To me, the task this week was fundamentally about paradigm flipping. Like language itself, the standards that have become accepted as the means of interaction on the World Wide Web facilitate communication – in this case between the user and the website with which they are interacting. In essence, the accepted practices for interaction are like any symbol set – alphabet, emojis, hand-signals. Much of the standard is built around the transmission of information rather than the content, but if the content cannot be sent or received then it is of no use. By upending the norms of interaction, ‘User Inyerface’ forces the user to carefully consider the elements of user interface design that have come to be accepted as the standard. Interesting, whether intended or not, in many cases it also reinforces the optimization of many of those standards. Working through the forms is not only difficult because they standards are different, but also because many of the choices are deliberately inefficient. I hesitate to use the word counterintuitive here because it is hard to separate, at this mature point in the development of the World Wide Web, how many of our expectations are in place because of familiarity over genuine ease of use. But certain aspects are clearly (dare I say objectively?) better than the choices made in this interface. A simple idea like a placeholder that automatically disappears when a text box is selected seems like a minor inconvenience until the opposite effect makes it clear that alternative is incredibly time consuming and unnecessarily difficult. My other favourite in this was the reverse colour choices in the buttons. Making the ‘chosen’ option stand out from the background seems an obvious choice, especially when faced with the opposite.
All of that being said, every choice on this website could become a learned behaviour. In fact, it became much easier to work through once I adopted the principle of assuming that everything simply worked opposite to what I was used to; or was designed to thwart my best efforts. For example, with no need to be factual about my data, I slid the age bar to a number that looked reasonable, and then constructed an arbitrary date of birth to match. This adaptation is, I think, a hallmark of much human communication. It made me think of the famous Cambridge University experiment:
Aoccdrnig to a rseearch at Cmabridge Uinervtisy, it deos not mttaer in what oredr the ltteers in a wrod are, the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae.
A study by Agrawal et al has determined that the key to this is a pattern matching algorithm in the human brain (2020). To me this suggests that pattern matching is a fundamental task of visual language processing. As a Computer Scientist I have long been convinced that this is the one of the great barriers between the human brain and any real measure of Artificial Intelligence. Many AI algorithms are really just attempts at improving the essentially brute force operation of pattern matching for computers but in no way come close to the natural ability of humans in the visual realm. As humans we pattern match relatively easily. A new standard for a user interface simply requires that we reset out pattern matching expectations. I would argue that it would take relatively few attempts for an individual to become competent at the new UI pattern (albeit still annoyed by the deliberate inefficiencies) – as was my own experience after a few tries. Having witnessed the development of the World Wide Web since its inception I believe that it is fair to say that it has evolved over time. Advantageous features have been selected, detrimental ones have fallen by the wayside. New computer input and output mechanisms and design breakthroughs continue to shape the way we interacted with the web, but ultimately it is a kind of natural selection that determines what is considered best practice. These best practices quickly become part of the standard set of expectations. That is why for this experience any change inevitably led to a reduction in efficiency, even when the new patter was learned.
References:
Agrawal, Hari, K., & Arun, S. P. (2020). A compositional neural code in high-level visual cortex can explain jumbled word reading. eLife, 9. https://doi.org/10.7554/eLife.54846. Accessed on November 12, 2021.