Kultus, K. B., & Balzer, H. - U. (2012). Analysis of Human-Horse-Relation. In K. Krueger (Ed.), Proceedings of the 2. International Equine Science Meeting (Vol. in press). Wald: Xenophon Publishing.
Abstract: The relation between humans and animals is one of the most famous factors for animal welfare in modern housekeeping systems. Words like intuition and empathy in exposure to the horse are difficult to describe, to measure or to teach. In German speaking countries there is the sentence: a good rider knows what the horse will do before it can do it; a bad rider only reacts to what the horse has done. By using the monitoring systemsmardwatch® in connection with chronobiological regulation diagnostics it becomes possible to get insight in the interaction between human and horse.Thesmardwatch® enables measuring of so called psycho-physiologicalparameters likeskin resistance, skin potential, electromyogram and skin temperature, measured 10 times per second; it also registersbehaviorinformation as 3D-acceleration and -position and over this environment information like temperature, noise and brightness. Cutting hooves, cleaning and riding a horse are monitored for example. The data were analyzed under distinct aspectsby chronobiological regulation diagnostics developed byBalzerand Hecht (2000). The physical and vegetative activities of the human and the animalwhere pointed out separately and in their interactionat different levels. Very interesting is the influence of different humans on one horse or the influence of one human on distinct horses. The synchronies or asynchronies in the behavior of different human-horse-pairs could be proved at the level of their vegetative functions. While riding phases of exhaustion of the horse could be shown just as the increasingactivity of the rider to compensate this exhaustion. The method could be a basic approach to develop new training methods which agree with individual rhythms of riders and horses to optimize their achievement.These analysis are not only important for riding, they also give useful directions for daily contact with horses. So it will be possible to detect harmony/disharmony between human and horse for their pairing in order to teach them and to buy or sell a horse, respectively. KW horse, human, chronobiology, synchronisation, smardwatch
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Hinde, R. A. (1969). Analyzing the roles of the partners in a behavioral interaction--mother-infant relations in rhesus macaques. Ann N Y Acad Sci, 159(3), 651–667.
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Gaunitz, C., Fages, A., Hanghøj, K., Albrechtsen, A., Khan, N., Schubert, M., et al. (2018). Ancient genomes revisit the ancestry of domestic and Przewalski's horses. Science, 360(6384), 111–114.
Abstract: The Eneolithic Botai culture of the Central Asian steppes provides the earliest archaeological evidence for horse husbandry, ~5,500 ya, but the exact nature of early horse domestication remains controversial. We generated 42 ancient horse genomes, including 20 from Botai. Compared to 46 published ancient and modern horse genomes, our data indicate that Przewalski's horses are the feral descendants of horses herded at Botai and not truly wild horses. All domestic horses dated from ~4,000 ya to present only show ~2.7% of Botai-related ancestry. This indicates that a massive genomic turnover underpins the expansion of the horse stock that gave rise to modern domesticates, which coincides with large-scale human population expansions during the Early Bronze Age.
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Hoy, R. (2005). Animal awareness: The (un)binding of multisensory cues in decision making by animals. Proc. Natl. Acad. Sci. U.S.A., 102(7), 2267–2268.
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Miller, G. (2006). Animal behavior. Signs of empathy seen in mice. Science, 312(5782), 1860–1861.
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Cohen, J. (2007). Animal behavior. The world through a chimp's eyes (Vol. 316).
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Real, L. A. (1991). Animal choice behavior and the evolution of cognitive architecture. Science, 253(5023), 980–986.
Abstract: Animals process sensory information according to specific computational rules and, subsequently, form representations of their environments that form the basis for decisions and choices. The specific computational rules used by organisms will often be evolutionarily adaptive by generating higher probabilities of survival, reproduction, and resource acquisition. Experiments with enclosed colonies of bumblebees constrained to foraging on artificial flowers suggest that the bumblebee's cognitive architecture is designed to efficiently exploit floral resources from spatially structured environments given limits on memory and the neuronal processing of information. A non-linear relationship between the biomechanics of nectar extraction and rates of net energetic gain by individual bees may account for sensitivities to both the arithmetic mean and variance in reward distributions in flowers. Heuristic rules that lead to efficient resource exploitation may also lead to subjective misperception of likelihoods. Subjective probability formation may then be viewed as a problem in pattern recognition subject to specific sampling schemes and memory constraints.
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Epstein, R. (1985). Animal cognition as the praxist views it. Neurosci Biobehav Rev, 9(4), 623–630.
Abstract: The distinction between psychology and praxics provides a clear answer to the question of animal cognition. As Griffin and others have noted, the kinds of behavioral phenomena that lead psychologists to speak of cognition in humans are also observed in nonhuman animals, and therefore those who are convinced of the legitimacy of psychology should not hesitate to speak of and to attempt to study animal cognition. The behavior of organisms is also a legitimate subject matter, and praxics, the study of behavior, has led to significant advances in our understanding of the kinds of behaviors that lead psychologists to speak of cognition. Praxics is a biological science; the attempt by students of behavior to appropriate psychology has been misguided. Generativity theory is an example of a formal theory of behavior that has proved useful both in the engineering of intelligent performances in nonhuman animals and in the prediction of intelligent performances in humans.
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Pennisi, E. (2006). Animal cognition. Man's best friend(s) reveal the possible roots of social intelligence (Vol. 312).
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Pennisi, E. (2006). Animal cognition. Social animals prove their smarts (Vol. 312).
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