Lots of people have been making great new visualizations of our color names data. Here are 4 more that folks have sent us.

Chris Harrison, a Ph.D. student at CMU HCI, combined our data with results from his own previous experiment, and created beautiful flower and spiral images. Unlike my and Martin’s color wheels, hue is scaled along the radius, creating a striking effect.

Next: network and cluster diagrams from David Sparks, Ph.D. student at Duke PoliSci. The layout below was computed from a similarity metric on color names. (I’m unclear whether it’s on labels or colors.) Size of node corresponds to the label’s frequency.

All of the visualizations so far have had to map three-dimensional color points into a 2D space. But Jeff Clark in Toronto went ahead and wrote a 3D explorer — you fly around a space of the color labels. He built it with the excellent Processing framework.

Finally, yet another tack: instead of creating a picture with all the labels, why not fit labels to a picture? Kristina Durivage, Chris Burg, and Scott Olson did that for an undergrad CS project at Winona State University. Their software takes any image and overlays color names. An example:

Four new visualizations in a month — whew!

To look at all our color posts, check out blog.doloreslabs.com/topics/colors.

-Brendan

In 2007, researchers at Aberdeen University’s Face Research Laboratory showed that women found the face of a man more attractive when the face of a woman was smiling at it. (NewScientist) Biologists have a term, “mate choice copying”, for similar behavior in birds.

I was looking at some photos of couples on FaceStat and wanted to run a quick and dirty version of this experiment, and including the other FaceStat variables. I photoshopped out the girl from guy/girl photos and reuploaded. The results followed the same trend as the Aberdeen study (although the Turkers are mixed gender). The strongest, most consistent difference for the photoshopped photo was in relationship status (more single, obviously) and in attractiveness (25% of people found them less attractive). Some examples:

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If you could ask dozens of random people on the internet questions like “Do I look smart?” or “Does this dress make me look fat?”, why wouldn’t you? Today there are no excuses! With the launch of FaceStat you have the power to decide exactly what profile picture is right for the 10 different social networks you’re a part of. You have the power to make sure the photo you’re about to upload to Match.com makes you look attractive and funny. You have the power to make sure you look trustworthy and never divorced before your girlfriend sends that picture to her parents.

A couple weeks ago we took some pictures and asked Turkers a bunch of questions. We thought this was really cool so we built a tool to let anybody see what people had to say about their picture.

You just upload a picture from your hard drive or Facebook, and we send the faces out to Mechanical Turk to be judged. Currently you can get statistics on one photo per day. Browse the latest faces, or even see who looks the most trustworthy.

Add some faces, and see how you stack up!

-Chris

Note: Unlike the other projects, this one was not done by Dolores Labs, but it was too interesting not to share.

In 2006, Aaron Koblin used Mechanical Turk to produce 10,000 hand drawn sheep. You can check them out (and buy some) at http://www.thesheepmarket.com.

Recently, he and Takashi Kawashima worked together to make an art project called Ten Thousand Cents, where they broke a one hundred dollar bill into ten thousand pieces and had turkers copy each piece.

The result is this (you can buy a copy for $100):

It’s a cool project on multiple levels. It struck me how visually obvious it is who is taking the task seriously and who isn’t (the boxes that look grainy in the above picture are probably examples of people who didn’t really do the stated task). Yet even with the noise there’s a very clear signal that comes through. In fact it looks like they made such a good replica bill that Google checkout shut down their orders.

-Lukas

Search engines control the information we see and use. Their key component is a ranking algorithm that tries to determine the most relevant web pages for your query. How good are these algorithms? Publicly, there’s a lot of hype, while privately, all the big engines run proprietary quality evaluation efforts. But there’s virtually no real data out there for the rest of us.

Using Mechanical Turk, we can evaluate engine relevance. We tried an experiment where we took five hundred queries and ran them against the top 4 English language web search engines: Ask, Google, Live, and Yahoo. The queries were a random sample from a real-world set of search queries. We had annotators rate the relevance of the top five results for each engine. Our results:

Ask clearly performed the worst. The other three engines were in a statistical tie. Their ordering was Google, Yahoo, then Live, but the differences were miniscule: the top 3 engines all answer about 80% of queries effectively.

What do these results mean?

People often talk about Google as being the most relevant search engine, with the best algorithms and the like. This study finds little evidence to support that. Sure, our methods are preliminary and could be improved in any number of ways; we can probably shrink those error bars and find more statistical differences. However, it is the case that for 500 typical queries, a rough but pretty objective measurement of search quality found that Google, Live, and Yahoo all performed about the same.

Note that these results don’t speak to the entire user experience. To be able to compare between engines, we extracted only the core web results with their titles, urls, and snippets. But a search engine also includes much more: the presentation, branding, video and image results, ads, etc. We only tested the relevance of core web search.

Many more details below.

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In the previous post I talked about how we scraped about one hundred photos from social networking sites and had Turkers guess those people’s traits – including age, race, intelligence, political affiliation, and intoxication.

Intoxication was listed as a checkbox. Averaging the guesses for each photo gives us an intoxication rating from 0 (sober) to 1 (smashed). In the histogram, you’ll notice the familiar power law distribution of drunkenness:

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