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PURPOSE: Although computer technology may be particularly useful for older adults (e.g., for communication, information access), they have been slower adopters than their younger counterparts. Perceptions about computers such as perceived usefulness and perceived ease of use can pose barriers to acceptance and universal access [1]. Therefore, understanding the precursors to these perceptions for older adult non-computer users may provide insight into the reasons for their non-adoption. METHODS: We examined the relationship between perceived usefulness and perceived ease of use of a computer interface designed for older users and demographic, technology experience, cognitive abilities, personality, and attitudinal variables in a sample of 300 non-computer using adults between the ages of 64 and 98, selected for being at high risk for social isolation. RESULTS: The strongest correlates of perceived usefulness and perceived ease of use were: technology experience, personality dimensions of agreeableness and openness to experience, and attitudes. The emotional stability personality dimension was significantly correlated with perceived ease of use but not perceived usefulness. Hierarchical regression analysis revealed that attitudes (i.e., self-efficacy, comfort, interest) remained predictive of perceptions of usefulness and ease of use when technology experience and personality variables were accounted for. CONCLUSION: Given that attitudes are more malleable than other variables, such as demographic and cognitive abilities, these findings highlight the potential to increase technology acceptance through positive experiences, appropriate training, and educational campaigns about the benefits of computers and other technologies.

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As technology advances, robots and virtual agents will be introduced into the home and healthcare settings to assist individuals, both young and old, with everyday living tasks. Understanding how users recognize an agent's social cues is therefore imperative, especially in social interactions. Facial expression, in particular, is one of the most common non-verbal cues used to display and communicate emotion in on-screen agents (Cassell, Sullivan, Prevost, & Churchill, 2000). Age is important to consider because age-related differences in emotion recognition of human facial expression have been supported (Ruffman et al., 2008), with older adults showing a deficit for recognition of negative facial expressions. Previous work has shown that younger adults can effectively recognize facial emotions displayed by agents (Bartneck & Reichenbach, 2005; Courgeon et al. 2009; 2011; Breazeal, 2003); however, little research has compared in-depth younger and older adults' ability to label a virtual agent's facial emotions, an import consideration because social agents will be required to interact with users of varying ages. If such age-related differences exist for recognition of virtual agent facial expressions, we aim to understand if those age-related differences are influenced by the intensity of the emotion, dynamic formation of emotion (i.e., a neutral expression developing into an expression of emotion through motion), or the type of virtual character differing by human-likeness. Study 1 investigated the relationship between age-related differences, the implication of dynamic formation of emotion, and the role of emotion intensity in emotion recognition of the facial expressions of a virtual agent (iCat). Study 2 examined age-related differences in recognition expressed by three types of virtual characters differing by human-likeness (non-humanoid iCat, synthetic human, and human). Study 2 also investigated the role of configural and featural processing as a possible explanation for age-related differences in emotion recognition. First, our findings show age-related differences in the recognition of emotions expressed by a virtual agent, with older adults showing lower recognition for the emotions of anger, disgust, fear, happiness, sadness, and neutral. These age-related difference might be explained by older adults having difficulty discriminating similarity in configural arrangement of facial features for certain emotions; for example, older adults often mislabeled the similar emotions of fear as surprise. Second, our results did not provide evidence for the dynamic formation improving emotion recognition; but, in general, the intensity of the emotion improved recognition. Lastly, we learned that emotion recognition, for older and younger adults, differed by character type, from best to worst: human, synthetic human, and then iCat. Our findings provide guidance for design, as well as the development of a framework of age-related differences in emotion recognition.

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Technology holds promise in terms of providing support to older adults. To date, there have been limited robust systematic efforts to evaluate the psychosocial benefits of technology for older people and identify factors that influence both the usability and uptake of technology systems. In response to these issues, we developed the Personal Reminder Information and Social Management System (PRISM), a software application designed for older adults to support social connectivity, memory, knowledge about topics, leisure activities and access to resources. This trail is evaluating the impact of access to the PRISM system on outcomes such as social isolation, social support and connectivity. This paper reports on the approach used to design the PRISM system, study design, methodology and baseline data for the trial. The trial is multi-site randomized field trial. PRISM is being compared to a Binder condition where participants received a binder that contained content similar to that found on PRISM. The sample includes 300 older adults, aged 65-98 years, who lived alone and at risk for being isolated. The primary outcome measures for the trial include indices of social isolation and support and well-being. Secondary outcomes measures include indices of computer proficiency, technology uptake and attitudes towards technology. Follow-up assessments occurred at 6 and 12 months post-randomization. The results of this study will yield important information about the potential value of technology for older adults. The study also demonstrates how a user-centered iterative design approach can be incorporated into the design and evaluation of an intervention protocol.

