Electroencephalographic correlates of paced breathing.

Multichannel EEG, respiration, blood pressure and ECG were recorded during paced breathing at five frequencies in 18 subjects in order to elucidate the effects of paced breathing on power changes in alpha, beta and theta bands, and on rhythmical variability of these parameters. Mean power in the beta band and low-frequency beta power variability (0.12-0.04 Hz) increased during paced breathing at frequencies of 0.25 and 0.20 Hz. The total variability of alpha power in the right parietal and occipital electrodes decreased during paced breathing at 0.1 Hz compared with initial rest. The results point to increased cortical excitability during paced breathing at eupnoeic frequencies and to diminished cortical sensitivity to desynchronizing influences during paced breathing at 0.1 Hz.

Strictly simultaneous classical eyelid conditioning in man.

Strictly simultaneous eyelid conditioning in human Ss on the one hand and forward (ISI: 0.45 s), backward (ISI: -0.45 s), and pseudoconditioning on the other hand were compared in two different experimental situations: one-session experiment (100 reinforcements, 20 isolated CS randomly interspersed), and ten-sessions experiment (20 reinforcements, 4 isolated CS in each session), UCS was a light flash (0.5 J, 20 microseconds duration), CS a tone (1000 Hz, 50 ms, 73 dB). In both the experiments, strictly simultaneous conditioning was significantly different from pseudoconditioning, but significantly less effective than forward conditioning. In the ten-sessions experiment, it was also less effective than backward conditioning, while in the one-session experiment, it did not differ from backward conditioning. Statistical mixture decomposition method applied to the one-session- and the ten-sessions- experimental samples of individual acquisition curves resulted into the division of both samples into 4 subgroups according to the trend of acquisition curves. A significant positive association between the conditioning procedure and the distribution of individual curves into the four subgroups was found.

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.