Application of Four Stochastic Models to Learning of Numerical Sequences
Bibliographic Data
| ID | 19277496 |
|---|---|
| Authors | Tarow Indow (Keio University), SAYOKO SUZUKI (Keio University) |
| Year | 1969 |
| Volume | 11 |
| Issue | 4 |
| Pages | 183-197 |
| Publication date | 1969-01-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Japanese Psychological Research (JOURNAL) |
| Journal identifiers | ISSN: 0021-5368 • E-ISSN: 1468-5884 |
| Publisher | The Japanese Psychological Association (PUBLISHER • JP) |
| DOI | 10.4992/psycholres1954.11.183 |
| OpenAlex | W1654221537 |
| Language | EN |
| References cited | 3 |
The task is to learn numerical sequences, 8 in total, by anticipating each new member of the sequences. With the data of 50 university students, four stochastic models were fitted to the decomposed subprocesses, each under a subrule. The subprocesses were 24 in all. The single-operator model of Bush and Sternberg accounts for the easier subprocesses where as the subprocesses of intermediate difficulty were best fitted by the two-phase model of Norman. The difficult subprocesses were accounted for by the all-or-none model of Bower and also by the two-phase model. None of the types of subprocess seemed to be in accordance with the random-trial-increment model of Norman. Concerning with the difficulty of the subprocess reflected into values of the parameters, some interesting trends were described
Algorithm · Natural language processing · Operator (biology) · Statistics · Stochastic modelling · Task (project management) · Artificial Intelligence · Computer Science · Engineering · Mathematics · Scientific Research and Discoveries · Simulation Techniques and Applications
| Highly cited | No |
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