of aging: older adults take longer to find a target (e.g., Madden et al., 1996). This effect is not on the duration of the fixations, but instead the number of movements or saccades (Maltz and Shinar, 1999). We need to understand in much more detail how older adults scan both static and dynamic visual scenes. In order to make large advances in this understanding we will need to increase the ability to interpret patterns of eye movements, patterns that can reveal the underlying or latent cognitive processes (Rayner, 1998). And in order to make these advances, we will need to be able to model the behavior of the
system so that we can better isolate the effect of aging on the individual components (processes) of the system (Reichle et al., 1998).
The advantages of head-up displays were initially thought to far outweigh their disadvantages. Indeed, early reports indicated that drivers more frequently identified distinctive stimuli in a traffic scenario when using a head-up display than when using a dashboard-mounted display (Sojourner and Antin, 1990). However, more recent tests with automobiles suggest that there may be problems with such devices, problems that need to be addressed right now as the first automotive head-up displays are coming to market (Tufano, 1997). These are problems with the very advantages that devices were supposed to provide.
First, there is now evidence that drivers do not focus at optical infinity when the image is collimated (Marran and Schor, 1997). Instead their mis-accommodation is inward, tending by some accounts toward their accommodative resting position in the dark (Iavecchia et al., 1988) and by other accounts toward their vergence resting position (Weintraub and Ensing, 1992). In either case, the result is that objects in the outside world appear more distant and smaller than in fact they really are. In the aviation literature, it has been argued that this is the cause of many of the very hard landings that pilots make, the runway appearing farther away than it actually is when a head-up display is in use (Roscoe, 1987). However, it is still not known exactly what causes the objects in the real world to appear both more distant and smaller in size. Nor is it well known how aging affects misaccommodation.
Second, it is no longer universally believed that a head-up display will make it easier for drivers to attend to events in the outside world that require some action. In fact, Weintraub and Ensing (1992) have argued that the display images may actually capture the attention of the individual, making it more difficult to recognize unusual events in the real world. Attention to displays on the dashboard is associated with a multitude of cues (e.g., changes in accommodation, vergence, head position, ambient illumination) that alert the driver that he or she is paying attention to something other than the traffic in the outside world. A head-up display, in contrast, does not provide the user with these cues and therefore can cognitively capture the attention of the driver. Evidence is now accumulating suggesting that this may be a real problem (Wickens and Long, 1994). And given older adults' inability easily to switch attention and their greatly constricted useful field of view (Ball et al., 1990; Owsley et al., 1991; Sanders, 1970), a head-up display may capture the attention of an older adult much longer and more completely than it does that of a younger adult. Clearly, more research is needed before head-up displays are universally installed in automobiles.
The opportunity to individualize the design of an interface for a specific older adult is great. As noted above, it depends on our having for a given task both a quantitative model that can predict performance for each of the different possible interface designs and a mathematical or computer method that can be used to identify the optimal design. Such a capability depends on our having an understanding of the effects of aging on the structure and duration of the cognitive processes underlying the performance (Fisher and Glaser, 1996). The general statement that older adults are slower on speeded tasks is almost always true in practice. Many explanations for this slowing have been offered, including decreases in memory capacity (Salthouse, 1980) and increases in a failure to inhibit competing sources of information. But it is the slowing of the latent processes themselves that explains most of the overall slowing in response times (Salthouse, 1996a, 1996b). This slowing, at first thought to be proportional across all processes (Birren, 1965, 1974), is now known to vary across domains (Hale et al., 1987; Lima et al., 1991) and possibly within domains across individual processes (Fisk and Rogers, 1991; Fisk et al., 1992). This specificity requires much more systematic analyses. Quantitative models are now available that use standard additive factors techniques (Sternberg, 1969, 1975) to tease apart the structure of complex tasks (Schweickert, 1978; Schweickert and Townsend, 1989). And methods are available that can be used to predict response times with these more complex networks (Goldstein and Fisher, 1991, 1992). It is important to extend these methods to a great many more tasks than have been studied so far.
