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M E Raijmakers

Publications and source records attributed to M E Raijmakers.

4 recordsLinked to original sources
PubMed2001

Finite mixture distribution models of simple discrimination learning.

Through the application of finite mixture distribution models, we investigated the existence of distinct modes of behavior in learning a simple discrimination. The data were obtained in a repeated measures study in which subjects aged 6 to 10 years carried out a simple discrimination learning task. In contrast to distribution models of exclusively rational learners or exclusively incremental learners, a mixture distribution model of rational learners and slow learners was found to fit the data of all measurement occasions and all age groups. Hence, the finite mixture distribution analysis provides strong support for the existence of distinct modes of learning behavior. The results of a second experiment support this conclusion by crossvalidation of the models that fit the data of the first experiment. The effect of verbally labeling the values on the relevant stimulus dimension and the consistency of behavior over measurement occasions are related to the mixture model estimates.

Bayes Theorem↗
PubMed2000

Confidence intervals for hidden Markov model parameters.

Three methods for computing confidence intervals (CIs) of hidden Markov model parameters are compared in the context of 'long' time series, T > 100, namely likelihood profiling, bootstrapping and CIs based on a finite-differences approximation to the Hessian. First it is shown that with 'long' time series computing the exact Hessian is not feasible. In simulation studies quadratic and cubic interpolation polynomials for the likelihood profiles are compared. Likelihood profiling and bootstrapping produce similar CIs, whereas the CIs from the finite-differences approximation of the Hessian are mostly too small.

Humans↗
PubMed1996

Numerical bifurcation analysis of distance-dependent on-center off-surround shunting neural networks.

On-center off-surround shunting neural networks are often applied as models for content-address-able memory (CAM), the equilibria being the stored memories. One important demand of biological plausible CAMs is that they function under a broad range of parameters, since several parameters vary due to postnatal maturation or learning. Ellias, Cohen and Grossberg have put much effort into showing the stability properties of several configurations of on-center off-surround shunting neural networks. In this article we present numerical bifurcation analysis of distance-dependent on-center off-surround shunting neural networks with fixed external input. We varied four parameters that may be subject to postnatal maturation: the range of both excitatory and inhibitory connections and the strength of both inhibitory and excitatory connections. These analyses show that fold bifurcations occur in the equilibrium behavior of the network by variation of all four parameters. The most important result is that the number of activation peaks in the equilibrium behavior varies from one to many if the range of inhibitory connections is decreased. Moreover, under a broad range of the parameters the stability of the network is maintained. The examined network is implemented in an ART network, Exact ART, where it functions as the classification layer F2. The stability of the ART network with the F2-field in different dynamic regimes is maintained and the behavior is functional in Exact ART. Through a bifurcation the learning behavior of Exact ART may even change from forming local representations to forming distributed representations.

Cybernetics↗