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Heart rate (HR) rhanRcs during sleep were analyzed, with the aid of a minicomputer, for a group of 20 healthy subjects. In stages 1, 2, 3, and 4, HR decreased against a background of increasing respiratory arrhythmia. REM sleep was characterized by increased HR and decreased respiratory arrhythmia. HR changes during sleep were dependent on the subject's initial autonomic HR control level. The evidence from 3 healthy subjects, who were studied under baseline conditions, propranolol (a sympatholytic agent), atropine (a parasympathetic depressant), and propranolol plus atropine, showed that the HR decrease during stages I, 2, 3, and 4 is caused by augmented parasympathetic input. The HR increase during REM sleep is due to a reduction in parasympathetic control. The sympathetic input remains relatively constant throughout all stages of sleep, except for its decrease during stage 1.DESCRIPTORS: Heart rhythm, Non-REM sleep, REM sleep, Sympathetic-parasympathetic control, Beta-adrenoblockade, Atropine. A relationship between sleep and blood pressure and heart rate (HR) has been known since the pioneering work by MacWilliams (1923). Brooks et al. (1956) described a significant increase in HR upon transition from "deep" to more "sujjerficial" sleep. Snyder, Hobson, Morrison, and Goldfrank (1964) observed an increase and decrease in HR. in rapid eye movement (REM) and non-REM sleep, respectively. According to Aldredge and Welch (1973), HR is significantly higher in REM sleep as compared with stages 2, 3, and 4, and HR variability decreases with increasing depth of sleep. Nishimuta, Masuda, Uchino, and Oomori (1978) observed large HR fluctuations during REM sleep and stage 1, small fluctuations during stages 3 and 4, and a combination of both during stage 2. A number of reports have appeared presenting evidence for decreased HR during stages I through 4 and increased HR during REM sleep in healthy subjects

The most important problem confronting color science is the construction of a uniform color space, i.e.. a geometrical model of color discrimination in which Euclidean distances between the points representing colors are proportional to perceived color differences. The traditional approach to the construction of a metric color space is based on the integration of just-noticeable color differences (Wyszecki <& Stiles, 1982). Experimental data show, however, that the integral of Justnoticeable differences between colors does not coincide with direct estimations of the subjective differences between the colors (Judd, 1967;Izmailov, 1980). We suggest another way to construct a uniform color space, namely, to analyze large color differences by multidimensional scaling. This paper reports three groups of experimental data of the measurement of large color differences. Based on these data, we suggest a new color space model taking into account nontraditional relations between threshold and suprathreshold differences. The first group of data includes the results of research on color discrimination for a set of equibright monochromatic lights. The second group includes data on the discrimination of achromatic light stimuli resulting from different relations between test and background luminances. The third group consists of results of color-naming classification of lights varying in chromaticity and brightness. Chromatic differences between spectral stimuli of equal brightness, varying in hue and saturation, and differences in brightness between achromatic lights varying in luminance were analyzed separately. The results are compared with a general color space of colors of different hue. saturation, and brightness. The color spaces were constructed by the same multidimensional scaling technique. An important advantage of multidimensional scaling is that it offers the possibility of finding the dimensionality of a color space directly from experimental data, as we demonstrate for the analysis of color discrimination data for equibright stimuli.

Despite a wide variety of emotions that can be subjectively experienced, the emotion space has consistently revealed a low dimensionality. The search for corresponding somato-visceral response patterns has been only moderately successful. The authors suggest a solution based on an assumed parallelism between emotion coding and color coding. According to the color detection model proposed by Sokolov and co-workers, neurons responsible for color detection are triggered by a combination of excitations in a limited number of input cells. Similarly, a limited number of input channels may feed complex emotion detectors being located on a hypersphere in a four-dimensional emotion space, the three angles of which correspond to emotional tone, intensity, and saturation, in parallel to hue, lightness, and saturation in color perception. The existence of such a four-dimensional emotion space in the subjective domain is shown by using schematic facial expressions as stimuli. A neurophysiological model is provided in which reticular, hypothalamic, and limbic structures constitute input channels of an emotion detecting system, thus acting as the first layer of emotion predetectors. Hypothalamic neurons with differential sensitivity for various transmitters may elicit a subsequent selective activation in a second layer of predetectors at the thalamic level. The latter are suggested to trigger emotion detectors located in cortical areas, the action of which should be revealed by measures of central nervous system activity. Preliminary results from evoked potential studies show that switching between schematic faces that express different emotions may be used as an objective measure for establishing a psychophysiological emotion space.