Keyboard shortcuts are generally accepted as the most efficient method for issuing commands, but previous research has suggested that many people do not use them. In this study we investigate the use of keyboard shortcuts further and explore reasons why they are underutilized by users. In Experiment 1, we establish two baseline findings: (1) people infrequently use keyboard shortcuts and (2) lack of knowledge of keyboard shortcuts cannot fully account for the low frequency of use. In Experiments 2 and 3, we furthermore establish that (3) even when put under time pressure users often fail to select those methods they themselves believe to be fastest and (4) the frequency of use of keyboard shortcuts can be increased by a tool that assists users learning keyboard shortcuts. We discuss how the theoretical notion of ‘satisficing’, adopted from economic and cognitive theory, can explain our results.
Keyboard Shortcut Usage: The Roles of Social Factors and Computer Experience
Previous research (Lane, Napier, Peres, & Sándor, in press) has shown that despite the fact that it typically takes half as much time to issue a command to a computer application using that command's keyboard shortcut, most people issue a particular command by clicking an icon on a toolbar or by selecting the command from a pull-down menu. This study examined reasons why that might be the case with a web survey that focused on demographic characteristics of people who do and do not use keyboard shortcuts, as well as social factors of computer use that might influence use of keyboard shortcuts. Participants' shortcut usage was influenced by social factors, such as working in an environment with other shortcut users and experiential factors, primarily the hours spent using a computer per week.
Hidden Costs of Graphical User Interfaces: Failure to Make the Transition from Menus and Icon Toolbars to Keyboard Shortcuts
Graphical interfaces allow users to issue commands using pull-down menus, icon toolbars, and keyboard shortcuts. Menus and icon toolbars are easier to learn, whereas keyboard shortcuts are more ef...
Demand characteristics in human–computer experiments
Demand characteristics refer to cues that can inform participants in experiments about the hypothesis and influence their behavior. They lead researchers to erroneously infer non-existing effects, undermining the experimental integrity of empirical studies. Despite a widespread acknowledgment of their confounding influence in experimental psychology, experiments involving humans and computers to a lesser extent consider effects of demand characteristics, as computerized protocols are thought to be immune to some experimenter biases. Furthermore, demand characteristics are considered to mainly effect subjective measures. As a result, demand characteristics often remain uncontrolled in studies involving computers, and in particular for objective measures such as performance. In this paper, we present two experiments that underline the importance of demand characteristics in human–computer interaction experiments. In a text-entry study, we made participants believe they were evaluating a research-based keyboard. This belief led to increased performance and self-reported user experience. In a second study, we conducted a thought experiment on the illusion of body ownership in virtual reality, where the experimental design indicated the study hypothesis. We found hypothesis-compliant responses from participants, even when they did not experience the illusion. We conclude that demand characteristics pose a significant challenge to the interpretation and validity of human–computer experiments, even when they are fully automated. We discuss the implications and offer guidelines to mitigate effects of demand characteristics.
Typing without having to type
25+ years into my career as a programmer I think I may finally be coming around to preferring type hints or even strong typing. I resisted those in the past …
The peril of laziness lost | The Observation Deck
In his classic Programming Perl — affectionately known to a generation of technologists as "the Camel Book" — Larry Wall famously wrote of the three virtues of a programmer as laziness, impatience, and hubris: If we’re going to talk about good software design, we have to talk about Laziness, Impatience, and Hubris, the basis of good software design. We’ve all fallen into the trap of using cut-and-paste when we should have defined a higher-level abstraction, if only just a loop or subroutine. To be sure, some folks have gone to the opposite extreme of defining ever-growing mounds of higher level abstractions when they should have used cut-and-paste. Generally, though, most of us need to think about using more abstraction rather than less.
Note-ifying all the things (with Boris Mann)
Conversation about note-taking, the overwhelm of tools, and future possibilities. (shortlink:…
Games Done Quick's Long, Difficult Journey To A Better Gaming Future
Progress isn't linear, and there are no skips or shortcuts
How an accessibility designer adds keyboard shortcuts to a web app
Keyboard shortcuts occupy a strange area for web design.
Eroding a virtue: AI trains people to expect instant answers – and that’s bad news for patience
Patience is a virtue that researchers have linked to many parts of well-being. But it’s also something that needs a bit of practice and training – and can be undermined by instant, easy gratification.
Eroding a virtue: AI trains people to expect instant answers – and that’s bad news for patience
Patience is a virtue that researchers have linked to many parts of well-being. But it’s also something that needs a bit of practice and training – and can be undermined by instant, easy gratification.
Using AI for Just 10 Minutes Might Make You Lazy and Dumb, Study Shows
New research suggests that reliance on AI assistants can have a negative impact on people’s ability to think and problem solve.
Shift Happens: A book about keyboards
Shift Happens tells the story of keyboards like no book ever before, covering 150 years from the early typewriters to the pixellated keyboards in our pockets.
Our interfaces have lost their senses
We programmed them by punching cards, plugging in wires, and flipping switches. Programmers walked among banks of switches and cables, physically choreographing their logic. Being on a computer used to be a full-body experience.
The unintended consequences of large language models as a labor-augmenting technology in science
As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.
The unintended consequences of large language models as a labor-augmenting technology in science
As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.