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Crystal, J. D. (1999). Systematic nonlinearities in the perception of temporal intervals. J Exp Psychol Anim Behav Process, 25(1), 3–17.
Abstract: Rats judged time intervals in a choice procedure in which accuracy was maintained at approximately 75% correct. Sensitivity to time (d') was approximately constant for short durations 2.0-32.0 s with 1.0- or 2.0-s spacing between intervals (n = 5 in each group, Experiment 1), 2.0-50.0 s with 2.0-s spacing (n = 2, Experiment 1), and 0.1-2.0 s with 0.1- or 0.2-s spacing (n = 6 in each group, Experiment 2). However, systematic departures from average sensitivity were observed, with local maxima in sensitivity at approximately 0.3, 1.2, 10.0, 24.0, and 36.0 s. Such systematic departures from an approximately constant d' are predicted by a connectionist theory of time with multiple oscillators and may require a modification of the linear timing hypothesis of scalar timing theory.
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Hasenjager, M. J., & Dugatkin, L. A. Social Network Analysis in Behavioral Ecology. Advances in the Study of Behavior. Academic Press.
Abstract: Abstract In recent years, behavioral ecologists have embraced social network analysis (SNA) in order to explore the structure of animal societies and the functional consequences of that structure. We provide a conceptual introduction to the field that focuses on historical developments, as well as on novel insights generated by recent work. First, we discuss major advances in the analysis of nonhuman societies, culminating in the use of SNA by behavioral ecologists. Next, we discuss how network-based approaches have enhanced our understanding of social structure and behavior over the past decade, focusing on: (1) information transmission, (2) collective behaviors, (3) animal personality, and (4) cooperation. These behaviors and phenomena possess several features—e.g., indirect effects, emergent properties—that network analysis is well equipped to handle. Finally, we highlight recent developments in SNA that are allowing behavioral ecologists to address increasingly sophisticated questions regarding the structure and function of animal sociality.
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Krause, J., Croft, D., & James, R. (2007). Social network theory in the behavioural sciences: potential applications. Behav. Ecol. Sociobiol., 62(1), 15–27.
Abstract: Abstract Social network theory has made major contributions to our understanding of human social organisation but has found relatively little application in the field of animal behaviour. In this review, we identify several broad research areas where the networks approach could greatly enhance our understanding of social patterns and processes in animals. The network theory provides a quantitative framework that can be used to characterise social structure both at the level of the individual and the population. These novel quantitative variables may provide a new tool in addressing key questions in behavioural ecology particularly in relation to the evolution of social organisation and the impact of social structure on evolutionary processes. For example, network measures could be used to compare social networks of different species or populations making full use of the comparative approach. However, the networks approach can in principle go beyond identifying structural patterns and also can help with the understanding of processes within animal populations such as disease transmission and information transfer. Finally, understanding the pattern of interactions in the network (i.e. who is connected to whom) can also shed some light on the evolution of behavioural strategies.
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Newman, M. E. J. (2003). The Structure and Function of Complex Networks. SIAM Rev., 45(2), 167–256.
Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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Scheibe, K. M., & Gromann, C. (2006). Application testing of a new three-dimensional acceleration measuring system with wireless data transfer (WAS) for behavior analysis (Vol. 38).
Abstract: A wireless acceleration measurement system was applied to free-moving cows and horses. Sensors were available as a collar and a flat box for measuring leg or trunk movements. Results were transmitted simultaneously by radio or stored in an 8-MB internal memory. As analytical procedures, frequency distributions with standard deviations, spectral analyses, and fractal analyses were applied. Bymeans of the collar sensor, basic behavior patterns (standing, grazing, walking, ruminating, drinking, and hay uptake) could be identified in cows. Lameness could be detected in cows and horses by means of the leg sensor. The portion of basic and harmonic spectral components was reduced; the fractal dimension was reduced. The system can be used for the detection and analysis of even small movements of free-moving humans or animals over several hours. It is convenient for the analysis of basic behaviors, emotional reactions, or events causing flight or fright or for comparing different housing elements, such as floors or fences.
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