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Comment by jedberg | Hacker News Reader

Although they just discovered the mechanism, we've known that this happens for quite some time. It's something you can take advantage of in UX design, for example, because if you provide a subtle queue before something new happens to trigger the brain, the new thing will be perceived faster.

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The word you meant to use is cue.

Cue: a thing said or done that serves as a signal

Queue: a line or sequence of people waiting

Good UX design. I was expecting "cue" and when I got "people in a line" or "a data structure" it felt like drinking ginger ale expecting apple juice.

You're totally right. I'd like to blame autocorrect, except that I typed it on my laptop. Instead I'll blame the fact that I was writing about queuing theory just before I made that comment.

Thinking of a subtle queue of cues

Progress bars make my application run 50% faster without touching the rest of the code. :)

Because most people don't need the computer to run faster, they need it to respond faster.

It's better to appear busy than actually being busy.

They have known it too. The first cited experiments in this article were done in 2012. I wonder how long it's existed as a theory in neuroscience.

In cognitive psychology, I've worked with a method known as "masked priming" [0], which is relatively well-known in the field to influence reaction times. Reaction time is a complex measure as it includes both "processing speed" as well as physical reaction times. The "prime" is considered subliminal, as one would not necessarily notice it being presented (although the mask definitely is noticeable). Interestingly, at short enough time scales, e.g. 16 ms duration times, the effect of the priming reverse, and it makes reaction times increase[1].

[0] http://www.u.arizona.edu/~kforster/priming/ [1] https://link.springer.com/article/10.3758%2FBF03196307

>I wonder how long it's existed as a theory in neuroscience.

Probably since a few minutes after the first time a neuroscientist saw a magician's act.

In 2006 I worked on analyzing data that showed this. The principal investigator was plagued by health problems and I didn't know how to connect what I was doing with the previous literature, so we didn't end up publishing anything. I felt like the idea was inevitable, but I don't know to what extent others believed this.

My model, which was basically guessed from common-sense principles (what would a flip-flop's output look like if there were no clock and the "update" signal were not discrete?), used equations that were similar to what would eventually become the GRU update equations. And the fit with the data was gorgeous: it took one parameter (a time constant that scaled how quickly sensory information was integrated with the current prediction) and the R^2 was north of 90%.

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