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PURPOSE OF THE STUDY: Computers and the Internet have the potential to enrich the lives of seniors and aid in the performance of important tasks required for independent living. A prerequisite for reaping these benefits is having the skills needed to use these systems, which is highly dependent on proper training. One prerequisite for efficient and effective training is being able to gauge current levels of proficiency. We developed a new measure (the Computer Proficiency Questionnaire, or CPQ) to measure computer proficiency in the domains of computer basics, printing, communication, Internet, calendaring software, and multimedia use. Our aim was to develop a measure appropriate for individuals with a wide range of proficiencies from noncomputer users to extremely skilled users. DESIGN AND METHODS: To assess the reliability and validity of the CPQ, a diverse sample of older adults, including 276 older adults with no or minimal computer experience, was recruited and asked to complete the CPQ. RESULTS: The CPQ demonstrated excellent reliability (Cronbach's α = .98), with subscale reliabilities ranging from .86 to .97. Age, computer use, and general technology use all predicted CPQ scores. Factor analysis revealed three main factors of proficiency related to Internet and e-mail use; communication and calendaring; and computer basics. Based on our findings, we also developed a short-form CPQ (CPQ-12) with similar properties but 21 fewer questions. IMPLICATIONS: The CPQ and CPQ-12 are useful tools to gauge computer proficiency for training and research purposes, even among low computer proficient older adults.

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Automation has the potential to aid humans with a diverse set of tasks and support overall system performance. Automated systems are not always reliable, and when automation errs, humans must engage in error management, which is the process of detecting, understanding, and correcting errors. However, this process of error management in the context of human-automation interaction is not well understood. Therefore, we conducted a systematic review of the variables that contribute to error management. We examined relevant research in human-automation interaction and human error to identify critical automation, person, task, and emergent variables. We propose a framework for management of automation errors to incorporate and build upon previous models. Further, our analysis highlights variables that may be addressed through design and training to positively influence error management. Additional efforts to understand the error management process will contribute to automation designed and implemented to support safe and effective system performance.

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This study examined potential issues faced by older adults in managing their homes and their proposed solutions for overcoming hypothetical difficulties. Forty-four diverse, independently living older adults (66-85) participated in structured group interviews in which they discussed potential solutions to manage difficulties presented in four scenarios: perceptual, mobility, physical, and cognitive difficulties. The proposed solutions were classified using the Selection, Optimization, and Compensation (SOC) model. Participants indicated they would continue performing most tasks and reported a range of strategies to manage home maintenance challenges. Most participants reported that they would manage home maintenance challenges using compensation; the most frequently mentioned compensation strategy was using tools and technologies. There were also differences across the scenarios: Optimization was discussed most frequently with perceptual and cognitive difficulty scenarios. These results provide insights into supporting older adults' potential needs for aging-in-place and provide evidence of the value of the SOC model in applied research.

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A critical construct related to human-robot interaction (HRI) is autonomy, which varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots may interact with one another. Thus, there is a need to understand HRI by identifying variables that influence - and are influenced by - robot autonomy. Our overarching goal is to develop a framework for levels of robot autonomy in HRI. To reach this goal, the framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, the framework proposes a process for determining a robot's autonomy level, by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as guidelines to determine autonomy, categorize the LORA along a qualitative taxonomy, and consider which HRI variables (e.g., acceptance, situation awareness, reliability) may be influenced by the LORA.

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An obstacle detection task supported by "imperfect" automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over relying on it during non-alarms states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behavior according to the characteristics of the automation similarly to younger adults, although it took them longer to do so. The results of this study suggest the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human-automation interaction can help designers of automated systems make predictions about human behavior and system performance as a function of the characteristics of the automation.

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The need to manage chronic diseases and multiple medications increases for many older adults. Older adults are aware of memory declines and incorporate compensatory techniques. Everyday memory strategies used to support medication adherence were investigated. A survey distributed to 2000 households in the Atlanta metropolitan area yielded a 19.9% response rate including 354 older adults, aged 60-80 years. Older adults reported forgetting to take their medications, more so as their activity deviated from normal routines, such as unexpected activities. The majority of older adults endorsed at least two compensatory strategies, which they perceived to be more helpful in normal routines. Compensatory strategies were associated with higher education, more medications, having concern, and self-efficacy to take medications. As memory changes, older adults rely on multiple cues, and perceive reliance on multiple cues to be helpful. These data have implications for the design and successful implementation of medication reminder systems and interventions.

