Statistical methods for a general theory of all-or-none learning

DOI10.1007/BF02290593zbMath0193.48602MaRDI QIDQ2538599

Peter G. Polson

Publication date: 1970

Published in: Psychometrika (Search for Journal in Brave)

zbMATH Keywords

mathematical biology

Related Items (5)

Statistical methods for absorbing Markov-chain models for learning: Estimation and identification ⋮ A Monte Carlo study of the Hoyt reliability coefficient in the Bower- Trabasso model ⋮ A theoretical and empirical comparison of the all-or-none multilevel theory and the mixed model ⋮ A quantitative theory of the concept identification processes in the Hull paradigm ⋮ An identifiable model of two-stage learning

Cites Work

• Statistical methods for a theory of cue learning
• A comparison of paired-associate learning models having different acquisition and retention axioms
• Markovian processes with identifiable states: General considerations and application to all-or-none learning
• Paired-associate learning with short-term retention: Mathematical analysis and data regarding identification of parameters
• Application of a model to paired-associate learning
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