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Unformatted text preview: e is exr1 a constant that represents the barrier height
f h1. For two mutually exclusive hypotheses
1 Br(h1m) or, equivalently, Pr(h0)
P ackground: Banburismus1at Bletchley
d assuming the weight problemdecision rule
of evidence is exFormalize the
bans, this rearranges to:
Pr(h1m) 1 1
.
B
10 Park P(h1m)
(4) indicates that the posterior probability of h1
B
nly on the value of the barrier, B, and not on
h1 is true
How much the evidence favors h1 over h0
lar samples of evidence, m, encountered. In
Decision rule: setting B to achieve an expected level of performance (confidence that h1
s, as ilong as the weight of evidence reaches
s true).
e probability that h1 is correct is a fixed value.
SpeedAccuracy trade off: more evidence needs to be accumulated in order to increase
Banburismus, setting thethe correctof the barrier
height answer.
the the probability of getting
mined the weight of evidence to accumulate
mmitting to a decision was equivalent to setpected level of performance. For example, Implementation: Banburismus
Performed by Neurons (in 2 AFC choice) • How does the activity of sensory neurons compute
the “weight of evidence”? • How does the brain make use of the sensory
evidence? • What is the decision rule based on this evidence? 992;(and assume opponency, giving an ,analogous expres ,
Salzman
Pr(x yh1)
2
2
in these areas h0):
2
√1
sion under
that the brain Banburismus Performed by Neurons
Q
where
e2
is was most
Sensory xSignalsTuring’s Weight of Evidence
,
(5)
Pr( ,yh1)
2
22
2
Neuron
(y
2 (x
1 2 (x √1 1)
0)
1)(y
302
Q
2
2
1
Using the difference (xy) in spike rates from two
where • (5) ) 0 , neurons (one 2 the common variance, other favors Down
favors Up motion, the and are the means of
2
is
cisions about (x
2
1
)
(y
)
2 (x
)(y
)0
1
1
0
1
0
motion) x and y, respectively, under h1, and , is the covariance
Qlike Tu 2
tities
2 1
between x and y. Solving for the weight of evidence (in
ically related
h1: Up motion ( x is larger)
2
is the common variance, 1 and 0...
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This note was uploaded on 09/15/2011 for the course COGS 1 taught by Professor Lewis during the Spring '08 term at UCSD.
 Spring '08
 LEWIS

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