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Diagnostic automation has been posited to alleviate the high demands of multiple-task environments; however, mixed effects have been found pertaining to performance aid success. To better understand these effects, attention allocation must be studied directly. We developed a multiple-task environment to study the effects of automation on visual attention. Participants interacted with a system providing varying levels of automation and automation reliability and then were transferred to a system with no support. Attention allocation was measured by tracking the number of times each task was viewed. We found that participants receiving automation allocated their time according to the task frequency and that tasks that benefited most from automation were most harmed when it was removed. The results suggest that the degree to which automation affects multiple-task performance is dependent on the relative attributes of the tasks involved. Moreover, there is an inverse relationship between support and cost when automation fails.

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Some task analysis methods break down a task into a hierarchy of subgoals. Although an important tool of many fields of study, learning to create such a hierarchy (redescription) is not trivial. To further the understanding of what makes task analysis a skill, the present research examined novices' problems with learning Hierarchical Task Analysis and captured practitioners' performance. All participants received a task description and analyzed three cooking and three communication tasks by drawing on their knowledge of those tasks. Thirty six younger adults (18-28 years) in Study 1 analyzed one task before training and five afterwards. Training consisted of a general handout that all participants received and an additional handout that differed between three conditions: a list of steps, a flow-diagram, and concept map. In Study 2, eight experienced task analysts received the same task descriptions as in Study 1 and demonstrated their understanding of task analysis while thinking aloud. Novices' initial task analysis scored low on all coding criteria. Performance improved on some criteria but was well below 100 % on others. Practitioners' task analyses were 2-3 levels deep but also scored low on some criteria. A task analyst's purpose of analysis may be the reason for higher specificity of analysis. This research furthers the understanding of Hierarchical Task Analysis and provides insights into the varying nature of task analyses as a function of experience. The derived skill components can inform training objectives.

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Age-related declines in capabilities may compromise older adults' ability to maintain their homes thus threatening successful aging in place. Structured interviews were conducted with forty-four independently living older adults (M(age) = 76.1, SD = 4.7) to discuss difficult home maintenance tasks and how they managed those tasks. Solutions to managing difficulties were categorized as person-related or environment-related. The majority (85%) of responses were person-related solutions. An understanding of the specific challenges that older adults face in maintaining their homes can guide redesign efforts and interventions to effectively support older adults' desire to age in place.

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Incidental task structure is consistent, potentially beneficial, information that is not necessary for successful task performance (i.e., is seemingly unrelated to the task). The authors investigated whether incidental task structure was differentially beneficial to younger and older adults. Across three experiments, 122 participants searched for targets among stimuli laid upon different patterns, such that certain patterns correlated with target location at varying degrees of consistency. An age-related difference was identified in the ability to learn an incidental structure under certain conditions and a strategy explanation for the difference was investigated. When older adults' were encouraged to orient at least some degree of attention toward the predictive information, learning occurred. Older adults are capable of learning incidental, environmental information but their learning was not identical to younger adults'. Younger adults showed performance benefits when provided with incidental task structure, but older adults may need to be made explicitly aware before it is useful.

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OBJECTIVE: This study examined how individuals, younger and older, interacted with an imperfect automated system. The impact of workload on performance and automation use was also investigated. BACKGROUND: Automation is used in situations characterized by varying levels of workload. As automated systems spread to domains such as transportation and the home, a diverse population of users will interact with automation. Research is needed to understand how different segments of the population use automation. METHOD: Workload was systematically manipulated to create three levels (low, moderate, high) in a dual-task scenario in which participants interacted with a 70% reliable automated aid. Two experiments were conducted to assess automation use for younger and older adults. RESULTS: Both younger and older adults relied on the automation more than they complied with it. Among younger adults, high workload led to poorer performance and higher compliance, even when that compliance was detrimental. Older adults' performance was negatively affected by workload, but their compliance and reliance were unaffected. CONCLUSION: Younger and older adults were both able to use and double-check an imperfect automated system. Workload affected how younger adults complied with automation, particularly with regard to detecting automation false alarms. Older adults tended to comply and rely at fairly high rates overall, and this did not change with increased workload. APPLICATION: Training programs for imperfect automated systems should vary workload and provide feedback about error types, and strategies for identifying errors. The ability to identify automation errors varies across individuals, thereby necessitating training.

