| Records |
| Author |
Crystal, J.D. |
| Title |
Systematic nonlinearities in the perception of temporal intervals |
Type |
Journal Article |
| Year |
1999 |
Publication |
Journal of Experimental Psychology. Animal Behavior Processes |
Abbreviated Journal  |
J Exp Psychol Anim Behav Process |
| Volume |
25 |
Issue |
1 |
Pages |
3-17 |
| Keywords |
Animals; *Attention; Awareness; Discrimination Learning; Male; Neural Networks (Computer); *Nonlinear Dynamics; Rats; Rats, Sprague-Dawley; Sensory Thresholds; *Time Perception |
| 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. |
| Address |
Department of Psychology, Brown University, USA. jdcrys@facstaff.wm.edu |
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| Language |
English |
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Edition |
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| ISSN |
0097-7403 |
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| Notes |
PMID:9987854 |
Approved |
no |
| Call Number |
Equine Behaviour @ team @ |
Serial |
2776 |
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| Author |
Newman, M.E.J. |
| Title |
The Structure and Function of Complex Networks |
Type |
Journal Article |
| Year |
2003 |
Publication |
SIAM Review |
Abbreviated Journal |
SIAM Rev. |
| Volume |
45 |
Issue |
2 |
Pages |
167-256 |
| Keywords |
networks; graph theory; complex systems; computer networks; social networks; random graphs; percolation theory |
| 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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Siam |
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no |
| Call Number |
Equine Behaviour @ team @ |
Serial |
5214 |
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