Liking, disliking, and averaging: Why average things are attractive but very attractive things are not average

By Gordon Rugg and Amy Martin

There are regularities in human desire. Often, though, the actual regularities are subtly but profoundly different from the apparent regularities.

In this article, we’ll look at one of these regularities. It starts with a significant insight from an article whose title neatly sums up a key finding, and implicitly raises a key question.

The article is a 1991 paper by Alley and Cunningham in Psychological Science. The title is: “Average faces are attractive, but very attractive faces are not average.” The implicit question is: “Why?”

There’s been a lot of work in this area. In this article, we’ll examine how a simple change in the way you represent the data can give powerful new insights into what’s actually going on, and into what you can do about it.

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Beauty, novelty and threat

By Gordon Rugg

In an earlier article in this short series, we looked at what happens when you treat two concepts such as liking and disliking as two separate scales, rather than as opposite ends on a single scale. The answer was that often this makes sense of results which would otherwise look contradictory.

In another article, we looked at what happens when you apply this approach to the well established literature on perceptions of attractiveness. The result was that this provided a clear, simple way of explaining an apparent paradox within the literature on perceptions of attractiveness in human faces.

Covering these topics raised a lot of other questions, which we’ll tackle in this article. The questions relate to three main themes:

• Social context
• Cognitive load
• Perceptions of novelty and threat

The apparent attraction of average faces

By Gordon Rugg

In a previous article, we looked at what happens when you take two concepts that are normally viewed as opposites, and instead treat them as two separate concepts. We used the example of what happens when you treat liking and disliking as two separate concepts, and ask people to rate items in relation to both liking and disliking.

The result is that people are willing and able to do so. The image below shows types of response that we’ve seen in real data.

Item A has been rated low both for liking and for disliking; it’s just boring, with little to be said for or against it.

Item B has been rated high both for liking and for disliking; it produces strong but ambivalent feelings. An example that we saw involved university departmental websites, where some were strongly liked because they signalled high quality, but simultaneously strongly disliked because that same signal of high quality was viewed as implying unforgivingly high expectations.

Item C has been rated high like/low dislike by some participants, and low like/high dislike by others. This is informally known in the UK as the Marmite effect, where people either love something or hate it, with few people in between.

This approach of uncoupling apparent opposites is well established in some fields, but isn’t yet widely known outside them. We’ve been using it for a while in software evaluation, where it’s invaluable for improving software mockups before committing to the final design. We’ve also blogged about ways of using it to represent expressive and instrumental behaviour; handedness; and gender roles, going back to the literature where we first encountered it, in Bem’s work on androgyny (Bem, 1974).

The advantages of using this approach are clear when you see examples. In the next section, we’ll look at the background theory on which it works. We’ll then apply it to an apparently paradoxical finding about facial attractiveness, to show how the underlying issues can be swiftly and easily teased apart via this representation.

Crisp and fuzzy categorisation

By Gordon Rugg

Categorisation occurs pretty much everywhere in human life. Most of the time, most of the categorisation appears so obvious that we don’t pay particular attention to it. Every once in a while, though, a case crops up which suddenly calls our assumptions about categorisation into question, and raises uncomfortable questions about whether there’s something fundamentally wrong in how we think about the world.

In this article, I’ll look at one important aspect of categorisation, namely the difference between crisp sets and fuzzy sets. It looks, and is, simple, but it has powerful and far-reaching implications for making sense of the world.

I’ll start with the example of whether or not you own a motorbike. At first glance, this looks like a straightforward question which divides people neatly into two groups, namely those who own motorbikes, and those who don’t. We can represent this visually as two boxes, with a crisp dividing line between them, like this.

However, when you’re dealing with real life, you encounter a surprising number of cases where the answer is unclear. Suppose, for instance, that someone has jointly bought a motorbike with their friend. Does that person count as being the owner of a motorbike, when they’re actually the joint owner? Or what about someone who has bought a motorbike on hire purchase, and has not yet finished the payments?

