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  • 1.
    Ghirlanda, Stefano
    Stockholm University, Faculty of Humanities, Centre for the Study of Cultural Evolution. CUNY, USA.
    On elemental and configural models of associative learning2015In: Journal of mathematical psychology (Print), ISSN 0022-2496, E-ISSN 1096-0880, Vol. 64-65, p. 8-16Article in journal (Refereed)
    Abstract [en]

    The elemental and configural approaches to associative learning are considered fundamentally distinct, with much theoretical and empirical work devoted to determining which one can better account for empirical data. Elemental models assume that each perceptual element is capable of acquiring associative strength independently of other elements. Configural models, on the other hand, assume that associative strength accrues to percepts as wholes. Here I derive a necessary and sufficient condition for an elemental and a configural model to be equivalent, i.e., to always make the same predictions. I then ask when the condition can be fulfilled. I show that it is always possible to construct a configural model equivalent to a given elemental model, provided we broaden somewhat the customary definition of a configural model. Constructing an elemental model equivalent to a given elemental one is possible provided the generalization function of the configural model is positive definite. The latter condition is satisfied by existing configural models. The arguments leading to these conclusions clarify the relationship between elemental and configural models, and show that both approaches have heuristic value for associative learning theory.

  • 2.
    Ghirlanda, Stefano
    Stockholm University, Faculty of Humanities, Centre for the Study of Cultural Evolution.
    On the relationship between elemental and configural models of associative learningIn: Journal of mathematical psychology (Print), ISSN 0022-2496, E-ISSN 1096-0880Article in journal (Refereed)
  • 3.
    Ghirlanda, Stefano
    Stockholm University, Faculty of Humanities, Department of Archaeology and Classical Studies, Centre for Cultural Evolution. City University of New York, United States.
    Studying associative learning without solving learning equations2018In: Journal of mathematical psychology (Print), ISSN 0022-2496, E-ISSN 1096-0880, Vol. 85, p. 55-61Article in journal (Refereed)
    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.

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