Experimental goal Experimental Paradigm Results Illusions of nonrigidity
- Slides: 16
Experimental goal
Experimental Paradigm
Results
Illusions of non-rigidity
Results (contd. ) 10% X-Y positional noise added 3 x 10% 2 x 20% 1 x 20% Training set 30% 100% 30% 75% 40% 50% 40% 25% 50% 0% 50% Training set Percentage of trials over which subjects reported non-rigidity Learning is object-specific. . . Scale factor
Inferences 1. Arbitrary associations between 2 D and 3 D structures can be learned. 2. Learning is object-specific. 3. Learning influences perception of kinetic depth and stereoscopic depth. 4. Recognition may, in some circumstances, precede 3 D shape perception. Recognition 3 D Shape Image
A model for incorporating high-level learning in early perception (Jones, Sinha, Poggio, Vetter, Current Biology, 1997)
The model schematically Learned knowledge about object-class n Learned knowledge about object-class 1 Image 1+attribute Image 2+attribute … Imagen+attribute Attribute estimation via prototype combination Result Recognition (somehow…) Image
Combining learned instances Object class Image 0 + Attribute 0 Image 1 + Attribute 1 Imagen + Attributen image_new + ? Step 1: image_new = Step 2: + C 0 * Image 0 C 1 * Image 1 + Cn * Imagen C 0 * Attribute 0 + C 1 * Attribute 1 + Cn * Attributen = Attribute_new
The model in action: Example 1 - implicit 3 D shape recovery Image 45 degree face image Attribute Frontal face image
The model in action: Example 1 - implicit 3 D shape recovery Image 45 degree face image Attribute Frontal face image
The model in action: Example 1 - implicit 3 D shape recovery Image 45 degree face image Attribute Frontal face image
The model in action: Example 2 - Clean edge-map extraction Image frontal face image Attribute Clean edge-map Input
The model in action: Example 2 - Clean edge-map extraction Image frontal face image Attribute Clean edge-map Input Model output
The model in action: Example 2 - Clean edge-map extraction Image frontal face image Attribute Clean edge-map Input Model output Bottom-up output
Example 2 (contd. ) Strengths of high-level learning based model: 1. Ability to complete missing information 2. Ability to handle noise
- Old paradigm meaning
- Labfinder/paradigm results
- Experimental vs non experimental research
- Non experimental design vs experimental
- Experimental vs non experimental
- Experimental vs non experimental
- Research approaches and designs
- Mc escher illusions
- Optical illsuions
- Cognitive illusions in decision making
- Echalk optical illusions
- What is positive illusion
- Strengths and weaknesses of gestalt psychology
- Mathematical illusions
- Hardest optical illusions
- Ambiguous images
- Visual organization psychology