Visser & Merikle 1999 Conscious and Unconscious Processes - The Effects of Motivation
Inference: Conscious and Unconscious
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Transcript of Inference: Conscious and Unconscious
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Inference: Conscious and Unconscious
J. McClellandSymSys 100
April 9, 2009
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A Conscious Inference• I wake up in morning and ask myself has it rained overnight?• I look out the window and notice that the ground is wet.• I consider whether I should conclude that it rained last night.• I consider the fact that it is April, and rain is relatively rare.• I consider that I have a sprinker system, and ask myself if it is set to
operate on Wednesdays.• I conclude that any conclusion I can reach by looking at my own
back yard would be tentative; so I go outside and notice that the street and sidewalks along my block are all wet.
• I conclude that it did indeed rain (a little) in the wee hours of this morning.
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Some Examples of “Unconscious Inferences”
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Eberhardt: Seeing Black
• Participants were shown very brief presentations of black faces, white faces, or nothing (20 stimuli).
• They then identified pictures that gradually became clearer frame by frame (500 msec/frame).
• Exposure to black faces led to faster recognition of crime objects, even though participants were not aware that they had been shown any faces.
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Perception as ‘Unconscious Inference’
• Helmholtz coined the term in the 19th century, drawing on ideas going back to the ancients.
• The terms suggests a ‘little man in the head’, entertaining hypotheses, weighing evidence, and making decisions.
• But we can think of it differently: perhaps your brain is wired to – Use context together with direct
information– Combine multiple sources of information– Allow ‘hypotheses’ to exert mutual
influences on each other
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Rest of This Lecture
• Examine a “normative theory” of inference under uncertainty– How can we reason from given information to
conclusions when everything we know is uncertain?
– How can we combine different sources of evidence?
• Consider two experiments that produce data consistent (in a sense) with the normative theory.
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Next two lectures
• A neuro-mechanistic examination of unconscious inference– How neurons can produce output matching
the normative theory– How networks of neurons can work together
to produce collective behavior consistent with the normative theory.
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An inference question
• An apartment building has a surveillance camera with an automatic face recognition software. If the camera sees a known terrorist, it will ring a bell with 99% probability. If the camera sees a non-terrorist, it will trigger the alarm 1% of the time.
• Suppose somebody triggers the alarm. What is the chance he/she is a known terrorist?
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The Normative Theory: Bayesian Inference. Definitions
• Hypothesis• Evidence• Probability
– NB: Sometimes these are estimated or subjective probabilities• P(E|H): Likelihood
– = P(E&H)/P(H): In this case: .99• P(E|~H)
– = P(E&~H)/P(~H): In this case: .01• P(H|E)
– = P(E&H)/P(E)• What is it that we want to compute in this case?
– P(H|E)• Can we derive P(H|E)?
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P(H|M) & P(H|F)
0
2
4
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5'2" 5'6" 5'10" -6'2" 6'6"
Female Male
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Deriving Bayes’ Rule• P(H|E) = P(E&H)/P(E) =>
– P(H|E)P(E) = P(E&H)• Similarly, P(E|H) = P(E&H)/P(H) =>
– P(E|H)P(H) = P(E&H)• So
– P(H|E)P(E) = P(E|H)P(H)• Divide both sides by P(E):
– P(H|E) = P(E|H)P(H)/P(E)• Final Step: Compute P(E)
– In our case, the probability that the alarm goes off when a person walks by the camera
• Can we derive P(E) from quantities we’ve already discussed?– P(E) = P(E|H)P(H) + P(E|~H)P(~H)
• Substituting, we get Bayes’ Rule:
( | ) ( )( | )
( | ) ( ) ( |~ ) (~ )P E H P H
P H EP E H P H P E H P H
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‘Sh’ and ‘ss’
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P(‘sh’|freq) & P(‘ss’|freq)[Hypothetical Data!]
0510152025303540
2500 3500 4500 5500 6500 7500
'Sh' 'ss'
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Stimulus value (1-9)
P(‘sh’)
Expected form of the data from Ganong Experiment
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Class Data: P(‘sh’) for ‘establi..’ and ‘malpracti…’ contexts
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How might we combine information from different sources?
• For example, visible and heard speech (The McGurk effect)
• Auditory cue: starting frequency of syllable• Visible cue: degree of mouth opening• Treat A and V as ‘conditionally independent’
– P(Ea&Ev|H) = P(Ea|H)P(Ev|H) for H = ‘ba’ and ‘da’
• Then we get this normative model:
)'(')'|'()'|'()'(')'|'()'|'(
)'(')'|'()'|'()&|'('
baPbaEvPbaEaPdaPdaEvPdaEaP
daPdaEvPdaEaPEbEadaP
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