Four students preview their presentations at the Human Factors Conference and Ergonomics Society Annual Meeting in San Antonio. The conference October 19-23.

Keith Kline: The influence of spatial ability on multimedia learning.

Jenay Beer : Emotion recognition of virtual agent facial expressions

Kathi Olson: Trust in Automation

Julia DeBlasio: Documentation in a Medical Setting: Effects of Technology on Perceived Quality of Care

Abstracts and bios are below.

Keith Kline:

Abstract: Multimedia instructions, in which information is presented in multiple formats, afford better learning than instructions presented in a single format. Multimedia formatting is either multimodal, using audition (e.g., spoken words) and vision (e.g., pictures), or unimodal, using text and pictures. Spatial ability has been found to moderate the multimodal multimedia effect, but not the unimodal effect with visual materials. In the current study, participants received unimodal lessons about three physical systems in textual or pictorial-and-textual format. The multimedia effect was greater for participants who performed well on a cube rotation task. A second measure of spatial ability, the surface development task, did not interact significantly with instructional format. We discuss the factors that might lead to a discrepancy between the two measures of spatial ability in predicting the effects of instructional format. Results suggest that high spatial ability learners, in particular, benefit from the addition of pictures to textual instructions (the unimodal multimedia effect).

Bio: Keith is a graduate student in the Engineering Psychology program at Georgia Tech. He received a B.S. from Texas A&M University and an M.S. in neuroscience from the University of Texas, Houston. He currently works in Richard Catrambone's lab where he studies instructional design and users' mental representations of electronic devices.

Jenay Beer

Abstract:Robots and virtual agents have the potential to assist older adults. However, it is important to understand how people interpret social cues displayed by the agent, such as facial expressions. Although a considerable amount of research has been conducted investigating age-related differences in emotion recognition of human faces, the effect of age on emotion identification of virtual agent facial expressions has been largely unexplored. The purpose of the current research was to investigate whether age-related differences in facial emotion recognition can extend to emotion-expressive virtual agents. Younger and older adults performed a recognition task with a virtual agent expressing six basic emotions. The results indicated that age-related differences were found for the recognition of certain emotions.

Bio: Jenay is a 3rd year Engineering Psychology graduate student in the Human Factors and Aging Laboratory (directed by Wendy A. Rogers and Arthur D. Fisk). She is currently working toward her Masters degree. Her research interests include human —robotic interaction, health care, and automation.

Kathi Olson

Abstract: Older adults may encounter automated systems in a variety of context such as health care and transportation. Consequently it is important to understand the interactions between system knowledge and reliance. In this study we were interested in investigating how older adults develop the mental model they need for interacting with automated systems. We were also interested in how older adults' accuracy (or inaccuracy) of their mental model influence their interactions with automated systems. We tested 40 older adults on their ability to form an accurate mental model and how they responded to an automated navigation aid with variable reliability. Some older adults were able to form highly accurate mental models and were able to detect when the collaborative automated system was faulty. However, others did not form accurate mental models and were likely to inappropriately trust the automation. Specially designed training programs to help augment cognition may be necessary for older adults who use collaborative automated systems and have difficulty developing highly accurate mental models.

Bio: Katherine is an Engineering Psychology 3rd year Ph.D student in the Human Factors and Aging Laboratory at the Georgia Institute of Technology. She received her B.A. from the University of California, Davis and an M.A. from the California State University, Sacramento. She is currently investigating age-related differences in trust in automation and investigating technology usage in older adults.

Julia DeBlasio

Abstract:The authors examine the social impact of introducing advanced exam-room technologies to the doctor- patient interaction. A total 342 participants viewed one of several video conditions portraying a physician conducting a medical interview in which he used one of 5 documenting methods/devices (nothing, pen and paper, PDA, desktop computer, wearable computer). After viewing the interaction, participants completed a series of questionnaires evaluating their general satisfaction with the quality of care (QoC) delivered during the medical interview. Results reveal that the type of technology used has a significant effect on QoC ratings. Though advanced technology offers the opportunity of better healthcare delivery, there may be a trade-off with lower ratings of interpersonal interactions.

Bio: Julia DeBlasio is a Engineering Psychology PhD student in her 4th year of the program. She works under advisor Dr. Bruce Walker in the Sonification Laboratory. She will be presenting a similar lecture at this year's Human Factors and Ergonomics Society conference in San Antonio.