The subtle change in the diameter of a glass can hide large changes in volume. Unfortunately for us, we’re terrible at estimating this, even when we logically know it to be true. For example, a few millimeters at the top of a pint glass equals an ounce of liquid, while the same height measure at the bottom of the glass is far less.
If you’re concerned about getting a “short pour,” you can use a rule of thumb (“Is the liquid more than one finger width from the top of the glass?”) but to be truly accurate you need a measurement tool. Look no further! The folks at Three Phase Designs have created a pint-glass ruler that will tell you exactly how much liquid is missing from your glass. These informative photos are from their site.
For those who haven’t taken or don’t remember their Intro Psych class: Piaget was a child development researcher who studied the errors children consistently make concerning the world around them. He used these errors to define “stages” of development… and one of the stages is represented by errors of “conservation.” Conservation means that an object retains its proportions despite changes in arrangement.
For example, small children think the same amount poured in a tall thin glass is “more” than that amount poured into a short, thick glass. Though we eventually become accurate for these simple problems of conservation, there are many instances (such as the beer glass) where we still have difficulties. See also “guessing how many miles lie between your car and the mountains” while driving toward them.
The medical domain is an area where human factors research is very active. Here are some highlights from the conference.
The following two presentations/proceedings papers examined Personal Health Records (user-maintained medical records):
Improving the user interface and adoption of online personal health records. (2009). Peters, K. A., Green, T. F., & Shumacher, R. M.
This paper was a usability evaluation of some of the most popular PHR web apps on the web (Google Health, Microsoft Health Vault). Thirty participants completed a variety of tasks using both systems while the number and types of errors were examined. In addition, qualitative analysis examined adoption factors (perceived usability, utility, etc). We’ve blogged about this study (which is also available as a white paper from UserCentric).
Examining non-critical health information seeking: A needs analysis for personal health records. (2009). Price, M. M., Breedlove, J., Pak, R., Muller, H., & Stronge, A.
This was in-progress work that my student and I conducted. We were interested in the generic adoption of health IT by older consumers. While PHR development has proceeded full-steam, there has not yet been research examining the utility of these systems for older adults–those who may benefit the most from accurate, user-maintained, always-available health information. The study was a diary study of the kinds of health-related questions that older adults (those over age 60) have in the course of 2 weeks. Other dimensions were number of health questions, severity, strategies used to answer the questions, and attitudes toward technology assistance.
Some papers examined health records from the perspective of the health care provider (Electronic Health Records; EHRs):
Healthcare workers’ perceptions of information in the electronic health record. (2009). Russ, A.L., Saleem, J. J., Justice, C. F., Hagg, H., Woodbridge, P. A., Doebbeling, B. N.
The researchers were interested in understanding why EHR adoption by health care workers was not as swift as one would assume. They conducted 14 interviews of health care workers to better understand how EHRs could be designed to improve health care work flow. Seventeen major aspects of EHRs that prevented adoption were identified but only 5 were discussed in detail: Customizeability (EHRs were difficult to customize or not customizeable), Prioritized (EHRs did not present ‘priority’ information; users often used external programs to add this information), Trendable (long term trends were not easily visualizeable), Locatable (classic information overload; there is just too much information and not an easy way to navigate it), and Accessible (technical issues such as login problems, network issues). The paper outlines specific examines of “work-arounds” used by workers to overcome some of these issues.
Technology & Health
On to the more generic topic of health care IT, there was
Documentation in a medical setting: effects of technology on perceived quality of care. (2009). DeBlasio, J., & Walker, B. N.
This paper found that perceptions of the technology used by the health care provider significantly influenced the patient’s perceived quality of care. In their study, they had undergraduates view video clips of a doctor taking notes from a patient. The researchers manipulated the method of note-taking (mental notes, paper, PDA, desktop PC, and wearable PC) and angle of view (head-on and offset 90 degrees). The lowest quality of care condition was desktop PC. The highest QoC conditions were wearable PC or mental notes)–presumably reflecting the negative perceptions of “obvious” technology.
The nurse’s role in health care was highlighted in many papers. Two noteworthy papers (because they both deal with prospective memory) were:
Distributed prospective memory: an approach to understanding how nurses remember tasks. (2009). Grundeiger, T., Sanderson, P. M., MacDougal, H. G., & Venkatesh, B.
Prospective memory in the nursing environment: effects of type of prospective task and prospective load. (2009). Fink, N., Pak, R., & Battisto, D.
In one of my courses this year I had students talk about the kinds of human factors problems they had run across when using computers. There were a number of great anecdotes, but one thing that interested me was the difficulty they had discerning the difference between a software bug and a human factors problem. For example, one student complained that if they clicked on a certain button in a program, it crashed the computer.
It was interesting trying to explain the difference. I basically said that a bug is an unintentional effect while a usability problem is due to some misunderstanding of how humans could or should use a system. This isn’t perfect; some human factors problems come from “bugs” that went undiagnosed during development. Generally, I think bugs are not representative of any misunderstanding of human abilities or limitations.
The difference between human factors problems and bugs is about to become very important for Toyota. These are excerpts from a recent ABC news story:
Refusing to accept the explanation of Toyota and the federal government, hundreds of Toyota owners are in rebellion after a series of accidents caused by what they call “runaway cars.” Safety analysts found an estimated 2000 cases in which owners of Toyota cars including Camry, Prius and Lexus, reported that their cars surged without warning up to speeds of 100 miles per hour.
Toyota says the incidents are caused by floor mats becoming stuck under gas pedals, but owners say that’s not what happened to them.
