I don't know about the rest of you, but I don't think that I have read text as strictly left to right from one letter to the next since I was a child. My intuition is that we look at the first couple of letters (and subconsciously take in the length of the word), and then we begin to make predictions about what the word might be. I've noticed this in my son as he was learning to read. He would see the first few letters and then try to guess the rest of the word based on what he thought the first part of the word sounded like. I had to admonish him several times to read the whole word and not just guess. But my intuition tells me that it may not be necessary to read every single letter in a word.
We are highly efficient pattern recognizers. I think our eyes instinctively saccad to areas of the pattern that will help us disambiguate what we are seeing. In the case of reading text, we might see the first two or three letters and begin making predictions based on them (and probably the approximate length of the word as roughly judged by our periphery vision). Our eyes then saccad to the location in the text that is (statistically speaking) the most likely to reduce the number of potential patterns that have letters at the locations we have already scanned with our fovea. (I think something similar to this allows us to achieve some degree of spatial invariance when reading at an angle or upside down.) We do this until we have a high enough confidence in our prediction to move on. (I'm sure context is also used in this process as well.) In the past, I have been trying to think about this problem in terms of Bayesian analysis, but more recently, my thoughts have been shifting more towards the CLA/HTM and sensor-motor integration. I think there is a tremendous amount of potential to perform pattern recognition utilizing both the spatial and temporal poolers through the use of saccads. But, I will save that discussion for the nupic-theory list if anyone is interested. Eric M. Collins On Tue, Mar 11, 2014 at 12:44 AM, Aseem Hegshetye <[email protected]>wrote: > Hi, > > Matt: that was a great question, because the whole debate depends on > understanding the input. > > suppose letters are input to the system. Every letter has a predefined > representation. > C=[1100000] > O=[1010000] > W=[1001000] > H=[1000100] > Since all four letters have 1st bit overlapping, their SRD's are going to > have overlapping bits. > suppose their SDR's are: > C=[00010000010000100010000000000100001] > O=[01000000010000100010000000000100100] > W=[00010000010000100010000001000000100] > H=[00010010000000100010000000000100001] > > I am trying to build a heirarchy. So from temporal pooler i am planning on > building higher level SDR. So a word 'COW' will have a SDR and 'HOW' will > have a SDR. > now a very simple question: > Would COW and HOW have overlapping bits? Cant say they will be > semantically similar coz both mean different. But actual semantics is too > high level. like in subutai's exp. fox had a representation which was > semantically similar to something that ate rodent, Do you think these two > words COW and HOW should have some representational similarities. > > I discussed this problem with my roomate, who knows nothing about AI or > brain or programming. He said both words sound same mostly. If there was > some noise in the surrounding when i heard these words,my prediction could > have predicted either of them with equal probability. two words that sound > similar, means our cochlea generates significantly similar signals for > both, have similar representation in our low level brain, they need not > have similar meaning. > > Both words are distinct[HOW] [COW}, but since last two letters are same, > they sound a bit similar, so should they have little semantic similarity? > > thanks > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org >
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