COMPUTATIONAL STUDIES OF PRESENTATION STRATEGIES FOR A MULTILEVEL MODEL OF LEARNING.
Abstract
We consider a class of 'look-ahead' rules for generating stimulus presentation strategies in learning experiments, i.e., rules on (local) optimization over the next one, two, or more trials--given the subject's state of conditioning at the current trial. In previous studies using a two-level (single-element) model from the stimulus-sampling theory of learning, we proved that R(1) indeed generated only globally optimal strategies. In the present work we hypothesize a more general, multilevel learning model and put forth two conjectures concerning the rule R(h). We report on computational studies performed to test these conjectures. The computations did not refute the conjectures (although they led to some modification). The conjectures have not yielded to analytical treatment. The primary conjecture asserts that for an m-level model of learning the R(m-1) rule will generate a globally optimal strategy. Roughly, the second conjecture is the intuitive one that R(k) is at least as good as (h) for k h. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 08, 1966
- Accession Number
- AD0636841
Entities
People
- R. E. Dear
- W. Karush
Organizations
- System Development Corporation