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