Pink Tentacle reports that researchers at Japan’s ATR Computational Neuroscience Laboratories have developed a system that can “reconstruct the images inside a person’s mind and display them on a computer monitor.”
Scientists Extract Images Directly from Brain: Pink Tentacle
The scientists were able to reconstruct various images viewed by a person by analyzing changes in their cerebral blood flow. Using a functional magnetic resonance imaging (fMRI) machine, the researchers first mapped the blood flow changes that occurred in the cerebral visual cortex as subjects viewed various images held in front of their eyes. Subjects were shown 400 random 10 x 10 pixel black-and-white images for a period of 12 seconds each. While the fMRI machine monitored the changes in brain activity, a computer crunched the data and learned to associate the various changes in brain activity with the different image designs.
Then, when the test subjects were shown a completely new set of images, such as the letters N-E-U-R-O-N, the system was able to reconstruct and display what the test subjects were viewing based solely on their brain activity.
Remember 1993? Clinton succeeded George Bush Senior as the next US President, The Buffalo Bills became the first NFL team to lose the Super Bowl three times in a row and the moon was bright and I mean BRIGHT! Well today and today only, the moon will be the closest it has been since 1993 and star gazers from around the world will be eyes to the sky. If you’re out and about, probably stumbling around in a drunken stuper may I suggest you stare up instead of down at the cracked pavement. You may just find some inspiration … or just get knocked to the ground by a bunch of people passing by. Either way, its going to be one hell of a spectacle.
Since our mention in Wired Magazine, we’ve had a lot of great feedback from users requesting changes to our reading interface. We had the whole gamut of requests from different colors to different fonts, changing of font sizes, adding words to each cluster, subtracting words from each cluster.
These are all great suggestions and they seem pretty commonplace. One has to ask, why haven’t the guys at Spreed already implemented many of the requests? To technically implement them is not that hard. What gives?
The answer is not simple.
The easy answer is that we are the first company entirely focused on reading enhancement. It is a new space with new challenges. Our goal is to become the experts in this field. We have developed some significant expertise and we try to use this when building in features. This means that we have a look at the existing sciences to justify a feature’s benefit in terms of reading efficiency.
We look at past research, we try to find new research, we try it out on ourselves and a portion of our community before a change to our reading interface ever makes it to the live site.
Our initial goal when we started Spreed was to let the computer do the heavy lifting of speed reading. We wanted to develop an algorithm and a reading interface that would be effective for most people. No doubt Spreed demands that people challenge themselves to learn how to get through information faster. We remain adamant that with a little (or in some cases a lot) of practice, we can help you read faster. When Spreed eventually catches on and is integrated with other content and technology providers, you’ll be able to let the computer do the ‘heavy lifting’ for all your digital reading.
You might even find an
increase in your speed when reading conventionally. Recently, we had a few people say that they are now reading faster on paper since they started using Spreed.
That’s the easy answer to the latency in adding features. I am going to pick a specific part of our research to have our users think about: Have you ever wondered how the human eye picks up words?
When reading traditionally your eyes do not move in a linear fashion across the page. The eye makes many “stops” and occasionally doubles back to words previously read. Even fast readers double back – only they are a lot faster at it than the average reader. A “stop” is called a saccade and it typically lasts in the range of 200-250 milliseconds.
The Science of Word Recognition – Kevin Larson, July 2004.
On each stop, the eye will focus on a word, look for first few letters of the next word and also go further ahead to gauge the length of upcoming words and the sentence as a whole. The eye’s ability to look forward might be the reason why single word flashing also know as RSVP might be less efficient than our algorithm.
So what does your eye process in a saccade?
There is a vast array of academic research in this area. I am going to quote Kevin Larson who is the leader in this field. Mr. Larson is a cognitive psychologist working at Microsoft with their advanced reading technologies team. Who knew Microsoft has such a team??? Regardless, we find his work very useful.
During a single fixation, there is a limit to the amount of information that can be recognized. The fovea, which is the clear center point of our vision, can only see three to four letters to the left and right of fixation at normal reading distances. Visual acuity decreases quickly in the parafovea, which extends out as far as 15 to 20 letters to the left and right of the fixation point
The Science of Word Recognition – Kevin Larson, July 2004.
Visual acuity? Fovea? Parafovea? What happens when font sizes are increased? when colours or contrasts are changed? when the number of words or characters in a cluster change? How will this affect how you can absorb information? You need not worry about these questions, but we do!
Parafoveal rules are the basis for the length of clusters in Spreed. The algorithm ensures that characters do not fall outside the average person’s parafoveal field of vision. Depending on the length of words in a cluster, the formation algorithm can produce a cluster with one, two, three and sometimes four words. At times the algorithm does not allow clusters to reach the maximum length because of another set of rules (i.e. grammar rules). The alogorithm tries to encompass speed reading principles, visual perception (eye science), and English grammar / linguistic rules. We’ll leave the other rules for another post.
We err on the side of caution when we make changes to the cluster formation algorithm and reading interface. Our goal was to allow the average (if not daring) person to read faster. There will always be outliers who require a larger font, or different colour scheme etc. Is the algorithm perfect? Certainly not. We feel we are at the inception of a reading revolution and will continue to innovate and test both within our labs and our community at large.
ps. We will be at the Web 2.0 Summit in San Francisco this week November 5-8. If you feel like talking about Spreed, shoot myself or Dave an email (firstname.lastname@example.org or email@example.com)