Posner and Keele Rosch et al Posner and

Posner and Keele; Rosch et al.

Posner and Keele: Two Main Points • Greatest generalization is to prototype. – Given noisy examples of prototype, prototype is as well classified as examples. • Prototype isn’t only thing learned. – Amount of variability in training examples influences representations.

How the Experiments Work • • Generate prototype. Then distortions. Train subject to know one level of distortions. Judge ability to classify – – Training set. More distorted ones Equally distorted but different. Prototypes. Matlab

From Palmeri and Flanery

http: //www. msu. edu/course/psy/200/Burns/p 200 f 030916_6. pdf

Conclusions of Experiment 3 • Train on distorted images. • Performance as good on prototype as training set. • Worse on less distorted images, of equal avg. distance to training set as prototype. • Even worse on new of equal distortion as training set. Representation favors prototypes.

Conclusions of Experiment 1&2 • Train on a) less or b) more distorted versions. • Test on even more distorted. • Better if you trained on more distorted. Representation is more than prototypes.

Questions • What is learned? – Prototype? • Not just prototype. • Examples plus prototype. – Distribution? • Gaussian? • Seems like examples mattered. Could distribution overfit the data? – Examples + Weighted distances.

• Is this domain sufficiently realistic? – Are dots just dots? • Maybe shape is important. This might mean forming links between dots, which noise disrupts. Perhaps exps 1&2 have more to do with set of shapes learned than variance in features. – If real categories have structure, does this domain have too little structure?

Rosch • Categories are in the world. – Objects have structure, correlations of attributes. • Basic categories are those with greatest cue validity. – Cue predicts category. – Superordinates don’t have many attributes in common. – Subordinates share many attributes with other subordinates. – Why cue validity?

Experiments 1 -4 • Main points – Categories have shared properties – Basic categories are where most of these first appear. • Experiments 1. Collect attribute lists. • Example: # common attributes for fruit: superordinate – 3, basic – 8. 3, subordinate – 9. 5 2. Document actions with objects. • Similar pattern of attributes. 3. Similarity of shape. 4. Average shape. • Basic categories have similar shape; no evidence subordinates are less similar.

Experiments 5 -12 • Main point – • Basic categories are psychologically real. Experiments 5 & 6 Prime with category name then compare/detect with quick exposure. 7 Category name is query compared to image, not prime. 1. 1 st task where performance is best for basic categories. 8 & 9 Children sort into basic categories earlier. 10 People name with basic categories. 11 Children learn these words first. 12 ASL is missing many subordinate and superordinate words.

Conclusions • Is this belaboring the obvious? – Well, it wasn’t obvious. – Convergence of evidence more convincing? • Categorization is for inference. – This seems like a very modern view. • Different kinds of attributes common at basic level. – This is what makes visual classification possible (if true). • Are categories completely in the world? – May depend on extent of knowledge. – Context.