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J Wackermann

Publications and source records attributed to J Wackermann.

23 records · Page 2Linked to original sources
PubMed1986

Statistical mixture decomposition as a method for type analysis of learning curves.

A method for type analysis of learning curves, based on the statistical mixture decomposition, is described. Some critical points in current data-analytic techniques are discussed. The mathematical rationale of the new method is outlined in a brief sketch. The possibilities of the method are documented by two examples. In the first study, done on simulated lata of a known structure (N = 200, 2 classes), it was possible to distinguish, with an average performance of 82%, between two types, and to reproduce their original curves. In the second study data from experiments in classical eye-lid conditioning in man were analysed (N = 80). The decomposition procedure resulted into the classification into four groups, with pronounced inter-class differences in the course of respective learning curves. The variety of class curves ranges from a group with only few CRs (C1, N = 26), through a group with an initial increase and final decrease in CR frequency (C2, N = 16), a group with an apparently biphasic course of CR frequency (C3, N = 20), to a group with a rapid increase of CR and then stable course of CR frequency (C4, N = 18). The results are consistent with earlier findings concerning the existence of distinct types of learning curves. The problem of interpretation is briefly discussed. The method can be applied principally to any problems, where different types of time development trends of an alternative response are to be distinguished.

Conditioning, Classical↗
PubMedDate not supplied

Global dimensional complexity of multichannel EEG in mild Alzheimer's disease and age-matched cohorts.

Multichannel EEG as sequence of momentary brain field maps constitutes a trajectory through K-dimensional state space (K = number of channels); the complexity of this trajectory is assessed by the nonlinear measure of global correlation dimension (Global Dimensional Complexity, GDC) with the number of electrodes as embedding dimension. We analyzed eyes-closed EEG of three age-matched subject groups: mild Alzheimer's disease (AD; n = 21), mild cognitive impairment (29) and subjective memory complaint (29). Kruskal-Wallis statistics showed an overall effect between groups. AD patients differed significantly (GDC = 4.56) from mild cognitive impairments (GDC = 4.98) and from subjective memory complaints (GDC = 4.93). GDC also had significant positive correlations with mental condition and performance (MMSE and WAIS-R scores). Thus, the dynamics of brain state development over time in mild AD differs from that in mild cognitive impairment and in subjective memory complaint cases.

Aged↗