If you intend to use this component with Finsweet's Table of Contents attributes follow these steps:
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Chapter 5: Exploiting vulnerabilities in comprehension

Literacy, numeracy and problem solving

In 2013, a huge worldwide study called the Program for the International Assessment of Adult Competencies (PIAAC) was published, involving over 165,000 working-age adults in 23 different countries.1 It looked at literacy, numeracy and problem-solving proficiencies across the world. The following summary is just for the United States, though the picture is fairly similar in many countries. According to the 2013 PIAAC findings:2

  • 30% of adults in the US are likely to have difficulty sorting through emails and organising them in folders provided for them.
  • 20% of adults in the US are unlikely to find the name of a congressperson with a summary information sheet that lists the district, name, year and place of birth.
  • 30% of adults in the US are unlikely to be able to calculate the total cost of daily car rental when provided with miles driven that day, cost per day and cost per mile.
  • 16% of adults in the US are digitally illiterate, and cannot use a computer to find a recipe, make a retail purchase or file taxes online.

As you can see, low literacy and numeracy is very common. With an exploitative mindset, this presents an exploitable vulnerability. If a business wants to hide unfair or unappealing aspects of a transaction, it can do so through the use of complex language or complex numerical content. With this in mind, it’s interesting to consider the writing style used on public service websites – plain language, short sentences, and enormous efforts taken for comprehension for all citizens – versus the writing style used in more exploitative products like crypto trading apps, where impenetrable technical terms are used extensively, very little is explained, and the user is enabled to make all kinds of risky trades with minimal safeguards or education.

How scan reading can be used to manipulate people

When we read, we don’t usually read every word on every page. Not unless we’re studying really hard or working our way through something we’re enjoying, like a novel for example. Take a look at this:3

On the left is a screen with the words ‘You will read this first’ in a large black typeface on a light background surrounded by a frame; below and outside the frame and at a smaller size are the words ‘And then you will read this’; farther below and smaller still are the words ‘Then this one’. On the right is a photo of a black puppy over which, in a large, white typeface outlined in black, are the words: (top) ‘WHAT I IF TOLD YOU…’; (middle) ‘THAT READ YOU THE FIRST LINE WRONG’; and (bottom) ‘YOU READ THAT LINE WRONG TOO :D [grin emoji]’.
A demonstration of how human visual perception can be manipulated.

On the left, you can see we tend to let visual hierarchy determine the order in which we read things. We’ve learned it’s a good idea to read the big, prominent things first and smaller things later. On the right, you can see how we glance at content and make educated guesses about individual words to save time. This isn’t something we’re born with. It’s a technique called ‘scan reading’, which we pick up naturally as we get better at reading. Similarly, good writers and page designers learn how to design for scan reading, to help people to do it more efficiently.

Steve Krug published Don’t Make Me Think in 2000. It’s now in its third edition, with over 350,000 copies in print. This book is highly regarded in the UX design industry, as it puts forward a clear explanation for the concept of scan reading by people who are using screens.

Let me show you two more images. The first image explains what we naively might assume is a natural way to read information. In theory, we’d expect readers to take in each successive word, thoroughly paying attention to every element of the design. This naive view of human information-seeking behaviour is similar to...

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Since 2010, Harry Brignull has dedicated his career to understanding and exposing the techniques that are employed to exploit users online, known as “deceptive patterns” or “dark patterns”. He is credited with coining a number of the terms that are now popularly used in this research area, and is the founder of the website He has worked as an expert witness on a number of cases, including Nichols v. Noom Inc. ($56 million settlement), and FTC v. Publishers Clearing House LLC ($18.5 million settlement). Harry is also an accomplished user experience practitioner, having worked for organisations that include Smart Pension, Spotify, Pearson, HMRC, and the Telegraph newspaper.