RE: Research for Visual Indicators

Hi David,

It is interesting, but these are fairly low-level perceptual experiments. For example, the stimulus from the first (and second) paper is a set of crosses on a (2002 era) screen:

When “All tasks were performed while subjects fixated a small spot in the center of the screen”, these are not equivalent tasks to looking around a screen.

This type of research is working at several layers below interface design, it doesn’t consider:

  *   Comparison of different features, where the task is to find targets in a mixture of surrounding non-targets.
  *   Meaning, where the user is trying to work out what things are actionable, rather than spotting pattern variations.

Design on the web has a bucket-load more variables, this type of research doesn’t relate easily.

I think what is needed is more at the HCI level of research (interfaces), rather than vision research, but I’m not aware of anything directly applicable.

Kind regards,


From: David Fazio

I’ve compiled some research into this Google Doc:

I’ve added links to the research, pulled out relevant excerpts and commented on how I feel they apply and what we can extrapolate from it.

From: Alastair Campbell <<>>
Date: Wednesday, April 22, 2020 at 5:56 AM
To: "WCAG list (<>)" <<>>
Cc: David Fazio <<>>
Subject: Research for Visual Indicators

Hi everyone,

On the call yesterday one of the things needed for Visual Indicators to progress is a solid research basis for the requirement. This is my overview of where I think we are with that need.

To be clear about what we need:

  *   If we have a general “interaction controls should have salience”, we need to define what that means with lots of examples for different types of control. (Big project in itself.)

  *   If it is restricted to buttons/links, that helps, but we still need to define salience in terms that work across different contexts.

  *   If it takes the approach of listing design attributes (e.g. font, border, background, spacing etc) then we need to know what a minimum difference is, AND whether these attributes are equivalent.

Ideally it would say something like “A border of X contrast improved visual acquisition by Y%”, or something we could take that sort of conclusion from.

The ultimate test is that we test several example sites, and the controls which fail are the ones which cause issues for people in practice (and almost as important, it does not catch controls which are not an issue).

David Fazio kindly provide an example [1], and there are several in the document from Lisa & the COGA TF [2].

This is the list of things I looked through, and my quick conclusion from each whilst hunting for suitable findings:


Gives background to flat design & the (usability) issues, links to the next article.


This is looking at the right level of attribute (e.g. colour, position). Highlights that location can be as useful as other indicators, and whether other items are available also has an impact (making this task harder). Arrow icons were not thought to be as useful. Not referencing research (that I could see), experience based.


Tested target acquisition performance by making 1 of 12 targets on screen ‘salient’, with either contrast change, movement or an extra ring around the target. It showed (I think) that more change = quicker acquisition.
This article linked to quite a few related research papers giving me more to look at, but mostly from the ‘90s.


Some things automatically stand out, some we can be attentive to. Deals with real life stimuli rather than interface design.


Spacing didn’t affect search times for finding an icon within a set, but small icons were harder to identify and took longer to find.

I really struggled to spot anything that would support a visual indicators SC, including from scanning the results of research listings.

However, I’m not a scholar and there are probably better terms to use when searching. If anyone knows some better sources for this information now would be the time to send them in…

Kind regards,



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Received on Thursday, 23 April 2020 22:57:57 UTC