Representation is representation of similarities

Edelman S., ‘Behavioral and Brain Sciences,’ 1998.

Naresh P. Cuntoor

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

Naresh P. Cuntoor

Analysis of Mapping

1234 ffffM

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

Naresh P. Cuntoor

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

Naresh P. Cuntoor

Challenges