“I’m absolutely certain that in my situation, it was not the floor mats,” Elizabeth James told ABC News. She was driving her Toyota Prius outside Denver, CO when she says it suddenly shot up to 90 miles an hour, even though her foot was on the brake and not the gas pedal. “I kept going faster and faster,” James said. “And all of a sudden& my foot was pressing on the brake super, super hard and I wasn’t slowing down.” James and some other Toyota owners suspect the accidents have been caused by some kind of glitch in the electronic computer system used in Toyotas that controls the throttle.
The National Highway Traffic Safety Administration has done six separate investigations of such acceleration surges in Toyotas since 2003 and found no defect in Toyota’s electronics.
“Toyota has announced a safety recall involving 3.8 million vehicles in which the accelerator pedal may become stuck at high vehicle speeds due to interference by the driver’s side floor mat, which is obviously a very dangerous situation.
On the human factors side, we have a known issue with the floormats (indeed, I’ve had to pull my Matrix floormat back for years as it creeps forward.) We also have prior incidents of personsmistaking the accelerator for the brake. On the bug side we have a car with a great deal of control given to electronics. Frankly, Toyota loses either way.
The interesting part is how the drivers believe it would be more Toyota’s fault if there is a “bug” in the electronics than if the problem has to do with human factors design. We appear to feel more control over a human factors issue, even when it is beyond our control, than we do with a software bug.
This attitude can be seen over in a Consumerist post of a 911 call by a family with a floormat-stuck gas pedal. (I don’t suggest listening to the call .) Read the comments instead, they include:
“Shouldn’t a CHP officer know that ? Tragic but completely avoidable.”
“Maybe I’m missing something, but couldn’t they just have turned the engine off?”
“It is sad but the driver had the ability to put the car in neutral or turn off the engine off at any point.”
“maybe it was the way I was raised, but I understand the concept of putting a car in neutral, or stepping on the clutch, or shutting the engine off… I’m not trying to be crass, but I feel like everyone should understand these concepts before driving.”
and (wow) –
We still have a long way to go in educating the public.
During the conference I had a very personal experience with the effects of automation reliability on trust and subsequent behaviors. First, a bit of background. There is a large body of research examining how humans interact with automated systems (Global positioning systems, for example). Human-automation interaction is quite complex; being affected by many factors.
Julian Sanchez (of MITRE) presented a poster at the conference summarizing the literature; presenting how the many variables of human-automation interaction relate to each other (figure 1). One factor being extensively investigated is the issue of how much the user/operator (you & me) trusts the automation.
I have used Google Maps on my phone extensively; and in the many cities I’ve used it, it has been a reliable tool for directions. Since the phone includes a GPS chip, it can track my movements as I walk showing me my distance to my destination. However, it failed miserably in San Antonio…twice. First, I tried to find a restaurant near the River Walk and following the directions led me to go almost in the complete opposite direction. I was so confident in Google we spent 20 minutes walking around until we asked a local policeman for directions. One reason I was confident was that as we walked toward our destination, the phone confirmed that our position was nearing the GPS destination.
The second failure was when we tried to find the Cowboy bar. Automation researchers would say that I was complacent–I over-relied on the automation which indicted that my trust was not calibrated correctly. My high level of trust came from thinking, “San Antonio is a big city, it must be fully and accurately mapped…” as well as past successful navigation attempts. This is one consequence of ultra-high reliability systems: the effect they have on users expectations and trust. Ever since my return, I’ve needed to use Google Maps (on the web or phone) and I have found myself very uncertain of the stated locations and directions offered by Google Maps. I confirm Gmaps using the competing service (Bing Maps).
Sanchez, J. (2009). Conceptual model of human-automation interaction. Proceedings of the Human Factors and Ergonomics Society 53rd Annual Meeting.
The paper described in this post was part of the Aging Technical Group sessions at HFES.
Hearing Levels Affect Higher-Order Cognitive Performance – Carryl L. Baldwin, George Mason University
Perhaps I was excited by this talk because I could see how the information could be used in the book Rich and I are working on. This presentation was a fascinating exploration of the types of trouble adults over sixty-five might have with auditory interfaces. These problems are not necessarily related to the function of the ear: in general, older adults may have more poor hearing, but it is due to environmental exposure rather than aging of the ear. Many older adults show no detectable hearing loss, yet still have trouble with auditory interfaces, as found by Carryl’s experiment.
An important contribution of this paper was the connection found between decline in a sensory ability (hearing) and decline in cognitive ability, even on tests that had no auditory elements. Carryl addressed this years ago in her article Designing in-vehicle technologies for older drivers: application of sensory-cognitive interaction theory. Essentially, when most of us study cognitive aging, we either omit or control for sensory ability. For example, in my work, all older adults must have corrected vision of 20/40 or better and if there is any auditory component, must meet hearing level requirements. Sensory ability may well predict their task performance, but I do not study it.
Carryl pointed out in her talk that even older adults with no measurable hearing loss showed worse working memory capacity as stimuli got harder to hear. This was true for younger adults as well, but the older listeners were harmed differentially worse as the stimuli dB levels decreased. It isn’t hard to see why telephone menus and other auditory interfaces can be so frustrating: what requires more working memory than a softly spoken voice menu with 9 options? Eek.
Take home messages:
“The observation that scores on an assessment of working memory capacity decreased in young listeners indicates that hearing level, irrespective of age, can impact performance on aurally presented working memory tests.” (p.124)
“…functional hearing level may play a substantial role in the performance of older adults. Subclinical hearing loss may result in the need to expend greater effort to process test stimuli – thus compromising performance in higher order stages.” (p.124)
Baldwin, C. (2009). Hearing Levels Affect Higher-Order Cognitive Performance.
Baldwin, C. (2002). Designing in-vehicle technologies for older drivers: application of sensory-cognitive interaction theory. Theoretical Issues in Ergonomic Science, 3, 307-329.