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Ingmar Visser

Publications and source records attributed to Ingmar Visser.

2 recordsLinked to original sources

Multiple learning modes in the development of performance on a rule-based category-learning task.

Behavioral and neuropsychological data suggest that multiple systems are involved in category-learning. In this paper, the existence and the development of multiple modes of learning of a rule-based category structure was examined, and features of different learning processes were identified. Data were obtained in a cross-sectional study by Raijmakers et al. [Raijmakers, M. E. J., Dolan, C. V., & Molenaar, P. C. M. (2001). Finite mixture distribution models of simple discrimination learning. Memory and Cognition, 29, 659-677], in which subjects aged 4-20 years carried out a rule-based category-learning task. Learning models were employed to investigate the development of the learning processes in the sample. The results support the hypothesis of two distinct learning modes, rather than a single general mode of learning with a continuum of appearances. One mode represents sudden rational learning by means of hypothesis testing. In the second, slow learning mode, learning also occurs suddenly as opposed to incrementally. The probability of rational learning increases with age, and seems to be related to dimension preference in the younger age groups. However, the finding of distinct learning modes does not necessarily imply that distinct learning systems are involved. Implications for the interpretation and clinical use of tasks with a category-learning component, such as the Wisconsin Card Sorting Test (WCST [Heaton, R. K., Chelune, G. J., Talley, J. L., Kay, G. G., & Curtis, G. (Eds.). (1993). Wisconsin card sorting test manual: Revised and expanded. Odessa, FL: Psychological Assessment Resources]), are discussed.

Adolescent↗

Inferring the structure of latent class models using a genetic algorithm.

Present optimization techniques in latent class analysis apply the expectation maximization algorithm or the Newton-Raphson algorithm for optimizing the parameter values of a prespecified model. These techniques can be used to find maximum likelihood estimates of the parameters, given the specified structure of the model, which is defined by the number of classes and, possibly, fixation and equality constraints. The model structure is usually chosen on theoretical grounds. A large variety of structurally different latent class models can be compared using goodness-of-fit indices of the chi-square family, Akaike's information criterion, the Bayesian information criterion, and various other statistics. However, finding the optimal structure for a given goodness-of-fit index often requires a lengthy search in which all kinds of model structures are tested. Moreover, solutions may depend on the choice of initial values for the parameters. This article presents a new method by which one can simultaneously infer the model structure from the data and optimize the parameter values. The method consists of a genetic algorithm in which any goodness-of-fit index can be used as a fitness criterion. In a number of test cases in which data sets from the literature were used, it is shown that this method provides models that fit equally well as or better than the models suggested in the original articles.

Algorithms↗