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Studying associative learning without solving learning equations
Stockholm University, Faculty of Humanities, Department of Archaeology and Classical Studies, Centre for Cultural Evolution. City University of New York, United States.
Number of Authors: 12018 (English)In: Journal of mathematical psychology (Print), ISSN 0022-2496, E-ISSN 1096-0880, Vol. 85, p. 55-61Article in journal (Refereed) Published
Abstract [en]

I introduce a simple mathematical method to calculate the associative strengths of stimuli in many models of associative learning, without solving the models' learning equations and without simulating the learning process. The method applies to many models, including the Rescorla and Wagner (1972) model, the replaced elements model of Brandon et al. (2000), and Pearce's (1987) configural model. I illustrate the method by calculating the predictions of these three models in summation and blocking experiments, allowing for a degree of similarity between the training stimuli as well as for the effects of contextual stimuli. The method clarifies the models' predictions and suggests new empirical tests.

Place, publisher, year, edition, pages
2018. Vol. 85, p. 55-61
Keywords [en]
Associative learning theory, Elemental model, Configural model, Connectionism, Kernel machine
National Category
Mathematics Psychology
Identifiers
URN: urn:nbn:se:su:diva-161212DOI: 10.1016/j.jmp.2018.07.003ISI: 000445443500006OAI: oai:DiVA.org:su-161212DiVA, id: diva2:1258711
Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2018-10-25Bibliographically approved

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