Bad questionnaires, gender and ethnicity: When researchers achieve profundity by mistake

By Gordon Rugg

My usual response to badly assembled questionnaires involves a rant, followed by a dissection of the methodological issues involved and of various relevant bodies of theory.

Sometimes, though, a questionnaire manages to achieve a level of badness so extreme that it transcends its own awfulness.

Today’s example is one of those. It’s a question from an unidentified questionnaire. It’s asking about sexuality. It offers one option which you don’t usually see in this context. Admittedly, it’s probably the result of a copy and paste error, but that’s a minor detail. (Yes, I’m being ironic there…)

Anyway, here it is, in all its blighted majesty…

How much is too much?

By Gordon Rugg

There are various well-established answers to the question of how much is too much. (Though being well-established doesn’t necessarily mean that they’re true…)

In this article, I’ll look briefly at four types of answer:

• Moral outrage
• An unforeseen price
• To infinity and beyond
• The statistics of uncanny valleys

I’ll look at the statistical type in most detail, because it’s received least attention in the past, and because it has some fascinating implications for fashion, the media, and inter-group relations.

This is a story that goes in some improbable-sounding directions. It starts with mediaeval pointy shoes, lust-crazed beetles, and beer bottles.

Images from Wikipedia; full details and acknowledgements at the end of this article

150 posts and counting

By Gordon Rugg

This article is an overview of our blog articles so far, and of what is coming next.

Image copyleft Hyde & Rugg, 2014

Gendered language in Shakespeare

By Gordon Rugg

To what extent does the language of Shakespeare’s plays indicate a male-dominated world? One way to see is by looking at the distribution of gendered words within the texts.

The figure below shows the location of the words he, him, his, she and her in Midsummer Night’s Dream. Each of the tiny rectangles represents a word in the text; the coloured words represent the keywords, and the blank rectangles represent the other words. This representation ignores linebreaks in the original text. The images are roughly equivalent to a miniaturised image of the text laid out as a scroll, with the keywords marked with coloured highlighter.

In each pair of images below, the nominative forms of the keywords are in red, and the other forms such as accusatives are in green, to show whether one gender appears more often in an active role.

(Apologies to any readers who are red/green colour blind; the Search Visualizer software itself takes account of colour blindness in its options, but shrinking the images down to fit into blog format loses the contrast.)

The column on the left shows the distribution of the words he/him/his in Midsummer Night’s Dream. The column on the right shows the distribution of the words she/her in the same play.

There are more male pronouns, but the difference is not huge; both male and female pronouns occur frequently throughout the play.

Here, for comparison, is the corresponding figure for Romeo and Juliet.

One hundred Hyde & Rugg articles, and the Verifier framework

By Gordon Rugg

This is the 100^th post on the Hyde & Rugg blog. We’re taking this opportunity to look back at what we’ve covered and look forward to what comes next.

The image below shows some of the main themes and outputs so far, in the “knowledge cycle” format that underlies our Verifier framework for tackling human error. If you’ve come to this blog after reading Blind Spot, you might be pleased to discover that we’ve been covering the contents of Verifier here in more depth than was possible in the book, and that we’re well on the way to a full description.

In the image below you can see some of the main themes and topics we’ve covered so far in the “knowledge cycle” format that underlies our Verifier framework for tackling human error. If you’ve come to this blog after reading Blind Spot, it’s worth knowing that we’ve covered some of the the contents of Verifier in more depth here than was possible in the book, and that we’re well on the way to a full description.

The knowledge cycle, and topics that we’ve blogged about

Copyleft Hyde & Rugg 2014

Categorisation of gender: Gordon’s art exhibition, part 2

By Gordon Rugg

I have an art exhibition at Keele University until October 25^th. The exhibition consists of twelve canvases.

The first six examine depictions of women in epic texts, described in my previous article.

Canvases seven to twelve examine ways of categorising gender, which will be the topic of this article.