Neural modeling fields: Difference between revisions

Revision as of 16:03, 3 October 2008

Revision as of 16:09, 3 October 2008

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During the learning process, concept-models are constantly modified. In this review we consider a case when functional forms of models, '''M_m'''('''S_m''',n), are all fixed and learning-adaptation involves only model parameters, '''S_m'''. From time to time a system forms a new concept, while retaining an old one as well; alternatively, old concepts are sometimes merged or eliminated. This requires a modification of the similarity measure L; ); the reason is that more models always result in a better fit between the models and data. This is a well known problem, it is addressed by reducing similarity L using a “skeptic penalty function,” p(N,M) that grows with the number of models M, and this growth is steeper for a smaller amount of data N. For example, an asymptotically unbiased maximum likelihood estimation leads to multiplicative p(N,M) = exp(-N_par/2), where N_par is a total number of adaptive parameters in all models (this penalty function is known as [[Akaike information criterion]], see (Perlovsky 2001) for further discussion and references).

A psychological interpretation of maximizing similarity L is the [[Perlovsky | knowledge instinct]], a drive to learn, to maximize the knowledge.

Psychologically, satisfaction of instincts is felt as pleasant emotions. Emotions related to satisfaction of the knowledge instinct (maximization of similarity measure L) are aesthetic emotions, they are “spiritual” in that they are related to working of the mind-brain (whereas bodily emotions are related to bodily instincts).

==Dynamic logic==