A comparison of paired-associate learning models having different acquisition and retention axioms

DOI10.1016/0022-2496(64)90005-7zbMath0134.37803OpenAlexW2008683376WikidataQ114685170 ScholiaQ114685170MaRDI QIDQ2396123

E. J. Crothers, Richard C. Atkinson

Publication date: 1964

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0022-2496(64)90005-7

zbMATH Keywords

mathematical biology

Related Items

Moments of transition-additive random variables defined on finite, regenerative random processes, Learning model solution to a problem in constrained optimization, Population states and eigenstructures: A simplifying view of Markov learning models, A Bayesian-optimal principle for learner-friendly adaptation in learning games, Individual differences and the all-or-none vs incremental learning controversy, Some targets for mathematical psychology, Equivalence classes of functions of finite Markov chains, Second responses in paired-associate learning, Derivation of learning process statistics for a general Markov model, Paired-associate learning with short-term retention: Mathematical analysis and data regarding identification of parameters, An all-or-none theory for learning on both the paired-associate and concept levels, Statistical methods for a general theory of all-or-none learning, A comparison of a Hullian paired-associate learning model with other models appropriate to an \(R_ N T_ s\) design with 1 : 1 pairing of stimuli and responses, Reinforcement-test intervals in paired-associate learning, An analysis of some conditions for representing \(N\) state Markov processes as general all or none models, Stochastic learning models with distributions of parameters, Formal requirements of Markov state models for paired associate learning

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• New developments in statistical behavior theory. Differential tests of axioms for associative learning
• Markovian processes with identifiable states: General considerations and application to all-or-none learning
• Incremental learning on random trials
• Application of a model to paired-associate learning