The practical advances just described cannot be achieved solely by basic research within a single discipline. For example, consider the development of a collision avoidance system, which helps the older driver both notice and steer around obstacles without taking complete control of the vehicle. We need to know not only how to direct the driver's attention to a particular position ahead of the automobile where the collision is predicted to occur. We would also need to generalize our understanding of elementary motions, such as reaching (Berthier, 1996), and of the effects of various forms of visual and force feedback on operator performance learned from studies of tele-operation (Sheridan, 1991) to the fast-paced environment of the automobile. We then need to understand how to interface the driver's responses with the collision avoidance algorithms (Grupen et al., 1995; Stan et al., 1994), so that the assistance provided enhances (rather than detracts from) the driver's own steering efforts, an effort that requires a better understanding of control theory (Djaferis, 1995). Finally, we need to understand how best to implement the
proposed assistive control system in hardware and software using the various microelectromechanical miniaturization technologies (Holm-Hansen and Gao, 1997). In summary, basic research is needed that is at the interface of psychology (visual search and attention, psychomotor control), computer science (path planning algorithm development), electrical engineering (control theory), mechanical engineering (microelectromechanical systems), and human factors (person-machine systems models). Moreover, the basic research requires a fair degree of quantitative sophistication among all disciplines, a sophistication that is essential if the models of the person and the machine are ever going to be coordinated with one another.
SUMMARY AND IMPLICATIONS
As adults age, they lose their ability to lead their lives independently. Perhaps they can no longer drive, so shopping becomes a problem. Perhaps their own home becomes too difficult to navigate, so they must be moved to a retirement community. Perhaps they can no longer reliably remember when to take their medications or when to see a physician, so that their health is jeopardized. Or perhaps the rapid advances in the technologies they must master and with which they must interact (e.g., ATMs) leaves them behind. Any one of these conditions can lead to a loss of independence.
Advances in adaptive technologies have been identified in this paper that can potentially lead to a greater independence, advances in the sensing of environmental information, in the displaying of that information to the older adult, and in the individualization and interactivity of the interface. Many such advances will require models of the person (or person-machine system) that can be used to infer the behavior of the individual from the pattern of responses, models that may need to run in real time. Thus, advances have also been discussed in our ability to model the simple and complex behaviors that govern performance in discrete and continuous-control tasks. Some of the advances in adaptive technologies have already been implemented. Selective implementations were discussed, including those used in the car, the home, and more general commerce. Finally, the ongoing development of adaptive technologies suggests areas of research that will be critically important to future implementations of such technologies. Three areas in particular were identified, one most relevant to sensors (visual search), one most relevant to displays (perception and attention), and one most relevant to interfaces (mathematical modeling of cognitive tasks).
Four related implications follow from the review of adaptive technologies. First and foremost is the need to continue research in basic cognitive aging. Other areas of aging are receiving and should be receiving new emphasis, most notably cognitive neuroscience. But it is not the advances in cogni-
tire neuroscience that will prepare the groundwork for the implementation of the first generation of adaptive technologies for older adults. The basic vocabulary is the elementary cognitive process, much as it was conceived by Donders (1868) over a century ago. Cognitive task analysis is at the center of the effort, and this level of abstraction is both necessary and sufficient at this point in time.
Second, and intimately related to the above, is the need to foster research and training that increases understanding not just of the effects of aging on performance, but also of the effects of aging on the structure and duration of the processes that underlie this performance. It is models of these processes, often quantitative, that will serve as the common language of the engineers and psychologists who try to bridge, through an adaptive technology, the worlds of cognition and control. Graduate training programs should be supported that emphasize modeling. Postdoctoral fellowships should be initiated that support individuals wanting to increase their understanding of cognitive models. And, to the extent possible, research initiatives should be encouraged that focus on both cognition and control.
Third, there is a clear need to support more multidisciplinary efforts. The National Science Foundation does this very successfully through vehicles such as the Science and Technology Centers, Engineering Research Centers, and major cross-disciplinary programs (e.g., Knowledge and Distributed Intelligence), which encourage collaboration across disciplines. The National Institutes of Health also have a mechanism in place for establishing centers. There is no obvious reason that the National Institute on Aging could not set out such programmatic initiatives, if not at the center level then certainly at the level of requests for proposals that are obviously multidisciplinary in focus.
Finally, there is a clear need to support the acquisition of adaptive technologies and the equipment that it takes to evaluate such technologies. For items that are relatively inexpensive, this would not seem to create a problem with the current funding mechanism. However, for more expensive items, a different mechanism may be needed, one that the National Institute on Aging is best positioned to detail.
A number of individuals have read and commented on the chapter. I would particularly like to thank Robert Gao, Kathleen Hancock and Rod Grupen (all of the University of Massachusetts at Amherst), Richard Jagacinski (Ohio State University) and William Yost (Loyola University Chicago).
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