JulieLeung.com: a life told in tidepools

The other day the girls and I stopped by the Burke Museum at the University of Washington. While walking through the lower floor’s exhibits on Pacific Rim cultures, I saw this quote describing how the Maori learn:

…Maori learn in complete cycles. When they learn the word for caterpillar, they are taken outside to the caterpillar’s home…

I wasn’t able to copy the rest of the quote which described how the children touch a caterpillar, put their feet in the grass and feel the caterpillar’s home. I was struck by how our concept of education is limited by culture. “Learning” in America often means blackboards and desks, reading and writing, spelling and speaking. Perhaps a teacher would order a caterpillar from a science catalog and let it grow a cocoon in the room. But going outside to experience all the aspects of caterpillar existence creates a different value for education and a different knowledge set.

Interesting that the complete cycle of learning was illustrated with a caterpillar – whose life is a cycle!

Speaking of learning, I appreciated this article I found via Slashdot by Kevin Larson on The Science of Word Recognition. The explanation of how the brain recognizes the shape of words has helped me understand how my daughter Abigail reads.

The Seidenberg & McClelland and Plaut et. al. models are able to simulate not only adult reading, but can also simulate a child learning to read. Initially the neural network model starts out with no knowledge about the relationship between letters and pronunciations, only that letters and sounds exist. The neural net goes through a training phase where the network is given examples of correct pronunciations for different words. After seeing a correct sample, the network will calculate the error in its guess of the pronunciation, and then modifies the strength of each of the nodes that are connected to it so that the error will be slightly less next time. This is analogous to what the brain does. After a few rounds of training, the model may be able to read a few of the most high frequency, regular words. After many rounds of training the model will be able to read not only words its seen before, but words it hasn’t seen before as well.

It is also helpful for bloggers who might want to know what is the most efficient way to be read – type in lowercase!

The weakest evidence is support of word shape is that lowercase text is read faster than uppercase text. This is entirely a practice effect. Most readers spend the bulk of their time reading lowercase text and are therefore more proficient at it. When readers are forced to read large quantities of uppercase text, their reading speed will eventually increase to the rate of lowercase text.