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Older adults (n = 113) participated in focus groups discussing their use of and attitudes about technology in the context of their home, work, and healthcare. Participants reported using a wide variety of technology items, particularly in their homes. Positive attitudes (i.e., likes) outnumbered negative attitudes (i.e., dislikes), suggesting that older adults perceive the benefits of technology use to outweigh the costs of such use. Positive attitudes were most frequently related to how the technology supported activities, enhanced convenience, and contained useful features. Negative attitudes were most frequently associated with technology creating inconveniences, unhelpful features, as well as security and reliability concerns. Given that older adults reported more positive than negative attitudes about the technologies they use, these results contradict stereotypes that older adults are afraid or unwilling to use technology. These findings also highlight the importance of perceived benefits of use and ease of use for models of technology acceptance. Emphasizing the benefits of technology in education and training programs may increase future technology adoption.

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OBJECTIVES: Technology represents advances in knowledge that change the way humans perform tasks. Ideally, technology will make the task easier, more efficient, safer, or perhaps more pleasurable. Unfortunately, new technologies can sometimes make a task more difficult, slower, dangerous, or perhaps more frustrating. Older adults interact with a variety of technologies in the course of their daily activities and thus products should be designed to be used by people of varying ages. METHODS: In this article, we provide an overview of what psychology has to offer to the design of technology-from understanding what people need, to identifying their preferences for design characteristics, and to defining their capabilities and limitations that will influence technology interactions. RESULTS: We identify how research in the field of psychology and aging has advanced understanding of technology interactions and how research on technology interactions can inform theories of aging. DISCUSSION: Design for aging involves understanding the unique capabilities and limitations of older adults; identifying their needs, preferences, and desires for technology in their lives; and involving them in the design process.

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A goal of many older adults is to remain in their own homes as they age (Beyond 50.05 Survey, 2005). However, a detailed assessment is lacking of the needs of older adults as they age in place. Using focus groups, twenty-six independently living older adults (mean age 78.8 years) from the Atlanta metropolitan area were asked to describe the tasks they perform to maintain their homes, as well as any difficulties they have performing these tasks. Participants described performing a wide range of tasks and focused primarily on physical difficulties. However, participants also reported solutions to manage these difficulties that fell into three broad categories: "Cessation," "Perseverance," and "Compensation." These categories represent classes of opportunities for interventions that may help older adults remain independent in their homes longer. By understanding the nature of home maintenance problems older adults encounter while aging in place, interventions and redesign efforts can be more effective. These data suggest that interventions should start with answering physical issues.

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The goal of this study was to investigate the effects of visual degradation on warning symbol comprehension across warning symbol types and age groups. Twenty-seven black and white ANSI symbols of four different types (prohibition, course of action, information, and hazard symbols) were presented to older (N = 21, M = 73.1) and younger adults (N = 20, M = 21.4) via computer at three degradation levels (0%, 30%, 40% of pixels inverted); accuracy and response time in answering yes-no questions about the symbols were recorded. Younger adults were more accurate and faster overall than older adults (p < .01). Regarding degradation, 0% and 30% inverted symbols did not significantly differ in comprehension (p ≥ .25), but both were comprehended better than 40% inverted symbols (p < .01); no interactions were observed. For degraded warning symbols, results suggest symbols must be substantially degraded to affect base comprehensibility, and age differences exist. These data have practical implications for warnings in environments susceptible to degradation.

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Researchers have suggested that attention is a key moderating variable predicting performance with an input device [e.g., Greenstein & Arnaut, 1988] without directly assessing the attention demands of devices We hypothesized that the attentional demands of input devices would be intricately linked to whether the device matched the input requirements of the on-screen task. Further, matching task and device should be more important for attentionally reduced groups, such as older adults. Younger and older adults used either a direct (touch screen) or indirect (rotary encoder) input device to perform matched or mismatched input tasks under a spectrum of attention allocation conditions. Input devices required attention - more so for older adults, especially in a mismatch situation. In addition, task performance was influenced by the mach between task demands and input device characteristics. Though both groups benefited from a match between input device and task input requirements, older adults benefited more and this benefit increased as less attention was available. We offer an a priori method to choose an input device for a task by considering the overlap between device attributes and input requirements. These data have implications for design decisions concerning input device selection across age groups and task contexts.

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Research has shown that changes in the way a website works or how it is laid out affects how well people are able to use that website. This study examined how changes in the content and procedures of a website-like system affect the way people recover from not being able to find information in that system. Participants were placed in one of four learning conditions, differing by the content and procedures taught for a simple website-like system. They were then tasked with finding certain pages in that system or systems with different procedures, content, or both. The first test (System B Online Test) showed that participants who had to learn new content were less efficient at finding that content, while participants who had to learn new content and procedures were the only ones slowed down. The second test (System C Online Test) showed that participants who had experienced a previous change in content responded to the new change faster, whereas people who started with inconsistent procedures (as compared to consistent) made fewer errors towards the end.