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Lecture

LEC 10 – Creativity and How We Learn Nov 19 2009


Department
Psychology
Course Code
PSY370H1
Professor
John Vervaeke

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PSY370: Thinking and Reasoning
LEC 10 Creativity & How we Learn
Nov, 19th, 2009
How do neuronetworks learn?
! parallel process
! the weight of connections are altered as we learn
! this is done through back propagation of error
! target value performance value = error
! learning algorithm use statistical contribution of each connection to assign blame for the error
! then the weight of connection is altered slightly to the degree to which it had blame, you do not
turn it all off, because the blame calculation is very probabilistic
! this is back propagation of error, and we do this many times until the network is able to do the
task well
! but this theory requires the ability to independently learn, because it is presupposing the thing it
is trying to explain
! presumption that we know the target value
How do we get unsupervised learning?
Gefford Hinton: Internalization
Terms used below are not from Hinto but from Vervaeke
! network break down into 2 parts
! letter for parts, number for stages
A
B
1 [Wake]
weak reinforcement of world,
because it does not know how big
the sample is for the population
WORLD
2 (a)
[Sleep]
B's sample act as A's
Population.
B will provide back propagation
for A, because it has the target
value of what the world is in A's
eyes
2 (b)
A is really good at solving
the problem, and it teaches
B how to model now.
1
Go back to stage 1 now,
the loop occurs again,
indefinitely until B has a
good model of the world
! but the criticism might be that the machine will never truly achieve a real representation of the
world but really just a good model
! bad criticism b/c humans don't have that either (we are not God, and its unfair to ask the
machine to be one)
Ito et. al
! cerebellum is structurally dense
www.notesolution.com
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