Transcript Representation is representation of similarities Edelman S., ‘Behavioral and Brain Sciences,’ 1998. Naresh P.

Representation is representation of
similarities
Edelman S., ‘Behavioral and Brain
Sciences,’ 1998.
Naresh P. Cuntoor
Introduction
Naresh P. Cuntoor
Groundwork
 Distal shape space
 Proximal shape space
 Representing similarity – distinctness, NN
preservation, full similarity spectrum
 Distal to proximal mapping, M - constraints
and composition, distance rank preservation
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Analysis of Mapping
M  f 4  f3  f 2  f1
f1(p): Geometry
f2(p,z): Imaging
f3(p,z): Measurements
f4(p): Dimensionality reduction
•f4 and f3 need to counteract the z-dependency of f2
•Absolute invariance not necessary –Need:
influence of shape space changes > view space changes
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Representation = Measurement +
Dimensionality Reduction
•Another example: Sarkar’s face space to
affine space
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Chorus of prototypes
 An ensemble of tuned classifiers
 Smooth response degradation
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Similarity
 Levels: basic, subordinate, superordinate
 Features of similarity: pi(A): ith classifier
 Measures of similarity
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What’s the brain doing?
 Novel objects – how chorus deals with it
 New Pandemonium –feature demons,
cognitive demons, master demons
 ‘Democracy’ in chorus
 Perception of similarity – ppl. classify maps
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Experiments and Predictions
 To test second order isormophism
 Computer rendered 3D animal shapes and nonsense shapes
 Predicting distortion in the MDS setting – can chorus do
well?
 Parameter space distances – some more important?
 Priming – how does it affect?
 Neurobiology – columns in IT cortex
 Qualia – attributes of objects
 Scene richness – humans don’t see everything
 Bottom-up vs. top-down – Sinha’s STICKS approach
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Challenges
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