Representation is representation of similarities

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

2. Introduction Naresh P. Cuntoor

3. 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

4. Analysis of Mapping 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

5. Representation = Measurement + Dimensionality Reduction • Another example: Sarkar’s face space to affine space Naresh P. Cuntoor

6. Chorus of prototypes • An ensemble of tuned classifiers • Smooth response degradation Naresh P. Cuntoor

7. Similarity • Levels: basic, subordinate, superordinate • Features of similarity: pi(A): ith classifier • Measures of similarity Naresh P. Cuntoor

8. 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 Naresh P. Cuntoor

9. 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

10. Challenges Naresh P. Cuntoor